\u65f6\u7a7a\u8fde\u7eed\u6027\u5047\u8bbe：\u76f8\u90bb\u70b9\u5177\u6709\u76f8\u4f3c\u7684\u8fd0\u52a8<\/p>\n<\/li>\n
\u5149\u6d41\u4f30\u8ba1\u7684\u6838\u5fc3\u601d\u60f3\u662f：\u5728\u89c6\u9891\u5e8f\u5217\u4e2d，\u901a\u8fc7\u5206\u6790\u76f8\u90bb\u5e27\u4e4b\u95f4\u50cf\u7d20\u5f3a\u5ea6\u7684\u53d8\u5316\u6765\u4f30\u8ba1\u50cf\u7d20\u7684\u8fd0\u52a8\u901f\u5ea6\u548c\u65b9\u5411\u3002<\/p>\n
\u4e8c\u3001\u51fd\u6570\u8be6\u89e3<\/h3>\n1.\u00a0cv2.goodFeaturesToTrack()\u00a0– Shi-Tomasi\u89d2\u70b9\u68c0\u6d4b<\/h4>\n
\u7528\u4e8e\u5728\u56fe\u50cf\u4e2d\u68c0\u6d4b\u89d2\u70b9（\u7279\u5f81\u70b9），\u8fd9\u4e9b\u70b9\u9002\u5408\u8fdb\u884c\u5149\u6d41\u8ddf\u8e2a\u3002<\/p>\n
\u53c2\u6570\u8bf4\u660e：<\/p>\n
p0 = cv2.goodFeaturesToTrack(image=old_gray, mask=None, **feature_params)<\/p>\n
- \n
- \n
image：\u8f93\u5165\u7684\u5355\u901a\u9053\u7070\u5ea6\u56fe\u50cf<\/p>\n<\/li>\n
- \n
maxCorners=100：\u8fd4\u56de\u7684\u6700\u5927\u89d2\u70b9\u6570\u91cf（\u22640\u8868\u793a\u65e0\u9650\u5236）<\/p>\n<\/li>\n
- \n
qualityLevel=0.3：\u89d2\u70b9\u8d28\u91cf\u9608\u503c（0-1\u4e4b\u95f4），\u503c\u8d8a\u5927\u68c0\u6d4b\u5230\u7684\u89d2\u70b9\u8d28\u91cf\u8d8a\u9ad8\u4f46\u6570\u91cf\u8d8a\u5c11<\/p>\n<\/li>\n
- \n
minDistance=7：\u89d2\u70b9\u4e4b\u95f4\u7684\u6700\u5c0f\u6b27\u6c0f\u8ddd\u79bb（\u50cf\u7d20），\u907f\u514d\u89d2\u70b9\u8fc7\u4e8e\u5bc6\u96c6<\/p>\n<\/li>\n
- \n
mask=None：\u53ef\u9009\u63a9\u7801，\u6307\u5b9a\u68c0\u6d4b\u533a\u57df<\/p>\n<\/li>\n
- \n
blockSize=3（\u9ed8\u8ba4）：\u8ba1\u7b97\u89d2\u70b9\u65f6\u8003\u8651\u7684\u90bb\u57df\u5927\u5c0f<\/p>\n<\/li>\n
- \n
useHarrisDetector=False（\u9ed8\u8ba4）：\u662f\u5426\u4f7f\u7528Harris\u89d2\u70b9\u68c0\u6d4b\u5668（False\u8868\u793a\u4f7f\u7528Shi-Tomasi）<\/p>\n<\/li>\n<\/ul>\n
\u8fd4\u56de\u503c：<\/p>\n
- \n
- \n
\u89d2\u70b9\u5750\u6807\u6570\u7ec4，\u5f62\u72b6\u4e3a(n, 1, 2)<\/p>\n<\/li>\n<\/ul>\n
2.\u00a0cv2.calcOpticalFlowPyrLK()\u00a0– Lucas-Kanade\u91d1\u5b57\u5854\u5149\u6d41\u6cd5<\/h4>\n
\u8fd9\u662f\u4ee3\u7801\u4e2d\u6838\u5fc3\u7684\u5149\u6d41\u8ba1\u7b97\u51fd\u6570，\u4f7f\u7528\u91d1\u5b57\u5854Lucas-Kanade\u65b9\u6cd5\u3002<\/p>\n
\u51fd\u6570\u539f\u578b：<\/p>\n
p1, st, err = cv2.calcOpticalFlowPyrLK(
