{"id":1137862,"date":"2025-01-08T21:53:27","date_gmt":"2025-01-08T13:53:27","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1137862.html"},"modified":"2025-01-08T21:53:29","modified_gmt":"2025-01-08T13:53:29","slug":"python%e5%9b%be%e5%83%8f%e4%b8%80%e5%a4%a7%e4%b8%80%e5%b0%8f%e5%a6%82%e4%bd%95%e8%9e%8d%e5%90%88","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1137862.html","title":{"rendered":"python\u56fe\u50cf\u4e00\u5927\u4e00\u5c0f\u5982\u4f55\u878d\u5408"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25101607\/0c273cf6-1eea-46d6-8661-5a303c538f83.webp\" alt=\"python\u56fe\u50cf\u4e00\u5927\u4e00\u5c0f\u5982\u4f55\u878d\u5408\" \/><\/p>\n<p><p> <strong>Python\u4e2d\u56fe\u50cf\u4e00\u5927\u4e00\u5c0f\u5982\u4f55\u878d\u5408\uff0c\u53ef\u4ee5\u901a\u8fc7\u56fe\u50cf\u7f29\u653e\u3001\u56fe\u50cf\u53e0\u52a0\u3001\u56fe\u50cf\u6df7\u5408\u7b49\u65b9\u6cd5\u5b9e\u73b0<\/strong>\u3002\u5176\u4e2d\uff0c\u901a\u8fc7\u56fe\u50cf\u7f29\u653e\u5c06\u4e24\u5f20\u56fe\u50cf\u8c03\u6574\u4e3a\u76f8\u540c\u5927\u5c0f\uff0c\u7136\u540e\u4f7f\u7528\u56fe\u50cf\u53e0\u52a0\u5c06\u4e24\u5f20\u56fe\u50cf\u5408\u5e76\u5728\u4e00\u8d77\uff0c\u6216\u8005\u901a\u8fc7\u56fe\u50cf\u6df7\u5408\u5b9e\u73b0\u56fe\u50cf\u7684\u900f\u660e\u5ea6\u53e0\u52a0\u6548\u679c\uff0c\u662f\u5e38\u89c1\u7684\u6280\u672f\u4e4b\u4e00\u3002<\/p>\n<\/p>\n<p><p>\u8be6\u7ec6\u6765\u8bf4\uff0c\u56fe\u50cf\u7f29\u653e\u662f\u5c06\u4e24\u5f20\u4e0d\u540c\u5927\u5c0f\u7684\u56fe\u50cf\u8c03\u6574\u4e3a\u76f8\u540c\u7684\u5927\u5c0f\uff0c\u4ee5\u4fbf\u4e8e\u540e\u7eed\u7684\u878d\u5408\u5904\u7406\u3002\u56fe\u50cf\u53e0\u52a0\u5219\u662f\u5c06\u4e24\u5f20\u56fe\u50cf\u6309\u4e00\u5b9a\u7684\u6743\u91cd\u6bd4\u4f8b\u8fdb\u884c\u53e0\u52a0\uff0c\u5f62\u6210\u65b0\u7684\u56fe\u50cf\u3002\u56fe\u50cf\u6df7\u5408\u662f\u901a\u8fc7\u8bbe\u7f6e\u900f\u660e\u5ea6\uff0c\u5c06\u4e24\u5f20\u56fe\u50cf\u8fdb\u884c\u6df7\u5408\u5904\u7406\uff0c\u5f62\u6210\u534a\u900f\u660e\u7684\u53e0\u52a0\u6548\u679c\u3002<\/p>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u4f7f\u7528Python\u6765\u5b9e\u73b0\u56fe\u50cf\u7684\u7f29\u653e\u3001\u53e0\u52a0\u548c\u6df7\u5408\uff0c\u4ee5\u5b9e\u73b0\u56fe\u50cf\u7684\u878d\u5408\u3002<\/p>\n<\/p>\n<p><h2>\u4e00\u3001\u56fe\u50cf\u7f29\u653e<\/h2>\n<\/p>\n<p><p>\u5728\u56fe\u50cf\u878d\u5408\u7684\u8fc7\u7a0b\u4e2d\uff0c\u9996\u5148\u8981\u89e3\u51b3\u7684\u95ee\u9898\u662f\u5982\u4f55\u5c06\u4e24\u5f20\u4e0d\u540c\u5927\u5c0f\u7684\u56fe\u50cf\u8c03\u6574\u4e3a\u76f8\u540c\u7684\u5927\u5c0f\u3002Python\u4e2d\u5e38\u7528\u7684\u56fe\u50cf\u5904\u7406\u5e93\u662fOpenCV\u548cPIL\uff08Pillow\uff09\u3002<\/p>\n<\/p>\n<p><h3>1\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u7f29\u653e<\/h3>\n<\/p>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u7f29\u653e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>def resize_image(image, size):<\/p>\n<p>    return cv2.resize(image, size)<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = cv2.imread(&#39;path_to_large_image.jpg&#39;)<\/p>\n<p>image2 = cv2.imread(&#39;path_to_small_image.jpg&#39;)<\/p>\n<h2><strong>\u83b7\u53d6\u76ee\u6807\u5c3a\u5bf8<\/strong><\/h2>\n<p>target_size = (image1.shape[1], image1.shape[0])<\/p>\n<h2><strong>\u7f29\u653e\u56fe\u50cf<\/strong><\/h2>\n<p>resized_image2 = resize_image(image2, target_size)<\/p>\n<h2><strong>\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n<p>cv2.imshow(&#39;Resized