{"id":1105738,"date":"2025-01-08T16:33:47","date_gmt":"2025-01-08T08:33:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1105738.html"},"modified":"2025-01-08T16:33:50","modified_gmt":"2025-01-08T08:33:50","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97%e4%b8%80%e5%bc%a0%e5%9b%be%e7%89%87%e7%9a%84%e6%96%b9%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1105738.html","title":{"rendered":"python\u5982\u4f55\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee"},"content":{"rendered":"

\"python\u5982\u4f55\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee\"<\/p>\n

Python\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee\uff1a<\/strong><\/p>\n<\/p>\n

\u8981\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684NumPy\u548cOpenCV\u5e93\u3002\u9996\u5148\u8bfb\u53d6\u56fe\u7247\u3001\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u3001\u8ba1\u7b97\u50cf\u7d20\u503c\u7684\u65b9\u5dee<\/strong>\u3002\u4e0b\u9762\u8be6\u7ec6\u8bb2\u89e3\u5176\u4e2d\u4e00\u4e2a\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5fc5\u8981\u7684\u5e93\uff0c\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n

pip install numpy opencv-python<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u63a5\u4e0b\u6765\u662f\u5177\u4f53\u7684\u4ee3\u7801\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u56fe\u7247<\/strong><\/h2>\n
image = cv2.imread('path_to_image.jpg')<\/p>\n
\u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe<\/strong><\/h2>\n
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
\u8ba1\u7b97\u7070\u5ea6\u56fe\u50cf\u7d20\u503c\u7684\u65b9\u5dee<\/strong><\/h2>\n
variance = np.var(gray_image)<\/p>\n
print(f"The variance of the image is: {variance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u8bfb\u53d6\u4e86\u56fe\u7247\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u3002\u7136\u540e\u4f7f\u7528NumPy\u7684var<\/code>\u51fd\u6570\u8ba1\u7b97\u4e86\u7070\u5ea6\u56fe\u50cf\u7d20\u503c\u7684\u65b9\u5dee\u3002\u65b9\u5dee<\/strong>\u662f\u56fe\u50cf\u4eae\u5ea6\u53d8\u5316\u7684\u4e00\u4e2a\u91cf\u5ea6\uff0c\u65b9\u5dee\u8d8a\u5927\uff0c\u8868\u660e\u56fe\u50cf\u4e2d\u7684\u4eae\u5ea6\u53d8\u5316\u8d8a\u5267\u70c8\u3002<\/p>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0\u7070\u5ea6\u56fe\u8f6c\u6362\u6b65\u9aa4<\/strong>\uff1a<\/p>\n
\u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u662f\u8ba1\u7b97\u65b9\u5dee\u7684\u91cd\u8981\u6b65\u9aa4\u4e4b\u4e00\u3002\u7070\u5ea6\u56fe\u662f\u6307\u6bcf\u4e2a\u50cf\u7d20\u53ea\u6709\u4e00\u4e2a\u7070\u5ea6\u503c\uff0c\u800c\u4e0d\u662f\u50cf\u5f69\u8272\u56fe\u50cf\u90a3\u6837\u6709\u591a\u4e2a\u901a\u9053\uff08\u4f8b\u5982RGB\uff09\u3002\u7070\u5ea6\u56fe\u66f4\u5bb9\u6613\u5904\u7406\u548c\u5206\u6790\uff0c\u56e0\u4e3a\u5b83\u51cf\u5c11\u4e86\u6570\u636e\u7684\u590d\u6742\u6027\u3002\u4f7f\u7528OpenCV\u7684cvtColor<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u3002<\/p>\n<\/p>\n
\n
\u4e00\u3001\u8bfb\u53d6\u56fe\u7247<\/h3>\n<\/p>\n
\u5728\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u8bfb\u53d6\u56fe\u7247\u3002\u4f7f\u7528OpenCV\u7684imread<\/code>\u51fd\u6570\u53ef\u4ee5\u65b9\u4fbf\u5730\u8bfb\u53d6\u672c\u5730\u5b58\u50a8\u7684\u56fe\u7247\u3002\u51fd\u6570\u8bed\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
