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Merge pull request #67 from luxonis/gen2_yolov4_tiny_demo
add (demo, test) added demo code and the test
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144 changes: 144 additions & 0 deletions
144
examples/src/tiny_yolo_v4_device_side_decoding_example.cpp
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| #include <cstdio> | ||
| #include <iostream> | ||
| #include <chrono> | ||
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| #include "utility.hpp" | ||
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| // Inludes common necessary includes for development using depthai library | ||
| #include "depthai/depthai.hpp" | ||
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| /* | ||
| The code is the same as for Tiny-yolo-V3, the only difference is the blob file. | ||
| The blob was compiled following this tutorial: https://github.com/TNTWEN/OpenVINO-YOLOV4 | ||
| */ | ||
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| static const auto labelMap = { | ||
| "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", | ||
| "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", | ||
| "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", | ||
| "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", | ||
| "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", | ||
| "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", | ||
| "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", | ||
| "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", | ||
| "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"}; | ||
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| static bool syncNN = true; | ||
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| dai::Pipeline createNNPipeline(std::string nnPath) { | ||
| dai::Pipeline p; | ||
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| auto colorCam = p.create<dai::node::ColorCamera>(); | ||
| auto xlinkOut = p.create<dai::node::XLinkOut>(); | ||
| auto nnOut = p.create<dai::node::XLinkOut>(); | ||
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| xlinkOut->setStreamName("preview"); | ||
| nnOut->setStreamName("detections"); | ||
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| colorCam->setPreviewSize(416, 416); | ||
| colorCam->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P); | ||
| colorCam->setInterleaved(false); | ||
| colorCam->setColorOrder(dai::ColorCameraProperties::ColorOrder::BGR); | ||
| colorCam->setFps(40); | ||
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| auto detectionNetwork = p.create<dai::node::YoloDetectionNetwork>(); | ||
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| // network specific!! | ||
| detectionNetwork->setConfidenceThreshold(0.5f); | ||
| detectionNetwork->setNumClasses(80); | ||
| detectionNetwork->setCoordinateSize(4); | ||
| detectionNetwork->setAnchors({10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319}); | ||
| detectionNetwork->setAnchorMasks({{"side13", {3,4,5}}, {"side26", {1,2,3}}); | ||
| detectionNetwork->setIouThreshold(0.5f); | ||
| detectionNetwork->setBlobPath(nnPath); | ||
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| // Link plugins CAM -> NN -> XLINK | ||
| colorCam->preview.link(detectionNetwork->input); | ||
| if(syncNN) detectionNetwork->passthrough.link(xlinkOut->input); | ||
| else colorCam->preview.link(xlinkOut->input); | ||
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| detectionNetwork->out.link(nnOut->input); | ||
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| return p; | ||
| } | ||
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| int main(int argc, char** argv) { | ||
| using namespace std; | ||
| using namespace std::chrono; | ||
| std::string nnPath(BLOB_PATH); | ||
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| // If path to blob specified, use that | ||
| if(argc > 1) { | ||
| nnPath = std::string(argv[1]); | ||
| } | ||
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| // Print which blob we are using | ||
| printf("Using blob at path: %s\n", nnPath.c_str()); | ||
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| // Create pipeline | ||
| dai::Pipeline p = createNNPipeline(nnPath); | ||
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| // Connect to device with above created pipeline | ||
| dai::Device d(p); | ||
| // Start the pipeline | ||
| d.startPipeline(); | ||
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| cv::Mat frame; | ||
| auto preview = d.getOutputQueue("preview", 4, false); | ||
| auto detections = d.getOutputQueue("detections", 4, false); | ||
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| auto startTime = std::chrono::steady_clock::now(); | ||
| int counter = 0; | ||
| float fps = 0; | ||
| while(1) { | ||
| auto imgFrame = preview->get<dai::ImgFrame>(); | ||
| auto det = detections->get<dai::ImgDetections>(); | ||
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| counter++; | ||
| auto currentTime = std::chrono::steady_clock::now(); | ||
| auto elapsed = duration_cast<duration<float>>(current_time - start_time); | ||
| if(elapsed > seconds(1)) { | ||
| fps = counter / elapsed; | ||
| counter = 0; | ||
| startTime = currentTime; | ||
| } | ||
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| if(imgFrame) { | ||
| frame = toMat(imgFrame->getData(), imgFrame->getWidth(), imgFrame->getHeight(), 3, 1); | ||
| } | ||
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| auto color = cv::Scalar(255, 255, 255); | ||
| auto dets = det->detections; | ||
| for(const auto& d : dets) { | ||
| int x1 = d.xmin * frame.cols; | ||
| int y1 = d.ymin * frame.rows; | ||
| int x2 = d.xmax * frame.cols; | ||
| int y2 = d.ymax * frame.rows; | ||
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| int labelIndex = d.label; | ||
| std::string labelStr = to_string(labelIndex); | ||
| if(labelIndex < labelMap.size()) { | ||
| labelStr = labelMap[labelIndex]; | ||
| } | ||
| cv::putText(frame, labelStr, cv::Point(x1 + 10, y1 + 20), cv::FONT_HERSHEY_TRIPLEX, 0.5, color); | ||
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| std::stringstream confStr; | ||
| confStr << std::fixed << std::setprecision(2) << d.confidence*100; | ||
| cv::putText(frame, confStr.str(), cv::Point(x1 + 10, y1 + 40), cv::FONT_HERSHEY_TRIPLEX, 0.5, color); | ||
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| cv::rectangle(frame, cv::Rect(cv::Point(x1, y1), cv::Point(x2, y2)), color, cv::FONT_HERSHEY_SIMPLEX); | ||
| } | ||
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| std::stringstream fpsStr; | ||
| fpsStr << std::fixed << std::setprecision(2) << fps; | ||
| cv::putText(frame, fpsStr.str(), cv::Point(2, imgFrame->getHeight()-4), cv::FONT_HERSHEY_TRIPLEX, 0.4, color); | ||
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| cv::imshow("preview", frame); | ||
| int key = cv::waitKey(1); | ||
| if(key == 'q') { | ||
| return 0; | ||
| } | ||
| } | ||
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| return 0; | ||
| } |