{"id":995965,"date":"2024-12-27T09:09:15","date_gmt":"2024-12-27T01:09:15","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/995965.html"},"modified":"2024-12-27T09:09:18","modified_gmt":"2024-12-27T01:09:18","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%ad%9b%e9%80%89%e5%9b%be%e7%89%87","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/995965.html","title":{"rendered":"\u5982\u4f55\u7528python\u7b5b\u9009\u56fe\u7247"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u7b5b\u9009\u56fe\u7247\"<\/p>\n

\u4e00\u3001\u7528Python\u7b5b\u9009\u56fe\u7247\u7684\u65b9\u6cd5<\/p>\n<\/p>\n

Python\u7b5b\u9009\u56fe\u7247\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\uff1a\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u683c\u5f0f\u4e0e\u5927\u5c0f\u7684\u7b5b\u9009\u3001\u901a\u8fc7OpenCV\u8fdb\u884c\u56fe\u50cf\u5185\u5bb9\u8bc6\u522b\u3001\u7ed3\u5408\u673a\u5668\u5b66\u4e60<\/a>\u6280\u672f\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b<\/strong>\u3002\u5176\u4e2d\uff0c\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u683c\u5f0f\u4e0e\u5927\u5c0f\u7684\u7b5b\u9009\u662f\u6700\u57fa\u7840\u7684\u65b9\u6cd5\u3002PIL\uff08Python Imaging Library\uff09\u662fPython\u4e2d\u7528\u4e8e\u5904\u7406\u56fe\u50cf\u7684\u57fa\u7840\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5bf9\u56fe\u50cf\u7684\u6253\u5f00\u3001\u64cd\u4f5c\u548c\u4fdd\u5b58\u7b49\u57fa\u672c\u529f\u80fd\u3002\u901a\u8fc7PIL\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u7b5b\u9009\u51fa\u7279\u5b9a\u683c\u5f0f\u7684\u56fe\u7247\uff0c\u6216\u8005\u662f\u7b5b\u9009\u51fa\u6587\u4ef6\u5927\u5c0f\u5728\u67d0\u4e2a\u8303\u56f4\u5185\u7684\u56fe\u7247\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u683c\u5f0f\u4e0e\u5927\u5c0f\u7684\u7b5b\u9009\u3002<\/p>\n<\/p>\n

PIL\u5e93\u7684\u5b89\u88c5\u4e0e\u4f7f\u7528\u975e\u5e38\u7b80\u5355\uff0c\u9996\u5148\u901a\u8fc7pip\u547d\u4ee4\u5b89\u88c5PIL\u5e93\uff08\u73b0\u4e3aPillow\uff09\uff0c\u7136\u540e\u53ef\u4ee5\u4f7f\u7528Image<\/code>\u6a21\u5757\u6253\u5f00\u56fe\u50cf\u3002\u6253\u5f00\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7format<\/code>\u5c5e\u6027\u83b7\u53d6\u56fe\u50cf\u7684\u683c\u5f0f\uff0c\u901a\u8fc7size<\/code>\u5c5e\u6027\u83b7\u53d6\u56fe\u50cf\u7684\u5927\u5c0f\u3002\u6839\u636e\u8fd9\u4e9b\u5c5e\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u7b5b\u9009\u51fa\u7b26\u5408\u8981\u6c42\u7684\u56fe\u7247\u3002\u5177\u4f53\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

from PIL import Image<\/p>\n
import os<\/p>\n
def filter_images_by_format_and_size(directory, required_format, size_limit):<\/p>\n
    for filename in os.listdir(directory):<\/p>\n
        if filename.endswith(required_format):<\/p>\n
            image_path = os.path.join(directory, filename)<\/p>\n
            with Image.open(image_path) as img:<\/p>\n
                if img.size[0] * img.size[1] <= size_limit:<\/p>\n
                    print(f"{filename} matches the criteria.")<\/p>\n
filter_images_by_format_and_size('path\/to\/images', 'JPEG', 1000000)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be5\u4ee3\u7801\u4f1a\u5728\u6307\u5b9a\u76ee\u5f55\u4e0b\u7b5b\u9009\u51fa\u683c\u5f0f\u4e3aJPEG\u4e14\u50cf\u7d20\u6570\u5c0f\u4e8e\u7b49\u4e8e100\u4e07\u7684\u56fe\u7247\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5229\u7528PIL\u5e93\u8fdb\u884c\u683c\u5f0f\u4e0e\u5927\u5c0f\u7b5b\u9009<\/p>\n<\/p>\n
