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Add camera/video/image input for C++ DNN object detection samples. Add nice display and computation time.
This commit is contained in:
catree
parent
21c8e6d02d
commit
48e07437f0
@ -23,23 +23,25 @@ const char* classNames[] = {"background",
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"motorbike", "person", "pottedplant",
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"sheep", "sofa", "train", "tvmonitor"};
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const char* about = "This sample uses Single-Shot Detector "
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"(https://arxiv.org/abs/1512.02325)"
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"to detect objects on image.\n"
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".caffemodel model's file is avaliable here: "
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"https://github.com/chuanqi305/MobileNet-SSD\n";
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const char* about = "This sample uses MobileNet Single-Shot Detector "
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"(https://arxiv.org/abs/1704.04861) "
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"to detect objects on camera/video/image.\n"
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".caffemodel model's file is available here: "
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"https://github.com/chuanqi305/MobileNet-SSD\n"
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"Default network is 300x300 and 20-classes VOC.\n";
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const char* params
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= "{ help | false | print usage }"
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"{ proto | MobileNetSSD_deploy.prototxt | model configuration }"
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"{ model | MobileNetSSD_deploy.caffemodel | model weights }"
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"{ video | | video for detection }"
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"{ camera_device | 0 | camera device number }"
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"{ video | | video or image for detection}"
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"{ out | | path to output video file}"
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"{ min_confidence | 0.2 | min confidence }";
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int main(int argc, char** argv)
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{
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cv::CommandLineParser parser(argc, argv, params);
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CommandLineParser parser(argc, argv, params);
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if (parser.get<bool>("help"))
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{
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@ -55,19 +57,40 @@ int main(int argc, char** argv)
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dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
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//! [Initialize network]
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VideoCapture cap(parser.get<String>("video"));
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if(!cap.isOpened()) // check if we succeeded
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if (net.empty())
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{
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cap = VideoCapture(0);
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cerr << "Can't load network by using the following files: " << endl;
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cerr << "prototxt: " << modelConfiguration << endl;
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cerr << "caffemodel: " << modelBinary << endl;
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cerr << "Models can be downloaded here:" << endl;
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cerr << "https://github.com/chuanqi305/MobileNet-SSD" << endl;
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exit(-1);
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}
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VideoCapture cap;
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if (parser.get<String>("video").empty())
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{
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int cameraDevice = parser.get<int>("camera_device");
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cap = VideoCapture(cameraDevice);
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if(!cap.isOpened())
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{
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cout << "Couldn't find camera" << endl;
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cout << "Couldn't find camera: " << cameraDevice << endl;
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return -1;
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}
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}
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else
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{
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cap.open(parser.get<String>("video"));
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if(!cap.isOpened())
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{
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cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
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return -1;
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}
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}
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Size inVideoSize = Size((int) cap.get(CV_CAP_PROP_FRAME_WIDTH), //Acquire input size
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(int) cap.get(CV_CAP_PROP_FRAME_HEIGHT));
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Size inVideoSize;
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inVideoSize = Size((int) cap.get(CV_CAP_PROP_FRAME_WIDTH), //Acquire input size
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(int) cap.get(CV_CAP_PROP_FRAME_HEIGHT));
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Size cropSize;
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if (inVideoSize.width / (float)inVideoSize.height > WHRatio)
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@ -93,9 +116,18 @@ int main(int argc, char** argv)
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for(;;)
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{
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Mat frame;
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cap >> frame; // get a new frame from camera
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//! [Prepare blob]
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cap >> frame; // get a new frame from camera/video or read image
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if (frame.empty())
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{
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waitKey();
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break;
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}
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if (frame.channels() == 4)
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cvtColor(frame, frame, COLOR_BGRA2BGR);
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//! [Prepare blob]
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Mat inputBlob = blobFromImage(frame, inScaleFactor,
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Size(inWidth, inHeight), meanVal, false); //Convert Mat to batch of images
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//! [Prepare blob]
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@ -108,15 +140,23 @@ int main(int argc, char** argv)
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Mat detection = net.forward("detection_out"); //compute output
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//! [Make forward pass]
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std::vector<double> layersTimings;
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vector<double> layersTimings;
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double freq = getTickFrequency() / 1000;
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double time = net.getPerfProfile(layersTimings) / freq;
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cout << "Inference time, ms: " << time << endl;
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Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
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frame = frame(crop);
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ostringstream ss;
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if (!outputVideo.isOpened())
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{
