{"id":1106190,"date":"2025-01-08T16:37:51","date_gmt":"2025-01-08T08:37:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1106190.html"},"modified":"2025-01-08T16:37:53","modified_gmt":"2025-01-08T08:37:53","slug":"python%e5%a6%82%e4%bd%95%e6%94%b9%e5%8f%98%e4%b8%80%e5%bc%a0%e5%9b%be%e7%89%87%e7%9a%84%e9%a2%9c%e8%89%b2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1106190.html","title":{"rendered":"python\u5982\u4f55\u6539\u53d8\u4e00\u5f20\u56fe\u7247\u7684\u989c\u8272"},"content":{"rendered":"

\"python\u5982\u4f55\u6539\u53d8\u4e00\u5f20\u56fe\u7247\u7684\u989c\u8272\"<\/p>\n

\u4f7f\u7528Python\u6539\u53d8\u4e00\u5f20\u56fe\u7247\u7684\u989c\u8272\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528PIL\u5e93\u3001\u4f7f\u7528OpenCV\u5e93\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528Matplotlib\u5e93\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528PIL\u5e93\u662f\u4e00\u79cd\u975e\u5e38\u5e38\u89c1\u4e14\u7b80\u5355\u7684\u65b9\u6cd5\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528PIL\u5e93\u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u5177\u4f53\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528PIL\u5e93<\/p>\n<\/p>\n

PIL\uff08Python Imaging Library\uff09\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u5305\u62ec\u56fe\u50cf\u7684\u6253\u5f00\u3001\u663e\u793a\u3001\u4fdd\u5b58\u3001\u53d8\u6362\u3001\u6ee4\u955c\u7b49\u3002\u4f7f\u7528PIL\u5e93\u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5PIL\u5e93<\/li>\n
  2. \u6253\u5f00\u56fe\u7247<\/li>\n
  3. \u4fee\u6539\u56fe\u7247\u989c\u8272<\/li>\n
  4. \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/li>\n<\/ol>\n

    \u5b89\u88c5PIL\u5e93<\/h3>\n<\/p>\n

    PIL\u5e93\u5df2\u7ecf\u88abPillow\u5e93\u6240\u66ff\u4ee3\uff0c\u56e0\u6b64\u6211\u4eec\u9700\u8981\u5b89\u88c5Pillow\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

    pip install Pillow<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6253\u5f00\u56fe\u7247<\/h3>\n<\/p>\n
    \u9996\u5148\uff0c\u5bfc\u5165PIL\u5e93\u5e76\u6253\u5f00\u4e00\u5f20\u56fe\u7247\uff1a<\/p>\n<\/p>\n
    from PIL import Image<\/p>\n
    \u6253\u5f00\u56fe\u7247<\/strong><\/h2>\n
    image = Image.open('path\/to\/your\/image.jpg')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4fee\u6539\u56fe\u7247\u989c\u8272<\/h3>\n<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528PIL\u5e93\u4e2d\u7684ImageEnhance<\/code>\u6a21\u5757\u6765\u4fee\u6539\u56fe\u7247\u7684\u989c\u8272\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
    from PIL import ImageEnhance<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u589e\u5f3a\u5668\u5bf9\u8c61<\/strong><\/h2>\n
    enhancer = ImageEnhance.Color(image)<\/p>\n
    \u589e\u5f3a\u56fe\u7247\u7684\u989c\u8272\uff08\u53c2\u65701.5\u8868\u793a\u989c\u8272\u589e\u5f3a\u4e3a\u539f\u6765\u76841.5\u500d\uff09<\/strong><\/h2>\n
    image_enhanced = enhancer.enhance(1.5)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/h3>\n<\/p>\n
    \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u4fee\u6539\u540e\u7684\u56fe\u7247\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff1a<\/p>\n<\/p>\n
    # \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/p>\n
    image_enhanced.save('path\/to\/your\/enhanced_image.jpg')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528PIL\u5e93\u8f7b\u677e\u5730\u6539\u53d8\u4e00\u5f20\u56fe\u7247\u7684\u989c\u8272\u4e86\u3002<\/p>\n<\/p>\n
    \u4e8c\u3001\u4f7f\u7528OpenCV\u5e93<\/p>\n<\/p>\n
    OpenCV\uff08Open Source Computer Vision Library\uff09\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u51fd\u6570\u3002\u4f7f\u7528OpenCV\u5e93\u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
      \n
    1. \u5b89\u88c5OpenCV\u5e93<\/li>\n
    2. \u6253\u5f00\u56fe\u7247<\/li>\n
