{"id":906,"date":"2025-01-12T10:06:50","date_gmt":"2025-01-12T02:06:50","guid":{"rendered":"https:\/\/www.laixuexila.com\/?p=906"},"modified":"2025-01-12T10:06:50","modified_gmt":"2025-01-12T02:06:50","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e8%bf%9b%e8%a1%8c%e8%ae%a1%e7%ae%97%e6%9c%ba%e8%a7%86%e8%a7%89%ef%bc%88cv%ef%bc%89%ef%bc%9a%e5%85%a5%e9%97%a8%e4%b8%8e%e5%ba%94%e7%94%a8","status":"publish","type":"post","link":"https:\/\/www.laixuexila.com\/index.php\/2025\/01\/12\/%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e8%bf%9b%e8%a1%8c%e8%ae%a1%e7%ae%97%e6%9c%ba%e8%a7%86%e8%a7%89%ef%bc%88cv%ef%bc%89%ef%bc%9a%e5%85%a5%e9%97%a8%e4%b8%8e%e5%ba%94%e7%94%a8\/","title":{"rendered":"\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u8ba1\u7b97\u673a\u89c6\u89c9\uff08CV\uff09\uff1a\u5165\u95e8\u4e0e\u5e94\u7528"},"content":{"rendered":"\n
\n

\u8ba1\u7b97\u673a\u89c6\u89c9\uff08Computer Vision\uff0cCV\uff09\u662f\u4eba\u5de5\u667a\u80fd\uff08AI\uff09\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u9886\u57df\uff0c\u65e8\u5728\u4f7f\u8ba1\u7b97\u673a\u80fd\u591f\u50cf\u4eba\u7c7b\u4e00\u6837\u7406\u89e3\u548c\u5206\u6790\u56fe\u50cf\u548c\u89c6\u9891\u3002\u4f7f\u7528 Python \u8fdb\u884c\u8ba1\u7b97\u673a\u89c6\u89c9\u5f00\u53d1\u662f\u975e\u5e38\u666e\u904d\u7684\uff0cPython \u62e5\u6709\u591a\u4e2a\u5f3a\u5927\u7684\u5e93\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5feb\u901f\u5b9e\u73b0\u56fe\u50cf\u5904\u7406\u3001\u7279\u5f81\u63d0\u53d6\u3001\u76ee\u6807\u68c0\u6d4b\u7b49\u5e94\u7528\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u4ece\u5165\u95e8\u5230\u5e94\u7528\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5b66\u4e60\u8def\u5f84\uff0c\u5e2e\u52a9\u4f60\u638c\u63e1\u57fa\u7840\u6982\u5ff5\u3001\u5de5\u5177\u3001\u6280\u5de7\uff0c\u5e76\u8fdb\u884c\u5b9e\u9645\u9879\u76ee\u5b9e\u8df5\u3002<\/p>\n<\/blockquote>\n\n\n\n

\u4e00\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u57fa\u7840\u77e5\u8bc6<\/h3>\n\n\n\n

1. \u56fe\u50cf\u4e0e\u89c6\u9891\u5904\u7406\u57fa\u7840<\/strong><\/h4>\n\n\n\n
    \n
  • \u56fe\u50cf\u7684\u57fa\u672c\u6982\u5ff5<\/strong>\uff1a\u56fe\u50cf\u662f\u7531\u50cf\u7d20\uff08Pixel\uff09\u7ec4\u6210\u7684\u4e8c\u7ef4\u77e9\u9635\uff0c\u6bcf\u4e2a\u50cf\u7d20\u5305\u542b\u989c\u8272\u4fe1\u606f\uff08\u901a\u5e38\u4e3aRGB\u503c\uff09\u3002<\/li>\n\n\n\n
  • \u56fe\u50cf\u7684\u901a\u9053<\/strong>\uff1a\u5e38\u89c1\u7684\u6709RGB\uff08\u5f69\u8272\u56fe\u50cf\uff09\u3001\u7070\u5ea6\u56fe\u50cf\uff08\u5355\u901a\u9053\uff09\uff0c\u6709\u65f6\u4e5f\u6d89\u53ca\u5230\u5176\u4ed6\u989c\u8272\u7a7a\u95f4\uff08\u5982HSV\uff09\u3002<\/li>\n\n\n\n
  • \u89c6\u9891\u5e27<\/strong>\uff1a\u89c6\u9891\u662f\u7531\u8fde\u7eed\u7684\u56fe\u50cf\u5e27\u7ec4\u6210\u7684\u3002<\/li>\n<\/ul>\n\n\n\n

