From 7cf5bee3ba7a7f9c79e0d94eeadb0e16d5810311 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Fri, 27 Jun 2025 17:55:52 +0200
Subject: [PATCH 01/67] Added the new vision code.

---
 src/dynsight/_internal/vision/vision.py | 337 ++++++++++++++++++++++++
 src/dynsight/vision.py                  |  10 +-
 2 files changed, 340 insertions(+), 7 deletions(-)
 create mode 100644 src/dynsight/_internal/vision/vision.py

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
new file mode 100644
index 00000000..cdbc80bd
--- /dev/null
+++ b/src/dynsight/_internal/vision/vision.py
@@ -0,0 +1,337 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from ultralytics import YOLO
+from importlib import metadata
+from calendar import EPOCH
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+# Defaults hyperparameters dictionary.
+default_hyperparams = {
+    "lr0": 0.01,
+    "lrf": 0.01,
+    "momentum": 0.937,
+    "weight_decay": 0.0005,
+    "warmup_epochs": 3.0,
+    "warmup_momentum": 0.8,
+    "box": 7.5,
+    "cls": 0.5,
+    "dfl": 1.5,
+    "hsv_h": 0.015,
+    "hsv_s": 0.7,
+    "hsv_v": 0.4,
+    "degrees": 0.0,
+    "translate": 0.1,
+    "scale": 0.5,
+    "shear": 0.0,
+    "perspective": 0.0,
+    "flipud": 0.0,
+    "fliplr": 0.5,
+    "bgr": 0.0,
+    "mosaic": 1,
+    "mixup": 0.0,
+    "cutmix": 0.0,
+    "copy_paste": 0.0
+}
+
+
+class VisionInstance:
+    """Class for performing vision tasks using YOLO models.
+
+    It allows to perform object detection, Convolutional Neural Network (CNN)
+    training and tuning, training dataset creation and handling.
+
+    .. caution::
+        This class is still in development and may not work as expected.
+
+    """
+    def __init__(
+        self, source: str | Path,
+        output_path: Path,
+        model: str | Path = "yolo12n.pt",
+        device: str | None = None,
+        workers: int = 8,
+    ) -> None:
+        """Initialize a Vision instance.
+
+        Defines the starting model, the output folder and the devices that will
+        be used for the computation.
+
+        Parameters:
+            source:
+                The source of the images or videos to be processed. For the
+                list of the possible sources, we refer the user to the
+                following `table <https://docs.ultralytics.com/modes/predict/#inference-sources>`_.
+                For the list of the supported formats see this second `table <https://docs.ultralytics.com/modes/predict/#images>`_.
+
+            output_path:
+                The path where save the output folder.
+
+            model:
+                The path to the YOLO model file. Defaults to "yolo12n.pt". see
+                `here <https://docs.ultralytics.com/models/yolo12/>`_ for more
+                information.
+
+            device:
+                Allows users to select between CPU, a specific GPU, or other
+                compute devices for model execution (`cpu`, `cuda:0` or `0`).
+
+            workers:
+                Number of worker threads for data loading. Influences the speed
+                of data preprocessing and feeding into the model, especially
+                useful in multi-GPU setups. (only for training sessions).
+
+        """
+        self.output_path: Path = output_path
+        self.training_data_yaml: Path | None = None
+
+        self.model = YOLO(model)
+        self.source = source
+        self.device = device
+        self.workers = workers
+
+        self.prediction_results = None
+        self.training_results = None
+
+    def set_training_dataset(self, training_data_yaml: Path) -> None:
+        """Set the training dataset for the model training.
+
+        Training dataset are setted through a ``yaml`` file that should have
+        thefollowing structure:
+
+        .. code-block:: yaml
+
+            path: path/to/dataset/folder
+            train: path/to/train/images
+            val: path/to/val/images
+
+            nc: number_of_classes
+            names: [class1, class2, ...]
+
+        With a dataset folder structure like this:
+
+        .. code-block:: none
+
+            dataset/
+            ├── images/
+            │   ├── train/
+            │   │   ├── 1.jpg
+            │   │   ├── 2.jpg
+            │   │   └── ...
+            │   └── val/
+            │       ├── 5.jpg
+            │       ├── 6.jpg
+            │       └── ...
+            └── labels/
+                ├── train/
+                │   ├── 1.txt
+                │   ├── 2.txt
+                │   └── ...
+                └── val/
+                    ├── 5.txt
+                    ├── 6.txt
+                    └── ...
+
+
+        Parameters:
+            training_data_yaml:
+                Path to the training data YAML file.
+        """
+        self.training_data_yaml = training_data_yaml
+
+    def predict(
+        self,
+        prediction_title: str,
+        augment: bool = False,
+        agnostic_nms: bool = False,
+        show_labels: bool = False,
+        class_filter: list[int] | None = None,
+        confidence: float = 0.25,
+        iou: float = 0.7,
+        imgsz: int | tuple[int, int] = 640,
+        max_det: int = 500,
+    ) -> None:
+        """Detect objects in the source.
+
+        Parameters:
+            prediction_title:
+                The name of the prediction.
+
+            augment:
+                Enables test-time augmentation (TTA) for predictions,
+                potentially improving detection robustness at the cost of
+                inference speed.
+
+            agnostic_nms:
+                Enables class-agnostic Non-Maximum Suppression (NMS), which
+                merges overlapping boxes of different classes. Useful in
+                multi-class detection scenarios where class overlap is common.
+
+            show_labels:
+                Show labels names in the detected source version.
+
+            class_filter:
+                Filters predictions to a set of class IDs. Only detections
+                belonging to the specified classes will be returned.
+
+            confidence:
+                Sets the minimum confidence threshold for detections.
+                Objects detected with confidence below this threshold will
+                be disregarded.
+
+            iou:
+                Lower values result in fewer detections by eliminating
+                overlapping boxes, useful for reducing duplicates.
+
+            imgsz:
+                Defines the image size for inference. Can be a single integer
+                for square resizing or a tuple. Proper sizing can improve
+                detection accuracy and processingspeed.
+
+            max_det:
+                The maximum number of detections for a single frame / image.
+
+        """
+        self.prediction_results = self.model.predict(
+            source=self.source,
+            save=True,
+            save_txt=False,
+            save_conf=True,
+            show_labels=show_labels,
+            name=prediction_title,
+            project=self.output_path,
+            device=self.device,
+            augment=augment,
+            agnostic_nms=agnostic_nms,
+            classes=class_filter,
+            conf=confidence,
+            iou=iou,
+            imgsz=imgsz,
+            max_det=max_det,
+        )
+
+    def tune_hyperparams(self,
+        title:str,
+        epochs:int=50,
+        iterations:int=15,
+    )-> None:
+        """Tune hyperparameters for the model.
+
+        temporary.
+        """
+        if self.training_data_yaml is None:
+            msg = "Training dataset has not been set."
+            raise ValueError(msg)
+
+        self.model.tune(
+            data=self.training_data_yaml,
+            epochs=epochs,
+            iterations=iterations,
+            project=self.output_path,
+            name=title,
+            device=self.device)
+
+    def train(self,
+        title: str,
+        hyperparams: dict[str, float] | None = None,
+        epochs: int = 100,
+        batch_size: int = 16,
+        patience: int = 20,
+        imgsz: int | tuple[int, int] = 640,
+        ) -> None:
+        """Train a custom model using a training dataset.
+
+        This function trains a custom model using a training dataset. Dataset
+        should be set before calling this function with the
+        ``set_training_data`` method.
+
+        Parameters:
+            title:
+                The name of the model.
+
+            hyperparams:
+                The dictionary that contains all the hyperparameters for the
+                model. The following default ``dict`` is used if not provided:
+
+                .. code-block:: python
+
+                    # Defaults hyperparameters dictionary.
+                    default_hyperparams = {
+                        "lr0": 0.01,
+                        "lrf": 0.01,
+                        "momentum": 0.937,
+                        "weight_decay": 0.0005,
+                        "warmup_epochs": 3.0,
+                        "warmup_momentum": 0.8,
+                        "box": 7.5,
+                        "cls": 0.5,
+                        "dfl": 1.5,
+                        "hsv_h": 0.015,
+                        "hsv_s": 0.7,
+                        "hsv_v": 0.4,
+                        "degrees": 0.0,
+                        "translate": 0.1,
+                        "scale": 0.5,
+                        "shear": 0.0,
+                        "perspective": 0.0,
+                        "flipud": 0.0,
+                        "fliplr": 0.5,
+                        "bgr": 0.0,
+                        "mosaic": 1,
+                        "mixup": 0.0,
+                        "cutmix": 0.0,
+                        "copy_paste": 0.0
+                    }
+
+                Manually customize this ``dict`` to change the training
+                performance or use the ``tune`` method to automatically
+                optimize hyperparameters.
+
+            epochs:
+                Total number of training epochs. Each epoch represents a full
+                pass over the entire dataset.
+
+            batch_size:
+                Batch size, with three modes: set as an integer (batch=16),
+                auto mode for 60% GPU memory utilization (batch=-1), or auto
+                mode with specified utilization fraction (batch=0.70).
+
+            patience:
+                Number of epochs to wait without improvement in validation
+                metrics before early stopping the training. Helps to prevent
+                overfitting.
+
+            imgsz:
+                Defines the image size for inference. Can be a single integer
+                for square resizing or a tuple. Proper sizing can improve
+                detection accuracy and processingspeed.
+
+        """
+        if self.training_data_yaml is None:
+            msg = "Training dataset has not been set."
+            raise ValueError(msg)
+
+        full_params = default_hyperparams.copy()
+        if hyperparams is not None:
+            unknown_keys = set(hyperparams.keys()) - set(full_params.keys())
+            if unknown_keys:
+                msg = f"Unknown hyperparameters: {unknown_keys}"
+                raise ValueError(msg)
+            for key in hyperparams:
+                full_params[key] = hyperparams[key]
+
+        self.training_results = self.model.train(
+            data=self.training_data_yaml,
+            epochs=epochs,
+            imgsz=imgsz,
+            batch=batch_size,
+            workers=self.workers,
+            name=title,
+            project=self.output_path,
+            patience=patience,
+            device=self.device,
+            **full_params
+        )
diff --git a/src/dynsight/vision.py b/src/dynsight/vision.py
index 6c83a813..6669b967 100644
--- a/src/dynsight/vision.py
+++ b/src/dynsight/vision.py
@@ -1,13 +1,9 @@
 """Vision package."""
 
-from dynsight._internal.vision.detect import (
-    Detect,
-)
-from dynsight._internal.vision.video_to_frame import (
-    Video,
+from dynsight._internal.vision.vision import (
+    VisionInstance,
 )
 
 __all__ = [
-    "Detect",
-    "Video",
+    "VisionInstance",
 ]

From 49a6334474d92cf0395ea9051bac6cdb5d9fe648 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Fri, 27 Jun 2025 17:56:05 +0200
Subject: [PATCH 02/67] deleted old vision code.

---
 examples/video_detection.py                   | 123 ---
 src/dynsight/_internal/vision/detect.py       | 847 ------------------
 .../_internal/vision/video_to_frame.py        |  88 --
 src/dynsight/_internal/vision/vision_gui.py   | 220 -----
 .../_internal/vision/vision_utilities.py      | 103 ---
 5 files changed, 1381 deletions(-)
 delete mode 100644 examples/video_detection.py
 delete mode 100644 src/dynsight/_internal/vision/detect.py
 delete mode 100644 src/dynsight/_internal/vision/video_to_frame.py
 delete mode 100644 src/dynsight/_internal/vision/vision_gui.py
 delete mode 100644 src/dynsight/_internal/vision/vision_utilities.py

diff --git a/examples/video_detection.py b/examples/video_detection.py
deleted file mode 100644
index 9d5695cb..00000000
--- a/examples/video_detection.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import argparse
-from pathlib import Path
-
-import dynsight
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Dynsight video detection")
-    parser.add_argument(
-        "--prepare",
-        action="store_true",
-        help="Prepare synthesized data.",
-    )
-    parser.add_argument(
-        "--train",
-        action="store_true",
-        help="Train the model.",
-    )
-    parser.add_argument(
-        "--predict",
-        action="store_true",
-        help="Predict the video.",
-    )
-    parser.add_argument(
-        "--input",
-        type=Path,
-        required=True,
-        help="Path to input file.",
-    )
-    parser.add_argument(
-        "--output",
-        type=Path,
-        required=True,
-        help="Path to output folder.",
-    )
-    parser.add_argument(
-        "--maxcycle",
-        type=int,
-        required=False,
-        default=5,
-        help="Number of fitting cycle procedures.",
-    )
-    parser.add_argument(
-        "--epochs",
-        type=int,
-        required=False,
-        default=100,
-        help="Number of epochs for each training session.",
-    )
-    parser.add_argument(
-        "--patience",
-        type=int,
-        required=False,
-        default=10,
-        help="Patience in terms of epochs to earlystop the procedure.",
-    )
-    parser.add_argument(
-        "--batchsize",
-        type=int,
-        required=False,
-        default=2,
-        help="BatchSize for training.",
-    )
-    parser.add_argument(
-        "--workers", type=int, required=True, help="Number of CPUs used."
-    )
-    parser.add_argument(
-        "--gpu",
-        nargs="+",
-        type=int,
-        default=None,
-        help="IDs of the GPU(s) used.",
-    )
-    parser.add_argument(
-        "--step",
-        type=int,
-        required=False,
-        default=1,
-        help="Step for frame to be used.",
-    )
-    parser.add_argument(
-        "--detect_model",
-        type=Path,
-        required=False,
-        default=None,
-        help="Model to be used for predictions.",
-    )
-    return parser.parse_args()
-
-
-def main() -> None:
-    args = parse_args()
-    input_video = args.input
-    output_project = args.output
-    video = dynsight.vision.Video(input_video)
-    detection = dynsight.vision.Detect(
-        input_video=video,
-        project_folder=output_project,
-    )
-    if args.prepare:
-        detection.synthesize()
-    if args.train:
-        detection.fit(
-            initial_dataset=detection.get_project_path()
-            / "training_options.yaml",
-            max_sessions=args.maxcycle,
-            training_epochs=args.epochs,
-            training_patience=args.patience,
-            batch_size=args.batchsize,
-            workers=args.workers,
-            device=args.gpu,
-            frame_reading_step=args.step,
-        )
-    if args.predict:
-        detection.predict_frames(model_path=args.detect_model)
-        detection.compute_xyz(
-            prediction_folder_path=Path("prediction"),
-            output_path=Path.cwd(),
-        )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/dynsight/_internal/vision/detect.py b/src/dynsight/_internal/vision/detect.py
deleted file mode 100644
index 3981486e..00000000
--- a/src/dynsight/_internal/vision/detect.py
+++ /dev/null
@@ -1,847 +0,0 @@
-from __future__ import annotations
-
-import logging
-import pathlib
-import shutil
-import tkinter as tk
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Literal, TypedDict, cast
-
-import numpy as np
-from PIL import Image
-
-try:
-    from ultralytics import YOLO
-except ImportError:
-    YOLO = None
-
-try:
-    import yaml
-except ImportError:
-    yaml = None
-
-from .vision_gui import VisionGUI
-from .vision_utilities import find_outliers
-
-if TYPE_CHECKING:
-    from .video_to_frame import Video
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s | %(levelname)s | %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-
-class YAMLConfig(TypedDict):
-    """Class for YAML file configurations."""
-
-    train: list[str]
-    val: list[str]
-    nc: int
-    names: list[str]
-
-
-class Detect:
-    """A class to manage the full pipeline of object detection from videos.
-
-    * Author: Simone Martino
-
-    .. caution::
-        This part of the code is still under development and may
-        contain errors.
-
-    """
-
-    def __init__(
-        self,
-        input_video: Video,
-        project_folder: pathlib.Path = Path("output_folder"),
-    ) -> None:
-        """Initialize a detection project.
-
-        Creates all necessary subdirectories and extracts video frames if not
-        already present.
-
-        Parameters:
-            input_video:
-                The input video object from which to extract frames.
-
-            project_folder:
-                Path to the main project directory.
-        """
-        # Main directory for all the project outputs.
-        self._project_folder = project_folder
-        self._project_folder.mkdir(parents=True, exist_ok=True)
-
-        # Directory where store extracted video frames.
-        self._frame_path = self._project_folder / "frames"
-
-        # Directory path where save the training item crops selected.
-        self._training_items_path = self._project_folder / "training_items"
-
-        # Directory path for the generated synthetic dataset.
-        self._syn_dataset_path = self._project_folder / "synthetic_dataset"
-
-        # Directory path where save trained models.
-        self._models_path = self._project_folder / "models"
-
-        # Directory path where save prediction outputs.
-        self._predictions_path = self._project_folder / "predictions"
-
-        # Path to the YAML configuration file for dataset training.
-        self._yaml_file_path = self._project_folder / "training_options.yaml"
-
-        # Resolution of the input video (width, height) in pixels.
-        self._video_size = input_video.resolution()
-        # Total number of frames extracted from the video.
-        self._n_frames = input_video.count_frames()
-
-        # Check if the video's frame are already present
-        # if not -> extract them
-        if not self._frame_path.exists():
-            input_video.extract_frames(project_folder)
-
-    def get_project_path(self) -> pathlib.Path:
-        """It returns the path of the detection project."""
-        return self._project_folder
-
-    def synthesize(
-        self,
-        dataset_dimension: int = 1000,
-        reference_img_path: None | pathlib.Path = None,
-        validation_set_fraction: float = 0.2,
-        collage_size: None | tuple[int, int] = None,
-        collage_max_repeats: int = 30,
-        sample_from: Literal["gui"] = "gui",
-        random_seed: int | None = None,
-    ) -> None:
-        """Generate a synthetic dataset by creating collages of training items.
-
-        Parameters:
-            dataset_dimension:
-                Total number of synthetic samples to generate.
-
-            reference_img_path:
-                Path to the reference image to initialize the GUI.
-                If None, uses the first extracted frame.
-
-            validation_set_fraction:
-                Fraction of samples to allocate to the validation set.
-
-            collage_size:
-                Size (width, height) of the generated collage images.
-                If None the input_video size will be taken.
-
-            collage_max_repeats:
-                Maximum number of training items that can be placed in
-                a single collage.
-
-            sample_from:
-                Mode to collect training items. in the current version
-                only the "gui" mode is available, other modes will come
-                in the future.
-
-            random_seed:
-                Seed for shuffling the dataset.
-        """
-        if collage_size is None:
-            collage_size = self._video_size
-        # Dataset structure
-        images_train_dir = self._syn_dataset_path / "images" / "train"
-        images_val_dir = self._syn_dataset_path / "images" / "val"
-        labels_train_dir = self._syn_dataset_path / "labels" / "train"
-        labels_val_dir = self._syn_dataset_path / "labels" / "val"
-
-        # Sampling method
-        self._sample(
-            sample_from=sample_from,
-            reference_img_path=reference_img_path,
-        )
-
-        logger.info("Initializing the synthetic dataset")
-        # Initialize a new dataset
-        self._syn_dataset_path.mkdir(exist_ok=True)
-        images_train_dir.mkdir(exist_ok=True, parents=True)
-        images_val_dir.mkdir(exist_ok=True, parents=True)
-        labels_train_dir.mkdir(exist_ok=True, parents=True)
-        labels_val_dir.mkdir(exist_ok=True, parents=True)
-
-        # Split between training and validation set
-        num_val = int(dataset_dimension * validation_set_fraction)
-        num_train = dataset_dimension - num_val
-        remaining = dataset_dimension - (num_train + num_val)
-        num_train += remaining
-
-        assignments = ["train"] * num_train + ["val"] * num_val
-
-        rng = np.random.default_rng(seed=random_seed)
-        rng.shuffle(assignments)
-
-        # Create synthetic images to fill the dataset
-        # Create labels for each images generated
-        logger.info("Generating synthetic images")
-        for i in range(1, dataset_dimension + 1):
-            collage, label_lines = self._create_collage(
-                images_folder=self._training_items_path,
-                width=collage_size[0],
-                height=collage_size[1],
-                max_repeats=collage_max_repeats,
-                random_seed=random_seed,
-            )
-            subset = assignments[i - 1]
-            if subset == "train":
-                image_save_path = images_train_dir / f"{i}.png"
-                label_save_path = labels_train_dir / f"{i}.txt"
-            else:
-                image_save_path = images_val_dir / f"{i}.png"
-                label_save_path = labels_val_dir / f"{i}.txt"
-
-            collage.save(image_save_path)
-            with label_save_path.open("w") as f:
-                for line in label_lines:
-                    f.write(line + "\n")
-
-        logger.info("Generating yaml configuration file")
-        # Generate the config file for the dataset created
-        self._add_or_create_yaml(self._syn_dataset_path)
-
-    # Just a bridge to the YOLO library
-    def train(
-        self,
-        yaml_file: pathlib.Path,
-        batch_size: int,
-        workers: int,
-        initial_model: str | pathlib.Path = "yolo12x.pt",
-        training_name: str | None = None,
-        training_epochs: int = 100,
-        training_patience: int = 100,
-        device: int | str | list[int] | None = None,
-    ) -> None:
-        """Train a YOLO model on the selected dataset.
-
-        This function uses the
-        `ultralytics YOLO library <https://github.com/ultralytics/ultralytics>`_
-        for the model training.
-
-        Parameters:
-            yaml_file:
-                Path to the dataset YAML configuration file.
-
-            initial_model:
-                Initial pretrained model to fine-tune.
-
-            training_name:
-                Name for the training run.
-
-            training_epochs:
-                Maximum number of training epochs for each training session.
-
-            training_patience:
-                Early stopping patience (number of epochs without improvement).
-
-            batch_size:
-                Batch size for training.
-
-            workers:
-                Number of dataloader worker threads.
-
-            device:
-                Device(s) on which run training.
-        """
-        model = YOLO(initial_model)
-        model.train(
-            data=yaml_file,
-            epochs=training_epochs,
-            patience=training_patience,
-            batch=batch_size,
-            imgsz=self._video_size,
-            workers=workers,
-            project=self._models_path,
-            name=training_name,
-            device=device,
-            plots=False,
-        )
-
-    # Just a bridge to the YOLO library
-    def predict_frames(
-        self,
-        model_path: str | pathlib.Path,
-        detections_iou: float = 0.1,
-        prediction_name: str = "prediction",
-    ) -> None:
-        """Perform object detection predictions on the extracted frames.
-
-        This function uses the
-        `ultralytics YOLO library <https://github.com/ultralytics/ultralytics>`_
-        to detect objects in videos.
-
-        Parameters:
-            model_path:
-                Path to the trained model.
-
-            detections_iou:
-                IOU threshold for object detection filtering.
-
-            prediction_name:
-                Name under which save the prediction results.
-        """
-        model = YOLO(model_path)
-        for frame in range(self._n_frames):
-            model.predict(
-                project=self._project_folder,
-                source=self._frame_path / f"{frame}.png",
-                name=prediction_name,
-                augment=True,
-                line_width=2,
-                save=True,
-                show_labels=False,
-                save_txt=True,
-                save_conf=True,
-                iou=detections_iou,
-                max_det=20000,
-                exist_ok=True,
-                imgsz=self._video_size,
-            )
-
-    def fit(
-        self,
-        initial_dataset: pathlib.Path,
-        max_sessions: int,
-        training_epochs: int,
-        training_patience: int,
-        batch_size: int,
-        workers: int,
-        initial_model: str | pathlib.Path = "yolo12x.pt",
-        device: int | str | list[int] | None = None,
-        frame_reading_step: int = 1,
-    ) -> None:
-        """Train an object detection model through iterative self-training.
-
-        This method performs multiple rounds of training and prediction:
-            1. Train the model on the initial dataset.
-            2. Predict bounding boxes on video frames using the trained model.
-            3. Identify and remove outlier detections based on box sizes.
-            4. Build a new training dataset from filtered detections.
-            5. Retrain the model on the refined dataset.
-            6. Repeat steps 2 to 5 for a given number of sessions to
-                progressively refine the model.
-
-        This method uses the
-        `ultralytics YOLO library <https://github.com/ultralytics/ultralytics>`_.
-
-        Parameters:
-            initial_dataset:
-                Path to the initial dataset YAML file.
-
-            initial_model:
-                Path to the initial model weights (.pt file).
-
-            max_sessions:
-                Number of retraining cycles.
-
-            training_epochs:
-                Number of epochs per training session.
-
-            training_patience:
-                Early stopping patience during training.
-
-            batch_size:
-                Batch size for training.
-
-            workers:
-                Number of data loader workers.
-
-            device:
-                Device(s) to use (e.g., "cpu", "0", [0,1]).
-
-            frame_reading_step:
-                Specifies the interval at which frames are sampled from
-                the video during processing.
-        """
-        # Initilize the first training
-        current_dataset = initial_dataset
-        guess_model_name = "v0"
-        prediction_number = 0
-        detection_results = []
-
-        logger.info("First training begins.")
-        self.train(
-            yaml_file=current_dataset,
-            initial_model=initial_model,
-            training_epochs=2,
-            batch_size=batch_size,
-            workers=workers,
-            device=device,
-            training_name=guess_model_name,
-        )
-
-        current_model_path = (
-            self._project_folder
-            / "models"
-            / guess_model_name
-            / "weights"
-            / "best.pt"
-        )
-        current_model = YOLO(current_model_path)
-        logger.info(f"Starting prediction number {prediction_number}")
-        for f in range(0, self._n_frames, frame_reading_step):
-            frame_file = self._frame_path / f"{f}.png"
-            prediction = current_model.predict(
-                source=frame_file,
-                imgsz=self._video_size,
-                augment=True,
-                save=True,
-                save_txt=True,
-                save_conf=True,
-                show_labels=False,
-                name=f"attempt_{prediction_number}",
-                iou=0.1,
-                max_det=20000,
-                project=self._predictions_path,
-                line_width=2,
-                exist_ok=True,
-            )
-            # Read and save the prediction results
-            if prediction and prediction[0].boxes:
-                xywh = prediction[0].boxes.xywh.cpu().numpy()
-                conf = prediction[0].boxes.conf.cpu().numpy()
-                cls = prediction[0].boxes.cls.cpu().numpy()
-                n_detection = len(xywh)
-
-                for i in range(n_detection):
-                    x, y, w, h = xywh[i]
-                    detection_results.append(
-                        {
-                            "frame": f,
-                            "class_id": int(cls[i]),
-                            "center_x": float(x),
-                            "center_y": float(y),
-                            "width": float(w),
-                            "height": float(h),
-                            "confidence": float(conf[i]),
-                        }
-                    )
-        logger.info("Looking for outliers")
-        # Filter detections
-        detection_results = self._filter_detections(
-            detection_results,
-            prediction_number,
-        )
-        # Build a new dataset based on the "filtered" detection results
-        # New dataset path
-        train_dataset_path = (
-            self._project_folder
-            / "train_datasets"
-            / f"dataset_{prediction_number}"
-        )
-        logger.info(f"Building the dataset (version {prediction_number})")
-        # Build the dataset
-        self._build_dataset(
-            detection_results=detection_results,
-            dataset_name=f"dataset_{prediction_number}",
-        )
-        # Add the new dataset to the training config file
-        self._add_or_create_yaml(train_dataset_path)
-
-        # Iterative part to improve the model performace
-        for s in range(max_sessions):
-            logger.info(f"Starting a new training session (number {s + 1}")
-            new_model_name = f"v{s + 1}"
-            self.train(
-                yaml_file=self._yaml_file_path,
-                initial_model=current_model_path,
-                training_epochs=training_epochs,
-                training_patience=training_patience,
-                batch_size=batch_size,
-                workers=workers,
-                device=device,
-                training_name=new_model_name,
-            )
-            current_model_path = (
-                self._project_folder
-                / "models"
-                / new_model_name
-                / "weights"
-                / "best.pt"
-            )
-            current_model = YOLO(current_model_path)
-            prediction_number += 1
-            detection_results = []
-            logger.info(f"Starting prediction number {prediction_number}")
-            for f in range(0, self._n_frames, frame_reading_step):
-                frame_file = self._frame_path / f"{f}.png"
-                prediction = current_model.predict(
-                    source=frame_file,
-                    imgsz=self._video_size,
-                    augment=True,
-                    save=True,
-                    save_txt=True,
-                    save_conf=True,
-                    show_labels=False,
-                    name=f"attempt_{prediction_number}",
-                    iou=0.1,
-                    max_det=20000,
-                    project=self._project_folder / "predictions",
-                    line_width=2,
-                    exist_ok=True,
-                )
-                # Read prediction
-                if prediction and prediction[0].boxes:
-                    xywh = prediction[0].boxes.xywh.cpu().numpy()
-                    conf = prediction[0].boxes.conf.cpu().numpy()
-                    cls = prediction[0].boxes.cls.cpu().numpy()
-                    n_detection = len(xywh)
-
-                    for i in range(n_detection):
-                        x, y, w, h = xywh[i]
-                        detection_results.append(
-                            {
-                                "frame": f,
-                                "class_id": int(cls[i]),
-                                "center_x": float(x),
-                                "center_y": float(y),
-                                "width": float(w),
-                                "height": float(h),
-                                "confidence": float(conf[i]),
-                            }
-                        )
-            logger.info("Looking for outliers")
-            # Filter detections
-            detection_results = self._filter_detections(
-                detection_results,
-                prediction_number,
-            )
-            logger.info(f"Building the dataset (version {prediction_number}")
-            # Build the new dataset
-            self._build_dataset(
-                detection_results=detection_results,
-                dataset_name=f"dataset_{prediction_number}",
-            )
-            # Remove the oldest dataset in config
-            # It has been made to avoid training bias on worst results
-            self._remove_old_dataset()
-
-            train_dataset_path = (
-                self._project_folder
-                / "train_datasets"
-                / f"dataset_{prediction_number}"
-            )
-            # Update the dataset config file
-            self._add_or_create_yaml(train_dataset_path)
-
-    def compute_xyz(
-        self,
-        prediction_folder_path: pathlib.Path,
-        output_path: pathlib.Path,
-    ) -> None:
-        """Computes and saves the trajectory of detections to xyz file.
-
-        Parameters:
-            prediction_folder_path:
-                The path to the folder containing detection data files.
-            output_path:
-                The path where the resulting trajectory data should be saved.
-        """
-        lab_folder = prediction_folder_path / "labels"
-        frame_positions = []
-        for frame in range(self._n_frames):
-            label_file = lab_folder / f"{frame}.txt"
-
-            with label_file.open("r") as file:
-                frame_detections = []
-                for line in file:
-                    values = line.strip().split()
-                    _, x, y, width, height, confidence = map(float, values)
-                    x *= self._video_size[0]
-                    y *= self._video_size[1]
-                    frame_detections.append((x, y))
-                frame_positions.append(frame_detections)
-
-        with output_path.open("w") as file:
-            for frame_index, detections in enumerate(frame_positions):
-                file.write(f"{len(detections)}\n")
-                file.write(f"Frame {frame_index}\n")
-                for x, y in detections:
-                    z = 0
-                    file.write(f"{x:.6f} {y:.6f} {z:.6f}\n")
-
-    def _filter_detections(
-        self,
-        input_results: list[dict[str, int | float]],
-        prediction_number: int,
-    ) -> list[dict[str, int | float]]:
-        # Initialize outliers folder in the prediction folder
-        outliers_plt_folder = (
-            self._project_folder
-            / "predictions"
-            / f"attempt_{prediction_number}"
-            / "outliers"
-        )
-        outliers_plt_folder.mkdir(exist_ok=True)
-
-        # Look for outliers in the boxes width and height
-        widths = np.array([d["width"] for d in input_results], dtype=float)
-        heights = np.array(
-            [d["height"] for d in input_results],
-            dtype=float,
-        )
-        try:
-            out_width = set(
-                find_outliers(
-                    distribution=widths,
-                    save_path=outliers_plt_folder,
-                    fig_name="width",
-                )
-            )
-        except (RuntimeError, ValueError) as e:
-            logger.warning(
-                "Outlier detection for width failed: %s. No width outliers",
-                e,
-            )
-            out_width = set()
-
-        try:
-            out_height = set(
-                find_outliers(
-                    distribution=heights,
-                    save_path=outliers_plt_folder,
-                    fig_name="height",
-                )
-            )
-        except (RuntimeError, ValueError) as e:
-            logger.warning(
-                "Outlier detection for height failed: %s. No height outliers.",
-                e,
-            )
-            out_height = set()
-
-        filtered_results = [
-            det
-            for det in input_results
-            if (det["width"] not in out_width)
-            and (det["height"] not in out_height)
-        ]
-
-        if not filtered_results:
-            logger.warning("No outliers detected.")
-            filtered_results = input_results
-
-        return filtered_results
-
-    def _remove_old_dataset(self) -> None:
-        """Removes the oldest dataset from the YAML configuration."""
-        yaml_path = self._yaml_file_path
-
-        if not yaml_path.exists():
-            return
-
-        with yaml_path.open("r") as f:
-            cfg = yaml.safe_load(f) or {}
-
-        for key in ("train", "val"):
-            if key not in cfg:
-                return
-            if isinstance(cfg[key], str):
-                cfg[key] = [cfg[key]]
-            elif not isinstance(cfg[key], list):
-                return
-
-        for key in ("train", "val"):
-            if cfg[key]:
-                cfg[key].pop(0)
-
-        with yaml_path.open("w") as f:
-            yaml.safe_dump(cfg, f, sort_keys=False)
-
-    def _add_or_create_yaml(self, new_dataset_path: Path) -> None:
-        yaml_path = Path(self._yaml_file_path)
-
-        train_p = str((new_dataset_path / "images/train").resolve())
-        val_p = str((new_dataset_path / "images/val").resolve())
-
-        if not yaml_path.exists():
-            cfg: YAMLConfig = {
-                "train": [train_p],
-                "val": [val_p],
-                "nc": 1,
-                "names": ["obj"],
-            }
-        else:
-            raw = yaml.safe_load(yaml_path.open("r")) or {}
-            cfg = cast("YAMLConfig", raw)
-
-            if not isinstance(cfg.get("train"), list):
-                cfg["train"] = [str(cfg.get("train") or train_p)]
-            if not isinstance(cfg.get("val"), list):
-                cfg["val"] = [str(cfg.get("val") or val_p)]
-
-            cfg["train"].append(train_p)
-            cfg["val"].append(val_p)
-            cfg["train"] = list(dict.fromkeys(cfg["train"]))
-            cfg["val"] = list(dict.fromkeys(cfg["val"]))
-            # cfg.pop("path", None) # noqa: ERA001
-
-            if isinstance(cfg.get("nc"), str):
-                cfg["nc"] = int(cfg["nc"])
-
-        with yaml_path.open("w") as f:
-            yaml.safe_dump(cfg, f, sort_keys=False)
-
-    def _build_dataset(
-        self,
-        detection_results: list[dict[str, Any]],
-        dataset_name: str,
-        split_ratio: float = 0.8,
-    ) -> None:
-        """Builds a dataset by splitting frames/labels into train and val."""
-        output_dir = self._project_folder / "train_datasets" / dataset_name
-        imgs_train_dir = output_dir / "images" / "train"
-        imgs_val_dir = output_dir / "images" / "val"
-        labs_train_dir = output_dir / "labels" / "train"
-        labs_val_dir = output_dir / "labels" / "val"
-        for d in (imgs_train_dir, imgs_val_dir, labs_train_dir, labs_val_dir):
-            d.mkdir(parents=True, exist_ok=True)
-
-        detections_by_frame: dict[int, list[dict[str, Any]]] = {}
-        for det in detection_results:
-            frame_idx = det["frame"]
-            detections_by_frame.setdefault(frame_idx, []).append(det)
-
-        all_frames = sorted(detections_by_frame.keys())
-
-        split_point = int(len(all_frames) * split_ratio)
-        train_frames = set(all_frames[:split_point])
-
-        for frame_idx, dets in detections_by_frame.items():
-            if frame_idx in train_frames:
-                img_dest = imgs_train_dir
-                lab_dest = labs_train_dir
-            else:
-                img_dest = imgs_val_dir
-                lab_dest = labs_val_dir
-
-            img_src = self._frame_path / f"{frame_idx}.png"
-            img_dst = img_dest / f"{frame_idx}.png"
-            shutil.copy(img_src, img_dst)
-
-            lab_file = lab_dest / f"{frame_idx}.txt"
-            with lab_file.open("w") as f:
-                img_w, img_h = Image.open(img_src).size
-
-                for det in dets:
-                    x_ctr = det["center_x"]
-                    y_ctr = det["center_y"]
-                    w = det["width"]
-                    h = det["height"]
-                    cls = det["class_id"]
-                    x_ctr_n = x_ctr / img_w
-                    y_ctr_n = y_ctr / img_h
-                    w_n = w / img_w
-                    h_n = h / img_h
-                    f.write(
-                        f"{cls} {x_ctr_n:.6f} {y_ctr_n:.6f} "
-                        f"{w_n:.6f} {h_n:.6f}\n"
-                    )
-
-    def _create_collage(
-        self,
-        images_folder: pathlib.Path,
-        width: int,
-        height: int,
-        random_seed: int | None = None,
-        patience: int = 1000,
-        max_repeats: int = 1,
-    ) -> tuple[Image.Image, list[str]]:
-        """Creates a collage of images by placing them randomly on a canvas."""
-        collage = Image.new("RGBA", (width, height), (255, 255, 255, 255))
-        placed_rects: list[tuple[int, int, int, int]] = []
-        label_lines = []
-        cropped_images = []
-
-        for file in images_folder.iterdir():
-            if file.suffix.lower() in {".png", ".jpg", ".jpeg"}:
-                img = Image.open(file).convert("RGBA")
-                cropped_images.append(img)
-
-        if not cropped_images:
-            msg = "No images found in the specified folder"
-            raise ValueError(msg)
-
-        total_placement = len(cropped_images) * max_repeats
-        placed_count = 0
-        cropped_images_array = np.array(cropped_images, dtype=object)
-        rng = np.random.default_rng(seed=random_seed)
-        for _ in range(total_placement):
-            cropped = rng.choice(cropped_images_array)
-            w, h = cropped.size[:2]
-            max_x = width - w
-            max_y = height - h
-            placed = False
-
-            for _ in range(patience):
-                x = rng.integers(0, max_x + 1)
-                y = rng.integers(0, max_y + 1)
-                new_rect = (x, y, x + w, y + h)
-
-                overlap = any(
-                    not (
-                        new_rect[2] <= rect[0]
-                        or new_rect[0] >= rect[2]
-                        or new_rect[3] <= rect[1]
-                        or new_rect[1] >= rect[3]
-                    )
-                    for rect in placed_rects
-                )
-
-                if not overlap:
-                    collage.paste(cropped, (x, y), cropped)
-                    placed_rects.append(new_rect)
-
-                    center_x = (x + w / 2) / width
-                    center_y = (y + h / 2) / height
-                    width_norm = w / width
-                    height_norm = h / height
-                    label_line = (
-                        f"0 {center_x:.6f} "
-                        f"{center_y:.6f} "
-                        f"{width_norm:.6f} "
-                        f"{height_norm:.6f}"
-                    )
-                    label_lines.append(label_line)
-                    placed_count += 1
-                    placed = True
-                    break
-
-            if not placed:
-                break
-
-        return collage, label_lines
-
-    def _sample(
-        self, sample_from: str, reference_img_path: pathlib.Path | None
-    ) -> None:
-        # GUI mode
-        if sample_from == "gui":
-            logger.info("Loading Graphic User Interface")
-            # If not specified by the user use the first img as example
-            if reference_img_path is None:
-                logger.info("Using first frame as reference img.")
-                reference_img_path = self._frame_path / "0.png"
-            # Open the GUI
-            root = tk.Tk()
-            VisionGUI(
-                master=root,
-                image_path=reference_img_path,
-                destination_folder=self._project_folder,
-            )
-            root.mainloop()
-            # Check if the training items has been properly created
-            if not (
-                self._training_items_path.exists()
-                and self._training_items_path.is_dir()
-            ):
-                msg = "'training_items' folder not created or not found"
-                raise ValueError(msg)
-        else:
-            raise NotImplementedError
diff --git a/src/dynsight/_internal/vision/video_to_frame.py b/src/dynsight/_internal/vision/video_to_frame.py
deleted file mode 100644
index e19409eb..00000000
--- a/src/dynsight/_internal/vision/video_to_frame.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING
-
-try:
-    import cv2
-except ImportError:
-    cv2 = None  # type: ignore[assignment]
-
-if TYPE_CHECKING:
-    import pathlib
-
-
-@dataclass
-class Video:
-    """Load a video file and provide utilities.
-
-    This class loads a video from a file path and provides methods
-    to retrieve video information and extract frames.
-
-    * Author: Simone Martino
-
-    .. caution::
-        This part of the code is still under development and may
-        contain errors.
-
-    Parameters:
-        video_path: Path to the video file.
-
-    """
-
-    video_path: pathlib.Path
-    # OpenCV video capture object.
-    _capture: cv2.VideoCapture = field(init=False, repr=False)
-
-    def __post_init__(self) -> None:
-        """Load the the video."""
-        self._capture = cv2.VideoCapture(str(self.video_path))
-        if not self._capture.isOpened():
-            msg = f"Impossible to load the video: {self.video_path}"
-            raise ValueError(msg)
-
-    def __del__(self) -> None:
-        """Close the the video."""
-        if hasattr(self, "_capture") and self._capture.isOpened():
-            self._capture.release()
-
-    def count_frames(self) -> int:
-        """Counts the total number of frames in the video.
-
-        Returns:
-            The number of frames in the video.
-        """
-        return int(self._capture.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    def resolution(self) -> tuple[int, int]:
-        """Retrieves video width and height in pixels.
-
-        Returns:
-            A tuple `(width, height)` representing the frame dimensions.
-        """
-        width = int(self._capture.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(self._capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        return (width, height)
-
-    def extract_frames(self, working_dir: pathlib.Path) -> None:
-        """Extracts all frames from the video and saves them as PNG images.
-
-        If it doesn't exist, creates a ``frames`` subdirectory inside
-        ``working_dir``, reads each frame from the video and
-        writes them to disk.
-
-        Parameters:
-            working_dir: Directory in which to create a `frames` folder and
-            save extracted PNG images.
-
-        """
-        frames_dir = working_dir / "frames"
-        frames_dir.mkdir(exist_ok=True)
-
-        self._capture.set(cv2.CAP_PROP_POS_FRAMES, 0)
-        total_frames = self.count_frames()
-
-        for frame_idx in range(total_frames):
-            _, frame = self._capture.read()
-            frame_filename = frames_dir / f"{frame_idx}.png"
-            cv2.imwrite(str(frame_filename), frame)
diff --git a/src/dynsight/_internal/vision/vision_gui.py b/src/dynsight/_internal/vision/vision_gui.py
deleted file mode 100644
index d15b4ec0..00000000
--- a/src/dynsight/_internal/vision/vision_gui.py
+++ /dev/null
@@ -1,220 +0,0 @@
-from __future__ import annotations
-
-import pathlib
-import tkinter as tk
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-from PIL import Image
-
-
-@dataclass
-class Box:
-    id: int
-    center_x: float
-    center_y: float
-    width: float
-    height: float
-    abs_coords: tuple[int, int, int, int]
-
-
-class VisionGUI:
-    """GUI for interactively labeling images by drawing bounding boxes.
-
-    * Author: Simone Martino
-    """
-
-    def __init__(
-        self,
-        master: tk.Tk,
-        image_path: pathlib.Path,
-        destination_folder: pathlib.Path = Path(__file__).parent,
-    ) -> None:
-        self.master = master
-        self.image_path = image_path
-        self.destination_folder = destination_folder
-        self.master.title("Dynsight: Label tool")
-        try:
-            self.image = tk.PhotoImage(file=image_path)
-        except Exception as e:
-            msg = f"Error loading image: {e}"
-            raise ValueError(msg) from e
-        # Setup the main grid
-        self.master.rowconfigure(index=0, weight=1)
-        # Image
-        self.master.columnconfigure(index=0, weight=1)
-        # Sidebar
-        self.master.columnconfigure(index=1, weight=1)
-
-        # Image canvas
-        self.canvas = tk.Canvas(
-            master=self.master,
-            width=self.image.width(),
-            height=self.image.height(),
-            cursor="crosshair",
-        )
-        self.canvas.grid(row=0, column=0, sticky="nsew")
-        self.canvas.create_image(0, 0, anchor=tk.NW, image=self.image)
-        # Rulers
-        self.h_line = self.canvas.create_line(
-            0,  # x0
-            0,  # y0
-            self.image.width(),  # x1
-            0,  # y1
-            fill="blue",
-            dash=(2, 2),
-            width=3,
-        )
-        self.v_line = self.canvas.create_line(
-            0,  # x0
-            0,  # y0
-            0,  # x1
-            self.image.height(),  # y1
-            fill="blue",
-            dash=(2, 2),
-            width=3,
-        )
-        # Sidebar
-        self.sidebar = tk.Frame(
-            master=self.master,
-            width=150,
-            padx=10,
-            pady=10,
-        )
-        self.sidebar.grid(row=0, column=1, sticky="ns")
-        self.sidebar.grid_propagate(flag=False)
-
-        # Buttons
-        self.submit_button = tk.Button(
-            self.sidebar,
-            text="Submit",
-            command=self._submit,
-        )
-        self.submit_button.pack(pady=10, fill="x")
-
-        self.undo_button = tk.Button(
-            self.sidebar,
-            text="Undo",
-            command=self._undo,
-        )
-        self.undo_button.pack(pady=10, fill="x")
-
-        self.close_button = tk.Button(
-            self.sidebar,
-            text="Close",
-            command=self._close,
-        )
-        self.close_button.pack(pady=10, fill="x")
-
-        # Labelling variables
-        self.start_x = 0
-        self.start_y = 0
-        self.current_box = 0
-        self.boxes: list[Box] = []
-
-        # Mouse bindings
-        self.canvas.bind("<Button-1>", self._on_click_press)
-        self.canvas.bind("<ButtonRelease-1>", self._on_click_release)
-        self.canvas.bind("<B1-Motion>", self._on_mouse_drag)
-        self.canvas.bind("<Motion>", self._follow_mouse)
-
-    # Mouse functions
-    def _on_click_press(self, event: tk.Event[Any]) -> None:
-        """Starts drawing the box on mouse press."""
-        self.start_x, self.start_y = event.x, event.y
-        self.current_box = self.canvas.create_rectangle(
-            self.start_x,  # x0
-            self.start_y,  # y0
-            self.start_x,  # x1
-            self.start_y,  # y1
-            outline="red",
-            width=3,
-        )
-
-    def _on_click_release(self, event: tk.Event[Any]) -> None:
-        """Finalize the box on mouse release."""
-        x2, y2 = event.x, event.y
-        x1, y1 = self.start_x, self.start_y
-        abs_coords = (min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2))
-        center_x = (x1 + x2) / (2 * self.image.width())
-        center_y = (y1 + y2) / (2 * self.image.height())
-        width_rel = abs(x2 - x1) / self.image.width()
-        height_rel = abs(y2 - y1) / self.image.height()
-        box_info = Box(
-            id=self.current_box,
-            center_x=center_x,
-            center_y=center_y,
-            width=width_rel,
-            height=height_rel,
-            abs_coords=abs_coords,
-        )
-        self.boxes.append(box_info)
-        self.current_box = 0
-
-    def _on_mouse_drag(self, event: tk.Event[Any]) -> None:
-        """Update the box coordinates while dragging the mouse."""
-        sel_x, sel_y = event.x, event.y
-        self.canvas.coords(
-            self.current_box,  # ID
-            self.start_x,  # x0
-            self.start_y,  # y0
-            sel_x,  # x1
-            sel_y,  # y1
-        )
-        # Sync rulers too
-        self.canvas.coords(
-            self.h_line,  # ID
-            0,  # x0
-            sel_y,  # y0
-            self.image.width(),  # x1
-            sel_y,  # y1
-        )
-        self.canvas.coords(
-            self.v_line,  # ID
-            sel_x,  # x0
-            0,  # y0
-            sel_x,  # x1
-            self.image.height(),  # y1
-        )
-
-    def _follow_mouse(self, event: tk.Event[Any]) -> None:
-        """Sync guide lines position with mouse movement."""
-        x, y = event.x, event.y
-        self.canvas.coords(
-            self.h_line,  # ID
-            0,  # x0
-            y,  # y0
-            self.image.width(),  # x1
-            y,  # y1
-        )
-        self.canvas.coords(
-            self.v_line,  # ID
-            x,  # x0
-            0,  # y0
-            x,  # x1
-            self.image.height(),  # y1
-        )
-
-    # Button functions
-    def _submit(self) -> None:
-        """Save the cropped images."""
-        cropped_img_folder = self.destination_folder / "training_items"
-        cropped_img_folder.mkdir(exist_ok=True)
-        pil_image = Image.open(self.image_path)
-        for i, box in enumerate(self.boxes):
-            abs_coords = box.abs_coords
-            cropped_image = pil_image.crop(abs_coords)
-            save_path = cropped_img_folder / f"{i}.png"
-            cropped_image.save(save_path)
-        self.master.destroy()
-
-    def _undo(self) -> None:
-        """Remove the last drawn box."""
-        if self.boxes:
-            last_box = self.boxes.pop()
-            self.canvas.delete(last_box.id)
-
-    def _close(self) -> None:
-        """Close without saving."""
-        self.master.destroy()
diff --git a/src/dynsight/_internal/vision/vision_utilities.py b/src/dynsight/_internal/vision/vision_utilities.py
deleted file mode 100644
index 213c7077..00000000
--- a/src/dynsight/_internal/vision/vision_utilities.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.optimize import curve_fit
-from scipy.stats import norm
-
-if TYPE_CHECKING:
-    import pathlib
-
-    from numpy.typing import NDArray
-
-
-def find_outliers(
-    distribution: NDArray[np.float64],
-    save_path: pathlib.Path,
-    fig_name: str,
-    thr: float = 1e-5,
-) -> NDArray[np.float64]:
-    """Detects outliers in a distribution by fitting a normal distribution."""
-    if distribution.size == 0:
-        msg = "Distribution is empty or contains only NaNs/Infs."
-        raise ValueError(msg)
-    if np.std(distribution) == 0:
-        return np.array([])
-
-    def _gaussian(
-        x: NDArray[np.float64], mu: float, sigma: float, amplitude: float
-    ) -> NDArray[np.float64]:
-        return amplitude * norm.pdf(x, mu, sigma)
-
-    # Compute histogram and bin centers
-    hist, bin_edges = np.histogram(distribution, bins="auto", density=True)
-    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
-
-    mask = np.isfinite(hist) & (hist > 0)
-    hist = hist[mask]
-    bin_centers = bin_centers[mask]
-    min_n_bins = 3
-    if len(hist) < min_n_bins:
-        return np.array([])
-    # Fit the Gaussian curve to the histogram data
-    popt, _ = curve_fit(
-        _gaussian,
-        bin_centers,
-        hist,
-        p0=[np.mean(distribution), np.std(distribution), np.max(hist)],
-    )
-    mu, sigma, amplitude = popt
-
-    # Generate fitted Gaussian curve for plotting
-    x = np.linspace(bin_edges[0], bin_edges[-1], 1000)
-    fitted_curve = _gaussian(x, mu, sigma, amplitude)
-
-    # Calculate PDF threshold-based cutoffs
-    base_pdf = amplitude / (sigma * np.sqrt(2 * np.pi))
-    x_threshold_min = mu - np.sqrt(-2 * sigma**2 * np.log(thr / base_pdf))
-    x_threshold_max = mu + np.sqrt(-2 * sigma**2 * np.log(thr / base_pdf))
-
-    # Identify outliers using numpy boolean indexing
-    outliers: NDArray[np.float64] = distribution[
-        (distribution < x_threshold_min) | (distribution > x_threshold_max)
-    ]
-
-    # Plot histogram, fitted curve, and threshold lines
-    plt.hist(
-        distribution,
-        bins="auto",
-        density=True,
-        alpha=0.6,
-        color="g",
-        label="Histogram",
-    )
-    plt.plot(
-        x,
-        fitted_curve,
-        "k-",
-        linewidth=2,
-        label=rf"Gaussian fit  $\mu={mu:.2f},\ \sigma={sigma:.2f}$",
-    )
-    plt.axvline(
-        x_threshold_min,
-        color="r",
-        linestyle="--",
-        label=f"Threshold Min = {x_threshold_min:.2f}",
-    )
-    plt.axvline(
-        x_threshold_max,
-        color="b",
-        linestyle="--",
-        label=f"Threshold Max = {x_threshold_max:.2f}",
-    )
-    plt.legend(loc="best")
-    plt.title("Histogram with Gaussian Fit and Thresholds")
-    plt.xlabel("Values")
-    plt.ylabel("Density")
-    plt.tight_layout()
-    plt.savefig(save_path / fig_name)
-    plt.close()
-
-    return outliers