cv2.imread(filename, flags)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5176\u4e2d\uff0cfilename<\/code>\u662f\u56fe\u7247\u7684\u8def\u5f84\uff0cflags<\/code>\u662f\u8bfb\u53d6\u56fe\u7247\u7684\u6a21\u5f0f\u3002\u9ed8\u8ba4\u6a21\u5f0f\u4e3acv2.IMREAD_COLOR<\/code>\uff0c\u8bfb\u53d6\u5f69\u8272\u56fe\u50cf\u3002<\/p>\n<\/p>\n
image = cv2.imread('path_to_image.jpg')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe<\/h3>\n<\/p>\n
\u8bfb\u53d6\u56fe\u7247\u540e\uff0c\u6211\u4eec\u9700\u8981\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u3002\u7070\u5ea6\u56fe\u7684\u6bcf\u4e2a\u50cf\u7d20\u90fd\u6709\u4e00\u4e2a\u7070\u5ea6\u503c\uff0c\u8868\u793a\u8be5\u50cf\u7d20\u7684\u4eae\u5ea6\u3002\u4f7f\u7528OpenCV\u7684cvtColor<\/code>\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e00\u8f6c\u6362\u3002\u51fd\u6570\u8bed\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
cv2.cvtColor(src, code)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5176\u4e2d\uff0csrc<\/code>\u662f\u6e90\u56fe\u50cf\uff0ccode<\/code>\u662f\u989c\u8272\u7a7a\u95f4\u8f6c\u6362\u4ee3\u7801\u3002\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u65f6\uff0c\u4f7f\u7528cv2.COLOR_BGR2GRAY<\/code>\u3002<\/p>\n<\/p>\n
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8ba1\u7b97\u65b9\u5dee<\/h3>\n<\/p>\n
\u7070\u5ea6\u56fe\u8f6c\u6362\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u7684var<\/code>\u51fd\u6570\u8ba1\u7b97\u7070\u5ea6\u56fe\u50cf\u7d20\u503c\u7684\u65b9\u5dee\u3002\u51fd\u6570\u8bed\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
numpy.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5176\u4e2d\uff0ca<\/code>\u662f\u8f93\u5165\u6570\u7ec4\uff0caxis<\/code>\u6307\u5b9a\u8ba1\u7b97\u65b9\u5dee\u7684\u8f74\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u8ba1\u7b97\u6574\u4e2a\u6570\u7ec4\u7684\u65b9\u5dee\u3002<\/p>\n<\/p>\n
variance = np.var(gray_image)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u663e\u793a\u7ed3\u679c<\/h3>\n<\/p>\n
\u8ba1\u7b97\u65b9\u5dee\u540e\uff0c\u53ef\u4ee5\u5c06\u7ed3\u679c\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
print(f"The variance of the image is: {variance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fdb\u4e00\u6b65\u4f18\u5316\u548c\u6269\u5c55<\/h3>\n<\/p>\n
\u9664\u4e86\u8ba1\u7b97\u56fe\u50cf\u7684\u65b9\u5dee\uff0cPython\u548cOpenCV\u8fd8\u53ef\u4ee5\u7528\u4e8e\u5176\u4ed6\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u4efb\u52a1\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8ba1\u7b97\u56fe\u50cf\u7684\u5747\u503c\u3001\u4e2d\u503c\u3001\u6807\u51c6\u5dee\u7b49\u7edf\u8ba1\u91cf\u3002\u8fd8\u53ef\u4ee5\u8fdb\u884c\u56fe\u50cf\u6ee4\u6ce2\u3001\u8fb9\u7f18\u68c0\u6d4b\u3001\u5f62\u6001\u5b66\u64cd\u4f5c\u7b49\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
\u8ba1\u7b97\u56fe\u50cf\u5747\u503c<\/h4>\n<\/p>\n
mean = np.mean(gray_image)<\/p>\n
print(f"The mean of the image is: {mean}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8ba1\u7b97\u56fe\u50cf\u6807\u51c6\u5dee<\/h4>\n<\/p>\n
std_dev = np.std(gray_image)<\/p>\n
print(f"The standard deviation of the image is: {std_dev}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56fe\u50cf\u6ee4\u6ce2<\/h4>\n<\/p>\n
