{"id":961939,"date":"2024-12-27T04:03:37","date_gmt":"2024-12-26T20:03:37","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/961939.html"},"modified":"2024-12-27T04:03:39","modified_gmt":"2024-12-26T20:03:39","slug":"%e7%94%a8python%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97ssd","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/961939.html","title":{"rendered":"\u7528python\u5982\u4f55\u8ba1\u7b97ssd"},"content":{"rendered":"

\"\u7528python\u5982\u4f55\u8ba1\u7b97ssd\"<\/p>\n

\u5728Python\u4e2d\u8ba1\u7b97SSD\uff08Sum of Squared Differences\uff0c\u5e73\u65b9\u5dee\u548c\uff09\u901a\u5e38\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u56fe\u50cf\u5339\u914d\u6216\u6bd4\u8f83\u3002\u8fd9\u662f\u4e00\u79cd\u8861\u91cf\u4e24\u4e2a\u56fe\u50cf\u6216\u56fe\u50cf\u5757\u4e4b\u95f4\u76f8\u4f3c\u5ea6\u7684\u65b9\u6cd5\u3002\u8ba1\u7b97SSD\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\u52a0\u8f7d\u56fe\u50cf\u3001\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u6216\u5176\u4ed6\u9002\u5408\u6bd4\u8f83\u7684\u683c\u5f0f\u3001\u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u5dee\u503c\u3001\u6c42\u5e73\u65b9\u548c\uff0c\u7136\u540e\u6c47\u603b\u8fd9\u4e9b\u5e73\u65b9\u503c\u3002<\/strong>\u5176\u4e2d\uff0c\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u683c\u5f0f<\/strong>\u662f\u4e00\u4e2a\u5173\u952e\u6b65\u9aa4\uff0c\u56e0\u4e3a\u8fd9\u6837\u53ef\u4ee5\u7b80\u5316\u8ba1\u7b97\u5e76\u63d0\u9ad8\u6548\u7387\u3002\u4e0b\u9762\u5c06\u5bf9\u8fd9\u4e00\u8fc7\u7a0b\u8fdb\u884c\u8be6\u7ec6\u63cf\u8ff0\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u56fe\u50cf\u52a0\u8f7d\u4e0e\u9884\u5904\u7406<\/h3>\n<\/p>\n

\u5728\u8ba1\u7b97SSD\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u52a0\u8f7d\u5e76\u9884\u5904\u7406\u56fe\u50cf\u3002Python\u4e2d\u5e38\u7528\u7684\u56fe\u50cf\u5904\u7406\u5e93\u5305\u62ecOpenCV\u548cPIL\u3002OpenCV\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u800cPIL\uff08Pillow\uff09\u5219\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u56fe\u50cf\u52a0\u8f7d\u548c\u5904\u7406\u63a5\u53e3\u3002<\/p>\n<\/p>\n

1. \u4f7f\u7528OpenCV\u52a0\u8f7d\u56fe\u50cf<\/h4>\n<\/p>\n

OpenCV\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u4e2d\u5e7f\u6cdb\u4f7f\u7528\u7684\u5e93\uff0c\u5177\u6709\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4f7f\u7528OpenCV\u52a0\u8f7d\u56fe\u50cf\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n

import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image1 = cv2.imread('image1.jpg')<\/p>\n
image2 = cv2.imread('image2.jpg')<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6<\/strong><\/h2>\n
gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)<\/p>\n
gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528cv2.imread()<\/code>\u51fd\u6570\u8bfb\u53d6\u4e24\u5e45\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528cv2.cvtColor()<\/code>\u51fd\u6570\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u683c\u5f0f\u3002\u8fd9\u662f\u56e0\u4e3a\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\uff0c\u7070\u5ea6\u56fe\u50cf\u8db3\u4ee5\u7528\u4e8e\u6bd4\u8f83\uff0c\u5e76\u4e14\u8ba1\u7b97\u66f4\u4e3a\u9ad8\u6548\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528Pillow\u52a0\u8f7d\u56fe\u50cf<\/h4>\n<\/p>\n
Pillow\u662fPython Imaging Library\uff08PIL\uff09\u7684\u4e00\u4e2a\u5206\u652f\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4f7f\u7528Pillow\u52a0\u8f7d\u56fe\u50cf\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
