diff --git a/models/palm_detection_mediapipe/CMakeLists.txt b/models/palm_detection_mediapipe/CMakeLists.txt new file mode 100644 index 00000000..823d7e17 --- /dev/null +++ b/models/palm_detection_mediapipe/CMakeLists.txt @@ -0,0 +1,11 @@ +cmake_minimum_required(VERSION 3.24.0) +project(opencv_zoo_qrcode_wechatqrcode) + +set(OPENCV_VERSION "4.10.0") +set(OPENCV_INSTALLATION_PATH "" CACHE PATH "Where to look for OpenCV installation") + +# Find OpenCV +find_package(OpenCV ${OPENCV_VERSION} REQUIRED HINTS ${OPENCV_INSTALLATION_PATH}) + +add_executable(demo demo.cpp) +target_link_libraries(demo ${OpenCV_LIBS}) diff --git a/models/palm_detection_mediapipe/README.md b/models/palm_detection_mediapipe/README.md index 22a53ced..ff889c13 100644 --- a/models/palm_detection_mediapipe/README.md +++ b/models/palm_detection_mediapipe/README.md @@ -13,6 +13,8 @@ SSD Anchors are generated from [GenMediaPipePalmDectionSSDAnchors](https://githu ## Demo +### Python + Run the following commands to try the demo: ```bash @@ -25,6 +27,23 @@ python demo.py -i /path/to/image -v python demo.py --help ``` +### C++ + +Install latest OpenCV (with opencv_contrib) and CMake >= 3.24.0 to get started with: + +```shell +# A typical and default installation path of OpenCV is /usr/local +cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation . +cmake --build build + +# detect on camera input +./build/demo +# detect on an image +./build/demo -i=/path/to/image -v +# get help messages +./build/demo -h +``` + ### Example outputs ![webcam demo](./example_outputs/mppalmdet_demo.gif) diff --git a/models/palm_detection_mediapipe/demo.cpp b/models/palm_detection_mediapipe/demo.cpp new file mode 100644 index 00000000..96bd07ee --- /dev/null +++ b/models/palm_detection_mediapipe/demo.cpp @@ -0,0 +1,2379 @@ +#include +#include +#include +#include +#include + +const std::vector> + backend_target_pairs = { + {cv::dnn::DNN_BACKEND_OPENCV, cv::dnn::DNN_TARGET_CPU}, + {cv::dnn::DNN_BACKEND_CUDA, cv::dnn::DNN_TARGET_CUDA}, + {cv::dnn::DNN_BACKEND_CUDA, cv::dnn::DNN_TARGET_CUDA_FP16}, + {cv::dnn::DNN_BACKEND_TIMVX, cv::dnn::DNN_TARGET_NPU}, + {cv::dnn::DNN_BACKEND_CANN, cv::dnn::DNN_TARGET_NPU}}; + +class MPPalmDet { + private: + std::string model_path; + float nms_threshold; + float score_threshold; + int topK; + int backend_id; + int target_id; + cv::Size input_size; + cv::dnn::Net model; + std::vector anchors; + + std::vector loadAnchors(); + + std::pair preprocess(const cv::Mat& image) { + cv::Point2i pad_bias(0, 0); + float ratio = + std::min(static_cast(input_size.width) / image.cols, + static_cast(input_size.height) / image.rows); + + cv::Mat processed_image; + if (image.rows != input_size.height || image.cols != input_size.width) { + cv::Size ratio_size(static_cast(image.cols * ratio), + static_cast(image.rows * ratio)); + cv::resize(image, processed_image, ratio_size); + + int pad_h = input_size.height - ratio_size.height; + int pad_w = input_size.width - ratio_size.width; + pad_bias.x = pad_w / 2; + pad_bias.y = pad_h / 2; + + cv::copyMakeBorder(processed_image, processed_image, pad_bias.y, + pad_h - pad_bias.y, pad_bias.x, + pad_w - pad_bias.x, cv::BORDER_CONSTANT, + cv::Scalar(0, 0, 0)); + } else { + processed_image = image.clone(); + } + + // Create blob with correct parameters + cv::Mat blob; + cv::dnn::Image2BlobParams params; + params.datalayout = cv::dnn::DNN_LAYOUT_NHWC; + params.ddepth = CV_32F; + params.mean = cv::Scalar::all(0); + params.scalefactor = cv::Scalar::all(1.0 / 255.0); + params.size = input_size; + params.swapRB = true; + params.paddingmode = cv::dnn::DNN_PMODE_NULL; + + blob = cv::dnn::blobFromImageWithParams(processed_image, params); + + pad_bias.x = static_cast(pad_bias.x / ratio); + pad_bias.y = static_cast(pad_bias.y / ratio); + + return {blob, pad_bias}; + } + + std::vector> postprocess( + const std::vector& output_blobs, const cv::Size& original_size, + const cv::Point2i& pad_bias) { + cv::Mat scores = + output_blobs[1].reshape(1, output_blobs[1].total() / 1); + cv::Mat boxes = + output_blobs[0].reshape(1, output_blobs[0].total() / 18); + + std::vector score_vec; + std::vector boxes_vec; + std::vector> landmarks_vec; + + float scale = std::max(original_size.height, original_size.width); + + // Process all detections first + for (int i = 0; i < scores.rows; i++) { + float score = 1.0f / (1.0f + std::exp(-scores.at(i, 0))); + + // Extract box and landmark deltas + cv::Mat box_delta = boxes.row(i).colRange(0, 4); + cv::Mat landmark_delta = boxes.row(i).colRange(4, 18); + cv::Point2f anchor = anchors[i]; + + // Normalize box deltas by input size + cv::Point2f cxy_delta(box_delta.at(0) / input_size.width, + box_delta.at(1) / input_size.height); + cv::Point2f wh_delta(box_delta.at(2) / input_size.width, + box_delta.at(3) / input_size.height); + + // Calculate box coordinates (scale first, then subtract pad_bias) + cv::Point2f xy1( + (cxy_delta.x - wh_delta.x / 2 + anchor.x) * scale - pad_bias.x, + (cxy_delta.y - wh_delta.y / 2 + anchor.y) * scale - pad_bias.y); + cv::Point2f xy2( + (cxy_delta.x + wh_delta.x / 2 + anchor.x) * scale - pad_bias.x, + (cxy_delta.y + wh_delta.y / 2 + anchor.y) * scale - pad_bias.y); + + if (score > score_threshold) { + score_vec.push_back(score); + boxes_vec.push_back( + cv::Rect2f(xy1.x, xy1.y, xy2.x - xy1.x, xy2.y - xy1.y)); + + // Process landmarks + std::vector landmarks; + for (int j = 0; j < 7; j++) { + // Normalize by input size + float dx = + landmark_delta.at(j * 2) / input_size.width; + float dy = + landmark_delta.at(j * 2 + 1) / input_size.height; + + // Add anchor + dx += anchor.x; + dy += anchor.y; + + // Scale and subtract pad_bias in one step + dx = dx * scale - pad_bias.x; + dy = dy * scale - pad_bias.y; + + landmarks.push_back(cv::Point2f(dx, dy)); + } + landmarks_vec.push_back(landmarks); + } + } + + // Perform NMS + std::vector indices; + std::vector boxes_int; + for (const auto& box : boxes_vec) { + boxes_int.push_back(cv::Rect( + static_cast(box.x), static_cast(box.y), + static_cast(box.width), static_cast(box.height))); + } + cv::dnn::NMSBoxes(boxes_int, score_vec, score_threshold, nms_threshold, + indices); + + // Prepare results + std::vector> results; + for (int idx : indices) { + std::vector result; + result.push_back(boxes_vec[idx].x); + result.push_back(boxes_vec[idx].y); + result.push_back(boxes_vec[idx].x + boxes_vec[idx].width); + result.push_back(boxes_vec[idx].y + boxes_vec[idx].height); + + for (const auto& point : landmarks_vec[idx]) { + result.push_back(point.x); + result.push_back(point.y); + } + result.push_back(score_vec[idx]); + results.push_back(result); + } + + return results; + } + + public: + MPPalmDet(const std::string& modelPath, float nmsThreshold = 0.3f, + float scoreThreshold = 0.5f, int topK = 