diff --git a/.devcontainer/arm/devcontainer.json b/.devcontainer/arm/devcontainer.json new file mode 100644 index 0000000..46e9df8 --- /dev/null +++ b/.devcontainer/arm/devcontainer.json @@ -0,0 +1,29 @@ +// For format details, see https://aka.ms/devcontainer.json. For config options, see the +// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-dockerfile +{ + "name": "ARM Dev Environment", + "build": { + // Sets the run context to one level up instead of the .devcontainer folder. + "context": "..", + // Update the 'dockerFile' property if you aren't using the standard 'Dockerfile' filename. + "dockerfile": "../../Dockerfile.ARM" + }, + + // Add arguments directly to the docker cli command + "runArgs": [] + + // Features to add to the dev container. More info: https://containers.dev/features. + // "features": {}, + + // Use 'forwardPorts' to make a list of ports inside the container available locally. + // "forwardPorts": [], + + // Uncomment the next line to run commands after the container is created. + // "postCreateCommand": "cat /etc/os-release", + + // Configure tool-specific properties. + // "customizations": {}, + + // Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root. + // "remoteUser": "devcontainer" +} diff --git a/.devcontainer/x86/devcontainer.json b/.devcontainer/x86/devcontainer.json new file mode 100644 index 0000000..0e3b920 --- /dev/null +++ b/.devcontainer/x86/devcontainer.json @@ -0,0 +1,29 @@ +// For format details, see https://aka.ms/devcontainer.json. For config options, see the +// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-dockerfile +{ + "name": "X86 Dev Environment", + "build": { + // Sets the run context to one level up instead of the .devcontainer folder. + "context": "..", + // Update the 'dockerFile' property if you aren't using the standard 'Dockerfile' filename. + "dockerfile": "../../Dockerfile.X86" + }, + + // Add arguments directly to the docker cli command + "runArgs": [] + + // Features to add to the dev container. More info: https://containers.dev/features. + // "features": {}, + + // Use 'forwardPorts' to make a list of ports inside the container available locally. + // "forwardPorts": [], + + // Uncomment the next line to run commands after the container is created. + // "postCreateCommand": "cat /etc/os-release", + + // Configure tool-specific properties. + // "customizations": {}, + + // Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root. + // "remoteUser": "devcontainer" +} diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index bda8f26..eede543 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -68,6 +68,14 @@ jobs: -w /work \ ${{ steps.meta.outputs.tags }} \ /bin/bash -c "mkdir -p build && cd build && cmake .. && make" + + - name: Upload CPack Log on Failure + if: failure() + uses: actions/upload-artifact@v4 + with: + name: cpack-log-amd64 + path: ${{ github.workspace }}/build/_CPack_Packages/Linux/DEB/PreinstallOutput.log + retention-days: 7 - name: List files for debugging run: ls -laR ./build @@ -139,6 +147,14 @@ jobs: ${{ steps.meta.outputs.tags }} \ /bin/bash -c "mkdir -p build && cd build && cmake .. && make" + - name: Upload CPack Log on Failure + if: failure() + uses: actions/upload-artifact@v4 + with: + name: cpack-log-arm64 + path: ${{ github.workspace }}/build/_CPack_Packages/Linux/DEB/PreinstallOutput.log + retention-days: 7 + - name: List files for debugging run: ls -laR ./build diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..da42b2a --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "files.associations": { + "__node_handle": "cpp", + "optional": "cpp", + "regex": "cpp" + } +} \ No newline at end of file diff --git a/.vscode/tasks.json b/.vscode/tasks.json new file mode 100644 index 0000000..0f72ece --- /dev/null +++ b/.vscode/tasks.json @@ -0,0 +1,42 @@ +{ + // See https://go.microsoft.com/fwlink/?LinkId=733558 + // for the documentation about the tasks.json format + "version": "2.0.0", + "tasks": [ + { + "label": "configure build", + "type": "shell", + "options": {"cwd": "${cwd}/build"}, + "command": "cmake", + "args": [ + "..", + ], + "group": "build", + "presentation": { + "reveal": "always" + }, + }, + { + "label": "build", + "type": "shell", + "options": {"cwd": "${cwd}/build"}, + "command": "make", + "args": [], + "group": "build", + "presentation": { + "reveal": "always" + }, + }, + { + "label": "run", + "type": "shell", + "options": {"cwd": "${cwd}/build/src"}, + "command": "./GompeiVision", + "args": [], + "group": "build", + "presentation": { + "reveal": "always" + }, + }, + ] +} diff --git a/CMakeLists.txt b/CMakeLists.txt index b5ecb5e..017ae49 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,6 +1,18 @@ cmake_minimum_required(VERSION 3.21) project(GompeiVision VERSION 1.0.3 LANGUAGES CXX) +# --- Set Architecture-Specific Compiler Optimizations --- +if(CMAKE_SYSTEM_PROCESSOR MATCHES "x86_64") + # Apply optimizations for Intel x86_64 CPUs (like Alder Lake) + message(STATUS "x86_64 architecture detected. Applying Raptor Lake optimizations (for intel 13th gen).") + add_compile_options(-O3 -march=core-avx2) + +elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64") + # Apply optimizations for 64-bit ARM CPUs (like Orange Pi) + message(STATUS "aarch64 architecture detected. Applying ARMv8-a optimizations.") + add_compile_options(-O3 -march=armv8-a) +endif() + # Explicitly generate compile_commands.json for IDEs set(CMAKE_EXPORT_COMPILE_COMMANDS ON) @@ -8,6 +20,20 @@ set(CMAKE_CXX_STANDARD 20) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CXX_EXTENSIONS OFF) +# Check if the target architecture is x86_64 +if(CMAKE_SYSTEM_PROCESSOR MATCHES "x86_64") + # Only on x86, we find OpenVINO and add a definition + message(STATUS "x86_64 architecture detected, enabling OpenVINO.") + find_package(OpenVINO REQUIRED) + set(USE_OPENVINO ON) + add_compile_definitions(USE_OPENVINO=1) +else() + # On other architectures (like ARM), we do nothing + message(STATUS "Non-x86 architecture detected, OpenVINO will be disabled.") + set(USE_OPENVINO OFF) + add_compile_definitions(USE_OPENVINO=0) +endif() + # Include the dependency fetching script. This will trigger the downloads # and set up the library targets (e.g., wpiutil, ntcore). add_subdirectory(third_party) @@ -35,5 +61,13 @@ install(FILES ${SYSTEM_LIBS} DESTINATION lib ) +# Install the models directory to the standard share location. +install( + DIRECTORY models/ + DESTINATION /usr/local/share/GompeiVision + FILE_PERMISSIONS OWNER_READ GROUP_READ WORLD_READ + DIRECTORY_PERMISSIONS OWNER_READ OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE +) + # Set up packaging with CPack include(packaging/CPackConfig.cmake) \ No newline at end of file diff --git a/Dockerfile.X86 b/Dockerfile.X86 index 7e7403b..af14fd3 100644 --- a/Dockerfile.X86 +++ b/Dockerfile.X86 @@ -48,8 +48,27 @@ RUN apt-get update && apt-get install -y \ libswscale-dev \ libzstd-dev \ file \ + gnupg \ + software-properties-common \ && rm -rf /var/lib/apt/lists/* +# ============================= +# Install Intel OpenVINO Runtime (for OpenCV DNN backend) +# ============================= + +RUN wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB +RUN gpg --output /etc/apt/trusted.gpg.d/intel.gpg --dearmor GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + +RUN echo "deb https://apt.repos.intel.com/openvino ubuntu24 main" | tee /etc/apt/sources.list.d/intel-openvino.list + +RUN apt update +RUN apt-cache search openvino +RUN apt install -y openvino intel-opencl-icd + +# Set up OpenVINO environment (optional for runtime path) +ENV OpenVINO_DIR=/usr/lib/cmake/openvino2025.3.0 + + # Install any pip packages needed RUN pip install --no-cache-dir --break-system-packages requests protobuf grpcio-tools diff --git a/build/.gitignore b/build/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/build/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/models/gamepiece_table.csv b/models/gamepiece_table.csv new file mode 100644 index 0000000..31e45d0 --- /dev/null +++ b/models/gamepiece_table.csv @@ -0,0 +1,30817 @@ 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100644 index 0000000..305e3ef --- /dev/null +++ b/models/reefscape.names @@ -0,0 +1,2 @@ +algae +coral \ No newline at end of file diff --git a/models/reefscape_yolo11n.onnx b/models/reefscape_yolo11n.onnx new file mode 100644 index 0000000..b5155cb Binary files /dev/null and b/models/reefscape_yolo11n.onnx differ diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 483c0ea..58820f7 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -11,15 +11,19 @@ set(MANAGER_SOURCES cpp/main.cpp cpp/pipeline/PipelineManager.cpp + include/estimator/GamePiecePosEstimator.h ) # Sources required for the pipeline worker executable. set(WORKER_SOURCES cpp/capture/Camera.cpp cpp/detector/FiducialDetector.cpp + cpp/detector/ObjectDetector.cpp + cpp/estimator/GamePiecePosEstimator.cpp cpp/estimator/CameraPoseEstimator.cpp cpp/estimator/SingleTagPoseEstimator.cpp cpp/estimator/TagAngleCalculator.cpp + cpp/estimator/ObjectEstimator.cpp cpp/io/ConfigInterface.cpp cpp/io/FieldInterface.cpp cpp/io/OutputPublisher.cpp @@ -64,6 +68,12 @@ target_link_libraries(GompeiVision PRIVATE target_link_libraries(GompeiVisionProcess PRIVATE third_party_libs ) +message(STATUS "DEBUG: The value of USE_OPENVINO is [${USE_OPENVINO}]") + +if (USE_OPENVINO) + find_package(OpenVINO REQUIRED) + target_link_libraries(GompeiVisionProcess PRIVATE openvino::runtime) +endif () # --- Installation --- # CRITICAL: Install BOTH executables into the same 'bin' directory. diff --git a/src/cpp/detector/ObjectDetector.cpp b/src/cpp/detector/ObjectDetector.cpp new file mode 100644 index 0000000..fb08306 --- /dev/null +++ b/src/cpp/detector/ObjectDetector.cpp @@ -0,0 +1,281 @@ +#include "detector/ObjectDetector.h" + +#include +#include +#include + +ObjectDetector::ObjectDetector(const std::string& model_path, + const std::string& class_names_path, + const float input_width, + const float input_height) { + logInfo("Initializing..."); + + // --- Load Class Names --- + std::ifstream ifs(class_names_path); + if (ifs.is_open()) { + std::string line; + while (std::getline(ifs, line)) { + m_class_names.push_back(line); + } + logInfo("Loaded " + std::to_string(m_class_names.size()) + " class names."); + } else { + logError("Could not open class names file: " + class_names_path); + } + + // --- Load ONNX Neural Network Model --- +#if USE_OPENVINO + // --- Use OpenVINO Runtime on x86 --- + logInfo("Using OpenVINO runtime."); + try { + // Load the model from the .onnx file + m_core = ov::Core(); + + auto model = m_core.read_model(model_path); + + ov::preprocess::PrePostProcessor ppp(model); + ppp.input() + .tensor() + .set_element_type(ov::element::u8) // Input will be uint8 (0-255) + .set_shape({1, input_height, input_width, 3}) + .set_color_format(ov::preprocess::ColorFormat::BGR) + .set_layout("NHWC"); // OpenCV format: batch, height, width, channels + + ppp.input().model().set_layout("NCHW"); + ppp.input() + .preprocess() + .convert_element_type(ov::element::f32) // Convert to float32 + .convert_color(ov::preprocess::ColorFormat::RGB) + .scale(255.0f) + .resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR, m_input_width, + m_input_height); + model = ppp.build(); + + // Compile the model for the optimal device (e.g., CPU, GPU) + // 'AUTO' lets OpenVINO choose the best available device. + m_core.set_property( + "GPU", ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY)); + + m_core.set_property("GPU", + ov::hint::model_priority(ov::hint::Priority::HIGH)); + + m_core.set_property("GPU", ov::hint::inference_precision(ov::element::f16)); + + m_compiled_model = m_core.compile_model(model, "HETERO:GPU,CPU", + ov::enable_profiling(false)); + + auto input_port = m_compiled_model.input(); + m_input_shape = input_port.get_shape(); + m_input_element_type = input_port.get_element_type(); + + logInfo("OpenVINO model compiled successfully for AUTO device."); + logInfo("Model input shape: " + std::to_string(m_input_shape[0]) + "x" + + std::to_string(m_input_shape[1]) + "x" + + std::to_string(m_input_shape[2]) + "x" + + std::to_string(m_input_shape[3])); + // Get input shape information + m_infer_request = m_compiled_model.create_infer_request(); + + // Print info + auto exec_devices = m_compiled_model.get_property(ov::execution_devices); + std::cout << "[ObjectDetector INFO] Running on device(s): "; + for (const auto& d : exec_devices) { + std::cout << d << " "; + } + std::cout << std::endl; + } catch (const ov::Exception& e) { + logError("OpenVINO exception: " + std::string(e.what())); + } catch (const std::exception& e) { + logError("Standard exception: " + std::string(e.what())); + } + +#else + // --- Use OpenCV DNN on ARM --- + m_net = cv::dnn::readNet(model_path); + if (m_net.empty()) { + logError("Failed to load object detection model from: " + model_path); + } else { + logInfo("ONNX model loaded successfully with OpenCV DNN."); + // Set backend for ARM + m_net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV); + m_net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU); + } +#endif +} + +ObjectDetector::~ObjectDetector() { logInfo("Shutting down..."); } + +void ObjectDetector::detect(const QueuedFrame& frame, + std::vector& observations, + std::vector& final_boxes, + std::vector& final_class_ids, + std::vector& final_confidences) { + std::vector class_ids; + std::vector confidences; + std::vector boxes; + +#if USE_OPENVINO + if (frame.frame.empty() || !m_compiled_model) { + return; + } + // ---1. Preprocess --- + ov::Tensor input_tensor(ov::element::u8, + {1, static_cast(frame.frame.rows), + static_cast(frame.frame.cols), 3}, + frame.frame.data); + + m_infer_request.set_input_tensor(input_tensor); + + // --- 2. Perform Inference --- + m_infer_request.infer(); + + // --- 3. Post-process Results --- + const ov::Tensor& output_tensor = m_infer_request.get_output_tensor(); + const float* detections = output_tensor.data(); + + // The output shape for YOLOv8 is [1, 84, 8400] + // where 84 = 4 (bbox) + 80 (class scores) + // We need to process this flat data structure. + const int num_classes = m_class_names.size(); + const int elements_per_detection = num_classes + 4; // 4 for bbox + const int num_detections = output_tensor.get_shape()[2]; + float x_factor = frame.frame.cols / m_input_width; + float y_factor = frame.frame.rows / m_input_height; + // Transpose the data from [1, 84, 8400] to [1, 8400, 84] for easier iteration + cv::Mat output_matrix(elements_per_detection, num_detections, CV_16F, + (void*)detections); + cv::Mat detections_mat = output_matrix.t(); + + for (int i = 0; i < detections_mat.rows; ++i) { + // For each detection, find the class with the highest score + cv::Mat classes_scores = detections_mat.row(i).colRange(4, 4 + num_classes); + cv::Point class_id_point; + double max_class_score; + cv::minMaxLoc(classes_scores, 0, &max_class_score, 0, &class_id_point); + + if (max_class_score > m_confidence_threshold) { + confidences.push_back(max_class_score); + class_ids.push_back(class_id_point.x); + + // Extract bounding box coordinates (center_x, center_y, width, height) + float cx = detections_mat.at(i, 0); + float cy = detections_mat.at(i, 1); + float w = detections_mat.at(i, 2); + float h = detections_mat.at(i, 3); + + // Convert from center coordinates to top-left corner + int left = static_cast((cx - 0.5 * w) * x_factor); + int top = static_cast((cy - 0.5 * h) * y_factor); + int width = static_cast(w * x_factor); + int height = static_cast(h * y_factor); + boxes.emplace_back(left, top, width, height); + } + } +#else + // --- ARM IMPLEMENTATION (Original OpenCV DNN) --- + if (frame.frame.empty() || m_net.empty()) { + return; + } + + // --- 1. Preprocess Frame --- + cv::Mat blob; + cv::dnn::blobFromImage(frame.frame, blob, 1. / 255., + cv::Size(m_input_width, m_input_height), cv::Scalar(), + true, false); + + // --- 2. Perform Inference --- + m_net.setInput(blob); + + std::vector outs; + m_net.forward(outs, m_net.getUnconnectedOutLayersNames()); + + if (outs.empty()) { + logError("Inference failed: model returned no outputs."); + return; // Exit the detect function early + } + + // --- 3. Post-process Results for YOLOv8/v11 --- + // The output of YOLOv8 is a single matrix of shape [1, 84, 8400] + // where 84 = 4 (bbox) + 80 (class scores) + // and 8400 is the number of detections. + // We need to transpose it to [1, 8400, 84] for easier processing. + cv::Mat detection_matrix = outs[0]; + cv::Mat detections = + detection_matrix + .reshape(1, {detection_matrix.size[1], detection_matrix.size[2]}) + .t(); + + float x_factor = frame.frame.cols / m_input_width; + float y_factor = frame.frame.rows / m_input_height; + + for (int i = 0; i < detections.rows; ++i) { + // For each detection, find the class with the highest score + cv::Mat classes_scores = + detections.row(i).colRange(4, 4 + m_class_names.size()); + cv::Point class_id_point; + double max_class_score; + cv::minMaxLoc(classes_scores, 0, &max_class_score, 0, &class_id_point); + + if (max_class_score > m_confidence_threshold) { + confidences.push_back(max_class_score); + class_ids.push_back(class_id_point.x); + + // Extract bounding box coordinates (center_x, center_y, width, height) + float cx = detections.at(i, 0); + float cy = detections.at(i, 1); + float w = detections.at(i, 2); + float h = detections.at(i, 3); + + // Convert from center coordinates to top-left corner + int left = static_cast((cx - 0.5 * w) * x_factor); + int top = static_cast((cy - 0.5 * h) * y_factor); + int width = static_cast(w * x_factor); + int height = static_cast(h * y_factor); + boxes.emplace_back(left, top, width, height); + } + } +#endif + + // Apply Non-Maximum Suppression to filter out overlapping boxes + std::vector nms_result; + cv::dnn::NMSBoxes(boxes, confidences, m_confidence_threshold, m_nms_threshold, + nms_result); + + observations.clear(); + final_boxes.clear(); + final_class_ids.clear(); + final_confidences.clear(); + + observations.reserve(nms_result.size()); + final_boxes.reserve(nms_result.size()); + final_class_ids.reserve(nms_result.size()); + final_confidences.reserve(nms_result.size()); + + for (int idx : nms_result) { + const auto& box = boxes[idx]; + final_boxes.push_back(box); + final_class_ids.push_back(class_ids[idx]); + final_confidences.push_back(confidences[idx]); + + ObjDetectObservation obs; + obs.obj_class = class_ids[idx]; + obs.confidence = confidences[idx]; + obs.corner_pixels = { + static_cast(box.tl().x), static_cast(box.tl().y), + static_cast(box.br().x), static_cast(box.tl().y), + static_cast(box.br().x), static_cast(box.br().y), + static_cast(box.tl().x), static_cast(box.br().y)}; + observations.push_back(obs); + } +} + +void ObjectDetector::logInfo(const std::string& message) const { + std::cout << "[ObjectDetector INFO] " << message << std::endl; +} + +void ObjectDetector::logError(const std::string& message) const { + std::cerr << "[ObjectDetector ERROR] " << message << std::endl; +} + +const std::vector& ObjectDetector::getClassNames() const { + return m_class_names; +} \ No newline at end of file diff --git a/src/cpp/estimator/GamePiecePosEstimator.cpp b/src/cpp/estimator/GamePiecePosEstimator.cpp new file mode 100644 index 0000000..6ff74fa --- /dev/null +++ b/src/cpp/estimator/GamePiecePosEstimator.cpp @@ -0,0 +1,61 @@ +#include "estimator/GamePiecePosEstimator.h" + +#include + +#include + +GamePiecePosEstimator::GamePiecePosEstimator( + const float imageWidth, const float imageHeight, + const std::vector& data) + : width(imageWidth), height(imageHeight), data(data) {} + +bool GamePiecePosEstimator::isClose(const double a, const double b, + const double tol) { + return std::abs(a - b) <= tol; +} + +std::optional> +GamePiecePosEstimator::estimatePosition(const double x, const double y, + const double w, const double h) const { + double centerX = x + w / 2.0; + double centerY = y + h / 2.0; + + int tolerance = 25; + const int maxTol = 40; + const int step = 3; + + while (tolerance <= maxTol) { + double sumX = 0.0; + double sumY = 0.0; + double sumAngle = 0.0; + int count = 0; + + for (const auto& row : data) { + if (isClose(row.centerX, centerX, tolerance) && + isClose(row.centerY, centerY, tolerance) && + isClose(row.width, w, tolerance) && + isClose(row.height, h, tolerance)) { + sumX += row.xPosition; + sumY += row.yPosition; + sumAngle += row.angle; + count++; + } + } + + if (count > 0) { + double meanX = sumX / count; + double meanY = sumY / count; + double meanAngle = sumAngle / count; + int certainty = 50 - tolerance; + + auto pose = frc::Pose2d(units::meter_t(meanX), units::meter_t(meanX), + frc::Rotation2d(units::degree_t(meanAngle))); + + return std::make_optional(std::make_pair(pose, certainty)); + } + + tolerance += step; + } + + return std::nullopt; +} diff --git a/src/cpp/estimator/ObjectEstimator.cpp b/src/cpp/estimator/ObjectEstimator.cpp new file mode 100644 index 0000000..f8c5152 --- /dev/null +++ b/src/cpp/estimator/ObjectEstimator.cpp @@ -0,0 +1,105 @@ +#include "estimator/ObjectEstimator.h" + +#include +#include +#include +#include +#include +#include + +static std::vector LoadGamePieceTable() { + std::vector table; + // Ensure this path matches where you deploy the file on the robot/coprocessor + std::string path = "/usr/local/share/GompeiVision/gamepiece_table.csv"; + std::ifstream file(path); + + if (!file.is_open()) { + std::cerr << "Failed to open " << path << std::endl; + return table; + } + + std::string