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fanfuhan OpenCV 教學084 ~ opencv-084-視頻分析（移動對象的KLT光流跟踪/5偵測算法） 資料來源: https://fanfuhan.github.io/ https://fanfuhan.github.io/2019/04/26/opencv-084/ GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV 光流跟踪方法分為密集密實光流跟踪與稀疏光流跟踪算法，KLT是稀疏光流跟踪算法，這個算法最初是由Bruce D. Lucas和Takeo Kanade兩位作者提出來的，所以又被稱為KLT 。 稀疏光流算法工作有三個假設前提條件：   ◎亮度恆定   ◎短距離移動   ◎空間一致性 C++ #include #include using namespace std; using namespace cv; vector featurePoints; RNG rng(12345); /* * 视频分析(移动对象的KLT光流跟踪算法) */ int main() { VideoCapture capture("../images/vtest.avi"); if (!capture.isOpened()) { cout << "could not open video..." << endl; return -1; } // 角点检测参数 double qualityLevel = 0.01; int minDistance = 10; int maxCorners = 100; // KLT光流跟踪参数 vector pts[2]; vector status; vector err; // 读取第一帧及其角点 Mat old_frame, old_gray; capture.read(old_frame); cvtColor(old_frame, old_gray, COLOR_BGR2GRAY); goodFeaturesToTrack(old_gray, featurePoints, maxCorners, qualityLevel, minDistance, Mat()); pts[0].insert(pts[0].end(), featurePoints.begin(), featurePoints.end()); int width = capture.get(CAP_PROP_FRAME_WIDTH); int height = capture.get(CAP_PROP_FRAME_HEIGHT); Rect roi(0, 0, width, height); Mat gray, frame; while (true) { bool ret = capture.read(frame); if (!ret) break; imshow("frame", frame); roi.x = 0; cvtColor(frame, gray, COLOR_BGR2GRAY); // 计算光流 calcOpticalFlowPyrLK(old_gray, gray, pts[0], pts[1], status, err, Size(31, 31)); size_t i, k; for (int i = k = 0; i < pts[1].size(); ++i) { // 根据状态选择 if (status[i]){ pts[0][k] = pts[0][i]; pts[1][k++] = pts[1][i]; int b = rng.uniform(0, 256); int g = rng.uniform(0, 256); int r = rng.uniform(0, 256); Scalar color(b, g, r); // 绘制跟踪线 circle(frame, pts[1][i], 4, color, -1); line(frame, pts[0][i], pts[1][i], color, 2); } } // resize 有用特征点 pts[0].resize(k); pts[1].resize(k); imshow("result", frame); roi.x = width; char c = waitKey(50); if (c == 27) break; // 更新old std::swap(pts[1], pts[0]); cv::swap(old_gray, gray); } return 0; } Python import numpy as np import cv2 as cv cap = cv.VideoCapture('D:/images/video/vtest.avi') # 角点检测参数 feature_params = dict(maxCorners=100, qualityLevel=0.01, minDistance=10, blockSize=3) # KLT光流参数 lk_params = dict(winSize=(31, 31), maxLevel=3, criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 30, 0.01)) # 随机颜色 color = np.random.randint(0,255,(100,3)) # 读取第一帧 ret, old_frame = cap.read() old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY) p0 = cv.goodFeaturesToTrack(old_gray, mask=None, **feature_params) # 光流跟踪 while True: ret, frame = cap.read() if ret is False: break frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) # 计算光流 p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params) # 根据状态选择 good_new = p1[st == 1] good_old = p0[st == 1] # 绘制跟踪线 for i, (new, old) in enumerate(zip(good_new,good_old)): a,b = new.ravel() c,d = old.ravel() frame = cv.line(frame, (a,b),(c,d), color[i].tolist(), 2) frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1) cv.imshow('frame',frame) k = cv.waitKey(30) & 0xff if k == 27: break # 更新 old_gray = frame_gray.copy() p0 = good_new.reshape(-1, 1, 2) cv.destroyAllWindows() cap.release()