Usuń 'old.cpp'
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old.cpp
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old.cpp
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// Usage example: ./mask_rcnn.out --video=
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// ./mask_rcnn.out --image=
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#include <fstream>
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#include <sstream>
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#include <iostream>
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#include <string.h>
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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const char* keys =
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"{help h usage ? | | Usage examples: \n\t\t./mask-rcnn.out --image=traffic.jpg \n\t\t./mask-rcnn.out --video=sample.mp4}"
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"{image i |<none>| input image }"
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"{video v |<none>| input video }"
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;
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using namespace cv;
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using namespace dnn;
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using namespace std;
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// Initialize the parameters
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float confThreshold = 0.5; // Confidence threshold
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float maskThreshold = 0.3; // Mask threshold
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vector<string> classes;
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vector<Scalar> colors;
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// Draw the predicted bounding box
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void drawBox(Mat& frame, int classId, float conf, Rect box, Mat& objectMask);
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// Postprocess the neural network's output for each frame
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void postprocess(Mat& frame, const vector<Mat>& outs);
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int main(int argc, char** argv)
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{
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CommandLineParser parser(argc, argv, keys);
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parser.about("Use this script to run object detection using YOLO3 in OpenCV.");
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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// Load names of classes
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string classesFile = "mscoco_labels.names";
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ifstream ifs(classesFile.c_str());
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string line;
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while (getline(ifs, line)) classes.push_back(line);
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// Load the colors
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string colorsFile = "colors.txt";
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ifstream colorFptr(colorsFile.c_str());
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while (getline(colorFptr, line)) {
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char* pEnd;
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double r, g, b;
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r = strtod (line.c_str(), &pEnd);
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g = strtod (pEnd, NULL);
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b = strtod (pEnd, NULL);
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Scalar color = Scalar(r, g, b, 255.0);
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colors.push_back(Scalar(r, g, b, 255.0));
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}
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// Give the configuration and weight files for the model
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String textGraph = "./mask_rcnn_inception_v2_coco_2018_01_28.pbtxt";
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String modelWeights = "./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb";
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// Load the network
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Net net = readNetFromTensorflow(modelWeights, textGraph);
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(DNN_TARGET_CPU);
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// Open a video file or an image file or a camera stream.
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string str, outputFile;
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VideoCapture cap;
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VideoWriter video;
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Mat frame, blob;
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try {
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outputFile = "mask_rcnn_out_cpp.avi";
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if (parser.has("image"))
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{
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// Open the image file
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str = parser.get<String>("image");
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//cout << "Image file input : " << str << endl;
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ifstream ifile(str);
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if (!ifile) throw("error");
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cap.open(str);
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str.replace(str.end()-4, str.end(), "_mask_rcnn_out.jpg");
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outputFile = str;
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}
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else if (parser.has("video"))
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{
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// Open the video file
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str = parser.get<String>("video");
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ifstream ifile(str);
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if (!ifile) throw("error");
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cap.open(str);
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str.replace(str.end()-4, str.end(), "_mask_rcnn_out.avi");
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outputFile = str;
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}
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// Open the webcam
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else cap.open(parser.get<int>("device"));
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}
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catch(...) {
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cout << "Could not open the input image/video stream" << endl;
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return 0;
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}
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// Get the video writer initialized to save the output video
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if (!parser.has("image")) {
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video.open(outputFile, VideoWriter::fourcc('M','J','P','G'), 28, Size(cap.get(CAP_PROP_FRAME_WIDTH), cap.get(CAP_PROP_FRAME_HEIGHT)));
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}
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// Create a window
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static const string kWinName = "Deep learning object detection in OpenCV";
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namedWindow(kWinName, WINDOW_NORMAL);
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// Process frames.
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while (waitKey(1) < 0)
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{
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// get frame from the video
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cap >> frame;
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// Stop the program if reached end of video
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if (frame.empty()) {
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cout << "Done processing !!!" << endl;
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cout << "Output file is stored as " << outputFile << endl;
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waitKey(3000);
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break;
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}
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// Create a 4D blob from a frame.
