2017-07-15 01:47:56 +08:00
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#include <opencv2/dnn.hpp>
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#include <opencv2/dnn/shape_utils.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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using namespace cv;
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using namespace cv::dnn;
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#include <fstream>
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#include <iostream>
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#include <cstdlib>
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using namespace std;
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const size_t inWidth = 300;
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const size_t inHeight = 300;
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const float WHRatio = inWidth / (float)inHeight;
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const float inScaleFactor = 0.007843f;
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const float meanVal = 127.5;
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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"
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".caffemodel model's file is avaliable here: "
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2017-08-09 00:53:07 +08:00
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"https://github.com/chuanqi305/MobileNet-SSD\n";
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2017-07-15 01:47:56 +08:00
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const char* params
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= "{ help | false | print usage }"
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2017-08-09 00:53:07 +08:00
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"{ proto | MobileNetSSD_deploy.prototxt | model configuration }"
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"{ model | MobileNetSSD_deploy.caffemodel | model weights }"
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2017-07-15 01:47:56 +08:00
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"{ video | | video 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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if (parser.get<bool>("help"))
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{
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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>("proto");
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String modelBinary = parser.get<string>("model");
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//! [Initialize network]
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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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{
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cap = VideoCapture(0);
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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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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 cropSize;
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if (inVideoSize.width / (float)inVideoSize.height > WHRatio)
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{
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cropSize = Size(static_cast<int>(inVideoSize.height * WHRatio),
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inVideoSize.height);
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}
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else
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{
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cropSize = Size(inVideoSize.width,
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static_cast<int>(inVideoSize.width / WHRatio));
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}
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Rect crop(Point((inVideoSize.width - cropSize.width) / 2,
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(inVideoSize.height - cropSize.height) / 2),
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cropSize);
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VideoWriter outputVideo;
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outputVideo.open(parser.get<String>("out") ,
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static_cast<int>(cap.get(CV_CAP_PROP_FOURCC)),
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cap.get(CV_CAP_PROP_FPS), cropSize, true);
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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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Mat inputBlob = blobFromImage(frame, inScaleFactor,
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Size(inWidth, inHeight), meanVal); //Convert Mat to batch of images
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//! [Prepare blob]
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//! [Set input blob]
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2017-08-02 22:27:58 +08:00
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net.setInput(inputBlob, "data"); //set the network input
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2017-07-15 01:47:56 +08:00
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//! [Set input blob]
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//! [Make forward pass]
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2017-08-02 22:27:58 +08:00
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Mat detection = net.forward("detection_out"); //compute output
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2017-07-15 01:47:56 +08:00
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//! [Make forward pass]
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2017-08-02 22:27:58 +08:00
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std::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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2017-07-15 01:47:56 +08:00
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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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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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ostringstream ss;
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ss << confidence;
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String conf(ss.str());
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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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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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if (outputVideo.isOpened())
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outputVideo << frame;
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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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