{"id":984455,"date":"2024-12-27T07:28:17","date_gmt":"2024-12-26T23:28:17","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/984455.html"},"modified":"2024-12-27T07:28:18","modified_gmt":"2024-12-26T23:28:18","slug":"python%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e5%9b%be%e7%89%87%e9%aa%8c%e8%af%81","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/984455.html","title":{"rendered":"python\u5982\u4f55\u5b9e\u73b0\u56fe\u7247\u9a8c\u8bc1"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24212046\/49fbea2e-b88d-4fcc-9d32-2790d626489a.webp\" alt=\"python\u5982\u4f55\u5b9e\u73b0\u56fe\u7247\u9a8c\u8bc1\" \/><\/p>\n<p><p> <strong>Python\u5b9e\u73b0\u56fe\u7247\u9a8c\u8bc1\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5176\u4e2d\u5e38\u7528\u7684\u5305\u62ec\uff1a\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u3001\u7ed3\u5408OpenCV\u8fdb\u884c\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff08\u5982CNN\uff09\u8fdb\u884c\u56fe\u50cf\u8bc6\u522b\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u5176\u7279\u70b9\u548c\u5e94\u7528\u573a\u666f\u3002<\/strong>\u5176\u4e2d\uff0c\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u662f\u6700\u57fa\u7840\u7684\u5b9e\u73b0\u65b9\u5f0f\uff0c\u9002\u5408\u521d\u5b66\u8005\u5feb\u901f\u4e0a\u624b\u3002PIL\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u56fe\u50cf\u8fdb\u884c\u8bfb\u53d6\u3001\u5904\u7406\u548c\u663e\u793a\uff0c\u5bf9\u4e8e\u7b80\u5355\u7684\u56fe\u7247\u9a8c\u8bc1\u4efb\u52a1\u8db3\u591f\u4f7f\u7528\u3002<\/p>\n<\/p>\n<p><p>\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u9a8c\u8bc1\u7684\u4e00\u4e2a\u5e38\u89c1\u65b9\u6cd5\u662f\u8fdb\u884c\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u6bd4\u8f83\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5bf9\u6bd4\u4e24\u5f20\u56fe\u7247\u7684\u50cf\u7d20\u503c\uff0c\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u4ece\u800c\u5224\u65ad\u4e24\u5f20\u56fe\u7247\u662f\u5426\u76f8\u4f3c\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u53ef\u4ee5\u901a\u8fc7\u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\uff0c\u7136\u540e\u8ba1\u7b97\u4e24\u5f20\u56fe\u7247\u7684\u76f4\u65b9\u56fe\u5dee\u5f02\uff0c\u6765\u5224\u65ad\u5b83\u4eec\u7684\u76f8\u4f3c\u5ea6\u3002<\/p>\n<\/p>\n<hr>\n<p><h2>\u4e00\u3001\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u5904\u7406<\/h2>\n<\/p>\n<p><p>Python Imaging Library\uff08PIL\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u6253\u5f00\u3001\u64cd\u4f5c\u548c\u4fdd\u5b58\u591a\u79cd\u683c\u5f0f\u7684\u56fe\u50cf\u6587\u4ef6\u3002PIL\u5e93\u7684\u4f7f\u7528\u975e\u5e38\u7b80\u5355\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e0e\u5176\u4ed6Python\u5e93\u914d\u5408\u4f7f\u7528\u3002<\/p>\n<\/p>\n<p><h3>1. \u56fe\u50cf\u6253\u5f00\u4e0e\u663e\u793a<\/h3>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528PIL\u5e93\u4e2d\u7684<code>Image<\/code>\u6a21\u5757\u6765\u6253\u5f00\u548c\u663e\u793a\u56fe\u50cf\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u6253\u5f00\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;path_to_image.jpg&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n<p>image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u5feb\u901f\u67e5\u770b\u548c\u64cd\u4f5c\u56fe\u50cf\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n<p><h3>2. \u56fe\u50cf\u683c\u5f0f\u8f6c\u6362<\/h3>\n<\/p>\n<p><p>PIL\u8fd8\u652f\u6301\u56fe\u50cf\u683c\u5f0f\u7684\u8f6c\u6362\uff0c\u4f8b\u5982\uff0c\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>gray_image = image.convert(&#39;L&#39;)<\/p>\n<p>gray_image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u7070\u5ea6\u56fe\u50cf\u53ea\u5305\u542b\u4eae\u5ea6\u4fe1\u606f\uff0c\u56e0\u6b64\u5728\u8fdb\u884c\u56fe\u50cf\u6bd4\u8f83\u65f6\u53ef\u4ee5\u51cf\u5c11\u8ba1\u7b97\u91cf\u3002<\/p>\n<\/p>\n<p><h2>\u4e8c\u3001\u7ed3\u5408OpenCV\u8fdb\u884c\u56fe\u50cf\u7279\u5f81\u63d0\u53d6<\/h2>\n<\/p>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4e0ePIL\u76f8\u6bd4\uff0cOpenCV\u5728\u5904\u7406\u590d\u6742\u56fe\u50cf\u4efb\u52a1\u65f6\u5177\u6709\u66f4\u9ad8\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n<p><h3>1. \u8bfb\u53d6\u4e0e\u663e\u793a\u56fe\u50cf<\/h3>\n<\/p>\n<p><p>\u4f7f\u7528OpenCV\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf\u975e\u5e38\u7b80\u5355\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;path_to_image.jpg&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n<p>cv2.imshow(&#39;Image&#39;, image)<\/p>\n<p>cv2.w<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>tKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u79cd\u65b9\u6cd5\u9002\u5408\u9700\u8981\u8fdb\u884c\u590d\u6742\u56fe\u50cf\u5904\u7406\u7684\u5e94\u7528\uff0c\u5982\u7279\u5f81\u63d0\u53d6\u548c\u7269\u4f53\u68c0\u6d4b\u3002<\/p>\n<\/p>\n<p><h3>2. \u4f7f\u7528\u7279\u5f81\u5339\u914d\u8fdb\u884c\u56fe\u50cf\u9a8c\u8bc1<\/h3>\n<\/p>\n<p><p>OpenCV\u63d0\u4f9b\u4e86\u591a\u79cd\u7279\u5f81\u5339\u914d\u7b97\u6cd5\uff0c\u5982SIFT\u3001SURF\u3001ORB\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u7b97\u6cd5\uff0c\u53ef\u4ee5\u63d0\u53d6\u56fe\u50cf\u7684\u5173\u952e\u70b9\u548c\u7279\u5f81\u63cf\u8ff0\u7b26\uff0c\u7136\u540e\u8fdb\u884c\u5339\u914d\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u521d\u59cb\u5316ORB\u7279\u5f81\u68c0\u6d4b\u5668<\/p>\n<p>orb = cv2.ORB_create()<\/p>\n<h2><strong>\u68c0\u6d4b\u5173\u952e\u70b9\u548c\u8ba1\u7b97\u63cf\u8ff0\u7b26<\/strong><\/h2>\n<p>kp1, des1 = orb.detectAndCompute(image1, None)<\/p>\n<p>kp2, des2 = orb.detectAndCompute(image2, None)<\/p>\n<h2><strong>\u4f7f\u7528BFMatcher\u8fdb\u884c\u7279\u5f81\u5339\u914d<\/strong><\/h2>\n<p>bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)<\/p>\n<p>matches = bf.match(des1, des2)<\/p>\n<h2><strong>\u6839\u636e\u5339\u914d\u7ed3\u679c\u8fdb\u884c\u9a8c\u8bc1<\/strong><\/h2>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e09\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u56fe\u50cf\u8bc6\u522b<\/h2>\n<\/p>\n<p><p>\u6df1\u5ea6\u5b66\u4e60\u5728\u56fe\u50cf\u8bc6\u522b\u9886\u57df\u8868\u73b0\u4f18\u5f02\uff0c\u7279\u522b\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u53ef\u4ee5\u81ea\u52a8\u63d0\u53d6\u56fe\u50cf\u7279\u5f81\uff0c\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u56fe\u50cf\u9a8c\u8bc1\u4efb\u52a1\u3002<\/p>\n<\/p>\n<p><h3>1. \u9884\u8bad\u7ec3\u6a21\u578b<\/h3>\n<\/p>\n<p><p>\u8bb8\u591a\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff08\u5982TensorFlow\u548cPyTorch\uff09\u63d0\u4f9b\u4e86\u9884\u8bad\u7ec3\u7684\u56fe\u50cf\u8bc6\u522b\u6a21\u578b\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u8fd9\u4e9b\u6a21\u578b\u8fdb\u884c\u56fe\u50cf\u9a8c\u8bc1\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from tensorflow.keras.applications import VGG16<\/p>\n<p>from tensorflow.keras.preprocessing import