\n prevImg=old_gray,
\n nextImg=frame_gray,
\n prevPts=p0,
\n nextPts=None,
\n **lk_params
\n)<\/p>\n\u53c2\u6570\u8be6\u7ec6\u8bf4\u660e：<\/p>\n
\u8f93\u5165\u53c2\u6570：<\/h5>\n
- \n
- \n
prevImg：\u524d\u4e00\u5e27\u7684\u7070\u5ea6\u56fe\u50cf<\/p>\n<\/li>\n
- \n
nextImg：\u5f53\u524d\u5e27\u7684\u7070\u5ea6\u56fe\u50cf<\/p>\n<\/li>\n
- \n
prevPts：\u524d\u4e00\u5e27\u4e2d\u9700\u8981\u8ddf\u8e2a\u7684\u7279\u5f81\u70b9\u5750\u6807<\/p>\n<\/li>\n
- \n
nextPts：\u521d\u59cb\u5316\u4e3aNone，\u51fd\u6570\u4f1a\u8ba1\u7b97\u5e76\u8fd4\u56de\u8fd9\u4e9b\u70b9\u5728\u5f53\u524d\u5e27\u4e2d\u7684\u4f4d\u7f6e<\/p>\n<\/li>\n<\/ul>\n
# Lucas-Kanade\u5149\u6d41\u6cd5\u53c2\u6570
\nlk_params = dict(
\n winSize=(15, 15), # \u7a97\u53e3\u5927\u5c0f
\n maxLevel=2, # \u91d1\u5b57\u5854\u5c42\u6570
\n # criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)
\n)<\/p>\nlk_params\u5b57\u5178\u4e2d\u7684\u53c2\u6570：<\/h5>\n
- \n
- \n
winSize=(15, 15)：\u641c\u7d22\u7a97\u53e3\u5927\u5c0f<\/p>\n
- \n
- \n
\u8f83\u5927\u7684\u7a97\u53e3\u53ef\u4ee5\u5904\u7406\u66f4\u5927\u7684\u8fd0\u52a8，\u4f46\u8ba1\u7b97\u66f4\u6162<\/p>\n<\/li>\n
- \n
\u8f83\u5c0f\u7684\u7a97\u53e3\u5bf9\u5feb\u901f\u8fd0\u52a8\u654f\u611f\u4f46\u53ef\u80fd\u4e22\u5931\u8ddf\u8e2a<\/p>\n<\/li>\n<\/ul>\n<\/li>\n
- \n
maxLevel=2：\u91d1\u5b57\u5854\u5c42\u6570<\/p>\n
- \n
- \n
\u91d1\u5b57\u5854\u7528\u4e8e\u5904\u7406\u5927\u4f4d\u79fb\u8fd0\u52a8<\/p>\n<\/li>\n
- \n
\u5c42\u65700\u8868\u793a\u4e0d\u4f7f\u7528\u91d1\u5b57\u5854，\u53ea\u4f7f\u7528\u539f\u59cb\u56fe\u50cf<\/p>\n<\/li>\n
- \n
\u6bcf\u589e\u52a0\u4e00\u5c42，\u56fe\u50cf\u5c3a\u5bf8\u51cf\u534a，\u53ef\u4ee5\u5904\u7406\u66f4\u5927\u7684\u8fd0\u52a8<\/p>\n<\/li>\n<\/ul>\n<\/li>\n
- \n
criteria（\u4ee3\u7801\u4e2d\u5df2\u6ce8\u91ca）：\u8fed\u4ee3\u7ec8\u6b62\u51c6\u5219<\/p>\n
- \n
- \n
\u4f8b\u5982：(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)<\/p>\n<\/li>\n
- \n
\u8868\u793a\u6700\u5927\u8fed\u4ee3\u6b21\u657010\u6216\u53d8\u5316\u5c0f\u4e8e0.03\u65f6\u505c\u6b62<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
\u53ef\u9009\u53c2\u6570（\u4ee3\u7801\u4e2d\u672a\u4f7f\u7528）：<\/h5>\n
- \n
- \n
flags：\u8ba1\u7b97\u9009\u9879<\/p>\n
- \n
- \n