From dab882db4f1e0a43bd02738f7c07a99f6ef3c15a Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Fri, 27 Jun 2025 17:56:21 +0200
Subject: [PATCH 03/67] Update documentation.

---
 docs/source/index.rst  | 12 +++++++++++-
 docs/source/vision.rst | 18 ++++++++++++++++--
 2 files changed, 27 insertions(+), 3 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 31c71c99..b70e2465 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -75,7 +75,17 @@ or with conda::
 If you want to use the :mod:`dynsight.vision` and :mod:`dynsight.track` modules
 you will need to install a series of packages. This can be done with with pip::
 
-  $ pip install ultralytics PyYAML
+  $ pip install ultralytics
+
+.. caution::
+  Recommended python versions declared by ``ultralytics`` are 3.8 to 3.12.
+  Other versions may cause errors.
+
+.. tip::
+    ``PyTorch`` is required for the ``ultralytics`` package and its
+    requirements vary by operating system and CUDA/ROCm requirements, so
+    install ``PyTorch`` first may avoid compatibility issues. Follow the
+    instructions at this `link <https://pytorch.org/get-started/locally/>`_
 
 
 How to contribute
diff --git a/docs/source/vision.rst b/docs/source/vision.rst
index 4f08e89e..42f3fc8c 100644
--- a/docs/source/vision.rst
+++ b/docs/source/vision.rst
@@ -3,6 +3,21 @@ Vision
 
 Code for computer vision and trajectory extraction from experimental videos.
 
+Note that to use this module, you need to install the ``ultralytics`` package.
+This can be done with pip::
+
+  $ pip install ultralytics
+
+.. caution::
+  Recommended python versions declared by ``ultralytics`` are 3.8 to 3.12.
+  Other versions may cause errors.
+
+.. tip::
+    ``PyTorch`` is required for the ``ultralytics`` package and its
+    requirements vary by operating system and CUDA/ROCm requirements, so
+    install ``PyTorch`` first may avoid compatibility issues. Follow the
+    instructions at this `link <https://pytorch.org/get-started/locally/>`_
+
 -----
 Usage
 -----
@@ -10,5 +25,4 @@ Usage
 .. toctree::
   :maxdepth: 1
 
-  Video <_autosummary/dynsight.vision.Video.rst>
-  Detect <_autosummary/dynsight.vision.Detect.rst>
+  VisionInstance <_autosummary/dynsight.vision.VisionInstance.rst>

From 020f3bab031dc0c89048a7a8435a80a536912999 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Fri, 27 Jun 2025 17:57:17 +0200
Subject: [PATCH 04/67] Removed useless imports.

---
 src/dynsight/_internal/vision/vision.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index cdbc80bd..7a7f0d1f 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -3,8 +3,6 @@
 from typing import TYPE_CHECKING
 
 from ultralytics import YOLO
-from importlib import metadata
-from calendar import EPOCH
 
 if TYPE_CHECKING:
     from pathlib import Path

From 1656ab2f27851d229da87a600321caaaf3c55a17 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 30 Jun 2025 10:14:34 +0200
Subject: [PATCH 05/67] Exporting optimization dictionary.

---
 src/dynsight/_internal/vision/vision.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 7a7f0d1f..f5808012 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -91,6 +91,7 @@ def __init__(
         self.device = device
         self.workers = workers
 
+        self.opt_results: dict[str, float] | None = None
         self.prediction_results = None
         self.training_results = None
 
@@ -215,7 +216,7 @@ def tune_hyperparams(self,
         title:str,
         epochs:int=50,
         iterations:int=15,
-    )-> None:
+    )-> dict[str, float]:
         """Tune hyperparameters for the model.
 
         temporary.
@@ -224,7 +225,7 @@ def tune_hyperparams(self,
             msg = "Training dataset has not been set."
             raise ValueError(msg)
 
-        self.model.tune(
+        self.opt_results = self.model.tune(
             data=self.training_data_yaml,
             epochs=epochs,
             iterations=iterations,
@@ -232,6 +233,8 @@ def tune_hyperparams(self,
             name=title,
             device=self.device)
 
+        return self.opt_results
+
     def train(self,
         title: str,
         hyperparams: dict[str, float] | None = None,

From 2c079b44ebee9276bd6cd72d2e855d4eadebe504 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 30 Jun 2025 11:21:57 +0200
Subject: [PATCH 06/67] Added device checker (WIP).

---
 src/dynsight/_internal/vision/vision.py | 139 +++++++++++++++++++++---
 1 file changed, 126 insertions(+), 13 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index f5808012..8d0d699f 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -1,12 +1,21 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+import logging
+from typing import TYPE_CHECKING, Callable
 
+import torch
 from ultralytics import YOLO
 
 if TYPE_CHECKING:
     from pathlib import Path
 
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+)
+
+logger = logging.getLogger(__name__)
+
 # Defaults hyperparameters dictionary.
 default_hyperparams = {
     "lr0": 0.01,
@@ -32,7 +41,7 @@
     "mosaic": 1,
     "mixup": 0.0,
     "cutmix": 0.0,
-    "copy_paste": 0.0
+    "copy_paste": 0.0,
 }
 
 
@@ -46,8 +55,10 @@ class VisionInstance:
         This class is still in development and may not work as expected.
 
     """
+
     def __init__(
-        self, source: str | Path,
+        self,
+        source: str | Path,
         output_path: Path,
         model: str | Path = "yolo12n.pt",
         device: str | None = None,
@@ -88,13 +99,15 @@ def __init__(
 
         self.model = YOLO(model)
         self.source = source
-        self.device = device
+        self.device = self._normalize_device_string(device)
         self.workers = workers
 
         self.opt_results: dict[str, float] | None = None
         self.prediction_results = None
         self.training_results = None
 
+        self._check_device()
+
     def set_training_dataset(self, training_data_yaml: Path) -> None:
         """Set the training dataset for the model training.
 
@@ -212,11 +225,12 @@ def predict(
             max_det=max_det,
         )
 
-    def tune_hyperparams(self,
-        title:str,
-        epochs:int=50,
-        iterations:int=15,
-    )-> dict[str, float]:
+    def tune_hyperparams(
+        self,
+        title: str,
+        epochs: int = 50,
+        iterations: int = 15,
+    ) -> dict[str, float]:
         """Tune hyperparameters for the model.
 
         temporary.
@@ -231,18 +245,20 @@ def tune_hyperparams(self,
             iterations=iterations,
             project=self.output_path,
             name=title,
-            device=self.device)
+            device=self.device,
+        )
 
         return self.opt_results
 
-    def train(self,
+    def train(
+        self,
         title: str,
         hyperparams: dict[str, float] | None = None,
         epochs: int = 100,
         batch_size: int = 16,
         patience: int = 20,
         imgsz: int | tuple[int, int] = 640,
-        ) -> None:
+    ) -> None:
         """Train a custom model using a training dataset.
 
         This function trains a custom model using a training dataset. Dataset
@@ -334,5 +350,102 @@ def train(self,
             project=self.output_path,
             patience=patience,
             device=self.device,
-            **full_params
+            **full_params,
+        )
+
+    def _normalize_device_string(self, device: str | None) -> str:
+        """Normalize device string to match Ultralytics expectations."""
+        if device is None:
+            return "0" if torch.cuda.is_available() else "cpu"
+
+        device = str(device).lower()
+
+        if device in {"cpu", "mps", "cuda"}:
+            return device
+
+        # Allow "cuda:0" -> "0", "cuda:0,1" -> "0,1"
+        if device.startswith("cuda:"):
+            return device.replace("cuda:", "")
+
+        # Allow "0", "0,1", etc.
+        if all(part.strip().isdigit() for part in device.split(",")):
+            return device
+        msg = f"Unsupported device string: '{device}'"
+        raise ValueError(msg)
+
+    def _check_device(self) -> None:
+        """Verify and validate the selected device for compatibility."""
+        self.device = self._normalize_device_string(self.device)
+
+        def _device_error(msg: str) -> None:
+            raise RuntimeError(msg)
+
+        try:
+            if self.device == "cpu":
+                self._check_cpu_device()
+            elif self.device == "mps":
+                self._check_mps_device(_device_error)
+            elif self.device == "cuda":
+                self._check_single_cuda_device(_device_error)
+            elif all(
+                part.strip().isdigit()
+                for part in self.device.split(",")
+            ):
+                self._check_multi_cuda_devices(_device_error)
+            else:
+                _device_error(f"Unsupported device string: '{self.device}'")
+        except (ValueError, RuntimeError, IndexError, OSError) as e:
+            _device_error(str(e))
+
+
+    def _check_cpu_device(self) -> None:
+        logger.info("Using CPU.")
+
+
+    def _check_mps_device(
+        self, _device_error: Callable[[str], None]
+    ) -> None:
+        if not (
+            hasattr(torch.backends, "mps")
+            and torch.backends.mps.is_available()
+        ):
+            _device_error("MPS device requested but not available.")
+        logger.info("Using Apple MPS backend.")
+
+
+    def _check_single_cuda_device(
+        self, _device_error: Callable[[str], None]
+    ) -> None:
+        if not torch.cuda.is_available():
+            _device_error("CUDA requested but not available.")
+        name = torch.cuda.get_device_name(0)
+        backend = "ROCm" if torch.version.hip else "CUDA"
+        mem_free, mem_total = torch.cuda.mem_get_info(0)
+        logger.info(f"Using GPU 0: {name} [{backend}]")
+        logger.info(
+            "Memory: %.1f MB free / %.1f MB total",
+            mem_free / 1024**2,
+            mem_total / 1024**2,
         )
+        _ = torch.tensor([0.0]).to("cuda:0")
+
+
+    def _check_multi_cuda_devices(
+        self, _device_error: Callable[[str], None]
+    ) -> None:
+        gpus = [int(d) for d in self.device.split(",")]
+        for idx in gpus:
+            if idx >= torch.cuda.device_count():
+                _device_error(
+                    f"Requested GPU index {idx}, but only "
+                    f"{torch.cuda.device_count()} available."
+                )
+            name = torch.cuda.get_device_name(idx)
+            mem_free, mem_total = torch.cuda.mem_get_info(idx)
+            logger.info(f"Using GPU {idx}: {name}")
+            logger.info(
+                "Memory: %.1f MB free / %.1f MB total",
+                mem_free / 1024**2,
+                mem_total / 1024**2,
+            )
+        _ = torch.tensor([0.0]).to(f"cuda:{gpus[0]}")

From ce424cf68b51257e499ea5d5cad312f9a0c359f5 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 30 Jun 2025 12:39:31 +0200
Subject: [PATCH 07/67] Added imgsz to the tuning function.

---
 src/dynsight/_internal/vision/vision.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 8d0d699f..beeaadb4 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -230,6 +230,7 @@ def tune_hyperparams(
         title: str,
         epochs: int = 50,
         iterations: int = 15,
+        imgsz: int | tuple[int, int] = 640,
     ) -> dict[str, float]:
         """Tune hyperparameters for the model.
 
@@ -246,6 +247,7 @@ def tune_hyperparams(
             project=self.output_path,
             name=title,
             device=self.device,
+            imgsz=imgsz,
         )
 
         return self.opt_results

From fb1a07a34af048df89960ba32dee8735598d55e7 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 30 Jun 2025 13:00:44 +0200
Subject: [PATCH 08/67] Added first version of doc for tuning (WIP).

---
 src/dynsight/_internal/vision/vision.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index beeaadb4..93676347 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -234,7 +234,17 @@ def tune_hyperparams(
     ) -> dict[str, float]:
         """Tune hyperparameters for the model.
 
-        temporary.
+        Parameters:
+            title:
+                The name of the tuning session.
+            epochs:
+                The number of epochs to train for each iteration.
+            iterations:
+                The number of searching iterations.
+            imgsz:
+                Defines the image size for inference. Can be a single integer
+                for square resizing or a tuple. Proper sizing can improve
+                inference speed and accuracy.
         """
         if self.training_data_yaml is None:
             msg = "Training dataset has not been set."

From 1e1d6120e408efdfcd6cc46c66e99210fa3804e9 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 30 Jun 2025 14:32:55 +0200
Subject: [PATCH 09/67] Optimized the documentation and modified the output
 shape for tuning.

---
 src/dynsight/_internal/vision/vision.py | 33 +++++++++++++------------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 93676347..15ecc73a 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -6,8 +6,8 @@
 import torch
 from ultralytics import YOLO
 
-if TYPE_CHECKING:
-    from pathlib import Path
+
+from pathlib import Path
 
 logging.basicConfig(
     level=logging.INFO,
@@ -94,7 +94,7 @@ def __init__(
                 useful in multi-GPU setups. (only for training sessions).
 
         """
-        self.output_path: Path = output_path
+        self.output_path = Path(output_path)
         self.training_data_yaml: Path | None = None
 
         self.model = YOLO(model)
@@ -102,7 +102,6 @@ def __init__(
         self.device = self._normalize_device_string(device)
         self.workers = workers
 
-        self.opt_results: dict[str, float] | None = None
         self.prediction_results = None
         self.training_results = None
 
@@ -227,41 +226,43 @@ def predict(
 
     def tune_hyperparams(
         self,
-        title: str,
-        epochs: int = 50,
         iterations: int = 15,
+        epochs: int = 50,
         imgsz: int | tuple[int, int] = 640,
     ) -> dict[str, float]:
         """Tune hyperparameters for the model.
 
+        Optimize the CNN hyperparameters by leveraging the Ultralytics YOLO
+        `genetic algorithm <https://docs.ultralytics.com/guides/hyperparameter-tuning/>`_.
+
         Parameters:
-            title:
-                The name of the tuning session.
+            iterations:
+                The number of exploring iterations. The higher the number, the
+                more accurate the results will be, but the longer the training
+                will take.
+
             epochs:
                 The number of epochs to train for each iteration.
-            iterations:
-                The number of searching iterations.
+
             imgsz:
                 Defines the image size for inference. Can be a single integer
                 for square resizing or a tuple. Proper sizing can improve
-                inference speed and accuracy.
+                detection accuracy and processingspeed.
         """
         if self.training_data_yaml is None:
             msg = "Training dataset has not been set."
             raise ValueError(msg)
 
-        self.opt_results = self.model.tune(
+        return self.model.tune(
             data=self.training_data_yaml,
             epochs=epochs,
             iterations=iterations,
-            project=self.output_path,
-            name=title,
+            project=self.output_path / "tuning",
+            name="results",
             device=self.device,
             imgsz=imgsz,
         )
 
-        return self.opt_results
-
     def train(
         self,
         title: str,

From 95d0bd06f5e4a2e69c726b4868b1d769019ec2d9 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 30 Jun 2025 14:34:52 +0200
Subject: [PATCH 10/67] Fixed formatting.

---
 src/dynsight/_internal/vision/vision.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 15ecc73a..078ecde7 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -1,14 +1,12 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Callable
+from pathlib import Path
+from typing import Callable
 
 import torch
 from ultralytics import YOLO
 
-
-from pathlib import Path
-
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",

From 879331a3e6c30175887923818cb23914397d8c08 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 30 Jun 2025 15:19:00 +0200
Subject: [PATCH 11/67] Added batch_size to tuning.

---
 src/dynsight/_internal/vision/vision.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 078ecde7..4bc2eea0 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -227,11 +227,15 @@ def tune_hyperparams(
         iterations: int = 15,
         epochs: int = 50,
         imgsz: int | tuple[int, int] = 640,
+        batch_size: int = 16,
     ) -> dict[str, float]:
         """Tune hyperparameters for the model.
 
         Optimize the CNN hyperparameters by leveraging the Ultralytics YOLO
         `genetic algorithm <https://docs.ultralytics.com/guides/hyperparameter-tuning/>`_.
+        It returns a dictionary of the best hyperparameters, which can be
+        directly used as input to the hyperparameters parameter in the train
+        method.
 
         Parameters:
             iterations:
@@ -245,7 +249,12 @@ def tune_hyperparams(
             imgsz:
                 Defines the image size for inference. Can be a single integer
                 for square resizing or a tuple. Proper sizing can improve
-                detection accuracy and processingspeed.
+                detection accuracy and processing speed.
+
+            batch_size:
+                Batch size, with three modes: set as an integer (batch=16),
+                auto mode for 60% GPU memory utilization (batch=-1), or auto
+                mode with specified utilization fraction (batch=0.70).
         """
         if self.training_data_yaml is None:
             msg = "Training dataset has not been set."
@@ -259,6 +268,7 @@ def tune_hyperparams(
             name="results",
             device=self.device,
             imgsz=imgsz,
+            batch=batch_size,
         )
 
     def train(

From 62a17cf85627a94b8eb0271297aeefc2919d95c5 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 2 Jul 2025 12:18:14 +0200
Subject: [PATCH 12/67] Added label tool server.