\u4f7f\u7528\u9ad8\u65af\u6ee4\u6ce2\u5668\u5e73\u6ed1\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528\u4e2d\u503c\u6ee4\u6ce2\u5668\u53bb\u9664\u566a\u58f0\uff1a<\/p>\n<\/p>\n
median_blurred_image = cv2.medianBlur(gray_image, 5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fb9\u7f18\u68c0\u6d4b<\/h4>\n<\/p>\n
\u4f7f\u7528Canny\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\uff1a<\/p>\n<\/p>\n
edges = cv2.Canny(gray_image, 100, 200)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
\u4f7f\u7528OpenCV\u7684imshow<\/code>\u51fd\u6570\u663e\u793a\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.imshow('Gray Image', gray_image)<\/p>\n
cv2.imshow('Edges', edges)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9ad8\u7ea7\u8bdd\u9898<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\uff0c\u53ef\u4ee5\u4f7f\u7528\u66f4\u9ad8\u7ea7\u7684\u6280\u672f\u548c\u5e93\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u5272\u7b49\u4efb\u52a1\u3002\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u5305\u62ecTensorFlow\u3001Keras\u3001PyTorch\u7b49\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4f7f\u7528Keras\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from keras.models import Sequential<\/p>\n
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten<\/p>\n
from keras.preprocessing.image import ImageDataGenerator<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n
train_datagen = ImageDataGenerator(rescale=1.\/255)<\/p>\n
train_generator = train_datagen.flow_from_directory(<\/p>\n
    'path_to_train_data',<\/p>\n
    target_size=(150, 150),<\/p>\n
    batch_size=32,<\/p>\n
    class_mode='binary'<\/p>\n
)<\/p>\n
\u6784\u5efa\u6a21\u578b<\/strong><\/h2>\n
model = Sequential([<\/p>\n
    Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),<\/p>\n
    MaxPooling2D((2, 2)),<\/p>\n
    Conv2D(64, (3, 3), activation='relu'),<\/p>\n
    MaxPooling2D((2, 2)),<\/p>\n
    Flatten(),<\/p>\n
    Dense(64, activation='relu'),<\/p>\n
    Dense(1, activation='sigmoid')<\/p>\n
])<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(train_generator, epochs=10)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Keras\u6784\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b\u3002\u9996\u5148\u5bf9\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u7136\u540e\u6784\u5efa\u548c\u7f16\u8bd1\u6a21\u578b\uff0c\u6700\u540e\u8bad\u7ec3\u6a21\u578b\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u8bb2\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee\uff0c\u5e76\u5c55\u793a\u4e86\u4e00\u4e9b\u5176\u4ed6\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u4efb\u52a1\u3002\u4f7f\u7528Python\u548cOpenCV\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u5404\u79cd\u56fe\u50cf\u5904\u7406\u64cd\u4f5c\u3002\u6b64\u5916\uff0c\u7ed3\u5408\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\uff0c\u53ef\u4ee5\u5b9e\u73b0\u66f4\u9ad8\u7ea7\u7684\u56fe\u50cf\u5206\u6790\u548c\u7406\u89e3\u4efb\u52a1\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff0c\u5e76\u6fc0\u53d1\u60a8\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u7684\u63a2\u7d22\u5174\u8da3\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u5e93\u8ba1\u7b97\u56fe\u50cf\u7684\u65b9\u5dee\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684NumPy\u548cOpenCV\u5e93\u6765\u8ba1\u7b97\u56fe\u50cf\u7684\u65b9\u5dee\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u3002\u63a5\u7740\uff0c\u5229\u7528NumPy\u7684\u65b9\u5dee\u51fd\u6570\u8ba1\u7b97\u56fe\u50cf\u7684\u65b9\u5dee\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
