{"id":976298,"date":"2024-12-27T06:20:27","date_gmt":"2024-12-26T22:20:27","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/976298.html"},"modified":"2024-12-27T06:20:29","modified_gmt":"2024-12-26T22:20:29","slug":"python%e7%94%a8opencv%e5%a6%82%e4%bd%95%e7%94%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/976298.html","title":{"rendered":"python\u7528opencv\u5982\u4f55\u7528"},"content":{"rendered":"

\"python\u7528opencv\u5982\u4f55\u7528\"<\/p>\n

\u5728Python\u4e2d\u4f7f\u7528OpenCV\uff0c\u53ef\u4ee5\u5b9e\u73b0\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684\u5404\u79cd\u529f\u80fd\uff0c\u5982\u56fe\u50cf\u5904\u7406\u3001\u89c6\u9891\u6355\u6349\u3001\u5bf9\u8c61\u8bc6\u522b\u7b49\u3002\u8981\u5728Python\u4e2d\u4f7f\u7528OpenCV\uff0c\u4f60\u9700\u8981\u5b89\u88c5OpenCV\u5e93\u3001\u5bfc\u5165\u5e76\u8bfb\u53d6\u56fe\u50cf\u6216\u89c6\u9891\u3001\u8fdb\u884c\u5904\u7406\u548c\u663e\u793a\u7ed3\u679c<\/strong>\u3002\u4e0b\u9762\uff0c\u6211\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5OpenCV<\/h3>\n<\/p>\n

1. \u5b89\u88c5OpenCV<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4f7f\u7528OpenCV\u4e4b\u524d\uff0c\u4f60\u9700\u8981\u5b89\u88c5OpenCV\u5e93\u3002\u4f7f\u7528pip\u53ef\u4ee5\u8f7b\u677e\u5730\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install opencv-python<\/p>\n
pip install opencv-python-headless  # \u5982\u679c\u4e0d\u9700\u8981GUI\u529f\u80fd<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u68c0\u67e5\u5b89\u88c5<\/h4>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u68c0\u67e5\u5b89\u88c5\u662f\u5426\u6210\u529f\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
print(cv2.__version__)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5982\u679c\u6ca1\u6709\u62a5\u9519\u5e76\u4e14\u8f93\u51fa\u4e86\u7248\u672c\u53f7\uff0c\u8bf4\u660eOpenCV\u5b89\u88c5\u6210\u529f\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/h3>\n<\/p>\n
1. \u8bfb\u53d6\u56fe\u50cf<\/h4>\n<\/p>\n
\u8981\u8bfb\u53d6\u56fe\u50cf\uff0c\u53ef\u4ee5\u4f7f\u7528cv2.imread()<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('path_to_image.jpg')<\/p>\n
\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u52a0\u8f7d\u6210\u529f<\/strong><\/h2>\n
if image is None:<\/p>\n
    print("Error: Could not open or find the image.")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
\u4f7f\u7528cv2.imshow()<\/code>\u51fd\u6570\u53ef\u4ee5\u663e\u793a\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
# \u663e\u793a\u56fe\u50cf<\/p>\n
cv2.imshow('Displayed Image', image)<\/p>\n
\u7b49\u5f85\u6309\u952e\u4e8b\u4ef6\uff0c0\u8868\u793a\u65e0\u9650\u7b49\u5f85<\/strong><\/h2>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
\u9500\u6bc1\u6240\u6709\u7a97\u53e3<\/strong><\/h2>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u56fe\u50cf\u5904\u7406\u57fa\u7840\u64cd\u4f5c<\/h3>\n<\/p>\n
