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fanfuhan OpenCV 教學088 ~ opencv-088-視頻分析(基於均值遷移的對象移動 偵測/抓取/標記/定位/分析) 資料來源: https://fanfuhan.github.io/ https://fanfuhan.github.io/2019/05/08/opencv-088/ GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV 均值遷移移動對象分析，主要是基於直方圖分佈與反向投影實現移動對象的軌跡跟踪，其核心的思想是對反向投影之後的圖像做均值遷移（meanshift）從而發現密度最高的區域，也是對象分佈最大的域。完整的算法流程如下：   01.讀取圖像一幀   02.HSV直方圖   03.反向投影該幀   04.使用means shift尋找最大分佈密度   05.更新窗口直至最後一幀 C++ #include #include #include using namespace cv; using namespace std; Mat image; int trackObject = 0; /* * 视频分析(基于均值迁移的对象移动分析) */ int main() { VideoCapture cap("../images/balltest.mp4"); Rect trackWindow; int hsize = 16; float hranges[] = {0, 180}; const float *phranges = hranges; if (!cap.isOpened()) { printf("could not open camera...\n"); return -1; } Mat frame, hsv, hue, mask, hist = Mat::zeros(200, 320, CV_8UC3), backproj; cap.read(frame); Rect selection = selectROI("CamShift Demo", frame, true, false); while (true) { bool ret = cap.read(frame); if (!ret) break; frame.copyTo(image); cvtColor(image, hsv, COLOR_BGR2HSV); inRange(hsv, Scalar(26, 43, 46), Scalar(34, 255, 255), mask); int ch[] = {0, 0}; hue.create(hsv.size(), hsv.depth()); mixChannels(&hsv, 1, &hue, 1, ch, 1); if (trackObject <= 0) { // Object has been selected by user, set up CAMShift search properties once Mat roi(hue, selection), maskroi(mask, selection); calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges); normalize(hist, hist, 0, 255, NORM_MINMAX); trackWindow = selection; trackObject = 1; // Don't set up again, unless user selects new ROI } // Perform meanShift calcBackProject(&hue, 1, 0, hist, backproj, &phranges); backproj &= mask; meanShift(backproj, trackWindow, TermCriteria(TermCriteria::EPS | TermCriteria::COUNT, 10, 1)); rectangle(image, trackWindow, Scalar(0, 0, 255), 3, LINE_AA); imshow("CamShift Demo", image); char c = (char) waitKey(50); if (c == 27) break; } return 0; } Python import cv2 as cv cap = cv.VideoCapture('D:/images/video/balltest.mp4') # 读取第一帧 ret,frame = cap.read() cv.namedWindow("CAS Demo", cv.WINDOW_AUTOSIZE) x, y, w, h = cv.selectROI("CAS Demo", frame, True, False) track_window = (x, y, w, h) # 获取ROI直方图 roi = frame[y:y+h, x:x+w] hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV) mask = cv.inRange(hsv_roi, (26, 43, 46), (34, 255, 255)) roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180]) cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX) # 设置搜索跟踪分析 term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 ) while True: ret, frame = cap.read() if ret is False: break; hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV) dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1) # 均值迁移，搜索更新roi区域 ret, track_window = cv.meanShift(dst, track_window, term_crit) # 绘制窗口 x,y,w,h = track_window cv.rectangle(frame, (x,y), (x+w,y+h), 255,2) cv.imshow('CAS Demo',frame) k = cv.waitKey(60) & 0xff if k == 27: break cv.destroyAllWindows() cap.release()