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blobFromImage(frame, blob, 1.0, Size(frame.cols, frame.rows), Scalar(), true, false);
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//blobFromImage(frame, blob);
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//Sets the input to the network
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net.setInput(blob);
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// Runs the forward pass to get output from the output layers
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std::vector<String> outNames(2);
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outNames[0] = "detection_out_final";
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outNames[1] = "detection_masks";
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vector<Mat> outs;
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net.forward(outs, outNames);
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// Extract the bounding box and mask for each of the detected objects
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postprocess(frame, outs);
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// Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
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vector<double> layersTimes;
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double freq = getTickFrequency() / 1000;
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double t = net.getPerfProfile(layersTimes) / freq;
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string label = format("Mask-RCNN on 2.5 GHz Intel Core i7 CPU, Inference time for a frame : %0.0f ms", t);
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putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
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// Write the frame with the detection boxes
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Mat detectedFrame;
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frame.convertTo(detectedFrame, CV_8U);
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if (parser.has("image")) imwrite(outputFile, detectedFrame);
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else video.write(detectedFrame);
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imshow(kWinName, frame);
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}
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cap.release();
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if (!parser.has("image")) video.release();
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return 0;
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}
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// For each frame, extract the bounding box and mask for each detected object
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void postprocess(Mat& frame, const vector<Mat>& outs)
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{
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Mat outDetections = outs[0];
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Mat outMasks = outs[1];
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// Output size of masks is NxCxHxW where
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// N - number of detected boxes
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// C - number of classes (excluding background)
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// HxW - segmentation shape
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const int numDetections = outDetections.size[2];
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const int numClasses = outMasks.size[1];
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outDetections = outDetections.reshape(1, outDetections.total() / 7);
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for (int i = 0; i < numDetections; ++i)
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{
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float score = outDetections.at<float>(i, 2);
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if (score > confThreshold)
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{
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// Extract the bounding box
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int classId = static_cast<int>(outDetections.at<float>(i, 1));
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int left = static_cast<int>(frame.cols * outDetections.at<float>(i, 3));
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int top = static_cast<int>(frame.rows * outDetections.at<float>(i, 4));
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int right = static_cast<int>(frame.cols * outDetections.at<float>(i, 5));
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int bottom = static_cast<int>(frame.rows * outDetections.at<float>(i, 6));
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left = max(0, min(left, frame.cols - 1));
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top = max(0, min(top, frame.rows - 1));
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right = max(0, min(right, frame.cols - 1));
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bottom = max(0, min(bottom, frame.rows - 1));
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Rect box = Rect(left, top, right - left + 1, bottom - top + 1);
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// Extract the mask for the object
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Mat objectMask(outMasks.size[2], outMasks.size[3],CV_32F, outMasks.ptr<float>(i,classId));
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// Draw bounding box, colorize and show the mask on the image
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drawBox(frame, classId, score, box, objectMask);
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}
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}
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}
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// Draw the predicted bounding box, colorize and show the mask on the image
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void drawBox(Mat& frame, int classId, float conf, Rect box, Mat& objectMask)
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{
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//Draw a rectangle displaying the bounding box
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rectangle(frame, Point(box.x, box.y), Point(box.x+box.width, box.y+box.height), Scalar(255, 178, 50), 3);
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//Get the label for the class name and its confidence
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string label = format("%.2f", conf);
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if (!classes.empty())
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{
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CV_Assert(classId < (int)classes.size());
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label = classes[classId] + ":" + label;
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}
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//Display the label at the top of the bounding box
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int baseLine;
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Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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box.y = max(box.y, labelSize.height);
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rectangle(frame, Point(box.x, box.y - round(1.5*labelSize.height)), Point(box.x + round(1.5*labelSize.width), box.y + baseLine), Scalar(255, 255, 255), FILLED);
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putText(frame, label, Point(box.x, box.y), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0,0,0),1);
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Scalar color = colors[classId%colors.size()];
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// Resize the mask, threshold, color and apply it on the image
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resize(objectMask, objectMask, Size(box.width, box.height));
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Mat mask = (objectMask > maskThreshold);
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Mat coloredRoi = (0.3 * color + 0.7 * frame(box));
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coloredRoi.convertTo(coloredRoi, CV_8UC3);
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// Draw the contours on the image
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vector<Mat> contours;
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Mat hierarchy;
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mask.convertTo(mask, CV_8U);
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findContours(mask, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
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drawContours(coloredRoi, contours, -1, color, 5, LINE_8, hierarchy, 100);
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coloredRoi.copyTo(frame(box), mask);
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}
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