    2. \u57fa\u672c\u64cd\u4f5c<\/strong><\/h4>\n\n\n\n
      \n
    • \u56fe\u50cf\u7684\u52a0\u8f7d\u3001\u4fdd\u5b58\u4e0e\u663e\u793a<\/strong>\uff1a\u5982\u4f55\u8bfb\u53d6\u3001\u663e\u793a\u548c\u4fdd\u5b58\u56fe\u50cf\u3002<\/li>\n\n\n\n
    • \u56fe\u50cf\u7684\u51e0\u4f55\u53d8\u6362<\/strong>\uff1a\u5982\u7f29\u653e\u3001\u65cb\u8f6c\u3001\u88c1\u526a\u3001\u5e73\u79fb\u7b49\u3002<\/li>\n\n\n\n
    • \u56fe\u50cf\u7684\u989c\u8272\u7a7a\u95f4\u8f6c\u6362<\/strong>\uff1a\u5982\u4eceRGB\u8f6c\u6362\u5230\u7070\u5ea6\u3001HSV\u3001LAB\u7b49\u3002<\/li>\n\n\n\n
    • \u56fe\u50cf\u7684\u6ee4\u6ce2\u4e0e\u53bb\u566a<\/strong>\uff1a\u5982\u9ad8\u65af\u6ee4\u6ce2\u3001\u8fb9\u7f18\u68c0\u6d4b\u7b49\u3002<\/li>\n<\/ul>\n\n\n\n

      \u4e8c\u3001\u5e38\u7528\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93<\/h3>\n\n\n\n
        \n
      1. OpenCV (Open Source Computer Vision Library)<\/strong><\/li>\n<\/ol>\n\n\n\n
          \n
        • OpenCV \u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u673a\u5668\u5b66\u4e60\u7b49\u9886\u57df\u3002<\/li>\n\n\n\n
        • \u5b89\u88c5\uff1a
          bash pip install opencv-python<\/code><\/li>\n<\/ul>\n\n\n\n
            \n
          1. Pillow (PIL Fork)<\/strong><\/li>\n<\/ol>\n\n\n\n
              \n
            • Pillow \u662f Python Imaging Library (PIL) \u7684\u5206\u652f\uff0c\u4e3b\u8981\u7528\u4e8e\u56fe\u50cf\u5904\u7406\uff0c\u5982\u6253\u5f00\u3001\u4fdd\u5b58\u3001\u8f6c\u6362\u683c\u5f0f\u7b49\u3002<\/li>\n\n\n\n
            • \u5b89\u88c5\uff1a
              bash pip install Pillow<\/code><\/li>\n<\/ul>\n\n\n\n
                \n
              1. scikit-image<\/strong><\/li>\n<\/ol>\n\n\n\n
                  \n
                • \u4e00\u4e2a\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u7684 Python \u5e93\uff0c\u63d0\u4f9b\u4e86\u4e00\u4e9b\u56fe\u50cf\u5904\u7406\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u56fe\u50cf\u7684\u57fa\u672c\u5206\u6790\u548c\u5904\u7406\u3002<\/li>\n\n\n\n
                • \u5b89\u88c5\uff1a
                  bash pip install scikit-image<\/code><\/li>\n<\/ul>\n\n\n\n
                    \n
                  1. TensorFlow\/Keras\/PyTorch<\/strong><\/li>\n<\/ol>\n\n\n\n
                      \n
                    • \u8fd9\u4e9b\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u63d0\u4f9b\u4e86\u8bb8\u591a\u9884\u8bad\u7ec3\u7684\u6a21\u578b\u548c\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u5272\u7b49\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002<\/li>\n<\/ul>\n\n\n\n

                      \u4e09\u3001\u56fe\u50cf\u5904\u7406\u4e0e\u57fa\u672c\u6280\u5de7<\/h3>\n\n\n\n

                      1. \u52a0\u8f7d\u4e0e\u663e\u793a\u56fe\u50cf<\/strong><\/h4>\n\n\n\n

                      \u4f7f\u7528 OpenCV \u6216 Pillow \u6765\u52a0\u8f7d\u3001\u663e\u793a\u548c\u4fdd\u5b58\u56fe\u50cf\u3002<\/p>\n\n\n\n