    3. \u4fee\u6539\u56fe\u7247\u989c\u8272<\/li>\n
    4. \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/li>\n<\/ol>\n
      \u5b89\u88c5OpenCV\u5e93<\/h3>\n<\/p>\n
      \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5OpenCV\u5e93\uff1a<\/p>\n<\/p>\n
      pip install opencv-python<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u6253\u5f00\u56fe\u7247<\/h3>\n<\/p>\n
      \u9996\u5148\uff0c\u5bfc\u5165OpenCV\u5e93\u5e76\u6253\u5f00\u4e00\u5f20\u56fe\u7247\uff1a<\/p>\n<\/p>\n
      import cv2<\/p>\n
      \u6253\u5f00\u56fe\u7247<\/strong><\/h2>\n
      image = cv2.imread('path\/to\/your\/image.jpg')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4fee\u6539\u56fe\u7247\u989c\u8272<\/h3>\n<\/p>\n
      \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u4e2d\u7684cv2.cvtColor<\/code>\u51fd\u6570\u6765\u4fee\u6539\u56fe\u7247\u7684\u989c\u8272\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
      # \u8f6c\u6362\u56fe\u7247\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n
      image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/h3>\n<\/p>\n
      \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u4fee\u6539\u540e\u7684\u56fe\u7247\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff1a<\/p>\n<\/p>\n
      # \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/p>\n
      cv2.imwrite('path\/to\/your\/gray_image.jpg', image_gray)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6539\u53d8\u56fe\u7247\u7684\u989c\u8272\u4e86\u3002<\/p>\n<\/p>\n
      \u4e09\u3001\u4f7f\u7528Numpy\u5e93<\/p>\n<\/p>\n
      Numpy\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\u3002\u4f7f\u7528Numpy\u5e93\u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
        \n
      1. \u5b89\u88c5Numpy\u5e93<\/li>\n
      2. \u6253\u5f00\u56fe\u7247<\/li>\n
      3. \u4fee\u6539\u56fe\u7247\u989c\u8272<\/li>\n
      4. \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/li>\n<\/ol>\n
        \u5b89\u88c5Numpy\u5e93<\/h3>\n<\/p>\n
        \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Numpy\u5e93\uff1a<\/p>\n<\/p>\n
        pip install numpy<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u6253\u5f00\u56fe\u7247<\/h3>\n<\/p>\n
        \u9996\u5148\uff0c\u5bfc\u5165Numpy\u5e93\u548cPIL\u5e93\uff0c\u5e76\u6253\u5f00\u4e00\u5f20\u56fe\u7247\uff1a<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        from PIL import Image<\/p>\n
        \u6253\u5f00\u56fe\u7247<\/strong><\/h2>\n
        image = Image.open('path\/to\/your\/image.jpg')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4fee\u6539\u56fe\u7247\u989c\u8272<\/h3>\n<\/p>\n
        \u6211\u4eec\u53ef\u4ee5\u5c06\u56fe\u7247\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4\uff0c\u7136\u540e\u5bf9\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c\u6765\u4fee\u6539\u56fe\u7247\u7684\u989c\u8272\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
        # \u5c06\u56fe\u7247\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4<\/p>\n
        image_array = np.array(image)<\/p>\n
        \u4fee\u6539\u56fe\u7247\u7684\u989c\u8272\uff08\u5c06\u6240\u6709\u50cf\u7d20\u7684\u7ea2\u8272\u901a\u9053\u503c\u589e\u52a050\uff09<\/strong><\/h2>\n
        image_array[:, :, 0] = np.clip(image_array[:, :, 0] + 50, 0, 255)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/h3>\n<\/p>\n
        \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u4fee\u6539\u540e\u7684Numpy\u6570\u7ec4\u8f6c\u6362\u56de\u56fe\u7247\uff0c\u5e76\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff1a<\/p>\n<\/p>\n
        # \u5c06Numpy\u6570\u7ec4\u8f6c\u6362\u56de\u56fe\u7247<\/p>\n
        image_enhanced = Image.fromarray(image_array)<\/p>\n
        \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/strong><\/h2>\n