\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u7b5b\u9009\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a\u9996\u5148\uff0c\u5b89\u88c5\u5e76\u5bfc\u5165PIL\u5e93\uff1b\u5176\u6b21\uff0c\u904d\u5386\u76ee\u6807\u76ee\u5f55\u4e2d\u7684\u6240\u6709\u6587\u4ef6\uff1b\u7136\u540e\uff0c\u68c0\u67e5\u6bcf\u4e2a\u6587\u4ef6\u7684\u683c\u5f0f\u548c\u5927\u5c0f\uff1b\u6700\u540e\uff0c\u7b5b\u9009\u51fa\u7b26\u5408\u6761\u4ef6\u7684\u56fe\u7247\u3002<\/p>\n<\/p>\n
    \n
  1. \u5b89\u88c5\u4e0e\u5bfc\u5165PIL\u5e93<\/strong><\/li>\n<\/ol>\n
    PIL\u5e93\u5728Python3\u4e2d\u5df2\u88abPillow\u5e93\u6240\u53d6\u4ee3\uff0c\u56e0\u6b64\u6211\u4eec\u9700\u8981\u5b89\u88c5Pillow\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
    pip install Pillow<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165PIL\u5e93\uff1a<\/p>\n<\/p>\n
    from PIL import Image<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u904d\u5386\u76ee\u6807\u76ee\u5f55<\/strong><\/li>\n<\/ol>\n
      \u901a\u8fc7os<\/code>\u6a21\u5757\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u904d\u5386\u76ee\u6807\u76ee\u5f55\u4e2d\u7684\u6240\u6709\u6587\u4ef6\u3002\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u904d\u5386\u4e00\u4e2a\u76ee\u5f55\uff1a<\/p>\n<\/p>\n
      import os<\/p>\n
      def list_files_in_directory(directory):<\/p>\n
          for filename in os.listdir(directory):<\/p>\n
              print(filename)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u68c0\u67e5\u56fe\u50cf\u683c\u5f0f\u4e0e\u5927\u5c0f<\/strong><\/li>\n<\/ol>\n
        \u5728\u904d\u5386\u6587\u4ef6\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7PIL\u5e93\u7684Image.open()<\/code>\u65b9\u6cd5\u6253\u5f00\u56fe\u50cf\u6587\u4ef6\uff0c\u5e76\u901a\u8fc7format<\/code>\u548csize<\/code>\u5c5e\u6027\u83b7\u53d6\u56fe\u50cf\u7684\u683c\u5f0f\u4e0e\u5927\u5c0f\uff1a<\/p>\n<\/p>\n
        def check_image_format_and_size(image_path, required_format, size_limit):<\/p>\n
            with Image.open(image_path) as img:<\/p>\n
                if img.format == required_format and img.size[0] * img.size[1] <= size_limit:<\/p>\n
                    print(f"{image_path} matches the criteria.")<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u7b5b\u9009\u51fa\u7b26\u5408\u6761\u4ef6\u7684\u56fe\u7247<\/strong><\/li>\n<\/ol>\n
          \u901a\u8fc7\u524d\u9762\u7684\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u7b5b\u9009\u51fa\u7b26\u5408\u683c\u5f0f\u4e0e\u5927\u5c0f\u8981\u6c42\u7684\u56fe\u7247\uff1a<\/p>\n<\/p>\n
          def filter_images_by_format_and_size(directory, required_format, size_limit):<\/p>\n
              for filename in os.listdir(directory):<\/p>\n
                  if filename.endswith(required_format):<\/p>\n
                      image_path = os.path.join(directory, filename)<\/p>\n
                      with Image.open(image_path) as img:<\/p>\n
                          if img.size[0] * img.size[1] <= size_limit:<\/p>\n
                              print(f"{filename} matches the criteria.")<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u901a\u8fc7OpenCV\u8fdb\u884c\u56fe\u50cf\u5185\u5bb9\u8bc6\u522b<\/p>\n<\/p>\n
          OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u3002\u5229\u7528OpenCV\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u56fe\u50cf\u5185\u5bb9\u8fdb\u884c\u8bc6\u522b\u548c\u7b5b\u9009\u3002<\/p>\n<\/p>\n
            \n
          1. \u5b89\u88c5\u4e0e\u5bfc\u5165OpenCV\u5e93<\/strong><\/li>\n<\/ol>\n
