{"id":1068297,"date":"2024-12-31T16:42:10","date_gmt":"2024-12-31T08:42:10","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1068297.html"},"modified":"2024-12-31T16:42:13","modified_gmt":"2024-12-31T08:42:13","slug":"python-%e5%a6%82%e4%bd%95%e8%af%86%e5%88%ab%e4%b8%80%e4%b8%aa%e7%89%a9%e4%bd%93%e7%9a%84%e9%a2%9c%e8%89%b2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1068297.html","title":{"rendered":"python \u5982\u4f55\u8bc6\u522b\u4e00\u4e2a\u7269\u4f53\u7684\u989c\u8272"},"content":{"rendered":"

\"python<\/p>\n

Python\u8bc6\u522b\u4e00\u4e2a\u7269\u4f53\u7684\u989c\u8272\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u5305\u62ecOpenCV\u5e93\u3001PIL\u5e93\u3001\u548c\u8272\u5f69\u7a7a\u95f4\u8f6c\u6362\u7b49\u3002<\/strong> \u5176\u4e2d\uff0cOpenCV<\/strong>\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u5de5\u5177\u3002\u901a\u8fc7OpenCV<\/strong>\uff0c\u6211\u4eec\u53ef\u4ee5\u8bfb\u53d6\u56fe\u50cf\u3001\u8f6c\u6362\u8272\u5f69\u7a7a\u95f4\u3001\u63d0\u53d6\u989c\u8272\u533a\u57df\u7b49\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528OpenCV\u8bc6\u522b\u7269\u4f53\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u8bfb\u53d6\u56fe\u50cf<\/h3>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u8bfb\u53d6\u56fe\u50cf\uff0c\u8fd9\u53ef\u4ee5\u901a\u8fc7OpenCV<\/strong>\u7684cv2.imread()<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u3002\u8fd9\u4e2a\u51fd\u6570\u5141\u8bb8\u6211\u4eec\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u5b58\u50a8\u4e3a\u4e00\u4e2aNumPy\u6570\u7ec4\u3002<\/p>\n<\/p>\n

import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('path_to_image.jpg')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8f6c\u6362\u8272\u5f69\u7a7a\u95f4<\/h3>\n<\/p>\n
\u56fe\u50cf\u901a\u5e38\u4ee5BGR<\/strong>\uff08\u84dd-\u7eff-\u7ea2\uff09\u683c\u5f0f\u8bfb\u53d6\uff0c\u4f46\u4e3a\u4e86\u66f4\u65b9\u4fbf\u5730\u8fdb\u884c\u989c\u8272\u8bc6\u522b\uff0c\u6211\u4eec\u901a\u5e38\u5c06\u5176\u8f6c\u6362\u4e3aHSV<\/strong>\uff08\u8272\u8c03-\u9971\u548c\u5ea6-\u660e\u5ea6\uff09\u8272\u5f69\u7a7a\u95f4\u3002HSV<\/strong>\u8272\u5f69\u7a7a\u95f4\u66f4\u52a0\u7b26\u5408\u4eba\u7c7b\u5bf9\u989c\u8272\u7684\u611f\u77e5\uff0c\u4f7f\u989c\u8272\u5206\u5272\u548c\u8bc6\u522b\u66f4\u52a0\u76f4\u89c2\u3002<\/p>\n<\/p>\n
# \u5c06\u56fe\u50cf\u4eceBGR\u8f6c\u6362\u4e3aHSV<\/p>\n
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5b9a\u4e49\u989c\u8272\u8303\u56f4<\/h3>\n<\/p>\n
\u4e3a\u4e86\u8bc6\u522b\u7279\u5b9a\u989c\u8272\uff0c\u6211\u4eec\u9700\u8981\u5b9a\u4e49\u989c\u8272\u7684HSV\u8303\u56f4\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy<\/strong>\u6570\u7ec4\u6765\u5b9a\u4e49\u8fd9\u4e9b\u8303\u56f4\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u5e38\u89c1\u989c\u8272\u7684HSV\u8303\u56f4\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u5b9a\u4e49\u7ea2\u8272\u7684HSV\u8303\u56f4<\/strong><\/h2>\n