0：\u666e\u901a\u65b9\u6cd5<\/p>\n<\/li>\n
- \n
cv2.OPTFLOW_LK_GET_MIN_EIGENVALS：\u4f7f\u7528\u6700\u5c0f\u7279\u5f81\u503c<\/p>\n<\/li>\n<\/ul>\n<\/li>\n
- \n
minEigThreshold：\u7279\u5f81\u503c\u9608\u503c，\u7528\u4e8e\u5224\u65ad\u8ddf\u8e2a\u8d28\u91cf<\/p>\n<\/li>\n<\/ul>\n
\u8fd4\u56de\u503c：<\/p>\n
- \n
- \n
p1：\u5f53\u524d\u5e27\u4e2d\u4f30\u8ba1\u7684\u7279\u5f81\u70b9\u4f4d\u7f6e<\/p>\n<\/li>\n
- \n
st：\u72b6\u6001\u5411\u91cf（\u4e0ep0\u540c\u5927\u5c0f）<\/p>\n
- \n
- \n
1：\u6210\u529f\u8ddf\u8e2a<\/p>\n<\/li>\n
- \n
0：\u8ddf\u8e2a\u5931\u8d25<\/p>\n<\/li>\n<\/ul>\n<\/li>\n
- \n
err：\u8bef\u5dee\u5411\u91cf，\u8868\u793a\u6bcf\u4e2a\u70b9\u7684\u8ddf\u8e2a\u8bef\u5dee<\/p>\n<\/li>\n<\/ul>\n
\u4e09\u3001\u7b97\u6cd5\u6d41\u7a0b\u89e3\u6790<\/h3>\n
1.\u00a0\u521d\u59cb\u5316\u9636\u6bb5<\/h4>\n
# \u8bfb\u53d6\u7b2c\u4e00\u5e27\u5e76\u8f6c\u4e3a\u7070\u5ea6
\nold_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)<\/p>\n# \u68c0\u6d4b\u89d2\u70b9\u4f5c\u4e3a\u7279\u5f81\u70b9
\np0 = cv2.goodFeaturesToTrack(old_gray, maxCorners=100, qualityLevel=0.3, minDistance=7)<\/p>\n2.\u00a0\u5149\u6d41\u8ba1\u7b97\u5faa\u73af<\/h4>\n
p1, st, err = cv2.calcOpticalFlowPyrLK(prevImg=old_gray, nextImg=frame_gray,prevPts=p0,nextPts=None,**lk_params)<\/p>\n
# \u9009\u62e9\u6210\u529f\u7684\u70b9（\u72b6\u6001\u4e3a1\u7684\u70b9）
\n good_new = p1[st == 1]
\n good_old = p0[st == 1]<\/p>\n# \u7ed8\u5236\u8f68\u8ff9
\n for i, (new, old) in enumerate(zip(good_new, good_old)):
\n # \u83b7\u53d6\u65b0\u70b9\u548c\u65e7\u70b9\u7684\u5750\u6807
\n a, b = new # \u65b0\u70b9\u5750\u6807
\n c, d = old # \u65e7\u70b9\u5750\u6807<\/p>\n# \u8f6c\u6362\u4e3a\u6574\u6570
\n a, b, c, d = int(a), int(b), int(c), int(d)<\/p>\n# \u5728\u63a9\u6a21\u4e0a\u7ed8\u5236\u7ebf\u6bb5，\u8fde\u63a5\u65b0\u70b9\u548c\u65e7\u70b9
\n mask = cv2.line(mask, (a, b), (c, d),
\n color[i].tolist(), thickness=2)
\n # \u663e\u793a\u63a9\u6a21
\n cv2.imshow('mask', mask)<\/p>\n# \u5c06\u63a9\u6a21\u6dfb\u52a0\u5230\u5f53\u524d\u5e27\u4e0a，\u751f\u6210\u6700\u7ec8\u56fe\u50cf
\n img = cv2.add(frame, mask)<\/p>\n# \u663e\u793a\u56fe\u50cf
\n cv2.imshow('frame', img)<\/p>\n# \u7b49\u5f8530ms，\u68c0\u6d4b\u662f\u5426\u6309\u4e0b\u4e86Esc\u952e（\u952e\u7801\u4e3a27）