1. \u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6<\/h4>\n<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u5728\u5f88\u591a\u5904\u7406\u64cd\u4f5c\u4e2d\u662f\u57fa\u7840\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u56fe\u50cf\u7f29\u653e<\/h4>\n<\/p>\n
\u7f29\u653e\u56fe\u50cf\u53ef\u4ee5\u4f7f\u7528cv2.resize()<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
resized_image = cv2.resize(image, (width, height))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u56fe\u50cf\u88c1\u526a<\/h4>\n<\/p>\n
\u88c1\u526a\u56fe\u50cf\u53ef\u4ee5\u901a\u8fc7\u6570\u7ec4\u5207\u7247\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
cropped_image = image[y_start:y_end, x_start:x_end]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u56fe\u50cf\u6ee4\u6ce2\u548c\u53d8\u6362<\/h3>\n<\/p>\n
1. \u56fe\u50cf\u5e73\u6ed1<\/h4>\n<\/p>\n
\u5e73\u6ed1\u56fe\u50cf\u53ef\u4ee5\u4f7f\u7528\u9ad8\u65af\u6a21\u7cca\uff1a<\/p>\n<\/p>\n
blurred_image = cv2.GaussianBlur(image, (5, 5), 0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8fb9\u7f18\u68c0\u6d4b<\/h4>\n<\/p>\n
\u4f7f\u7528Canny\u8fb9\u7f18\u68c0\u6d4b\u5668\u53ef\u4ee5\u68c0\u6d4b\u8fb9\u7f18\uff1a<\/p>\n<\/p>\n
edges = cv2.Canny(image, 100, 200)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u89c6\u9891\u5904\u7406<\/h3>\n<\/p>\n
1. \u8bfb\u53d6\u89c6\u9891<\/h4>\n<\/p>\n
\u901a\u8fc7cv2.VideoCapture()<\/code>\u8bfb\u53d6\u89c6\u9891\uff1a<\/p>\n<\/p>\n
cap = cv2.VideoCapture('path_to_video.mp4')<\/p>\n
while cap.isOpened():<\/p>\n
    ret, frame = cap.read()<\/p>\n
    if not ret:<\/p>\n
        break<\/p>\n
    cv2.imshow('Video Frame', frame)<\/p>\n
    if cv2.waitKey(1) & 0xFF == ord('q'):<\/p>\n
        break<\/p>\n
cap.release()<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6355\u83b7\u6444\u50cf\u5934<\/h4>\n<\/p>\n
\u4f7f\u7528\u6444\u50cf\u5934\u6355\u83b7\u89c6\u9891\u6d41\uff1a<\/p>\n<\/p>\n
cap = cv2.VideoCapture(0)  # 0\u4ee3\u8868\u9ed8\u8ba4\u6444\u50cf\u5934<\/p>\n
while True:<\/p>\n
    ret, frame = cap.read()<\/p>\n
    cv2.imshow('Webcam', frame)<\/p>\n
    if cv2.waitKey(1) & 0xFF == ord('q'):<\/p>\n
        break<\/p>\n
cap.release()<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5bf9\u8c61\u68c0\u6d4b\u548c\u8bc6\u522b<\/h3>\n<\/p>\n
1. \u4eba\u8138\u68c0\u6d4b<\/h4>\n<\/p>\n
\u4f7f\u7528\u9884\u8bad\u7ec3\u7684Haar\u7ea7\u8054\u5206\u7c7b\u5668\u8fdb\u884c\u4eba\u8138\u68c0\u6d4b\uff1a<\/p>\n<\/p>\n
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')<\/p>\n
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
faces = face_cascade.detectMultiScale(gray, 1.1, 4)<\/p>\n
for (x, y, w, h) in faces:<\/p>\n
    cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)<\/p>\n
cv2.imshow('Detected Faces', image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8f6e\u5ed3\u68c0\u6d4b<\/h4>\n<\/p>\n
OpenCV\u63d0\u4f9b\u4e86\u8f6e\u5ed3\u68c0\u6d4b\u529f\u80fd\uff1a<\/p>\n<\/p>\n
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)<\/p>\n
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)<\/p>\n