        image_enhanced.save('path\/to\/your\/enhanced_image.jpg')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Numpy\u5e93\u6539\u53d8\u56fe\u7247\u7684\u989c\u8272\u4e86\u3002<\/p>\n<\/p>\n
        \u56db\u3001\u4f7f\u7528Matplotlib\u5e93<\/p>\n<\/p>\n
        Matplotlib\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u51fd\u6570\u3002\u4f7f\u7528Matplotlib\u5e93\u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
          \n
        1. \u5b89\u88c5Matplotlib\u5e93<\/li>\n
        2. \u6253\u5f00\u56fe\u7247<\/li>\n
        3. \u4fee\u6539\u56fe\u7247\u989c\u8272<\/li>\n
        4. \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/li>\n<\/ol>\n
          \u5b89\u88c5Matplotlib\u5e93<\/h3>\n<\/p>\n
          \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Matplotlib\u5e93\uff1a<\/p>\n<\/p>\n
          pip install matplotlib<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u6253\u5f00\u56fe\u7247<\/h3>\n<\/p>\n
          \u9996\u5148\uff0c\u5bfc\u5165Matplotlib\u5e93\u548cPIL\u5e93\uff0c\u5e76\u6253\u5f00\u4e00\u5f20\u56fe\u7247\uff1a<\/p>\n<\/p>\n
          import matplotlib.pyplot as plt<\/p>\n
          from PIL import Image<\/p>\n
          \u6253\u5f00\u56fe\u7247<\/strong><\/h2>\n
          image = Image.open('path\/to\/your\/image.jpg')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4fee\u6539\u56fe\u7247\u989c\u8272<\/h3>\n<\/p>\n
          \u6211\u4eec\u53ef\u4ee5\u5c06\u56fe\u7247\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4\uff0c\u7136\u540e\u4f7f\u7528Matplotlib\u5e93\u4e2d\u7684imshow<\/code>\u51fd\u6570\u6765\u663e\u793a\u4fee\u6539\u540e\u7684\u56fe\u7247\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
          # \u5c06\u56fe\u7247\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4<\/p>\n
          image_array = np.array(image)<\/p>\n
          \u4fee\u6539\u56fe\u7247\u7684\u989c\u8272\uff08\u5c06\u6240\u6709\u50cf\u7d20\u7684\u7ea2\u8272\u901a\u9053\u503c\u589e\u52a050\uff09<\/strong><\/h2>\n
          image_array[:, :, 0] = np.clip(image_array[:, :, 0] + 50, 0, 255)<\/p>\n
          \u663e\u793a\u4fee\u6539\u540e\u7684\u56fe\u7247<\/strong><\/h2>\n
          plt.imshow(image_array)<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/h3>\n<\/p>\n
          \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u4fee\u6539\u540e\u7684Numpy\u6570\u7ec4\u8f6c\u6362\u56de\u56fe\u7247\uff0c\u5e76\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff1a<\/p>\n<\/p>\n
          # \u5c06Numpy\u6570\u7ec4\u8f6c\u6362\u56de\u56fe\u7247<\/p>\n
          image_enhanced = Image.fromarray(image_array)<\/p>\n
          \u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u7247<\/strong><\/h2>\n
          image_enhanced.save('path\/to\/your\/enhanced_image.jpg')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6539\u53d8\u56fe\u7247\u7684\u989c\u8272\u4e86\u3002<\/p>\n<\/p>\n
          \u603b\u7ed3<\/p>\n<\/p>\n
          \u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u4f7f\u7528Python\u6539\u53d8\u4e00\u5f20\u56fe\u7247\u7684\u989c\u8272\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528PIL\u5e93\u3001\u4f7f\u7528OpenCV\u5e93\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528Matplotlib\u5e93\u3002<\/strong>\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u7f3a\u70b9\u548c\u9002\u7528\u573a\u666f\uff0c\u5927\u5bb6\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u5904\u7406\u56fe\u7247\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u5927\u5bb6\u66f4\u597d\u5730\u7406\u89e3\u548c\u638c\u63e1Python\u56fe\u50cf\u5904\u7406\u7684\u76f8\u5173\u77e5\u8bc6\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u5982\u4f55\u4f7f\u7528Python\u6539\u53d8\u56fe\u7247\u7684\u989c\u8272\uff1f<\/strong>