            \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5OpenCV\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
            pip install opencv-python<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165OpenCV\u5e93\uff1a<\/p>\n<\/p>\n
            import cv2<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u52a0\u8f7d\u4e0e\u5904\u7406\u56fe\u50cf<\/strong><\/li>\n<\/ol>\n
              \u4f7f\u7528OpenCV\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u52a0\u8f7d\u548c\u5904\u7406\u56fe\u50cf\u3002\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u52a0\u8f7d\u548c\u663e\u793a\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
              def display_image(image_path):<\/p>\n
                  img = cv2.imread(image_path)<\/p>\n
                  cv2.imshow('Image', img)<\/p>\n
                  cv2.wAI<\/a>tKey(0)<\/p>\n
                  cv2.destroyAllWindows()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u56fe\u50cf\u5185\u5bb9\u8bc6\u522b\u4e0e\u7b5b\u9009<\/strong><\/li>\n<\/ol>\n
                \u5229\u7528OpenCV\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u884c\u56fe\u50cf\u5185\u5bb9\u7684\u8bc6\u522b\u4e0e\u7b5b\u9009\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7\u8fb9\u7f18\u68c0\u6d4b\u3001\u8f6e\u5ed3\u8bc6\u522b\u7b49\u6280\u672f\u7b5b\u9009\u51fa\u5305\u542b\u7279\u5b9a\u5185\u5bb9\u7684\u56fe\u7247\u3002\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Canny\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\u8fdb\u884c\u56fe\u50cf\u7b5b\u9009\uff1a<\/p>\n<\/p>\n
                def filter_images_by_content(directory, threshold1, threshold2):<\/p>\n
                    for filename in os.listdir(directory):<\/p>\n
                        image_path = os.path.join(directory, filename)<\/p>\n
                        img = cv2.imread(image_path, 0)<\/p>\n
                        edges = cv2.Canny(img, threshold1, threshold2)<\/p>\n
                        if cv2.countNonZero(edges) > 1000:  # \u5047\u8bbe\u8fb9\u7f18\u6570\u5927\u4e8e1000\u4f5c\u4e3a\u7b5b\u9009\u6761\u4ef6<\/p>\n
                            print(f"{filename} contains significant edges.")<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001\u7ed3\u5408\u673a\u5668\u5b66\u4e60\u6280\u672f\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b<\/p>\n<\/p>\n
                \u673a\u5668\u5b66\u4e60\u6280\u672f\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u7684\u9ad8\u7ea7\u5206\u7c7b\u548c\u7b5b\u9009\uff0c\u4f8b\u5982\u901a\u8fc7\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u5bf9\u56fe\u50cf\u8fdb\u884c\u81ea\u52a8\u5206\u7c7b\u3002<\/p>\n<\/p>\n
                  \n
                1. \u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6<\/strong><\/li>\n<\/ol>\n
                  \u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\u5305\u62ecTensorFlow\u548cPyTorch\u3002\u53ef\u4ee5\u6839\u636e\u4e2a\u4eba\u504f\u597d\u548c\u9879\u76ee\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u6846\u67b6\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u51c6\u5907\u6570\u636e\u96c6\u4e0e\u6a21\u578b<\/strong><\/li>\n<\/ol>\n
                    \u5728\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b\u4e4b\u524d\uff0c\u9700\u8981\u51c6\u5907\u6570\u636e\u96c6\u5e76\u8bad\u7ec3\u6a21\u578b\u3002\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528\u9884\u8bad\u7ec3\u6a21\u578b\uff08\u5982ResNet\u3001VGG\u7b49\uff09\u8fdb\u884c\u8fc1\u79fb\u5b66\u4e60\uff0c\u8fd9\u6837\u53ef\u4ee5\u5728\u8f83\u5c0f\u7684\u6570\u636e\u96c6\u4e0a\u83b7\u5f97\u4e0d\u9519\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u52a0\u8f7d\u6a21\u578b\u4e0e\u5206\u7c7b\u56fe\u50cf<\/strong><\/li>\n<\/ol>\n