red_lower = np.array([0, 120, 70])<\/p>\n
red_upper = np.array([10, 255, 255])<\/p>\n
\u5b9a\u4e49\u7eff\u8272\u7684HSV\u8303\u56f4<\/strong><\/h2>\n
green_lower = np.array([36, 25, 25])<\/p>\n
green_upper = np.array([86, 255, 255])<\/p>\n
\u5b9a\u4e49\u84dd\u8272\u7684HSV\u8303\u56f4<\/strong><\/h2>\n
blue_lower = np.array([94, 80, 2])<\/p>\n
blue_upper = np.array([126, 255, 255])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u521b\u5efa\u63a9\u819c<\/h3>\n<\/p>\n
\u4f7f\u7528\u5b9a\u4e49\u7684\u989c\u8272\u8303\u56f4\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u63a9\u819c\u3002\u63a9\u819c\u662f\u4e00\u4e2a\u4e0e\u539f\u59cb\u56fe\u50cf\u5927\u5c0f\u76f8\u540c\u7684\u4e8c\u503c\u56fe\u50cf\uff0c\u5176\u4e2d\u767d\u8272\u533a\u57df\u8868\u793a\u989c\u8272\u7b26\u5408\u8303\u56f4\u7684\u50cf\u7d20\uff0c\u9ed1\u8272\u533a\u57df\u8868\u793a\u4e0d\u7b26\u5408\u8303\u56f4\u7684\u50cf\u7d20\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u7ea2\u8272\u63a9\u819c<\/p>\n
red_mask = cv2.inRange(hsv_image, red_lower, red_upper)<\/p>\n
\u521b\u5efa\u7eff\u8272\u63a9\u819c<\/strong><\/h2>\n
green_mask = cv2.inRange(hsv_image, green_lower, green_upper)<\/p>\n
\u521b\u5efa\u84dd\u8272\u63a9\u819c<\/strong><\/h2>\n
blue_mask = cv2.inRange(hsv_image, blue_lower, blue_upper)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5e94\u7528\u63a9\u819c<\/h3>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u5c06\u63a9\u819c\u5e94\u7528\u5230\u539f\u59cb\u56fe\u50cf\uff0c\u4ee5\u63d0\u53d6\u7279\u5b9a\u989c\u8272\u7684\u533a\u57df\u3002<\/p>\n<\/p>\n
# \u5e94\u7528\u7ea2\u8272\u63a9\u819c<\/p>\n
red_result = cv2.bitwise_and(image, image, mask=red_mask)<\/p>\n
\u5e94\u7528\u7eff\u8272\u63a9\u819c<\/strong><\/h2>\n
green_result = cv2.bitwise_and(image, image, mask=green_mask)<\/p>\n
\u5e94\u7528\u84dd\u8272\u63a9\u819c<\/strong><\/h2>\n
blue_result = cv2.bitwise_and(image, image, mask=blue_mask)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u663e\u793a\u7ed3\u679c<\/h3>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV<\/strong>\u7684cv2.imshow()<\/code>\u51fd\u6570\u6765\u663e\u793a\u7ed3\u679c\u56fe\u50cf\u3002<\/p>\n<\/p>\n
# \u663e\u793a\u7ed3\u679c\u56fe\u50cf<\/p>\n
cv2.imshow('Red Result', red_result)<\/p>\n
cv2.imshow('Green Result', green_result)<\/p>\n
cv2.imshow('Blue Result', blue_result)<\/p>\n