import numpy as np<\/p>\n
\u6253\u5f00\u56fe\u50cf<\/strong><\/h2>\n
image1 = Image.open('image1.jpg')<\/p>\n
image2 = Image.open('image2.jpg')<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6<\/strong><\/h2>\n
gray1 = image1.convert('L')<\/p>\n
gray2 = image2.convert('L')<\/p>\n
\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n
array1 = np.array(gray1)<\/p>\n
array2 = np.array(gray2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528Image.open()<\/code>\u51fd\u6570\u6253\u5f00\u4e24\u5e45\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528convert('L')<\/code>\u65b9\u6cd5\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u683c\u5f0f\u3002\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528np.array()<\/code>\u5c06\u7070\u5ea6\u56fe\u50cf\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4\uff0c\u4ee5\u4fbf\u540e\u7eed\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u8ba1\u7b97SSD<\/h3>\n<\/p>\n
\u5728\u9884\u5904\u7406\u5b8c\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u5f00\u59cb\u8ba1\u7b97SSD\u3002SSD\u7684\u8ba1\u7b97\u5305\u62ec\u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u5dee\u503c\u3001\u6c42\u5e73\u65b9\u548c\uff0c\u7136\u540e\u6c47\u603b\u8fd9\u4e9b\u5e73\u65b9\u503c\u3002<\/p>\n<\/p>\n
1. \u8ba1\u7b97\u50cf\u7d20\u5dee\u503c<\/h4>\n<\/p>\n
\u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u4e4b\u95f4\u7684\u5dee\u503c\u662fSSD\u8ba1\u7b97\u7684\u7b2c\u4e00\u6b65\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u6570\u7ec4\u7684\u64cd\u4f5c\u6765\u9ad8\u6548\u5730\u5b8c\u6210\u8fd9\u4e00\u4efb\u52a1\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u50cf\u7d20\u5dee\u503c<\/p>\n
diff = array1.astype(float) - array2.astype(float)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5c06\u4e24\u4e2a\u56fe\u50cf\u6570\u7ec4\u8f6c\u6362\u4e3a\u6d6e\u70b9\u7c7b\u578b\uff0c\u7136\u540e\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u7684\u5dee\u503c\u3002\u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u5728\u540e\u7eed\u8ba1\u7b97\u5e73\u65b9\u65f6\u51fa\u73b0\u6570\u636e\u6ea2\u51fa\u7684\u95ee\u9898\u3002<\/p>\n<\/p>\n
2. \u8ba1\u7b97\u5e73\u65b9\u548c<\/h4>\n<\/p>\n
\u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u5dee\u503c\u7684\u5e73\u65b9\u548c\u662fSSD\u8ba1\u7b97\u7684\u7b2c\u4e8c\u6b65\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u5e73\u65b9\u548c<\/p>\n
squared_diff = diff  2<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e00\u6b65\u9aa4\u4e2d\uff0c\u6211\u4eec\u5bf9\u6bcf\u4e2a\u50cf\u7d20\u5dee\u503c\u6c42\u5e73\u65b9\uff0c\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u6570\u7ec4 squared_diff<\/code>\uff0c\u5176\u4e2d\u5305\u542b\u6240\u6709\u50cf\u7d20\u5dee\u503c\u7684\u5e73\u65b9\u3002<\/p>\n<\/p>\n
3. \u6c47\u603b\u5e73\u65b9\u503c<\/h4>\n<\/p>\n
\u6700\u540e\u4e00\u6b65\u662f\u5c06\u6240\u6709\u5e73\u65b9\u503c\u76f8\u52a0\uff0c\u5f97\u5230\u6700\u7ec8\u7684SSD\u503c\u3002<\/p>\n<\/p>\n
# \u6c47\u603b\u5e73\u65b9\u503c<\/p>\n
ssd = np.sum(squared_diff)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7np.sum()<\/code>\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6240\u6709\u5e73\u65b9\u503c\u76f8\u52a0\uff0c\u4ece\u800c\u5f97\u5230SSD\u503c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f18\u5316\u4e0e\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n
