{"id":1116884,"date":"2025-01-08T18:20:47","date_gmt":"2025-01-08T10:20:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1116884.html"},"modified":"2025-01-08T18:20:50","modified_gmt":"2025-01-08T10:20:50","slug":"python%e5%a6%82%e4%bd%95%e5%af%b9%e5%9b%be%e7%89%87%e8%bf%9b%e8%a1%8c%e4%ba%8c%e5%80%bc%e5%8c%96%e6%98%be%e7%a4%ba-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1116884.html","title":{"rendered":"python\u5982\u4f55\u5bf9\u56fe\u7247\u8fdb\u884c\u4e8c\u503c\u5316\u663e\u793a"},"content":{"rendered":"

\"python\u5982\u4f55\u5bf9\u56fe\u7247\u8fdb\u884c\u4e8c\u503c\u5316\u663e\u793a\"<\/p>\n

Python\u5bf9\u56fe\u7247\u8fdb\u884c\u4e8c\u503c\u5316\u663e\u793a\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528OpenCV\u3001PIL\u548cScikit-image\u5e93\u3002<\/strong> \u5728\u8fd9\u4e09\u79cd\u65b9\u6cd5\u4e2d\uff0cOpenCV\u662f\u6700\u5e38\u7528\u7684\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u4e14\u9ad8\u6548\uff0cPIL\u9002\u5408\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\uff0c\u800cScikit-image\u5219\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u3002\u5728\u8fd9\u91cc\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e09\u79cd\u65b9\u6cd5\u5bf9\u56fe\u7247\u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406<\/h3>\n<\/p>\n

OpenCV\uff08Open Source Computer Vision Library\uff09\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u673a\u5668\u5b66\u4e60<\/a>\u8f6f\u4ef6\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u5305\u62ec\u56fe\u50cf\u4e8c\u503c\u5316\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5OpenCV<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u4f60\u9700\u8981\u5b89\u88c5OpenCV\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install opencv-python<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528OpenCV\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('path_to_your_image.jpg', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(image, cmap='gray')<\/p>\n
plt.title('Original Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5e94\u7528\u4e8c\u503c\u5316\u5904\u7406<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684cv2.threshold<\/code>\u51fd\u6570\u6765\u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406\u3002\u8be5\u51fd\u6570\u6709\u591a\u4e2a\u53c2\u6570\uff0c\u4f46\u6700\u91cd\u8981\u7684\u662f\u9608\u503c\u548c\u6700\u5927\u503c\u3002\u9608\u503c\u7528\u4e8e\u51b3\u5b9a\u50cf\u7d20\u503c\u662f\u5426\u88ab\u7f6e\u4e3a0\u6216\u6700\u5927\u503c\u3002<\/p>\n<\/p>\n
# \u5e94\u7528\u4e8c\u503c\u5316\u5904\u7406<\/p>\n
ret, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)<\/p>\n
\u663e\u793a\u4e8c\u503c\u5316\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(binary_image, cmap='gray')<\/p>\n
plt.title('Binary Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u9608\u503c\u8bbe\u7f6e\u4e3a127\uff0c\u6700\u5927\u503c\u8bbe\u7f6e\u4e3a255\u3002\u5982\u679c\u50cf\u7d20\u503c\u5927\u4e8e127\uff0c\u5219\u5c06\u5176\u7f6e\u4e3a255\uff0c\u5426\u5219\u7f6e\u4e3a0\u3002<\/p>\n<\/p>\n
4. \u81ea\u9002\u5e94\u9608\u503c\u5904\u7406<\/h4>\n<\/p>\n
OpenCV\u8fd8\u63d0\u4f9b\u4e86\u81ea\u9002\u5e94\u9608\u503c\u5904\u7406\u65b9\u6cd5\uff0c\u8fd9\u5728\u5904\u7406\u5149\u7167\u4e0d\u5747\u5300\u7684\u56fe\u50cf\u65f6\u975e\u5e38\u6709\u7528\u3002\u81ea\u9002\u5e94\u9608\u503c\u5904\u7406\u6839\u636e\u56fe\u50cf\u7684\u5c40\u90e8\u53d8\u5316\u6765\u8ba1\u7b97\u9608\u503c\uff0c\u800c\u4e0d\u662f\u4f7f\u7528\u5168\u5c40\u9608\u503c\u3002<\/p>\n<\/p>\n
# \u5e94\u7528\u81ea\u9002\u5e94\u9608\u503c\u5904\u7406<\/p>\n
adaptive_thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)<\/p>\n
\u663e\u793a\u81ea\u9002\u5e94\u9608\u503c\u5904\u7406\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(adaptive_thresh, cmap='gray')<\/p>\n
plt.title('Adaptive Threshold Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0ccv2.ADAPTIVE_THRESH_GAUSSIAN_C<\/code>\u8868\u793a\u4f7f\u7528\u9ad8\u65af\u52a0\u6743\u7684\u81ea\u9002\u5e94\u9608\u503c\u65b9\u6cd5\uff0c\u5757\u5927\u5c0f\u4e3a11\uff0c\u5e38\u6570C\u4e3a2\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528PIL\u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406<\/h3>\n<\/p>\n