line; + // Skip the header line (assuming the first line contains "Image,x_center...") + std::getline(file, line); + + while (std::getline(file, line)) { + if (line.empty()) { + continue; + } + + std::istringstream iss(line); + std::string image_name_dummy; // To hold the "Image" column (filename) + char comma; + double image_angle_dummy; // To hold "Image_angle" + GamePieceRow row; + + // 1. Read the filename string up to the comma + if (std::getline(iss, image_name_dummy, ',') && + iss >> row.centerX >> comma >> row.centerY >> comma >> row.width >> + comma >> row.height >> comma >> image_angle_dummy >> comma >> + row.xPosition >> comma >> row.yPosition >> comma >> row.angle) { + table.push_back(row); + } + } + + return table; +} + +ObjectEstimator* ObjectEstimator::instance = nullptr; + +ObjectEstimator& ObjectEstimator::GetInstance(const float width, + const float height) { + if (instance == nullptr) { + instance = new ObjectEstimator(width, height); + } + return *instance; +} + +ObjectEstimator::ObjectEstimator(const float width, const float height) + : posEstimator(width, height, LoadGamePieceTable()) {} + +void ObjectEstimator::calculate(ObjDetectObservation& observation, + const cv::Mat& cameraMatrix, + const cv::Mat& distCoeffs) const { + // Must have exactly 4 corners + if (observation.corner_pixels.size() != 8) { + return; + } + + // 1. Collect distorted corner pixels + std::vector image_points; + image_points.reserve(4); + + for (size_t i = 0; i < 8; i += 2) { + image_points.emplace_back(observation.corner_pixels[i], + observation.corner_pixels[i + 1]); + } + + // 2. Undistort corner pixels into pixel space + std::vector undistorted_points; + cv::undistortPoints(image_points, undistorted_points, cameraMatrix, + distCoeffs, cv::noArray(), cameraMatrix); + + // 3. Compute bounding box in undistorted space + cv::Rect2f bbox = cv::boundingRect(undistorted_points); + + // Reject degenerate boxes + if (bbox.width <= 1.0f || bbox.height <= 1.0f) { + return; + } + + // 4. Estimate field position using lookup table + auto estimate = + posEstimator.estimatePosition(bbox.x, bbox.y, bbox.width, bbox.height); + + if (!estimate.has_value()) { + return; + } + + auto [position, certainty] = estimate.value(); + + // 5. Write results to observation + observation.pose = position; + + observation.confidence = certainty; +} diff --git a/src/cpp/io/ConfigInterface.cpp b/src/cpp/io/ConfigInterface.cpp index d6d5da7..c6e57d4 100644 --- a/src/cpp/io/ConfigInterface.cpp +++ b/src/cpp/io/ConfigInterface.cpp @@ -18,14 +18,13 @@ constexpr auto kGain = "gain"; constexpr auto kWidth = "width"; constexpr auto kHeight = "height"; constexpr auto kCompressed = "compressed"; +constexpr auto kModelIndex = "model_index"; } // namespace nt_keys ConfigInterface::ConfigInterface(const std::string& hardwareID, const nt::NetworkTableInstance& nt_inst) { - m_table = - nt_inst.GetTable("/cameras/" + hardwareID); - m_configTable = nt_inst.GetTable( - "/cameras/" + hardwareID + "/config"); + m_table = nt_inst.GetTable("/cameras/" + hardwareID); + m_configTable = nt_inst.GetTable("/cameras/" + hardwareID + "/config"); if (m_table) { std::cout << "Initializing subscribers" << std::endl; @@ -56,6 +55,8 @@ ConfigInterface::ConfigInterface(const std::string& hardwareID, m_configTable->GetIntegerTopic(nt_keys::kHeight).Subscribe(0, options); m_compressedSub = m_configTable->GetBooleanTopic(nt_keys::kCompressed) .Subscribe(false, options); + m_modelIndexSub = m_configTable->GetIntegerTopic(nt_keys::kModelIndex) + .Subscribe(0, options); // Start the initialization thread m_initThread = @@ -122,6 +123,7 @@ void ConfigInterface::update() { m_width = m_widthSub.Get(); m_height = m_heightSub.Get(); m_compressed = m_compressedSub.Get(); + m_modelIndex = m_modelIndexSub.Get(); } // --- State Getters --- @@ -154,6 +156,8 @@ int ConfigInterface::getHeight() const { return m_height; } bool ConfigInterface::getCompressed() const { return m_compressed; } +int ConfigInterface::getModelIndex() const { return m_modelIndex; } + // --- Logging Helpers --- void ConfigInterface::logInfo(const std::string& message) { diff --git a/src/cpp/io/FieldInterface.cpp b/src/cpp/io/FieldInterface.cpp index dfd0a0c..33593a7 100644 --- a/src/cpp/io/FieldInterface.cpp +++ b/src/cpp/io/FieldInterface.cpp @@ -1,5 +1,5 @@ #include "io/FieldInterface.h" -#include + #include "networktables/NetworkTableInstance.h" void FieldInterface::initialize(const nt::NetworkTableInstance& nt_inst) { @@ -16,7 +16,7 @@ void FieldInterface::initialize(const nt::NetworkTableInstance& nt_inst) { subscribers.emplace(tag_id, std::move(sub)); } - numTags = 22; // we know how many tags to expect + numTags = 22; // we know how many tags to expect } bool FieldInterface::update() { @@ -24,14 +24,12 @@ bool FieldInterface::update() { for (const auto& [tag_id, sub] : subscribers) { if (std::vector pose_data = sub.Get(); pose_data.size() == 7) { - const frc::Translation3d t{ - units::meter_t{pose_data[0]}, - units::meter_t{pose_data[1]}, - units::meter_t{pose_data[2]}}; + const frc::Translation3d t{units::meter_t{pose_data[0]}, + units::meter_t{pose_data[1]}, + units::meter_t{pose_data[2]}}; - const frc::Rotation3d r{ - frc::Quaternion{pose_data[3], pose_data[4], - pose_data[5], pose_data[6]}}; + const frc::Rotation3d r{frc::Quaternion{pose_data[3], pose_data[4], + pose_data[5], pose_data[6]}}; tagMap.emplace(tag_id, frc::Pose3d{t, r}); @@ -44,10 +42,6 @@ bool FieldInterface::update() { return tagMap.size() == numTags; } -std::map FieldInterface::getMap() { - return tagMap; -} +std::map FieldInterface::getMap() { return tagMap; } -bool FieldInterface::isInitialized() { - return !subscribers.empty(); -} \ No newline at end of file +bool FieldInterface::isInitialized() { return !subscribers.empty(); } \ No newline at end of file diff --git a/src/cpp/io/OutputPublisher.cpp b/src/cpp/io/OutputPublisher.cpp index 5ae8d02..e6e2181 100644 --- a/src/cpp/io/OutputPublisher.cpp +++ b/src/cpp/io/OutputPublisher.cpp @@ -36,7 +36,9 @@ NTOutputPublisher::NTOutputPublisher(const std::string_view hardware_id, observations_pub_ = table->GetDoubleArrayTopic("observations").Publish(options); + objects_pub_ = table->GetDoubleArrayTopic("objects").Publish(options); apriltags_fps_pub_ = table->GetIntegerTopic("fps_apriltags").Publish(); + objects_fps_pub_ = table->GetIntegerTopic("fps_objects").Publish(); connection_status_pub_ = table->GetBooleanTopic("connected").Publish(); usb_speed_pub_ = table->GetDoubleTopic("usb_speed").Publish(); } @@ -80,6 +82,28 @@ void NTOutputPublisher::SendAprilTagResult(const AprilTagResult& result) { apriltags_fps_pub_.Set(result.fps); } +void NTOutputPublisher::SendObjectDetectResult( + const ObjectDetectResult& result) { + // --- Object detection data --- + std::vector object_data; + + // Serialize the results into a flat array of doubles. + // Data format is repeating blocks of: + // [class_id, confidence, c0_x_angle, c0_y_angle, c1_x_angle, ... ] + for (const auto& obs : result.observations) { + object_data.push_back(obs.obj_class); + object_data.push_back(obs.confidence); + AppendPoseData(object_data, frc::Pose3d(obs.pose)); + } + + // Publish