\u5728\u8ba1\u7b97SSD\u65f6\uff0c\u6709\u51e0\u4e2a\u4f18\u5316\u548c\u6ce8\u610f\u4e8b\u9879\u9700\u8981\u8003\u8651\uff0c\u4ee5\u63d0\u9ad8\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
1. \u56fe\u50cf\u5c3a\u5bf8\u5339\u914d<\/h4>\n<\/p>\n
\u5728\u6bd4\u8f83\u4e24\u4e2a\u56fe\u50cf\u65f6\uff0c\u786e\u4fdd\u5b83\u4eec\u7684\u5c3a\u5bf8\u76f8\u540c\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u5982\u679c\u56fe\u50cf\u5c3a\u5bf8\u4e0d\u540c\uff0c\u9700\u8981\u5148\u8fdb\u884c\u5c3a\u5bf8\u5339\u914d\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u6216Pillow\u4e2d\u7684\u56fe\u50cf\u7f29\u653e\u529f\u80fd\u6765\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
# \u4f7f\u7528OpenCV\u8c03\u6574\u56fe\u50cf\u5927\u5c0f<\/p>\n
resized_image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0]))<\/p>\n
\u4f7f\u7528Pillow\u8c03\u6574\u56fe\u50cf\u5927\u5c0f<\/strong><\/h2>\n
resized_image2 = image2.resize(image1.size)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6570\u636e\u7c7b\u578b\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u8ba1\u7b97SSD\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u7c7b\u578b\u7684\u5904\u7406\u53ef\u4ee5\u907f\u514d\u6ea2\u51fa\u6216\u7cbe\u5ea6\u635f\u5931\u7684\u95ee\u9898\u3002\u901a\u5e38\u5c06\u56fe\u50cf\u6570\u636e\u8f6c\u6362\u4e3a\u6d6e\u70b9\u7c7b\u578b\u662f\u4e00\u4e2a\u597d\u7684\u5b9e\u8df5\u3002<\/p>\n<\/p>\n
3. \u6027\u80fd\u4f18\u5316<\/h4>\n<\/p>\n
\u5982\u679c\u9700\u8981\u5904\u7406\u5927\u91cf\u56fe\u50cf\u6216\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528NumPy\u7684\u5e76\u884c\u8ba1\u7b97\u529f\u80fd\u6216GPU\u52a0\u901f\u5e93\uff08\u5982CuPy\uff09\u6765\u63d0\u9ad8\u6027\u80fd\u3002<\/p>\n<\/p>\n
4. \u4f7f\u7528\u5176\u4ed6\u8ddd\u79bb\u5ea6\u91cf<\/h4>\n<\/p>\n
\u867d\u7136SSD\u662f\u4e00\u79cd\u5e38\u7528\u7684\u8ddd\u79bb\u5ea6\u91cf\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u5176\u4ed6\u5ea6\u91cf\uff08\u5982\u5f52\u4e00\u5316\u7684\u76f8\u5173\u6027\u7cfb\u6570\u6216\u7ed3\u6784\u76f8\u4f3c\u6027\uff09\u53ef\u80fd\u66f4\u9002\u5408\u3002\u56e0\u6b64\uff0c\u6839\u636e\u5177\u4f53\u5e94\u7528\u573a\u666f\u9009\u62e9\u5408\u9002\u7684\u5ea6\u91cf\u65b9\u6cd5\u662f\u5f88\u91cd\u8981\u7684\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
SSD\u5728\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u5e94\u7528\u975e\u5e38\u5e7f\u6cdb\uff0c\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
1. \u6a21\u677f\u5339\u914d<\/h4>\n<\/p>\n
\u5728\u6a21\u677f\u5339\u914d\u4e2d\uff0cSSD\u88ab\u7528\u4f5c\u8861\u91cf\u6a21\u677f\u4e0e\u56fe\u50cf\u4e2d\u4e0d\u540c\u4f4d\u7f6e\u7684\u76f8\u4f3c\u5ea6\u3002\u901a\u8fc7\u8ba1\u7b97SSD\uff0c\u53ef\u4ee5\u627e\u5230\u6a21\u677f\u5728\u56fe\u50cf\u4e2d\u6700\u4f73\u5339\u914d\u7684\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
2. \u7acb\u4f53\u89c6\u89c9<\/h4>\n<\/p>\n
\u5728\u7acb\u4f53\u89c6\u89c9\u4e2d\uff0cSSD\u7528\u4e8e\u8ba1\u7b97\u89c6\u5dee\u56fe\u50cf\u3002\u901a\u8fc7\u6bd4\u8f83\u5de6\u53f3\u89c6\u56fe\u4e2d\u7684\u5bf9\u5e94\u5757\uff0c\u53ef\u4ee5\u4f30\u8ba1\u666f\u6df1\u4fe1\u606f\u3002<\/p>\n<\/p>\n
3. \u56fe\u50cf\u53bb\u566a<\/h4>\n<\/p>\n