\u7b49\u5f85\u6309\u952e\u6309\u4e0b<\/strong><\/h2>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV<\/strong>\u6765\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u7279\u5b9a\u989c\u8272\u3002\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\u8bfb\u53d6\u56fe\u50cf\u3001\u8f6c\u6362\u8272\u5f69\u7a7a\u95f4\u3001\u5b9a\u4e49\u989c\u8272\u8303\u56f4\u3001\u521b\u5efa\u63a9\u819c\u3001\u5e94\u7528\u63a9\u819c\u3001\u663e\u793a\u7ed3\u679c\u3002<\/strong>\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u7528\u4e8e\u9759\u6001\u56fe\u50cf\uff0c\u8fd8\u53ef\u4ee5\u7528\u4e8e\u5b9e\u65f6\u89c6\u9891\u6d41\u4e2d\u7684\u989c\u8272\u8bc6\u522b\u3002<\/p>\n<\/p>\n
\u516b\u3001\u6269\u5c55\u5e94\u7528<\/h3>\n<\/p>\n
\u989c\u8272\u8bc6\u522b\u5728\u8bb8\u591a\u5e94\u7528\u4e2d\u90fd\u975e\u5e38\u6709\u7528\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    \n
  1. \u7269\u4f53\u68c0\u6d4b\u4e0e\u8ddf\u8e2a<\/strong>\uff1a\u5728\u673a\u5668\u4eba\u89c6\u89c9\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u989c\u8272\u8bc6\u522b\u7279\u5b9a\u7269\u4f53\uff0c\u5e76\u8fdb\u884c\u8ffd\u8e2a\u3002<\/li>\n
  2. \u56fe\u50cf\u5206\u5272<\/strong>\uff1a\u5728\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u989c\u8272\u8bc6\u522b\u8fdb\u884c\u56fe\u50cf\u5206\u5272\uff0c\u5c06\u56fe\u50cf\u4e2d\u7684\u4e0d\u540c\u90e8\u5206\u5206\u79bb\u51fa\u6765\u3002<\/li>\n
  3. \u589e\u5f3a\u73b0\u5b9e<\/strong>\uff1a\u5728\u589e\u5f3a\u73b0\u5b9e\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u989c\u8272\u8bc6\u522b\u7279\u5b9a\u6807\u8bb0\uff0c\u4ece\u800c\u5b9e\u73b0\u865a\u62df\u5185\u5bb9\u7684\u53e0\u52a0\u3002<\/li>\n
  4. \u5de5\u4e1a\u68c0\u6d4b<\/strong>\uff1a\u5728\u5de5\u4e1a\u751f\u4ea7\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u989c\u8272\u8bc6\u522b\u68c0\u6d4b\u4ea7\u54c1\u7684\u8d28\u91cf\uff0c\u4f8b\u5982\u8bc6\u522b\u7455\u75b5\u6216\u9519\u8bef\u7684\u989c\u8272\u3002<\/li>\n<\/ol>\n
    \u4e5d\u3001\u4ee3\u7801\u4f18\u5316\u4e0e\u6539\u8fdb<\/h3>\n<\/p>\n
    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5bf9\u4ee3\u7801\u8fdb\u884c\u4f18\u5316\u548c\u6539\u8fdb\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
      \n
    • \u52a8\u6001\u8c03\u6574\u989c\u8272\u8303\u56f4<\/strong>\uff1a\u5728\u4e0d\u540c\u7684\u5149\u7167\u6761\u4ef6\u4e0b\uff0c\u989c\u8272\u7684HSV\u503c\u53ef\u80fd\u4f1a\u6709\u6240\u4e0d\u540c\u3002\u6211\u4eec\u53ef\u4ee5\u52a8\u6001\u8c03\u6574\u989c\u8272\u8303\u56f4\uff0c\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u73af\u5883\u3002<\/li>\n
    • \u566a\u58f0\u8fc7\u6ee4<\/strong>\uff1a\u5728\u521b\u5efa\u63a9\u819c\u540e\uff0c\u53ef\u80fd\u4f1a\u6709\u4e00\u4e9b\u566a\u58f0\u50cf\u7d20\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5f62\u6001\u5b66\u64cd\u4f5c\uff08\u5982\u8150\u8680\u548c\u81a8\u80c0\uff09\u6765\u8fc7\u6ee4\u6389\u8fd9\u4e9b\u566a\u58f0\u3002<\/li>\n