\n key = cv2.waitKey(30)
\n if key == 27: # \u6309\u4e0bEsc\u952e，\u9000\u51fa\u5faa\u73af
\n break<\/p>\n# \u66f4\u65b0\u7070\u5ea6\u56fe\u548c\u7279\u5f81\u70b9
\n old_gray = frame_gray.copy()
\n p0 = good_new.reshape(-1, 1, 2) <\/p>\n\u56db\u3001\u91d1\u5b57\u5854Lucas-Kanade\u7b97\u6cd5\u539f\u7406<\/h3>\n
1.\u00a0\u57fa\u672cLucas-Kanade\u65b9\u7a0b<\/h4>\n
\u5bf9\u4e8e\u6bcf\u4e2a\u7279\u5f81\u70b9，\u5047\u8bbe\u5728\u4e00\u4e2a\u5c0f\u7684\u90bb\u57df\u7a97\u53e3\u5185，\u6240\u6709\u50cf\u7d20\u5177\u6709\u76f8\u540c\u7684\u8fd0\u52a8：<\/p>\n
I(x,y,t) = I(x+dx, y+dy, t+dt)<\/p>\n
\u901a\u8fc7\u6cf0\u52d2\u5c55\u5f00\u5f97\u5230\u5149\u6d41\u65b9\u7a0b：<\/p>\n
I_x * u + I_y * v + I_t = 0<\/p>\n
\u5176\u4e2d：<\/p>\n
- \n
- \n
I_x, I_y：\u56fe\u50cf\u5728x,y\u65b9\u5411\u7684\u68af\u5ea6<\/p>\n<\/li>\n
- \n
I_t：\u65f6\u95f4\u68af\u5ea6<\/p>\n<\/li>\n
- \n
u, v：x,y\u65b9\u5411\u7684\u901f\u5ea6<\/p>\n<\/li>\n<\/ul>\n
2.\u00a0\u91d1\u5b57\u5854\u65b9\u6cd5<\/h4>\n
- \n
- \n
\u6784\u5efa\u56fe\u50cf\u91d1\u5b57\u5854：\u4ece\u539f\u59cb\u56fe\u50cf\u5f00\u59cb，\u6bcf\u5c42\u5c3a\u5bf8\u51cf\u534a<\/p>\n<\/li>\n
- \n
\u4ece\u9876\u5c42\u5f00\u59cb\u8ba1\u7b97：\u5728\u4f4e\u5206\u8fa8\u7387\u5c42\u8ba1\u7b97\u7c97\u7565\u5149\u6d41<\/p>\n<\/li>\n
- \n
\u9010\u5c42\u7ec6\u5316：\u5c06\u7c97\u7565\u7ed3\u679c\u4f5c\u4e3a\u4e0b\u4e00\u5c42\u7684\u521d\u59cb\u503c<\/p>\n<\/li>\n
- \n
\u6700\u7ec8\u5728\u539f\u59cb\u5206\u8fa8\u7387\u5c42\u5f97\u5230\u7cbe\u786e\u7ed3\u679c<\/p>\n<\/li>\n<\/ul>\n
3.\u00a0\u7a97\u53e3\u641c\u7d22<\/h4>\n
\u5bf9\u4e8e\u6bcf\u4e2a\u7279\u5f81\u70b9，\u5728\u5176\u5468\u56f4\u7684winSize\u7a97\u53e3\u5185\u641c\u7d22\u6700\u4f73\u5339\u914d\u4f4d\u7f6e\u3002<\/p>\n
\u4e94\u3001\u5e94\u7528\u573a\u666f<\/h3>\n
- \n
\u8fd0\u52a8\u68c0\u6d4b\u4e0e\u5206\u6790<\/p>\n<\/li>\n
- \n
\u89c6\u9891\u7a33\u5b9a<\/p>\n<\/li>\n
- \n
\u76ee\u6807\u8ddf\u8e2a<\/p>\n<\/li>\n
- \n
\u52a8\u4f5c\u8bc6\u522b<\/p>\n<\/li>\n
- \n
\u4e09\u7ef4\u91cd\u5efa<\/p>\n<\/li>\n
- \n
\u81ea\u52a8\u9a7e\u9a76（\u8f66\u8f86\/\u884c\u4eba\u8fd0\u52a8\u4f30\u8ba1）<\/p>\n<\/li>\n
\u516d\u3001\u4f18\u7f3a\u70b9<\/h3>\n
\u4f18\u70b9：<\/h4>\n
- \n
- \n