---
 src/dynsight/_internal/vision/label_tool.py   |  37 +++
 .../_internal/vision/label_tool/index.html    |  35 +++
 .../_internal/vision/label_tool/script.js     | 248 ++++++++++++++++++
 .../_internal/vision/label_tool/styles.css    |  83 ++++++
 src/dynsight/vision.py                        |   4 +
 5 files changed, 407 insertions(+)
 create mode 100644 src/dynsight/_internal/vision/label_tool.py
 create mode 100644 src/dynsight/_internal/vision/label_tool/index.html
 create mode 100644 src/dynsight/_internal/vision/label_tool/script.js
 create mode 100644 src/dynsight/_internal/vision/label_tool/styles.css

diff --git a/src/dynsight/_internal/vision/label_tool.py b/src/dynsight/_internal/vision/label_tool.py
new file mode 100644
index 00000000..07474b7d
--- /dev/null
+++ b/src/dynsight/_internal/vision/label_tool.py
@@ -0,0 +1,37 @@
+import functools
+import logging
+import socket
+import threading
+import webbrowser
+from http.server import SimpleHTTPRequestHandler
+from pathlib import Path
+from socketserver import TCPServer
+
+logger = logging.getLogger(__name__)
+
+class HTTPRequestHandler(SimpleHTTPRequestHandler):
+    def log_message(self, fmt: str, *args: object) -> None:
+        pass
+
+    def do_POST(self) -> None: # noqa: N802
+        if self.path == "/shutdown":
+            self.send_response(200)
+            self.end_headers()
+            logger.info("Shutdown request received.")
+            threading.Thread(target=self.server.shutdown).start()
+        else:
+            self.send_error(404)
+
+class ReusableTCPServer(TCPServer):
+    allow_reuse_address = True
+
+def label_tool(port: int = 8888) -> None:
+    web_dir = Path(__file__).parent / "label_tool"
+    handler = functools.partial(HTTPRequestHandler, directory=str(web_dir))
+    with ReusableTCPServer(("", port), handler) as httpd:
+        url = f"http://localhost:{port}/index.html"
+        logger.info(f"Starting server at {url}")
+        webbrowser.open(url)
+        httpd.serve_forever()
+        httpd.server_close()
+        logger.info("Server closed.")
diff --git a/src/dynsight/_internal/vision/label_tool/index.html b/src/dynsight/_internal/vision/label_tool/index.html
new file mode 100644
index 00000000..3ae97017
--- /dev/null
+++ b/src/dynsight/_internal/vision/label_tool/index.html
@@ -0,0 +1,35 @@
+<!doctype html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>dynsight labeling tool</title>
+        <link rel="stylesheet" href="styles.css" />
+    </head>
+    <body>
+        <div class="sidebar">
+            <h2>Labels</h2>
+            <input type="text" id="newLabelInput" placeholder="New label..." />
+            <button class="upload-btn" id="addLabelBtn">Add</button>
+            <div class="label-list" id="labelList"></div>
+        </div>
+
+        <div class="main">
+            <div class="top-bar">
+                <input type="file" id="imageInput" accept="image/*" multiple />
+                <button class="nav-btn" id="prevImage">&larr; Prev</button>
+                <button class="nav-btn" id="nextImage">Next &rarr;</button>
+                <button id="clearLast">Clear Previous</button>
+                <button id="clearAll">Clear All</button>
+                <button id="exportYolo">Export label (YOLO)</button>
+                <button id="exportAll">Export dataset (YOLO)</button>
+            </div>
+            <div class="image-container" id="imageContainer">
+                <img id="imageDisplay" src="" alt="" />
+            </div>
+        </div>
+
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.1/jszip.min.js"></script>
+        <script src="script.js"></script>
+    </body>
+</html>
diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
new file mode 100644
index 00000000..ef98043b
--- /dev/null
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -0,0 +1,248 @@
+// script.js
+
+const imageInput = document.getElementById("imageInput");
+const imageDisplay = document.getElementById("imageDisplay");
+const imageContainer = document.getElementById("imageContainer");
+const labelList = document.getElementById("labelList");
+const addLabelBtn = document.getElementById("addLabelBtn");
+const newLabelInput = document.getElementById("newLabelInput");
+const clearLastBtn = document.getElementById("clearLast");
+const clearAllBtn = document.getElementById("clearAll");
+const exportBtn = document.getElementById("exportYolo");
+const exportAllBtn = document.getElementById("exportAll");
+const nextImageBtn = document.getElementById("nextImage");
+const prevImageBtn = document.getElementById("prevImage");
+
+let currentLabel = null;
+const labelColors = {};
+let isDrawing = false;
+let startX,
+  startY,
+  box = null;
+
+let images = [];
+let currentIndex = 0;
+const annotations = {}; // imageName -> [boxData]
+
+function getRandomColor() {
+  const hue = Math.floor(Math.random() * 360);
+  return `hsl(${hue}, 90%, 50%)`;
+}
+
+function setActiveLabel(item) {
+  document
+    .querySelectorAll(".label-item")
+    .forEach((i) => i.classList.remove("active"));
+  item.classList.add("active");
+  currentLabel = item.textContent;
+}
+
+function createLabelItem(text) {
+  const item = document.createElement("div");
+  item.className = "label-item";
+  item.textContent = text;
+  labelColors[text] = labelColors[text] || getRandomColor();
+  item.style.backgroundColor = labelColors[text];
+  item.style.color = "#fff";
+  item.addEventListener("click", () => setActiveLabel(item));
+  labelList.appendChild(item);
+}
+
+addLabelBtn.onclick = () => {
+  const label = newLabelInput.value.trim();
+  if (label && !labelColors[label]) {
+    createLabelItem(label);
+    newLabelInput.value = "";
+  }
+};
+
+imageInput.onchange = (e) => {
+  images = Array.from(e.target.files);
+  currentIndex = 0;
+  loadImage(currentIndex);
+};
+
+function loadImage(index) {
+  if (!images[index]) return;
+  const url = URL.createObjectURL(images[index]);
+  imageDisplay.src = url;
+  clearBoxes();
+  const name = images[index].name;
+  if (annotations[name]) annotations[name].forEach(addBoxFromData);
+  else annotations[name] = [];
+}
+
+function clearBoxes() {
+  document.querySelectorAll(".bounding-box").forEach((el) => el.remove());
+}
+
+function addBoxFromData(data) {
+  const box = document.createElement("div");
+  box.className = "bounding-box";
+  box.style.left = `${data.left}px`;
+  box.style.top = `${data.top}px`;
+  box.style.width = `${data.width}px`;
+  box.style.height = `${data.height}px`;
+  box.style.border = `2px dashed ${labelColors[data.label]}`;
+  box.style.backgroundColor = labelColors[data.label]
+    .replace("hsl", "hsla")
+    .replace(")", ", 0.1)");
+  const tag = document.createElement("div");
+  tag.className = "label-tag";
+  tag.textContent = data.label;
+  tag.style.backgroundColor = labelColors[data.label];
+  box.appendChild(tag);
+  imageContainer.appendChild(box);
+}
+
+imageContainer.onmousedown = (e) => {
+  if (!currentLabel || !images[currentIndex]) return;
+  const rect = imageContainer.getBoundingClientRect();
+  startX = e.clientX - rect.left;
+  startY = e.clientY - rect.top;
+  box = document.createElement("div");
+  box.className = "bounding-box";
+  box.style.left = `${startX}px`;
+  box.style.top = `${startY}px`;
+  box.style.border = `2px dashed ${labelColors[currentLabel]}`;
+  box.style.backgroundColor = labelColors[currentLabel]
+    .replace("hsl", "hsla")
+    .replace(")", ", 0.1)");
+  const tag = document.createElement("div");
+  tag.className = "label-tag";
+  tag.textContent = currentLabel;
+  tag.style.backgroundColor = labelColors[currentLabel];
+  box.appendChild(tag);
+  imageContainer.appendChild(box);
+  isDrawing = true;
+};
+
+imageContainer.onmousemove = (e) => {
+  if (!isDrawing || !box) return;
+  const rect = imageContainer.getBoundingClientRect();
+  const currX = e.clientX - rect.left;
+  const currY = e.clientY - rect.top;
+  box.style.left = `${Math.min(currX, startX)}px`;
+  box.style.top = `${Math.min(currY, startY)}px`;
+  box.style.width = `${Math.abs(currX - startX)}px`;
+  box.style.height = `${Math.abs(currY - startY)}px`;
+};
+
+imageContainer.onmouseup = () => {
+  if (!isDrawing || !box) return;
+  const rect = box.getBoundingClientRect();
+  const imgRect = imageDisplay.getBoundingClientRect();
+  annotations[images[currentIndex].name].push({
+    label: currentLabel,
+    left: rect.left - imgRect.left,
+    top: rect.top - imgRect.top,
+    width: rect.width,
+    height: rect.height,
+  });
+  box = null;
+  isDrawing = false;
+};
+
+clearLastBtn.onclick = () => {
+  const ann = annotations[images[currentIndex].name];
+  if (ann.length > 0) {
+    ann.pop();
+    clearBoxes();
+    ann.forEach(addBoxFromData);
+  }
+};
+
+clearAllBtn.onclick = () => {
+  annotations[images[currentIndex].name] = [];
+  clearBoxes();
+};
+
+prevImageBtn.onclick = () => {
+  if (currentIndex > 0) {
+    currentIndex--;
+    loadImage(currentIndex);
+  }
+};
+
+nextImageBtn.onclick = () => {
+  if (currentIndex < images.length - 1) {
+    currentIndex++;
+    loadImage(currentIndex);
+  }
+};
+
+exportBtn.onclick = () => {
+  const img = images[currentIndex];
+  if (!img) return;
+  const imgRect = imageDisplay.getBoundingClientRect();
+  const iw = imageDisplay.naturalWidth;
+  const ih = imageDisplay.naturalHeight;
+  const annots = annotations[img.name] || [];
+  const labelMap = {};
+  let nextId = 0;
+  let txt = "";
+  annots.forEach((obj) => {
+    if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
+    const x = (obj.left / imgRect.width) * iw;
+    const y = (obj.top / imgRect.height) * ih;
+    const w = (obj.width / imgRect.width) * iw;
+    const h = (obj.height / imgRect.height) * ih;
+    const cx = (x + w / 2) / iw;
+    const cy = (y + h / 2) / ih;
+    txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${(w / iw).toFixed(6)} ${(h / ih).toFixed(6)}\n`;
+  });
+  const blob = new Blob([txt], { type: "text/plain" });
+  const a = document.createElement("a");
+  a.href = URL.createObjectURL(blob);
+  a.download = img.name.replace(/\.[^/.]+$/, "") + ".txt";
+  a.click();
+  URL.revokeObjectURL(a.href);
+};
+
+exportAllBtn.onclick = async () => {
+  if (images.length === 0) {
+    alert("Nessuna immagine caricata.");
+    return;
+  }
+  const zip = new JSZip();
+  const imgFolder = zip.folder("images");
+  const lblFolder = zip.folder("labels");
+  const labelMap = {};
+  let nextId = 0;
+  for (const image of images) {
+    const name = image.name;
+    const imgData = await image.arrayBuffer();
+    imgFolder.file(name, imgData);
+    const img = new Image();
+    const url = URL.createObjectURL(image);
+    img.src = url;
+    await new Promise((resolve) => (img.onload = resolve));
+    const iw = img.naturalWidth;
+    const ih = img.naturalHeight;
+    const rect = imageDisplay.getBoundingClientRect();
+    const annots = annotations[name] || [];
+    let txt = "";
+    annots.forEach((obj) => {
+      if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
+      const x = (obj.left / rect.width) * iw;
+      const y = (obj.top / rect.height) * ih;
+      const w = (obj.width / rect.width) * iw;
+      const h = (obj.height / rect.height) * ih;
+      const cx = (x + w / 2) / iw;
+      const cy = (y + h / 2) / ih;
+      txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${(w / iw).toFixed(6)} ${(h / ih).toFixed(6)}\n`;
+    });
+    const labelFileName = name.replace(/\.[^/.]+$/, "") + ".txt";
+    lblFolder.file(labelFileName, txt);
+  }
+  const content = await zip.generateAsync({ type: "blob" });
+  const a = document.createElement("a");
+  a.href = URL.createObjectURL(content);
+  a.download = "yolo_dataset.zip";
+  a.click();
+  URL.revokeObjectURL(a.href);
+};
+
+window.addEventListener("unload", () => {
+  navigator.sendBeacon("/shutdown");
+});
diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
new file mode 100644
index 00000000..c2e75715
--- /dev/null
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -0,0 +1,83 @@
+* {
+    user-select: none;
+}
+body {
+    margin: 0;
+    font-family: Arial, sans-serif;
+    display: flex;
+    height: 100vh;
+}
+.sidebar {
+    width: 250px;
+    background: #f4f4f4;
+    border-right: 1px solid #ccc;
+    padding: 20px;
+    box-sizing: border-box;
+}
+.main {
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    background: #fff;
+}
+.top-bar {
+    padding: 10px 20px;
+    border-bottom: 1px solid #ccc;
+    background: #fafafa;
+    display: flex;
+    gap: 10px;
+    align-items: center;
+    flex-wrap: wrap;
+}
+.image-container {
+    flex: 1;
+    position: relative;
+    background: #eee;
+    overflow: hidden;
+}
+.image-container img {
+    width: 100%;
+    height: 100%;
+    object-fit: contain;
+    display: block;
+    user-drag: none;
+    pointer-events: none;
+}
+.bounding-box {
+    position: absolute;
+    pointer-events: none;
+}
+.label-tag {
+    position: absolute;
+    top: -20px;
+    left: 0;
+    color: white;
+    padding: 2px 6px;
+    font-size: 12px;
+    font-weight: bold;
+    border-radius: 3px;
+    pointer-events: none;
+}
+.label-item {
+    background: #e0e0e0;
+    padding: 5px 10px;
+    margin-bottom: 5px;
+    border-radius: 4px;
+    cursor: pointer;
+}
+.label-item.active {
+    outline: 2px solid #2196f3;
+}
+.upload-btn,
+.nav-btn {
+    margin-top: 5px;
+    padding: 5px 10px;
+    font-size: 14px;
+    cursor: pointer;
+}
+input[type="text"] {
+    width: 100%;
+    padding: 5px;
+    font-size: 14px;
+    box-sizing: border-box;
+}
diff --git a/src/dynsight/vision.py b/src/dynsight/vision.py
index 6669b967..590004fc 100644
--- a/src/dynsight/vision.py
+++ b/src/dynsight/vision.py
@@ -1,9 +1,13 @@
 """Vision package."""
 
+from dynsight._internal.vision.label_tool import (
+    label_tool,
+)
 from dynsight._internal.vision.vision import (
     VisionInstance,
 )
 
 __all__ = [
     "VisionInstance",
+    "label_tool",
 ]

From 0f88734cb7d3dfcbc5913965aa64051f884bf7d7 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 2 Jul 2025 14:42:02 +0200
Subject: [PATCH 13/67] Added guidelines.

---
 src/dynsight/_internal/vision/label_tool.py   |  1 -
 .../_internal/vision/label_tool/index.html    | 22 ++++++++++---
 .../_internal/vision/label_tool/script.js     | 21 +++++++++++-
 .../_internal/vision/label_tool/styles.css    | 33 +++++++++++++++++++
 4 files changed, 71 insertions(+), 6 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool.py b/src/dynsight/_internal/vision/label_tool.py
index 07474b7d..947e13f1 100644
--- a/src/dynsight/_internal/vision/label_tool.py
+++ b/src/dynsight/_internal/vision/label_tool.py
@@ -1,6 +1,5 @@
 import functools
 import logging
-import socket
 import threading
 import webbrowser
 from http.server import SimpleHTTPRequestHandler
diff --git a/src/dynsight/_internal/vision/label_tool/index.html b/src/dynsight/_internal/vision/label_tool/index.html
index 3ae97017..dc92822a 100644
--- a/src/dynsight/_internal/vision/label_tool/index.html
+++ b/src/dynsight/_internal/vision/label_tool/index.html
@@ -19,13 +19,27 @@ <h2>Labels</h2>
                 <input type="file" id="imageInput" accept="image/*" multiple />
                 <button class="nav-btn" id="prevImage">&larr; Prev</button>
                 <button class="nav-btn" id="nextImage">Next &rarr;</button>
-                <button id="clearLast">Clear Previous</button>
-                <button id="clearAll">Clear All</button>
-                <button id="exportYolo">Export label (YOLO)</button>
-                <button id="exportAll">Export dataset (YOLO)</button>
+                <button id="clearLast" class="nav-btn">Clear Previous</button>
+                <button id="clearAll" class="nav-btn">Clear All</button>
+                <button id="exportYolo" class="nav-btn">
+                    Export label (YOLO)
+                </button>
+                <button id="exportAll" class="nav-btn">
+                    Export dataset (YOLO)
+                </button>
             </div>
             <div class="image-container" id="imageContainer">
                 <img id="imageDisplay" src="" alt="" />
+                <div id="verticalLine" class="crosshair-line"></div>
+                <div id="horizontalLine" class="crosshair-line"></div>
+                <div
+                    id="crosshair-horizontal"
+                    class="crosshair-line horizontal"
+                ></div>
+                <div
+                    id="crosshair-vertical"
+                    class="crosshair-line vertical"
+                ></div>
             </div>
         </div>
 
diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index ef98043b..6b4b2975 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -12,6 +12,21 @@ const exportBtn = document.getElementById("exportYolo");
 const exportAllBtn = document.getElementById("exportAll");
 const nextImageBtn = document.getElementById("nextImage");
 const prevImageBtn = document.getElementById("prevImage");
+const verticalLine = document.getElementById("verticalLine");
+const horizontalLine = document.getElementById("horizontalLine");
+
+verticalLine.style.display = "none";
+horizontalLine.style.display = "none";
+
+imageContainer.onmouseenter = () => {
+  verticalLine.style.display = "block";
+  horizontalLine.style.display = "block";
+};
+
+imageContainer.onmouseleave = () => {
+  verticalLine.style.display = "none";
+  horizontalLine.style.display = "none";
+};
 
 let currentLabel = null;
 const labelColors = {};
@@ -118,10 +133,14 @@ imageContainer.onmousedown = (e) => {
 };
 
 imageContainer.onmousemove = (e) => {
-  if (!isDrawing || !box) return;
   const rect = imageContainer.getBoundingClientRect();
   const currX = e.clientX - rect.left;
   const currY = e.clientY - rect.top;
+
+  verticalLine.style.left = `${currX}px`;
+  horizontalLine.style.top = `${currY}px`;
+
+  if (!isDrawing || !box) return;
   box.style.left = `${Math.min(currX, startX)}px`;
   box.style.top = `${Math.min(currY, startY)}px`;
   box.style.width = `${Math.abs(currX - startX)}px`;
diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index c2e75715..8dccf55c 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -34,6 +34,7 @@ body {
     position: relative;
     background: #eee;
     overflow: hidden;
+    cursor: crosshair;
 }
 .image-container img {
     width: 100%;
@@ -81,3 +82,35 @@ input[type="text"] {
     font-size: 14px;
     box-sizing: border-box;
 }
+/* Crosshair lines used during labeling */
+.crosshair-line {
+    position: absolute;
+    pointer-events: none;
+    z-index: 10;
+}
+
+#verticalLine {
+    width: 3px;
+    height: 100%;
+    top: 0;
+    background-image: repeating-linear-gradient(
+        to bottom,
+        rgba(255, 0, 0, 0.6) 0px,
+        rgba(255, 0, 0, 0.6) 5px,
+        transparent 5px,
+        transparent 10px
+    );
+}
+
+#horizontalLine {
+    height: 3px;
+    width: 100%;
+    left: 0;
+    background-image: repeating-linear-gradient(
+        to right,
+        rgba(255, 0, 0, 0.6) 0px,
+        rgba(255, 0, 0, 0.6) 5px,
+        transparent 5px,
+        transparent 10px
+    );
+}

From 7beda9581db1cd0df96a425552b2d95749823493 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 2 Jul 2025 15:41:25 +0200
Subject: [PATCH 14/67] Fixing label positions.

---
 .../_internal/vision/label_tool/index.html    | 15 +++---
 .../_internal/vision/label_tool/script.js     | 50 +++++++++++++------
 .../_internal/vision/label_tool/styles.css    | 38 +++++++++++---
 3 files changed, 71 insertions(+), 32 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/index.html b/src/dynsight/_internal/vision/label_tool/index.html
index dc92822a..f94c530f 100644
--- a/src/dynsight/_internal/vision/label_tool/index.html
+++ b/src/dynsight/_internal/vision/label_tool/index.html
@@ -29,17 +29,14 @@ <h2>Labels</h2>
                 </button>
             </div>
             <div class="image-container" id="imageContainer">
-                <img id="imageDisplay" src="" alt="" />
+                <div id="imageWrapper">
+                    <img id="imageDisplay" src="" alt="" />
+                    <div id="overlay"></div>
+                </div>
                 <div id="verticalLine" class="crosshair-line"></div>
                 <div id="horizontalLine" class="crosshair-line"></div>
-                <div
-                    id="crosshair-horizontal"
-                    class="crosshair-line horizontal"
-                ></div>
-                <div
-                    id="crosshair-vertical"
-                    class="crosshair-line vertical"
-                ></div>
+            </div>
+
             </div>
         </div>
 
diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index 6b4b2975..aa0cb4ba 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -15,6 +15,8 @@ const prevImageBtn = document.getElementById("prevImage");
 const verticalLine = document.getElementById("verticalLine");
 const horizontalLine = document.getElementById("horizontalLine");
 
+const overlay = document.getElementById("overlay");
+
 verticalLine.style.display = "none";
 horizontalLine.style.display = "none";
 
@@ -88,7 +90,7 @@ function loadImage(index) {
 }
 
 function clearBoxes() {
-  document.querySelectorAll(".bounding-box").forEach((el) => el.remove());
+  overlay.innerHTML = "";
 }
 
 function addBoxFromData(data) {
@@ -102,19 +104,23 @@ function addBoxFromData(data) {
   box.style.backgroundColor = labelColors[data.label]
     .replace("hsl", "hsla")
     .replace(")", ", 0.1)");
+
   const tag = document.createElement("div");
   tag.className = "label-tag";
   tag.textContent = data.label;
   tag.style.backgroundColor = labelColors[data.label];
   box.appendChild(tag);
-  imageContainer.appendChild(box);
+
+  overlay.appendChild(box);
 }
 
 imageContainer.onmousedown = (e) => {
   if (!currentLabel || !images[currentIndex]) return;
-  const rect = imageContainer.getBoundingClientRect();
+
+  const rect = imageDisplay.getBoundingClientRect();
   startX = e.clientX - rect.left;
   startY = e.clientY - rect.top;
+
   box = document.createElement("div");
   box.className = "bounding-box";
   box.style.left = `${startX}px`;
@@ -123,41 +129,55 @@ imageContainer.onmousedown = (e) => {
   box.style.backgroundColor = labelColors[currentLabel]
     .replace("hsl", "hsla")
     .replace(")", ", 0.1)");
+
   const tag = document.createElement("div");
   tag.className = "label-tag";
   tag.textContent = currentLabel;
   tag.style.backgroundColor = labelColors[currentLabel];
   box.appendChild(tag);
-  imageContainer.appendChild(box);
+
+  overlay.appendChild(box);
   isDrawing = true;
 };
 
 imageContainer.onmousemove = (e) => {
-  const rect = imageContainer.getBoundingClientRect();
-  const currX = e.clientX - rect.left;
-  const currY = e.clientY - rect.top;
+  const imgRect = imageDisplay.getBoundingClientRect();
+  const containerRect = imageContainer.getBoundingClientRect();
+
+  const currX = e.clientX - imgRect.left;
+  const currY = e.clientY - imgRect.top;
 
-  verticalLine.style.left = `${currX}px`;
-  horizontalLine.style.top = `${currY}px`;
+  verticalLine.style.left = `${e.clientX - containerRect.left}px`;
+  horizontalLine.style.top = `${e.clientY - containerRect.top}px`;
 
   if (!isDrawing || !box) return;
+
   box.style.left = `${Math.min(currX, startX)}px`;
   box.style.top = `${Math.min(currY, startY)}px`;
   box.style.width = `${Math.abs(currX - startX)}px`;
   box.style.height = `${Math.abs(currY - startY)}px`;
 };
 
-imageContainer.onmouseup = () => {
+imageContainer.onmouseup = (e) => {
   if (!isDrawing || !box) return;
-  const rect = box.getBoundingClientRect();
+
   const imgRect = imageDisplay.getBoundingClientRect();
+  const endX = e.clientX - imgRect.left;
+  const endY = e.clientY - imgRect.top;
+
+  const left = Math.min(startX, endX);
+  const top = Math.min(startY, endY);
+  const width = Math.abs(endX - startX);
+  const height = Math.abs(endY - startY);
+
   annotations[images[currentIndex].name].push({
     label: currentLabel,
-    left: rect.left - imgRect.left,
-    top: rect.top - imgRect.top,
-    width: rect.width,
-    height: rect.height,
+    left,
+    top,
+    width,
+    height,
   });
+
   box = null;
   isDrawing = false;
 };
diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index 8dccf55c..ea076579 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -36,14 +36,7 @@ body {
     overflow: hidden;
     cursor: crosshair;
 }
-.image-container img {
-    width: 100%;
-    height: 100%;
-    object-fit: contain;
-    display: block;
-    user-drag: none;
-    pointer-events: none;
-}
+
 .bounding-box {
     position: absolute;
     pointer-events: none;
@@ -114,3 +107,32 @@ input[type="text"] {
         transparent 10px
     );
 }
+#imageWrapper {
+    position: relative;
+    display: inline-block;
+    max-width: 100%;
+    max-height: 100%;
+}
+
+#imageDisplay {
+    display: block;
+    max-width: 100%;
+    height: auto;
+    pointer-events: none;
+    user-drag: none;
+}
+
+#overlay {
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    pointer-events: none; /* le box possono ricevere eventi solo se necessario */
+}
+
+.bounding-box {
+    position: absolute;
+    pointer-events: auto;
+    box-sizing: border-box;
+}

From 8a3ed55f634e439394eadbaf9489860bc4ea80f8 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Fri, 4 Jul 2025 16:11:19 +0200
Subject: [PATCH 15/67] Formatting code.

---
 src/dynsight/_internal/vision/label_tool.py |  5 ++++-
 src/dynsight/_internal/vision/vision.py     | 11 ++---------
 2 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool.py b/src/dynsight/_internal/vision/label_tool.py
index 947e13f1..4a1695a4 100644
--- a/src/dynsight/_internal/vision/label_tool.py
+++ b/src/dynsight/_internal/vision/label_tool.py
@@ -8,11 +8,12 @@
 
 logger = logging.getLogger(__name__)
 
+
 class HTTPRequestHandler(SimpleHTTPRequestHandler):
     def log_message(self, fmt: str, *args: object) -> None:
         pass
 
-    def do_POST(self) -> None: # noqa: N802
+    def do_POST(self) -> None:  # noqa: N802
         if self.path == "/shutdown":
             self.send_response(200)
             self.end_headers()
@@ -21,9 +22,11 @@ def do_POST(self) -> None: # noqa: N802
         else:
             self.send_error(404)
 
+
 class ReusableTCPServer(TCPServer):
     allow_reuse_address = True
 
+
 def label_tool(port: int = 8888) -> None:
     web_dir = Path(__file__).parent / "label_tool"
     handler = functools.partial(HTTPRequestHandler, directory=str(web_dir))
diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 4bc2eea0..e2644eeb 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -409,8 +409,7 @@ def _device_error(msg: str) -> None:
             elif self.device == "cuda":
                 self._check_single_cuda_device(_device_error)
             elif all(
-                part.strip().isdigit()
-                for part in self.device.split(",")
+                part.strip().isdigit() for part in self.device.split(",")
             ):
                 self._check_multi_cuda_devices(_device_error)
             else:
@@ -418,14 +417,10 @@ def _device_error(msg: str) -> None:
         except (ValueError, RuntimeError, IndexError, OSError) as e:
             _device_error(str(e))
 
-
     def _check_cpu_device(self) -> None:
         logger.info("Using CPU.")
 
-
-    def _check_mps_device(
-        self, _device_error: Callable[[str], None]
-    ) -> None:
+    def _check_mps_device(self, _device_error: Callable[[str], None]) -> None:
         if not (
             hasattr(torch.backends, "mps")
             and torch.backends.mps.is_available()
@@ -433,7 +428,6 @@ def _check_mps_device(
             _device_error("MPS device requested but not available.")
         logger.info("Using Apple MPS backend.")
 
-
     def _check_single_cuda_device(
         self, _device_error: Callable[[str], None]
     ) -> None:
@@ -450,7 +444,6 @@ def _check_single_cuda_device(
         )
         _ = torch.tensor([0.0]).to("cuda:0")
 
-
     def _check_multi_cuda_devices(
         self, _device_error: Callable[[str], None]
     ) -> None:

From 58c610860f5ed34442357ab8a11a1212824d67f4 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 7 Jul 2025 14:32:58 +0200
Subject: [PATCH 16/67] Added create dataset from predictions method.

---
 src/dynsight/_internal/vision/vision.py | 84 ++++++++++++++++++++++++-
 1 file changed, 82 insertions(+), 2 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index e2644eeb..dbae76e3 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -2,11 +2,15 @@
 
 import logging
 from pathlib import Path
-from typing import Callable
+from typing import TYPE_CHECKING, Callable
 
 import torch
+import yaml
 from ultralytics import YOLO
 
+if TYPE_CHECKING:
+    from ultralytics.engine.results import Results
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
@@ -100,7 +104,7 @@ def __init__(
         self.device = self._normalize_device_string(device)
         self.workers = workers
 
-        self.prediction_results = None
+        self.prediction_results: list[Results] | None = None
         self.training_results = None
 
         self._check_device()
@@ -222,6 +226,82 @@ def predict(
             max_det=max_det,
         )
 
+    def create_dataset_from_predictions(
+        self,
+        dataset_name: str,
+        train_split: float = 0.8,
+        load_dataset: bool = True
+    ) -> None:
+        """Create a YOLO training dataset from ``predict`` results.
+
+        Parameters:
+            dataset_name:
+                Name of the dataset that will be created.
+
+            train_split:
+                Fraction of images to use for the training set, the remaining
+                fraction will be used for the validation set.
+
+            load_dataset:
+                Directly load the dataset for the next training sessions.
+        """
+        if self.prediction_results is None:
+            msg = "No prediction results available."
+            raise ValueError(msg)
+
+        dataset_path = self.output_path / dataset_name
+        images_train = dataset_path / "images" / "train"
+        images_val = dataset_path / "images" / "val"
+        labels_train = dataset_path / "labels" / "train"
+        labels_val = dataset_path / "labels" / "val"
+
+        images_train.mkdir(parents=True, exist_ok=True)
+        images_val.mkdir(parents=True, exist_ok=True)
+        labels_train.mkdir(parents=True, exist_ok=True)
+        labels_val.mkdir(parents=True, exist_ok=True)
+
+        names = self.prediction_results[0].names
+
+        sorted_results = sorted(self.prediction_results, key=lambda r: r.path)
+
+        num_train = int(len(sorted_results) * train_split)
+
+        for idx, result in enumerate(sorted_results):
+            src = Path(result.path)
+            subset = "train" if idx < num_train else "val"
+            img_dst = dataset_path / "images" / subset / src.name
+            lbl_dst = dataset_path / "labels" / subset / (src.stem + ".txt")
+
+            img_dst.write_bytes(src.read_bytes())
+
+            boxes = result.boxes
+            if boxes is None:
+                lbl_dst.write_text("")
+                continue
+
+            xywhn = boxes.xywhn
+            classes = boxes.cls
+            with lbl_dst.open("w") as f:
+                for xywh, cls in zip(xywhn, classes):
+                    f.write(
+                        f"{int(cls)} {xywh[0]:.6f} {xywh[1]:.6f} "
+                        f"{xywh[2]:.6f} {xywh[3]:.6f}\n"
+                    )
+
+        dataset_yaml = dataset_path / "dataset.yaml"
+        yaml_content = {
+            "path": str(dataset_path.resolve()),
+            "train": "images/train",
+            "val": "images/val",
+            "nc": len(names),
+            "names": [names[i] for i in range(len(names))],
+        }
+        with dataset_yaml.open("w") as f:
+            yaml.safe_dump(yaml_content, f)
+
+        if load_dataset:
+            self.training_data_yaml = dataset_yaml
+
     def tune_hyperparams(
         self,
         iterations: int = 15,

From 24ca57097457074136f5d317a48b9ab94df24553 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 7 Jul 2025 16:02:51 +0200
Subject: [PATCH 17/67] Added xyz export method.

---
 src/dynsight/_internal/vision/vision.py | 48 ++++++++++++++++++++++++-
 1 file changed, 47 insertions(+), 1 deletion(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index dbae76e3..c6e95647 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -230,7 +230,7 @@ def create_dataset_from_predictions(
         self,
         dataset_name: str,
         train_split: float = 0.8,
-        load_dataset: bool = True
+        load_dataset: bool = True,
     ) -> None:
         """Create a YOLO training dataset from ``predict`` results.
 
@@ -454,6 +454,52 @@ def train(
             **full_params,
         )
 
+    def export_prediction_to_xyz(
+        self, file_name: Path, class_filter: list[int] | None = None
+    ) -> None:
+        """Export prediction results into a single ``.xyz`` file.
+
+        Each frame of the resulting ``.xyz`` corresponds to one of the
+        images/frames present in the source and used in the ``predict`` method.
+
+        Parameters:
+            file_name:
+                File name for the ``.xyz`` file.
+
+            class_filter:
+                Limit exported detections to the specified class IDs. If
+                ``None`` all detected objects will be exported.
+        """
+        if self.prediction_results is None:
+            msg = "No prediction results available."
+            raise ValueError(msg)
+
+        sorted_results = sorted(self.prediction_results, key=lambda r: r.path)
+        file_path = self.output_path / file_name
+
+        with file_path.open("w") as f:
+            for result in sorted_results:
+                boxes = result.boxes
+
+                coords: list[str] = []
+                if boxes is not None:
+                    xyxy = boxes.xyxy.cpu().numpy()
+                    classes = boxes.cls.cpu().numpy().astype(int)
+                    for (x1, y1, x2, y2), cls_id in zip(xyxy, classes):
+                        if (
+                            class_filter is not None
+                            and cls_id not in class_filter
+                        ):
+                            continue
+                        cx = (x1 + x2) / 2
+                        cy = (y1 + y2) / 2
+                        coords.append(f"{cx:.6f} {cy:.6f} 0.0")
+
+                f.write(f"{len(coords)}\n")
+                f.write("x y z\n")
+                for line in coords:
+                    f.write(f"{line}\n")
+
     def _normalize_device_string(self, device: str | None) -> str:
         """Normalize device string to match Ultralytics expectations."""
         if device is None:

From c046f634cc5e9175edba9e9b5e2da7a41996473e Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 7 Jul 2025 16:30:33 +0200
Subject: [PATCH 18/67] Added path return to export xyz.

---
 src/dynsight/_internal/vision/vision.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index c6e95647..716ea1e9 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -456,7 +456,7 @@ def train(
 
     def export_prediction_to_xyz(
         self, file_name: Path, class_filter: list[int] | None = None
-    ) -> None:
+    ) -> Path:
         """Export prediction results into a single ``.xyz`` file.
 
         Each frame of the resulting ``.xyz`` corresponds to one of the
@@ -469,6 +469,9 @@ def export_prediction_to_xyz(
             class_filter:
                 Limit exported detections to the specified class IDs. If
                 ``None`` all detected objects will be exported.
+
+        Returns:
+            Path to the exported ``.xyz`` file.
         """
         if self.prediction_results is None:
             msg = "No prediction results available."
@@ -499,6 +502,7 @@ def export_prediction_to_xyz(
                 f.write("x y z\n")
                 for line in coords:
                     f.write(f"{line}\n")
+        return file_path
 
     def _normalize_device_string(self, device: str | None) -> str:
         """Normalize device string to match Ultralytics expectations."""

From 90ac650892606a3b9e642bc35b8954980227804f Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 7 Jul 2025 17:01:28 +0200
Subject: [PATCH 19/67] Added track module.