    • \u591a\u989c\u8272\u8bc6\u522b<\/strong>\uff1a\u5982\u679c\u9700\u8981\u540c\u65f6\u8bc6\u522b\u591a\u79cd\u989c\u8272\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u591a\u4e2a\u63a9\u819c\u8fdb\u884c\u903b\u8f91\u8fd0\u7b97\uff08\u5982\u6309\u4f4d\u6216\u8fd0\u7b97\uff09\uff0c\u4ee5\u521b\u5efa\u4e00\u4e2a\u7efc\u5408\u63a9\u819c\u3002<\/li>\n<\/ul>\n
      # \u52a8\u6001\u8c03\u6574\u989c\u8272\u8303\u56f4\uff08\u793a\u4f8b\uff09<\/p>\n
      def adjust_hsv_range(hsv, lower_bound, upper_bound):<\/p>\n
          # \u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u52a8\u6001\u8c03\u6574HSV\u8303\u56f4<\/p>\n
          pass<\/p>\n
      \u566a\u58f0\u8fc7\u6ee4\uff08\u793a\u4f8b\uff09<\/strong><\/h2>\n
      kernel = np.ones((5, 5), np.uint8)<\/p>\n
      red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel)<\/p>\n
      red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_CLOSE, kernel)<\/p>\n
      \u591a\u989c\u8272\u8bc6\u522b\uff08\u793a\u4f8b\uff09<\/strong><\/h2>\n
      combined_mask = cv2.bitwise_or(red_mask, green_mask)<\/p>\n
      combined_mask = cv2.bitwise_or(combined_mask, blue_mask)<\/p>\n
      combined_result = cv2.bitwise_and(image, image, mask=combined_mask)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u4e00\u6b65\u4f18\u5316\u548c\u6539\u8fdb\u989c\u8272\u8bc6\u522b\u7684\u6548\u679c\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u5e94\u7528\u7684\u9700\u6c42\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u4f7f\u7528Python\u548cOpenCV\u8fdb\u884c\u989c\u8272\u8bc6\u522b\uff0c\u5e76\u5e94\u7528\u5230\u5b9e\u9645\u9879\u76ee\u4e2d\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u4f7f\u7528Python\u8bc6\u522b\u7269\u4f53\u7684\u989c\u8272\uff1f<\/strong>
      \u4f7f\u7528Python\u8bc6\u522b\u7269\u4f53\u989c\u8272\u7684\u5e38\u89c1\u65b9\u6cd5\u662f\u501f\u52a9\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5982OpenCV\u3002\u901a\u8fc7\u6444\u50cf\u5934\u83b7\u53d6\u56fe\u50cf\uff0c\u5c06\u5176\u8f6c\u6362\u4e3aHSV\u989c\u8272\u7a7a\u95f4\uff0c\u7136\u540e\u5b9a\u4e49\u7279\u5b9a\u989c\u8272\u8303\u56f4\uff0c\u6700\u540e\u5229\u7528\u63a9\u819c\u6765\u8bc6\u522b\u548c\u63d0\u53d6\u6240\u9700\u7684\u989c\u8272\u533a\u57df\u3002<\/p>\n
      \u5728Python\u4e2d\u8bc6\u522b\u989c\u8272\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong>
      \u901a\u5e38\u60c5\u51b5\u4e0b\uff0cOpenCV\u662f\u8fdb\u884c\u989c\u8272\u8bc6\u522b\u7684\u4e3b\u8981\u5e93\u3002\u6b64\u5916\uff0cNumPy\u4e5f\u5e38\u88ab\u7528\u6765\u5904\u7406\u56fe\u50cf\u6570\u636e\u3002\u5b89\u88c5\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u5b9e\u73b0\uff0c\u5982pip install opencv-python numpy<\/code>\u3002<\/p>\n
      \u5982\u4f55\u5728\u56fe\u50cf\u4e2d\u63d0\u53d6\u7279\u5b9a\u989c\u8272\u7684\u533a\u57df\uff1f<\/strong>
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