diff --git a/models/person_detection_mediapipe/CMakeLists.txt b/models/person_detection_mediapipe/CMakeLists.txt new file mode 100644 index 00000000..aeb552a8 --- /dev/null +++ b/models/person_detection_mediapipe/CMakeLists.txt @@ -0,0 +1,29 @@ +cmake_minimum_required(VERSION 3.24.0) +set(project_name "opencv_zoo_person_detection_mediapipe") + +PROJECT (${project_name}) + +set(OPENCV_VERSION "4.7.0") +set(OPENCV_INSTALLATION_PATH "" CACHE PATH "Where to look for OpenCV installation") +find_package(OpenCV ${OPENCV_VERSION} REQUIRED HINTS ${OPENCV_INSTALLATION_PATH}) +# Find OpenCV, you may need to set OpenCV_DIR variable +# to the absolute path to the directory containing OpenCVConfig.cmake file +# via the command line or GUI + +file(GLOB SourceFile + "demo.cpp") +# If the package has been found, several variables will +# be set, you can find the full list with descriptions +# in the OpenCVConfig.cmake file. +# Print some message showing some of them +message(STATUS "OpenCV library status:") +message(STATUS " config: ${OpenCV_DIR}") +message(STATUS " version: ${OpenCV_VERSION}") +message(STATUS " libraries: ${OpenCV_LIBS}") +message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}") + +# Declare the executable target built from your sources +add_executable(${project_name} ${SourceFile}) + +# Link your application with OpenCV libraries +target_link_libraries(${project_name} PRIVATE ${OpenCV_LIBS}) diff --git a/models/person_detection_mediapipe/README.md b/models/person_detection_mediapipe/README.md index a3b80472..e09ab4fc 100644 --- a/models/person_detection_mediapipe/README.md +++ b/models/person_detection_mediapipe/README.md @@ -9,6 +9,8 @@ SSD Anchors are generated from [GenMediaPipePalmDectionSSDAnchors](https://githu ## Demo +### Python + Run the following commands to try the demo: ```bash @@ -21,6 +23,23 @@ python demo.py -i /path/to/image -v python demo.py --help ``` +### C++ + +Install latest OpenCV 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/opencv_zoo_person_detection_mediapipe +# detect on an image +./build/opencv_zoo_person_detection_mediapipe -m=/path/to/model -i=/path/to/image -v +# get help messages +./build/opencv_zoo_person_detection_mediapipe -h +``` + ### Example outputs ![webcam demo](./example_outputs/mppersondet_demo.webp) diff --git a/models/person_detection_mediapipe/demo.cpp b/models/person_detection_mediapipe/demo.cpp new file mode 100644 index 00000000..0e4f0378 --- /dev/null +++ b/models/person_detection_mediapipe/demo.cpp @@ -0,0 +1,2531 @@ +#include +#include +#include + +#include + +using namespace std; +using namespace cv; +using namespace dnn; + +vector< pair > backendTargetPairs = { + std::make_pair(dnn::DNN_BACKEND_OPENCV, dnn::DNN_TARGET_CPU), + std::make_pair(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA), + std::make_pair(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA_FP16), + std::make_pair(dnn::DNN_BACKEND_TIMVX, dnn::DNN_TARGET_NPU), + std::make_pair(dnn::DNN_BACKEND_CANN, dnn::DNN_TARGET_NPU) }; + + +Mat getMediapipeAnchor(); + +class MPPersonDet { +private: + Net net; + string modelPath; + Size inputSize; + float scoreThreshold; + float nmsThreshold; + dnn::Backend backendId; + dnn::Target targetId; + int topK; + Mat anchors; + +public: + MPPersonDet(string modPath, float nmsThresh = 0.5, float scoreThresh = 0.3, int tok = 1, dnn::Backend bId = DNN_BACKEND_DEFAULT, dnn::Target tId = DNN_TARGET_CPU) : + modelPath(modPath), nmsThreshold(nmsThresh), + scoreThreshold(scoreThresh), topK(tok), + backendId(bId), targetId(tId) + { + this->inputSize = Size(224, 224); + this->net = readNet(this->modelPath); + this->net.setPreferableBackend(this->backendId); + this->net.setPreferableTarget(this->targetId); + this->anchors = getMediapipeAnchor(); + } + + void setBackendAndTarget(dnn::Backend bId, dnn::Target tId) + { + this->backendId = bId; + this->targetId = tId; + this->net.setPreferableBackend(this->backendId); + this->net.setPreferableTarget(this->targetId); + } + + pair preprocess(Mat img) + { + Mat blob; + Image2BlobParams paramMediapipe; + paramMediapipe.datalayout = DNN_LAYOUT_NCHW; + paramMediapipe.ddepth = CV_32F; + paramMediapipe.mean = Scalar::all(127.5); + paramMediapipe.scalefactor = Scalar::all(1 / 127.5); + paramMediapipe.size = this->inputSize; + paramMediapipe.swapRB = true; + paramMediapipe.paddingmode = DNN_PMODE_LETTERBOX; + + double ratio = min(this->inputSize.height / double(img.rows), this->inputSize.width / double(img.cols)); + Size padBias(0, 0); + if (img.rows != this->inputSize.height || img.cols != this->inputSize.width) + { + // keep aspect ratio when resize + Size ratioSize(int(img.cols * ratio), int(img.rows * ratio)); + int padH = this->inputSize.height - ratioSize.height; + int padW = this->inputSize.width - ratioSize.width; + padBias.width = padW / 2; + padBias.height = padH / 2; + } + blob = blobFromImageWithParams(img, paramMediapipe); + padBias = Size(int(padBias.width / ratio), int(padBias.height / ratio)); + return pair(blob, padBias); + } + + Mat infer(Mat srcimg) + { + pair w = this->preprocess(srcimg); + Mat inputBlob = get<0>(w); + Size padBias = get<1>(w); + this->net.setInput(inputBlob); + vector outs; + this->net.forward(outs, this->net.getUnconnectedOutLayersNames()); + Mat predictions = this->postprocess(outs, Size(srcimg.cols, srcimg.rows), padBias); + return predictions; + } + + Mat postprocess(vector outputs, Size orgSize, Size padBias) + { + Mat score = outputs[1].reshape(0, outputs[1].size[0]); + Mat boxLandDelta = outputs[0].reshape(outputs[0].size[0], outputs[0].size[1]); + Mat boxDelta = boxLandDelta.colRange(0, 4); + Mat landmarkDelta = boxLandDelta.colRange(4, boxLandDelta.cols); + double scale = max(orgSize.height, orgSize.width); + Mat mask = score < -100; + score.setTo(-100, mask); + mask = score > 100; + score.setTo(100, mask); + Mat deno; + exp(-score, deno); + divide(1.0, 1 + deno, score); + boxDelta.colRange(0, 1) = boxDelta.colRange(0, 1) / this->inputSize.width; + boxDelta.colRange(1, 2) = boxDelta.colRange(1, 2) / this->inputSize.height; + boxDelta.colRange(2, 3) = boxDelta.colRange(2, 3) / this->inputSize.width; + boxDelta.colRange(3, 4) = boxDelta.colRange(3, 4) / this->inputSize.height; + Mat xy1 = (boxDelta.colRange(0, 2) - boxDelta.colRange(2, 4) / 2 + this->anchors) * scale; + Mat xy2 = (boxDelta.colRange(0, 2) + boxDelta.colRange(2, 4) / 2 + this->anchors) * scale; + Mat boxes; + hconcat(xy1, xy2, boxes); + vector< Rect2d > rotBoxes(boxes.rows); + boxes.colRange(0, 1) = boxes.colRange(0, 1) - padBias.width; + boxes.colRange(1, 2) = boxes.colRange(1, 2) - padBias.height; + boxes.colRange(2, 3) = boxes.colRange(2, 3) - padBias.width; + boxes.colRange(3, 4) = boxes.colRange(3, 4) - padBias.height; + for (int i = 0; i < boxes.rows; i++) + { + rotBoxes[i] = Rect2d(Point2d(boxes.at(i, 0), boxes.at(i, 1)), Point2d(boxes.at(i, 2), boxes.at(i, 3))); + } + vector< int > keep; + NMSBoxes(rotBoxes, score, this->scoreThreshold, this->nmsThreshold, keep, this->topK); + if (keep.size() == 0) + return Mat(); + int nbCols = landmarkDelta.cols + boxes.cols + 1; + Mat candidates(int(keep.size()), nbCols, CV_32FC1); + int row = 0; + for (auto idx : keep) + { + candidates.at(row, nbCols - 1) = score.at(idx); + boxes.row(idx).copyTo(candidates.row(row).colRange(0, 4)); + candidates.at(row, 4) = (landmarkDelta.at(idx, 0) / this->inputSize.width + this->anchors.at(idx, 0)) * scale - padBias.width; + candidates.at(row, 5) = (landmarkDelta.at(idx, 1) / this->inputSize.height + this->anchors.at(idx, 1)) * scale - padBias.height; + candidates.at(row, 6) = (landmarkDelta.at(idx, 2) / this->inputSize.width + this->anchors.at(idx, 0)) * scale - padBias.width; + candidates.at(row, 7) = (landmarkDelta.at(idx, 3) / this->inputSize.height + this->anchors.at(idx, 1)) * scale - padBias.height; + candidates.at(row, 8) = (landmarkDelta.at(idx, 4) / this->inputSize.width + this->anchors.at(idx, 0)) * scale - padBias.width; + candidates.at(row, 9) = (landmarkDelta.at(idx, 5) / this->inputSize.height + this->anchors.at(idx, 1)) * scale - padBias.height; + candidates.at(row, 10) = (landmarkDelta.at(idx, 6) / this->inputSize.width + this->anchors.at(idx, 0)) * scale - padBias.width; + candidates.at(row, 11) = (landmarkDelta.at(idx, 7) / this->inputSize.height + this->anchors.at(idx, 1)) * scale - padBias.height; + row++; + } + return candidates; + + } + + +}; +std::string keys = +"{ help h | | Print help message. }" +"{ model m | person_detection_mediapipe_2023mar.onnx | Usage: Path to the model, defaults to person_detection_mediapipe_2023mar.onnx }" +"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}" +"{ score_threshold | 0.5 | Usage: Set the minimum needed confidence for the model to identify a person, defaults to 0.5. Smaller values may result in faster detection, but will limit accuracy. Filter out persons of confidence < conf_threshold. }" +"{ nms_threshold | 0.3 | Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3. }" +"{ top_k | 1 | Usage: Keep top_k bounding boxes before NMS. }" +"{ save s | 0 | Usage: Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input. }" +"{ vis v | 1 | Usage: Specify to open a new window to show results. Invalid in case of camera input. }" +"{ backend bt | 0 | Choose one of computation backends: " +"0: (default) OpenCV implementation + CPU, " +"1: CUDA + GPU (CUDA), " +"2: CUDA + GPU (CUDA FP16), " +"3: TIM-VX + NPU, " +"4: CANN + NPU}"; + +Mat visualize(Mat img, Mat results, double fps = -1) +{ + Mat resImg = img.clone(); + if (fps > 0) + putText(resImg, format("FPS: %2f", fps), Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255)); + + for (int row = 0; row < results.rows; row++) + { + float score = results.at(row, results.cols - 1); + Mat personLandmarks; + results.row(row).colRange(4, results.cols - 1).reshape(0, 4).convertTo(personLandmarks, CV_32S); + + Point hipPoint = Point(personLandmarks.row(0)); + Point fullBody = Point(personLandmarks.row(1)); + Point shoulderPoint = Point(personLandmarks.row(2)); + Point upperBody = Point(personLandmarks.row(3)); + + // draw circle for full body + int radius = int(norm(hipPoint - fullBody)); + circle(resImg, hipPoint, radius, Scalar(255, 0, 0), 2); + + // draw circle for upper body + radius = int(norm(shoulderPoint - upperBody)); + circle(resImg, shoulderPoint, radius, Scalar(0, 255, 255), 2); + + // draw points for each keypoint + for (int iRow=0; iRow < personLandmarks.rows; iRow++) + circle(resImg, Point(personLandmarks.row(iRow)), 2, Scalar(0, 0, 255), 2); + putText(resImg, format("Score: %4f", score), Point(0, resImg.rows - 48), FONT_HERSHEY_DUPLEX, 0.5, Scalar(0, 255, 0)); + } + // put score + putText(resImg, string("Yellow: upper body circle"), Point(0, resImg.rows - 36), FONT_HERSHEY_DUPLEX, 0.5, Scalar(0, 255, 255)); + putText(resImg, string("Blue: full body circle"), Point(0, resImg.rows - 24), FONT_HERSHEY_DUPLEX, 0.5, Scalar(255, 0, 0)); + putText(resImg, string("Red: keypoint"), Point(0, resImg.rows - 12), FONT_HERSHEY_DUPLEX, 0.5, Scalar(0, 0, 255)); + + return resImg; +} + +int main(int argc, char** argv) +{ + CommandLineParser parser(argc, argv, keys); + + parser.about("Person Detector from MediaPipe"); + if (parser.has("help")) + { + parser.printMessage(); + return 0; + } + + string model = parser.get("model"); + float scoreThreshold = parser.get("score_threshold"); + float nmsThreshold = parser.get("nms_threshold"); + int topK = parser.get("top_k"); + bool vis = parser.get("vis"); + bool save = parser.get("save"); + int backendTargetid = parser.get("backend"); + + if (model.empty()) + { + CV_Error(Error::StsError, "Model file " + model + " not found"); + } + VideoCapture cap; + if (parser.has("input")) + cap.open(samples::findFile(parser.get("input"))); + else + cap.open(0); + Mat frame; + + MPPersonDet modelNet(model, nmsThreshold, scoreThreshold, topK, + backendTargetPairs[backendTargetid].first, backendTargetPairs[backendTargetid].second); + //! [Open a video file or an image file or a camera stream] + if (!cap.isOpened()) + CV_Error(Error::StsError, "Cannot opend video or file"); + + static const std::string kWinName = "MPPersonDet Demo"; + int nbInference = 0; + while (waitKey(1) < 0) + { + cap >> frame; + if (frame.empty()) + { + cout << "Frame is empty" << endl; + waitKey(); + break; + } + TickMeter tm; + tm.start(); + Mat results = modelNet.infer(frame); + tm.stop(); + cout << "Inference time: " << tm.getTimeMilli() << " ms\n"; + Mat img = visualize(frame, results, tm.getFPS()); + if (save && parser.has("input")) + { + cout << "Results saved to result.jpg\n"; + imwrite("result.jpg", img); + } + + if (vis || !parser.has("input")) + { + imshow(kWinName, img); + } + } + return 0; +} + + +Mat getMediapipeAnchor() +{ + Mat anchor= (Mat_(2254,2) << 0.017857142857142856, 0.017857142857142856, + 0.017857142857142856, 0.017857142857142856, + 0.05357142857142857, 0.017857142857142856, + 0.05357142857142857, 0.017857142857142856, + 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