{"id":967659,"date":"2024-12-27T04:59:14","date_gmt":"2024-12-26T20:59:14","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/967659.html"},"modified":"2024-12-27T04:59:17","modified_gmt":"2024-12-26T20:59:17","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a9%e5%9b%be%e7%89%87%e9%a2%9c%e8%89%b2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/967659.html","title":{"rendered":"python\u5982\u4f55\u8ba9\u56fe\u7247\u989c\u8272"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24182828\/d28ead2d-5136-4092-9ef7-3187c6e285bb.webp\" alt=\"python\u5982\u4f55\u8ba9\u56fe\u7247\u989c\u8272\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\uff0c\u6539\u53d8\u56fe\u7247\u989c\u8272\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u5305\u62ec\u4f7f\u7528PIL\u5e93\u3001OpenCV\u5e93\u3001\u8c03\u6574\u8272\u5f69\u5e73\u8861\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u56fe\u50cf\u8fdb\u884c\u8c03\u8272\u3001\u8f6c\u6362\u8272\u5f69\u7a7a\u95f4\u4ee5\u53ca\u5e94\u7528\u6ee4\u955c\u7b49\u64cd\u4f5c\u3002<\/strong>\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8\u4f7f\u7528Python\u6765\u6539\u53d8\u56fe\u7247\u989c\u8272\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u8be6\u7ec6\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u4ee5\u5e2e\u52a9\u4f60\u638c\u63e1\u8fd9\u4e9b\u6280\u672f\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u4f7f\u7528PIL\u5e93\u6765\u6539\u53d8\u56fe\u7247\u989c\u8272<\/p>\n<\/p>\n<p><p>PIL\uff08Python Imaging Library\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\u3002\u5728PIL\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u6765\u6539\u53d8\u56fe\u7247\u989c\u8272\u3002<\/p>\n<\/p>\n<ol>\n<li>\u52a0\u8f7d\u548c\u663e\u793a\u56fe\u7247<\/li>\n<\/ol>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u52a0\u8f7d\u56fe\u7247\u5e76\u663e\u793a\u5b83\u3002PIL\u5e93\u4e2d\u7684<code>Image<\/code>\u6a21\u5757\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n<p>image = Image.open(&#39;example.jpg&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u7247<\/strong><\/h2>\n<p>image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u8f6c\u6362\u989c\u8272\u6a21\u5f0f<\/li>\n<\/ol>\n<p><p>PIL\u5141\u8bb8\u6211\u4eec\u5c06\u56fe\u50cf\u4ece\u4e00\u79cd\u989c\u8272\u6a21\u5f0f\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u989c\u8272\u6a21\u5f0f\uff0c\u4f8b\u5982\u4eceRGB\u8f6c\u6362\u4e3a\u7070\u5ea6\u6a21\u5f0f\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8f6c\u6362\u4e3a\u7070\u5ea6\u6a21\u5f0f<\/p>\n<p>gray_image = image.convert(&#39;L&#39;)<\/p>\n<p>gray_image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u8c03\u6574\u8272\u5f69\u5e73\u8861<\/li>\n<\/ol>\n<p><p>\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u56fe\u50cf\u7684\u8272\u5f69\u5e73\u8861\u6765\u6539\u53d8\u5176\u989c\u8272\u3002\u4f8b\u5982\uff0c\u589e\u52a0\u56fe\u50cf\u7684\u7ea2\u8272\u5206\u91cf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import ImageEnhance<\/p>\n<h2><strong>\u589e\u5f3a\u7ea2\u8272\u5206\u91cf<\/strong><\/h2>\n<p>enhancer = ImageEnhance.Color(image)<\/p>\n<p>enhanced_image = enhancer.enhance(1.5)  # \u589e\u5f3a\u8272\u5f69<\/p>\n<p>enhanced_image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528OpenCV\u5e93\u6765\u6539\u53d8\u56fe\u7247\u989c\u8272<\/p>\n<\/p>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u5e94\u7528\u3002\u5728OpenCV\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u6539\u53d8\u56fe\u50cf\u7684\u989c\u8272\u3002<\/p>\n<\/p>\n<ol>\n<li>\u52a0\u8f7d\u548c\u663e\u793a\u56fe\u7247<\/li>\n<\/ol>\n<p><p>OpenCV\u63d0\u4f9b\u4e86<code>cv2.imread<\/code>\u548c<code>cv2.imshow<\/code>\u51fd\u6570\u6765\u52a0\u8f7d\u548c\u663e\u793a\u56fe\u7247\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n<p>image = cv2.imread(&#39;example.jpg&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u7247<\/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<ol start=\"2\">\n<li>\u8f6c\u6362\u989c\u8272\u7a7a\u95f4<\/li>\n<\/ol>\n<p><p>OpenCV\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u989c\u8272\u7a7a\u95f4\u8f6c\u6362\u529f\u80fd\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u56fe\u50cf\u4ece\u4e00\u79cd\u989c\u8272\u7a7a\u95f4\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u989c\u8272\u7a7a\u95f4\u3002\u4f8b\u5982\uff0c\u4eceBGR\u8f6c\u6362\u4e3aHSV\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8f6c\u6362\u4e3aHSV\u989c\u8272\u7a7a\u95f4<\/p>\n<p>hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)<\/p>\n<p>cv2.imshow(&#39;HSV Image&#39;, hsv_image)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u5e94\u7528\u989c\u8272\u6ee4\u955c<\/li>\n<\/ol>\n<p><p>\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV\u6765\u5e94\u7528\u989c\u8272\u6ee4\u955c\uff0c\u4f8b\u5982\u589e\u52a0\u56fe\u50cf\u7684\u84dd\u8272\u5206\u91cf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u589e\u52a0\u84dd\u8272\u5206\u91cf<\/p>\n<p>(B, G, R) = cv2.split(image)<\/p>\n<p>B = cv2.add(B, 50)<\/p>\n<p>filtered_image = cv2.merge([B, G, R])<\/p>\n<p>cv2.imshow(&#39;Filtered Image&#39;, filtered_image)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e09\u3001\u4f7f\u7528Numpy\u76f4\u63a5\u64cd\u4f5c\u50cf\u7d20\u503c<\/p>\n<\/p>\n<p><p>Numpy\u662fPython\u7684\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u76f4\u63a5\u64cd\u4f5c\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u3002\u901a\u8fc7Numpy\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u56fe\u50cf\u989c\u8272\u7684\u7ec6\u7c92\u5ea6\u63a7\u5236\u3002<\/p>\n<\/p>\n<ol>\n<li>\u76f4\u63a5\u64cd\u4f5c\u50cf\u7d20\u503c<\/li>\n<\/ol>\n<p><p>\u901a\u8fc7Numpy\u6570\u7ec4\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u4fee\u6539\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u3002\u4f8b\u5982\uff0c\u5c06\u56fe\u50cf\u7684\u7eff\u8272\u5206\u91cf\u589e\u52a050\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n<p>image = cv2.imread(&#39;example.jpg&#39;)<\/p>\n<h2><strong>\u589e\u52a0\u7eff\u8272\u5206\u91cf<\/strong><\/h2>\n<p>image[:, :, 1] = np.clip(image[:, :, 1] + 50, 0, 255)<\/p>\n<p>cv2.imshow(&#39;Numpy Modified Image&#39;, image)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u5e94\u7528\u81ea\u5b9a\u4e49\u8272\u5f69\u53d8\u6362<\/li>\n<\/ol>\n<p><p>\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Numpy\u6765\u5b9e\u73b0\u81ea\u5b9a\u4e49\u7684\u8272\u5f69\u53d8\u6362\u3002\u4f8b\u5982\uff0c\u5c06\u56fe\u50cf\u7684RGB\u503c\u8f6c\u6362\u4e3a\u53cd\u8272\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8f6c\u6362\u4e3a\u53cd\u8272<\/p>\n<p>inverted_image = 255 - image<\/p>\n<p>cv2.imshow(&#39;Inverted