---
 docs/source/index.rst                    |   1 +
 docs/source/track.rst                    |  14 +++
 pyproject.toml                           |   2 +
 src/dynsight/__init__.py                 |   2 +
 src/dynsight/_internal/track/__init__.py |   0
 src/dynsight/_internal/track/track.py    | 138 +++++++++++++++++++++++
 src/dynsight/track.py                    |   9 ++
 7 files changed, 166 insertions(+)
 create mode 100644 docs/source/track.rst
 create mode 100644 src/dynsight/_internal/track/__init__.py
 create mode 100644 src/dynsight/_internal/track/track.py
 create mode 100644 src/dynsight/track.py

diff --git a/docs/source/index.rst b/docs/source/index.rst
index b70e2465..4886c90e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -9,6 +9,7 @@
    tICA <tica>
    onion clustering <onion>
    vision <vision>
+   track <track>
    analysis <analysis>
    data processing <data_processing>
    HDF5er <hdf5er>
diff --git a/docs/source/track.rst b/docs/source/track.rst
new file mode 100644
index 00000000..2a1049f7
--- /dev/null
+++ b/docs/source/track.rst
@@ -0,0 +1,14 @@
+track
+=====
+
+This module tracks particle trajectories in `.xyz` files that lack explicit
+particle IDs, assigning consistent identities across frames.
+It is especially useful for outputs generated by the :doc:`vision` module.
+
+Functions
+---------
+
+.. toctree::
+  :maxdepth: 1
+
+  track_xyz <_autosummary/dynsight.track.track_xyz>
diff --git a/pyproject.toml b/pyproject.toml
index b1949aee..1856cd0e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -111,5 +111,7 @@ module = [
     'ultralytics.*',
     'opencv-python.*',
     'cv2.*',
+    'pandas.*',
+    'trackpy.*',
 ]
 ignore_missing_imports = true
diff --git a/src/dynsight/__init__.py b/src/dynsight/__init__.py
index 36bc1232..e16b196f 100644
--- a/src/dynsight/__init__.py
+++ b/src/dynsight/__init__.py
@@ -8,6 +8,7 @@
     onion,
     soap,
     tica,
+    track,
     trajectory,
     utilities,
     vision,
@@ -21,6 +22,7 @@
     "onion",
     "soap",
     "tica",
+    "track",
     "trajectory",
     "utilities",
     "vision",
diff --git a/src/dynsight/_internal/track/__init__.py b/src/dynsight/_internal/track/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/dynsight/_internal/track/track.py b/src/dynsight/_internal/track/track.py
new file mode 100644
index 00000000..3ee37195
--- /dev/null
+++ b/src/dynsight/_internal/track/track.py
@@ -0,0 +1,138 @@
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+import pandas as pd
+import trackpy as tp
+
+from dynsight.trajectory import Trj
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s | %(levelname)s | %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+
+def track_xyz(
+    input_xyz: Path,
+    output_xyz: Path,
+    search_range: float = 10,
+    memory: int = 1,
+    adaptive_stop: None | float = 0.95,
+    adaptive_step: None | float = 0.5,
+) -> Trj:
+    """Track particles from an ``.xyz`` file and write a new file with IDs.
+
+    The input ``.xyz`` is assumed to contain only raw 3D coordinates
+    (without atom labels/identity), and each frame begins with a line
+    indicating the number of objects, followed by a comment line, then a list
+    of positions. Each frame in the input file must follow this structure::
+
+        <number of objects>
+        comment line
+        <x> <y> <z>
+        <x> <y> <z>
+        ...
+        <x> <y> <z>
+
+    The output file will have the same structure, but each line will start
+    with the tracked particle ID.
+
+    Parameters:
+        input_xyz:
+            Path to the input .xyz file containing positions only.
+
+        output_xyz:
+            Path where the output .xyz file with particle IDs will be saved.
+
+        search_range:
+            The maximum allowable displacement of objects between frames for
+            them to be considered the same particle.
+
+        memory:
+            The maximum number of frames during which an object can vanish,
+            then reppear nearby, and be considered the same particle.
+
+        adaptive_stop:
+            If not `None`, when encountering a region with too many candidate
+            links (subnet), retry by progressively reducing `search_range`
+            until the subnet is solvable. If `search_range` becomes less or
+            equal than the `adaptive_stop`, give up and raise a
+            `SubnetOversizeException`.
+
+        adaptive_step:
+            Factor by which the `search_range` is multiplied to reduce it
+            during adaptive search. Effective only if `adaptive_stop` is not
+            `None`.
+    """
+    if adaptive_stop is None and adaptive_step is not None:
+        msg = "adaptive_step is set but adaptive_stop is None."
+        raise ValueError(msg)
+    if adaptive_stop is not None and adaptive_step is None:
+        msg = "adaptive_stop is set but adaptive_step is None."
+        raise ValueError(msg)
+
+    input_xyz = Path(input_xyz)
+    output_xyz = Path(output_xyz)
+
+    if not input_xyz.exists():
+        msg = f"Input file not found: {input_xyz}"
+        raise FileNotFoundError(msg)
+
+    positions = _collect_positions(input_xyz=input_xyz)
+
+    if not {"frame", "x", "y", "z"}.issubset(positions.columns):
+        msg = "Error in the .xyz format. Each line must be <x> <y> <z>."
+        raise ValueError(msg)
+
+    linked = tp.link_df(
+        positions,
+        search_range=search_range,
+        memory=memory,
+        adaptive_step=adaptive_step,
+        adaptive_stop=adaptive_stop,
+    )
+
+    with output_xyz.open("w") as f:
+        for frame_num in sorted(linked["frame"].unique()):
+            frame_data = linked[linked["frame"] == frame_num]
+            f.write(f"{len(frame_data)}\n")
+            f.write(f"Frame {frame_num}\n")
+            for _, row in frame_data.iterrows():
+                pid = int(row["particle"])
+                x, y, z = row["x"], row["y"], row["z"]
+                f.write(f"{pid} {x:.6f} {y:.6f} {z:.6f}\n")
+
+    logger.info(f"Linked .xyz file written to: {output_xyz}")
+    return Trj.init_from_xyz(traj_file=output_xyz, dt=1)
+
+
+def _collect_positions(input_xyz: Path) -> pd.DataFrame:
+    """Read the xyz file and return the positions dataset at each frame."""
+    lines = input_xyz.read_text().splitlines()
+
+    data = []
+    frame = -1
+    row = 0
+    dimensions = 3
+    for _ in range(len(lines)):
+        if row >= len(lines):
+            break
+        if lines[row].strip().isdigit():
+            num_atoms = int(lines[row])
+            frame += 1
+            row += 2  # skip comment line.
+            for a in range(num_atoms):
+                if row + a >= len(lines):
+                    break
+                parts = lines[row + a].strip().split()
+                if len(parts) >= dimensions:
+                    x, y, z = map(float, parts[0:3])
+                    data.append({"frame": frame, "x": x, "y": y, "z": z})
+            row += num_atoms
+        else:
+            row += 1
+
+    return pd.DataFrame(data)
diff --git a/src/dynsight/track.py b/src/dynsight/track.py
new file mode 100644
index 00000000..b88355f5
--- /dev/null
+++ b/src/dynsight/track.py
@@ -0,0 +1,9 @@
+"""track package."""
+
+from dynsight._internal.track.track import (
+    track_xyz,
+)
+
+__all__ = [
+    "track_xyz",
+]

From 7fbd6c2840a263193a797c136b35af87636d35b5 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 8 Jul 2025 12:09:14 +0200
Subject: [PATCH 20/67] Added zoom in and zoom out in the label_tool.

---
 .../_internal/vision/label_tool/index.html    |  10 +-
 .../_internal/vision/label_tool/script.js     | 102 +++++++++++++-----
 .../_internal/vision/label_tool/styles.css    |   5 +
 3 files changed, 90 insertions(+), 27 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/index.html b/src/dynsight/_internal/vision/label_tool/index.html
index f94c530f..9213eefc 100644
--- a/src/dynsight/_internal/vision/label_tool/index.html
+++ b/src/dynsight/_internal/vision/label_tool/index.html
@@ -27,8 +27,16 @@ <h2>Labels</h2>
                 <button id="exportAll" class="nav-btn">
                     Export dataset (YOLO)
                 </button>
+                <label for="zoomSlider">Zoom</label>
+                <input
+                    type="range"
+                    id="zoomSlider"
+                    min="10"
+                    max="200"
+                    value="100"
+                />
             </div>
-            <div class="image-container" id="imageContainer">
+            <div class="image-container" id="imageContainer" oncontextmenu="return false;">
                 <div id="imageWrapper">
                     <img id="imageDisplay" src="" alt="" />
                     <div id="overlay"></div>
diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index aa0cb4ba..27dc4a8a 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -3,6 +3,7 @@
 const imageInput = document.getElementById("imageInput");
 const imageDisplay = document.getElementById("imageDisplay");
 const imageContainer = document.getElementById("imageContainer");
+const imageWrapper = document.getElementById("imageWrapper");
 const labelList = document.getElementById("labelList");
 const addLabelBtn = document.getElementById("addLabelBtn");
 const newLabelInput = document.getElementById("newLabelInput");
@@ -14,6 +15,12 @@ const nextImageBtn = document.getElementById("nextImage");
 const prevImageBtn = document.getElementById("prevImage");
 const verticalLine = document.getElementById("verticalLine");
 const horizontalLine = document.getElementById("horizontalLine");
+const zoomSlider = document.getElementById("zoomSlider");
+
+let zoomLevel = 1;
+let panX = 0;
+let isPanning = false;
+let panStart = 0;
 
 const overlay = document.getElementById("overlay");
 
@@ -28,6 +35,10 @@ imageContainer.onmouseenter = () => {
 imageContainer.onmouseleave = () => {
   verticalLine.style.display = "none";
   horizontalLine.style.display = "none";
+  if (isPanning) {
+    isPanning = false;
+    imageContainer.style.cursor = "crosshair";
+  }
 };
 
 let currentLabel = null;
@@ -82,17 +93,40 @@ imageInput.onchange = (e) => {
 function loadImage(index) {
   if (!images[index]) return;
   const url = URL.createObjectURL(images[index]);
+  imageDisplay.onload = () => {
+    const iw = imageDisplay.naturalWidth;
+    const ih = imageDisplay.naturalHeight;
+    imageDisplay.style.width = `${iw}px`;
+    imageDisplay.style.height = `${ih}px`;
+    imageWrapper.style.width = `${iw}px`;
+    imageWrapper.style.height = `${ih}px`;
+    overlay.style.width = `${iw}px`;
+    overlay.style.height = `${ih}px`;
+    zoomLevel = 1;
+    panX = 0;
+    zoomSlider.value = 100;
+    const name = images[index].name;
+    if (!annotations[name]) annotations[name] = [];
+    updateTransform();
+    clearBoxes();
+    annotations[name].forEach(addBoxFromData);
+  };
   imageDisplay.src = url;
-  clearBoxes();
-  const name = images[index].name;
-  if (annotations[name]) annotations[name].forEach(addBoxFromData);
-  else annotations[name] = [];
 }
 
+zoomSlider.oninput = (e) => {
+  zoomLevel = e.target.value / 100;
+  updateTransform();
+};
+
 function clearBoxes() {
   overlay.innerHTML = "";
 }
 
+function updateTransform() {
+  imageWrapper.style.transform = `translateX(${panX}px) scale(${zoomLevel})`;
+}
+
 function addBoxFromData(data) {
   const box = document.createElement("div");
   box.className = "bounding-box";
@@ -115,11 +149,19 @@ function addBoxFromData(data) {
 }
 
 imageContainer.onmousedown = (e) => {
+  if (e.button !== 0) {
+    isPanning = true;
+    panStart = e.clientX;
+    imageContainer.style.cursor = "grabbing";
+    e.preventDefault();
+    return;
+  }
+
   if (!currentLabel || !images[currentIndex]) return;
 
   const rect = imageDisplay.getBoundingClientRect();
-  startX = e.clientX - rect.left;
-  startY = e.clientY - rect.top;
+  startX = (e.clientX - rect.left) / zoomLevel;
+  startY = (e.clientY - rect.top) / zoomLevel;
 
   box = document.createElement("div");
   box.className = "bounding-box";
@@ -144,12 +186,20 @@ imageContainer.onmousemove = (e) => {
   const imgRect = imageDisplay.getBoundingClientRect();
   const containerRect = imageContainer.getBoundingClientRect();
 
-  const currX = e.clientX - imgRect.left;
-  const currY = e.clientY - imgRect.top;
+  const currX = (e.clientX - imgRect.left) / zoomLevel;
+  const currY = (e.clientY - imgRect.top) / zoomLevel;
 
   verticalLine.style.left = `${e.clientX - containerRect.left}px`;
   horizontalLine.style.top = `${e.clientY - containerRect.top}px`;
 
+  if (isPanning) {
+    const dx = e.clientX - panStart;
+    panStart = e.clientX;
+    panX += dx;
+    updateTransform();
+    return;
+  }
+
   if (!isDrawing || !box) return;
 
   box.style.left = `${Math.min(currX, startX)}px`;
@@ -159,11 +209,17 @@ imageContainer.onmousemove = (e) => {
 };
 
 imageContainer.onmouseup = (e) => {
+  if (isPanning) {
+    isPanning = false;
+    imageContainer.style.cursor = "crosshair";
+    return;
+  }
+
   if (!isDrawing || !box) return;
 
   const imgRect = imageDisplay.getBoundingClientRect();
-  const endX = e.clientX - imgRect.left;
-  const endY = e.clientY - imgRect.top;
+  const endX = (e.clientX - imgRect.left) / zoomLevel;
+  const endY = (e.clientY - imgRect.top) / zoomLevel;
 
   const left = Math.min(startX, endX);
   const top = Math.min(startY, endY);
@@ -213,7 +269,6 @@ nextImageBtn.onclick = () => {
 exportBtn.onclick = () => {
   const img = images[currentIndex];
   if (!img) return;
-  const imgRect = imageDisplay.getBoundingClientRect();
   const iw = imageDisplay.naturalWidth;
   const ih = imageDisplay.naturalHeight;
   const annots = annotations[img.name] || [];
@@ -222,13 +277,11 @@ exportBtn.onclick = () => {
   let txt = "";
   annots.forEach((obj) => {
     if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
-    const x = (obj.left / imgRect.width) * iw;
-    const y = (obj.top / imgRect.height) * ih;
-    const w = (obj.width / imgRect.width) * iw;
-    const h = (obj.height / imgRect.height) * ih;
-    const cx = (x + w / 2) / iw;
-    const cy = (y + h / 2) / ih;
-    txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${(w / iw).toFixed(6)} ${(h / ih).toFixed(6)}\n`;
+    const cx = (obj.left + obj.width / 2) / iw;
+    const cy = (obj.top + obj.height / 2) / ih;
+    const w = obj.width / iw;
+    const h = obj.height / ih;
+    txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
   });
   const blob = new Blob([txt], { type: "text/plain" });
   const a = document.createElement("a");
@@ -258,18 +311,15 @@ exportAllBtn.onclick = async () => {
     await new Promise((resolve) => (img.onload = resolve));
     const iw = img.naturalWidth;
     const ih = img.naturalHeight;
-    const rect = imageDisplay.getBoundingClientRect();
     const annots = annotations[name] || [];
     let txt = "";
     annots.forEach((obj) => {
       if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
-      const x = (obj.left / rect.width) * iw;
-      const y = (obj.top / rect.height) * ih;
-      const w = (obj.width / rect.width) * iw;
-      const h = (obj.height / rect.height) * ih;
-      const cx = (x + w / 2) / iw;
-      const cy = (y + h / 2) / ih;
-      txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${(w / iw).toFixed(6)} ${(h / ih).toFixed(6)}\n`;
+      const cx = (obj.left + obj.width / 2) / iw;
+      const cy = (obj.top + obj.height / 2) / ih;
+      const w = obj.width / iw;
+      const h = obj.height / ih;
+      txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
     });
     const labelFileName = name.replace(/\.[^/.]+$/, "") + ".txt";
     lblFolder.file(labelFileName, txt);
diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index ea076579..451da9ef 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -75,6 +75,10 @@ input[type="text"] {
     font-size: 14px;
     box-sizing: border-box;
 }
+
+input[type="range"] {
+    width: 300px;
+}
 /* Crosshair lines used during labeling */
 .crosshair-line {
     position: absolute;
@@ -112,6 +116,7 @@ input[type="text"] {
     display: inline-block;
     max-width: 100%;
     max-height: 100%;
+    transform-origin: top left;
 }
 
 #imageDisplay {

From 1e31e4f1bb520e70809dca30bbc9bf81f601309c Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 8 Jul 2025 14:26:52 +0200
Subject: [PATCH 21/67] Image size fixs.

---
 .../_internal/vision/label_tool/script.js     | 75 ++++++++-----------
 .../_internal/vision/label_tool/styles.css    |  4 +-
 2 files changed, 34 insertions(+), 45 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index 27dc4a8a..a035c219 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -18,9 +18,8 @@ const horizontalLine = document.getElementById("horizontalLine");
 const zoomSlider = document.getElementById("zoomSlider");
 
 let zoomLevel = 1;
-let panX = 0;
-let isPanning = false;
-let panStart = 0;
+let naturalWidth = 0;
+let naturalHeight = 0;
 
 const overlay = document.getElementById("overlay");
 
@@ -35,10 +34,6 @@ imageContainer.onmouseenter = () => {
 imageContainer.onmouseleave = () => {
   verticalLine.style.display = "none";
   horizontalLine.style.display = "none";
-  if (isPanning) {
-    isPanning = false;
-    imageContainer.style.cursor = "crosshair";
-  }
 };
 
 let currentLabel = null;
@@ -98,16 +93,17 @@ function loadImage(index) {
     const ih = imageDisplay.naturalHeight;
     imageDisplay.style.width = `${iw}px`;
     imageDisplay.style.height = `${ih}px`;
-    imageWrapper.style.width = `${iw}px`;
-    imageWrapper.style.height = `${ih}px`;
-    overlay.style.width = `${iw}px`;
-    overlay.style.height = `${ih}px`;
-    zoomLevel = 1;
-    panX = 0;
-    zoomSlider.value = 100;
+    naturalWidth = iw;
+    naturalHeight = ih;
+    zoomLevel = Math.min(
+      imageContainer.clientWidth / iw,
+      imageContainer.clientHeight / ih,
+      1,
+    );
+    zoomSlider.value = zoomLevel * 100;
+    updateTransform();
     const name = images[index].name;
     if (!annotations[name]) annotations[name] = [];
-    updateTransform();
     clearBoxes();
     annotations[name].forEach(addBoxFromData);
   };
@@ -117,6 +113,8 @@ function loadImage(index) {
 zoomSlider.oninput = (e) => {
   zoomLevel = e.target.value / 100;
   updateTransform();
+  clearBoxes();
+  annotations[images[currentIndex].name].forEach(addBoxFromData);
 };
 
 function clearBoxes() {
@@ -124,16 +122,23 @@ function clearBoxes() {
 }
 
 function updateTransform() {
-  imageWrapper.style.transform = `translateX(${panX}px) scale(${zoomLevel})`;
+  const w = naturalWidth * zoomLevel;
+  const h = naturalHeight * zoomLevel;
+  imageDisplay.style.width = `${w}px`;
+  imageDisplay.style.height = `${h}px`;
+  imageWrapper.style.width = `${w}px`;
+  imageWrapper.style.height = `${h}px`;
+  overlay.style.width = `${w}px`;
+  overlay.style.height = `${h}px`;
 }
 
 function addBoxFromData(data) {
   const box = document.createElement("div");
   box.className = "bounding-box";
-  box.style.left = `${data.left}px`;
-  box.style.top = `${data.top}px`;
-  box.style.width = `${data.width}px`;
-  box.style.height = `${data.height}px`;
+  box.style.left = `${data.left * zoomLevel}px`;
+  box.style.top = `${data.top * zoomLevel}px`;
+  box.style.width = `${data.width * zoomLevel}px`;
+  box.style.height = `${data.height * zoomLevel}px`;
   box.style.border = `2px dashed ${labelColors[data.label]}`;
   box.style.backgroundColor = labelColors[data.label]
     .replace("hsl", "hsla")
@@ -150,10 +155,6 @@ function addBoxFromData(data) {
 
 imageContainer.onmousedown = (e) => {
   if (e.button !== 0) {
-    isPanning = true;
-    panStart = e.clientX;
-    imageContainer.style.cursor = "grabbing";
-    e.preventDefault();
     return;
   }
 
@@ -165,8 +166,8 @@ imageContainer.onmousedown = (e) => {
 
   box = document.createElement("div");
   box.className = "bounding-box";
-  box.style.left = `${startX}px`;
-  box.style.top = `${startY}px`;
+  box.style.left = `${startX * zoomLevel}px`;
+  box.style.top = `${startY * zoomLevel}px`;
   box.style.border = `2px dashed ${labelColors[currentLabel]}`;
   box.style.backgroundColor = labelColors[currentLabel]
     .replace("hsl", "hsla")
@@ -192,29 +193,15 @@ imageContainer.onmousemove = (e) => {
   verticalLine.style.left = `${e.clientX - containerRect.left}px`;
   horizontalLine.style.top = `${e.clientY - containerRect.top}px`;
 
-  if (isPanning) {
-    const dx = e.clientX - panStart;
-    panStart = e.clientX;
-    panX += dx;
-    updateTransform();
-    return;
-  }
-
   if (!isDrawing || !box) return;
 
-  box.style.left = `${Math.min(currX, startX)}px`;
-  box.style.top = `${Math.min(currY, startY)}px`;
-  box.style.width = `${Math.abs(currX - startX)}px`;
-  box.style.height = `${Math.abs(currY - startY)}px`;
+  box.style.left = `${Math.min(currX, startX) * zoomLevel}px`;
+  box.style.top = `${Math.min(currY, startY) * zoomLevel}px`;
+  box.style.width = `${Math.abs(currX - startX) * zoomLevel}px`;
+  box.style.height = `${Math.abs(currY - startY) * zoomLevel}px`;
 };
 
 imageContainer.onmouseup = (e) => {
-  if (isPanning) {
-    isPanning = false;
-    imageContainer.style.cursor = "crosshair";
-    return;
-  }
-
   if (!isDrawing || !box) return;
 
   const imgRect = imageDisplay.getBoundingClientRect();
diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index 451da9ef..3e3ec5ef 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -13,6 +13,7 @@ body {
     border-right: 1px solid #ccc;
     padding: 20px;
     box-sizing: border-box;
+    flex-shrink: 0;
 }
 .main {
     flex: 1;
@@ -28,12 +29,13 @@ body {
     gap: 10px;
     align-items: center;
     flex-wrap: wrap;
+    flex-shrink: 0;
 }
 .image-container {
     flex: 1;
     position: relative;
     background: #eee;
-    overflow: hidden;
+    overflow: auto;
     cursor: crosshair;
 }
 

From 8186f762763370968ce4f483d1be7d3caa1e93b7 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 8 Jul 2025 14:49:08 +0200
Subject: [PATCH 22/67] Zoom fixs.

---
 src/dynsight/_internal/vision/label_tool/script.js  | 8 +++++---
 src/dynsight/_internal/vision/label_tool/styles.css | 1 +
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index a035c219..cc938b0a 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -18,6 +18,7 @@ const horizontalLine = document.getElementById("horizontalLine");
 const zoomSlider = document.getElementById("zoomSlider");
 
 let zoomLevel = 1;
+let baseZoom = 1;
 let naturalWidth = 0;
 let naturalHeight = 0;
 
@@ -95,12 +96,13 @@ function loadImage(index) {
     imageDisplay.style.height = `${ih}px`;
     naturalWidth = iw;
     naturalHeight = ih;
-    zoomLevel = Math.min(
+    baseZoom = Math.min(
       imageContainer.clientWidth / iw,
       imageContainer.clientHeight / ih,
       1,
     );
-    zoomSlider.value = zoomLevel * 100;
+    zoomLevel = baseZoom;
+    zoomSlider.value = 100;
     updateTransform();
     const name = images[index].name;
     if (!annotations[name]) annotations[name] = [];
@@ -111,7 +113,7 @@ function loadImage(index) {
 }
 
 zoomSlider.oninput = (e) => {
-  zoomLevel = e.target.value / 100;
+  zoomLevel = (e.target.value / 100) * baseZoom;
   updateTransform();
   clearBoxes();
   annotations[images[currentIndex].name].forEach(addBoxFromData);
diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index 3e3ec5ef..e8ff438b 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -17,6 +17,7 @@ body {
 }
 .main {
     flex: 1;
+    min-width: 0;
     display: flex;
     flex-direction: column;
     background: #fff;

From 5bda1aa9cf4a92d13d0270cd10d29967920504b1 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 8 Jul 2025 15:03:20 +0200
Subject: [PATCH 23/67] Fixing red guidelines.

---
 src/dynsight/_internal/vision/label_tool/script.js  | 8 ++++++--
 src/dynsight/_internal/vision/label_tool/styles.css | 4 ++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index cc938b0a..c714d8be 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -192,8 +192,12 @@ imageContainer.onmousemove = (e) => {
   const currX = (e.clientX - imgRect.left) / zoomLevel;
   const currY = (e.clientY - imgRect.top) / zoomLevel;
 
-  verticalLine.style.left = `${e.clientX - containerRect.left}px`;
-  horizontalLine.style.top = `${e.clientY - containerRect.top}px`;
+  verticalLine.style.left = `${
+    e.clientX - containerRect.left + imageContainer.scrollLeft
+  }px`;
+  horizontalLine.style.top = `${
+    e.clientY - containerRect.top + imageContainer.scrollTop
+  }px`;
 
   if (!isDrawing || !box) return;
 
diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index e8ff438b..d6033236 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -91,7 +91,7 @@ input[type="range"] {
 
 #verticalLine {
     width: 3px;
-    height: 100%;
+    height: 300%;
     top: 0;
     background-image: repeating-linear-gradient(
         to bottom,
@@ -104,7 +104,7 @@ input[type="range"] {
 
 #horizontalLine {
     height: 3px;
-    width: 100%;
+    width: 300%;
     left: 0;
     background-image: repeating-linear-gradient(
         to right,

From 2d58ba818ebbe00b57beacc1956e12624c1008fd Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 8 Jul 2025 15:17:30 +0200
Subject: [PATCH 24/67] Image stretch bug fix.

---
 src/dynsight/_internal/vision/label_tool/styles.css | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index d6033236..0836828e 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -117,14 +117,14 @@ input[type="range"] {
 #imageWrapper {
     position: relative;
     display: inline-block;
-    max-width: 100%;
-    max-height: 100%;
+    max-width: none;
+    max-height: none;
     transform-origin: top left;
 }
 
 #imageDisplay {
     display: block;
-    max-width: 100%;
+    max-width: none;
     height: auto;
     pointer-events: none;
     user-drag: none;

From 04dc8461c2a7e3485e22ae03a9792fa44ee3d7f1 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 8 Jul 2025 16:42:25 +0200
Subject: [PATCH 25/67] Fixing bounding box positions rules.

---
 .../_internal/vision/label_tool/script.js     | 31 ++++++++++++++-----
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index c714d8be..d2c89007 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -166,6 +166,17 @@ imageContainer.onmousedown = (e) => {
   startX = (e.clientX - rect.left) / zoomLevel;
   startY = (e.clientY - rect.top) / zoomLevel;
 
+  // Ignore clicks started outside the image boundaries
+  if (
+    startX < 0 ||
+    startY < 0 ||
+    startX > naturalWidth ||
+    startY > naturalHeight
+  ) {
+    isDrawing = false;
+    return;
+  }
+
   box = document.createElement("div");
   box.className = "bounding-box";
   box.style.left = `${startX * zoomLevel}px`;
@@ -191,6 +202,8 @@ imageContainer.onmousemove = (e) => {
 
   const currX = (e.clientX - imgRect.left) / zoomLevel;
   const currY = (e.clientY - imgRect.top) / zoomLevel;
+  const clampedX = Math.max(0, Math.min(naturalWidth, currX));
+  const clampedY = Math.max(0, Math.min(naturalHeight, currY));
 
   verticalLine.style.left = `${
     e.clientX - containerRect.left + imageContainer.scrollLeft
@@ -201,10 +214,10 @@ imageContainer.onmousemove = (e) => {
 
   if (!isDrawing || !box) return;
 
-  box.style.left = `${Math.min(currX, startX) * zoomLevel}px`;
-  box.style.top = `${Math.min(currY, startY) * zoomLevel}px`;
-  box.style.width = `${Math.abs(currX - startX) * zoomLevel}px`;
-  box.style.height = `${Math.abs(currY - startY) * zoomLevel}px`;
+  box.style.left = `${Math.min(clampedX, startX) * zoomLevel}px`;
+  box.style.top = `${Math.min(clampedY, startY) * zoomLevel}px`;
+  box.style.width = `${Math.abs(clampedX - startX) * zoomLevel}px`;
+  box.style.height = `${Math.abs(clampedY - startY) * zoomLevel}px`;
 };
 
 imageContainer.onmouseup = (e) => {
@@ -213,11 +226,13 @@ imageContainer.onmouseup = (e) => {
   const imgRect = imageDisplay.getBoundingClientRect();
   const endX = (e.clientX - imgRect.left) / zoomLevel;
   const endY = (e.clientY - imgRect.top) / zoomLevel;
+  const clampedX = Math.max(0, Math.min(naturalWidth, endX));
+  const clampedY = Math.max(0, Math.min(naturalHeight, endY));
 
-  const left = Math.min(startX, endX);
-  const top = Math.min(startY, endY);
-  const width = Math.abs(endX - startX);
-  const height = Math.abs(endY - startY);
+  const left = Math.min(startX, clampedX);
+  const top = Math.min(startY, clampedY);
+  const width = Math.abs(clampedX - startX);
+  const height = Math.abs(clampedY - startY);
 
   annotations[images[currentIndex].name].push({
     label: currentLabel,

From f6810788a63aba351de82e8b627c4857f22b16b9 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 9 Jul 2025 09:44:45 +0200
Subject: [PATCH 26/67] Fixing server shutdown.

---
 src/dynsight/_internal/vision/label_tool/script.js | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index d2c89007..29318607 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -340,6 +340,16 @@ exportAllBtn.onclick = async () => {
   URL.revokeObjectURL(a.href);
 };
 
-window.addEventListener("unload", () => {
-  navigator.sendBeacon("/shutdown");
+let navigatingAway = false;
+document.addEventListener("click", (e) => {
+  const link = e.target.closest("a");
+  if (link && link.href) {
+    navigatingAway = true;
+  }
+});
+
+window.addEventListener("pagehide", () => {
+  if (!navigatingAway) {
+    navigator.sendBeacon("/shutdown");
+  }
 });

From 763936d4947aacf83d3f5f9aee5353a6b629b407 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 9 Jul 2025 11:17:36 +0200
Subject: [PATCH 27/67] Added vision tests.

---
 tests/vision/__init__.py        |   0
 tests/vision/test_label_tool.py |  46 +++++++++++++
 tests/vision/test_vision.py     | 116 ++++++++++++++++++++++++++++++++
 3 files changed, 162 insertions(+)
 create mode 100644 tests/vision/__init__.py
 create mode 100644 tests/vision/test_label_tool.py
 create mode 100644 tests/vision/test_vision.py

diff --git a/tests/vision/__init__.py b/tests/vision/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/vision/test_label_tool.py b/tests/vision/test_label_tool.py
new file mode 100644
index 00000000..cb5ac94c
--- /dev/null
+++ b/tests/vision/test_label_tool.py
@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import http.client
+import socket
+import threading
+import time
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import pytest
+
+from dynsight.vision import label_tool
+
+
+def free_port() -> int:
+    sock = socket.socket()
+    sock.bind(("", 0))
+    port = sock.getsockname()[1]
+    sock.close()
+    return port
+
+
+def test_label_tool(monkeypatch: pytest.MonkeyPatch) -> None:
+    port = free_port()
+    opened: list[str] = []
+    monkeypatch.setattr("webbrowser.open", lambda url: opened.append(url))
+
+    thread = threading.Thread(target=label_tool, kwargs={"port": port})
+    thread.start()
+    time.sleep(0.5)
+
+    conn = http.client.HTTPConnection("localhost", port)
+    conn.request("GET", "/index.html")
+    resp = conn.getresponse()
+    status_num = 200
+    assert resp.status == status_num
+    conn.close()
+
+    conn = http.client.HTTPConnection("localhost", port)
+    conn.request("POST", "/shutdown")
+    conn.getresponse()
+    conn.close()
+
+    thread.join(timeout=5)
+    assert not thread.is_alive()
+    assert opened == [f"http://localhost:{port}/index.html"]
diff --git a/tests/vision/test_vision.py b/tests/vision/test_vision.py
new file mode 100644
index 00000000..371c3b96
--- /dev/null
+++ b/tests/vision/test_vision.py
@@ -0,0 +1,116 @@
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any, Iterator
+
+import numpy as np
+import pytest
+import yaml
+
+from dynsight.vision import VisionInstance
+
+
+class DummyTensor:
+    def __init__(self, arr: np.ndarray[Any, Any]) -> None:
+        self.arr = np.asarray(arr, dtype=float)
+
+    def cpu(self) -> DummyTensor:
+        return self
+
+    def numpy(self) -> np.ndarray[Any, Any]:
+        return np.asarray(self.arr)
+
+    def __iter__(self) -> Iterator[float]:
+        return iter(self.arr)
+
+
+class DummyBoxes:
+    def __init__(
+        self,
+        xyxy: np.ndarray[Any, Any],
+        cls_ids: np.ndarray[Any, Any],
+    ) -> None:
+        self._xyxy = np.array(xyxy, dtype=float)
+        self._cls = np.array(cls_ids, dtype=float)
+
+    @property
+    def xyxy(self) -> DummyTensor:
+        return DummyTensor(self._xyxy)
+
+    @property
+    def cls(self) -> DummyTensor:
+        return DummyTensor(self._cls)
+
+    @property
+    def xywhn(self) -> np.ndarray[Any, Any]:
+        xywh = np.column_stack(
+            [
+                (self._xyxy[:, 0] + self._xyxy[:, 2]) / 2,
+                (self._xyxy[:, 1] + self._xyxy[:, 3]) / 2,
+                self._xyxy[:, 2] - self._xyxy[:, 0],
+                self._xyxy[:, 3] - self._xyxy[:, 1],
+            ]
+        )
+        return xywh / 100.0
+
+
+class DummyResult:
+    def __init__(self, path: Path, boxes: DummyBoxes) -> None:
+        self.path = str(path)
+        self.names = {0: "obj"}
+        self.boxes = boxes
+
+
+@pytest.fixture(autouse=True)
+def patch_yolo(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(
+        "dynsight._internal.vision.vision.YOLO",
+        lambda *_: SimpleNamespace(),
+    )
+    monkeypatch.setattr(VisionInstance, "_check_device", lambda _: None)
+
+def test_set_training_dataset(tmp_path: Path) -> None:
+    vi = VisionInstance(source="src", output_path=tmp_path)
+    yaml_file = tmp_path / "data.yaml"
+    vi.set_training_dataset(yaml_file)
+    assert vi.training_data_yaml == yaml_file
+
+
+def create_fake_results(tmp_path: Path) -> list[DummyResult]:
+    files: list[Path] = []
+    results: list[DummyResult] = []
+    for i in range(3):
+        img = tmp_path / f"img{i}.jpg"
+        img.write_bytes(b"test")
+        boxes = DummyBoxes(np.array([[0, 0, 10, 10]]), np.array([0]))
+        results.append(DummyResult(img, boxes))
+        files.append(img)
+    return results
+
+
+def test_create_dataset_from_predictions(tmp_path: Path) -> None:
+    vi = VisionInstance(source="src", output_path=tmp_path)
+    vi.prediction_results = create_fake_results(tmp_path)
+    vi.create_dataset_from_predictions("dataset", train_split=0.5)
+    dataset = tmp_path / "dataset"
+    assert (dataset / "images/train/img0.jpg").exists()
+    assert (dataset / "labels/train/img0.txt").exists()
+    assert (dataset / "images/val/img1.jpg").exists()
+    yaml_path = dataset / "dataset.yaml"
+    data = yaml.safe_load(yaml_path.read_text())
+    assert data["nc"] == 1
+    assert data["names"] == ["obj"]
+    assert vi.training_data_yaml == yaml_path
+
+
+def test_export_prediction_to_xyz(tmp_path: Path) -> None:
+    vi = VisionInstance(source="src", output_path=tmp_path)
+    vi.prediction_results = create_fake_results(tmp_path)
+    xyz_path = vi.export_prediction_to_xyz(Path("out.xyz"))
+    lines = xyz_path.read_text().splitlines()
+    lines_num = 9
+    assert len(lines) == lines_num
+    assert lines[0] == "1"
+    assert lines[1] == "x y z"
+    assert xyz_path.exists()

From 913bc5ad646f5445e20dd043a1f3aa1b8da5d1b5 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 9 Jul 2025 14:33:56 +0200
Subject: [PATCH 28/67] Removed tests.

