15 视频追踪.zip

import numpy as np import cv2 as cv # 1获取图像 cap = cv.VideoCapture('./image/dog.wmv') # 2获取第一帧图像，并指定目标位置 ret, frame = cap.read() # 2.1目标位置（行，高，列，宽） r, h, c, w = 197, 141, 0, 208 track_window = (c, r, w, h) # 2.2指定目标的感兴趣区域 roi = frame[r:r + h, c:c + w] # 3.计算直方图 # 3.1转换色彩空间(HSV) hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV) # 3.2去除低亮度的值 mask = cv.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) # 3.3计算直方图 roi_hist = cv.calcHist([hsv_roi], [0], None, [180], [0, 180]) # 3.4归一化 cv.normalize(roi_hist, roi_hist, 0, 255, cv.NORM_MINMAX) # 4.目标追踪 # 4.1设置窗口搜索终止条件：最大迭代次数，窗口中心漂移最小值 term_crit = cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 while True: # 4.2获取每一帧图像 ret, frame = cap.read() if ret: # 4.3计算直方图的反向投影 hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV) dst = cv.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) # 4.4进行meanshifti追踪 ret, track_window = cv.meanShift(dst, track_window, term_crit) # 4.5将追踪的位置绘制在视频上，并进行显示 x, y, w, h = track_window img2 = cv.rectangle(frame, (x, y), (x + w, y + h), 255, 2) cv.imshow('frame', img2) if cv.waitKey(60) & 0xFF == ord('q'): break else: break # 5.资源释放 cap.release() cv.destroyAllWindows()