all data + objects_pub_.Set(object_data, + std::chrono::duration_cast( + result.timestamp.time_since_epoch()) + .count()); + objects_fps_pub_.Set(result.fps); +} + void NTOutputPublisher::sendConnectionStatus(const bool isConnected) { connection_status_pub_.Set(isConnected); } diff --git a/src/cpp/pipeline/Pipeline.cpp b/src/cpp/pipeline/Pipeline.cpp index 7773a2b..b7031ef 100644 --- a/src/cpp/pipeline/Pipeline.cpp +++ b/src/cpp/pipeline/Pipeline.cpp @@ -15,10 +15,14 @@ #include #include "capture/Camera.h" +#include "detector/ObjectDetector.h" #include "estimator/CameraPoseEstimator.h" +#include "estimator/ObjectEstimator.h" #include "estimator/SingleTagPoseEstimator.h" #include "estimator/TagAngleCalculator.h" +#include "io/FieldInterface.h" #include "io/OutputPublisher.h" +#include "pipeline/ModelManager.h" #include "pipeline/PipelineHelper.h" Pipeline::Pipeline(const std::string& device_path, @@ -44,6 +48,28 @@ Pipeline::Pipeline(const std::string& device_path, std::cout << "[" << m_role << "] Initializing pipeline..." << std::endl; + // --- Initialize Object Detector based on NT index --- + const int initial_model_index = m_config_interface->getModelIndex(); + + // Validate the index + if (initial_model_index >= 0 && + initial_model_index < available_models.size()) { + const auto& selected_model = available_models[initial_model_index]; + std::cout << "[" << m_role + << "] Loading initial object detection model (index " + << initial_model_index << "): " << selected_model.modelPath + << std::endl; + + m_ObjectDetector = std::make_unique( + selected_model.modelPath, selected_model.namesPath, m_active_width, + m_active_height); + m_active_model_index = initial_model_index; // Set the active index + } else { + std::cerr << "[" << m_role << "] ERROR: Invalid initial model index (" + << initial_model_index << "). Object detection disabled." + << std::endl; + } + m_camera = std::make_unique(device_path, hardware_id, m_active_width, m_active_height); @@ -61,10 +87,21 @@ Pipeline::Pipeline(const std::string& device_path, FieldInterface::initialize(nt_inst); } - while (!FieldInterface::update()) + while (!FieldInterface::update()) { + } + + m_frames_for_object_detection.setMaxQueue(1); + m_frames_for_annotation.setMaxQueue(1); + + m_intrinsics_loaded = PipelineHelper::load_camera_intrinsics( + *m_config_interface, m_camera_matrix, m_dist_coeffs); + m_object_detections.setMaxQueue(1); + + m_output_publisher = + std::make_unique(m_hardware_id, nt_inst); - m_intrinsics_loaded = PipelineHelper::load_camera_intrinsics( - *m_config_interface, m_camera_matrix, m_dist_coeffs); + m_intrinsics_loaded = PipelineHelper::load_camera_intrinsics( + *m_config_interface, m_camera_matrix, m_dist_coeffs); if (m_intrinsics_loaded) { std::cout << "[" << m_role << "] Initial Camera Matrix: " @@ -94,6 +131,10 @@ void Pipeline::start() { m_is_running = true; m_processing_thread = std::thread(&Pipeline::processing_loop, this); m_networktables_thread = std::thread(&Pipeline::networktables_loop, this); + m_apriltag_thread = std::thread(&Pipeline::apriltag_detection_loop, this); + m_object_detection_thread = + std::thread(&Pipeline::object_detection_loop, this); + m_annotation_thread = std::thread(&Pipeline::annotation_loop, this); } void Pipeline::during() { @@ -101,6 +142,27 @@ void Pipeline::during() { m_config_interface->update(); + // --- Check for Model Change --- + const int new_model_index = m_config_interface->getModelIndex(); + if (new_model_index != m_active_model_index) { + if (new_model_index >= 0 && new_model_index < available_models.size()) { + const auto& new_model = available_models[new_model_index]; + std::cout << "[" << m_role << "] Model index changed to " + << new_model_index + << ". Reloading object detector with: " << new_model.modelPath + << std::endl; + + // Replace the old detector with a new one + m_ObjectDetector = std::make_unique( + new_model.modelPath, new_model.namesPath, m_active_width, + m_active_height); + m_active_model_index = new_model_index; // Update the active index + } else { + std::cerr << "[" << m_role << "] ERROR: Received invalid model index (" + << new_model_index << "). Keeping current model." << std::endl; + } + } + cv::Mat new_camera_matrix; cv::Mat new_dist_coeffs; const bool new_intrinsics_loaded = PipelineHelper::load_camera_intrinsics( @@ -205,15 +267,32 @@ void Pipeline::during() { std::cout << "[" << m_role << "] MJPEG stream available at port " << m_stream_port << std::endl; + + m_annotated_mjpeg_server = std::make_unique( + m_role + "_stream_annotated", m_stream_port + 1); + m_annotated_cv_source = std::make_unique( + m_role + "_source_annotated", cs::VideoMode::PixelFormat::kBGR, + m_active_width, m_active_height, 60); + + CS_Status annotated_status = 0; + cs::SetSinkSource(m_annotated_mjpeg_server->GetHandle(), + m_annotated_cv_source->GetHandle(), &annotated_status); + + std::cout << "[" << m_role + << "] Annotated MJPEG stream available at port " + << m_stream_port + 1 << std::endl; } } else { // --- We are NOT in Setup Mode --- + if (!m_config_interface->isSetupMode() && m_mjpeg_server) std::cout << "[" << m_role << "] Setup mode disabled. Stopping servers." << std::endl; m_mjpeg_server.reset(); m_cv_source.reset(); + m_annotated_mjpeg_server.reset(); + m_annotated_cv_source.reset(); } const double usb_speed = m_camera->getSpeed(); @@ -232,6 +311,11 @@ void Pipeline::stop() { m_is_running = false; m_estimated_poses.shutdown(); + m_object_detections.shutdown(); + m_frames_for_apriltag_detection.shutdown(); + m_frames_for_object_detection.shutdown(); + m_frames_for_annotation.shutdown(); + m_frames_for_annotation.shutdown(); if (m_processing_thread.joinable()) m_processing_thread.join(); if (m_networktables_thread.joinable()) m_networktables_thread.join(); @@ -242,38 +326,76 @@ void Pipeline::stop() { bool Pipeline::isRunning() const { return m_is_running; } void Pipeline::processing_loop() { - auto last_time = std::chrono::steady_clock::now(); - double smoothed_fps = 0.0; - QueuedFrame frame; std::chrono::time_point timestamp; while (m_is_running) { - bool frame_was_captured = false; - if (m_camera) { // The definitive test for a camera connection is whether we can get a // frame. if (m_camera->getFrame(frame.frame, timestamp)) { - frame_was_captured = true; + frame.cameraRole = m_role; + frame.timestamp = timestamp; + + if (m_config_interface->isSetupMode() && m_cv_source) { + m_cv_source->PutFrame(frame.frame); + } + + // Push the frame to both processing queues + m_frames_for_apriltag_detection.push(frame); + if (m_ObjectDetector) { + m_frames_for_object_detection.push(frame); + } + } else { - // If getting a frame fails, the camera is considered disconnected. - // Attempt to reconnect for the next cycle. m_camera->attemptReconnect(); + std::this_thread::sleep_for(std::chrono::milliseconds(100)); } + } else { + std::this_thread::sleep_for(std::chrono::seconds(1)); } + } +} - // Update the shared connection status flag once per loop iteration. - if (frame_was_captured) { - // --- Frame processing continues only if a frame was captured --- - constexpr double alpha = 0.05; +void Pipeline::networktables_loop() { + while (m_is_running) { + // Always publish the latest known connection status. + if (m_output_publisher) { + m_output_publisher->sendConnectionStatus(m_camera->isConnected()); + } + + AprilTagResult apriltag_result; + // Use a timed wait so that we still publish connection status regularly + // even if no new AprilTag results are available. + if (m_estimated_poses.waitAndPopWithTimeout( + apriltag_result, std::chrono::milliseconds(50))) { + // If we get a apriltag_result, publish it. + if (m_output_publisher) { + m_output_publisher->SendAprilTagResult(apriltag_result); + } + } - if (m_config_interface->isSetupMode() && m_cv_source) { - m_cv_source->PutFrame(frame.frame); + ObjectDetectResult object_result; + if (m_object_detections.waitAndPopWithTimeout( + object_result, std::chrono::milliseconds(50))) { + // If we get a object_result, publish it. + if (m_output_publisher) { + m_output_publisher->SendObjectDetectResult(object_result); } + } + } +} +void Pipeline::apriltag_detection_loop() { + auto last_time = std::chrono::steady_clock::now(); + double smoothed_fps = 0.0; + + QueuedFrame frame; + while (m_is_running) { + if (m_frames_for_apriltag_detection.waitAndPop(frame)) { + constexpr double alpha = 0.05; FiducialImageObservation frame_observation; - frame_observation.timestamp = timestamp; + frame_observation.timestamp = frame.timestamp; m_AprilTagDetector.detect(frame, frame_observation); @@ -284,23 +406,74 @@ void Pipeline::processing_loop() { smoothed_fps = (1.0 - alpha) * smoothed_fps + alpha * instant_fps; if (!frame_observation.tag_ids.empty() && m_intrinsics_loaded) { - AprilTagResult result; - result.timestamp = frame_observation.timestamp; - result.camera_role = m_role; + AprilTagResult apriltag_result; + apriltag_result.timestamp = frame_observation.timestamp; + apriltag_result.camera_role = m_role; constexpr double tag_size_m = 0.1651; cv::Mat cameraMatrix = m_camera_matrix.clone(); cv::Mat distCoeffs = m_dist_coeffs.clone(); - CameraPoseEstimator::estimatePose(frame_observation, result, + CameraPoseEstimator::estimatePose(frame_observation, apriltag_result, cameraMatrix, distCoeffs, tag_size_m, FieldInterface::getMap()); - TagAngleCalculator::calculate(frame_observation, result, cameraMatrix, - distCoeffs, tag_size_m); + TagAngleCalculator::calculate(frame_observation, apriltag_result, + cameraMatrix, distCoeffs, tag_size_m); - result.fps = smoothed_fps; - m_estimated_poses.push(result); + apriltag_result.fps = smoothed_fps; + m_estimated_poses.push(apriltag_result); + } + } + } +} + +void Pipeline::object_detection_loop() { + auto last_time = std::chrono::steady_clock::now(); + double smoothed_fps = 0.0; + + QueuedFrame frame; + while (m_is_running) { + // Wait for a frame to become available + if (m_frames_for_object_detection.waitAndPop(frame)) { + constexpr double alpha = 0.05; + // --- Calculate FPS for this thread --- + auto current_time = std::chrono::steady_clock::now(); + std::chrono::duration elapsed_seconds = current_time - last_time; + last_time = current_time; + const double instant_fps = 1.0 / elapsed_seconds.count(); + smoothed_fps = (1.0 - alpha) * smoothed_fps + alpha * instant_fps; + + // --- Run Object Detection --- + std::vector raw_observations; + std::vector detected_boxes; + std::vector detected_class_ids; + std::vector detected_confidences; + m_ObjectDetector->detect(frame, raw_observations, detected_boxes, + detected_class_ids, detected_confidences); + + // --- Offload annotation to the dedicated thread --- + if (m_config_interface->isSetupMode() && m_annotated_cv_source) { + AnnotationData data; + data.q_frame = frame; + data.boxes = detected_boxes; + data.class_ids = detected_class_ids; + data.confidences = detected_confidences; + m_frames_for_annotation.push(data); + } + + if (!raw_observations.empty() && m_intrinsics_loaded) { + ObjectDetectResult object_result; + object_result.timestamp = frame.timestamp; + object_result.camera_role = frame.cameraRole; + object_result.fps = smoothed_fps; + + for (auto& obs : raw_observations) { + ObjectEstimator::GetInstance(m_active_width, m_active_height) + .calculate(obs, m_camera_matrix, m_dist_coeffs); + object_result.observations.push_back(obs); + } + m_object_detections.push(object_result); } } else { // If no frame was captured, wait a moment before the next attempt. @@ -309,27 +482,49 @@ void Pipeline::processing_loop() { } } -void Pipeline::networktables_loop() { +void Pipeline::annotation_loop() { + AnnotationData data; while (m_is_running) { - // Always publish the latest known connection status. - if (m_output_publisher) { - m_output_publisher->sendConnectionStatus(m_camera->isConnected()); - } - if (m_output_publisher) { - m_output_publisher->sendUSBSpeed(m_camera->getSpeed()); // ( in Mbps) + if (!m_config_interface->isSetupMode()) { + std::this_thread::sleep_for(std::chrono::seconds(1)); + continue; } + if (m_frames_for_annotation.waitAndPop(data)) { + cv::UMat annotated_frame = + data.q_frame.frame.getUMat(cv::ACCESS_READ).clone(); + const auto& class_names = m_ObjectDetector->getClassNames(); + + for (size_t i = 0; i < data.boxes.size(); ++i) { + const auto& box = data.boxes[i]; + const auto& class_id = data.class_ids[i]; + const auto& confidence = data.confidences[i]; + + // Draw the rectangle + cv::rectangle(annotated_frame, box, cv::Scalar(0, 255, 0), 2); + + // --- THIS IS THE RESTORED LABEL DRAWING CODE --- + std::string label = + class_names[class_id] + ": " + cv::format("%.2f", confidence); + + int baseLine; + cv::Size labelSize = + cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); + int top = std::max(box.y, labelSize.height); + + cv::rectangle(annotated_frame, cv::Point(box.x, top - labelSize.height), + cv::Point(box.x + labelSize.width, top + baseLine), + cv::Scalar(0, 0, 0), cv::FILLED); + cv::putText(annotated_frame, label, cv::Point(box.x, top), + cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(255, 255, 255), + 1); + } - AprilTagResult result; - // Use a timed wait so that we still publish connection status regularly - // even if no new AprilTag results are available. - if (!m_estimated_poses.waitAndPopWithTimeout( - result, std::chrono::milliseconds(50))) { - continue; // No new data, but we’ll loop again soon. - } + cv::Mat annotated_frame_mat; + annotated_frame.copyTo(annotated_frame_mat); - // If we get a result, publish it. - if (m_output_publisher) { - m_output_publisher->SendAprilTagResult(result); + if (m_annotated_cv_source) { + m_annotated_cv_source->PutFrame(annotated_frame_mat); + } } } } diff --git a/src/cpp/pipeline/PipelineHelper.cpp b/src/cpp/pipeline/PipelineHelper.cpp index b38e78a..c2826bf 100644 --- a/src/cpp/pipeline/PipelineHelper.cpp +++ b/src/cpp/pipeline/PipelineHelper.cpp @@ -1,18 +1,8 @@ #include "pipeline/PipelineHelper.h" -#include -#include -#include -#include -#include -#include - -#include #include #include -#include #include -#include #include "io/ConfigInterface.h" diff --git a/src/include/detector/FiducialDetector.h b/src/include/detector/FiducialDetector.h index 2815310..e2f73f3 100644 --- a/src/include/detector/FiducialDetector.h +++ b/src/include/detector/FiducialDetector.h @@ -2,9 +2,6 @@ #include -#include -#include - #include "util/QueuedFiducialData.h" #include "util/QueuedFrame.h" diff --git a/src/include/detector/ObjectDetector.h b/src/include/detector/ObjectDetector.h new file mode 100644 index 0000000..d9e8260 --- /dev/null +++ b/src/include/detector/ObjectDetector.h @@ -0,0 +1,77 @@ +#pragma once + +#include +#include + +#if USE_OPENVINO +#include +#endif + +#include + +#include "util/QueuedFrame.h" +#include "util/QueuedObjectData.h" + +class ObjectDetector { + public: + /** + * @brief Constructs the ObjectDetector and loads the ONNX model. + * @param model_path The file path to the .onnx object detection model. + * @param class_names_path The file path to the list of class names. + * @param input_width Width of camera input + * @param input_height Height of camera input + * @parem input_width Width of input frame + */ + ObjectDetector(const std::string& model_path, + const std::string& class_names_path, const float input_width, + const float input_height); + + ~ObjectDetector(); + + // Disable copy and move semantics for resource safety. + ObjectDetector(const ObjectDetector&) = delete; + ObjectDetector& operator=(const ObjectDetector&) = delete; + + /** + * @brief Detects all objects in a given frame. + * @param frame The input image from the camera. + * @param observations A reference to the result object to be populated. + * @param final_boxes A vector to be populated with the cv::Rect for each + * observation. + * @param final_class_ids Class ID (based on the .names file) of the model + * @param final_confidences Confidence score for each detection. + */ + void detect(const QueuedFrame& frame, + std::vector& observations, + std::vector& final_boxes, + std::vector& final_class_ids, + std::vector& final_confidences); + + /** + * @brief Gets the class names used by the model. + * @return A constant reference to the vector of class name strings. + */ + const std::vector& getClassNames() const; + + private: + void logInfo(const std::string& message) const; + void logError(const std::string& message) const; + +#if USE_OPENVINO + ov::Core m_core; + ov::CompiledModel m_compiled_model; + ov::InferRequest m_infer_request; + ov::Shape m_input_shape; + ov::element::Type m_input_element_type; + +#else + cv::dnn::Net m_net; +#endif + std::vector m_class_names; + + // MODEL PARAMETERS + const float m_input_width = 640; + const float m_input_height = 640; + const float m_confidence_threshold = 0.5f; + const float m_nms_threshold = 0.4f; +}; diff --git a/src/include/estimator/CameraPoseEstimator.h b/src/include/estimator/CameraPoseEstimator.h index b0e91fe..8fd5b46 100644 --- a/src/include/estimator/CameraPoseEstimator.h +++ b/src/include/estimator/CameraPoseEstimator.h @@ -1,10 +1,7 @@ #pragma once #include -#include -#include -#include "fieldImages/fields/fields.h" #include "opencv2/opencv.hpp" #include "util/QueuedFiducialData.h" diff --git a/src/include/estimator/GamePiecePosEstimator.h b/src/include/estimator/GamePiecePosEstimator.h new file mode 100644 index 0000000..5c5d192 --- /dev/null +++ b/src/include/estimator/GamePiecePosEstimator.h @@ -0,0 +1,35 @@ +#pragma once + +#include + +#include +#include +#include + +struct GamePieceRow { + double centerX; + double centerY; + double width; + double height; + double xPosition; + double yPosition; + double angle; +}; + +class GamePiecePosEstimator { + public: + GamePiecePosEstimator(float imageWidth, float imageHeight, + const std::vector& data); + + std::optional> estimatePosition(double x, + double y, + double w, + double h) const; + + private: + float width; + float height; + std::vector data; + + static bool isClose(double a, double b, double tol); +}; diff --git a/src/include/estimator/ObjectEstimator.h b/src/include/estimator/ObjectEstimator.h new file mode 100644 index 0000000..8cb5dc8 --- /dev/null +++ b/src/include/estimator/ObjectEstimator.h @@ -0,0 +1,42 @@ +#pragma once + +#include + +#include "GamePiecePosEstimator.h" +#include "util/QueuedObjectData.h" + +/** + * @class ObjectEstimator + * @brief Calculates the viewing angles to the corners of a detected object + * and its distance from the camera. + * + * This class is analogous to the TagAngleCalculator and provides the necessary + * data for robot alignment and targeting. + */ +class ObjectEstimator { + public: + /** + * @brief Calculates angles and distances for all detected objects. + * + * For each object in the observation, this function: + * 1. Undistorts the 2D corner points of the bounding box. + * 2. Calculates the horizontal and vertical viewing angle to each corner. + * 3. Uses the known physical width of the object to estimate distance. + * 4. Populates the `corner_angles` vector in the provided observation. + * + * @param observation A reference to the object observation to be populated. + * @param cameraMatrix The camera's intrinsic matrix. + * @param distCoeffs The camera's distortion coefficients. + */ + void calculate(ObjDetectObservation& observation, const cv::Mat& cameraMatrix, + const cv::Mat& distCoeffs) const; + + static ObjectEstimator& GetInstance(float width, float height); + + private: + ObjectEstimator(float width, float height); // private + + static ObjectEstimator* instance; + + GamePiecePosEstimator posEstimator; +}; diff --git a/src/include/io/ConfigInterface.h b/src/include/io/ConfigInterface.h index 95bacdd..542443c 100644 --- a/src/include/io/ConfigInterface.h +++ b/src/include/io/ConfigInterface.h @@ -14,65 +14,22 @@ #include #include -/** - * @class ConfigInterface - * @brief Manages loading and providing configuration parameters from - * NetworkTables. - * - * This class subscribes to a specific NetworkTables table (e.g., - * "GompeiVision") to dynamically load and update camera and vision processing - * settings. It provides a centralized, in-memory cache of these settings for - * other parts of the application to use. - * - * When the "setup_mode" topic is true, this class can also be used to publish - * new values for the settings, allowing for live tuning. - */ class ConfigInterface { public: - /** - * @brief Constructs the ConfigInterface and initializes NetworkTables - * subscribers and publishers. - * - * On construction, it establishes connections to the relevant NetworkTables - * topics and performs an initial load of all configuration values. - */ explicit ConfigInterface(const std::string&, const nt::NetworkTableInstance& nt_inst); ~ConfigInterface(); - /** - * @brief Blocks until the initial configuration values have been received - * from NetworkTables. - */ void waitForInitialization(); - - /** - * @brief Fetches the latest values from all NetworkTables topics. - * - * This method should be called periodically (e.g., once per main loop - * iteration) to ensure the configuration remains up-to-date with any changes - * published by a client, such as a dashboard or the robot code. - */ void update(); // --- State Getters --- - - /** - * @brief Checks if the system is currently in setup mode. - * @return True if setup mode is active, false otherwise. - */ bool isSetupMode() const; // --- State Setters --- - - /** - * @brief sets the setup mode - * @param setupMode The state of the