---
 src/dynsight/_internal/vision/vision.py |   1 +
 tests/vision/__init__.py                |   0
 tests/vision/test_label_tool.py         |  46 ----------
 tests/vision/test_vision.py             | 116 ------------------------
 4 files changed, 1 insertion(+), 162 deletions(-)
 delete mode 100644 tests/vision/__init__.py
 delete mode 100644 tests/vision/test_label_tool.py
 delete mode 100644 tests/vision/test_vision.py

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 716ea1e9..3deeffa0 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -453,6 +453,7 @@ def train(
             device=self.device,
             **full_params,
         )
+        self.model = YOLO(self.output_path / title / "weights" / "best.pt")
 
     def export_prediction_to_xyz(
         self, file_name: Path, class_filter: list[int] | None = None
diff --git a/tests/vision/__init__.py b/tests/vision/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/vision/test_label_tool.py b/tests/vision/test_label_tool.py
deleted file mode 100644
index cb5ac94c..00000000
--- a/tests/vision/test_label_tool.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from __future__ import annotations
-
-import http.client
-import socket
-import threading
-import time
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    import pytest
-
-from dynsight.vision import label_tool
-
-
-def free_port() -> int:
-    sock = socket.socket()
-    sock.bind(("", 0))
-    port = sock.getsockname()[1]
-    sock.close()
-    return port
-
-
-def test_label_tool(monkeypatch: pytest.MonkeyPatch) -> None:
-    port = free_port()
-    opened: list[str] = []
-    monkeypatch.setattr("webbrowser.open", lambda url: opened.append(url))
-
-    thread = threading.Thread(target=label_tool, kwargs={"port": port})
-    thread.start()
-    time.sleep(0.5)
-
-    conn = http.client.HTTPConnection("localhost", port)
-    conn.request("GET", "/index.html")
-    resp = conn.getresponse()
-    status_num = 200
-    assert resp.status == status_num
-    conn.close()
-
-    conn = http.client.HTTPConnection("localhost", port)
-    conn.request("POST", "/shutdown")
-    conn.getresponse()
-    conn.close()
-
-    thread.join(timeout=5)
-    assert not thread.is_alive()
-    assert opened == [f"http://localhost:{port}/index.html"]
diff --git a/tests/vision/test_vision.py b/tests/vision/test_vision.py
deleted file mode 100644
index 371c3b96..00000000
--- a/tests/vision/test_vision.py
+++ /dev/null
@@ -1,116 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from types import SimpleNamespace
-from typing import Any, Iterator
-
-import numpy as np
-import pytest
-import yaml
-
-from dynsight.vision import VisionInstance
-
-
-class DummyTensor:
-    def __init__(self, arr: np.ndarray[Any, Any]) -> None:
-        self.arr = np.asarray(arr, dtype=float)
-
-    def cpu(self) -> DummyTensor:
-        return self
-
-    def numpy(self) -> np.ndarray[Any, Any]:
-        return np.asarray(self.arr)
-
-    def __iter__(self) -> Iterator[float]:
-        return iter(self.arr)
-
-
-class DummyBoxes:
-    def __init__(
-        self,
-        xyxy: np.ndarray[Any, Any],
-        cls_ids: np.ndarray[Any, Any],
-    ) -> None:
-        self._xyxy = np.array(xyxy, dtype=float)
-        self._cls = np.array(cls_ids, dtype=float)
-
-    @property
-    def xyxy(self) -> DummyTensor:
-        return DummyTensor(self._xyxy)
-
-    @property
-    def cls(self) -> DummyTensor:
-        return DummyTensor(self._cls)
-
-    @property
-    def xywhn(self) -> np.ndarray[Any, Any]:
-        xywh = np.column_stack(
-            [
-                (self._xyxy[:, 0] + self._xyxy[:, 2]) / 2,
-                (self._xyxy[:, 1] + self._xyxy[:, 3]) / 2,
-                self._xyxy[:, 2] - self._xyxy[:, 0],
-                self._xyxy[:, 3] - self._xyxy[:, 1],
-            ]
-        )
-        return xywh / 100.0
-
-
-class DummyResult:
-    def __init__(self, path: Path, boxes: DummyBoxes) -> None:
-        self.path = str(path)
-        self.names = {0: "obj"}
-        self.boxes = boxes
-
-
-@pytest.fixture(autouse=True)
-def patch_yolo(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(
-        "dynsight._internal.vision.vision.YOLO",
-        lambda *_: SimpleNamespace(),
-    )
-    monkeypatch.setattr(VisionInstance, "_check_device", lambda _: None)
-
-def test_set_training_dataset(tmp_path: Path) -> None:
-    vi = VisionInstance(source="src", output_path=tmp_path)
-    yaml_file = tmp_path / "data.yaml"
-    vi.set_training_dataset(yaml_file)
-    assert vi.training_data_yaml == yaml_file
-
-
-def create_fake_results(tmp_path: Path) -> list[DummyResult]:
-    files: list[Path] = []
-    results: list[DummyResult] = []
-    for i in range(3):
-        img = tmp_path / f"img{i}.jpg"
-        img.write_bytes(b"test")
-        boxes = DummyBoxes(np.array([[0, 0, 10, 10]]), np.array([0]))
-        results.append(DummyResult(img, boxes))
-        files.append(img)
-    return results
-
-
-def test_create_dataset_from_predictions(tmp_path: Path) -> None:
-    vi = VisionInstance(source="src", output_path=tmp_path)
-    vi.prediction_results = create_fake_results(tmp_path)
-    vi.create_dataset_from_predictions("dataset", train_split=0.5)
-    dataset = tmp_path / "dataset"
-    assert (dataset / "images/train/img0.jpg").exists()
-    assert (dataset / "labels/train/img0.txt").exists()
-    assert (dataset / "images/val/img1.jpg").exists()
-    yaml_path = dataset / "dataset.yaml"
-    data = yaml.safe_load(yaml_path.read_text())
-    assert data["nc"] == 1
-    assert data["names"] == ["obj"]
-    assert vi.training_data_yaml == yaml_path
-
-
-def test_export_prediction_to_xyz(tmp_path: Path) -> None:
-    vi = VisionInstance(source="src", output_path=tmp_path)
-    vi.prediction_results = create_fake_results(tmp_path)
-    xyz_path = vi.export_prediction_to_xyz(Path("out.xyz"))
-    lines = xyz_path.read_text().splitlines()
-    lines_num = 9
-    assert len(lines) == lines_num
-    assert lines[0] == "1"
-    assert lines[1] == "x y z"
-    assert xyz_path.exists()

From db66c5cb000cfe7868ca84ba240d49e8715e2c72 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Thu, 10 Jul 2025 16:09:21 +0200
Subject: [PATCH 29/67] Adding vision test.

---
 src/dynsight/_internal/vision/vision.py |  13 +-
 tests/vision/__init__.py                |   0
 tests/vision/test_vision.py             | 192 ++++++++++++++++++++++++
 3 files changed, 203 insertions(+), 2 deletions(-)
 create mode 100644 tests/vision/__init__.py
 create mode 100644 tests/vision/test_vision.py

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 3deeffa0..8eff55b9 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -10,6 +10,7 @@
 
 if TYPE_CHECKING:
     from ultralytics.engine.results import Results
+    from ultralytics.utils.metrics import DetMetrics
 
 logging.basicConfig(
     level=logging.INFO,
@@ -105,7 +106,7 @@ def __init__(
         self.workers = workers
 
         self.prediction_results: list[Results] | None = None
-        self.training_results = None
+        self.training_results: DetMetrics | None = None
 
         self._check_device()
 
@@ -340,7 +341,7 @@ def tune_hyperparams(
             msg = "Training dataset has not been set."
             raise ValueError(msg)
 
-        return self.model.tune(
+        self.model.tune(
             data=self.training_data_yaml,
             epochs=epochs,
             iterations=iterations,
@@ -350,6 +351,14 @@ def tune_hyperparams(
             imgsz=imgsz,
             batch=batch_size,
         )
+        yaml_path = (
+            self.output_path
+            / "tuning"
+            / "results"
+            / "best_hyperparameters.yaml"
+        )
+        with yaml_path.open("r") as f:
+            return yaml.safe_load(f)
 
     def train(
         self,
diff --git a/tests/vision/__init__.py b/tests/vision/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/vision/test_vision.py b/tests/vision/test_vision.py
new file mode 100644
index 00000000..32398fb2
--- /dev/null
+++ b/tests/vision/test_vision.py
@@ -0,0 +1,192 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import yaml
+from PIL import Image
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+from dynsight._internal.vision.vision import VisionInstance
+
+DEFAULT_MODEL = "yolo12n.pt"
+
+
+def create_dummy_yolo_dataset(
+    root_path: Path,
+    num_train: int = 5,
+    num_val: int = 2,
+    image_size: tuple[int, int] = (100, 100),
+    num_classes: int = 1,
+    class_names: list[str] | None = None,
+    rng: np.random.Generator | None = None,
+) -> None:
+    if rng is None:
+        rng = np.random.default_rng()
+    if class_names is None:
+        class_names = [f"class_{i}" for i in range(num_classes)]
+
+    def generate_split(split_name: str, num_images: int) -> None:
+        images_dir = root_path / "dataset" / "images" / split_name
+        labels_dir = root_path / "dataset" / "labels" / split_name
+        images_dir.mkdir(parents=True, exist_ok=True)
+        labels_dir.mkdir(parents=True, exist_ok=True)
+
+        for i in range(num_images):
+            array = rng.integers(
+                0, 256, size=(image_size[1], image_size[0], 3), dtype=np.uint8
+            )
+            img = Image.fromarray(array)
+            img_path = images_dir / f"img_{i}.jpg"
+            img.save(img_path)
+
+            x_center = np.round(rng.uniform(0.3, 0.7), 6)
+            y_center = np.round(rng.uniform(0.3, 0.7), 6)
+            width = np.round(rng.uniform(0.1, 0.3), 6)
+            height = np.round(rng.uniform(0.1, 0.3), 6)
+            class_id = int(rng.integers(0, num_classes))
+
+            label_path = labels_dir / f"img_{i}.txt"
+            label_path.write_text(
+                f"{class_id} {x_center} {y_center} {width} {height}\n"
+            )
+
+    generate_split("train", num_train)
+    generate_split("val", num_val)
+
+    data_yaml = {
+        "path": str((root_path / "dataset").resolve()),
+        "train": str((root_path / "dataset" / "images" / "train").resolve()),
+        "val": str((root_path / "dataset" / "images" / "val").resolve()),
+        "nc": num_classes,
+        "names": class_names,
+    }
+    yaml_path = root_path / "data.yaml"
+    yaml_path.write_text(yaml.dump(data_yaml))
+
+
+def test_vision_instance_creation(tmp_path: Path) -> None:
+    source_path = tmp_path / "source.jpg"
+    img = Image.new("RGB", (100, 100))
+    img.save(source_path)
+    model_path = tmp_path / DEFAULT_MODEL
+    out_path = tmp_path / "output"
+
+    instance = VisionInstance(
+        source=source_path,
+        output_path=out_path,
+        model=model_path,
+        device="cpu",
+        workers=1,
+    )
+    assert model_path.exists()
+    assert instance.training_data_yaml is None
+    assert instance.training_results is None
+    assert instance.prediction_results is None
+    assert instance.device == "cpu"
+
+
+def test_vision_training(tmp_path: Path) -> None:
+    source_path = tmp_path / "source.jpg"
+    img = Image.new("RGB", (100, 100))
+    img.save(source_path)
+    model_path = tmp_path / DEFAULT_MODEL
+    out_path = tmp_path / "output"
+
+    instance = VisionInstance(
+        source=source_path,
+        output_path=out_path,
+        model=model_path,
+        device="cpu",
+        workers=1,
+    )
+    old_model = instance.model
+    create_dummy_yolo_dataset(tmp_path)
+    instance.set_training_dataset(tmp_path / "data.yaml")
+    assert (tmp_path / "data.yaml").exists()
+    instance.train(
+        title="test_train",
+        epochs=1,
+        batch_size=-1,
+        imgsz=100,
+    )
+    new_model = instance.model
+    new_model_path = out_path / "test_train" / "weights" / "best.pt"
+    assert instance.training_results is not None
+
+    assert str(instance.training_results.names[0]) == "class_0"
+    assert str(instance.training_results.task) == "detect"
+
+    assert new_model_path.exists()
+    assert old_model != new_model
+
+
+def test_vision_predict(tmp_path: Path) -> None:
+    out_path = tmp_path / "output"
+
+    source_path = tmp_path / "imgs"
+    source_path.mkdir(parents=True, exist_ok=True)
+    for i in range(10):
+        img = Image.new("RGB", (100, 100))
+        img.save(source_path / f"img_{i}.jpg")
+    model_path = tmp_path / DEFAULT_MODEL
+
+    instance = VisionInstance(
+        source=source_path,
+        output_path=out_path,
+        model=model_path,
+        device="cpu",
+        workers=1,
+    )
+    instance.predict(prediction_title="test_predict")
+    assert instance.prediction_results is not None
+    for i in range(10):
+        assert (source_path / f"img_{i}.jpg").exists()
+    instance.create_dataset_from_predictions("test_dataset_from_pred")
+
+    dataset_img_t = out_path / "test_dataset_from_pred" / "images" / "train"
+    dataset_img_v = out_path / "test_dataset_from_pred" / "images" / "val"
+    dataset_lab_t = out_path / "test_dataset_from_pred" / "labels" / "train"
+    dataset_lab_v = out_path / "test_dataset_from_pred" / "labels" / "val"
+
+    files_img_t = list(dataset_img_t.glob("*.jpg"))
+    files_img_v = list(dataset_img_v.glob("*.jpg"))
+    files_lab_t = list(dataset_lab_t.glob("*.txt"))
+    files_lab_v = list(dataset_lab_v.glob("*.txt"))
+
+    expected_train_set_len = 8
+    expected_val_set_len = 2
+    assert len(files_img_t) == expected_train_set_len
+    assert len(files_img_v) == expected_val_set_len
+    assert len(files_lab_t) == expected_train_set_len
+    assert len(files_lab_v) == expected_val_set_len
+
+
+def test_vision_tuning(tmp_path: Path) -> None:
+    source_path = tmp_path / "source.jpg"
+    img = Image.new("RGB", (100, 100))
+    img.save(source_path)
+    model_path = tmp_path / DEFAULT_MODEL
+    out_path = tmp_path / "output"
+
+    instance = VisionInstance(
+        source=source_path,
+        output_path=out_path,
+        model=model_path,
+        device="cpu",
+        workers=1,
+    )
+    create_dummy_yolo_dataset(tmp_path)
+    instance.set_training_dataset(tmp_path / "data.yaml")
+    hyp = instance.tune_hyperparams(
+        iterations=1,
+        epochs=1,
+        imgsz=100,
+        batch_size=-1,
+    )
+    assert (
+        out_path / "tuning" / "results" / "best_hyperparameters.yaml"
+    ).exists()
+    assert isinstance(hyp, dict)

From 63c72d59e45ff717e8e613431b535c7b7e5d87a7 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Thu, 10 Jul 2025 16:17:14 +0200
Subject: [PATCH 30/67] adding dependecies.

---
 pyproject.toml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 1856cd0e..35074c18 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,6 +29,10 @@ dev = [
     "twine",
     "furo",
     "numba",
+    "pandas",
+    "PyYAML",
+    "trackpy",
+    "ultralytics",
 ]
 
 [project.urls]

From f8010cbe6797cdeed0ddf5d769da130bc7835bdb Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Fri, 11 Jul 2025 16:45:15 +0200
Subject: [PATCH 31/67] added test for track.

---
 tests/systems/lj_id.xyz   | 6012 +++++++++++++++++++++++++++++++++++++
 tests/systems/lj_noid.xyz | 6012 +++++++++++++++++++++++++++++++++++++
 tests/track/__init__.py   |    0
 tests/track/test_track.py |   28 +
 4 files changed, 12052 insertions(+)
 create mode 100644 tests/systems/lj_id.xyz
 create mode 100644 tests/systems/lj_noid.xyz
 create mode 100644 tests/track/__init__.py
 create mode 100644 tests/track/test_track.py

diff --git a/tests/systems/lj_id.xyz b/tests/systems/lj_id.xyz
new file mode 100644
index 00000000..6ddd5ac6
--- /dev/null
+++ b/tests/systems/lj_id.xyz
@@ -0,0 +1,6012 @@
+10
+Frame at timestep 0
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+2 19.0161 18.663 0
+3 18.4347 17.7033 0
+4 2.1178 13.9009 0
+5 4.85295 3.55566 0
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+7 12.0908 9.97606 0
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+9 2.52339 10.6278 0
+10 11.3372 3.4803 0
+10
+Frame at timestep 200
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+2 18.8951 19.2155 0
+3 18.8975 18.0409 0
+4 2.98407 13.5399 0
+5 5.40991 2.46274 0
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+7 12.3286 10.4689 0
+8 11.8941 7.66172 0
+9 2.09912 9.37473 0
+10 10.8882 4.20984 0
+10
+Frame at timestep 400
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+2 18.7757 18.7849 0
+3 18.4196 17.7175 0
+4 3.8227 13.1831 0
+5 5.96041 1.17672 0
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+7 12.5619 10.9499 0
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+9 1.31201 8.13621 0
+10 10.4444 4.93092 0
+10
+Frame at timestep 600
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+2 19.1831 19.1626 0
+3 18.5583 18.3597 0
+4 4.62492 12.8376 0
+5 6.4572 1.74709 0
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+7 12.7101 11.2474 0
+8 11.8118 9.9799 0
+9 1.47086 7.01853 0
+10 10.044 5.58165 0
+10
+Frame at timestep 800
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+2 18.6901 19.1813 0
+3 18.5046 18.0665 0
+4 4.77713 12.6536 0
+5 6.86153 2.35792 0
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+7 13.0462 11.5858 0
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+9 1.64634 6.10887 0
+10 9.71807 6.11127 0
+10
+Frame at timestep 1000
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+Frame at timestep 1200
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+10
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diff --git a/tests/systems/lj_noid.xyz b/tests/systems/lj_noid.xyz
new file mode 100644
index 00000000..929912c3
--- /dev/null
+++ b/tests/systems/lj_noid.xyz
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diff --git a/tests/track/__init__.py b/tests/track/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/track/test_track.py b/tests/track/test_track.py
new file mode 100644
index 00000000..c2e9954e
--- /dev/null
+++ b/tests/track/test_track.py
@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+
+from dynsight.track import track_xyz
+from dynsight.trajectory import Trj
+
+
+def test_track_xyz(tmp_path: Path) -> None:
+    original_dir = Path(__file__).resolve().parent
+    filename = original_dir / "../systems/lj_noid.xyz"
+    file_with_id = original_dir / "../systems/lj_id.xyz"
+    output = tmp_path / "trajectory.xyz"
+
+    ref_trajectory = Trj.init_from_xyz(traj_file=file_with_id, dt=1)
+
+    trajectory = track_xyz(input_xyz=filename, output_xyz=output)
+
+    ref_pos = ref_trajectory.get_coordinates(selection="all")
+    tst_pos = trajectory.get_coordinates(selection="all")
+
+    r_sorted = np.array([frame[np.argsort(frame[:, 0])] for frame in ref_pos])
+    t_sorted = np.array([frame[np.argsort(frame[:, 0])] for frame in tst_pos])
+
+    assert r_sorted.shape == t_sorted.shape
+    assert np.allclose(r_sorted[:, :, :], t_sorted[:, :, :])

From a3c519459b684a9ea64a8a93a931e626ca22ae94 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 21 Jul 2025 14:38:07 +0200
Subject: [PATCH 32/67] Added synthesize dataset button to the label tool.

---
 .../_internal/vision/label_tool/index.html    |   3 +
 .../_internal/vision/label_tool/script.js     | 661 +++++++++++-------
 2 files changed, 416 insertions(+), 248 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/index.html b/src/dynsight/_internal/vision/label_tool/index.html
index 9213eefc..d7e05bfd 100644
--- a/src/dynsight/_internal/vision/label_tool/index.html
+++ b/src/dynsight/_internal/vision/label_tool/index.html
@@ -27,6 +27,9 @@ <h2>Labels</h2>
                 <button id="exportAll" class="nav-btn">
                     Export dataset (YOLO)
                 </button>
+                <button id="synthesize" class="nav-btn">
+                    Synthesize dataset
+                </button>
                 <label for="zoomSlider">Zoom</label>
                 <input
                     type="range"
diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index 29318607..00a1e39f 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -11,6 +11,7 @@ const clearLastBtn = document.getElementById("clearLast");
 const clearAllBtn = document.getElementById("clearAll");
 const exportBtn = document.getElementById("exportYolo");
 const exportAllBtn = document.getElementById("exportAll");
+const synthBtn = document.getElementById("synthesize");
 const nextImageBtn = document.getElementById("nextImage");
 const prevImageBtn = document.getElementById("prevImage");
 const verticalLine = document.getElementById("verticalLine");
@@ -28,328 +29,492 @@ verticalLine.style.display = "none";
 horizontalLine.style.display = "none";
 
 imageContainer.onmouseenter = () => {
-  verticalLine.style.display = "block";
-  horizontalLine.style.display = "block";
+    verticalLine.style.display = "block";
+    horizontalLine.style.display = "block";
 };
 
 imageContainer.onmouseleave = () => {
-  verticalLine.style.display = "none";
-  horizontalLine.style.display = "none";
+    verticalLine.style.display = "none";
+    horizontalLine.style.display = "none";
 };
 
 let currentLabel = null;
 const labelColors = {};
 let isDrawing = false;
 let startX,
-  startY,
-  box = null;
+    startY,
+    box = null;
 
 let images = [];
 let currentIndex = 0;
 const annotations = {}; // imageName -> [boxData]
 
 function getRandomColor() {
-  const hue = Math.floor(Math.random() * 360);
-  return `hsl(${hue}, 90%, 50%)`;
+    const hue = Math.floor(Math.random() * 360);
+    return `hsl(${hue}, 90%, 50%)`;
 }
 
 function setActiveLabel(item) {
-  document
-    .querySelectorAll(".label-item")
-    .forEach((i) => i.classList.remove("active"));
-  item.classList.add("active");
-  currentLabel = item.textContent;
+    document
+        .querySelectorAll(".label-item")
+        .forEach((i) => i.classList.remove("active"));
+    item.classList.add("active");
+    currentLabel = item.textContent;
 }
 
 function createLabelItem(text) {
-  const item = document.createElement("div");
-  item.className = "label-item";
-  item.textContent = text;
-  labelColors[text] = labelColors[text] || getRandomColor();
-  item.style.backgroundColor = labelColors[text];
-  item.style.color = "#fff";
-  item.addEventListener("click", () => setActiveLabel(item));
-  labelList.appendChild(item);
+    const item = document.createElement("div");
+    item.className = "label-item";
+    item.textContent = text;
+    labelColors[text] = labelColors[text] || getRandomColor();
+    item.style.backgroundColor = labelColors[text];
+    item.style.color = "#fff";
+    item.addEventListener("click", () => setActiveLabel(item));
+    labelList.appendChild(item);
 }
 
 addLabelBtn.onclick = () => {
-  const label = newLabelInput.value.trim();
-  if (label && !labelColors[label]) {
-    createLabelItem(label);
-    newLabelInput.value = "";
-  }
+    const label = newLabelInput.value.trim();
+    if (label && !labelColors[label]) {
+        createLabelItem(label);
+        newLabelInput.value = "";
+    }
 };
 
 imageInput.onchange = (e) => {
-  images = Array.from(e.target.files);
-  currentIndex = 0;
-  loadImage(currentIndex);
+    images = Array.from(e.target.files);
+    currentIndex = 0;
+    loadImage(currentIndex);
 };
 
 function loadImage(index) {
-  if (!images[index]) return;
-  const url = URL.createObjectURL(images[index]);
-  imageDisplay.onload = () => {
-    const iw = imageDisplay.naturalWidth;
-    const ih = imageDisplay.naturalHeight;
-    imageDisplay.style.width = `${iw}px`;
-    imageDisplay.style.height = `${ih}px`;
-    naturalWidth = iw;
-    naturalHeight = ih;
-    baseZoom = Math.min(
-      imageContainer.clientWidth / iw,
-      imageContainer.clientHeight / ih,
-      1,
-    );
-    zoomLevel = baseZoom;
-    zoomSlider.value = 100;
-    updateTransform();
-    const name = images[index].name;
-    if (!annotations[name]) annotations[name] = [];
-    clearBoxes();
-    annotations[name].forEach(addBoxFromData);
-  };
-  imageDisplay.src = url;
+    if (!images[index]) return;
+    const url = URL.createObjectURL(images[index]);
+    imageDisplay.onload = () => {
+        const iw = imageDisplay.naturalWidth;
+        const ih = imageDisplay.naturalHeight;
+        imageDisplay.style.width = `${iw}px`;
+        imageDisplay.style.height = `${ih}px`;
+        naturalWidth = iw;
+        naturalHeight = ih;
+        baseZoom = Math.min(
+            imageContainer.clientWidth / iw,
+            imageContainer.clientHeight / ih,
+            1,
+        );
+        zoomLevel = baseZoom;
+        zoomSlider.value = 100;
+        updateTransform();
+        const name = images[index].name;
+        if (!annotations[name]) annotations[name] = [];
+        clearBoxes();
+        annotations[name].forEach(addBoxFromData);
+    };
+    imageDisplay.src = url;
 }
 
 zoomSlider.oninput = (e) => {
-  zoomLevel = (e.target.value / 100) * baseZoom;
-  updateTransform();
-  clearBoxes();
-  annotations[images[currentIndex].name].forEach(addBoxFromData);
+    zoomLevel = (e.target.value / 100) * baseZoom;
+    updateTransform();
+    clearBoxes();
+    annotations[images[currentIndex].name].forEach(addBoxFromData);
 };
 
 function clearBoxes() {
-  overlay.innerHTML = "";
+    overlay.innerHTML = "";
 }
 
 function updateTransform() {
-  const w = naturalWidth * zoomLevel;
-  const h = naturalHeight * zoomLevel;
-  imageDisplay.style.width = `${w}px`;
-  imageDisplay.style.height = `${h}px`;
-  imageWrapper.style.width = `${w}px`;
-  imageWrapper.style.height = `${h}px`;
-  overlay.style.width = `${w}px`;
-  overlay.style.height = `${h}px`;
+    const w = naturalWidth * zoomLevel;
+    const h = naturalHeight * zoomLevel;
+    imageDisplay.style.width = `${w}px`;
+    imageDisplay.style.height = `${h}px`;
+    imageWrapper.style.width = `${w}px`;
+    imageWrapper.style.height = `${h}px`;
+    overlay.style.width = `${w}px`;
+    overlay.style.height = `${h}px`;
 }
 
 function addBoxFromData(data) {
-  const box = document.createElement("div");
-  box.className = "bounding-box";
-  box.style.left = `${data.left * zoomLevel}px`;
-  box.style.top = `${data.top * zoomLevel}px`;
-  box.style.width = `${data.width * zoomLevel}px`;
-  box.style.height = `${data.height * zoomLevel}px`;
-  box.style.border = `2px dashed ${labelColors[data.label]}`;
-  box.style.backgroundColor = labelColors[data.label]
-    .replace("hsl", "hsla")
-    .replace(")", ", 0.1)");
-
-  const tag = document.createElement("div");
-  tag.className = "label-tag";
-  tag.textContent = data.label;
-  tag.style.backgroundColor = labelColors[data.label];
-  box.appendChild(tag);
-
-  overlay.appendChild(box);
+    const box = document.createElement("div");
+    box.className = "bounding-box";
+    box.style.left = `${data.left * zoomLevel}px`;
+    box.style.top = `${data.top * zoomLevel}px`;
+    box.style.width = `${data.width * zoomLevel}px`;
+    box.style.height = `${data.height * zoomLevel}px`;
+    box.style.border = `2px dashed ${labelColors[data.label]}`;
+    box.style.backgroundColor = labelColors[data.label]
+        .replace("hsl", "hsla")
+        .replace(")", ", 0.1)");
+
+    const tag = document.createElement("div");
+    tag.className = "label-tag";
+    tag.textContent = data.label;
+    tag.style.backgroundColor = labelColors[data.label];
+    box.appendChild(tag);
+
+    overlay.appendChild(box);
 }
 
 imageContainer.onmousedown = (e) => {
-  if (e.button !== 0) {
-    return;
-  }
-
-  if (!currentLabel || !images[currentIndex]) return;
-
-  const rect = imageDisplay.getBoundingClientRect();
-  startX = (e.clientX - rect.left) / zoomLevel;
-  startY = (e.clientY - rect.top) / zoomLevel;
-
-  // Ignore clicks started outside the image boundaries
-  if (
-    startX < 0 ||
-    startY < 0 ||
-    startX > naturalWidth ||
-    startY > naturalHeight
-  ) {
-    isDrawing = false;
-    return;
-  }
-
-  box = document.createElement("div");
-  box.className = "bounding-box";
-  box.style.left = `${startX * zoomLevel}px`;
-  box.style.top = `${startY * zoomLevel}px`;
-  box.style.border = `2px dashed ${labelColors[currentLabel]}`;
-  box.style.backgroundColor = labelColors[currentLabel]
-    .replace("hsl", "hsla")
-    .replace(")", ", 0.1)");
-
-  const tag = document.createElement("div");
-  tag.className = "label-tag";
-  tag.textContent = currentLabel;
-  tag.style.backgroundColor = labelColors[currentLabel];
-  box.appendChild(tag);
-
-  overlay.appendChild(box);
-  isDrawing = true;
+    if (e.button !== 0) {
+        return;
+    }
+
+    if (!currentLabel || !images[currentIndex]) return;
+
+    const rect = imageDisplay.getBoundingClientRect();
+    startX = (e.clientX - rect.left) / zoomLevel;
+    startY = (e.clientY - rect.top) / zoomLevel;
+
+    // Ignore clicks started outside the image boundaries
+    if (
+        startX < 0 ||
+        startY < 0 ||
+        startX > naturalWidth ||
+        startY > naturalHeight
+    ) {
+        isDrawing = false;
+        return;
+    }
+
+    box = document.createElement("div");
+    box.className = "bounding-box";
+    box.style.left = `${startX * zoomLevel}px`;
+    box.style.top = `${startY * zoomLevel}px`;
+    box.style.border = `2px dashed ${labelColors[currentLabel]}`;
+    box.style.backgroundColor = labelColors[currentLabel]
+        .replace("hsl", "hsla")
+        .replace(")", ", 0.1)");
+
+    const tag = document.createElement("div");
+    tag.className = "label-tag";
+    tag.textContent = currentLabel;
+    tag.style.backgroundColor = labelColors[currentLabel];
+    box.appendChild(tag);
+
+    overlay.appendChild(box);
+    isDrawing = true;
 };
 
 imageContainer.onmousemove = (e) => {
-  const imgRect = imageDisplay.getBoundingClientRect();
-  const containerRect = imageContainer.getBoundingClientRect();
-
-  const currX = (e.clientX - imgRect.left) / zoomLevel;
-  const currY = (e.clientY - imgRect.top) / zoomLevel;
-  const clampedX = Math.max(0, Math.min(naturalWidth, currX));
-  const clampedY = Math.max(0, Math.min(naturalHeight, currY));
-
-  verticalLine.style.left = `${
-    e.clientX - containerRect.left + imageContainer.scrollLeft
-  }px`;
-  horizontalLine.style.top = `${
-    e.clientY - containerRect.top + imageContainer.scrollTop
-  }px`;
-
-  if (!isDrawing || !box) return;
-
-  box.style.left = `${Math.min(clampedX, startX) * zoomLevel}px`;
-  box.style.top = `${Math.min(clampedY, startY) * zoomLevel}px`;
-  box.style.width = `${Math.abs(clampedX - startX) * zoomLevel}px`;
-  box.style.height = `${Math.abs(clampedY - startY) * zoomLevel}px`;
+    const imgRect = imageDisplay.getBoundingClientRect();
+    const containerRect = imageContainer.getBoundingClientRect();
+
+    const currX = (e.clientX - imgRect.left) / zoomLevel;
+    const currY = (e.clientY - imgRect.top) / zoomLevel;
+    const clampedX = Math.max(0, Math.min(naturalWidth, currX));
+    const clampedY = Math.max(0, Math.min(naturalHeight, currY));
+
+    verticalLine.style.left = `${
+        e.clientX - containerRect.left + imageContainer.scrollLeft
+    }px`;
+    horizontalLine.style.top = `${
+        e.clientY - containerRect.top + imageContainer.scrollTop
+    }px`;
+
+    if (!isDrawing || !box) return;
+
+    box.style.left = `${Math.min(clampedX, startX) * zoomLevel}px`;
+    box.style.top = `${Math.min(clampedY, startY) * zoomLevel}px`;
+    box.style.width = `${Math.abs(clampedX - startX) * zoomLevel}px`;
+    box.style.height = `${Math.abs(clampedY - startY) * zoomLevel}px`;
 };
 
 imageContainer.onmouseup = (e) => {
-  if (!isDrawing || !box) return;
-
-  const imgRect = imageDisplay.getBoundingClientRect();
-  const endX = (e.clientX - imgRect.left) / zoomLevel;
-  const endY = (e.clientY - imgRect.top) / zoomLevel;
-  const clampedX = Math.max(0, Math.min(naturalWidth, endX));
-  const clampedY = Math.max(0, Math.min(naturalHeight, endY));
-
-  const left = Math.min(startX, clampedX);
-  const top = Math.min(startY, clampedY);
-  const width = Math.abs(clampedX - startX);
-  const height = Math.abs(clampedY - startY);
-
-  annotations[images[currentIndex].name].push({
-    label: currentLabel,
-    left,
-    top,
-    width,
-    height,
-  });
-
-  box = null;
-  isDrawing = false;
+    if (!isDrawing || !box) return;
+
+    const imgRect = imageDisplay.getBoundingClientRect();
+    const endX = (e.clientX - imgRect.left) / zoomLevel;
+    const endY = (e.clientY - imgRect.top) / zoomLevel;
+    const clampedX = Math.max(0, Math.min(naturalWidth, endX));
+    const clampedY = Math.max(0, Math.min(naturalHeight, endY));
+
+    const left = Math.min(startX, clampedX);
+    const top = Math.min(startY, clampedY);
+    const width = Math.abs(clampedX - startX);
+    const height = Math.abs(clampedY - startY);
+
+    annotations[images[currentIndex].name].push({
+        label: currentLabel,
+        left,
+        top,
+        width,
+        height,
+    });
+
+    box = null;
+    isDrawing = false;
 };
 
 clearLastBtn.onclick = () => {
-  const ann = annotations[images[currentIndex].name];
-  if (ann.length > 0) {
-    ann.pop();
-    clearBoxes();
-    ann.forEach(addBoxFromData);
-  }
+    const ann = annotations[images[currentIndex].name];
+    if (ann.length > 0) {
+        ann.pop();
+        clearBoxes();
+        ann.forEach(addBoxFromData);
+    }
 };
 
 clearAllBtn.onclick = () => {
-  annotations[images[currentIndex].name] = [];
-  clearBoxes();
+    annotations[images[currentIndex].name] = [];
+    clearBoxes();
 };
 
 prevImageBtn.onclick = () => {
-  if (currentIndex > 0) {
-    currentIndex--;
-    loadImage(currentIndex);
-  }
+    if (currentIndex > 0) {
+        currentIndex--;
+        loadImage(currentIndex);
+    }
 };
 
 nextImageBtn.onclick = () => {
-  if (currentIndex < images.length - 1) {
-    currentIndex++;
-    loadImage(currentIndex);
-  }
+    if (currentIndex < images.length - 1) {
+        currentIndex++;
+        loadImage(currentIndex);
+    }
 };
 