\u8ba1\u7b97\u6548\u7387\u9ad8，\u9002\u5408\u5b9e\u65f6\u5e94\u7528<\/p>\n<\/li>\n
- \n
\u5bf9\u4eae\u5ea6\u53d8\u5316\u6709\u4e00\u5b9a\u9c81\u68d2\u6027<\/p>\n<\/li>\n
- \n
\u91d1\u5b57\u5854\u7ed3\u6784\u53ef\u4ee5\u5904\u7406\u8f83\u5927\u4f4d\u79fb<\/p>\n<\/li>\n<\/ul>\n
\u5c40\u9650\u6027：<\/h4>\n
- \n
- \n
\u4f9d\u8d56\u4e8e\u4eae\u5ea6\u6052\u5b9a\u5047\u8bbe<\/p>\n<\/li>\n
- \n
\u5bf9\u5feb\u901f\u8fd0\u52a8\u6216\u906e\u6321\u5904\u7406\u4e0d\u4f73<\/p>\n<\/li>\n
- \n
\u9700\u8981\u597d\u7684\u7279\u5f81\u70b9（\u89d2\u70b9）<\/p>\n<\/li>\n<\/ul>\n
\u4e03\u3001\u5b9e\u9645\u8fd0\u7528<\/h3>\n
import cv2
\nimport numpy as np<\/p>\n# \u8bfb\u53d6\u89c6\u9891\u6587\u4ef6
\ncap = cv2.VideoCapture('renwushipin.avi')<\/p>\n# \u8bfb\u53d6\u7b2c\u4e00\u5e27
\nret, old_frame = cap.read()<\/p>\n# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf
\nold_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)<\/p>\n# Shi-Tomasi\u89d2\u70b9\u68c0\u6d4b\u53c2\u6570
\nfeature_params = dict(
\n maxCorners=100, # \u6700\u5927\u89d2\u70b9\u6570\u91cf
\n qualityLevel=0.3, # \u89d2\u70b9\u8d28\u91cf\u7684\u9608\u503c
\n minDistance=7 # \u6700\u5c0f\u8ddd\u79bb，\u7528\u4e8e\u5206\u6563\u89d2\u70b9
\n)<\/p>\n# Shi-Tomasi\u89d2\u70b9\u68c0\u6d4b
\np0 = cv2.goodFeaturesToTrack(image=old_gray,mask=None,**feature_params)<\/p>\n# \u521b\u5efa\u7528\u4e8e\u7ed8\u5236\u8f68\u8ff9\u7684\u63a9\u6a21
\nmask = np.zeros_like(old_frame)<\/p>\n# Lucas-Kanade\u5149\u6d41\u6cd5\u53c2\u6570
\nlk_params = dict(
\n winSize=(15, 15), # \u7a97\u53e3\u5927\u5c0f
\n maxLevel=2, # \u91d1\u5b57\u5854\u5c42\u6570
\n # criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)
\n)
\n# \u751f\u6210\u968f\u673a\u989c\u8272\u7528\u4e8e\u7ed8\u5236\u8f68\u8ff9
\ncolor = np.random.randint(0, 255, (100, 3))<\/p>\nwhile True:
\n # \u8bfb\u53d6\u4e0b\u4e00\u5e27
\n ret, frame = cap.read()
\n if not ret:
\n break<\/p>\n# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe
\n frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)<\/p>\n# \u8ba1\u7b97\u5149\u6d41
\n p1, st, err = cv2.calcOpticalFlowPyrLK(prevImg=old_gray, nextImg=frame_gray,prevPts=p0,nextPts=None,**lk_params)<\/p>\n# \u9009\u62e9\u6210\u529f\u7684\u70b9（\u72b6\u6001\u4e3a1\u7684\u70b9）