cv2.drawContours(image, contours, -1, (0, 255, 0), 3)<\/p>\n
cv2.imshow('Contours', image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u56fe\u50cf\u589e\u5f3a\u548c\u53d8\u6362<\/h3>\n<\/p>\n
1. \u76f4\u65b9\u56fe\u5747\u8861\u5316<\/h4>\n<\/p>\n
\u7528\u4e8e\u589e\u5f3a\u5bf9\u6bd4\u5ea6\uff1a<\/p>\n<\/p>\n
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
equalized_image = cv2.equalizeHist(gray)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u56fe\u50cf\u65cb\u8f6c<\/h4>\n<\/p>\n
\u4f7f\u7528OpenCV\u7684\u4eff\u5c04\u53d8\u6362\u8fdb\u884c\u56fe\u50cf\u65cb\u8f6c\uff1a<\/p>\n<\/p>\n
(h, w) = image.shape[:2]<\/p>\n
center = (w \/\/ 2, h \/\/ 2)<\/p>\n
M = cv2.getRotationMatrix2D(center, angle, 1.0)<\/p>\n
rotated_image = cv2.warpAffine(image, M, (w, h))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u989c\u8272\u7a7a\u95f4\u8f6c\u6362<\/h3>\n<\/p>\n
1. \u8f6c\u6362\u4e3aHSV<\/h4>\n<\/p>\n
HSV\u989c\u8272\u7a7a\u95f4\u5728\u989c\u8272\u68c0\u6d4b\u4e2d\u975e\u5e38\u6709\u7528\uff1a<\/p>\n<\/p>\n
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u9762\u7f69\u521b\u5efa<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u989c\u8272\u8303\u56f4\u521b\u5efa\u9762\u7f69\uff1a<\/p>\n<\/p>\n
lower_bound = np.array([lower_hue, lower_saturation, lower_value])<\/p>\n
upper_bound = np.array([upper_hue, upper_saturation, upper_value])<\/p>\n
mask = cv2.inRange(hsv_image, lower_bound, upper_bound)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u7ed3\u8bba<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u4f7f\u7528OpenCV\u8fdb\u884c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u975e\u5e38\u5f3a\u5927\u548c\u7075\u6d3b\u3002\u901a\u8fc7\u638c\u63e1\u57fa\u672c\u7684\u56fe\u50cf\u548c\u89c6\u9891\u5904\u7406\u64cd\u4f5c\u3001\u5bf9\u8c61\u68c0\u6d4b\u548c\u989c\u8272\u7a7a\u95f4\u8f6c\u6362\u7b49\u529f\u80fd\uff0c\u4f60\u53ef\u4ee5\u5f00\u53d1\u51fa\u590d\u6742\u7684\u89c6\u89c9\u5e94\u7528<\/strong>\u3002OpenCV\u8fd8\u5728\u6301\u7eed\u66f4\u65b0\u548c\u6269\u5c55\u529f\u80fd\uff0c\u4f7f\u5f97\u5b83\u5728\u5b66\u672f\u7814\u7a76\u548c\u5de5\u4e1a\u5e94\u7528\u4e2d\u90fd\u5360\u6709\u4e00\u5e2d\u4e4b\u5730\u3002\u968f\u7740\u4f60\u7684\u6280\u672f\u6c34\u5e73\u4e0d\u65ad\u63d0\u9ad8\uff0c\u4f60\u5c06\u80fd\u591f\u66f4\u597d\u5730\u5229\u7528OpenCV\u7684\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u6df1\u5ea6\u5b66\u4e60\u96c6\u6210\u548c3D\u89c6\u89c9\u5904\u7406\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5b89\u88c5OpenCV\u5e93\uff1f<\/strong>
\u5728Python\u4e2d\u4f7f\u7528OpenCV\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u6765\u5b89\u88c5\u3002\u6253\u5f00\u7ec8\u7aef\u6216\u547d\u4ee4\u63d0\u793a\u7b26\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1apip install opencv-python<\/code>\u3002\u5982\u679c\u9700\u8981\u989d\u5916\u7684\u529f\u80fd\uff08\u5982\u89c6\u9891\u5904\u7406\uff09\uff0c\u53ef\u4ee5\u5b89\u88c5opencv-python-headless<\/code>\u548copencv-contrib-python<\/code>\u3002\u786e\u4fdd\u5728\u5b89\u88c5\u524d\u66f4\u65b0pip\uff0c\u4ee5\u907f\u514d\u517c\u5bb9\u6027\u95ee\u9898\u3002<\/p>\n