image<\/p>\n<p>from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u52a0\u8f7dVGG16\u6a21\u578b<\/strong><\/h2>\n<p>model = VGG16(weights=&#39;imagenet&#39;)<\/p>\n<h2><strong>\u9884\u5904\u7406\u56fe\u50cf<\/strong><\/h2>\n<p>img_path = &#39;path_to_image.jpg&#39;<\/p>\n<p>img = image.load_img(img_path, target_size=(224, 224))<\/p>\n<p>x = image.img_to_array(img)<\/p>\n<p>x = np.expand_dims(x, axis=0)<\/p>\n<p>x = preprocess_input(x)<\/p>\n<h2><strong>\u9884\u6d4b<\/strong><\/h2>\n<p>preds = model.predict(x)<\/p>\n<p>print(&#39;Predicted:&#39;, decode_predictions(preds, top=3)[0])<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2. \u81ea\u5b9a\u4e49\u6a21\u578b<\/h3>\n<\/p>\n<p><p>\u5982\u679c\u9700\u8981\u5904\u7406\u7279\u5b9a\u7684\u56fe\u50cf\u9a8c\u8bc1\u4efb\u52a1\uff0c\u53ef\u4ee5\u901a\u8fc7\u8fc1\u79fb\u5b66\u4e60\u7684\u65b9\u6cd5\u8bad\u7ec3\u81ea\u5b9a\u4e49\u6a21\u578b\u3002\u8fc1\u79fb\u5b66\u4e60\u5229\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u7279\u5f81\u63d0\u53d6\u80fd\u529b\uff0c\u51cf\u5c11\u8bad\u7ec3\u65f6\u95f4\u548c\u6570\u636e\u9700\u6c42\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from tensorflow.keras.models import Model<\/p>\n<p>from tensorflow.keras.layers import Dense, GlobalAveragePooling2D<\/p>\n<p>from tensorflow.keras.applications import MobileNetV2<\/p>\n<h2><strong>\u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<p>base_model = MobileNetV2(weights=&#39;imagenet&#39;, include_top=False)<\/p>\n<h2><strong>\u6dfb\u52a0\u81ea\u5b9a\u4e49\u5c42<\/strong><\/h2>\n<p>x = base_model.output<\/p>\n<p>x = GlobalAveragePooling2D()(x)<\/p>\n<p>x = Dense(1024, activation=&#39;relu&#39;)(x)<\/p>\n<p>predictions = Dense(num_classes, activation=&#39;softmax&#39;)(x)<\/p>\n<h2><strong>\u5b9a\u4e49\u65b0\u6a21\u578b<\/strong><\/h2>\n<p>model = Model(inputs=base_model.input, outputs=predictions)<\/p>\n<h2><strong>\u51bb\u7ed3\u90e8\u5206\u5c42<\/strong><\/h2>\n<p>for layer in base_model.layers:<\/p>\n<p>    layer.trainable = False<\/p>\n<h2><strong>\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n<p>model.compile(optimizer=&#39;rmsprop&#39;, loss=&#39;categorical_crossentropy&#39;)<\/p>\n<h2><strong>\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<h2><strong>model.fit(...)<\/strong><\/h2>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u56db\u3001\u603b\u7ed3\u4e0e\u5e94\u7528\u573a\u666f<\/h2>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u9700\u6c42\u548c\u573a\u666f\u3002\u5bf9\u4e8e\u7b80\u5355\u7684\u56fe\u50cf\u9a8c\u8bc1\u4efb\u52a1\uff0c\u4f7f\u7528PIL\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u5df2\u7ecf\u8db3\u591f\uff1b\u5bf9\u4e8e\u9700\u8981\u590d\u6742\u7279\u5f81\u63d0\u53d6\u7684\u5e94\u7528\uff0cOpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff1b\u800c\u5bf9\u4e8e\u9700\u8981\u9ad8\u7cbe\u5ea6\u548c\u81ea\u52a8\u5316\u7279\u5f81\u63d0\u53d6\u7684\u4efb\u52a1\uff0c\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u662f\u6700\u4f73\u9009\u62e9\u3002<\/p>\n<\/p>\n<p><p>\u5728\u7535\u5b50\u5546\u52a1\u3001\u56fe\u50cf\u5206\u7c7b\u3001\u8eab\u4efd\u9a8c\u8bc1\u7b49\u9886\u57df\uff0c\u56fe\u50cf\u9a8c\u8bc1\u6280\u672f\u90fd\u5f97\u5230\u4e86\u5e7f\u6cdb\u5e94\u7528\u3002\u901a\u8fc7\u5408\u7406\u9009\u62e9\u548c\u7ec4\u5408\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5b9e\u73b0\u9ad8\u6548\u3001\u53ef\u9760\u7684\u56fe\u50cf\u9a8c\u8bc1\u7cfb\u7edf\u3002\u65e0\u8bba\u662f\u521d\u5b66\u8005\u8fd8\u662f\u6709\u7ecf\u9a8c\u7684\u5f00\u53d1\u8005\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u7075\u6d3b\u5e94\u7528\u8fd9\u4e9b\u6280\u672f\uff0c\u4e3a\u9879\u76ee\u589e\u6dfb\u4ef7\u503c\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u521b\u5efa\u56fe\u50cf\u9a8c\u8bc1\u7801\uff1f<\/strong><br \/>\u8981\u521b\u5efa\u56fe\u50cf\u9a8c\u8bc1\u7801\uff0c\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u751f\u6210\u56fe\u7247\uff0c\u5e76\u7ed3\u5408\u968f\u673a\u5b57\u7b26\u751f\u6210\u9a8c\u8bc1\u7801\u6587\u672c\u3002\u60a8\u9700\u8981\u5b89\u88c5Pillow\u5e93\uff08\u4f7f\u7528<code>pip install Pillow<\/code>\uff09\uff0c\u7136\u540e\u53ef\u4ee5\u7f16\u5199\u4e00\u4e2a\u811a\u672c\uff0c\u968f\u673a\u751f\u6210\u5b57\u7b26\u5e76\u5c06\u5176\u7ed8\u5236\u5728\u56fe\u50cf\u4e0a\u3002\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u56fe\u50cf\u7684\u5927\u5c0f\u3001\u5b57\u4f53\u3001\u989c\u8272\u7b49\u6765\u5b9e\u73b0\u4e2a\u6027\u5316\u7684\u9a8c\u8bc1\u7801\u3002<\/p>\n<p><strong>Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u56fe\u7247\u9a8c\u8bc1\uff1f<\/strong><br \/>\u9664\u4e86Pillow\u5e93\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u5e93\u6765\u5b9e\u73b0\u56fe\u7247\u9a8c\u8bc1\u3002\u6bd4\u5982\uff0c<code>captcha<\/code>\u5e93\u662f\u4e13\u95e8\u7528\u4e8e\u751f\u6210\u9a8c\u8bc1\u7801\u7684\uff0c\u4f7f\u7528\u8d77\u6765\u975e\u5e38\u65b9\u4fbf\u3002\u5b83\u652f\u6301\u591a\u79cd\u9a8c\u8bc1\u7801\u6837\u5f0f\uff0c\u60a8\u53ea\u9700\u8c03\u7528\u76f8\u5173\u65b9\u6cd5\u5e76\u4f20\u5165\u6240\u9700\u53c2\u6570\u5373\u53ef\u521b\u5efa\u9a8c\u8bc1\u7801\u3002<\/p>\n<p><strong>\u5982\u4f55\u5728Flask\u5e94\u7528\u4e2d\u96c6\u6210\u56fe\u50cf\u9a8c\u8bc1\u7801\uff1f<\/strong><br \/>\u5728Flask\u5e94\u7528\u4e2d\u96c6\u6210\u56fe\u50cf\u9a8c\u8bc1\u7801\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u8def\u7531\u6765\u751f\u6210\u9a8c\u8bc1\u7801\u56fe\u50cf\uff0c\u5e76\u5c06\u5176\u8fd4\u56de\u7ed9\u524d\u7aef\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528Pillow\u6216captcha\u5e93\u751f\u6210\u9a8c\u8bc1\u7801\u56fe\u50cf\uff0c\u5e76\u4f7f\u7528Flask\u7684<code>send_file<\/code>\u529f\u80fd\u5c06\u751f\u6210\u7684\u56fe\u50cf\u53d1\u9001\u7ed9\u7528\u6237\u3002\u786e\u4fdd\u5728\u524d\u7aef\u7684\u8868\u5355\u4e2d\u6b63\u786e\u5f15\u7528\u9a8c\u8bc1\u7801\u56fe\u50cf\uff0c\u4ee5\u4fbf\u7528\u6237\u53ef\u4ee5\u770b\u5230\u5e76\u8f93\u5165\u6b63\u786e\u7684\u9a8c\u8bc1\u7801\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5b9e\u73b0\u56fe\u7247\u9a8c\u8bc1\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5176\u4e2d\u5e38\u7528\u7684\u5305\u62ec\uff1a\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u3001\u7ed3\u5408OpenCV\u8fdb\u884c\u56fe\u50cf\u7279\u5f81\u63d0 [&hellip;]","protected":false},"author":3,"featured_media":984463,"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\/984455"}],"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=984455"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/984455\/revisions"}],"predecessor-version":[{"id":984464,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/984455\/revisions\/984464"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/984463"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=984455"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=984455"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=984455"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}