setup mode - */ void setSetupMode(bool setupMode); // --- Configuration Getters --- - std::string getRole() const; cv::Mat getCameraMatrix() const; cv::Mat getDistortionCoeffs() const; @@ -81,6 +38,7 @@ class ConfigInterface { int getWidth() const; int getHeight() const; bool getCompressed() const; + int getModelIndex() const; private: /** @@ -104,6 +62,7 @@ class ConfigInterface { nt::IntegerSubscriber m_widthSub; nt::IntegerSubscriber m_heightSub; nt::BooleanSubscriber m_compressedSub; + nt::IntegerSubscriber m_modelIndexSub; // --- In-Memory Configuration Cache --- // These members hold the last valid values received from NetworkTables. @@ -116,6 +75,7 @@ class ConfigInterface { int m_width; int m_height; bool m_compressed; + int m_modelIndex; // --- Threading and Synchronization for Initialization --- std::thread m_initThread; diff --git a/src/include/io/OutputPublisher.h b/src/include/io/OutputPublisher.h index 3159f6a..31c7acf 100644 --- a/src/include/io/OutputPublisher.h +++ b/src/include/io/OutputPublisher.h @@ -5,10 +5,10 @@ #include #include -#include #include #include "../util/QueuedFiducialData.h" +#include "../util/QueuedObjectData.h" /** * @class OutputPublisher @@ -24,6 +24,12 @@ class OutputPublisher { */ virtual void SendAprilTagResult(const AprilTagResult& result) = 0; + /** + * @brief Sends a complete object detection result packet. + * @param result The object detection result data for a single frame. + */ + virtual void SendObjectDetectResult(const ObjectDetectResult& result) = 0; + /** * @brief Sends the current camera connection status. * @param isConnected True if the camera is connected, false otherwise. @@ -42,12 +48,15 @@ class NTOutputPublisher final : public OutputPublisher { explicit NTOutputPublisher(const std::string_view hardware_id, const nt::NetworkTableInstance& nt_inst); void SendAprilTagResult(const AprilTagResult& result) override; + void SendObjectDetectResult(const ObjectDetectResult& result) override; void sendConnectionStatus(bool isConnected) override; void sendUSBSpeed(double speed) override; private: nt::DoubleArrayPublisher observations_pub_; + nt::DoubleArrayPublisher objects_pub_; nt::IntegerPublisher apriltags_fps_pub_; + nt::IntegerPublisher objects_fps_pub_; nt::BooleanPublisher connection_status_pub_; nt::DoublePublisher usb_speed_pub_; }; diff --git a/src/include/pipeline/ModelManager.h b/src/include/pipeline/ModelManager.h new file mode 100644 index 0000000..1cf50de --- /dev/null +++ b/src/include/pipeline/ModelManager.h @@ -0,0 +1,18 @@ +#pragma once + +#include +#include + +// Define a structure to hold the paths for a single model. +struct ObjectModel { + std::string modelPath; + std::string namesPath; +}; + +// Define the global, constant list of all available models. +inline const std::vector available_models = { + // Index 0: Your primary model for reefscape objects + {"/usr/local/share/GompeiVision/reefscape_yolo11n.onnx", + "/usr/local/share/GompeiVision/reefscape.names"}, + // To add a new model, just add a new line here. +}; \ No newline at end of file diff --git a/src/include/pipeline/Pipeline.h b/src/include/pipeline/Pipeline.h index 3927150..6db0fe9 100644 --- a/src/include/pipeline/Pipeline.h +++ b/src/include/pipeline/Pipeline.h @@ -1,10 +1,8 @@ #pragma once -#include #include #include -#include #include #include #include @@ -14,9 +12,12 @@ #include "capture/Camera.h" #include "cscore_cv.h" #include "detector/FiducialDetector.h" +#include "detector/ObjectDetector.h" #include "io/ConfigInterface.h" -#include "io/FieldInterface.h" +#include "util/AnnotationData.h" #include "util/QueuedFiducialData.h" +#include "util/QueuedFrame.h" +#include "util/QueuedObjectData.h" #include "util/ThreadSafeQueue.h" struct UdevContextDeleter { @@ -40,6 +41,9 @@ class Pipeline { private: void processing_loop(); void networktables_loop(); + void apriltag_detection_loop(); + void object_detection_loop(); + void annotation_loop(); std::atomic m_is_running{false}; @@ -57,20 +61,33 @@ class Pipeline { cv::Mat m_dist_coeffs; bool m_intrinsics_loaded = false; + int m_active_model_index = -1; + std::unique_ptr m_camera; std::unique_ptr m_mjpeg_server; std::unique_ptr m_cv_source; + std::unique_ptr m_annotated_mjpeg_server; + std::unique_ptr m_annotated_cv_source; + // --- Detectors --- FiducialDetector m_AprilTagDetector; + std::unique_ptr m_ObjectDetector; // --- Pipeline Data Flow --- ThreadSafeQueue m_estimated_poses; + ThreadSafeQueue m_object_detections; + ThreadSafeQueue m_frames_for_apriltag_detection; + ThreadSafeQueue m_frames_for_object_detection; + ThreadSafeQueue m_frames_for_annotation; // Threads & Control std::thread m_processing_thread; std::thread m_networktables_thread; + std::thread m_apriltag_thread; + std::thread m_object_detection_thread; + std::thread m_annotation_thread; // --- NetworkTables Interface --- std::unique_ptr m_config_interface; diff --git a/src/include/pipeline/PipelineHelper.h b/src/include/pipeline/PipelineHelper.h index 6159331..8929f9f 100644 --- a/src/include/pipeline/PipelineHelper.h +++ b/src/include/pipeline/PipelineHelper.h @@ -1,13 +1,9 @@ #pragma once -#include #include -#include -#include #include "frc/geometry/Pose3d.h" #include "io/ConfigInterface.h" -#include "nlohmann/json.hpp" class PipelineHelper { public: diff --git a/src/include/util/AnnotationData.h b/src/include/util/AnnotationData.h new file mode 100644 index 0000000..98db142 --- /dev/null +++ b/src/include/util/AnnotationData.h @@ -0,0 +1,18 @@ +#pragma once + +#include +#include + +#include "util/QueuedFrame.h" + +/** + * @struct AnnotationData + * @brief Holds all the necessary data for the annotation thread to draw on a + * frame. + */ +struct AnnotationData { + QueuedFrame q_frame; + std::vector boxes; + std::vector class_ids; + std::vector confidences; +}; \ No newline at end of file diff --git a/src/include/util/QueuedObjectData.h b/src/include/util/QueuedObjectData.h index 1dd26f0..004971b 100644 --- a/src/include/util/QueuedObjectData.h +++ b/src/include/util/QueuedObjectData.h @@ -1,5 +1,7 @@ #pragma once +#include + #include #include #include @@ -7,18 +9,25 @@ /** * @struct ObjDetectObservation * @brief Represents a single detected object in a frame. - * Corresponds to the Python 'ObjDetectObservation'. */ struct ObjDetectObservation { int obj_class; - double confidence; - std::vector corner_angles; - std::vector corner_pixels; -}; + float confidence; + std::vector corner_pixels; // tl, tr, br, bl (x, y) + frc::Pose2d pose; -// --- Main Result Data Packets --- -// These are the structs that will be placed into the ThreadSafeQueue -// for the NetworkTables thread to consume. + // Default constructor + ObjDetectObservation() = default; + + // MODIFIED: Constructor for efficient emplace_back in the detector + ObjDetectObservation(int cls, float conf, double tl_x, double tl_y, + double tr_x, double tr_y, double br_x, double br_y, + double bl_x, double bl_y, frc::Pose2d pose) + : obj_class(cls), confidence(conf), pose(pose) { + // The vector is now initialized inside the constructor body + corner_pixels = {tl_x, tl_y, tr_x, tr_y, br_x, br_y, bl_x, bl_y}; + } +}; /** * @struct ObjectDetectResult