\n good_new = p1[st == 1]
\n good_old = p0[st == 1]<\/p>\n# \u7ed8\u5236\u8f68\u8ff9
\n for i, (new, old) in enumerate(zip(good_new, good_old)):
\n # \u83b7\u53d6\u65b0\u70b9\u548c\u65e7\u70b9\u7684\u5750\u6807
\n a, b = new # \u65b0\u70b9\u5750\u6807
\n c, d = old # \u65e7\u70b9\u5750\u6807<\/p>\n# \u8f6c\u6362\u4e3a\u6574\u6570
\n a, b, c, d = int(a), int(b), int(c), int(d)<\/p>\n# \u5728\u63a9\u6a21\u4e0a\u7ed8\u5236\u7ebf\u6bb5，\u8fde\u63a5\u65b0\u70b9\u548c\u65e7\u70b9
\n mask = cv2.line(mask, (a, b), (c, d),
\n color[i].tolist(), thickness=2)
\n # \u663e\u793a\u63a9\u6a21
\n cv2.imshow('mask', mask)<\/p>\n# \u5c06\u63a9\u6a21\u6dfb\u52a0\u5230\u5f53\u524d\u5e27\u4e0a，\u751f\u6210\u6700\u7ec8\u56fe\u50cf
\n img = cv2.add(frame, mask)<\/p>\n# \u663e\u793a\u56fe\u50cf
\n cv2.imshow('frame', img)<\/p>\n# \u7b49\u5f8530ms，\u68c0\u6d4b\u662f\u5426\u6309\u4e0b\u4e86Esc\u952e（\u952e\u7801\u4e3a27）
\n key = cv2.waitKey(30)
\n if key == 27: # \u6309\u4e0bEsc\u952e，\u9000\u51fa\u5faa\u73af
\n break<\/p>\n# \u66f4\u65b0\u7070\u5ea6\u56fe\u548c\u7279\u5f81\u70b9
\n old_gray = frame_gray.copy()
\n p0 = good_new.reshape(-1, 1, 2) # \u91cd\u65b0\u6574\u7406\u7279\u5f81\u70b9\u4e3a\u9002\u5408\u4e0b\u6b21\u8ba1\u7b97\u7684\u5f62\u72b6（n,2）->（n,1,2）<\/p>\n# \u91ca\u653e\u8d44\u6e90
\ncap.release()
\ncv2.destroyAllWindows()<\/p>\n![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260131142450-697e10b215397.png\")
<\/p>\n\u8fd9\u4e2a\u4ee3\u7801\u793a\u4f8b\u5b8c\u6574\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528OpenCV\u5b9e\u73b0\u57fa\u4e8e\u7279\u5f81\u70b9\u7684\u5149\u6d41\u8ddf\u8e2a，\u5305\u62ec\u7279\u5f81\u70b9\u68c0\u6d4b\u3001\u5149\u6d41\u8ba1\u7b97\u3001\u8f68\u8ff9\u7ed8\u5236\u7b49\u5b8c\u6574\u6d41\u7a0b\u3002\u4ece\u4ee3\u7801\u4f17，\u53ef\u4ee5\u770b\u5230，\u8fd9\u4e2a\u4ee3\u7801\u4f7f\u7528\u7684\u662f\u89c6\u9891，\u548c\u4e4b\u524d\u4f7f\u7528\u56fe\u7247\u65f6\u7684\u4ee3\u7801\u8bfb\u53d6\u65b9\u5f0f\u6709\u6240\u4e0d\u540c，\u90a3\u4e48\u6709\u4ec0\u4e48\u4e0d\u540c\u5462？<\/p>\n
\u516b\u3001\u89c6\u9891\u8bfb\u53d6\u65b9\u6cd5\u53ca\u4e0e\u56fe\u7247\u7684\u533a\u522b<\/h3>\n
1.\u89c6\u9891\u8bfb\u53d6\u7684\u8be6\u7ec6\u673a\u5236<\/h4>\n