5000, + int backendId = cv::dnn::DNN_BACKEND_DEFAULT, + int targetId = cv::dnn::DNN_TARGET_CPU) + : model_path(modelPath), + nms_threshold(nmsThreshold), + score_threshold(scoreThreshold), + topK(topK), + backend_id(backendId), + target_id(targetId), + input_size(192, 192) { + model = cv::dnn::readNet(model_path); + model.setPreferableBackend(backend_id); + model.setPreferableTarget(target_id); + anchors = loadAnchors(); + } + + void setBackendAndTarget(int backendId, int targetId) { + backend_id = backendId; + target_id = targetId; + model.setPreferableBackend(backend_id); + model.setPreferableTarget(target_id); + } + + std::vector> infer(const cv::Mat& image) { + std::pair preprocess_result = preprocess(image); + cv::Mat preprocessed_image = preprocess_result.first; + cv::Point2i pad_bias = preprocess_result.second; + model.setInput(preprocessed_image); + std::vector outputs; + model.forward(outputs, model.getUnconnectedOutLayersNames()); + return postprocess(outputs, image.size(), pad_bias); + } +}; + +cv::Mat visualize(const cv::Mat& image, + const std::vector>& results, + bool print_results = false, float fps = 0.0f) { + cv::Mat output = image.clone(); + + if (fps > 0) { + cv::putText(output, cv::format("FPS: %.2f", fps), cv::Point(0, 15), + cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 255)); + } + + for (size_t i = 0; i < results.size(); i++) { + const std::vector& result = results[i]; + float score = result.back(); + + // Draw box - using direct coordinates + cv::rectangle( + output, + cv::Point(static_cast(result[0]), static_cast(result[1])), + cv::Point(static_cast(result[2]), static_cast(result[3])), + cv::Scalar(0, 255, 0), 2); + + // Put score - using first coordinate of box + cv::putText(output, cv::format("%.4f", score), + cv::Point(static_cast(result[0]), + static_cast(result[1]) + 12), + cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar(0, 255, 0)); + + // Draw landmarks + for (size_t j = 0; j < 7; j++) { + cv::Point point(static_cast(result[4 + j * 2]), + static_cast(result[4 + j * 2 + 1])); + cv::circle(output, point, 2, cv::Scalar(0, 0, 255), 2); + } + + if (print_results) { + std::cout << "-----------palm " << i + 1 << "-----------\n"; + std::cout << "score: " << score << "\n"; + std::cout << "palm box: [" << result[0] << ", " << result[1] << ", " + << result[2] << ", " << result[3] << "]\n"; + std::cout << "palm landmarks:\n"; + for (size_t j = 0; j < 7; j++) { + std::cout << "\t(" << result[4 + j * 2] << ", " + << result[4 + j * 2 + 1] << ")\n"; + } + } + } + + return output; +} + +int main(int argc, char** argv) { + cv::CommandLineParser parser( + argc, argv, + "{help h usage ? | | print this message }" + "{input i | | path to input image }" + "{model m | palm_detection_mediapipe_2023feb.onnx | path to " + "model file }" + "{backend_target bt | 0 | backend-target pair (0:OpenCV CPU, 1:CUDA, " + "2:CUDA FP16, 3:TIM-VX NPU, 4:CANN NPU) }" + "{score_threshold | 0.8 | minimum confidence threshold }" + "{nms_threshold | 0.3 | NMS threshold }" + "{save s | | save results to file }" + "{vis v | | visualize results }"); + + if (parser.has("help")) { + parser.printMessage(); + return 0; + } + + int backend_target = parser.get("backend_target"); + if (backend_target < 0 || backend_target >= backend_target_pairs.size()) { + std::cerr << "Error: Invalid backend_target value" << std::endl; + return -1; + } + + int backend_id = backend_target_pairs[backend_target].first; + int target_id = backend_target_pairs[backend_target].second; + + // Create detector directly + MPPalmDet detector(parser.get("model"), + parser.get("nms_threshold"), + parser.get("score_threshold"), + 5000, // topK + backend_id, target_id); + + // Process image if input is provided + if (parser.has("input")) { + cv::Mat image = cv::imread(parser.get("input")); + if (image.empty()) { + std::cerr << "Error: Could not read image: " + << parser.get("input") << std::endl; + return -1; + } + + std::vector> results = detector.infer(image); + if (results.empty()) { + std::cout << "Hand not detected" << std::endl; + } + + cv::Mat output = visualize(image, results, true); + + if (parser.has("save")) { + cv::imwrite("result.jpg", output); + std::cout << "Results saved to result.jpg\n" << std::endl; + } + + if (parser.has("vis")) { + cv::namedWindow(parser.get("input"), + cv::WINDOW_AUTOSIZE); + cv::imshow(parser.get("input"), output); + cv::waitKey(0); + } + } + // Process camera feed if no input image + else { + cv::VideoCapture cap(0); + if (!cap.isOpened()) { + std::cerr << "Error: Could not open camera" << std::endl; + return -1; + } + + std::chrono::steady_clock::time_point start_time = + std::chrono::steady_clock::now(); + int frame_count = 0; + + while (true) { + cv::Mat frame; + cap >> frame; + if (frame.empty()) { + std::cout << "No frames grabbed!" << std::endl; + break; + } + + std::vector> results = detector.infer(frame); + frame_count++; + + std::chrono::steady_clock::time_point current_time = + std::chrono::steady_clock::now(); + float fps = + frame_count / (std::chrono::duration_cast( + current_time - start_time) + .count() + + 1); + + cv::Mat output = visualize(frame, results, false, fps); + cv::imshow("MPPalmDet Demo", output); + + if (cv::waitKey(1) >= 0) break; + } + } + + return 0; +} + +std::vector MPPalmDet::loadAnchors() { + std::vector anchors = { +{0.02083333f, 0.02083333f}, + {0.02083333f, 0.02083333f}, + {0.06250000f, 0.02083333f}, + {0.06250000f, 0.02083333f}, + {0.10416666f, 0.02083333f}, + {0.10416666f, 0.02083333f}, + {0.14583333f, 0.02083333f}, + {0.14583333f, 0.02083333f}, + {0.18750000f, 0.02083333f}, + {0.18750000f, 0.02083333f}, + {0.22916667f, 0.02083333f}, + {0.22916667f, 0.02083333f}, + {0.27083334f, 0.02083333f}, + {0.27083334f, 0.02083333f}, + {0.31250000f, 0.02083333f}, + {0.31250000f, 0.02083333f}, + {0.35416666f, 0.02083333f}, + {0.35416666f, 0.02083333f}, + {0.39583334f, 0.02083333f}, + {0.39583334f, 0.02083333f}, + {0.43750000f, 0.02083333f}, + {0.43750000f, 0.02083333f}, + {0.47916666f, 0.02083333f}, + {0.47916666f, 0.02083333f}, + {0.52083330f, 0.02083333f}, + {0.52083330f, 0.02083333f}, + {0.56250000f, 0.02083333f}, + {0.56250000f, 0.02083333f}, + {0.60416670f, 0.02083333f}, + {0.60416670f, 0.02083333f}, + {0.64583330f, 0.02083333f}, + {0.64583330f, 0.02083333f}, + {0.68750000f, 0.02083333f}, + {0.68750000f, 0.02083333f}, + {0.72916670f, 0.02083333f}, + {0.72916670f, 0.02083333f}, + {0.77083330f, 0.02083333f}, + {0.77083330f, 0.02083333f}, + {0.81250000f, 0.02083333f}, + {0.81250000f, 0.02083333f}, + {0.85416670f, 0.02083333f}, + {0.85416670f, 0.02083333f}, + {0.89583330f, 0.02083333f}, + {0.89583330f, 0.02083333f}, + {0.93750000f, 0.02083333f}, + {0.93750000f, 0.02083333f}, + {0.97916670f, 0.02083333f}, + {0.97916670f, 