PIL\uff08Python Imaging Library\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u867d\u7136\u5b83\u5df2\u7ecf\u88abPillow\u5e93\u66ff\u4ee3\uff0c\u4f46Pillow\u517c\u5bb9PIL\u7684\u6240\u6709\u529f\u80fd\u3002\u6211\u4eec\u5c06\u4f7f\u7528Pillow\u5e93\u8fdb\u884c\u56fe\u50cf\u4e8c\u503c\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Pillow<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u4f60\u9700\u8981\u5b89\u88c5Pillow\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\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
2. \u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528Pillow\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = Image.open('path_to_your_image.jpg').convert('L')<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(image, cmap='gray')<\/p>\n
plt.title('Original Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5e94\u7528\u4e8c\u503c\u5316\u5904\u7406<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pillow\u7684point<\/code>\u65b9\u6cd5\u6765\u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406\u3002\u8be5\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u5e94\u7528\u4e00\u4e2a\u51fd\u6570\u5230\u56fe\u50cf\u7684\u6bcf\u4e2a\u50cf\u7d20\u3002<\/p>\n<\/p>\n
# \u5b9a\u4e49\u4e8c\u503c\u5316\u51fd\u6570<\/p>\n
def binarize(image, threshold):<\/p>\n
    return image.point(lambda p: p > threshold and 255)<\/p>\n
\u5e94\u7528\u4e8c\u503c\u5316\u5904\u7406<\/strong><\/h2>\n
binary_image = binarize(image, 127)<\/p>\n
\u663e\u793a\u4e8c\u503c\u5316\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(binary_image, cmap='gray')<\/p>\n
plt.title('Binary Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u9608\u503c\u8bbe\u7f6e\u4e3a127\u3002\u5982\u679c\u50cf\u7d20\u503c\u5927\u4e8e127\uff0c\u5219\u5c06\u5176\u7f6e\u4e3a255\uff0c\u5426\u5219\u7f6e\u4e3a0\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Scikit-image\u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406<\/h3>\n<\/p>\n
Scikit-image\u662f\u4e00\u4e2a\u57fa\u4e8eScipy\u6784\u5efa\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Scikit-image<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u4f60\u9700\u8981\u5b89\u88c5Scikit-image\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install scikit-image<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528Scikit-image\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
from skimage import io, color<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = io.imread('path_to_your_image.jpg')<\/p>\n
gray_image = color.rgb2gray(image)<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(gray_image, cmap='gray')<\/p>\n
plt.title('Original Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5e94\u7528\u4e8c\u503c\u5316\u5904\u7406<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Scikit-image\u7684threshold_otsu<\/code>\u65b9\u6cd5\u6765\u8ba1\u7b97\u5168\u5c40\u9608\u503c\uff0c\u7136\u540e\u4f7f\u7528\u8be5\u9608\u503c\u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n
from skimage.filters import threshold_otsu<\/p>\n
\u8ba1\u7b97\u5168\u5c40\u9608\u503c<\/strong><\/h2>\n
thresh = threshold_otsu(gray_image)<\/p>\n
\u5e94\u7528\u4e8c\u503c\u5316\u5904\u7406<\/strong><\/h2>\n
binary_image = gray_image > thresh<\/p>\n
\u663e\u793a\u4e8c\u503c\u5316\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(binary_image, cmap='gray')<\/p>\n
plt.title('Binary Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cthreshold_otsu<\/code>\u65b9\u6cd5\u81ea\u52a8\u8ba1\u7b97\u4e00\u4e2a\u6700\u4f18\u7684\u5168\u5c40\u9608\u503c\uff0c\u7136\u540e\u6211\u4eec\u4f7f\u7528\u8be5\u9608\u503c\u5c06\u56fe\u50cf\u8fdb\u884c\u4e8c\u503c\u5316\u3002<\/p>\n<\/p>\n
4. \u81ea\u9002\u5e94\u9608\u503c\u5904\u7406<\/h4>\n<\/p>\n
Scikit-image\u8fd8\u63d0\u4f9b\u4e86\u81ea\u9002\u5e94\u9608\u503c\u5904\u7406\u65b9\u6cd5\uff0c\u8fd9\u5728\u5904\u7406\u5149\u7167\u4e0d\u5747\u5300\u7684\u56fe\u50cf\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
from skimage.filters import threshold_local<\/p>\n
\u8ba1\u7b97\u5c40\u90e8\u9608\u503c<\/strong><\/h2>\n
block_size = 35<\/p>\n
local_thresh = threshold_local(gray_image, block_size, offset=10)<\/p>\n
\u5e94\u7528\u4e8c\u503c\u5316\u5904\u7406<\/strong><\/h2>\n
binary_local = gray_image > local_thresh<\/p>\n
\u663e\u793a\u81ea\u9002\u5e94\u9608\u503c\u5904\u7406\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