1）\u00a0VideoCapture\u5bf9\u8c61<\/h5>\n
# \u521b\u5efa\u89c6\u9891\u6355\u83b7\u5bf9\u8c61
\ncap = cv2.VideoCapture('renwushipin.avi')
\n# \u6216\u4ece\u6444\u50cf\u5934\u8bfb\u53d6
\n# cap = cv2.VideoCapture(0) # 0\u8868\u793a\u9ed8\u8ba4\u6444\u50cf\u5934<\/p>\n\u5173\u952e\u5c5e\u6027\u548c\u65b9\u6cd5：<\/p>\n
- \n
- \n
cap.isOpened(): \u68c0\u67e5\u662f\u5426\u6210\u529f\u6253\u5f00<\/p>\n<\/li>\n
- \n
cap.get(propId): \u83b7\u53d6\u89c6\u9891\u5c5e\u6027（\u5e27\u7387\u3001\u5c3a\u5bf8\u7b49）<\/p>\n<\/li>\n
- \n
cap.set(propId, value): \u8bbe\u7f6e\u89c6\u9891\u5c5e\u6027<\/p>\n<\/li>\n<\/ul>\n
2）\u9010\u5e27\u8bfb\u53d6\u5faa\u73af<\/h5>\n
while True:
\n # \u8bfb\u53d6\u4e00\u5e27
\n ret, frame = cap.read()<\/p>\n# ret: \u5e03\u5c14\u503c，\u8868\u793a\u662f\u5426\u6210\u529f\u8bfb\u53d6
\n # frame: \u5f53\u524d\u5e27\u7684\u56fe\u50cf\u6570\u636e（numpy\u6570\u7ec4）<\/p>\nif not ret: # \u89c6\u9891\u7ed3\u675f\u6216\u8bfb\u53d6\u5931\u8d25
\n break<\/p>\n# \u5904\u7406\u5f53\u524d\u5e27（\u5149\u6d41\u8ba1\u7b97\u3001\u663e\u793a\u7b49）<\/p>\n
2. \u5bf9\u6bd4<\/h4>\n
1）\u6570\u636e\u7ed3\u6784\u4e0d\u540c<\/h5>\n
# \u89c6\u9891\u8bfb\u53d6 – \u8fde\u7eed\u5e27\u5e8f\u5217
\ncap = cv2.VideoCapture('video.avi')
\nret, frame = cap.read() # \u6bcf\u6b21\u8bfb\u53d6\u4e00\u5e27<\/p>\n# \u56fe\u7247\u8bfb\u53d6 – \u5355\u5f20\u56fe\u50cf
\nimg = cv2.imread('image.jpg') # \u4e00\u6b21\u6027\u8bfb\u53d6\u6574\u4e2a\u56fe\u50cf<\/p>\n\u5982\u679c\u60f3\u4f7f\u7528\u6444\u50cf\u5934，\u53ea\u9700\u8981\u5c06cap = cv2.VideoCapture('video.avi')\u6539\u4e3acap = cv2.VideoCapture(0)<\/p>\n
2）\u00a0\u8bfb\u53d6\u65b9\u5f0f\u4e0d\u540c<\/h5>\n
# \u89c6\u9891 – \u5faa\u73af\u9010\u5e27\u8bfb\u53d6
\nwhile True:
\n ret, frame = cap.read() # \u9700\u8981\u624b\u52a8\u63a7\u5236\u8bfb\u53d6
\n if not ret: # \u68c0\u67e5\u662f\u5426\u8bfb\u53d6\u6210\u529f
\n break
\n # \u5904\u7406\u6bcf\u4e00\u5e27<\/p>\n# \u56fe\u7247 – \u4e00\u6b21\u6027\u8bfb\u53d6
\nimg = cv2.imread('image.jpg') # \u5355\u6b21\u8c03\u7528\u8bfb\u53d6\u6240\u6709\u6570\u636e<\/p>\n3.\u5728\u5149\u6d41\u4f30\u8ba1\u4e2d\u7684\u7279\u6b8a\u610f\u4e49<\/h4>\n
1）\u00a0\u65f6\u95f4\u8fde\u7eed\u6027\u8981\u6c42<\/h5>\n