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ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
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putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
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}
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else
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cout << "Inference time, ms: " << time << endl;
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float confidenceThreshold = parser.get<float>("min_confidence");
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for(int i = 0; i < detectionMat.rows; i++)
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{
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@ -131,7 +171,7 @@ int main(int argc, char** argv)
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int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
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int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
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ostringstream ss;
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ss.str("");
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ss << confidence;
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String conf(ss.str());
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@ -40,15 +40,26 @@ static Mat preprocess(const Mat& frame)
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return preprocessed;
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}
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const char* classNames[] = {"background",
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"aeroplane", "bicycle", "bird", "boat",
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"bottle", "bus", "car", "cat", "chair",
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"cow", "diningtable", "dog", "horse",
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"motorbike", "person", "pottedplant",
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"sheep", "sofa", "train", "tvmonitor"};
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const char* about = "This sample uses Single-Shot Detector "
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"(https://arxiv.org/abs/1512.02325)"
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"to detect objects on image\n"; // TODO: link
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"(https://arxiv.org/abs/1512.02325) "
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"to detect objects on camera/video/image.\n"
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".caffemodel model's file is available here: "
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"https://github.com/weiliu89/caffe/tree/ssd#models\n"
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"Default network is 300x300 and 20-classes VOC.\n";
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const char* params
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= "{ help | false | print usage }"
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"{ proto | | model configuration }"
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"{ model | | model weights }"
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"{ image | | image for detection }"
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"{ camera_device | 0 | camera device number}"
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"{ video | | video or image for detection}"
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"{ min_confidence | 0.5 | min confidence }";
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int main(int argc, char** argv)
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@ -57,7 +68,7 @@ int main(int argc, char** argv)
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if (parser.get<bool>("help"))
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{
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std::cout << about << std::endl;
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cout << about << endl;
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parser.printMessage();
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return 0;
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}
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@ -79,58 +90,101 @@ int main(int argc, char** argv)
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exit(-1);
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}
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cv::Mat frame = cv::imread(parser.get<string>("image"), -1);
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if (frame.channels() == 4)
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cvtColor(frame, frame, COLOR_BGRA2BGR);
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//! [Prepare blob]
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Mat preprocessedFrame = preprocess(frame);
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Mat inputBlob = blobFromImage(preprocessedFrame, 1.0f, Size(), Scalar(), false); //Convert Mat to batch of images
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//! [Prepare blob]
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//! [Set input blob]
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net.setInput(inputBlob, "data"); //set the network input
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//! [Set input blob]
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//! [Make forward pass]
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Mat detection = net.forward("detection_out"); //compute output
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//! [Make forward pass]
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Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
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float confidenceThreshold = parser.get<float>("min_confidence");
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for(int i = 0; i < detectionMat.rows; i++)
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VideoCapture cap;
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if (parser.get<String>("video").empty())
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{
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float confidence = detectionMat.at<float>(i, 2);
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if(confidence > confidenceThreshold)
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int cameraDevice = parser.get<int>("camera_device");
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cap = VideoCapture(cameraDevice);
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if(!cap.isOpened())
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{
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size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
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float xLeftBottom = detectionMat.at<float>(i, 3) * frame.cols;
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float yLeftBottom = detectionMat.at<float>(i, 4) * frame.rows;
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float xRightTop = detectionMat.at<float>(i, 5) * frame.cols;
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float yRightTop = detectionMat.at<float>(i, 6) * frame.rows;
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std::cout << "Class: " << objectClass << std::endl;
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std::cout << "Confidence: " << confidence << std::endl;
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std::cout << " " << xLeftBottom
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<< " " << yLeftBottom
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<< " " << xRightTop
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<< " " << yRightTop << std::endl;
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Rect object((int)xLeftBottom, (int)yLeftBottom,
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(int)(xRightTop - xLeftBottom),
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(int)(yRightTop - yLeftBottom));
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rectangle(frame, object, Scalar(0, 255, 0));
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cout << "Couldn't find camera: " << cameraDevice << endl;
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return -1;
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}
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}
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else
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{
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cap.open(parser.get<String>("video"));
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if(!cap.isOpened())
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{
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cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
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return -1;
