Merge branch 'master' of https://github.com/superannotateai/superannotate-python-sdk into public_links_dev

Author: ero1311

docs/source/conf.py

@@ -20,7 +20,7 @@
 
 project = 'SuperAnnotate Python SDK'
 copyright = '2021, SuperAnnotate AI'
-author = 'Hovnatan Karapetyan, Vahagn Tumanyan, Armen Gyurjinyan, Shabin Dilanchian, Erik Harutyunyan, Arsine Mangasaryan'
+author = 'SuperAnnotate AI'
 
 # The full version, including alpha/beta/rc tags
 release = superannotate.__version__

superannotate/input_converters/converters/coco_converters/coco_to_sa_pixel.py

@@ -60,7 +60,7 @@ def coco_panoptic_segmentation_to_sa_pixel(coco_path, output_dir):
         annot_name = Path(annot["file_name"]).stem
         img_cv = cv2.imread(str(output_dir / ("%s.png" % annot_name)))
         if img_cv is None:
-            images_not_converted.append("%s.jpg" % annot_name)
+            images_not_converted.append(annot['file_name'])
             logger.warning(
                 "'%s' file dosen't exist!",
                 output_dir / ("%s.png" % annot_name)
@@ -84,10 +84,12 @@ def coco_panoptic_segmentation_to_sa_pixel(coco_path, output_dir):
             sa_instances.append(sa_obj)
 
         img = cv2.cvtColor(img.reshape((H, W, C)), cv2.COLOR_RGB2BGR)
-        cv2.imwrite(str(output_dir / ("%s.jpg___save.png" % annot_name)), img)
+        cv2.imwrite(
+            str(output_dir / ("%s___save.png" % annot['file_name'])), img
+        )
 
-        images_converted.append("%s.jpg" % annot_name)
-        file_name = "%s.jpg___pixel.json" % annot_name
+        images_converted.append(annot['file_name'])
+        file_name = "%s___pixel.json" % annot['file_name']
         sa_metadata = {
             'name': annot_name,
             'width': img_id_to_shape[str(annot['image_id'])]['width'],

superannotate/input_converters/export_from_sa_conversions.py

@@ -16,7 +16,7 @@
 logger = logging.getLogger("superannotate-python-sdk")
 
 
-def _load_files(path_to_imgs, ptype):
+def _load_files(path_to_imgs, task, ptype):
     suffix = None
     if ptype == "Pixel":
         suffix = '___pixel.json'
@@ -27,16 +27,21 @@ def _load_files(path_to_imgs, ptype):
     orig_images = [str(x).replace(suffix, '') for x in orig_images]
     all_files = None
 
-    if ptype == 'Pixel':
+    if task == 'keypoint_detection':
+        all_files = np.array([[fname, fname + suffix] for fname in orig_images])
+    elif ptype == 'Pixel':
         all_files = np.array(
             [
-                (str(fname), str(fname) + suffix, str(fname) + '___save.png')
+                [fname, fname + suffix, fname + '___save.png']
                 for fname in orig_images
             ]
         )
     elif ptype == 'Vector':
         all_files = np.array(
-            [(str(fname), str(fname) + suffix) for fname in orig_images]
+            [
+                [fname, fname + suffix, fname + '___fuse.png']
+                for fname in orig_images
+            ]
         )
 
     return all_files
@@ -47,7 +52,8 @@ def _move_files(data_set, src):
     if data_set is not None:
         for tup in data_set:
             for i in tup:
-                shutil.copy(i, train_path / Path(i).name)
+                if Path(i).exists():
+                    shutil.copy(i, train_path / Path(i).name)
     else:
         logger.warning("Images doesn't exist")
 
@@ -78,7 +84,7 @@ def export_from_sa(args):
     except Exception as e:
         _create_classes_mapper(args.input_dir, args.output_dir)
 
-    data_set = _load_files(args.input_dir, args.project_type)
+    data_set = _load_files(args.input_dir, args.task, args.project_type)
     _move_files(data_set, args.output_dir)
 
     args.__dict__.update(

superannotate/version.py

@@ -1 +1 @@
-__version__ = "4.1.0b18"
+__version__ = "4.1.0"