                      \u4e00\u65e6\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\uff0c\u5c31\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u5e76\u5bf9\u56fe\u50cf\u8fdb\u884c\u5206\u7c7b\u3002\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528TensorFlow\u52a0\u8f7d\u6a21\u578b\u5e76\u5bf9\u56fe\u50cf\u8fdb\u884c\u5206\u7c7b\uff1a<\/p>\n<\/p>\n
                      import tensorflow as tf<\/p>\n
                      from tensorflow.keras.preprocessing import image<\/p>\n
                      def classify_image(model_path, image_path, labels):<\/p>\n
                          model = tf.keras.models.load_model(model_path)<\/p>\n
                          img = image.load_img(image_path, target_size=(224, 224))<\/p>\n
                          img_array = image.img_to_array(img) \/ 255.0<\/p>\n
                          img_array = tf.expand_dims(img_array, 0)<\/p>\n
                          predictions = model.predict(img_array)<\/p>\n
                          predicted_label = labels[tf.argmax(predictions[0])]<\/p>\n
                          print(f"{image_path} is classified as {predicted_label}.")<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u4e94\u3001\u603b\u7ed3\u4e0e\u4f18\u5316\u5efa\u8bae<\/p>\n<\/p>\n
                      \u5728\u4f7f\u7528Python\u8fdb\u884c\u56fe\u50cf\u7b5b\u9009\u65f6\uff0c\u53ef\u4ee5\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u6765\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002\u5bf9\u4e8e\u7b80\u5355\u7684\u683c\u5f0f\u4e0e\u5927\u5c0f\u7b5b\u9009\uff0cPIL\u5e93\u5df2\u7ecf\u8db3\u591f\uff1b\u5bf9\u4e8e\u56fe\u50cf\u5185\u5bb9\u7684\u8bc6\u522b\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\uff1b\u5bf9\u4e8e\u590d\u6742\u7684\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\uff0c\u53ef\u4ee5\u7ed3\u5408\u673a\u5668\u5b66\u4e60\u6280\u672f\u3002<\/p>\n<\/p>\n
                      \u5728\u5b9e\u8df5\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5de5\u5177\u662f\u5173\u952e\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u8003\u8651\u4ee5\u4e0b\u4f18\u5316\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
                        \n
                      • \u6279\u5904\u7406\u4e0e\u5e76\u884c\u5904\u7406<\/strong>\uff1a\u5bf9\u4e8e\u5927\u91cf\u56fe\u7247\u7684\u7b5b\u9009\u4efb\u52a1\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u6279\u5904\u7406\u6216\u5e76\u884c\u5904\u7406\u7684\u65b9\u6cd5\u63d0\u9ad8\u6548\u7387\u3002<\/li>\n
                      • \u56fe\u50cf\u9884\u5904\u7406<\/strong>\uff1a\u5728\u8fdb\u884c\u5185\u5bb9\u8bc6\u522b\u6216\u5206\u7c7b\u4e4b\u524d\uff0c\u8fdb\u884c\u9002\u5f53\u7684\u56fe\u50cf\u9884\u5904\u7406\uff08\u5982\u5f52\u4e00\u5316\u3001\u53bb\u566a\u7b49\uff09\u53ef\u4ee5\u63d0\u9ad8\u8bc6\u522b\u7684\u51c6\u786e\u6027\u3002<\/li>\n
                      • \u6a21\u578b\u4f18\u5316<\/strong>\uff1a\u5728\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u6a21\u578b\u4f18\u5316\uff08\u5982\u8c03\u53c2\u3001\u526a\u679d\u7b49\uff09\u63d0\u9ad8\u5206\u7c7b\u6548\u679c\u3002<\/li>\n<\/ul>\n