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}
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}
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imshow("detections", frame);
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waitKey();
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for (;;)
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{
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cv::Mat frame;
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cap >> frame; // get a new frame from camera/video or read image
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if (frame.empty())
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{
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waitKey();
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break;
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}
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if (frame.channels() == 4)
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cvtColor(frame, frame, COLOR_BGRA2BGR);
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//! [Prepare blob]
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Mat preprocessedFrame = preprocess(frame);
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Mat inputBlob = blobFromImage(preprocessedFrame, 1.0f, Size(), Scalar(), false); //Convert Mat to batch of images
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//! [Prepare blob]
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//! [Set input blob]
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net.setInput(inputBlob, "data"); //set the network input
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//! [Set input blob]
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//! [Make forward pass]
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Mat detection = net.forward("detection_out"); //compute output
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//! [Make forward pass]
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vector<double> layersTimings;
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double freq = getTickFrequency() / 1000;
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double time = net.getPerfProfile(layersTimings) / freq;
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ostringstream ss;
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ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
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putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
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Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
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float confidenceThreshold = parser.get<float>("min_confidence");
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for(int i = 0; i < detectionMat.rows; i++)
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{
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float confidence = detectionMat.at<float>(i, 2);
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if(confidence > confidenceThreshold)
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{
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size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
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int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
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int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
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int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
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int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
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ss.str("");
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ss << confidence;
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String conf(ss.str());
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Rect object(xLeftBottom, yLeftBottom,
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xRightTop - xLeftBottom,
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yRightTop - yLeftBottom);
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rectangle(frame, object, Scalar(0, 255, 0));
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String label = String(classNames[objectClass]) + ": " + conf;
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int baseLine = 0;
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Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
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Size(labelSize.width, labelSize.height + baseLine)),
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Scalar(255, 255, 255), CV_FILLED);
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putText(frame, label, Point(xLeftBottom, yLeftBottom),
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FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
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}
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}
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imshow("detections", frame);
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if (waitKey(1) >= 0) break;
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}
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return 0;
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} // main
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@ -15,29 +15,36 @@ const size_t network_width = 416;
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const size_t network_height = 416;
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const char* about = "This sample uses You only look once (YOLO)-Detector "
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"(https://arxiv.org/abs/1612.08242)"
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"to detect objects on image\n"; // TODO: link
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"(https://arxiv.org/abs/1612.08242) "
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"to detect objects on camera/video/image.\n"
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"Models can be downloaded here: "
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"https://pjreddie.com/darknet/yolo/\n"
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"Default network is 416x416.\n"
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"Class names can be downloaded here: "
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"https://github.com/pjreddie/darknet/tree/master/data\n";
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const char* params
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= "{ help | false | print usage }"
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"{ cfg | | model configuration }"
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"{ model | | model weights }"
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"{ image | | image for detection }"
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"{ min_confidence | 0.24 | min confidence }";
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"{ camera_device | 0 | camera device number}"
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"{ video | | video or image for detection}"
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"{ min_confidence | 0.24 | min confidence }"
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"{ class_names | | class names }";
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int main(int argc, char** argv)
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{
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cv::CommandLineParser parser(argc, argv, params);
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CommandLineParser parser(argc, argv, params);
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if (parser.get<bool>("help"))
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{
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std::cout << about << std::endl;
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cout << about << endl;
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parser.printMessage();
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return 0;
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}
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String modelConfiguration = parser.get<string>("cfg");
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String modelBinary = parser.get<string>("model");
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String modelConfiguration = parser.get<String>("cfg");
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String modelBinary = parser.get<String>("model");
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//! [Initialize network]
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dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
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@ -53,65 +60,130 @@ int main(int argc, char** argv)
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exit(-1);