# \u5149\u6d41\u9700\u8981\u8fde\u7eed\u5e27\u7684\u65f6\u95f4\u5e8f\u5217
\nold_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)<\/p>\nwhile True:
\n ret, frame = cap.read()
\n frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)<\/p>\n# \u8ba1\u7b97\u76f8\u90bb\u5e27\u4e4b\u95f4\u7684\u8fd0\u52a8
\n p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)<\/p>\n# \u66f4\u65b0\u4e3a\u4e0b\u4e00\u5e27\u51c6\u5907
\n old_gray = frame_gray.copy()<\/p>\n2）\u00a0\u7279\u5f81\u70b9\u8ffd\u8e2a\u7684\u6301\u7eed\u6027<\/h5>\n
# \u9700\u8981\u4fdd\u6301\u7279\u5f81\u70b9\u7684\u8fde\u7eed\u6027
\np0 = good_new.reshape(-1, 1, 2) # \u4e3a\u4e0b\u4e00\u5e27\u66f4\u65b0\u7279\u5f81\u70b9<\/p>\n# \u5728\u89c6\u9891\u4e2d，\u7279\u5f81\u70b9\u53ef\u4ee5\u6301\u7eed\u8ddf\u8e2a\u591a\u5e27
\n# \u5728\u9759\u6001\u56fe\u7247\u5bf9\u4e2d，\u53ea\u80fd\u8ba1\u7b97\u5355\u6b21\u8fd0\u52a8<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e00\u3001\u5149\u6d41\u4f30\u8ba1\u6982\u5ff5\u5149\u6d41（Optical Flow）\u00a0\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7528\u4e8e\u63cf\u8ff0\u56fe\u50cf\u4e2d\u50cf\u7d20\u70b9\u8fd0\u52a8\u6a21\u5f0f\u7684\u6280\u672f\u3002\u5b83\u57fa\u4e8e\u4ee5\u4e0b\u4e24\u4e2a\u57fa\u672c\u5047\u8bbe：\u4eae\u5ea6\u6052\u5b9a\u5047\u8bbe：\u540c\u4e00\u7269\u4f53\u70b9\u5728\u8fde\u7eed\u5e27\u4e2d\u7684\u4eae\u5ea6\u4fdd\u6301\u4e0d\u53d8\u65f6\u7a7a\u8fde\u7eed\u6027\u5047\u8bbe：\u76f8\u90bb\u70b9\u5177\u6709\u76f8\u4f3c\u7684\u8fd0\u52a8\u5149\u6d41\u4f30\u8ba1\u7684\u6838\u5fc3\u601d\u60f3\u662f：\u5728\u89c6\u9891\u5e8f\u5217\u4e2d，\u901a\u8fc7\u5206\u6790\u76f8\u90bb\u5e27\u4e4b\u95f4\u50cf\u7d20\u5f3a\u5ea6\u7684\u53d8\u5316\u6765\u4f30\u8ba1\u50cf\u7d20\u7684\u8fd0\u52a8\u901f\u5ea6\u548c\u65b9\u5411\u3002\u4e8c\u3001\u51fd\u6570\u8be6\u89e31.\u00a0cv2.goodFeaturesToTrack()\u00a0– Sh<\/p>\n","protected":false},"author":2,"featured_media":69615,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[7322,7323,7324,1460,82,81,523],"topic":[],"class_list":["post-69616","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-7322","tag-7323","tag-7324","tag-opencv","tag-pycharm","tag-python","tag-523"],"yoast_head":"\n
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