plt.imshow(binary_local, cmap='gray')<\/p>\n
plt.title('Adaptive Threshold Image')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cthreshold_local<\/code>\u65b9\u6cd5\u8ba1\u7b97\u5c40\u90e8\u9608\u503c\uff0c\u5757\u5927\u5c0f\u4e3a35\uff0c\u504f\u79fb\u91cf\u4e3a10\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u5bf9\u56fe\u7247\u8fdb\u884c\u4e8c\u503c\u5316\u663e\u793a\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528OpenCV\u3001Pillow\u548cScikit-image\u5e93\u3002OpenCV\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\uff0cPillow\u9002\u5408\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\uff0c\u800cScikit-image\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u3002<\/strong> \u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u4f60\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u6765\u8fdb\u884c\u56fe\u50cf\u4e8c\u503c\u5316\u5904\u7406\u3002\u65e0\u8bba\u4f60\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u4e86\u89e3\u6bcf\u79cd\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\u548c\u9002\u7528\u573a\u666f\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528Python\u8fdb\u884c\u56fe\u50cf\u4e8c\u503c\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u56fe\u7247\u7684\u4e8c\u503c\u5316\u5904\u7406\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\u5982OpenCV\u548cPIL\uff08Pillow\uff09\u6765\u5b9e\u73b0\u56fe\u7247\u7684\u4e8c\u503c\u5316\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5\u76f8\u5e94\u7684\u5e93\uff0c\u4f8b\u5982\u4f7f\u7528pip install opencv-python<\/code>\u548cpip install Pillow<\/code>\u3002\u63a5\u4e0b\u6765\uff0c\u8bfb\u53d6\u56fe\u7247\u540e\uff0c\u901a\u8fc7\u8bbe\u5b9a\u9608\u503c\u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u9ed1\u767d\u8272\uff0c\u901a\u5e38\u4f7f\u7528cv2.threshold()<\/code>\u51fd\u6570\u6216Image.convert('1')<\/code>\u65b9\u6cd5\u6765\u5b8c\u6210\u8fd9\u4e00\u64cd\u4f5c\u3002<\/p>\n
\u4e8c\u503c\u5316\u5904\u7406\u7684\u5e94\u7528\u573a\u666f\u6709\u54ea\u4e9b\uff1f<\/strong>
\u4e8c\u503c\u5316\u5904\u7406\u5728\u8bb8\u591a\u573a\u666f\u4e2d\u90fd\u975e\u5e38\u6709\u7528\uff0c\u4f8b\u5982\u5728\u6587\u6863\u56fe\u50cf\u5904\u7406\u3001\u8fb9\u7f18\u68c0\u6d4b\u548c\u7279\u5f81\u63d0\u53d6\u4e2d\u3002\u5b83\u53ef\u4ee5\u5e2e\u52a9\u63d0\u9ad8\u56fe\u50cf\u7684\u5bf9\u6bd4\u5ea6\uff0c\u4f7f\u5f97\u540e\u7eed\u7684\u56fe\u50cf\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u53d8\u5f97\u66f4\u52a0\u5bb9\u6613\u548c\u9ad8\u6548\u3002\u6b64\u5916\uff0c\u4e8c\u503c\u5316\u5728OCR\uff08\u5149\u5b66\u5b57\u7b26\u8bc6\u522b\uff09\u548c\u56fe\u50cf\u538b\u7f29\u4e2d\u4e5f\u8d77\u7740\u5173\u952e\u4f5c\u7528\u3002<\/p>\n
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\u9009\u62e9\u5408\u9002\u7684\u9608\u503c\u5bf9\u4e8e\u4e8c\u503c\u5316\u7684\u6548\u679c\u81f3\u5173\u91cd\u8981\u3002\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u6765\u786e\u5b9a\u6700\u4f73\u9608\u503c\uff0c\u4f8b\u5982\u4f7f\u7528Otsu\u2019s\u65b9\u6cd5\uff0c\u8be5\u65b9\u6cd5\u80fd\u591f\u81ea\u52a8\u8ba1\u7b97\u51fa\u4e00\u4e2a\u5168\u5c40\u9608\u503c\uff0c\u6700\u5927\u5316\u7c7b\u95f4\u65b9\u5dee\uff0c\u4ece\u800c\u533a\u5206\u524d\u666f\u548c\u80cc\u666f\u3002\u4e5f\u53ef\u4ee5\u901a\u8fc7\u8bd5\u9a8c\u4e0d\u540c\u7684\u9608\u503c\u5e76\u67e5\u770b\u6548\u679c\u6765\u627e\u5230\u6700\u9002\u5408\u7279\u5b9a\u56fe\u50cf\u7684\u9608\u503c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5bf9\u56fe\u7247\u8fdb\u884c\u4e8c\u503c\u5316\u663e\u793a\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528OpenCV\u3001PIL\u548cScikit-image\u5e93\u3002 \u5728\u8fd9\u4e09\u79cd\u65b9\u6cd5 […]","protected":false},"author":3,"featured_media":1116895,"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\/1116884"}],"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=1116884"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1116884\/revisions"}],"predecessor-version":[{"id":1116896,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1116884\/revisions\/1116896"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1116895"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1116884"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1116884"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1116884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}