 exportBtn.onclick = () => {
-  const img = images[currentIndex];
-  if (!img) return;
-  const iw = imageDisplay.naturalWidth;
-  const ih = imageDisplay.naturalHeight;
-  const annots = annotations[img.name] || [];
-  const labelMap = {};
-  let nextId = 0;
-  let txt = "";
-  annots.forEach((obj) => {
-    if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
-    const cx = (obj.left + obj.width / 2) / iw;
-    const cy = (obj.top + obj.height / 2) / ih;
-    const w = obj.width / iw;
-    const h = obj.height / ih;
-    txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
-  });
-  const blob = new Blob([txt], { type: "text/plain" });
-  const a = document.createElement("a");
-  a.href = URL.createObjectURL(blob);
-  a.download = img.name.replace(/\.[^/.]+$/, "") + ".txt";
-  a.click();
-  URL.revokeObjectURL(a.href);
-};
-
-exportAllBtn.onclick = async () => {
-  if (images.length === 0) {
-    alert("Nessuna immagine caricata.");
-    return;
-  }
-  const zip = new JSZip();
-  const imgFolder = zip.folder("images");
-  const lblFolder = zip.folder("labels");
-  const labelMap = {};
-  let nextId = 0;
-  for (const image of images) {
-    const name = image.name;
-    const imgData = await image.arrayBuffer();
-    imgFolder.file(name, imgData);
-    const img = new Image();
-    const url = URL.createObjectURL(image);
-    img.src = url;
-    await new Promise((resolve) => (img.onload = resolve));
-    const iw = img.naturalWidth;
-    const ih = img.naturalHeight;
-    const annots = annotations[name] || [];
+    const img = images[currentIndex];
+    if (!img) return;
+    const iw = imageDisplay.naturalWidth;
+    const ih = imageDisplay.naturalHeight;
+    const annots = annotations[img.name] || [];
+    const labelMap = {};
+    let nextId = 0;
     let txt = "";
     annots.forEach((obj) => {
-      if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
-      const cx = (obj.left + obj.width / 2) / iw;
-      const cy = (obj.top + obj.height / 2) / ih;
-      const w = obj.width / iw;
-      const h = obj.height / ih;
-      txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
+        if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
+        const cx = (obj.left + obj.width / 2) / iw;
+        const cy = (obj.top + obj.height / 2) / ih;
+        const w = obj.width / iw;
+        const h = obj.height / ih;
+        txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
     });
-    const labelFileName = name.replace(/\.[^/.]+$/, "") + ".txt";
-    lblFolder.file(labelFileName, txt);
-  }
-  const content = await zip.generateAsync({ type: "blob" });
-  const a = document.createElement("a");
-  a.href = URL.createObjectURL(content);
-  a.download = "yolo_dataset.zip";
-  a.click();
-  URL.revokeObjectURL(a.href);
+    const blob = new Blob([txt], { type: "text/plain" });
+    const a = document.createElement("a");
+    a.href = URL.createObjectURL(blob);
+    a.download = img.name.replace(/\.[^/.]+$/, "") + ".txt";
+    a.click();
+    URL.revokeObjectURL(a.href);
+};
+
+exportAllBtn.onclick = async () => {
+    if (images.length === 0) {
+        alert("Nessuna immagine caricata.");
+        return;
+    }
+    const zip = new JSZip();
+    const imgFolder = zip.folder("images");
+    const lblFolder = zip.folder("labels");
+    const labelMap = {};
+    let nextId = 0;
+    for (const image of images) {
+        const name = image.name;
+        const imgData = await image.arrayBuffer();
+        imgFolder.file(name, imgData);
+        const img = new Image();
+        const url = URL.createObjectURL(image);
+        img.src = url;
+        await new Promise((resolve) => (img.onload = resolve));
+        const iw = img.naturalWidth;
+        const ih = img.naturalHeight;
+        const annots = annotations[name] || [];
+        let txt = "";
+        annots.forEach((obj) => {
+            if (!(obj.label in labelMap)) labelMap[obj.label] = nextId++;
+            const cx = (obj.left + obj.width / 2) / iw;
+            const cy = (obj.top + obj.height / 2) / ih;
+            const w = obj.width / iw;
+            const h = obj.height / ih;
+            txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
+        });
+        const labelFileName = name.replace(/\.[^/.]+$/, "") + ".txt";
+        lblFolder.file(labelFileName, txt);
+    }
+    const content = await zip.generateAsync({ type: "blob" });
+    const a = document.createElement("a");
+    a.href = URL.createObjectURL(content);
+    a.download = "yolo_dataset.zip";
+    a.click();
+    URL.revokeObjectURL(a.href);
+};
+
+synthBtn.onclick = async () => {
+    if (images.length === 0) {
+        alert("Nessuna immagine caricata.");
+        return;
+    }
+
+    const numImages = parseInt(
+        prompt("Numero di immagini sintetiche da generare?", "10"),
+        10,
+    );
+    const width = parseInt(prompt("Larghezza delle immagini?", "640"), 10);
+    const height = parseInt(prompt("Altezza delle immagini?", "640"), 10);
+
+    if (
+        !numImages ||
+        Number.isNaN(numImages) ||
+        !width ||
+        Number.isNaN(width) ||
+        !height ||
+        Number.isNaN(height)
+    ) {
+        alert("Parametri non validi.");
+        return;
+    }
+
+    const crops = [];
+    const labelMap = {};
+    let nextId = 0;
+    for (const file of images) {
+        const ann = annotations[file.name] || [];
+        for (const c of ann) {
+            crops.push({ file, ...c });
+            if (!(c.label in labelMap)) labelMap[c.label] = nextId++;
+        }
+    }
+
+    if (crops.length === 0) {
+        alert("Nessuna label trovata.");
+        return;
+    }
+
+    async function loadImage(file) {
+        return await new Promise((resolve) => {
+            const img = new Image();
+            img.src = URL.createObjectURL(file);
+            img.onload = () => {
+                URL.revokeObjectURL(img.src);
+                resolve(img);
+            };
+        });
+    }
+
+    function overlaps(x, y, w, h, boxes) {
+        return boxes.some((b) => {
+            return !(
+                x + w <= b.x ||
+                x >= b.x + b.w ||
+                y + h <= b.y ||
+                y >= b.y + b.h
+            );
+        });
+    }
+
+    async function createCollage() {
+        const canvas = document.createElement("canvas");
+        canvas.width = width;
+        canvas.height = height;
+        const ctx = canvas.getContext("2d");
+        ctx.fillStyle = "white";
+        ctx.fillRect(0, 0, width, height);
+
+        const placed = [];
+        const numObj = Math.min(5, crops.length);
+        for (let i = 0; i < numObj; i++) {
+            const crop = crops[Math.floor(Math.random() * crops.length)];
+            const img = await loadImage(crop.file);
+            const c = document.createElement("canvas");
+            c.width = crop.width;
+            c.height = crop.height;
+            c.getContext("2d").drawImage(
+                img,
+                crop.left,
+                crop.top,
+                crop.width,
+                crop.height,
+                0,
+                0,
+                crop.width,
+                crop.height,
+            );
+
+            const scale =
+                (0.15 + 0.15 * Math.random()) *
+                (Math.min(width, height) / Math.max(crop.width, crop.height));
+            const w = crop.width * scale;
+            const h = crop.height * scale;
+
+            let x, y;
+            let tries = 0;
+            do {
+                x = Math.random() * (width - w);
+                y = Math.random() * (height - h);
+                tries += 1;
+            } while (tries < 50 && overlaps(x, y, w, h, placed));
+
+            if (tries === 50) continue;
+
+            ctx.drawImage(c, 0, 0, crop.width, crop.height, x, y, w, h);
+            placed.push({ label: crop.label, x, y, w, h });
+        }
+
+        let txt = "";
+        placed.forEach((p) => {
+            const cls = labelMap[p.label];
+            const cx = (p.x + p.w / 2) / width;
+            const cy = (p.y + p.h / 2) / height;
+            const ww = p.w / width;
+            const hh = p.h / height;
+            txt += `${cls} ${cx.toFixed(6)} ${cy.toFixed(6)} ${ww.toFixed(
+                6,
+            )} ${hh.toFixed(6)}\n`;
+        });
+
+        const blob = await new Promise((resolve) =>
+            canvas.toBlob((b) => resolve(b), "image/jpeg"),
+        );
+        return { blob, txt };
+    }
+
+    const zip = new JSZip();
+    const imgTrain = zip.folder("images/train");
+    const imgVal = zip.folder("images/val");
+    const lblTrain = zip.folder("labels/train");
+    const lblVal = zip.folder("labels/val");
+
+    const numTrain = Math.floor(numImages * 0.8);
+
+    for (let i = 0; i < numImages; i++) {
+        const { blob, txt } = await createCollage();
+        const imgName = `synt_${i}.jpg`;
+        const txtName = `synt_${i}.txt`;
+        if (i < numTrain) {
+            imgTrain.file(imgName, blob);
+            lblTrain.file(txtName, txt);
+        } else {
+            imgVal.file(imgName, blob);
+            lblVal.file(txtName, txt);
+        }
+    }
+
+    const names = Object.keys(labelMap);
+    const yaml = `path: .\ntrain: images/train\nval: images/val\nnc: ${names.length}\nnames: [${names
+        .map((n) => `'${n}'`)
+        .join(", ")}]\n`;
+    zip.file("dataset.yaml", yaml);
+
+    const content = await zip.generateAsync({ type: "blob" });
+    const a = document.createElement("a");
+    a.href = URL.createObjectURL(content);
+    a.download = "synt_dataset.zip";
+    a.click();
+    URL.revokeObjectURL(a.href);
 };
 
 let navigatingAway = false;
 document.addEventListener("click", (e) => {
-  const link = e.target.closest("a");
-  if (link && link.href) {
-    navigatingAway = true;
-  }
+    const link = e.target.closest("a");
+    if (link && link.href) {
+        navigatingAway = true;
+    }
 });
 
 window.addEventListener("pagehide", () => {
-  if (!navigatingAway) {
-    navigator.sendBeacon("/shutdown");
-  }
+    if (!navigatingAway) {
+        navigator.sendBeacon("/shutdown");
+    }
 });

From b961d6d388f2927eeefe9aea4f2f0e4efbf60405 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 21 Jul 2025 15:02:20 +0200
Subject: [PATCH 33/67] Make the label tool executable standalone.

---
 pyproject.toml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 35074c18..21a7ac3b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,6 +39,9 @@ dev = [
 github = "https://github.com/GMPavanLab/dynsight"
 documentation = "https://dynsight.readthedocs.io/en/latest/"
 
+[project.scripts]
+label_tool = "dynsight.vision:label_tool"
+
 [tool.setuptools_scm]
 
 [tool.ruff]

From 2b4d316955dd9feed49b05abb80fe313d7dd7224 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 23 Jul 2025 17:48:36 +0200
Subject: [PATCH 34/67] Fixing bug for video source dataset creation.

---
 src/dynsight/_internal/vision/vision.py | 23 ++++++++++++++++++++---
 1 file changed, 20 insertions(+), 3 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 8eff55b9..bc961fda 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -267,13 +267,30 @@ def create_dataset_from_predictions(
 
         num_train = int(len(sorted_results) * train_split)
 
+        video_exts = {".mp4", ".avi", ".mov", ".mkv", ".webm"}
+        is_video = False
+        if isinstance(self.source, (str, Path)):
+            if Path(self.source).suffix.lower() in video_exts:
+                is_video = True
+
         for idx, result in enumerate(sorted_results):
             src = Path(result.path)
             subset = "train" if idx < num_train else "val"
-            img_dst = dataset_path / "images" / subset / src.name
-            lbl_dst = dataset_path / "labels" / subset / (src.stem + ".txt")
+            if is_video:
+                frame_name = f"{src.stem}_{idx:06d}.jpg"
+                img_dst = dataset_path / "images" / subset / frame_name
+                lbl_dst = dataset_path / "labels" / subset / (
+                    Path(frame_name).stem + ".txt"
+                )
+                from PIL import Image
+
+                img = Image.fromarray(result.orig_img[..., ::-1])
+                img.save(img_dst)
+            else:
+                img_dst = dataset_path / "images" / subset / src.name
+                lbl_dst = dataset_path / "labels" / subset / (src.stem + ".txt")
 
-            img_dst.write_bytes(src.read_bytes())
+                img_dst.write_bytes(src.read_bytes())
 
             boxes = result.boxes
             if boxes is None:

From c60cc6743c6d8f4d5d9f4cf30289891180fe69a5 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 23 Jul 2025 17:54:20 +0200
Subject: [PATCH 35/67] ruff fixes.

---
 src/dynsight/_internal/vision/vision.py | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index bc961fda..b82ac185 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -6,6 +6,7 @@
 
 import torch
 import yaml
+from PIL import Image
 from ultralytics import YOLO
 
 if TYPE_CHECKING:
@@ -269,9 +270,11 @@ def create_dataset_from_predictions(
 
         video_exts = {".mp4", ".avi", ".mov", ".mkv", ".webm"}
         is_video = False
-        if isinstance(self.source, (str, Path)):
-            if Path(self.source).suffix.lower() in video_exts:
-                is_video = True
+        if (
+            isinstance(self.source, (str, Path))
+            and Path(self.source).suffix.lower() in video_exts
+        ):
+            is_video = True
 
         for idx, result in enumerate(sorted_results):
             src = Path(result.path)
@@ -279,16 +282,20 @@ def create_dataset_from_predictions(
             if is_video:
                 frame_name = f"{src.stem}_{idx:06d}.jpg"
                 img_dst = dataset_path / "images" / subset / frame_name
-                lbl_dst = dataset_path / "labels" / subset / (
-                    Path(frame_name).stem + ".txt"
+                lbl_dst = (
+                    dataset_path
+                    / "labels"
+                    / subset
+                    / (Path(frame_name).stem + ".txt")
                 )
-                from PIL import Image
 
                 img = Image.fromarray(result.orig_img[..., ::-1])
                 img.save(img_dst)
             else:
                 img_dst = dataset_path / "images" / subset / src.name
-                lbl_dst = dataset_path / "labels" / subset / (src.stem + ".txt")
+                lbl_dst = (
+                    dataset_path / "labels" / subset / (src.stem + ".txt")
+                )
 
                 img_dst.write_bytes(src.read_bytes())
 

From 629ea8cb7e30f1dfd787f1bb7b1afe26444a0d29 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Thu, 24 Jul 2025 16:31:16 +0200
Subject: [PATCH 36/67] Added example code WIP.

---
 examples/video_to_trajectory.py               |  77 ++++++++++++++++++
 .../video_to_trajectory/example_video.mp4     | Bin 0 -> 1036652 bytes
 2 files changed, 77 insertions(+)
 create mode 100644 examples/video_to_trajectory.py
 create mode 100644 examples/video_to_trajectory/example_video.mp4

diff --git a/examples/video_to_trajectory.py b/examples/video_to_trajectory.py
new file mode 100644
index 00000000..5455b08c
--- /dev/null
+++ b/examples/video_to_trajectory.py
@@ -0,0 +1,77 @@
+"""Virtualization of a video in order to extract trajectory information.
+
+In this example, we show an example of how to use the dynsight Vision module
+combinedwith the track module to obtain a trajectory file from a video.
+
+A very simple video is used to demonstrate the workflow, for this reason the
+default detection model is used. For more complex videos is recommended to
+exploit the label_tool to create a synthesized dataset as a starting point to
+train the initial model and then follow the subsequent steps.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+from dynsight.vision import VisionInstance
+
+
+def plot_results(
+    instance: VisionInstance,
+    output_path: Path,
+    name: str,
+) -> None:
+    if instance.prediction_results is None:
+        msg = "No prediction results found"
+        raise ValueError(msg)
+
+    n_detections = [len(result) for result in instance.prediction_results]
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+
+    ax1.plot(n_detections, marker="o")
+    ax1.set_title("N° Detections in Time")
+    ax1.set_xlabel("Frame")
+    ax1.set_ylabel("N° Detections")
+
+    ax2.hist(n_detections, bins="auto", edgecolor="black")
+    ax2.set_title("Detection Distribution")
+    ax2.set_xlabel("N° Detections")
+    ax2.set_ylabel("Frequency")
+
+    plt.tight_layout()
+    plt.savefig(output_path / f"{name}.png", dpi=600)
+    plt.close()
+
+
+def main() -> None:
+    video_path = Path("video_to_trajectory/example_video.mp4")
+    n_iterations = 5
+    instance = VisionInstance(
+        source=video_path,
+        output_path=Path("output"),
+        device="0",
+        workers=8,
+    )
+    for it in range(n_iterations):
+        instance.predict(prediction_title=f"prediction_{it}")
+        plot_results(
+            instance=instance,
+            output_path=instance.output_path,
+            name=f"results_plot_{it}",
+        )
+        instance.create_dataset_from_predictions(
+            dataset_name=f"dataset_{it}",
+        )
+        instance.set_training_dataset(
+            training_data_yaml=instance.output_path
+            / f"dataset_{it}"
+            / "dataset.yaml",
+        )
+        instance.train(title=f"train_{it}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/video_to_trajectory/example_video.mp4 b/examples/video_to_trajectory/example_video.mp4
new file mode 100644
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From afc708a728d1f31ff5c9ad5baa88cd313e666558 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 28 Jul 2025 12:00:04 +0200
Subject: [PATCH 37/67] Added extraction to xyz and tracked xyz in example
 script.

---
 examples/video_to_trajectory.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/examples/video_to_trajectory.py b/examples/video_to_trajectory.py
index 5455b08c..be6657d4 100644
--- a/examples/video_to_trajectory.py
+++ b/examples/video_to_trajectory.py
@@ -15,6 +15,7 @@
 
 import matplotlib.pyplot as plt
 
+from dynsight.track import track_xyz
 from dynsight.vision import VisionInstance
 
 
@@ -71,6 +72,10 @@ def main() -> None:
             / "dataset.yaml",
         )
         instance.train(title=f"train_{it}")
+    traj_path = instance.export_prediction_to_xyz(
+        file_name=Path("trajectory.xyz")
+    )
+    track_xyz(input_xyz=traj_path, output_xyz=Path("output/tracked_traj.xyz"))
 
 
 if __name__ == "__main__":

From 3af25283a42388dd1caec521347a20c8598ede86 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Mon, 28 Jul 2025 13:40:43 +0200
Subject: [PATCH 38/67] Added dependencies.

---
 pyproject.toml | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 21a7ac3b..44d271ac 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,15 @@ maintainers = [
     { name = "Simone Martino", email = "s.martino0898@gmail.com" },
 ]
 
-dependencies = ["numpy", "dscribe", "tropea-clustering", "MDAnalysis"]
+dependencies = [
+    "numpy",
+    "dscribe",
+    "tropea-clustering",
+    "MDAnalysis",
+    "PyYAML",
+    "trackpy",
+    "ultralytics",
+]
 # Set by cpctools.
 requires-python = ">=3.8"
 dynamic = ["version"]

From 4caaec967f9c6748a133d1ea13264536573c1aed Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 11:06:32 +0200
Subject: [PATCH 39/67] Added a custom css file to modify the documentation
 layout.

---
 docs/source/_static/style.css | 25 +++++++++++++++++++++++++
 docs/source/conf.py           |  9 +++++++++
 2 files changed, 34 insertions(+)
 create mode 100644 docs/source/_static/style.css

diff --git a/docs/source/_static/style.css b/docs/source/_static/style.css
new file mode 100644
index 00000000..b4cd6dd2
--- /dev/null
+++ b/docs/source/_static/style.css
@@ -0,0 +1,25 @@
+/*DOCS SIDEBARS AND CENTERING*/
+.sidebar-drawer,
+.sidebar-container,
+.toc-drawer {
+    width: 16em;
+    min-width: 16em;
+}
+@media (min-width: 65em) {
+    .content {
+        width: 60em;
+        margin-left: auto;
+        margin-right: auto;
+        padding-right: 16em;
+    }
+}
+@media (min-width: 82em) {
+    .page {
+        justify-content: center;
+    }
+    .toc-drawer {
+        position: fixed;
+        right: 0;
+        top: 0;
+    }
+}
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 376d1d05..47c9d6a9 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -8,6 +8,10 @@
 from __future__ import annotations
 
 import importlib
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from sphinx.application import Sphinx
 
 project = "dynsight"
 project_copyright = "2023, Andrew Tarzia"
@@ -53,3 +57,8 @@
 
 html_theme = "furo"
 html_static_path = ["_static"]
+
+
+def setup(app: Sphinx) -> None:
+    """Configure the Sphinx app by adding a custom CSS file."""
+    app.add_css_file("style.css")

From a878df4f494f857920d57495a39169a3c598ed3e Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 11:07:01 +0200
Subject: [PATCH 40/67] Vision doc updated.

---
 src/dynsight/_internal/vision/vision.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index b82ac185..185a6187 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -52,12 +52,12 @@
 class VisionInstance:
     """Class for performing vision tasks using YOLO models.
 
-    It allows to perform object detection, Convolutional Neural Network (CNN)
-    training and tuning, training dataset creation and handling.
+    This class supports object detection, Convolutional Neural Network (CNN)
+    training and fine-tuning, as well as the creation and management of
+    training datasets.
 
     .. caution::
-        This class is still in development and may not work as expected.
-
+        This class is still under development and may not function as intended.
     """
 
     def __init__(

From 0c4a54c73e979e25ff3e07fffa3516e78fd9ac78 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 11:54:55 +0200
Subject: [PATCH 41/67] Remove ultralytics manual dependency and add the label
 tool documentation file.

---
 docs/source/_static/label_bar.png     | Bin 0 -> 14081 bytes
 docs/source/_static/label_menu.png    | Bin 0 -> 9545 bytes
 docs/source/_static/label_tool.png    | Bin 0 -> 1347489 bytes
 docs/source/index.rst                 |  16 ------
 docs/source/label_tool/label_tool.rst |  68 ++++++++++++++++++++++++++
 docs/source/vision.rst                |  16 +-----
 6 files changed, 69 insertions(+), 31 deletions(-)
 create mode 100644 docs/source/_static/label_bar.png
 create mode 100644 docs/source/_static/label_menu.png
 create mode 100644 docs/source/_static/label_tool.png
 create mode 100644 docs/source/label_tool/label_tool.rst

diff --git a/docs/source/_static/label_bar.png b/docs/source/_static/label_bar.png
new file mode 100644
index 0000000000000000000000000000000000000000..da11d3891bf400df9a590bdc98aaf0ea9f6db8d6
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HcmV?d00001

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 4886c90e..44c65400 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -73,22 +73,6 @@ or with conda::
 
   $ conda install -c conda-forge deeptime
 
-If you want to use the :mod:`dynsight.vision` and :mod:`dynsight.track` modules
-you will need to install a series of packages. This can be done with with pip::
-
-  $ pip install ultralytics
-
-.. caution::
-  Recommended python versions declared by ``ultralytics`` are 3.8 to 3.12.
-  Other versions may cause errors.
-
-.. tip::
-    ``PyTorch`` is required for the ``ultralytics`` package and its
-    requirements vary by operating system and CUDA/ROCm requirements, so
-    install ``PyTorch`` first may avoid compatibility issues. Follow the
-    instructions at this `link <https://pytorch.org/get-started/locally/>`_
-
-
 How to contribute
 -----------------
 
diff --git a/docs/source/label_tool/label_tool.rst b/docs/source/label_tool/label_tool.rst
new file mode 100644
index 00000000..ddceb0e7
--- /dev/null
+++ b/docs/source/label_tool/label_tool.rst
@@ -0,0 +1,68 @@
+The Label Tool
+==============
+
+The ``dynsight label_tool`` is a simple web application that allows users to
+label images. Picture labelling is a crucial step in many computer vision tasks,
+such as, the creation of initial training dataset to train Convolutional Neural
+Networks (CNNs) model. The current version of `dynsight vision <../_autosummary/dynsight.vision.VisionInstance.html>`_
+exploits the power of the `YOLO models <https://docs.ultralytics.com/models/yolo12/>`_
+for computer vision tasks. So that the ``label_tool`` has been specifically
+designed to work with the YOLO dataset format.
+
+.. image:: ../_static/label_tool.png
+
+----------
+How to Use
+----------
+
+The ``label_tool`` application can be executed in 2 main ways:
+
+* As standalone application by typing in your terminal environment:
+
+.. code-block:: bash
+
+    $ label_tool
+
+* From python code:
+
+.. code-block:: python
+
+    import dynsight
+
+    dynsight.vision.label_tool(port=8888) #port selection is optional
+
+In both cases a localhost server should start and the application should
+automatically appear in your default web browser.
+
+.. tip::
+
+    In case the application does
+    not open automatically, you can manually open it by copying and pasting
+    the URL provided in the terminal output.
+
+-------
+The GUI
+-------
+The ``label_tool`` Graphical User Interface is divided by three main panels:
+
+* **The image panel**: where loaded images appear and labels can be drawn.
+
+* **The label menu panel**: where labels can be created and edited.
+
+.. image:: ../_static/label_menu.png
+    :align: center
+
+* **The commands panel**: where all the available commands can be executed.
+
+.. image:: ../_static/label_bar.png
+
+Using the ``Choose File`` button, users can select the image(s) they want to
+label. Once the image is loaded, users can start drawing labels by clicking and
+dragging on the image panel. The label menu panel allows users to create and
+edit labels. Finally, the commands panel provides a set of exporting options:
+
+* **Export label**: Download the single txt file containing the label ``txt`` file in YOLO format for the current image.
+
+* **Export dataset**: Download a YOLO dataset from the loaded images with the labels and create the initial yaml configuration file to be used in the YOLO training process.
+
+* **Synthesize dataset**: Create a synthetic dataset from the drawn labels randomizing the object position in different images (useful when a low number of images is available).
diff --git a/docs/source/vision.rst b/docs/source/vision.rst
index 42f3fc8c..62b7264a 100644
--- a/docs/source/vision.rst
+++ b/docs/source/vision.rst
@@ -3,21 +3,6 @@ Vision
 
 Code for computer vision and trajectory extraction from experimental videos.
 
-Note that to use this module, you need to install the ``ultralytics`` package.
-This can be done with pip::
-
-  $ pip install ultralytics
-
-.. caution::
-  Recommended python versions declared by ``ultralytics`` are 3.8 to 3.12.
-  Other versions may cause errors.
-
-.. tip::
-    ``PyTorch`` is required for the ``ultralytics`` package and its
-    requirements vary by operating system and CUDA/ROCm requirements, so
-    install ``PyTorch`` first may avoid compatibility issues. Follow the
-    instructions at this `link <https://pytorch.org/get-started/locally/>`_
-
 -----
 Usage
 -----
@@ -25,4 +10,5 @@ Usage
 .. toctree::
   :maxdepth: 1
 
+  label_tool <label_tool/label_tool.rst>
   VisionInstance <_autosummary/dynsight.vision.VisionInstance.rst>

From 052f3682f76178a949260ff646ee0d1d72e95705 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 14:08:37 +0200
Subject: [PATCH 42/67] Minor fixes.

---
 src/dynsight/_internal/vision/label_tool/styles.css | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/styles.css b/src/dynsight/_internal/vision/label_tool/styles.css
index 0836828e..fffb761f 100644
--- a/src/dynsight/_internal/vision/label_tool/styles.css
+++ b/src/dynsight/_internal/vision/label_tool/styles.css
@@ -82,7 +82,6 @@ input[type="text"] {
 input[type="range"] {
     width: 300px;
 }
-/* Crosshair lines used during labeling */
 .crosshair-line {
     position: absolute;
     pointer-events: none;
@@ -136,7 +135,7 @@ input[type="range"] {
     left: 0;
     width: 100%;
     height: 100%;
-    pointer-events: none; /* le box possono ricevere eventi solo se necessario */
+    pointer-events: none;
 }
 
 .bounding-box {

From f7de55b69a6b3b235ab23f4978c899fda0671745 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 14:08:53 +0200
Subject: [PATCH 43/67] Solving dataset exportation bug and minor fixes.

---
 .../_internal/vision/label_tool/script.js     | 80 +++++++++++++++----
 1 file changed, 65 insertions(+), 15 deletions(-)

diff --git a/src/dynsight/_internal/vision/label_tool/script.js b/src/dynsight/_internal/vision/label_tool/script.js
index 00a1e39f..79b6f9de 100644
--- a/src/dynsight/_internal/vision/label_tool/script.js
+++ b/src/dynsight/_internal/vision/label_tool/script.js
@@ -290,7 +290,17 @@ exportBtn.onclick = () => {
         const cy = (obj.top + obj.height / 2) / ih;
         const w = obj.width / iw;
         const h = obj.height / ih;
-        txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
+        txt +=
+            labelMap[obj.label] +
+            " " +
+            cx.toFixed(6) +
+            " " +
+            cy.toFixed(6) +
+            " " +
+            w.toFixed(6) +
+            " " +
+            h.toFixed(6) +
+            "\n";
     });
     const blob = new Blob([txt], { type: "text/plain" });
     const a = document.createElement("a");
@@ -302,18 +312,35 @@ exportBtn.onclick = () => {
 
 exportAllBtn.onclick = async () => {
     if (images.length === 0) {
-        alert("Nessuna immagine caricata.");
+        alert("No images uploaded.");
         return;
     }
+    let trainPercent = parseFloat(
+        prompt("Percentage of images for training?", "80"),
+    );
+    if (
+        Number.isNaN(trainPercent) ||
+        trainPercent <= 0 ||
+        trainPercent >= 100
+    ) {
+        trainPercent = 80;
+    }
+    const numTrain = Math.floor(images.length * (trainPercent / 100));
     const zip = new JSZip();
-    const imgFolder = zip.folder("images");
-    const lblFolder = zip.folder("labels");
+    const imgTrain = zip.folder("images/train");
+    const imgVal = zip.folder("images/val");
+    const lblTrain = zip.folder("labels/train");
+    const lblVal = zip.folder("labels/val");
     const labelMap = {};
     let nextId = 0;
-    for (const image of images) {
+    for (let i = 0; i < images.length; i++) {
+        const image = images[i];
         const name = image.name;
         const imgData = await image.arrayBuffer();
+        const imgFolder = i < numTrain ? imgTrain : imgVal;
+        const lblFolder = i < numTrain ? lblTrain : lblVal;
         imgFolder.file(name, imgData);
+
         const img = new Image();
         const url = URL.createObjectURL(image);
         img.src = url;
@@ -328,11 +355,31 @@ exportAllBtn.onclick = async () => {
             const cy = (obj.top + obj.height / 2) / ih;
             const w = obj.width / iw;
             const h = obj.height / ih;
-            txt += `${labelMap[obj.label]} ${cx.toFixed(6)} ${cy.toFixed(6)} ${w.toFixed(6)} ${h.toFixed(6)}\n`;
+            txt +=
+                labelMap[obj.label] +
+                " " +
+                cx.toFixed(6) +
+                " " +
+                cy.toFixed(6) +
+                " " +
+                w.toFixed(6) +
+                " " +
+                h.toFixed(6) +
+                "\n";
         });
         const labelFileName = name.replace(/\.[^/.]+$/, "") + ".txt";
         lblFolder.file(labelFileName, txt);
     }
+
+    const names = Object.keys(labelMap);
+    const yaml = `path: .
+    train: images/train
+    val: images/val
+    nc: ${names.length}
+    names: [${names.map((n) => `'${n}'`).join(", ")}]
+    `;
+    zip.file("dataset.yaml", yaml);
+
     const content = await zip.generateAsync({ type: "blob" });
     const a = document.createElement("a");
     a.href = URL.createObjectURL(content);
@@ -343,16 +390,16 @@ exportAllBtn.onclick = async () => {
 
 synthBtn.onclick = async () => {
     if (images.length === 0) {
-        alert("Nessuna immagine caricata.");
+        alert("No images uploaded.");
         return;
     }
 
     const numImages = parseInt(
-        prompt("Numero di immagini sintetiche da generare?", "10"),
+        prompt("Number of synthetic images to generate?", "10"),
         10,
     );
-    const width = parseInt(prompt("Larghezza delle immagini?", "640"), 10);
-    const height = parseInt(prompt("Altezza delle immagini?", "640"), 10);
+    const width = parseInt(prompt("Image width?", "640"), 10);
+    const height = parseInt(prompt("Image height?", "640"), 10);
 
     if (
         !numImages ||
@@ -362,7 +409,7 @@ synthBtn.onclick = async () => {
         !height ||
         Number.isNaN(height)
     ) {
-        alert("Parametri non validi.");
+        alert("Invalid parameters.");
         return;
     }
 
@@ -378,7 +425,7 @@ synthBtn.onclick = async () => {
     }
 
     if (crops.length === 0) {
-        alert("Nessuna label trovata.");
+        alert("No label found.");
         return;
     }
 
@@ -492,9 +539,12 @@ synthBtn.onclick = async () => {
     }
 
     const names = Object.keys(labelMap);
-    const yaml = `path: .\ntrain: images/train\nval: images/val\nnc: ${names.length}\nnames: [${names
-        .map((n) => `'${n}'`)
-        .join(", ")}]\n`;
+    const yaml = `path: .
+    train: images/train
+    val: images/val
+    nc: ${names.length}
+    names: [${names.map((n) => `'${n}'`).join(", ")}]
+    `;
     zip.file("dataset.yaml", yaml);
 
     const content = await zip.generateAsync({ type: "blob" });

From 35247e32258093a2b2a17f77ecee48c9451c8e29 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 14:40:26 +0200
Subject: [PATCH 44/67] removed noqa.

---
 src/dynsight/_internal/vision/label_tool.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/dynsight/_internal/vision/label_tool.py b/src/dynsight/_internal/vision/label_tool.py
index 4a1695a4..7b47bcff 100644
--- a/src/dynsight/_internal/vision/label_tool.py
+++ b/src/dynsight/_internal/vision/label_tool.py
@@ -13,7 +13,7 @@ class HTTPRequestHandler(SimpleHTTPRequestHandler):
     def log_message(self, fmt: str, *args: object) -> None:
         pass
 
-    def do_POST(self) -> None:  # noqa: N802
+    def do_POST(self) -> None:
         if self.path == "/shutdown":
             self.send_response(200)
             self.end_headers()

From 83a33128bc2ce86c25c56e7d664874aa42e17330 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 14:52:07 +0200
Subject: [PATCH 45/67] removed tica.

---
 src/dynsight/__init__.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/dynsight/__init__.py b/src/dynsight/__init__.py
index 112981b6..f8663e1a 100644
--- a/src/dynsight/__init__.py
+++ b/src/dynsight/__init__.py
@@ -9,7 +9,6 @@
     logs,
     onion,
     soap,
-    tica,
     track,
     trajectory,
     utilities,
@@ -25,7 +24,6 @@
     "logs",
     "onion",
     "soap",
-    "tica",
     "track",
     "trajectory",
     "utilities",

From f2c5e1402d63a470db4298d45ae3da277b0ed7ba Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Tue, 29 Jul 2025 15:16:48 +0200
Subject: [PATCH 46/67] TEMPORARY - removed 3.13 tests from workflow.

---
 .github/workflows/tests.yaml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
index 3921413d..c5f178fe 100644
--- a/.github/workflows/tests.yaml
+++ b/.github/workflows/tests.yaml
@@ -10,7 +10,7 @@ jobs:
         runs-on: ubuntu-latest
         strategy:
             matrix:
-                python-version: ["3.10", "3.13"]
+                python-version: ["3.10"]
         name: ruff on ${{ matrix.python-version }}
         steps:
             - uses: actions/checkout@v3
@@ -24,7 +24,7 @@ jobs:
         runs-on: ubuntu-latest
         strategy:
             matrix:
-                python-version: ["3.10", "3.13"]
+                python-version: ["3.10"]
         name: mypy on ${{ matrix.python-version }}
         steps:
             - uses: actions/checkout@v3
@@ -38,7 +38,7 @@ jobs:
         runs-on: ubuntu-latest
         strategy:
             matrix:
-                python-version: ["3.10", "3.13"]
+                python-version: ["3.10"]
         name: ruff-format on ${{ matrix.python-version }}
         steps:
             - uses: actions/checkout@v3
@@ -52,7 +52,7 @@ jobs:
         runs-on: ubuntu-latest
         strategy:
             matrix:
-                python-version: ["3.10", "3.13"]
+                python-version: ["3.10"]
         name: pytest on ${{ matrix.python-version }}
         steps:
             - uses: actions/checkout@v3
@@ -66,7 +66,7 @@ jobs:
         runs-on: ubuntu-latest
         strategy:
             matrix:
-                python-version: ["3.10", "3.13"]
+                python-version: ["3.10"]
         name: doctest on ${{ matrix.python-version }}
         steps:
             - uses: actions/checkout@v3

From e17ba6a63af2581e55db2c7929bc03752e123881 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 30 Jul 2025 11:03:33 +0200
Subject: [PATCH 47/67] Documentation minor fixes.

---
 docs/source/label_tool/label_tool.rst | 8 ++++----
 examples/video_to_trajectory.py       | 8 ++++----
 src/dynsight/_internal/track/track.py | 2 ++
 3 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/docs/source/label_tool/label_tool.rst b/docs/source/label_tool/label_tool.rst
index ddceb0e7..aa7911c8 100644
--- a/docs/source/label_tool/label_tool.rst
+++ b/docs/source/label_tool/label_tool.rst
@@ -3,10 +3,10 @@ The Label Tool
 
 The ``dynsight label_tool`` is a simple web application that allows users to
 label images. Picture labelling is a crucial step in many computer vision tasks,
-such as, the creation of initial training dataset to train Convolutional Neural
+such as the creation of initial training dataset to train Convolutional Neural
 Networks (CNNs) model. The current version of `dynsight vision <../_autosummary/dynsight.vision.VisionInstance.html>`_
 exploits the power of the `YOLO models <https://docs.ultralytics.com/models/yolo12/>`_
-for computer vision tasks. So that the ``label_tool`` has been specifically
+for computer vision tasks. Thus, the ``label_tool`` has been specifically
 designed to work with the YOLO dataset format.
 
 .. image:: ../_static/label_tool.png
@@ -43,7 +43,7 @@ automatically appear in your default web browser.
 -------
 The GUI
 -------
-The ``label_tool`` Graphical User Interface is divided by three main panels:
+The ``label_tool`` Graphical User Interface is divided in three main panels:
 
 * **The image panel**: where loaded images appear and labels can be drawn.
 
@@ -61,7 +61,7 @@ label. Once the image is loaded, users can start drawing labels by clicking and
 dragging on the image panel. The label menu panel allows users to create and
 edit labels. Finally, the commands panel provides a set of exporting options:
 
-* **Export label**: Download the single txt file containing the label ``txt`` file in YOLO format for the current image.
+* **Export label**: Download a single ``.txt`` file in YOLO format containing the labels for the current image.
 
 * **Export dataset**: Download a YOLO dataset from the loaded images with the labels and create the initial yaml configuration file to be used in the YOLO training process.
 
diff --git a/examples/video_to_trajectory.py b/examples/video_to_trajectory.py
index be6657d4..15dbdf9f 100644
--- a/examples/video_to_trajectory.py
+++ b/examples/video_to_trajectory.py
@@ -1,7 +1,7 @@
 """Virtualization of a video in order to extract trajectory information.
 
-In this example, we show an example of how to use the dynsight Vision module
-combinedwith the track module to obtain a trajectory file from a video.
+In this example, we show how to use the dynsight ``vision` module
+combined with the track module to obtain a trajectory file from a video.
 
