#include #include #include #include using namespace cv; using namespace cv::dnn; #include #include #include using namespace std; const size_t inWidth = 300; const size_t inHeight = 300; const float inScaleFactor = 0.007843f; const float meanVal = 127.5; const char* classNames[] = {"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; const char* params = "{ help | false | print usage }" "{ proto | MobileNetSSD_deploy.prototxt | model configuration }" "{ model | MobileNetSSD_deploy.caffemodel | model weights }" "{ camera_device | 0 | camera device number }" "{ video | | video or image for detection}" "{ out | | path to output video file}" "{ min_confidence | 0.2 | min confidence }" "{ opencl | false | enable OpenCL }" ; int main(int argc, char** argv) { CommandLineParser parser(argc, argv, params); parser.about("This sample uses MobileNet Single-Shot Detector " "(https://arxiv.org/abs/1704.04861) " "to detect objects on camera/video/image.\n" ".caffemodel model's file is available here: " "https://github.com/chuanqi305/MobileNet-SSD\n" "Default network is 300x300 and 20-classes VOC.\n"); if (parser.get("help") || argc == 1) { parser.printMessage(); return 0; } String modelConfiguration = parser.get("proto"); String modelBinary = parser.get("model"); CV_Assert(!modelConfiguration.empty() && !modelBinary.empty()); //! [Initialize network] dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary); //! [Initialize network] if (parser.get("opencl")) { net.setPreferableTarget(DNN_TARGET_OPENCL); } if (net.empty()) { cerr << "Can't load network by using the following files: " << endl; cerr << "prototxt: " << modelConfiguration << endl; cerr << "caffemodel: " << modelBinary << endl; cerr << "Models can be downloaded here:" << endl; cerr << "https://github.com/chuanqi305/MobileNet-SSD" << endl; exit(-1); } VideoCapture cap; if (!parser.has("video")) { int cameraDevice = parser.get("camera_device"); cap = VideoCapture(cameraDevice); if(!cap.isOpened()) { cout << "Couldn't find camera: " << cameraDevice << endl; return -1; } } else { cap.open(parser.get("video")); if(!cap.isOpened()) { cout << "Couldn't open image or video: " << parser.get("video") << endl; return -1; } } //Acquire input size Size inVideoSize((int) cap.get(CV_CAP_PROP_FRAME_WIDTH), (int) cap.get(CV_CAP_PROP_FRAME_HEIGHT)); double fps = cap.get(CV_CAP_PROP_FPS); int fourcc = static_cast(cap.get(CV_CAP_PROP_FOURCC)); VideoWriter outputVideo; outputVideo.open(parser.get("out") , (fourcc != 0 ? fourcc : VideoWriter::fourcc('M','J','P','G')), (fps != 0 ? fps : 10.0), inVideoSize, true); for(;;) { Mat frame; cap >> frame; // get a new frame from camera/video or read image if (frame.empty()) { waitKey(); break; } if (frame.channels() == 4) cvtColor(frame, frame, COLOR_BGRA2BGR); //! [Prepare blob] Mat inputBlob = blobFromImage(frame, inScaleFactor, Size(inWidth, inHeight), Scalar(meanVal, meanVal, meanVal), false, false); //Convert Mat to batch of images //! [Prepare blob] //! [Set input blob] net.setInput(inputBlob); //set the network input //! [Set input blob] //! [Make forward pass] Mat detection = net.forward(); //compute output //! [Make forward pass] vector layersTimings; double freq = getTickFrequency() / 1000; double time = net.getPerfProfile(layersTimings) / freq; Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr()); if (!outputVideo.isOpened()) { putText(frame, format("FPS: %.2f ; time: %.2f ms", 1000.f/time, time), Point(20,20), 0, 0.5, Scalar(0,0,255)); } else cout << "Inference time, ms: " << time << endl; float confidenceThreshold = parser.get("min_confidence"); for(int i = 0; i < detectionMat.rows; i++) { float confidence = detectionMat.at(i, 2); if(confidence > confidenceThreshold) { size_t objectClass = (size_t)(detectionMat.at(i, 1)); int left = static_cast(detectionMat.at(i, 3) * frame.cols); int top = static_cast(detectionMat.at(i, 4) * frame.rows); int right = static_cast(detectionMat.at(i, 5) * frame.cols); int bottom = static_cast(detectionMat.at(i, 6) * frame.rows); rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0)); String label = format("%s: %.2f", classNames[objectClass], confidence); int baseLine = 0; Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); top = max(top, labelSize.height); rectangle(frame, Point(left, top - labelSize.height), Point(left + labelSize.width, top + baseLine), Scalar(255, 255, 255), CV_FILLED); putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0)); } } if (outputVideo.isOpened()) outputVideo << frame; imshow("detections", frame); if (waitKey(1) >= 0) break; } return 0; } // main