Image&#39;, inverted_image)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u56db\u3001\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\u9ad8\u7ea7\u56fe\u50cf\u5904\u7406<\/p>\n<\/p>\n<p><p>\u5728\u56fe\u50cf\u5904\u7406\u9879\u76ee\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u9ad8\u7ea7\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002\u4f8b\u5982\uff0c\u5148\u4f7f\u7528OpenCV\u8fdb\u884c\u989c\u8272\u7a7a\u95f4\u8f6c\u6362\uff0c\u518d\u4f7f\u7528PIL\u8fdb\u884c\u7cbe\u7ec6\u5316\u8c03\u8272\u3002<\/p>\n<\/p>\n<ol>\n<li>\u7ed3\u5408OpenCV\u548cPIL<\/li>\n<\/ol>\n<p><p>\u901a\u8fc7\u5c06OpenCV\u7684\u7ed3\u679c\u4f20\u9012\u7ed9PIL\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u4e0d\u540c\u7684\u5e93\u4e4b\u95f4\u5171\u4eab\u56fe\u50cf\u6570\u636e\uff0c\u5b9e\u73b0\u590d\u6742\u7684\u5904\u7406\u6d41\u7a0b\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u4f7f\u7528OpenCV\u52a0\u8f7d\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3a\u7070\u5ea6<\/p>\n<p>gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n<h2><strong>\u4f7f\u7528PIL\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406<\/strong><\/h2>\n<p>pil_image = Image.fromarray(gray_image)<\/p>\n<p>enhancer = ImageEnhance.Contrast(pil_image)<\/p>\n<p>enhanced_image = enhancer.enhance(1.5)<\/p>\n<p>enhanced_image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u56fe\u50cf\u6ee4\u955c\u548c\u7279\u6548<\/li>\n<\/ol>\n<p><p>\u901a\u8fc7\u7ec4\u5408\u4e0d\u540c\u7684\u56fe\u50cf\u5904\u7406\u6280\u672f\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u51fa\u8272\u7684\u56fe\u50cf\u6ee4\u955c\u548c\u7279\u6548\u3002\u4f8b\u5982\uff0c\u7ed3\u5408\u989c\u8272\u589e\u5f3a\u548c\u8fb9\u7f18\u68c0\u6d4b\u6765\u521b\u5efa\u7535\u5f71\u98ce\u683c\u7684\u56fe\u50cf\u6548\u679c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u589e\u5f3a\u8272\u5f69<\/p>\n<p>color_enhancer = ImageEnhance.Color(pil_image)<\/p>\n<p>color_enhanced_image = color_enhancer.enhance(1.5)<\/p>\n<h2><strong>\u5e94\u7528\u8fb9\u7f18\u68c0\u6d4b<\/strong><\/h2>\n<p>edges = cv2.Canny(image, 100, 200)<\/p>\n<h2><strong>\u53e0\u52a0\u8fb9\u7f18\u6548\u679c<\/strong><\/h2>\n<p>final_image = cv2.addWeighted(cv2.cvtColor(np.array(color_enhanced_image), cv2.COLOR_RGB2BGR), 0.8, edges, 0.2, 0)<\/p>\n<p>cv2.imshow(&#39;Final Image&#39;, final_image)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u6211\u4eec\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528Python\u4e2d\u7684PIL\u548cOpenCV\u5e93\u6765\u6539\u53d8\u56fe\u7247\u989c\u8272\uff0c\u5e76\u63a2\u7d22\u4e86Numpy\u5728\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u5e94\u7528\u3002\u638c\u63e1\u8fd9\u4e9b\u6280\u672f\u540e\uff0c\u4f60\u53ef\u4ee5\u5728\u56fe\u50cf\u5904\u7406\u4e2d\u5b9e\u73b0\u66f4\u590d\u6742\u548c\u7cbe\u7ec6\u7684\u8272\u5f69\u8c03\u6574\uff0c\u521b\u9020\u51fa\u66f4\u5177\u89c6\u89c9\u51b2\u51fb\u529b\u7684\u56fe\u50cf\u6548\u679c\u3002\u5e0c\u671b\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u4e3a\u4f60\u7684\u56fe\u50cf\u5904\u7406\u9879\u76ee\u63d0\u4f9b\u5e2e\u52a9\u548c\u542f\u53d1\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u6539\u53d8\u56fe\u7247\u7684\u989c\u8272\uff1f<\/strong><br \/>\u4f7f\u7528Python\u6539\u53d8\u56fe\u7247\u989c\u8272\u53ef\u4ee5\u501f\u52a9\u591a\u4e2a\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u4f8b\u5982Pillow\u3001OpenCV\u7b49\u3002Pillow\u662f\u4e00\u4e2a\u7b80\u5355\u6613\u7528\u7684\u5e93\uff0c\u80fd\u591f\u8fdb\u884c\u989c\u8272\u53d8\u6362\u3001\u6ee4\u955c\u5e94\u7528\u7b49\u64cd\u4f5c\u3002\u60a8\u53ef\u4ee5\u52a0\u8f7d\u56fe\u7247\uff0c\u4f7f\u7528<code>ImageEnhance<\/code>\u6a21\u5757\u8c03\u6574\u9971\u548c\u5ea6\u3001\u4eae\u5ea6\u6216\u8005\u5bf9\u6bd4\u5ea6\uff0c\u4ece\u800c\u5b9e\u73b0\u989c\u8272\u7684\u53d8\u5316\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u5bfc\u5165\u5e93\u3001\u8bfb\u53d6\u56fe\u50cf\u3001\u5e94\u7528\u989c\u8272\u8f6c\u6362\uff0c\u5e76\u4fdd\u5b58\u6216\u663e\u793a\u7ed3\u679c\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5904\u7406\u56fe\u7247\u989c\u8272\uff1f<\/strong><br \/>Python\u4e2d\u5904\u7406\u56fe\u7247\u989c\u8272\u7684\u5e38\u7528\u5e93\u5305\u62ecPillow\u3001OpenCV\u548cMatplotlib\u7b49\u3002Pillow\u63d0\u4f9b\u4e86\u57fa\u672c\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u9002\u5408\u521d\u5b66\u8005\uff1bOpenCV\u5219\u529f\u80fd\u5f3a\u5927\uff0c\u9002\u5408\u9700\u8981\u590d\u6742\u56fe\u50cf\u5904\u7406\u7684\u7528\u6237\uff1bMatplotlib\u5219\u5e38\u7528\u4e8e\u53ef\u89c6\u5316\u56fe\u50cf\uff0c\u80fd\u591f\u5e2e\u52a9\u7528\u6237\u5c55\u793a\u5904\u7406\u540e\u7684\u7ed3\u679c\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u9879\u76ee\u590d\u6742\u5ea6\u6765\u51b3\u5b9a\u3002<\/p>\n<p><strong>\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528OpenCV\u8c03\u6574\u56fe\u50cf\u7684\u989c\u8272\uff1f<\/strong><br \/>\u4f7f\u7528OpenCV\u8fdb\u884c\u989c\u8272\u8c03\u6574\u7684\u8fc7\u7a0b\u76f8\u5bf9\u7b80\u5355\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5b89\u88c5OpenCV\u5e93\u3002\u901a\u8fc7<code>cv2.imread()<\/code>\u8bfb\u53d6\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528<code>cv2.cvtColor()<\/code>\u51fd\u6570\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u4e0d\u540c\u7684\u989c\u8272\u7a7a\u95f4\uff0c\u6bd4\u5982BGR\u5230HSV\u3002\u63a5\u7740\uff0c\u60a8\u53ef\u4ee5\u8c03\u6574\u8272\u8c03\u3001\u9971\u548c\u5ea6\u548c\u4eae\u5ea6\uff0c\u6700\u540e\u4f7f\u7528<code>cv2.imshow()<\/code>\u663e\u793a\u7ed3\u679c\uff0c\u5e76\u901a\u8fc7<code>cv2.imwrite()<\/code>\u4fdd\u5b58\u4fee\u6539\u540e\u7684\u56fe\u50cf\u3002\u8fd9\u6837\u7684\u5904\u7406\u65b9\u5f0f\u9002\u5408\u9700\u8981\u8fdb\u884c\u66f4\u9ad8\u7ea7\u989c\u8272\u8c03\u6574\u7684\u7528\u6237\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u6539\u53d8\u56fe\u7247\u989c\u8272\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u5305\u62ec\u4f7f\u7528PIL\u5e93\u3001OpenCV\u5e93\u3001\u8c03\u6574\u8272\u5f69\u5e73\u8861\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b [&hellip;]","protected":false},"author":3,"featured_media":967664,"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\/967659"}],"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=967659"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/967659\/revisions"}],"predecessor-version":[{"id":967665,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/967659\/revisions\/967665"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/967664"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=967659"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=967659"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=967659"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}