                         import cv2\n\n   # \u52a0\u8f7d\u56fe\u50cf\n   image = cv2.imread('image.jpg')\n\n   # \u663e\u793a\u56fe\u50cf\n   cv2.imshow('Image', image)\n   cv2.waitKey(0)\n   cv2.destroyAllWindows()\n\n   # \u4fdd\u5b58\u56fe\u50cf\n   cv2.imwrite('output.jpg', image)<\/code><\/pre>\n\n\n\n
                      2. \u56fe\u50cf\u7684\u57fa\u672c\u64cd\u4f5c<\/strong><\/h4>\n\n\n\n
                        \n
                      • \u7070\u5ea6\u5316<\/strong>\uff1a\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u3002<\/li>\n<\/ul>\n\n\n\n
                           gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/code><\/pre>\n\n\n\n
                          \n
                        • \u7f29\u653e\u4e0e\u88c1\u526a<\/strong>\uff1a<\/li>\n<\/ul>\n\n\n\n
                             resized_image = cv2.resize(image, (width, height))  # \u5bbd\u9ad8\u6307\u5b9a\n   cropped_image = image[y1:y2, x1:x2]  # \u88c1\u526a\u56fe\u50cf<\/code><\/pre>\n\n\n\n
                            \n
                          • \u65cb\u8f6c<\/strong>\uff1a<\/li>\n<\/ul>\n\n\n\n
                               rows, cols = image.shape[:2]\n   rotation_matrix = cv2.getRotationMatrix2D((cols \/ 2, rows \/ 2), 45, 1)  # \u65cb\u8f6c45\u5ea6\n   rotated_image = cv2.warpAffine(image, rotation_matrix, (cols, rows))<\/code><\/pre>\n\n\n\n
                            3. \u56fe\u50cf\u6ee4\u6ce2\u4e0e\u53bb\u566a<\/strong><\/h4>\n\n\n\n
                              \n
                            • \u9ad8\u65af\u6a21\u7cca<\/strong>\uff1a\u7528\u4e8e\u53bb\u566a\u548c\u5e73\u6ed1\u56fe\u50cf\u3002<\/li>\n<\/ul>\n\n\n\n
                                 blurred_image = cv2.GaussianBlur(image, (5, 5), 0)<\/code><\/pre>\n\n\n\n
                                \n
                              • \u8fb9\u7f18\u68c0\u6d4b<\/strong>\uff1a\u4f7f\u7528 Canny \u7b97\u6cd5\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b\u3002<\/li>\n<\/ul>\n\n\n\n
                                   edges = cv2.Canny(image, 100, 200)<\/code><\/pre>\n\n\n\n
                                4. \u989c\u8272\u7a7a\u95f4\u8f6c\u6362<\/strong><\/h4>\n\n\n\n
                                   hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)  # RGB\u8f6cHSV<\/code><\/pre>\n\n\n\n
                                \u56db\u3001\u8fdb\u9636\u5e94\u7528\u4e0e\u6280\u5de7<\/h3>\n\n\n\n
                                1. \u7279\u5f81\u68c0\u6d4b\u4e0e\u63cf\u8ff0\u5b50<\/strong><\/h4>\n\n\n\n
                                \u7279\u5f81\u68c0\u6d4b\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u57fa\u7840\u4efb\u52a1\uff0c\u5e38\u7528\u7684\u7b97\u6cd5\u5305\u62ec\uff1a<\/p>\n\n\n\n
                                  \n
                                • SIFT\uff08\u5c3a\u5ea6\u4e0d\u53d8\u7279\u5f81\u53d8\u6362\uff09<\/strong>\uff1a\u7528\u4e8e\u68c0\u6d4b\u548c\u63cf\u8ff0\u56fe\u50cf\u4e2d\u7684\u5c40\u90e8\u7279\u5f81\u70b9\u3002<\/li>\n\n\n\n
                                • SURF\uff08\u52a0\u901f\u7a33\u5065\u7279\u5f81\uff09<\/strong>\uff1a\u6539\u8fdb\u4e86SIFT\u7b97\u6cd5\u3002<\/li>\n\n\n\n
                                • ORB\uff08Oriented FAST and Rotated BRIEF\uff09<\/strong>\uff1a\u7528\u4e8e\u5feb\u901f\u548c\u9ad8\u6548\u7684\u7279\u5f81\u5339\u914d\u3002<\/li>\n<\/ul>\n\n\n\n
                                     orb = cv2.ORB_create()\n   kp, des = orb.detectAndCompute(image, None)\n   image_with_kp = cv2.drawKeypoints(image, kp, None)\n   cv2.imshow('ORB Keypoints', image_with_kp)\n   cv2.waitKey(0)\n   cv2.destroyAllWindows()<\/code><\/pre>\n\n\n\n
                                  2. \u56fe\u50cf\u5206\u7c7b\u4e0e\u5bf9\u8c61\u8bc6\u522b<\/strong><\/h4>\n\n\n\n
                                  \u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b\uff08\u5982\u4f7f\u7528CNN\uff09\u3002<\/p>\n\n\n\n
                                    \n
                                  • \u4f7f\u7528Keras<\/strong>\u6216PyTorch<\/strong>\u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\uff08\u5982VGG16\u3001ResNet50\u7b49\uff09\u3002<\/li>\n\n\n\n