 A very simple video is used to demonstrate the workflow, for this reason the
 default detection model is used. For more complex videos is recommended to
@@ -53,8 +53,8 @@ def main() -> None:
     instance = VisionInstance(
         source=video_path,
         output_path=Path("output"),
-        device="0",
-        workers=8,
+        device="0",  # select GPU id, "cpu" or "mps" for MacOS users.
+        workers=8,  # number of cores used.
     )
     for it in range(n_iterations):
         instance.predict(prediction_title=f"prediction_{it}")
diff --git a/src/dynsight/_internal/track/track.py b/src/dynsight/_internal/track/track.py
index 3ee37195..eef0e6b3 100644
--- a/src/dynsight/_internal/track/track.py
+++ b/src/dynsight/_internal/track/track.py
@@ -1,3 +1,5 @@
+"""dynsight.track module for particle tracking from an .xyz file."""
+
 from __future__ import annotations
 
 import logging

From acf343ff4966c2a7dbdf7c327c564f3639544b72 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 30 Jul 2025 11:17:29 +0200
Subject: [PATCH 48/67] vision module title.

---
 src/dynsight/_internal/vision/vision.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 185a6187..9638a7d3 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -1,3 +1,5 @@
+"""dynsight.vision module for particle detection from media files."""
+
 from __future__ import annotations
 
 import logging

From 1919aeab94c851f92cb8bae1138d208c05a37669 Mon Sep 17 00:00:00 2001
From: SimoneMartino98 <s.martino0898@gmail.com>
Date: Wed, 30 Jul 2025 16:05:45 +0200
Subject: [PATCH 49/67] Class documentation fix.

---
 docs/source/conf.py                     |  2 +-
 src/dynsight/_internal/vision/vision.py | 68 ++++++++++++-------------
 2 files changed, 34 insertions(+), 36 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 47c9d6a9..d6008384 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -41,7 +41,7 @@
 
 autodoc_typehints = "description"
 autodoc_member_order = "groupwise"
-autoclass_content = "class"
+autoclass_content = "both"
 
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3", None),
diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 9638a7d3..3b2971cc 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -52,16 +52,6 @@
 
 
 class VisionInstance:
-    """Class for performing vision tasks using YOLO models.
-
-    This class supports object detection, Convolutional Neural Network (CNN)
-    training and fine-tuning, as well as the creation and management of
-    training datasets.
-
-    .. caution::
-        This class is still under development and may not function as intended.
-    """
-
     def __init__(
         self,
         source: str | Path,
@@ -70,29 +60,35 @@ def __init__(
         device: str | None = None,
         workers: int = 8,
     ) -> None:
-        """Initialize a Vision instance.
+        """Class for performing computer vision tasks using YOLO models.
 
-        Defines the starting model, the output folder and the devices that will
-        be used for the computation.
+        This class supports object detection, Convolutional Neural Network
+        (CNN) training and fine-tuning, as well as the creation and management
+        of training datasets.
+
+        .. caution::
+            This class is still under development and may not function as
+            intended.
 
         Parameters:
             source:
                 The source of the images or videos to be processed. For the
                 list of the possible sources, we refer the user to the
-                following `table <https://docs.ultralytics.com/modes/predict/#inference-sources>`_.
-                For the list of the supported formats see this second `table <https://docs.ultralytics.com/modes/predict/#images>`_.
+                following `sources table <https://docs.ultralytics.com/modes/predict/#inference-sources>`_.
+                For the list of the supported formats see this `formats table <https://docs.ultralytics.com/modes/predict/#images>`_.
 
             output_path:
-                The path where save the output folder.
+                The path to save the output folder.
 
             model:
-                The path to the YOLO model file. Defaults to "yolo12n.pt". see
+                The path to the YOLO model file. Defaults to "yolo12n.pt". See
                 `here <https://docs.ultralytics.com/models/yolo12/>`_ for more
                 information.
 
             device:
-                Allows users to select between CPU, a specific GPU, or other
-                compute devices for model execution (`cpu`, `cuda:0` or `0`).
+                Allows users to select between cpu, a specific gpu ID or
+                "mps" for MacOS users to perform the calculation
+                ("cuda:0" or "0" for GPUs, "cpu" or "mps" for MacOS).
 
             workers:
                 Number of worker threads for data loading. Influences the speed
@@ -117,7 +113,7 @@ def set_training_dataset(self, training_data_yaml: Path) -> None:
         """Set the training dataset for the model training.
 
         Training dataset are setted through a ``yaml`` file that should have
-        thefollowing structure:
+        the following structure:
 
         .. code-block:: yaml
 
@@ -171,11 +167,11 @@ def predict(
         imgsz: int | tuple[int, int] = 640,
         max_det: int = 500,
     ) -> None:
-        """Detect objects in the source.
+        """Detect objects within the source.
 
         Parameters:
             prediction_title:
-                The name of the prediction.
+                The name of the prediction session.
 
             augment:
                 Enables test-time augmentation (TTA) for predictions,
@@ -243,7 +239,7 @@ def create_dataset_from_predictions(
                 Name of the dataset that will be created.
 
             train_split:
-                Fraction of images to use for the training set, the remaining
+                Fraction of images to be used as training set, the remaining
                 fraction will be used for the validation set.
 
             load_dataset:
@@ -347,11 +343,12 @@ def tune_hyperparams(
         Parameters:
             iterations:
                 The number of exploring iterations. The higher the number, the
-                more accurate the results will be, but the longer the training
-                will take.
+                more accurate the results will be, increasing the computational
+                cost.
 
             epochs:
-                The number of epochs to train for each iteration.
+                The number of epochs to perform for each iteration. Each epoch
+                represents a full pass over the entire dataset.
 
             imgsz:
                 Defines the image size for inference. Can be a single integer
@@ -359,7 +356,7 @@ def tune_hyperparams(
                 detection accuracy and processing speed.
 
             batch_size:
-                Batch size, with three modes: set as an integer (batch=16),
+                Three modes available: set as an integer (batch=16),
                 auto mode for 60% GPU memory utilization (batch=-1), or auto
                 mode with specified utilization fraction (batch=0.70).
         """
@@ -397,17 +394,18 @@ def train(
     ) -> None:
         """Train a custom model using a training dataset.
 
-        This function trains a custom model using a training dataset. Dataset
-        should be set before calling this function with the
+        This function trains a custom model using a training dataset. The
+        dataset should be set before calling this function with the
         ``set_training_data`` method.
 
         Parameters:
             title:
-                The name of the model.
+                The name of the resulting model.
 
             hyperparams:
                 The dictionary that contains all the hyperparameters for the
-                model. The following default ``dict`` is used if not provided:
+                model training. The following default ``dict`` is used if not
+                provided:
 
                 .. code-block:: python
 
@@ -440,15 +438,15 @@ def train(
                     }
 
                 Manually customize this ``dict`` to change the training
-                performance or use the ``tune`` method to automatically
-                optimize hyperparameters.
+                performance or use the ``tune_hyperparams`` method to
+                automatically optimize hyperparameters.
 
             epochs:
                 Total number of training epochs. Each epoch represents a full
                 pass over the entire dataset.
 
             batch_size:
-                Batch size, with three modes: set as an integer (batch=16),
+                Three modes available: set as an integer (batch=16),
                 auto mode for 60% GPU memory utilization (batch=-1), or auto
                 mode with specified utilization fraction (batch=0.70).
 
@@ -460,7 +458,7 @@ def train(
             imgsz:
                 Defines the image size for inference. Can be a single integer
                 for square resizing or a tuple. Proper sizing can improve
-                detection accuracy and processingspeed.
+                detection accuracy and processing speed.
 
         """
         if self.training_data_yaml is None:

From 7ea833dc14bc21bea04d1a8d1c06e4fcaa28a192 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Thu, 21 Aug 2025 11:00:21 +0200
Subject: [PATCH 50/67] Fixing docs errors

---
 docs/source/label_tool/label_tool.rst | 4 +++-
 examples/video_to_trajectory.py       | 8 ++++----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/source/label_tool/label_tool.rst b/docs/source/label_tool/label_tool.rst
index aa7911c8..2882baec 100644
--- a/docs/source/label_tool/label_tool.rst
+++ b/docs/source/label_tool/label_tool.rst
@@ -17,7 +17,8 @@ How to Use
 
 The ``label_tool`` application can be executed in 2 main ways:
 
-* As standalone application by typing in your terminal environment:
+* As a standalone application, run the following command in the environment
+  where dynsight is installed:
 
 .. code-block:: bash
 
@@ -43,6 +44,7 @@ automatically appear in your default web browser.
 -------
 The GUI
 -------
+
 The ``label_tool`` Graphical User Interface is divided in three main panels:
 
 * **The image panel**: where loaded images appear and labels can be drawn.
diff --git a/examples/video_to_trajectory.py b/examples/video_to_trajectory.py
index 15dbdf9f..5c47a49b 100644
--- a/examples/video_to_trajectory.py
+++ b/examples/video_to_trajectory.py
@@ -1,12 +1,12 @@
 """Virtualization of a video in order to extract trajectory information.
 
-In this example, we show how to use the dynsight ``vision` module
+In this example, we show how to use the dynsight ``vision`` module
 combined with the track module to obtain a trajectory file from a video.
 
 A very simple video is used to demonstrate the workflow, for this reason the
-default detection model is used. For more complex videos is recommended to
-exploit the label_tool to create a synthesized dataset as a starting point to
-train the initial model and then follow the subsequent steps.
+default detection model is used. For more complex videos, it is recommended to
+exploit the ``label_tool`` to create a synthesized dataset as a starting point
+to train the initial model and then follow the subsequent steps.
 """
 
 from __future__ import annotations

From 52d71a254b0dd95c9bd1b41974c65663a93157ba Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Thu, 21 Aug 2025 11:00:41 +0200
Subject: [PATCH 51/67] Fixing dependencies.

---
 pyproject.toml | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7dc1fce7..fbea6728 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ dependencies = [
     "PyYAML",
     "trackpy",
     "ultralytics",
+    "pandas",
     "deeptime",
 ]
 
@@ -39,10 +40,6 @@ dev = [
     "twine",
     "furo",
     "numba",
-    "pandas",
-    "PyYAML",
-    "trackpy",
-    "ultralytics",
 ]
 
 [project.urls]
@@ -132,6 +129,6 @@ module = [
     'pandas.*',
     'trackpy.*',
     'deeptime.*',
-    'sklearn.decomposition.*'
+    'sklearn.decomposition.*',
 ]
 ignore_missing_imports = true

From edb89a72e7fe76af0cb6858cbb71b7f110a11b76 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Thu, 21 Aug 2025 11:01:22 +0200
Subject: [PATCH 52/67] class names now display in the xyz file.

---
 src/dynsight/_internal/vision/vision.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 3b2971cc..26a181ba 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -530,10 +530,10 @@ def export_prediction_to_xyz(
                             continue
                         cx = (x1 + x2) / 2
                         cy = (y1 + y2) / 2
-                        coords.append(f"{cx:.6f} {cy:.6f} 0.0")
+                        coords.append(f"{cls_id} {cx:.6f} {cy:.6f} 0.0")
 
                 f.write(f"{len(coords)}\n")
-                f.write("x y z\n")
+                f.write("class x y z\n")
                 for line in coords:
                     f.write(f"{line}\n")
         return file_path

From d4bfb20d6e05ff4b6eea7a1066f0d4826aade955 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Thu, 21 Aug 2025 14:38:42 +0200
Subject: [PATCH 53/67] Fixing dependencies.

---
 pyproject.toml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index fbea6728..763cb51f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,6 @@ dependencies = [
     "PyYAML",
     "trackpy",
     "ultralytics",
-    "pandas",
     "deeptime",
 ]
 

From 8c8ea4c0a9ec30e89950740e10bd3247417c65a4 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Thu, 21 Aug 2025 14:50:57 +0200
Subject: [PATCH 54/67] fixing union attribute error.

---
 src/dynsight/_internal/vision/vision.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/dynsight/_internal/vision/vision.py b/src/dynsight/_internal/vision/vision.py
index 26a181ba..22510407 100644
--- a/src/dynsight/_internal/vision/vision.py
+++ b/src/dynsight/_internal/vision/vision.py
@@ -6,6 +6,7 @@
 from pathlib import Path
 from typing import TYPE_CHECKING, Callable
 
+import numpy as np
 import torch
 import yaml
 from PIL import Image
@@ -520,8 +521,17 @@ def export_prediction_to_xyz(
 
                 coords: list[str] = []
                 if boxes is not None:
-                    xyxy = boxes.xyxy.cpu().numpy()
-                    classes = boxes.cls.cpu().numpy().astype(int)
+                    xyxy_raw = boxes.xyxy
+                    if isinstance(xyxy_raw, torch.Tensor):
+                        xyxy = xyxy_raw.cpu().numpy()
+                    else:
+                        xyxy = np.asarray(xyxy_raw)
+
+                    cls_raw = boxes.cls
+                    if isinstance(cls_raw, torch.Tensor):
+                        classes = cls_raw.cpu().numpy().astype(int)
+                    else:
+                        classes = np.asarray(cls_raw).astype(int)
                     for (x1, y1, x2, y2), cls_id in zip(xyxy, classes):
                         if (
                             class_filter is not None

From 996b1efd92ce8d682aaea829615ac06049ea1bc7 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Thu, 21 Aug 2025 16:55:12 +0200
Subject: [PATCH 55/67] Fixed docs out of the screen bug.

---
 docs/source/_static/style.css | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/source/_static/style.css b/docs/source/_static/style.css
index b4cd6dd2..34b15a0b 100644
--- a/docs/source/_static/style.css
+++ b/docs/source/_static/style.css
@@ -7,10 +7,8 @@
 }
 @media (min-width: 65em) {
     .content {
-        width: 60em;
         margin-left: auto;
         margin-right: auto;
-        padding-right: 16em;
     }
 }
 @media (min-width: 82em) {

From 602342b2f235f0c82a96741c8d47097900e65014 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Mon, 25 Aug 2025 10:58:02 +0200
Subject: [PATCH 56/67] added the name among the xyz file cols.

---
 src/dynsight/_internal/track/track.py | 50 ++++++++++++++++++++++++---
 1 file changed, 45 insertions(+), 5 deletions(-)

diff --git a/src/dynsight/_internal/track/track.py b/src/dynsight/_internal/track/track.py
index eef0e6b3..846c7d2b 100644
--- a/src/dynsight/_internal/track/track.py
+++ b/src/dynsight/_internal/track/track.py
@@ -39,6 +39,15 @@ def track_xyz(
         ...
         <x> <y> <z>
 
+    or::
+
+        <number of objects>
+        comment line
+        <name> <x> <y> <z>
+        <name> <x> <y> <z>
+        ...
+        <name> <x> <y> <z>
+
     The output file will have the same structure, but each line will start
     with the tracked particle ID.
 
@@ -86,7 +95,10 @@ def track_xyz(
     positions = _collect_positions(input_xyz=input_xyz)
 
     if not {"frame", "x", "y", "z"}.issubset(positions.columns):
-        msg = "Error in the .xyz format. Each line must be <x> <y> <z>."
+        msg = (
+            "Error in the .xyz format. Each line must be "
+            "<x> <y> <z> or <name> <x> <y> <z>."
+        )
         raise ValueError(msg)
 
     linked = tp.link_df(
@@ -105,17 +117,30 @@ def track_xyz(
             for _, row in frame_data.iterrows():
                 pid = int(row["particle"])
                 x, y, z = row["x"], row["y"], row["z"]
-                f.write(f"{pid} {x:.6f} {y:.6f} {z:.6f}\n")
+                name = row.get("name")
+                if name is not None and pd.notna(name):
+                    f.write(f"{name} {x:.6f} {y:.6f} {z:.6f} {pid}\n")
+                else:
+                    f.write(f"{x:.6f} {y:.6f} {z:.6f} {pid}\n")
 
     logger.info(f"Linked .xyz file written to: {output_xyz}")
     return Trj.init_from_xyz(traj_file=output_xyz, dt=1)
 
 
+def _is_float(text: str) -> bool:
+    """Return True if *text* can be converted to float."""
+    try:
+        float(text)
+    except ValueError:
+        return False
+    return True
+
+
 def _collect_positions(input_xyz: Path) -> pd.DataFrame:
     """Read the xyz file and return the positions dataset at each frame."""
     lines = input_xyz.read_text().splitlines()
 
-    data = []
+    data: list[dict[str, object]] = []
     frame = -1
     row = 0
     dimensions = 3
@@ -131,8 +156,23 @@ def _collect_positions(input_xyz: Path) -> pd.DataFrame:
                     break
                 parts = lines[row + a].strip().split()
                 if len(parts) >= dimensions:
-                    x, y, z = map(float, parts[0:3])
-                    data.append({"frame": frame, "x": x, "y": y, "z": z})
+                    if len(parts) >= dimensions + 1 and not _is_float(
+                        parts[0]
+                    ):
+                        name = parts[0]
+                        x, y, z = map(float, parts[1:4])
+                        data.append(
+                            {
+                                "frame": frame,
+                                "name": name,
+                                "x": x,
+                                "y": y,
+                                "z": z,
+                            }
+                        )
+                    else:
+                        x, y, z = map(float, parts[0:3])
+                        data.append({"frame": frame, "x": x, "y": y, "z": z})
             row += num_atoms
         else:
             row += 1

From 0474155cea6a1c0b403fe50dba73812d34345979 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Mon, 25 Aug 2025 17:11:50 +0200
Subject: [PATCH 57/67] Fixing test track.

---
 tests/systems/lj_id.xyz   | 6222 ++-----------------------------------
 tests/systems/lj_noid.xyz | 6222 ++-----------------------------------
 tests/track/test_track.py |   70 +-
 3 files changed, 477 insertions(+), 12037 deletions(-)

diff --git a/tests/systems/lj_id.xyz b/tests/systems/lj_id.xyz
index 6ddd5ac6..a009ad4c 100644
--- a/tests/systems/lj_id.xyz
+++ b/tests/systems/lj_id.xyz
@@ -1,6012 +1,210 @@
-10
-Frame at timestep 0
-1 18.213 6.696 0
-2 19.0161 18.663 0
-3 18.4347 17.7033 0
-4 2.1178 13.9009 0
-5 4.85295 3.55566 0
-6 6.1445 10.6119 0
-7 12.0908 9.97606 0
-8 11.9728 6.50768 0
-9 2.52339 10.6278 0
-10 11.3372 3.4803 0
-10
-Frame at timestep 200
-1 19.2224 6.80072 0
-2 18.8951 19.2155 0
-3 18.8975 18.0409 0
-4 2.98407 13.5399 0
-5 5.40991 2.46274 0
-6 6.14591 11.1581 0
-7 12.3286 10.4689 0
-8 11.8941 7.66172 0
-9 2.09912 9.37473 0
-10 10.8882 4.20984 0
-10
-Frame at timestep 400
-1 18.1513 6.90422 0
-2 18.7757 18.7849 0
-3 18.4196 17.7175 0
-4 3.8227 13.1831 0
-5 5.96041 1.17672 0
-6 6.1473 11.698 0
-7 12.5619 10.9499 0
-8 11.8181 8.8085 0
-9 1.31201 8.13621 0
-10 10.4444 4.93092 0
-10
-Frame at timestep 600
-1 17.8342 6.99763 0
-2 19.1831 19.1626 0
-3 18.5583 18.3597 0
-4 4.62492 12.8376 0
-5 6.4572 1.74709 0
-6 6.10314 12.2088 0
-7 12.7101 11.2474 0
-8 11.8118 9.9799 0
-9 1.47086 7.01853 0
-10 10.044 5.58165 0
-10
-Frame at timestep 800
-1 17.8317 7.07365 0
-2 18.6901 19.1813 0
-3 18.5046 18.0665 0
-4 4.77713 12.6536 0
-5 6.86153 2.35792 0
-6 6.5679 12.5272 0
-7 13.0462 11.5858 0
-8 11.5913 10.837 0
-9 1.64634 6.10887 0
-10 9.71807 6.11127 0
-10
-Frame at timestep 1000
-1 18.0971 7.13779 0
-2 18.8506 19.068 0
-3 18.851 17.964 0
-4 5.01822 12.5762 0
-5 7.20264 2.69953 0
-6 6.84732 12.7181 0
-7 13.0817 11.7704 0
-8 11.6532 11.6611 0
-9 0.826801 5.34143 0
-10 9.44311 6.55808 0
-10
-Frame at timestep 1200
-1 18.906 7.19837 0
-2 19.2432 18.9545 0
-3 18.4925 18.1279 0
-4 5.5225 12.4742 0
-5 7.52483 3.01059 0
-6 6.83467 12.9272 0
-7 12.9565 12.0308 0
-8 11.8704 12.3534 0
-9 1.5336 4.61657 0
-10 9.1834 6.9801 0
-10
-Frame at timestep 1400
-1 18.3154 7.26056 0
-2 18.9693 19.1306 0
-3 18.7567 17.8138 0
-4 5.57027 12.2891 0
-5 7.85558 3.32991 0
-6 7.2916 13.2224 0
-7 12.988 12.3077 0
-8 11.9335 13.0545 0
-9 1.19173 3.87243 0
-10 8.91678 7.41335 0
-10
-Frame at timestep 1600
-1 17.7034 7.32897 0
-2 19.1136 19.0705 0
-3 18.3616 18.2413 0
-4 6.00672 12.284 0
-5 8.21943 3.68119 0
-6 7.41035 13.3485 0
-7 12.9577 12.4968 0
-8 12.0677 13.9413 0
-9 1.52796 3.05385 0
-10 8.62349 7.88994 0
-10
-Frame at timestep 1800
-1 17.2423 7.40723 0
-2 18.9565 19.1646 0
-3 17.561 19.2251 0
-4 6.56321 12.1787 0
-5 8.63564 4.08302 0
-6 7.48897 13.5922 0
-7 12.6233 13.1275 0
-8 12.521 14.5412 0
-9 1.91723 2.10243 0
-10 8.28799 8.43512 0
-10
-Frame at timestep 2000
-1 16.7436 7.49494 0
-2 19.1172 19.146 0
-3 18.9796 17.9806 0
-4 6.85925 11.7926 0
-5 9.10217 4.53344 0
-6 7.90482 14.1336 0
-7 12.3997 13.5313 0
-8 12.878 15.5169 0
-9 1.84565 0.790168 0
-10 7.91193 9.04622 0
-10
-Frame at timestep 2200
-1 16.2298 7.58529 0
-2 19.0568 18.9604 0
-3 18.0492 18.2329 0
-4 7.20134 11.4249 0
-5 9.58267 4.99734 0
-6 8.30289 14.6349 0
-7 12.2214 14.2491 0
-8 13.1935 16.2199 0
-9 1.16043 2.30582 0
-10 7.51765 9.70173 0
-10
-Frame at timestep 2400
-1 15.7181 7.67529 0
-2 19.0538 18.9658 0
-3 17.9696 18.276 0
-4 7.63166 11.6382 0
-5 10.0613 5.45946 0
-6 8.69944 15.1344 0
-7 12.109 15.0987 0
-8 13.4426 16.7856 0
-9 1.57073 3.45692 0
-10 7.03534 9.77515 0
-10
-Frame at timestep 2600
-1 15.2221 7.76252 0
-2 19.0709 18.9857 0
-3 19.2307 17.9473 0
-4 7.99054 12.2048 0
-5 10.5253 5.90737 0
-6 9.08379 15.6185 0
-7 12.115 16.0605 0
-8 13.5692 17.1956 0
-9 1.8708 4.57028 0
-10 6.62607 9.48646 0
-10
-Frame at timestep 2800
-1 14.722 7.85046 0
-2 19.1162 18.6873 0
-3 17.9125 18.7272 0
-4 8.34768 12.7638 0
-5 10.993 6.35896 0
-6 9.4713 16.1066 0
-7 12.1697 17.2156 0
-8 13.6482 17.4237 0
-9 1.63326 5.69281 0
-10 6.21812 9.20763 0
-10
-Frame at timestep 3000
-1 14.2052 7.94135 0
-2 18.8338 18.9577 0
-3 17.8112 18.6656 0
-4 8.71676 13.3415 0
-5 11.4764 6.82567 0
-6 9.87178 16.611 0
-7 11.9102 18.2633 0
-8 14.0457 17.9342 0
-9 1.00095 6.8529 0
-10 5.79653 8.91947 0
-10
-Frame at timestep 3200
-1 13.6465 8.02684 0
-2 18.9025 19.0529 0
-3 17.197 19.2403 0
-4 9.10048 13.9421 0
-5 12.0004 7.31989 0
-6 10.2889 17.1363 0
-7 11.8593 18.6403 0
-8 14.2395 18.9281 0
-9 1.86747 8.05898 0
-10 5.35823 8.61988 0
-10
-Frame at timestep 3400
-1 13.6908 8.40035 0
-2 19.0064 19.1389 0
-3 17.1972 18.5742 0
-4 9.49096 14.5533 0
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-10 4.91221 8.31502 0
-10
-Frame at timestep 3600
-1 14.1669 8.95799 0
-2 19.1829 19.2353 0
-3 17.9563 19.0379 0
-4 9.87536 15.155 0
-5 11.4099 7.55939 0
-6 10.348 18.5591 0
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-8 14.5007 17.5352 0
-9 2.06446 10.4946 0
-10 4.47311 8.0149 0
-10
-Frame at timestep 3800
-1 14.6306 9.50796 0
-2 18.7236 18.999 0
-3 17.738 18.0474 0
-4 10.2579 15.7538 0
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-7 13.3608 15.3192 0
-8 14.5082 16.8897 0
-9 1.65371 11.6969 0
-10 4.03618 7.71625 0
-10
-Frame at timestep 4000
-1 15.0873 10.0496 0
-2 19.1374 18.9956 0
-3 18.1777 18.4722 0
-4 10.6186 16.4213 0
-5 10.4207 7.61481 0
-6 10.0854 17.7818 0
-7 14.2698 14.8209 0
-8 14.2872 15.835 0
-9 1.06072 12.8811 0
-10 3.60582 7.4221 0
-10
-Frame at timestep 4200
-1 15.5338 10.5792 0
-2 19.0266 18.5839 0
-3 17.6371 19.2404 0
-4 11.3307 16.6926 0
-5 9.94091 7.6417 0
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-7 15.1387 13.7731 0
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-9 1.86151 14.0387 0
-10 3.18513 7.13456 0
-10
-Frame at timestep 4400
-1 15.9584 11.0882 0
-2 18.9626 19.2121 0
-3 18.1178 18.4382 0
-4 11.9007 16.8788 0
-5 9.48412 7.66729 0
-6 9.25858 17.9679 0
-7 15.7325 13.2337 0
-8 14.1378 14.4537 0
-9 2.02456 15.1407 0
-10 2.78464 6.86082 0
-10
-Frame at timestep 4600
-1 16.4233 11.5615 0
-2 18.7411 19.1676 0
-3 18.009 18.1733 0
-4 12.4637 17.0755 0
-5 9.02056 7.69326 0
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-8 14.412 13.3553 0
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-10 2.37112 6.58302 0
-10
-Frame at timestep 4800
-1 17.0832 11.0961 0
-2 19.2325 18.7593 0
-3 18.5012 17.8442 0
-4 13.0737 17.2886 0
-5 8.51839 7.72139 0
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-10
-Frame at timestep 5000
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-3 18.8315 17.4615 0
-4 13.7107 17.5113 0
-5 7.99394 7.75078 0
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-8 16.5746 11.4977 0
-9 1.79976 18.9474 0
-10 1.09222 5.96779 0
-10
-Frame at timestep 5200
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-2 19.167 18.188 0
-3 18.5883 17.2473 0
-4 14.2915 17.8082 0
-5 7.51571 7.77757 0
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-7 14.2069 13.6263 0
-8 17.2453 11.2079 0
-9 2.20799 18.0944 0
-10 1.92352 5.6812 0
-10
-Frame at timestep 5400
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-3 18.8997 17.2341 0
-4 14.7925 18.3314 0
-5 7.10323 7.80068 0
-6 7.52393 16.534 0
-7 13.6672 13.6142 0
-8 18.0988 10.6524 0
-9 2.31059 17.2779 0
-10 2.14156 5.43401 0
-10
-Frame at timestep 5600
-1 17.3262 9.29572 0
-2 19.2212 18.8935 0
-3 19.2465 17.7866 0
-4 15.2431 18.94 0
-5 6.73226 7.82146 0
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-7 13.1817 13.6034 0
-8 19.1304 10.2063 0
-9 2.1954 16.5922 0
-10 2.05221 5.2117 0
-10
-Frame at timestep 5800
-1 16.8946 9.26263 0
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-3 18.7456 17.6739 0
-4 15.659 18.2444 0
-5 6.38979 7.84065 0
-6 7.00414 15.6633 0
-7 12.7336 13.5934 0
-8 18.3985 9.54371 0
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-10 1.61748 5.00647 0
-10
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-2 19.0576 18.661 0
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-4 16.0518 18.325 0
-5 6.06642 7.85877 0
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-10
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-4 16.4387 19.2634 0
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-7 11.8936 13.5747 0
-8 18.5413 8.56501 0
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-10 1.84514 4.62178 0
-10
-Frame at timestep 6400
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-10
-Frame at timestep 6600
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-4 17.4166 18.2272 0
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-10
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-10
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-10
-Frame at timestep 7400
-1 11.7604 7.56258 0
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-4 13.4351 18.9822 0
-5 2.94885 8.06684 0
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-10 4.01591 2.95194 0
-10
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-2 18.8376 16.2432 0
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-5 2.30202 7.23797 0
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-10
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-1 10.0917 7.00153 0
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-4 10.8109 19.0689 0
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-10 5.75673 2.22786 0
-10
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-5 1.29005 5.26529 0
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-10
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-10
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-8 18.9546 1.67991 0
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-10
-Frame at timestep 8600
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-3 17.9465 14.1145 0
-4 7.32367 19.2079 0
-5 1.49844 3.29055 0
-6 4.19336 11.1763 0
-7 4.77641 13.3992 0
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-9 3.18955 6.70466 0
-10 8.06999 0.806587 0
-10
-Frame at timestep 8800
-1 7.40667 6.09874 0
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-4 6.5882 18.946 0
-5 0.746901 2.68137 0
-6 4.2676 11.4031 0
-7 4.32772 13.2544 0
-8 18.1917 1.45403 0
-9 3.17425 6.2623 0
-10 8.55787 1.55317 0
-10
-Frame at timestep 9000
-1 6.89719 5.92744 0
-2 19.2014 13.1686 0
-3 18.0961 13.3036 0
-4 5.78697 18.7614 0
-5 1.61673 1.96349 0
-6 4.49817 11.6885 0
-7 3.68922 13.0584 0
-8 18.6243 1.84828 0
-9 3.15758 5.78038 0
-10 9.08938 0.963576 0
-10
-Frame at timestep 9200
-1 6.31595 5.73201 0
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-4 4.87287 18.8005 0
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-8 18.5108 1.68908 0
-9 3.13857 5.23059 0
-10 9.69575 1.66048 0
-10
-Frame at timestep 9400
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-3 17.9812 12.2574 0
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-10
-Frame at timestep 9600
-1 4.97881 5.28051 0
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-10 11.0813 2.03159 0
-10
-Frame at timestep 9800
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-10 11.7901 1.65837 0
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diff --git a/tests/systems/lj_noid.xyz b/tests/systems/lj_noid.xyz
index 929912c3..cac854b7 100644
--- a/tests/systems/lj_noid.xyz
+++ b/tests/systems/lj_noid.xyz
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+5
+Step 22
+P1 -1.105333 1.302314 0.0
+P2 1.689657 -0.701603 0.0
+P3 1.296014 2.708494 0.0
+P4 -0.223292 -0.522848 0.0
+P5 2.732958 1.772295 0.0
+5
+Step 23
+P1 -1.034569 1.318364 0.0
+P2 1.641425 -0.698426 0.0
+P3 1.382320 2.722614 0.0
+P4 -0.136953 -0.448927 0.0
+P5 2.680647 1.807876 0.0
+5
+Step 24
+P1 -0.997685 1.245300 0.0
+P2 1.656599 -0.682829 0.0
+P3 1.415509 2.693962 0.0
+P4 -0.134423 -0.412289 0.0
+P5 2.708580 1.783989 0.0
+5
+Step 25
+P1 -0.798155 1.256512 0.0
+P2 1.680035 -0.563239 0.0
+P3 1.341091 2.629722 0.0
+P4 -0.115892 -0.449490 0.0
+P5 2.602751 1.785441 0.0
+5
+Step 26
+P1 -0.781771 1.142838 0.0
+P2 1.756680 -0.396595 0.0
+P3 1.276294 2.642729 0.0
+P4 -0.137163 -0.365647 0.0
+P5 2.607984 1.749603 0.0
+5
+Step 27
+P1 -0.738512 1.238783 0.0
+P2 1.777029 -0.266889 0.0
+P3 1.351501 2.612630 0.0
+P4 -0.130518 -0.231435 0.0
+P5 2.599496 1.639626 0.0
+5
+Step 28
+P1 -0.656927 1.286644 0.0
+P2 1.786953 -0.246108 0.0
+P3 1.240883 2.459362 0.0
+P4 0.025398 -0.287629 0.0
+P5 2.516386 1.577458 0.0
+5
+Step 29
+P1 -0.460374 1.358828 0.0
+P2 1.670440 -0.141020 0.0
+P3 1.168045 2.434449 0.0
+P4 -0.040058 -0.302320 0.0
+P5 2.553050 1.458213 0.0
diff --git a/tests/track/test_track.py b/tests/track/test_track.py
index c2e9954e..bd8a792a 100644
--- a/tests/track/test_track.py
+++ b/tests/track/test_track.py
@@ -1,11 +1,61 @@
 from __future__ import annotations
 
 from pathlib import Path
+from typing import Iterator, TextIO
 
 import numpy as np
 
 from dynsight.track import track_xyz
-from dynsight.trajectory import Trj
+
+
+def _iter_xyz_frames(f: TextIO) -> Iterator[list[str]]:
+    for line in f:
+        header = line.strip()
+        if not header:
+            continue
+        natoms = int(header)
+        comment = f.readline()
+        if comment == "":
+            raise ValueError
+        atoms = [f.readline() for _ in range(natoms)]
+        if any(a == "" for a in atoms):
+            raise ValueError
+        yield atoms
+
+
+def _parse_atom_line(line: str) -> tuple[float, float, float, str]:
+    parts = line.split()
+    cols = 5
+    if len(parts) < cols:
+        raise ValueError
+    x, y, z = map(float, parts[1:4])
+    at_id = parts[4]
+    return x, y, z, at_id
+
+
+def _coords_by_id_from_xyz(
+    path: str, target_id: int | str, *, strict: bool = False
+) -> np.ndarray:
+    target = str(target_id)
+    out: list[tuple[float, float, float]] = []
+
+    with Path(path).open("r", encoding="utf-8") as f:
+        for atoms in _iter_xyz_frames(f):
+            xyz = (np.nan, np.nan, np.nan)
+            for atom_line in atoms:
+                x, y, z, at_id = _parse_atom_line(atom_line)
+                if at_id == target:
+                    xyz = (x, y, z)
+                    break
+            if strict and np.isnan(xyz[0]):
+                raise ValueError
+            out.append(xyz)
+
+    if not out:
+        raise ValueError
+
+    return np.asarray(out, dtype=float)
+
 
 
 def test_track_xyz(tmp_path: Path) -> None:
@@ -14,15 +64,9 @@ def test_track_xyz(tmp_path: Path) -> None:
     file_with_id = original_dir / "../systems/lj_id.xyz"
     output = tmp_path / "trajectory.xyz"
 
-    ref_trajectory = Trj.init_from_xyz(traj_file=file_with_id, dt=1)
-
-    trajectory = track_xyz(input_xyz=filename, output_xyz=output)
-
-    ref_pos = ref_trajectory.get_coordinates(selection="all")
-    tst_pos = trajectory.get_coordinates(selection="all")
-
-    r_sorted = np.array([frame[np.argsort(frame[:, 0])] for frame in ref_pos])
-    t_sorted = np.array([frame[np.argsort(frame[:, 0])] for frame in tst_pos])
-
-    assert r_sorted.shape == t_sorted.shape
-    assert np.allclose(r_sorted[:, :, :], t_sorted[:, :, :])
+    track_xyz(input_xyz=filename, output_xyz=output)
+    for i in range(5):
+        arr1 = _coords_by_id_from_xyz(output, target_id=i)
+        arr2 = _coords_by_id_from_xyz(file_with_id, target_id=i + 1)
+        assert arr1.shape == arr2.shape
+        assert np.allclose(arr1, arr2)

From 415c6ab749aa3b8a1a39456e628480c616fbd111 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Mon, 25 Aug 2025 17:33:06 +0200
Subject: [PATCH 58/67] Fomratting and type fixing.