\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u7684\u57fa\u672c\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
\u5728\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u65f6\uff0c\u901a\u5e38\u9700\u8981\u9075\u5faa\u51e0\u4e2a\u57fa\u672c\u6b65\u9aa4\u3002\u9996\u5148\uff0c\u901a\u8fc7cv2.imread()<\/code>\u51fd\u6570\u8bfb\u53d6\u56fe\u50cf\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u5404\u79cd\u5904\u7406\u51fd\u6570\uff0c\u5982cv2.cvtColor()<\/code>\u8f6c\u6362\u989c\u8272\u7a7a\u95f4\uff0ccv2.GaussianBlur()<\/code>\u8fdb\u884c\u6a21\u7cca\u5904\u7406\uff0c\u6216cv2.Canny()<\/code>\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b\u3002\u5904\u7406\u5b8c\u56fe\u50cf\u540e\uff0c\u4f7f\u7528cv2.imshow()<\/code>\u663e\u793a\u7ed3\u679c\uff0c\u5e76\u901a\u8fc7cv2.waitKey()<\/code>\u7b49\u5f85\u7528\u6237\u8f93\u5165\uff0c\u6700\u540e\u7528cv2.destroyAllWindows()<\/code>\u5173\u95ed\u6240\u6709\u7a97\u53e3\u3002<\/p>\n
OpenCV\u652f\u6301\u54ea\u4e9b\u56fe\u50cf\u6587\u4ef6\u683c\u5f0f\uff1f<\/strong>
OpenCV\u652f\u6301\u591a\u79cd\u56fe\u50cf\u6587\u4ef6\u683c\u5f0f\uff0c\u5305\u62ec\u4f46\u4e0d\u9650\u4e8eJPEG\u3001PNG\u3001BMP\u3001TIFF\u548cGIF\u3002\u4f7f\u7528cv2.imread()<\/code>\u51fd\u6570\u65f6\uff0c\u53ef\u4ee5\u76f4\u63a5\u8bfb\u53d6\u8fd9\u4e9b\u683c\u5f0f\u7684\u56fe\u50cf\u6587\u4ef6\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u4e0d\u540c\u683c\u5f0f\u7684\u56fe\u50cf\u5728\u5904\u7406\u65f6\u53ef\u80fd\u4f1a\u6709\u4e0d\u540c\u7684\u7279\u70b9\uff0c\u6bd4\u5982JPEG\u683c\u5f0f\u901a\u5e38\u4f1a\u6709\u538b\u7f29\u635f\u5931\uff0c\u800cPNG\u683c\u5f0f\u652f\u6301\u900f\u660e\u5ea6\u3002\u56e0\u6b64\uff0c\u9009\u62e9\u5408\u9002\u7684\u683c\u5f0f\u5bf9\u4e8e\u56fe\u50cf\u5904\u7406\u7684\u6548\u679c\u81f3\u5173\u91cd\u8981\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u4f7f\u7528OpenCV\uff0c\u53ef\u4ee5\u5b9e\u73b0\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684\u5404\u79cd\u529f\u80fd\uff0c\u5982\u56fe\u50cf\u5904\u7406\u3001\u89c6\u9891\u6355\u6349\u3001\u5bf9\u8c61\u8bc6\u522b\u7b49\u3002\u8981\u5728Py […]","protected":false},"author":3,"featured_media":976302,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/976298"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=976298"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/976298\/revisions"}],"predecessor-version":[{"id":976305,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/976298\/revisions\/976305"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/976302"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=976298"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=976298"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=976298"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}