{"id":1093747,"date":"2025-01-08T14:34:11","date_gmt":"2025-01-08T06:34:11","guid":{"rendered":""},"modified":"2025-01-08T14:34:13","modified_gmt":"2025-01-08T06:34:13","slug":"python%e5%a6%82%e4%bd%95%e6%8a%8argb%e5%9b%be%e5%83%8f%e5%8f%98%e6%88%90%e7%81%b0%e5%ba%a6%e5%9b%be%e5%83%8f-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1093747.html","title":{"rendered":"python\u5982\u4f55\u628argb\u56fe\u50cf\u53d8\u6210\u7070\u5ea6\u56fe\u50cf"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24210205\/f52b943e-1d97-41a6-9255-9c5038e4d5ab.webp\" alt=\"python\u5982\u4f55\u628argb\u56fe\u50cf\u53d8\u6210\u7070\u5ea6\u56fe\u50cf\" \/><\/p>\n<p><p> \u5728Python\u4e2d\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u7684\u51e0\u79cd\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528<code>OpenCV<\/code>\u3001<code>Pillow<\/code>\u548c<code>Matplotlib<\/code>\u5e93\u3002<strong>\u4f7f\u7528OpenCV\u3001\u4f7f\u7528Pillow\u3001\u4f7f\u7528Matplotlib<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528OpenCV\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><p><strong>\u4f7f\u7528OpenCV\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong>\uff1a<\/p>\n<p>OpenCV\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u652f\u6301\u56fe\u50cf\u548c\u89c6\u9891\u5904\u7406\u3002\u4f7f\u7528OpenCV\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u975e\u5e38\u7b80\u5355\u3002\u4e0b\u9762\u662f\u8be6\u7ec6\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6RGB\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;path_to_your_image.jpg&#39;)<\/p>\n<h2><strong>\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n<p>gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n<h2><strong>\u4fdd\u5b58\u6216\u663e\u793a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n<p>cv2.imwrite(&#39;gray_image.jpg&#39;, gray_image)<\/p>\n<p>cv2.imshow(&#39;Gray Image&#39;, gray_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>\u901a\u8fc7\u4e0a\u8ff0\u4ee3\u7801\uff0c\u6211\u4eec\u9996\u5148\u8bfb\u53d6RGB\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528<code>cv2.cvtColor()<\/code>\u51fd\u6570\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u6700\u540e\u4fdd\u5b58\u6216\u663e\u793a\u7070\u5ea6\u56fe\u50cf\u3002<code>cv2.COLOR_BGR2GRAY<\/code>\u662fOpenCV\u4e2d\u7528\u4e8e\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u7684\u6807\u5fd7\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u4f7f\u7528Pillow<\/p>\n<\/p>\n<p><p>Pillow\u662fPython\u56fe\u50cf\u5904\u7406\u5e93PIL\u7684\u4e00\u4e2a\u5206\u652f\u548c\u6539\u8fdb\u7248\u672c\u3002\u5b83\u975e\u5e38\u9002\u5408\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Pillow\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u6253\u5f00RGB\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;path_to_your_image.jpg&#39;)<\/p>\n<h2><strong>\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n<p>gray_image = image.convert(&#39;L&#39;)<\/p>\n<h2><strong>\u4fdd\u5b58\u6216\u663e\u793a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n<p>gray_image.save(&#39;gray_image.jpg&#39;)<\/p>\n<p>gray_image.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528<code>Image.open()<\/code>\u51fd\u6570\u6253\u5f00RGB\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528<code>convert(&#39;L&#39;)<\/code>\u65b9\u6cd5\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4fdd\u5b58\u6216\u663e\u793a\u7070\u5ea6\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528Matplotlib<\/p>\n<\/p>\n<p><p>Matplotlib\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u9759\u6001\u3001\u52a8\u753b\u548c\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u7684\u7efc\u5408\u5e93\u3002\u5c3d\u7ba1\u5b83\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u4f46\u4e5f\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Matplotlib\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import matplotlib.image as