0.02083333f}, + {0.02083333f, 0.06250000f}, + {0.02083333f, 0.06250000f}, + {0.06250000f, 0.06250000f}, + {0.06250000f, 0.06250000f}, + {0.10416666f, 0.06250000f}, + {0.10416666f, 0.06250000f}, + {0.14583333f, 0.06250000f}, + {0.14583333f, 0.06250000f}, + {0.18750000f, 0.06250000f}, + {0.18750000f, 0.06250000f}, + {0.22916667f, 0.06250000f}, + {0.22916667f, 0.06250000f}, + {0.27083334f, 0.06250000f}, + {0.27083334f, 0.06250000f}, + {0.31250000f, 0.06250000f}, + {0.31250000f, 0.06250000f}, + {0.35416666f, 0.06250000f}, + {0.35416666f, 0.06250000f}, + {0.39583334f, 0.06250000f}, + {0.39583334f, 0.06250000f}, + {0.43750000f, 0.06250000f}, + {0.43750000f, 0.06250000f}, + {0.47916666f, 0.06250000f}, + {0.47916666f, 0.06250000f}, + {0.52083330f, 0.06250000f}, + {0.52083330f, 0.06250000f}, + {0.56250000f, 0.06250000f}, + {0.56250000f, 0.06250000f}, + {0.60416670f, 0.06250000f}, + {0.60416670f, 0.06250000f}, + {0.64583330f, 0.06250000f}, + {0.64583330f, 0.06250000f}, + {0.68750000f, 0.06250000f}, + {0.68750000f, 0.06250000f}, + {0.72916670f, 0.06250000f}, + {0.72916670f, 0.06250000f}, + {0.77083330f, 0.06250000f}, + {0.77083330f, 0.06250000f}, + {0.81250000f, 0.06250000f}, + {0.81250000f, 0.06250000f}, + {0.85416670f, 0.06250000f}, + {0.85416670f, 0.06250000f}, + {0.89583330f, 0.06250000f}, + {0.89583330f, 0.06250000f}, + {0.93750000f, 0.06250000f}, + {0.93750000f, 0.06250000f}, + {0.97916670f, 0.06250000f}, + {0.97916670f, 0.06250000f}, + {0.02083333f, 0.10416666f}, + {0.02083333f, 0.10416666f}, + {0.06250000f, 0.10416666f}, + {0.06250000f, 0.10416666f}, + {0.10416666f, 0.10416666f}, + {0.10416666f, 0.10416666f}, + {0.14583333f, 0.10416666f}, + {0.14583333f, 0.10416666f}, + {0.18750000f, 0.10416666f}, + {0.18750000f, 0.10416666f}, + {0.22916667f, 0.10416666f}, + {0.22916667f, 0.10416666f}, + {0.27083334f, 0.10416666f}, + {0.27083334f, 0.10416666f}, + {0.31250000f, 0.10416666f}, + {0.31250000f, 0.10416666f}, + {0.35416666f, 0.10416666f}, + {0.35416666f, 0.10416666f}, + {0.39583334f, 0.10416666f}, + {0.39583334f, 0.10416666f}, + {0.43750000f, 0.10416666f}, + {0.43750000f, 0.10416666f}, + {0.47916666f, 0.10416666f}, + {0.47916666f, 0.10416666f}, + {0.52083330f, 0.10416666f}, + {0.52083330f, 0.10416666f}, + {0.56250000f, 0.10416666f}, + {0.56250000f, 0.10416666f}, + {0.60416670f, 0.10416666f}, + {0.60416670f, 0.10416666f}, + {0.64583330f, 0.10416666f}, + {0.64583330f, 0.10416666f}, + {0.68750000f, 0.10416666f}, + {0.68750000f, 0.10416666f}, + {0.72916670f, 0.10416666f}, + {0.72916670f, 0.10416666f}, + {0.77083330f, 0.10416666f}, + {0.77083330f, 0.10416666f}, + {0.81250000f, 0.10416666f}, + {0.81250000f, 0.10416666f}, + {0.85416670f, 0.10416666f}, + {0.85416670f, 0.10416666f}, + {0.89583330f, 0.10416666f}, + {0.89583330f, 0.10416666f}, + {0.93750000f, 0.10416666f}, + {0.93750000f, 0.10416666f}, + {0.97916670f, 0.10416666f}, + {0.97916670f, 0.10416666f}, + {0.02083333f, 0.14583333f}, + {0.02083333f, 0.14583333f}, + {0.06250000f, 0.14583333f}, + {0.06250000f, 0.14583333f}, + {0.10416666f, 0.14583333f}, + {0.10416666f, 0.14583333f}, + {0.14583333f, 0.14583333f}, + {0.14583333f, 0.14583333f}, + {0.18750000f, 0.14583333f}, + {0.18750000f, 0.14583333f}, + {0.22916667f, 0.14583333f}, + {0.22916667f, 0.14583333f}, + {0.27083334f, 0.14583333f}, + {0.27083334f, 0.14583333f}, + {0.31250000f, 0.14583333f}, + 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