import cv2\nimport numpy as np\n\n# \u8bfb\u53d6\u56fe\u50cf\nimage = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)\n\n# \u8ba1\u7b97\u65b9\u5dee\nvariance = np.var(image)\nprint("\u65b9\u5dee\u4e3a:", variance)\n<\/code><\/pre>\n
\u4e3a\u4ec0\u4e48\u8ba1\u7b97\u56fe\u50cf\u7684\u65b9\u5dee\u6709\u7528\uff1f<\/strong>
\u56fe\u50cf\u7684\u65b9\u5dee\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u56fe\u50cf\u7684\u5bf9\u6bd4\u5ea6\u548c\u4eae\u5ea6\u53d8\u5316\u3002\u8f83\u9ad8\u7684\u65b9\u5dee\u503c\u610f\u5473\u7740\u56fe\u50cf\u4e2d\u7684\u4eae\u5ea6\u53d8\u5316\u8f83\u5927\uff0c\u901a\u5e38\u8868\u793a\u56fe\u50cf\u5177\u6709\u8f83\u9ad8\u7684\u5bf9\u6bd4\u5ea6\u3002\u76f8\u53cd\uff0c\u8f83\u4f4e\u7684\u65b9\u5dee\u503c\u5219\u8868\u793a\u56fe\u50cf\u8f83\u4e3a\u5747\u5300\uff0c\u7f3a\u4e4f\u7ec6\u8282\u3002\u8fd9\u5728\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\uff0c\u5c24\u5176\u662f\u5728\u56fe\u50cf\u589e\u5f3a\u548c\u7279\u5f81\u63d0\u53d6\u7684\u5e94\u7528\u4e2d\u3002<\/p>\n
\u662f\u5426\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u8ba1\u7b97\u56fe\u50cf\u7684\u65b9\u5dee\uff1f<\/strong>
\u9664\u4e86\u4f7f\u7528NumPy\u548cOpenCV\uff0c\u8fd8\u6709\u4e00\u4e9b\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u8ba1\u7b97\u56fe\u50cf\u7684\u65b9\u5dee\u3002\u4f8b\u5982\uff0c\u4f7f\u7528PIL\uff08Python Imaging Library\uff09\u5e93\u4e5f\u53ef\u4ee5\u5b9e\u73b0\u76f8\u4f3c\u7684\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528PIL\u7684\u793a\u4f8b\uff1a  <\/p>\n
from PIL import Image\nimport numpy as np\n\n# \u8bfb\u53d6\u56fe\u50cf\nimage = Image.open('image.jpg').convert('L')\nimage_array = np.array(image)\n\n# \u8ba1\u7b97\u65b9\u5dee\nvariance = np.var(image_array)\nprint("\u65b9\u5dee\u4e3a:", variance)\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u6cd5\u540c\u6837\u6709\u6548\uff0c\u5e76\u4e14PIL\u5728\u5904\u7406\u67d0\u4e9b\u7c7b\u578b\u7684\u56fe\u50cf\u65f6\u53ef\u80fd\u4f1a\u66f4\u65b9\u4fbf\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee\uff1a \u8981\u8ba1\u7b97\u4e00\u5f20\u56fe\u7247\u7684\u65b9\u5dee\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684NumPy\u548cOpenCV\u5e93\u3002\u9996 […]","protected":false},"author":3,"featured_media":1105746,"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\/1105738"}],"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=1105738"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1105738\/revisions"}],"predecessor-version":[{"id":1105750,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1105738\/revisions\/1105750"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1105746"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1105738"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1105738"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1105738"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}