                                  • \u5bf9\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\u548c\u9884\u6d4b\u3002<\/li>\n<\/ul>\n\n\n\n
                                       from tensorflow.keras.applications import VGG16\n   from tensorflow.keras.preprocessing import image\n   from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\n   import numpy as np\n\n   model = VGG16(weights='imagenet')\n\n   img_path = 'elephant.jpg'\n   img = image.load_img(img_path, target_size=(224, 224))\n   img_array = image.img_to_array(img)\n   img_array = np.expand_dims(img_array, axis=0)\n   img_array = preprocess_input(img_array)\n\n   predictions = model.predict(img_array)\n   decoded_predictions = decode_predictions(predictions, top=3)[0]\n   print(decoded_predictions)<\/code><\/pre>\n\n\n\n
                                    3. \u76ee\u6807\u68c0\u6d4b<\/strong><\/h4>\n\n\n\n
                                      \n
                                    • Haar\u7ea7\u8054\u5206\u7c7b\u5668<\/strong>\uff1a\u7528\u4e8e\u4eba\u8138\u68c0\u6d4b\u548c\u7269\u4f53\u68c0\u6d4b\u3002<\/li>\n<\/ul>\n\n\n\n
                                         face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n   gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n   faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)\n\n   for (x, y, w, h) in faces:\n       cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)\n   cv2.imshow('Face Detection', image)\n   cv2.waitKey(0)\n   cv2.destroyAllWindows()<\/code><\/pre>\n\n\n\n
                                        \n
                                      • YOLO\uff08You Only Look Once\uff09<\/strong> \u6216 SSD\uff08Single Shot Multibox Detector\uff09<\/strong> \u7b49\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u5b9e\u65f6\u548c\u9ad8\u7cbe\u5ea6\u7684\u76ee\u6807\u68c0\u6d4b\u3002<\/li>\n<\/ul>\n\n\n\n
                                        4. \u56fe\u50cf\u5206\u5272<\/strong><\/h4>\n\n\n\n
                                          \n
                                        • \u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u5982 U-Net<\/strong> \u6216 Mask R-CNN<\/strong>\uff0c\u8fdb\u884c\u8bed\u4e49\u5206\u5272\u548c\u5b9e\u4f8b\u5206\u5272\u3002<\/li>\n\n\n\n
                                        • \u4f7f\u7528 OpenCV<\/strong> \u6216 TensorFlow\/Keras<\/strong> \u5b9e\u73b0\u56fe\u50cf\u5206\u5272\u4efb\u52a1\u3002<\/li>\n<\/ul>\n\n\n\n
                                          \u4e94\u3001\u6df1\u5ea6\u5b66\u4e60\u4e0e\u8ba1\u7b97\u673a\u89c6\u89c9\u7ed3\u5408<\/h3>\n\n\n\n
                                            \n
                                          1. \u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09<\/strong>
                                            CNN \u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u5e7f\u6cdb\u5e94\u7528\uff0c\u9002\u7528\u4e8e\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u5206\u5272\u7b49\u4efb\u52a1\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528 TensorFlow \u6216 PyTorch \u6765\u6784\u5efa CNN \u6a21\u578b\u3002<\/li>\n<\/ol>\n\n\n\n
                                               import tensorflow as tf\n   from tensorflow.keras import layers, models\n\n   model = models.Sequential([\n       layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),\n       layers.MaxPooling2D((2, 2)),\n       layers.Conv2D(64, (3, 3), activation='relu'),\n       layers.MaxPooling2D((2, 2)),\n       layers.Conv2D(64, (3, 3), activation='relu'),\n       layers.Flatten(),\n       layers.Dense(64, activation='relu'),\n       layers.Dense(10, activation='softmax')\n   ])\n\n   model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])<\/code><\/pre>\n\n\n\n
                                              \n
                                            1. \u8fc1\u79fb\u5b66\u4e60<\/strong>