          \u4f7f\u7528Python\u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528PIL\uff08Pillow\uff09\u5e93\u548cOpenCV\u5e93\u3002Pillow\u5e93\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u9002\u5408\u521d\u5b66\u8005\uff1b\u800cOpenCV\u5219\u66f4\u4e3a\u5f3a\u5927\uff0c\u9002\u7528\u4e8e\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u9700\u6c42\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u8c03\u6574\u989c\u8272\u3001\u5e94\u7528\u6ee4\u955c\u6216\u8f6c\u6362\u5230\u4e0d\u540c\u7684\u8272\u5f69\u7a7a\u95f4\u3002<\/p>\n
          \u5728Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u6765\u5904\u7406\u56fe\u7247\u989c\u8272\uff1f<\/strong>
          Python\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u56fe\u7247\u989c\u8272\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u5305\u62ecPillow\u3001OpenCV\u548cMatplotlib\u3002Pillow\u9002\u5408\u57fa\u672c\u7684\u56fe\u50cf\u64cd\u4f5c\uff0c\u5982\u6253\u5f00\u3001\u4fdd\u5b58\u3001\u8c03\u6574\u989c\u8272\u7b49\uff1bOpenCV\u5219\u63d0\u4f9b\u4e86\u66f4\u4e3a\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u5305\u62ec\u989c\u8272\u7a7a\u95f4\u8f6c\u6362\u548c\u7279\u6548\u5e94\u7528\uff1bMatplotlib\u53ef\u4ee5\u7528\u4e8e\u53ef\u89c6\u5316\u56fe\u50cf\u6570\u636e\uff0c\u5e76\u5bf9\u989c\u8272\u8fdb\u884c\u5206\u6790\u548c\u5c55\u793a\u3002<\/p>\n
          \u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u8fc7\u7a0b\u4e2d\u662f\u5426\u4f1a\u5f71\u54cd\u56fe\u7247\u8d28\u91cf\uff1f<\/strong>
          \u6539\u53d8\u56fe\u7247\u989c\u8272\u65f6\uff0c\u53ef\u80fd\u4f1a\u5bf9\u56fe\u7247\u8d28\u91cf\u4ea7\u751f\u5f71\u54cd\uff0c\u5c24\u5176\u662f\u5728\u8fdb\u884c\u538b\u7f29\u6216\u683c\u5f0f\u8f6c\u6362\u65f6\u3002\u4f7f\u7528\u65e0\u635f\u683c\u5f0f\u5982PNG\u53ef\u4ee5\u51cf\u5c11\u8d28\u91cf\u635f\u5931\uff0c\u800cJPEG\u7b49\u6709\u635f\u683c\u5f0f\u5728\u989c\u8272\u8c03\u6574\u540e\u53ef\u80fd\u4f1a\u5bfc\u81f4\u7ec6\u8282\u4e22\u5931\u3002\u4e3a\u4e86\u4fdd\u6301\u56fe\u7247\u8d28\u91cf\uff0c\u5efa\u8bae\u5728\u5904\u7406\u65f6\u5c3d\u91cf\u4f7f\u7528\u539f\u59cb\u56fe\u50cf\uff0c\u5e76\u5728\u5b8c\u6210\u540e\u4fdd\u5b58\u4e3a\u9ad8\u8d28\u91cf\u683c\u5f0f\u3002<\/p>\n
          \u5982\u4f55\u901a\u8fc7Python\u5b9e\u73b0\u7279\u5b9a\u7684\u989c\u8272\u8f6c\u6362\uff1f<\/strong>
          \u5b9e\u73b0\u7279\u5b9a\u989c\u8272\u8f6c\u6362\u7684\u6b65\u9aa4\u901a\u5e38\u5305\u62ec\u52a0\u8f7d\u56fe\u7247\u3001\u9009\u62e9\u76ee\u6807\u989c\u8272\u548c\u5e94\u7528\u8f6c\u6362\u3002\u53ef\u4ee5\u4f7f\u7528Pillow\u7684ImageEnhance<\/code>\u6a21\u5757\u8fdb\u884c\u989c\u8272\u589e\u5f3a\uff0c\u6216\u8005\u5229\u7528OpenCV\u7684\u989c\u8272\u8f6c\u6362\u51fd\u6570\uff08\u5982cv2.cvtColor<\/code>\uff09\u6765\u5b9e\u73b0\u7279\u5b9a\u7684\u989c\u8272\u5904\u7406\uff0c\u5982\u4eceRGB\u8f6c\u6362\u4e3aHSV\u6216LAB\u8272\u5f69\u7a7a\u95f4\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u9700\u6c42\u548c\u76ee\u6807\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u6539\u53d8\u4e00\u5f20\u56fe\u7247\u7684\u989c\u8272\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528PIL\u5e93\u3001\u4f7f\u7528OpenCV\u5e93\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528 […]","protected":false},"author":3,"featured_media":1106192,"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\/1106190"}],"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=1106190"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1106190\/revisions"}],"predecessor-version":[{"id":1106193,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1106190\/revisions\/1106193"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1106192"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1106190"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1106190"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1106190"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}