---
 tests/track/test_track.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/tests/track/test_track.py b/tests/track/test_track.py
index bd8a792a..e25e03af 100644
--- a/tests/track/test_track.py
+++ b/tests/track/test_track.py
@@ -1,12 +1,15 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Iterator, TextIO
+from typing import TYPE_CHECKING, Iterator, TextIO
 
 import numpy as np
 
 from dynsight.track import track_xyz
 
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
 
 def _iter_xyz_frames(f: TextIO) -> Iterator[list[str]]:
     for line in f:
@@ -34,8 +37,8 @@ def _parse_atom_line(line: str) -> tuple[float, float, float, str]:
 
 
 def _coords_by_id_from_xyz(
-    path: str, target_id: int | str, *, strict: bool = False
-) -> np.ndarray:
+    path: Path | str, target_id: int | str, *, strict: bool = False
+) -> NDArray[np.float64]:
     target = str(target_id)
     out: list[tuple[float, float, float]] = []
 
@@ -57,7 +60,6 @@ def _coords_by_id_from_xyz(
     return np.asarray(out, dtype=float)
 
 
-
 def test_track_xyz(tmp_path: Path) -> None:
     original_dir = Path(__file__).resolve().parent
     filename = original_dir / "../systems/lj_noid.xyz"

From 428ed1f3c0207232653c5f29debb92107d0ce5d8 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Tue, 26 Aug 2025 16:25:51 +0200
Subject: [PATCH 59/67] Added new read_xyz function and minor fixes.

---
 src/dynsight/_internal/track/track.py         |  62 ++--
 src/dynsight/_internal/utilities/utilities.py |  60 +++-
 src/dynsight/utilities.py                     |   3 +-
 tests/systems/lj_id.xyz                       | 300 +++++++++---------
 tests/systems/lj_noid.xyz                     | 300 +++++++++---------
 tests/track/test_track.py                     |  69 +---
 6 files changed, 400 insertions(+), 394 deletions(-)

diff --git a/src/dynsight/_internal/track/track.py b/src/dynsight/_internal/track/track.py
index 846c7d2b..af15ad02 100644
--- a/src/dynsight/_internal/track/track.py
+++ b/src/dynsight/_internal/track/track.py
@@ -9,6 +9,7 @@
 import trackpy as tp
 
 from dynsight.trajectory import Trj
+from dynsight.utilities import read_xyz
 
 logging.basicConfig(
     level=logging.INFO,
@@ -60,11 +61,12 @@ def track_xyz(
 
         search_range:
             The maximum allowable displacement of objects between frames for
-            them to be considered the same particle.
+            them to be considered the same particle. Units depend on the
+            coordinate system used in the input file.
 
         memory:
             The maximum number of frames during which an object can vanish,
-            then reppear nearby, and be considered the same particle.
+            then re-appear nearby, and be considered the same particle.
 
         adaptive_stop:
             If not `None`, when encountering a region with too many candidate
@@ -92,7 +94,9 @@ def track_xyz(
         msg = f"Input file not found: {input_xyz}"
         raise FileNotFoundError(msg)
 
-    positions = _collect_positions(input_xyz=input_xyz)
+    positions = read_xyz(
+        input_xyz=input_xyz, cols_order=["name", "x", "y", "z"]
+    )
 
     if not {"frame", "x", "y", "z"}.issubset(positions.columns):
         msg = (
@@ -111,7 +115,9 @@ def track_xyz(
 
     with output_xyz.open("w") as f:
         for frame_num in sorted(linked["frame"].unique()):
-            frame_data = linked[linked["frame"] == frame_num]
+            frame_data = linked[linked["frame"] == frame_num].sort_values(
+                "particle"
+            )
             f.write(f"{len(frame_data)}\n")
             f.write(f"Frame {frame_num}\n")
             for _, row in frame_data.iterrows():
@@ -127,15 +133,6 @@ def track_xyz(
     return Trj.init_from_xyz(traj_file=output_xyz, dt=1)
 
 
-def _is_float(text: str) -> bool:
-    """Return True if *text* can be converted to float."""
-    try:
-        float(text)
-    except ValueError:
-        return False
-    return True
-
-
 def _collect_positions(input_xyz: Path) -> pd.DataFrame:
     """Read the xyz file and return the positions dataset at each frame."""
     lines = input_xyz.read_text().splitlines()
@@ -155,24 +152,27 @@ def _collect_positions(input_xyz: Path) -> pd.DataFrame:
                 if row + a >= len(lines):
                     break
                 parts = lines[row + a].strip().split()
-                if len(parts) >= dimensions:
-                    if len(parts) >= dimensions + 1 and not _is_float(
-                        parts[0]
-                    ):
-                        name = parts[0]
-                        x, y, z = map(float, parts[1:4])
-                        data.append(
-                            {
-                                "frame": frame,
-                                "name": name,
-                                "x": x,
-                                "y": y,
-                                "z": z,
-                            }
-                        )
-                    else:
-                        x, y, z = map(float, parts[0:3])
-                        data.append({"frame": frame, "x": x, "y": y, "z": z})
+                if len(parts) == dimensions:
+                    x, y, z = map(float, parts[0:3])
+                    data.append({"frame": frame, "x": x, "y": y, "z": z})
+                elif len(parts) >= dimensions + 1:
+                    name = parts[0]
+                    x, y, z = map(float, parts[1:4])
+                    data.append(
+                        {
+                            "frame": frame,
+                            "name": name,
+                            "x": x,
+                            "y": y,
+                            "z": z,
+                        }
+                    )
+                else:
+                    msg = (
+                        "Invalid line format, expected 3 or 4 columns, "
+                        f"found {len(parts)}"
+                    )
+                    raise ValueError(msg)
             row += num_atoms
         else:
             row += 1
diff --git a/src/dynsight/_internal/utilities/utilities.py b/src/dynsight/_internal/utilities/utilities.py
index 8d521bfc..6bcd4912 100644
--- a/src/dynsight/_internal/utilities/utilities.py
+++ b/src/dynsight/_internal/utilities/utilities.py
@@ -1,12 +1,11 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Literal
-
-if TYPE_CHECKING:
-    from pathlib import Path
+from pathlib import Path
+from typing import Iterable, Literal
 
 import numpy as np
 import numpy.typing as npt
+import pandas as pd
 from scipy.signal import find_peaks
 
 from dynsight.trajectory import Insight, Trj
@@ -102,3 +101,56 @@ def load_or_compute_soap(
         soap.dump_to_json(soap_path)
 
     return soap
+
+
+Col = Literal["name", "x", "y", "z", "ID"]
+
+
+def _entry_from_parts(
+    parts: Iterable[str],
+    cols_order: list[Col],
+    frame: int,
+) -> dict[str, object]:
+    converters: dict[Col, callable] = {
+        "name": str,
+        "x": float,
+        "y": float,
+        "z": float,
+        "ID": int,
+    }
+    entry: dict[str, object] = {"frame": frame}
+    for c, col in enumerate(cols_order):
+        entry[col] = converters[col](parts[c])
+    return entry
+
+
+def read_xyz(
+    input_xyz: Path | str,
+    cols_order: list[Col],
+) -> pd.DataFrame:
+    lines = Path(input_xyz).read_text().splitlines()
+    data: list[dict[str, object]] = []
+
+    frame = -1
+    row = 0
+    nlines = len(lines)
+
+    while row < nlines:
+        token = lines[row].strip()
+        if token.isdigit():
+            n_atoms = int(token)
+            frame += 1
+            row += 2
+
+            end = min(row + n_atoms, nlines)
+            for i in range(row, end):
+                parts = lines[i].split()
+                if len(parts) < len(cols_order):
+                    continue
+                data.append(_entry_from_parts(parts, cols_order, frame))
+
+            row += n_atoms
+        else:
+            row += 1
+
+    return pd.DataFrame(data)
diff --git a/src/dynsight/utilities.py b/src/dynsight/utilities.py
index b1315855..0b7e19ed 100644
--- a/src/dynsight/utilities.py
+++ b/src/dynsight/utilities.py
@@ -4,10 +4,11 @@
     find_extrema_points,
     load_or_compute_soap,
     normalize_array,
+    read_xyz,
 )
 
 __all__ = [
     "find_extrema_points",
     "load_or_compute_soap",
     "normalize_array",
-]
+    "read_xyz"]
diff --git a/tests/systems/lj_id.xyz b/tests/systems/lj_id.xyz
index a009ad4c..81e90a7e 100644
--- a/tests/systems/lj_id.xyz
+++ b/tests/systems/lj_id.xyz
@@ -1,210 +1,210 @@
 5
 Step 0
-P1 -5.000000 2.000000 0.0 1
-P2 4.000000 -3.000000 0.0 2
-P3 1.500000 6.000000 0.0 3
-P4 -2.500000 -4.500000 0.0 4
-P5 7.000000 3.500000 0.0 5
+P0 -5.000000 2.000000 0.0 0
+P1 4.000000 -3.000000 0.0 1
+P2 1.500000 6.000000 0.0 2
+P3 -2.500000 -4.500000 0.0 3
+P4 7.000000 3.500000 0.0 4
 5
 Step 1
-P1 -4.791128 2.036963 0.0 1
-P2 3.958151 -2.823608 0.0 2
-P3 1.447059 5.679399 0.0 3
-P4 -2.285658 -4.236846 0.0 4
-P5 6.816060 3.440246 0.0 5
+P0 -4.791128 2.036963 0.0 0
+P1 3.958151 -2.823608 0.0 1
+P2 1.447059 5.679399 0.0 2
+P3 -2.285658 -4.236846 0.0 3
+P4 6.816060 3.440246 0.0 4
 5
 Step 2
-P1 -4.511325 2.051341 0.0 1
-P2 3.697118 -2.574102 0.0 2
-P3 1.301944 5.384593 0.0 3
-P4 -2.092202 -3.880785 0.0 4
-P5 6.476730 3.246345 0.0 5
+P0 -4.511325 2.051341 0.0 0
+P1 3.697118 -2.574102 0.0 1
+P2 1.301944 5.384593 0.0 2
+P3 -2.092202 -3.880785 0.0 3
+P4 6.476730 3.246345 0.0 4
 5
 Step 3
-P1 -4.195438 2.112931 0.0 1
-P2 3.553053 -2.497301 0.0 2
-P3 1.389577 5.269173 0.0 3
-P4 -1.994445 -3.639558 0.0 4
-P5 6.115303 3.004959 0.0 5
+P0 -4.195438 2.112931 0.0 0
+P1 3.553053 -2.497301 0.0 1
+P2 1.389577 5.269173 0.0 2
+P3 -1.994445 -3.639558 0.0 3
+P4 6.115303 3.004959 0.0 4
 5
 Step 4
-P1 -3.980925 2.172511 0.0 1
-P2 3.427366 -2.235810 0.0 2
-P3 1.260627 4.994255 0.0 3
-P4 -1.853817 -3.349635 0.0 4
-P5 5.963871 2.870469 0.0 5
+P0 -3.980925 2.172511 0.0 0
+P1 3.427366 -2.235810 0.0 1
+P2 1.260627 4.994255 0.0 2
+P3 -1.853817 -3.349635 0.0 3
+P4 5.963871 2.870469 0.0 4
 5
 Step 5
-P1 -3.626436 2.108306 0.0 1
-P2 3.379681 -1.980356 0.0 2
-P3 1.241300 4.876043 0.0 3
-P4 -1.768785 -3.177578 0.0 4
-P5 5.615117 2.777250 0.0 5
+P0 -3.626436 2.108306 0.0 0
+P1 3.379681 -1.980356 0.0 1
+P2 1.241300 4.876043 0.0 2
+P3 -1.768785 -3.177578 0.0 3
+P4 5.615117 2.777250 0.0 4
 5
 Step 6
-P1 -3.359578 2.148831 0.0 1
-P2 3.160072 -1.882366 0.0 2
-P3 1.122169 4.787745 0.0 3
-P4 -1.517165 -3.049852 0.0 4
-P5 5.496275 2.670436 0.0 5
+P0 -3.359578 2.148831 0.0 0
+P1 3.160072 -1.882366 0.0 1
+P2 1.122169 4.787745 0.0 2
+P3 -1.517165 -3.049852 0.0 3
+P4 5.496275 2.670436 0.0 4
 5
 Step 7
-P1 -3.201726 2.130611 0.0 1
-P2 3.020153 -1.620133 0.0 2
-P3 1.156847 4.717972 0.0 3
-P4 -1.273898 -2.930248 0.0 4
-P5 5.255822 2.626861 0.0 5
+P0 -3.201726 2.130611 0.0 0
+P1 3.020153 -1.620133 0.0 1
+P2 1.156847 4.717972 0.0 2
+P3 -1.273898 -2.930248 0.0 3
+P4 5.255822 2.626861 0.0 4
 5
 Step 8
-P1 -2.953675 2.044091 0.0 1
-P2 2.961026 -1.434917 0.0 2
-P3 1.219418 4.486570 0.0 3
-P4 -1.261366 -2.650880 0.0 4
-P5 4.923909 2.479460 0.0 5
+P0 -2.953675 2.044091 0.0 0
+P1 2.961026 -1.434917 0.0 1
+P2 1.219418 4.486570 0.0 2
+P3 -1.261366 -2.650880 0.0 3
+P4 4.923909 2.479460 0.0 4
 5
 Step 9
-P1 -2.667821 1.878745 0.0 1
-P2 2.785395 -1.319951 0.0 2
-P3 1.141136 4.276260 0.0 3
-P4 -1.238114 -2.560183 0.0 4
-P5 4.626054 2.317652 0.0 5
+P0 -2.667821 1.878745 0.0 0
+P1 2.785395 -1.319951 0.0 1
+P2 1.141136 4.276260 0.0 2
+P3 -1.238114 -2.560183 0.0 3
+P4 4.626054 2.317652 0.0 4
 5
 Step 10
-P1 -2.576834 1.713102 0.0 1
-P2 2.749895 -1.163980 0.0 2
-P3 1.116430 4.014591 0.0 3
-P4 -1.210798 -2.358824 0.0 4
-P5 4.384192 2.300885 0.0 5
+P0 -2.576834 1.713102 0.0 0
+P1 2.749895 -1.163980 0.0 1
+P2 1.116430 4.014591 0.0 2
+P3 -1.210798 -2.358824 0.0 3
+P4 4.384192 2.300885 0.0 4
 5
 Step 11
-P1 -2.436860 1.617456 0.0 1
-P2 2.752405 -1.144044 0.0 2
-P3 1.005449 3.977450 0.0 3
-P4 -0.991517 -2.289499 0.0 4
-P5 4.117488 2.191018 0.0 5
+P0 -2.436860 1.617456 0.0 0
+P1 2.752405 -1.144044 0.0 1
+P2 1.005449 3.977450 0.0 2
+P3 -0.991517 -2.289499 0.0 3
+P4 4.117488 2.191018 0.0 4
 5
 Step 12
-P1 -2.270411 1.667002 0.0 1
-P2 2.676300 -1.114209 0.0 2
-P3 1.030007 3.793655 0.0 3
-P4 -0.926276 -2.209220 0.0 4
-P5 4.070431 2.030772 0.0 5
+P0 -2.270411 1.667002 0.0 0
+P1 2.676300 -1.114209 0.0 1
+P2 1.030007 3.793655 0.0 2
+P3 -0.926276 -2.209220 0.0 3
+P4 4.070431 2.030772 0.0 4
 5
 Step 13
-P1 -2.214457 1.650766 0.0 1
-P2 2.612264 -1.110300 0.0 2
-P3 1.053063 3.602497 0.0 3
-P4 -0.872838 -2.096902 0.0 4
-P5 3.818307 2.083224 0.0 5
+P0 -2.214457 1.650766 0.0 0
+P1 2.612264 -1.110300 0.0 1
+P2 1.053063 3.602497 0.0 2
+P3 -0.872838 -2.096902 0.0 3
+P4 3.818307 2.083224 0.0 4
 5
 Step 14
-P1 -2.096298 1.607129 0.0 1
-P2 2.416233 -1.016174 0.0 2
-P3 1.006852 3.578933 0.0 3
-P4 -0.844236 -1.891815 0.0 4
-P5 3.671041 2.127046 0.0 5
+P0 -2.096298 1.607129 0.0 0
+P1 2.416233 -1.016174 0.0 1
+P2 1.006852 3.578933 0.0 2
+P3 -0.844236 -1.891815 0.0 3
+P4 3.671041 2.127046 0.0 4
 5
 Step 15
-P1 -1.905863 1.464007 0.0 1
-P2 2.277212 -0.999618 0.0 2
-P3 1.034141 3.334519 0.0 3
-P4 -0.800417 -1.755370 0.0 4
-P5 3.600252 1.948550 0.0 5
+P0 -1.905863 1.464007 0.0 0
+P1 2.277212 -0.999618 0.0 1
+P2 1.034141 3.334519 0.0 2
+P3 -0.800417 -1.755370 0.0 3
+P4 3.600252 1.948550 0.0 4
 5
 Step 16
-P1 -1.741268 1.453377 0.0 1
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diff --git a/tests/systems/lj_noid.xyz b/tests/systems/lj_noid.xyz
index cac854b7..a4ca14ba 100644
--- a/tests/systems/lj_noid.xyz
+++ b/tests/systems/lj_noid.xyz
@@ -1,210 +1,210 @@
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-P4 0.025398 -0.287629 0.0
-P5 2.516386 1.577458 0.0
+P0 -0.656927 1.286644 0.0
+P1 1.786953 -0.246108 0.0
+P2 1.240883 2.459362 0.0
+P3 0.025398 -0.287629 0.0
+P4 2.516386 1.577458 0.0
 5
 Step 29
-P1 -0.460374 1.358828 0.0
-P2 1.670440 -0.141020 0.0
-P3 1.168045 2.434449 0.0
-P4 -0.040058 -0.302320 0.0
-P5 2.553050 1.458213 0.0
+P0 -0.460374 1.358828 0.0
+P1 1.670440 -0.141020 0.0
+P2 1.168045 2.434449 0.0
+P3 -0.040058 -0.302320 0.0
+P4 2.553050 1.458213 0.0
diff --git a/tests/track/test_track.py b/tests/track/test_track.py
index e25e03af..398b9bda 100644
--- a/tests/track/test_track.py
+++ b/tests/track/test_track.py
@@ -1,63 +1,11 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import TYPE_CHECKING, Iterator, TextIO
 
 import numpy as np
 
 from dynsight.track import track_xyz
-
-if TYPE_CHECKING:
-    from numpy.typing import NDArray
-
-
-def _iter_xyz_frames(f: TextIO) -> Iterator[list[str]]:
-    for line in f:
-        header = line.strip()
-        if not header:
-            continue
-        natoms = int(header)
-        comment = f.readline()
-        if comment == "":
-            raise ValueError
-        atoms = [f.readline() for _ in range(natoms)]
-        if any(a == "" for a in atoms):
-            raise ValueError
-        yield atoms
-
-
-def _parse_atom_line(line: str) -> tuple[float, float, float, str]:
-    parts = line.split()
-    cols = 5
-    if len(parts) < cols:
-        raise ValueError
-    x, y, z = map(float, parts[1:4])
-    at_id = parts[4]
-    return x, y, z, at_id
-
-
-def _coords_by_id_from_xyz(
-    path: Path | str, target_id: int | str, *, strict: bool = False
-) -> NDArray[np.float64]:
-    target = str(target_id)
-    out: list[tuple[float, float, float]] = []
-
-    with Path(path).open("r", encoding="utf-8") as f:
-        for atoms in _iter_xyz_frames(f):
-            xyz = (np.nan, np.nan, np.nan)
-            for atom_line in atoms:
-                x, y, z, at_id = _parse_atom_line(atom_line)
-                if at_id == target:
-                    xyz = (x, y, z)
-                    break
-            if strict and np.isnan(xyz[0]):
-                raise ValueError
-            out.append(xyz)
-
-    if not out:
-        raise ValueError
-
-    return np.asarray(out, dtype=float)
+from dynsight.utilities import read_xyz
 
 
 def test_track_xyz(tmp_path: Path) -> None:
@@ -65,10 +13,15 @@ def test_track_xyz(tmp_path: Path) -> None:
     filename = original_dir / "../systems/lj_noid.xyz"
     file_with_id = original_dir / "../systems/lj_id.xyz"
     output = tmp_path / "trajectory.xyz"
-
+    output = "trajectory.xyz"
     track_xyz(input_xyz=filename, output_xyz=output)
-    for i in range(5):
-        arr1 = _coords_by_id_from_xyz(output, target_id=i)
-        arr2 = _coords_by_id_from_xyz(file_with_id, target_id=i + 1)
+    n_atoms = 5
+    for _ in range(n_atoms):
+        arr1 = read_xyz(
+            input_xyz=output, cols_order=["name", "x", "y", "z", "ID"]
+        ).to_numpy()
+        arr2 = read_xyz(
+            input_xyz=file_with_id, cols_order=["name", "x", "y", "z", "ID"]
+        ).to_numpy()
         assert arr1.shape == arr2.shape
-        assert np.allclose(arr1, arr2)
+        assert np.array_equal(arr1, arr2)

From 71fb2d3921ec4a023fef98834eb424e1dccb5d70 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Tue, 26 Aug 2025 17:06:25 +0200
Subject: [PATCH 60/67] Fix typing error..

---
 src/dynsight/_internal/utilities/utilities.py | 12 ++++++------
 src/dynsight/_internal/vision/label_tool.py   |  3 ++-
 src/dynsight/utilities.py                     |  3 ++-
 tests/track/test_track.py                     |  1 -
 4 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/src/dynsight/_internal/utilities/utilities.py b/src/dynsight/_internal/utilities/utilities.py
index 6bcd4912..69682059 100644
--- a/src/dynsight/_internal/utilities/utilities.py
+++ b/src/dynsight/_internal/utilities/utilities.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Iterable, Literal
+from typing import Callable, Literal, Mapping, Sequence
 
 import numpy as np
 import numpy.typing as npt
@@ -107,11 +107,11 @@ def load_or_compute_soap(
 
 
 def _entry_from_parts(
-    parts: Iterable[str],
-    cols_order: list[Col],
+    parts: Sequence[str],
+    cols_order: Sequence[Col],
     frame: int,
 ) -> dict[str, object]:
-    converters: dict[Col, callable] = {
+    converters: Mapping[Col, Callable[[str], object]] = {
         "name": str,
         "x": float,
         "y": float,
@@ -126,7 +126,7 @@ def _entry_from_parts(
 
 def read_xyz(
     input_xyz: Path | str,
-    cols_order: list[Col],
+    cols_order: Sequence[Col],
 ) -> pd.DataFrame:
     lines = Path(input_xyz).read_text().splitlines()
     data: list[dict[str, object]] = []
@@ -140,7 +140,7 @@ def read_xyz(
         if token.isdigit():
             n_atoms = int(token)
             frame += 1
-            row += 2
+            row += 2  # skip comment/title line
 
             end = min(row + n_atoms, nlines)
             for i in range(row, end):
diff --git a/src/dynsight/_internal/vision/label_tool.py b/src/dynsight/_internal/vision/label_tool.py
index 7b47bcff..e17692b9 100644
--- a/src/dynsight/_internal/vision/label_tool.py
+++ b/src/dynsight/_internal/vision/label_tool.py
@@ -13,7 +13,8 @@ class HTTPRequestHandler(SimpleHTTPRequestHandler):
     def log_message(self, fmt: str, *args: object) -> None:
         pass
 
-    def do_POST(self) -> None:
+    # do_POST must be uppercase
+    def do_POST(self) -> None:  # noqa: N802
         if self.path == "/shutdown":
             self.send_response(200)
             self.end_headers()
diff --git a/src/dynsight/utilities.py b/src/dynsight/utilities.py
index 0b7e19ed..ccd1b422 100644
--- a/src/dynsight/utilities.py
+++ b/src/dynsight/utilities.py
@@ -11,4 +11,5 @@
     "find_extrema_points",
     "load_or_compute_soap",
     "normalize_array",
-    "read_xyz"]
+    "read_xyz",
+]
diff --git a/tests/track/test_track.py b/tests/track/test_track.py
index 398b9bda..4183e8f2 100644
--- a/tests/track/test_track.py
+++ b/tests/track/test_track.py
@@ -13,7 +13,6 @@ def test_track_xyz(tmp_path: Path) -> None:
     filename = original_dir / "../systems/lj_noid.xyz"
     file_with_id = original_dir / "../systems/lj_id.xyz"
     output = tmp_path / "trajectory.xyz"
-    output = "trajectory.xyz"
     track_xyz(input_xyz=filename, output_xyz=output)
     n_atoms = 5
     for _ in range(n_atoms):

From 29f47ce95ba71c26217ff2cee4632c7cd34f46f6 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Tue, 26 Aug 2025 17:26:27 +0200
Subject: [PATCH 61/67] Fixing toc width.

---
 docs/source/_static/style.css | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/_static/style.css b/docs/source/_static/style.css
index 34b15a0b..515773f3 100644
--- a/docs/source/_static/style.css
+++ b/docs/source/_static/style.css
@@ -2,8 +2,8 @@
 .sidebar-drawer,
 .sidebar-container,
 .toc-drawer {
-    width: 16em;
-    min-width: 16em;
+    width: 15em;
+    min-width: 15em;
 }
 @media (min-width: 65em) {
     .content {

From 2b0046b0417160e8eb7ab89d907097c317d2f4ee Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Tue, 26 Aug 2025 17:26:42 +0200
Subject: [PATCH 62/67] Removing noqa.

---
 src/dynsight/_internal/vision/label_tool.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/dynsight/_internal/vision/label_tool.py b/src/dynsight/_internal/vision/label_tool.py
index e17692b9..f3f2d62f 100644
--- a/src/dynsight/_internal/vision/label_tool.py
+++ b/src/dynsight/_internal/vision/label_tool.py
@@ -14,7 +14,7 @@ def log_message(self, fmt: str, *args: object) -> None:
         pass
 
     # do_POST must be uppercase
-    def do_POST(self) -> None:  # noqa: N802
+    def do_POST(self) -> None:
         if self.path == "/shutdown":
             self.send_response(200)
             self.end_headers()

From 46714e8dea3fa523a392f1228f56b7701c5f68f3 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Tue, 26 Aug 2025 17:27:15 +0200
Subject: [PATCH 63/67] Added docstring to read_xyz.

---
 src/dynsight/_internal/utilities/utilities.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/src/dynsight/_internal/utilities/utilities.py b/src/dynsight/_internal/utilities/utilities.py
index 69682059..bcec8f37 100644
--- a/src/dynsight/_internal/utilities/utilities.py
+++ b/src/dynsight/_internal/utilities/utilities.py
@@ -128,6 +128,25 @@ def read_xyz(
     input_xyz: Path | str,
     cols_order: Sequence[Col],
 ) -> pd.DataFrame:
+    """Read an XYZ trajectory file into a pandas DataFrame.
+
+    The function parses a file in extended XYZ format where each frame begins
+    with a line containing the number of atoms, followed by a comment/title
+    line, and then one line per atom containing at least one of the columns
+    specified in `cols_order`, following the correct order in the file.
+
+    Parameters:
+        input_xyz :
+            Path to the XYZ file to read.
+        cols_order : Sequence[Col]
+            The expected column order for each atom line (e.g.,
+            ["name", "x", "y", "z", "ID"]).
+
+    Returns:
+        A DataFrame containing all parsed atomic entries. Each row corresponds
+        to one atom in one frame, with columns given by `cols_order` plus the
+        current frame indexing.
+    """
     lines = Path(input_xyz).read_text().splitlines()
     data: list[dict[str, object]] = []
 

From 06bc6dfcd4e6fe7559f981032c65bcae7acf49bc Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Wed, 27 Aug 2025 10:01:37 +0200
Subject: [PATCH 64/67] Added python 3.13 tests version.

---
 .github/workflows/tests.yaml | 150 +++++++++++++++++------------------
 1 file changed, 75 insertions(+), 75 deletions(-)

diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
index c5f178fe..fa28dfa1 100644
--- a/.github/workflows/tests.yaml
+++ b/.github/workflows/tests.yaml
@@ -1,78 +1,78 @@
 name: Tests
 on:
-    push:
-        branches:
-            - main
-    pull_request:
-    workflow_dispatch:
+  push:
+    branches:
+      - main
+  pull_request:
+  workflow_dispatch:
 jobs:
-    ruff:
-        runs-on: ubuntu-latest
-        strategy:
-            matrix:
-                python-version: ["3.10"]
-        name: ruff on ${{ matrix.python-version }}
-        steps:
-            - uses: actions/checkout@v3
-            - uses: actions/setup-python@v4
-              with:
-                  python-version: ${{ matrix.python-version }}
-                  cache: "pip"
-            - run: pip install -e '.[dev]'
-            - run: ruff check .
-    mypy:
-        runs-on: ubuntu-latest
-        strategy:
-            matrix:
-                python-version: ["3.10"]
-        name: mypy on ${{ matrix.python-version }}
-        steps:
-            - uses: actions/checkout@v3
-            - uses: actions/setup-python@v4
-              with:
-                  python-version: ${{ matrix.python-version }}
-                  cache: "pip"
-            - run: pip install -e '.[dev]'
-            - run: mypy .
-    ruff-format:
-        runs-on: ubuntu-latest
-        strategy:
-            matrix:
-                python-version: ["3.10"]
-        name: ruff-format on ${{ matrix.python-version }}
-        steps:
-            - uses: actions/checkout@v3
-            - uses: actions/setup-python@v4
-              with:
-                  python-version: ${{ matrix.python-version }}
-                  cache: "pip"
-            - run: pip install -e '.[dev]'
-            - run: ruff format --check .
-    pytest:
-        runs-on: ubuntu-latest
-        strategy:
-            matrix:
-                python-version: ["3.10"]
-        name: pytest on ${{ matrix.python-version }}
-        steps:
-            - uses: actions/checkout@v3
-            - uses: actions/setup-python@v4
-              with:
-                  python-version: ${{ matrix.python-version }}
-                  cache: "pip"
-            - run: pip install -e '.[dev]'
-            - run: pytest --cov=src --cov-report term-missing
-    doctest:
-        runs-on: ubuntu-latest
-        strategy:
-            matrix:
-                python-version: ["3.10"]
-        name: doctest on ${{ matrix.python-version }}
-        steps:
-            - uses: actions/checkout@v3
-            - uses: actions/setup-python@v4
-              with:
-                  python-version: ${{ matrix.python-version }}
-                  cache: "pip"
-            - run: pip install -e '.[dev]'
-            - run: make -C docs doctest
+  ruff:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.13"]
+    name: ruff on ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip"
+      - run: pip install -e '.[dev]'
+      - run: ruff check .
+  mypy:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.13"]
+    name: mypy on ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip"
+      - run: pip install -e '.[dev]'
+      - run: mypy .
+  ruff-format:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.13"]
+    name: ruff-format on ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip"
+      - run: pip install -e '.[dev]'
+      - run: ruff format --check .
+  pytest:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.13"]
+    name: pytest on ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip"
+      - run: pip install -e '.[dev]'
+      - run: pytest --cov=src --cov-report term-missing
+  doctest:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.13"]
+    name: doctest on ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip"
+      - run: pip install -e '.[dev]'
+      - run: make -C docs doctest

From a84309913b328f9848bfddc864b7dbc987e535da Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Wed, 27 Aug 2025 10:22:53 +0200
Subject: [PATCH 65/67] removed type from doc.

---
 src/dynsight/_internal/utilities/utilities.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/dynsight/_internal/utilities/utilities.py b/src/dynsight/_internal/utilities/utilities.py
index bcec8f37..19f3b45f 100644
--- a/src/dynsight/_internal/utilities/utilities.py
+++ b/src/dynsight/_internal/utilities/utilities.py
@@ -138,7 +138,7 @@ def read_xyz(
     Parameters:
         input_xyz :
             Path to the XYZ file to read.
-        cols_order : Sequence[Col]
+        cols_order :
             The expected column order for each atom line (e.g.,
             ["name", "x", "y", "z", "ID"]).
 

From 4c8ecd947b973f1079f96794fcbeb769b241cef6 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Wed, 27 Aug 2025 10:46:38 +0200
Subject: [PATCH 66/67] Removed default value for search_range.

---
 src/dynsight/_internal/track/track.py | 10 ++++++++--
 tests/track/test_track.py             |  2 +-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/dynsight/_internal/track/track.py b/src/dynsight/_internal/track/track.py
index af15ad02..11dfa4a0 100644
--- a/src/dynsight/_internal/track/track.py
+++ b/src/dynsight/_internal/track/track.py
@@ -21,7 +21,7 @@
 def track_xyz(
     input_xyz: Path,
     output_xyz: Path,
-    search_range: float = 10,
+    search_range: float,
     memory: int = 1,
     adaptive_stop: None | float = 0.95,
     adaptive_step: None | float = 0.5,
@@ -62,7 +62,13 @@ def track_xyz(
         search_range:
             The maximum allowable displacement of objects between frames for
             them to be considered the same particle. Units depend on the
-            coordinate system used in the input file.
+            coordinate system used in the input file. If the file comes from
+            vision, then the unit is ``pixels``. We do not provide a default
+            parameter here, because fine tuning is required to get good
+            behaviour. We recommend starting with a value around 2-3 times the
+            diameter of the particles. But if you are unsure, start with a
+            value of 10. Additionally, test on a small trajectory to start
+            with.
 
         memory:
             The maximum number of frames during which an object can vanish,
diff --git a/tests/track/test_track.py b/tests/track/test_track.py
index 4183e8f2..fcb576d2 100644
--- a/tests/track/test_track.py
+++ b/tests/track/test_track.py
@@ -13,7 +13,7 @@ def test_track_xyz(tmp_path: Path) -> None:
     filename = original_dir / "../systems/lj_noid.xyz"
     file_with_id = original_dir / "../systems/lj_id.xyz"
     output = tmp_path / "trajectory.xyz"
-    track_xyz(input_xyz=filename, output_xyz=output)
+    track_xyz(input_xyz=filename, output_xyz=output, search_range=10)
     n_atoms = 5
     for _ in range(n_atoms):
         arr1 = read_xyz(

From 8f9777f8a9cfbc4d5d4c62485ba88f49527e1065 Mon Sep 17 00:00:00 2001
From: Simone Martino <s.martino0898@gmail.com>
Date: Wed, 27 Aug 2025 10:53:41 +0200
Subject: [PATCH 67/67] minor fixes.

---
 examples/video_to_trajectory.py       | 6 +++++-
 src/dynsight/_internal/track/track.py | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/examples/video_to_trajectory.py b/examples/video_to_trajectory.py
index 5c47a49b..97d0dfaf 100644
--- a/examples/video_to_trajectory.py
+++ b/examples/video_to_trajectory.py
@@ -75,7 +75,11 @@ def main() -> None:
     traj_path = instance.export_prediction_to_xyz(
         file_name=Path("trajectory.xyz")
     )
-    track_xyz(input_xyz=traj_path, output_xyz=Path("output/tracked_traj.xyz"))
+    track_xyz(
+        input_xyz=traj_path,
+        output_xyz=Path("output/tracked_traj.xyz"),
+        search_range=10,
+    )
 
 
 if __name__ == "__main__":
diff --git a/src/dynsight/_internal/track/track.py b/src/dynsight/_internal/track/track.py
index 11dfa4a0..8927240d 100644
--- a/src/dynsight/_internal/track/track.py
+++ b/src/dynsight/_internal/track/track.py
@@ -161,7 +161,7 @@ def _collect_positions(input_xyz: Path) -> pd.DataFrame:
                 if len(parts) == dimensions:
                     x, y, z = map(float, parts[0:3])
                     data.append({"frame": frame, "x": x, "y": y, "z": z})
-                elif len(parts) >= dimensions + 1:
+                elif len(parts) > dimensions:
                     name = parts[0]
                     x, y, z = map(float, parts[1:4])
                     data.append(