OpenCV3.3深度神经网络(DNN)模块-应用课程配套源代码.7z

#include <opencv2/opencv.hpp> #include <opencv2/dnn.hpp> #include <iostream> using namespace cv; using namespace cv::dnn; using namespace std; String goturn_model = "D:/vcprojects/images/dnn/goturn_files/goturn.caffemodel"; String goturn_prototxt = "D:/vcprojects/images/dnn/goturn_files/goturn.prototxt.txt"; Net net; void initGoturn(); Rect trackObjects(Mat &frame, Mat &prevFrame); Mat frame, prevFrame; Rect prevBB; int main(int argc, char** argv) { initGoturn(); VideoCapture capture; capture.open("D:/vcprojects/images/visit.mp4"); capture.read(frame); frame.copyTo(prevFrame); prevBB = selectROI(frame, true, true); namedWindow("frame", CV_WINDOW_AUTOSIZE); while (capture.read(frame)) { Rect currentBB = trackObjects(frame, prevFrame); rectangle(frame, currentBB, Scalar(0, 0, 255), 2, 8, 0); // ready for next frame frame.copyTo(prevFrame); prevBB.x = currentBB.x; prevBB.y = currentBB.y; prevBB.width = currentBB.width; prevBB.height = currentBB.height; imshow("frame", frame); char c = waitKey(50); if (c == 27) { break; } } } void initGoturn() { Ptr<Importer> importer; importer = createCaffeImporter(goturn_prototxt, goturn_model); importer->populateNet(net); importer.release(); } Rect trackObjects(Mat& frame, Mat &prevFrame) { Rect rect; int INPUT_SIZE = 227; //Using prevFrame & prevBB from model and curFrame GOTURN calculating curBB Mat curFrame = frame.clone(); Rect2d curBB; float padTargetPatch = 2.0; Rect2f searchPatchRect, targetPatchRect; Point2f currCenter, prevCenter; Mat prevFramePadded, curFramePadded; Mat searchPatch, targetPatch; prevCenter.x = (float)(prevBB.x + prevBB.width / 2); prevCenter.y = (float)(prevBB.y + prevBB.height / 2); targetPatchRect.width = (float)(prevBB.width*padTargetPatch); targetPatchRect.height = (float)(prevBB.height*padTargetPatch); targetPatchRect.x = (float)(prevCenter.x - prevBB.width*padTargetPatch / 2.0 + targetPatchRect.width); targetPatchRect.y = (float)(prevCenter.y - prevBB.height*padTargetPatch / 2.0 + targetPatchRect.height); copyMakeBorder(prevFrame, prevFramePadded, (int)targetPatchRect.height, (int)targetPatchRect.height, (int)targetPatchRect.width, (int)targetPatchRect.width, BORDER_REPLICATE); targetPatch = prevFramePadded(targetPatchRect).clone(); copyMakeBorder(curFrame, curFramePadded, (int)targetPatchRect.height, (int)targetPatchRect.height, (int)targetPatchRect.width, (int)targetPatchRect.width, BORDER_REPLICATE); searchPatch = curFramePadded(targetPatchRect).clone(); //Preprocess //Resize resize(targetPatch, targetPatch, Size(INPUT_SIZE, INPUT_SIZE)); resize(searchPatch, searchPatch, Size(INPUT_SIZE, INPUT_SIZE)); //Mean Subtract targetPatch = targetPatch - 128; searchPatch = searchPatch - 128; //Convert to Float type targetPatch.convertTo(targetPatch, CV_32F); searchPatch.convertTo(searchPatch, CV_32F); Mat targetBlob = blobFromImage(targetPatch); Mat searchBlob = blobFromImage(searchPatch); net.setInput(targetBlob, ".data1"); net.setInput(searchBlob, ".data2"); Mat res = net.forward("scale"); Mat resMat = res.reshape(1, 1); //printf("width : %d, height : %d\n", (resMat.at<float>(2) - resMat.at<float>(0)), (resMat.at<float>(3) - resMat.at<float>(1))); curBB.x = targetPatchRect.x + (resMat.at<float>(0) * targetPatchRect.width / INPUT_SIZE) - targetPatchRect.width; curBB.y = targetPatchRect.y + (resMat.at<float>(1) * targetPatchRect.height / INPUT_SIZE) - targetPatchRect.height; curBB.width = (resMat.at<float>(2) - resMat.at<float>(0)) * targetPatchRect.width / INPUT_SIZE; curBB.height = (resMat.at<float>(3) - resMat.at<float>(1)) * targetPatchRect.height / INPUT_SIZE; //Predicted BB Rect boundingBox = curBB; return boundingBox; }