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}
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cv::Mat frame = cv::imread(parser.get<string>("image"));
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//! [Resizing without keeping aspect ratio]
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cv::Mat resized;
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cv::resize(frame, resized, cv::Size(network_width, network_height));
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//! [Resizing without keeping aspect ratio]
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//! [Prepare blob]
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Mat inputBlob = blobFromImage(resized, 1 / 255.F); //Convert Mat to batch of images
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//! [Prepare blob]
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//! [Set input blob]
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net.setInput(inputBlob, "data"); //set the network input
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//! [Set input blob]
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//! [Make forward pass]
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cv::Mat detectionMat = net.forward("detection_out"); //compute output
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//! [Make forward pass]
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float confidenceThreshold = parser.get<float>("min_confidence");
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for (int i = 0; i < detectionMat.rows; i++)
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VideoCapture cap;
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if (parser.get<String>("video").empty())
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{
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const int probability_index = 5;
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const int probability_size = detectionMat.cols - probability_index;
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float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
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size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
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float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
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if (confidence > confidenceThreshold)
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int cameraDevice = parser.get<int>("camera_device");
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cap = VideoCapture(cameraDevice);
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if(!cap.isOpened())
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{
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float x = detectionMat.at<float>(i, 0);
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float y = detectionMat.at<float>(i, 1);
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float width = detectionMat.at<float>(i, 2);
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float height = detectionMat.at<float>(i, 3);
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float xLeftBottom = (x - width / 2) * frame.cols;
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float yLeftBottom = (y - height / 2) * frame.rows;
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float xRightTop = (x + width / 2) * frame.cols;
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float yRightTop = (y + height / 2) * frame.rows;
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std::cout << "Class: " << objectClass << std::endl;
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std::cout << "Confidence: " << confidence << std::endl;
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std::cout << " " << xLeftBottom
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<< " " << yLeftBottom
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<< " " << xRightTop
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<< " " << yRightTop << std::endl;
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Rect object((int)xLeftBottom, (int)yLeftBottom,
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(int)(xRightTop - xLeftBottom),
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(int)(yRightTop - yLeftBottom));
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rectangle(frame, object, Scalar(0, 255, 0));
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cout << "Couldn't find camera: " << cameraDevice << endl;
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return -1;
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}
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}
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else
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{
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cap.open(parser.get<String>("video"));
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if(!cap.isOpened())
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{
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cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
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return -1;
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}
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}
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imshow("detections", frame);
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waitKey();
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vector<string> classNamesVec;
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ifstream classNamesFile(parser.get<String>("class_names").c_str());
|
||||
if (classNamesFile.is_open())
|
||||
{
|
||||
string className = "";
|
||||
while (classNamesFile >> className)
|
||||
classNamesVec.push_back(className);
|
||||
}
|
||||
|
||||
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);
|
||||
|
||||
//! [Resizing without keeping aspect ratio]
|
||||
Mat resized;
|
||||
resize(frame, resized, Size(network_width, network_height));
|
||||
//! [Resizing without keeping aspect ratio]
|
||||
|
||||
//! [Prepare blob]
|
||||
Mat inputBlob = blobFromImage(resized, 1 / 255.F); //Convert Mat to batch of images
|
||||
//! [Prepare blob]
|
||||
|
||||
//! [Set input blob]
|
||||
net.setInput(inputBlob, "data"); //set the network input
|
||||
//! [Set input blob]
|
||||
|
||||
//! [Make forward pass]
|
||||
Mat detectionMat = net.forward("detection_out"); //compute output
|
||||
//! [Make forward pass]
|
||||
|
||||
vector<double> layersTimings;
|
||||
double freq = getTickFrequency() / 1000;
|
||||
double time = net.getPerfProfile(layersTimings) / freq;
|
||||
ostringstream ss;
|
||||
ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
|
||||
putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
|
||||
|
||||
float confidenceThreshold = parser.get<float>("min_confidence");
|
||||
for (int i = 0; i < detectionMat.rows; i++)
|
||||
{
|
||||
const int probability_index = 5;
|
||||
const int probability_size = detectionMat.cols - probability_index;
|
||||
float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
|
||||
|
||||
size_t objectClass = max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
|
||||
float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
|
||||
|
||||
if (confidence > confidenceThreshold)
|
||||
{
|
||||
float x = detectionMat.at<float>(i, 0);
|
||||
float y = detectionMat.at<float>(i, 1);
|
||||
float width = detectionMat.at<float>(i, 2);
|
||||
float height = detectionMat.at<float>(i, 3);
|
||||
int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols);
|
||||
int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows);
|
||||
int xRightTop = static_cast<int>((x + width / 2) * frame.cols);
|
||||
int yRightTop = static_cast<int>((y + height / 2) * frame.rows);
|
||||
|
||||
Rect object(xLeftBottom, yLeftBottom,
|
||||
xRightTop - xLeftBottom,
|
||||
yRightTop - yLeftBottom);
|
||||
|
||||
rectangle(frame, object, Scalar(0, 255, 0));
|
||||
|
||||
if (objectClass < classNamesVec.size())
|
||||
{
|
||||
ss.str("");
|
||||
ss << confidence;
|
||||
String conf(ss.str());
|
||||
String label = String(classNamesVec[objectClass]) + ": " + conf;
|
||||
int baseLine = 0;
|
||||
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
||||
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
|
||||
Size(labelSize.width, labelSize.height + baseLine)),
|
||||
Scalar(255, 255, 255), CV_FILLED);
|
||||
putText(frame, label, Point(xLeftBottom, yLeftBottom),
|
||||
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
|
||||
}
|
||||
else
|
||||
{
|
||||
cout << "Class: " << objectClass << endl;
|
||||
cout << "Confidence: " << confidence << endl;
|
||||
cout << " " << xLeftBottom
|
||||
<< " " << yLeftBottom
|
||||
<< " " << xRightTop
|
||||
<< " " << yRightTop << endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
imshow("detections", frame);
|
||||
if (waitKey(1) >= 0) break;
|
||||
}
|
||||
|
||||
return 0;
|
||||
} // main
|
||||
} // main
|
||||
|
||||
Loading…
Reference in New Issue
Block a user