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                        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                         \u5982\u4f55\u4f7f\u7528Python\u5bf9\u56fe\u7247\u8fdb\u884c\u7b5b\u9009\u548c\u5904\u7406\uff1f<\/strong>
                        \u4f7f\u7528Python\u7b5b\u9009\u56fe\u7247\u901a\u5e38\u6d89\u53ca\u5230\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982PIL\uff08Pillow\uff09\u3001OpenCV\u7b49\u3002\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e9b\u5e93\u8bfb\u53d6\u56fe\u7247\u6587\u4ef6\uff0c\u8fdb\u884c\u683c\u5f0f\u8f6c\u6362\u3001\u5c3a\u5bf8\u8c03\u6574\u3001\u989c\u8272\u8fc7\u6ee4\u7b49\u64cd\u4f5c\uff0c\u4ee5\u4fbf\u6ee1\u8db3\u7279\u5b9a\u9700\u6c42\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60a8\u60f3\u8981\u7b5b\u9009\u7279\u5b9a\u5c3a\u5bf8\u7684\u56fe\u7247\uff0c\u53ef\u4ee5\u4f7f\u7528PIL\u5e93\u4e2d\u7684Image.open()<\/code>\u65b9\u6cd5\u52a0\u8f7d\u56fe\u7247\uff0c\u5e76\u901a\u8fc7resize()<\/code>\u65b9\u6cd5\u8c03\u6574\u5c3a\u5bf8\u3002<\/p>\n
                        Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u7b5b\u9009\uff1f<\/strong>
                        Python\u4e2d\u5e38\u7528\u7684\u56fe\u50cf\u5904\u7406\u5e93\u5305\u62ecPillow\u3001OpenCV\u548cscikit-image\u7b49\u3002Pillow\u662f\u4e00\u4e2a\u975e\u5e38\u9002\u5408\u521d\u5b66\u8005\u7684\u5e93\uff0c\u529f\u80fd\u5f3a\u5927\u4e14\u6613\u4e8e\u4f7f\u7528\uff1bOpenCV\u5219\u63d0\u4f9b\u4e86\u66f4\u4e3a\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u9002\u5408\u5bf9\u56fe\u50cf\u8fdb\u884c\u6df1\u5165\u5206\u6790\u548c\u5904\u7406\uff1bscikit-image\u5219\u662f\u4e00\u4e2a\u57fa\u4e8eSciPy\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u4e13\u6ce8\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u56fe\u50cf\u5206\u6790\u3002<\/p>\n
                        \u5982\u4f55\u901a\u8fc7Python\u7b5b\u9009\u7279\u5b9a\u7c7b\u578b\u7684\u56fe\u7247\u6587\u4ef6\uff1f<\/strong>
                        \u8981\u7b5b\u9009\u7279\u5b9a\u7c7b\u578b\u7684\u56fe\u7247\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684os\u5e93\u7ed3\u5408\u6587\u4ef6\u6269\u5c55\u540d\u8fdb\u884c\u7b5b\u9009\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7os.listdir()<\/code>\u5217\u51fa\u76ee\u5f55\u4e0b\u6240\u6709\u6587\u4ef6\uff0c\u5e76\u4f7f\u7528\u5b57\u7b26\u4e32\u64cd\u4f5c\u68c0\u67e5\u6587\u4ef6\u6269\u5c55\u540d\uff0c\u5982.jpg<\/code>\u3001.png<\/code>\u7b49\u3002\u8fd9\u6837\uff0c\u60a8\u5c31\u80fd\u591f\u8f7b\u677e\u83b7\u53d6\u6307\u5b9a\u7c7b\u578b\u7684\u56fe\u7247\u6587\u4ef6\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e00\u3001\u7528Python\u7b5b\u9009\u56fe\u7247\u7684\u65b9\u6cd5 Python\u7b5b\u9009\u56fe\u7247\u7684\u65b9\u6cd5\u4e3b\u8981\u6709\uff1a\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u683c\u5f0f\u4e0e\u5927\u5c0f\u7684\u7b5b\u9009\u3001\u901a\u8fc7 […]","protected":false},"author":3,"featured_media":995975,"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\/995965"}],"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=995965"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/995965\/revisions"}],"predecessor-version":[{"id":995977,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/995965\/revisions\/995977"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/995975"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=995965"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=995965"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=995965"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}