mpimg<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u8bfb\u53d6RGB\u56fe\u50cf<\/strong><\/h2>\n<p>image = mpimg.imread(&#39;path_to_your_image.jpg&#39;)<\/p>\n<h2><strong>\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n<p>gray_image = np.dot(image[..., :3], [0.2989, 0.5870, 0.1140])<\/p>\n<h2><strong>\u663e\u793a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n<p>plt.imshow(gray_image, cmap=plt.get_cmap(&#39;gray&#39;))<\/p>\n<p>plt.imsave(&#39;gray_image.jpg&#39;, gray_image, cmap=plt.get_cmap(&#39;gray&#39;))<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528<code>mpimg.imread()<\/code>\u51fd\u6570\u8bfb\u53d6RGB\u56fe\u50cf\u3002\u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528<code>np.dot()<\/code>\u51fd\u6570\u6839\u636e\u52a0\u6743\u5e73\u5747\u503c\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528<code>plt.imshow()<\/code>\u548c<code>plt.imsave()<\/code>\u51fd\u6570\u663e\u793a\u548c\u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u7070\u5ea6\u56fe\u50cf\u7684\u5de5\u4f5c\u539f\u7406<\/p>\n<\/p>\n<p><p>\u7070\u5ea6\u56fe\u50cf\u662f\u6307\u56fe\u50cf\u4e2d\u7684\u6bcf\u4e2a\u50cf\u7d20\u4ec5\u5305\u542b\u4eae\u5ea6\u4fe1\u606f\uff0c\u800c\u6ca1\u6709\u989c\u8272\u4fe1\u606f\u3002\u8fd9\u610f\u5473\u7740\u6bcf\u4e2a\u50cf\u7d20\u4ec5\u6709\u4e00\u4e2a\u503c\u6765\u8868\u793a\u5176\u4eae\u5ea6\u3002\u8981\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u901a\u5e38\u91c7\u7528\u52a0\u6743\u5e73\u5747\u503c\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u4eae\u5ea6\u3002\u5177\u4f53\u516c\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<p><pre><code>Gray = 0.2989 * R + 0.5870 * G + 0.1140 * B<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5176\u4e2d\uff0cR\u3001G\u3001B\u5206\u522b\u8868\u793a\u7ea2\u8272\u3001\u7eff\u8272\u548c\u84dd\u8272\u901a\u9053\u7684\u503c\u3002\u8fd9\u4e9b\u6743\u91cd\u662f\u6839\u636e\u4eba\u7c7b\u89c6\u89c9\u7cfb\u7edf\u5bf9\u4e0d\u540c\u989c\u8272\u654f\u611f\u5ea6\u7684\u7814\u7a76\u5f97\u51fa\u7684\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u7070\u5ea6\u56fe\u50cf\u7684\u5e94\u7528<\/p>\n<\/p>\n<p><p>\u7070\u5ea6\u56fe\u50cf\u5728\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u4e2d\u975e\u5e38\u6709\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\uff1a<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u8fb9\u7f18\u68c0\u6d4b<\/strong>\uff1a\u7070\u5ea6\u56fe\u50cf\u901a\u5e38\u7528\u4e8e\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\u4e2d\uff0c\u5982Canny\u8fb9\u7f18\u68c0\u6d4b\u3002\u8fb9\u7f18\u68c0\u6d4b\u662f\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u7684\u57fa\u7840\uff0c\u5982\u5bf9\u8c61\u8bc6\u522b\u548c\u56fe\u50cf\u5206\u5272\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u56fe\u50cf\u5206\u5272<\/strong>\uff1a\u5728\u56fe\u50cf\u5206\u5272\u4efb\u52a1\u4e2d\uff0c\u7070\u5ea6\u56fe\u50cf\u53ef\u4ee5\u7b80\u5316\u5904\u7406\u8fc7\u7a0b\uff0c\u56e0\u4e3a\u5b83\u4eec\u53ea\u6709\u4e00\u4e2a\u901a\u9053\uff0c\u8fd9\u4f7f\u5f97\u7b97\u6cd5\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u7279\u5f81\u63d0\u53d6<\/strong>\uff1a\u8bb8\u591a\u7279\u5f81\u63d0\u53d6\u7b97\u6cd5\uff08\u5982SIFT\u3001SURF\uff09\u5728\u7070\u5ea6\u56fe\u50cf\u4e0a\u8868\u73b0\u66f4\u597d\uff0c\u56e0\u4e3a\u5b83\u4eec\u4e13\u6ce8\u4e8e\u4eae\u5ea6\u53d8\u5316\uff0c\u800c\u4e0d\u662f\u989c\u8272\u53d8\u5316\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u56fe\u50cf\u589e\u5f3a<\/strong>\uff1a\u7070\u5ea6\u56fe\u50cf\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u589e\u5f3a\u6280\u672f\uff0c\u5982\u76f4\u65b9\u56fe\u5747\u8861\u5316\uff0c\u4ee5\u6539\u5584\u56fe\u50cf\u7684\u5bf9\u6bd4\u5ea6\u548c\u7ec6\u8282\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n<p><p>\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u5728Python\u4e2d\u975e\u5e38\u7b80\u5355\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u5b9e\u73b0\uff0c\u5982OpenCV\u3001Pillow\u548cMatplotlib\u3002\u6bcf\u4e2a\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f\u3002<strong>\u4f7f\u7528OpenCV\u3001\u4f7f\u7528Pillow\u3001\u4f7f\u7528Matplotlib<\/strong>\u3002\u6b64\u5916\uff0c\u7070\u5ea6\u56fe\u50cf\u5728\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u4e2d\u975e\u5e38\u6709\u7528\uff0c\u5982\u8fb9\u7f18\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u5272\u3001\u7279\u5f81\u63d0\u53d6\u548c\u56fe\u50cf\u589e\u5f3a\u3002\u4e86\u89e3\u5982\u4f55\u8f6c\u6362\u548c\u4f7f\u7528\u7070\u5ea6\u56fe\u50cf\u5bf9\u4e8e\u4ece\u4e8b\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u5f00\u53d1\u8005\u6765\u8bf4\u662f\u4e00\u4e2a\u57fa\u672c\u6280\u80fd\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u5e93\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff1f<\/strong><br \/>\u4f7f\u7528Python\u7684Pillow\u5e93\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002\u60a8\u53ea\u9700\u5bfc\u5165PIL\u5e93\u4e2d\u7684Image\u6a21\u5757\uff0c\u52a0\u8f7d\u56fe\u50cf\uff0c\u5e76\u4f7f\u7528<code>convert(&#39;L&#39;)<\/code>\u65b9\u6cd5\u8fdb\u884c\u8f6c\u6362\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<pre><code class=\"language-python\">from PIL import Image\n\n# \u52a0\u8f7d\u56fe\u50cf\nimg = Image.open(&#39;your_image.jpg&#39;)\n\n# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\ngray_img = img.convert(&#39;L&#39;)\n\n# \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf\ngray_img.save(&#39;gray_image.jpg&#39;)\n<\/code><\/pre>\n<p><strong>\u5728\u5904\u7406\u56fe\u50cf\u65f6\uff0c\u4e3a\u4ec0\u4e48\u9009\u62e9\u7070\u5ea6\u56fe\u50cf\u800c\u4e0d\u662fRGB\u56fe\u50cf\uff1f<\/strong><br \/>\u7070\u5ea6\u56fe\u50cf\u4ec5\u5305\u542b\u4eae\u5ea6\u4fe1\u606f\uff0c\u901a\u5e38\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002\u76f8\u6bd4\u4e8eRGB\u56fe\u50cf\uff0c\u7070\u5ea6\u56fe\u50cf\u5360\u7528\u7684\u5b58\u50a8\u7a7a\u95f4\u66f4\u5c0f\uff0c\u5904\u7406\u901f\u5ea6\u66f4\u5feb\u3002\u6b64\u5916\uff0c\u8bb8\u591a\u56fe\u50cf\u5206\u6790\u7b97\u6cd5\u5728\u5904\u7406\u7070\u5ea6\u56fe\u50cf\u65f6\u6548\u679c\u66f4\u4f73\uff0c\u56e0\u4e3a\u5b83\u4eec\u51cf\u5c11\u4e86\u989c\u8272\u7ef4\u5ea6\u7684\u590d\u6742\u6027\u3002<\/p>\n<p><strong>\u5982\u4f55\u4f7f\u7528OpenCV\u5e93\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff1f<\/strong><br \/>OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u4e5f\u53ef\u4ee5\u7528\u6765\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528<code>cv2.cvtColor()<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<pre><code class=\"language-python\">import cv2\n\n# \u52a0\u8f7d\u56fe\u50cf\nimg = cv2.imread(&#39;your_image.jpg&#39;)\n\n# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\ngray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf\ncv2.imwrite(&#39;gray_image.jpg&#39;, gray_img)\n<\/code><\/pre>\n<p>\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u7b80\u5355\u6613\u7528\uff0c\u8fd8\u80fd\u5904\u7406\u89c6\u9891\u6d41\u548c\u5b9e\u65f6\u56fe\u50cf\u8f6c\u6362\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u5c06RGB\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u7684\u51e0\u79cd\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528OpenCV\u3001Pillow\u548cMatplotlib\u5e93 [&hellip;]","protected":false},"author":3,"featured_media":1093754,"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\/1093747"}],"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=1093747"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1093747\/revisions"}],"predecessor-version":[{"id":1093757,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1093747\/revisions\/1093757"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1093754"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1093747"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1093747"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1093747"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}