                                              \u8fc1\u79fb\u5b66\u4e60\u662f\u901a\u8fc7\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u6a21\u578b\uff08\u5982VGG\u3001ResNet\u3001Inception\uff09\u6765\u52a0\u901f\u8bad\u7ec3\u5e76\u63d0\u9ad8\u6027\u80fd\u3002<\/li>\n<\/ol>\n\n\n\n
                                              \u516d\u3001\u5b9e\u8df5\u9879\u76ee<\/h3>\n\n\n\n
                                                \n
                                              1. \u4eba\u8138\u8bc6\u522b<\/strong><\/li>\n<\/ol>\n\n\n\n
                                                  \n
                                                • \u4f7f\u7528 OpenCV \u6216 dlib \u8fdb\u884c\u4eba\u8138\u68c0\u6d4b\u548c\u4eba\u8138\u8bc6\u522b\u3002<\/li>\n<\/ul>\n\n\n\n
                                                    \n
                                                  1. \u56fe\u50cf\u5206\u7c7b\u9879\u76ee<\/strong><\/li>\n<\/ol>\n\n\n\n
                                                      \n
                                                    • \u4f7f\u7528 CIFAR-10\u3001MNIST\u3001ImageNet \u6570\u636e\u96c6\u8bad\u7ec3\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u3002<\/li>\n<\/ul>\n\n\n\n
                                                        \n
                                                      1. \u76ee\u6807\u68c0\u6d4b\u9879\u76ee<\/strong><\/li>\n<\/ol>\n\n\n\n
                                                          \n
                                                        • \u5b9e\u73b0\u57fa\u4e8e YOLO \u6216 Faster R-CNN \u7684\u76ee\u6807\u68c0\u6d4b\u9879\u76ee\u3002<\/li>\n<\/ul>\n\n\n\n
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                                                          1. \u56fe\u50cf\u5206\u5272\u9879\u76ee<\/strong><\/li>\n<\/ol>\n\n\n\n
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                                                            • \u4f7f\u7528 U-Net \u5b9e\u73b0\u533b\u5b66\u56fe\u50cf\u7684\u5206\u5272\u3002<\/li>\n<\/ul>\n\n\n\n
                                                              \u7ed3\u8bba<\/h3>\n\n\n\n
                                                              \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u9010\u6b65\u638c\u63e1\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u57fa\u672c\u539f\u7406\u548c\u6280\u80fd\uff0c\u5e76\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u53d6\u5f97\u4e0d\u9519\u7684\u6210\u7ee9\u3002\u7ee7\u7eed\u6df1\u5165\u7814\u7a76\u6df1\u5ea6\u5b66\u4e60\u7684\u6a21\u578b\uff0c\u5982\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u3001\u76ee\u6807\u68c0\u6d4b\uff08YOLO\u3001SSD\uff09\u548c\u56fe\u50cf\u5206\u5272\uff08U-Net\u3001Mask R-CNN\uff09\u7b49\uff0c\u90fd\u4f1a\u8ba9\u4f60\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u53d6\u5f97\u66f4\u9ad8\u7684\u6210\u5c31\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
                                                              \u8ba1\u7b97\u673a\u89c6\u89c9\uff08Computer Vision\uff0cCV\uff09\u662f\u4eba\u5de5\u667a\u80fd\uff08AI\uff09\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u9886\u57df\uff0c\u65e8\u5728\u4f7f\u8ba1\u7b97\u673a\u80fd\u591f\u50cf\u4eba\u7c7b\u4e00 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54],"tags":[],"class_list":["post-906","post","type-post","status-publish","format-standard","hentry","category-python"],"_links":{"self":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/906","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/comments?post=906"}],"version-history":[{"count":1,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/906\/revisions"}],"predecessor-version":[{"id":907,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/906\/revisions\/907"}],"wp:attachment":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/media?parent=906"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/categories?post=906"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/tags?post=906"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}