\u5728\u56fe\u50cf\u53bb\u566a\u4e2d\uff0cSSD\u7528\u4e8e\u8bc4\u4f30\u53bb\u566a\u7b97\u6cd5\u7684\u6027\u80fd\u3002\u901a\u8fc7\u8ba1\u7b97\u53bb\u566a\u540e\u56fe\u50cf\u4e0e\u539f\u59cb\u56fe\u50cf\u4e4b\u95f4\u7684SSD\uff0c\u53ef\u4ee5\u8861\u91cf\u53bb\u566a\u6548\u679c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u4e86\u89e3\u4e86\u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97SSD\uff0c\u5305\u62ec\u56fe\u50cf\u52a0\u8f7d\u4e0e\u9884\u5904\u7406\u3001SSD\u8ba1\u7b97\u7684\u5177\u4f53\u6b65\u9aa4\uff0c\u4ee5\u53ca\u4f18\u5316\u4e0e\u5e94\u7528\u573a\u666f\u3002\u5728\u8fdb\u884cSSD\u8ba1\u7b97\u65f6\uff0c\u52a1\u5fc5\u6ce8\u610f\u56fe\u50cf\u5c3a\u5bf8\u5339\u914d\u548c\u6570\u636e\u7c7b\u578b\u5904\u7406\uff0c\u4ee5\u786e\u4fdd\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u8ba1\u7b97\u7684\u6548\u7387\u3002<\/strong>\u6b64\u5916\uff0c\u6839\u636e\u5177\u4f53\u5e94\u7528\u9009\u62e9\u5408\u9002\u7684\u8ddd\u79bb\u5ea6\u91cf\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u89e3\u51b3\u95ee\u9898\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u80fd\u591f\u5e2e\u52a9\u4f60\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u66f4\u597d\u5730\u5229\u7528SSD\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97SSD\u7684\u6027\u80fd\u6307\u6807\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u8ba1\u7b97SSD\u7684\u6027\u80fd\u6307\u6807\uff0c\u4f8b\u5982time<\/code>\u5e93\u6765\u6d4b\u91cf\u8bfb\u5199\u901f\u5ea6\uff0cos<\/code>\u5e93\u6765\u83b7\u53d6\u5b58\u50a8\u4fe1\u606f\u3002\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7\u7f16\u5199\u7b80\u5355\u7684\u811a\u672c\u6765\u6d4b\u8bd5SSD\u7684\u8bfb\u5199\u901f\u5ea6\uff0c\u5e76\u8bb0\u5f55\u8fd9\u4e9b\u6570\u636e\u8fdb\u884c\u5206\u6790\u3002<\/p>\n
\u6709\u54ea\u4e9bPython\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u7ba1\u7406SSD\u7684\u5b58\u50a8\u4fe1\u606f\uff1f<\/strong>
\u6709\u51e0\u4e2a\u6d41\u884c\u7684Python\u5e93\u53ef\u4ee5\u5e2e\u52a9\u4f60\u7ba1\u7406SSD\u7684\u5b58\u50a8\u4fe1\u606f\uff0c\u4f8b\u5982psutil<\/code>\u548cpySMART<\/code>\u3002psutil<\/code>\u53ef\u4ee5\u63d0\u4f9b\u5173\u4e8e\u7cfb\u7edf\u548c\u8fdb\u7a0b\u7684\u8be6\u7ec6\u4fe1\u606f\uff0c\u5305\u62ec\u78c1\u76d8\u4f7f\u7528\u60c5\u51b5\uff0c\u800cpySMART<\/code>\u53ef\u4ee5\u8ba9\u4f60\u8bbf\u95eeSSD\u7684SMART\u6570\u636e\uff0c\u4ee5\u76d1\u63a7\u5176\u5065\u5eb7\u72b6\u51b5\u3002<\/p>\n
\u5982\u4f55\u68c0\u6d4bSSD\u7684\u5065\u5eb7\u72b6\u6001\u548c\u5269\u4f59\u5bff\u547d\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528pySMART<\/code>\u5e93\u6765\u83b7\u53d6SSD\u7684SMART\u4fe1\u606f\uff0c\u8fd9\u4e9b\u4fe1\u606f\u5305\u542b\u4e86\u786c\u76d8\u7684\u5065\u5eb7\u72b6\u6001\u3001\u6e29\u5ea6\u3001\u5199\u5165\u6b21\u6570\u7b49\u6307\u6807\u3002\u901a\u8fc7\u5206\u6790\u8fd9\u4e9b\u6570\u636e\uff0c\u53ef\u4ee5\u5224\u65adSSD\u7684\u5065\u5eb7\u72b6\u51b5\u548c\u5269\u4f59\u5bff\u547d\uff0c\u5e2e\u52a9\u7528\u6237\u505a\u51fa\u7ef4\u62a4\u6216\u66f4\u6362\u7684\u51b3\u7b56\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8ba1\u7b97SSD\uff08Sum of Squared Differences\uff0c\u5e73\u65b9\u5dee\u548c\uff09\u901a\u5e38\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u4e2d […]","protected":false},"author":3,"featured_media":961946,"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\/961939"}],"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=961939"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/961939\/revisions"}],"predecessor-version":[{"id":961948,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/961939\/revisions\/961948"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/961946"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=961939"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=961939"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=961939"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}