Image&#39;, resized_image2)<\/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><h3>2\u3001\u4f7f\u7528PIL\u8fdb\u884c\u56fe\u50cf\u7f29\u653e<\/h3>\n<\/p>\n<p><p>PIL\uff08Pillow\uff09\u662f\u53e6\u4e00\u4e2a\u5e38\u7528\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528PIL\u8fdb\u884c\u56fe\u50cf\u7f29\u653e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<p>def resize_image(image, size):<\/p>\n<p>    return image.resize(size, Image.ANTIALIAS)<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = Image.open(&#39;path_to_large_image.jpg&#39;)<\/p>\n<p>image2 = Image.open(&#39;path_to_small_image.jpg&#39;)<\/p>\n<h2><strong>\u83b7\u53d6\u76ee\u6807\u5c3a\u5bf8<\/strong><\/h2>\n<p>target_size = (image1.width, image1.height)<\/p>\n<h2><strong>\u7f29\u653e\u56fe\u50cf<\/strong><\/h2>\n<p>resized_image2 = resize_image(image2, target_size)<\/p>\n<h2><strong>\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n<p>resized_image2.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e8c\u3001\u56fe\u50cf\u53e0\u52a0<\/h2>\n<\/p>\n<p><p>\u56fe\u50cf\u53e0\u52a0\u662f\u5c06\u4e24\u5f20\u56fe\u50cf\u6309\u4e00\u5b9a\u7684\u6743\u91cd\u6bd4\u4f8b\u8fdb\u884c\u53e0\u52a0\uff0c\u5f62\u6210\u65b0\u7684\u56fe\u50cf\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV\u4e2d\u7684<code>addWeighted<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u56fe\u50cf\u53e0\u52a0\u3002<\/p>\n<\/p>\n<p><h3>1\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/h3>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>def overlay_images(image1, image2, alpha=0.5, beta=0.5, gamma=0):<\/p>\n<p>    return cv2.addWeighted(image1, alpha, image2, beta, gamma)<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = cv2.imread(&#39;path_to_large_image.jpg&#39;)<\/p>\n<p>image2 = cv2.imread(&#39;path_to_small_image.jpg&#39;)<\/p>\n<h2><strong>\u7f29\u653e\u56fe\u50cf2<\/strong><\/h2>\n<p>resized_image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0]))<\/p>\n<h2><strong>\u53e0\u52a0\u56fe\u50cf<\/strong><\/h2>\n<p>result = overlay_images(image1, resized_image2)<\/p>\n<h2><strong>\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n<p>cv2.imshow(&#39;Overlay Image&#39;, result)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e09\u3001\u56fe\u50cf\u6df7\u5408<\/h2>\n<\/p>\n<p><p>\u56fe\u50cf\u6df7\u5408\u662f\u901a\u8fc7\u8bbe\u7f6e\u900f\u660e\u5ea6\uff0c\u5c06\u4e24\u5f20\u56fe\u50cf\u8fdb\u884c\u6df7\u5408\u5904\u7406\uff0c\u5f62\u6210\u534a\u900f\u660e\u7684\u53e0\u52a0\u6548\u679c\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCV\u548cPIL\u8fdb\u884c\u56fe\u50cf\u6df7\u5408\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n<p><h3>1\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u6df7\u5408<\/h3>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u6df7\u5408\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>def blend_images(image1, image2, alpha=0.5):<\/p>\n<p>    return cv2.addWeighted(image1, alpha, image2, 1 - alpha, 0)<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = cv2.imread(&#39;path_to_large_image.jpg&#39;)<\/p>\n<p>image2 = cv2.imread(&#39;path_to_small_image.jpg&#39;)<\/p>\n<h2><strong>\u7f29\u653e\u56fe\u50cf2<\/strong><\/h2>\n<p>resized_image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0]))<\/p>\n<h2><strong>\u6df7\u5408\u56fe\u50cf<\/strong><\/h2>\n<p>result = blend_images(image1, resized_image2)<\/p>\n<h2><strong>\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n<p>cv2.imshow(&#39;Blended Image&#39;, result)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2\u3001\u4f7f\u7528PIL\u8fdb\u884c\u56fe\u50cf\u6df7\u5408<\/h3>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4f7f\u7528PIL\u8fdb\u884c\u56fe\u50cf\u6df7\u5408\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<p>def blend_images(image1, image2, alpha=0.5):<\/p>\n<p>    return Image.blend(image1, image2, alpha)<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = Image.open(&#39;path_to_large_image.jpg&#39;)<\/p>\n<p>image2 = Image.open(&#39;path_to_small_image.jpg&#39;)<\/p>\n<h2><strong>\u7f29\u653e\u56fe\u50cf2<\/strong><\/h2>\n<p>resized_image2 = image2.resize((image1.width, image1.height))<\/p>\n<h2><strong>\u6df7\u5408\u56fe\u50cf<\/strong><\/h2>\n<p>result = blend_images(image1, resized_image2)<\/p>\n<h2><strong>\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n<p>result.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u56db\u3001\u56fe\u50cf\u878d\u5408\u7684\u5b9e\u9645\u5e94\u7528<\/h2>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u56fe\u50cf\u878d\u5408\u6280\u672f\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u79cd\u9886\u57df\uff0c\u5982\u533b\u5b66\u56fe\u50cf\u5904\u7406\u3001\u9065\u611f\u56fe\u50cf\u5904\u7406\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u7b49\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u5b9e\u9645\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n<p><h3>1\u3001\u533b\u5b66\u56fe\u50cf\u5904\u7406<\/h3>\n<\/p>\n<p><p>\u5728\u533b\u5b66\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u56fe\u50cf\u878d\u5408\u6280\u672f\u53ef\u4ee5\u7528\u4e8e\u5c06\u4e0d\u540c\u6a21\u6001\u7684\u533b\u5b66\u56fe\u50cf\uff08\u5982CT\u548cMRI\u56fe\u50cf\uff09\u8fdb\u884c\u878d\u5408\uff0c\u4ee5\u4fbf\u533b\u751f\u66f4\u597d\u5730\u8fdb\u884c\u8bca\u65ad\u548c\u6cbb\u7597\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u5c06CT\u56fe\u50cf\u548cMRI\u56fe\u50cf\u8fdb\u884c\u878d\u5408\uff0c\u53ef\u4ee5\u540c\u65f6\u83b7\u53d6\u9aa8\u9abc\u548c\u8f6f\u7ec4\u7ec7\u7684\u4fe1\u606f\uff0c\u4ece\u800c\u63d0\u9ad8\u8bca\u65ad\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n<p><h3>2\u3001\u9065\u611f\u56fe\u50cf\u5904\u7406<\/h3>\n<\/p>\n<p><p>\u5728\u9065\u611f\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u56fe\u50cf\u878d\u5408\u6280\u672f\u53ef\u4ee5\u7528\u4e8e\u5c06\u4e0d\u540c\u5206\u8fa8\u7387\u7684\u9065\u611f\u56fe\u50cf\u8fdb\u884c\u878d\u5408\uff0c\u4ee5\u4fbf\u4e8e\u66f4\u597d\u5730\u8fdb\u884c\u5730\u7269\u8bc6\u522b\u548c\u5206\u7c7b\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u5c06\u9ad8\u5206\u8fa8\u7387\u7684\u5168\u8272\u56fe\u50cf\u548c\u4f4e\u5206\u8fa8\u7387\u7684\u591a\u5149\u8c31\u56fe\u50cf\u8fdb\u884c\u878d\u5408\uff0c\u53ef\u4ee5\u540c\u65f6\u83b7\u53d6\u9ad8\u7a7a\u95f4\u5206\u8fa8\u7387\u548c\u9ad8\u5149\u8c31\u5206\u8fa8\u7387\u7684\u4fe1\u606f\uff0c\u4ece\u800c\u63d0\u9ad8\u5730\u7269\u8bc6\u522b\u7684\u7cbe\u5ea6\u3002<\/p>\n<\/p>\n<p><h3>3\u3001\u8ba1\u7b97\u673a\u89c6\u89c9<\/h3>\n<\/p>\n<p><p>\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\uff0c\u56fe\u50cf\u878d\u5408\u6280\u672f\u53ef\u4ee5\u7528\u4e8e\u589e\u5f3a\u56fe\u50cf\u7684\u7ec6\u8282\u4fe1\u606f\uff0c\u4ee5\u4fbf\u4e8e\u66f4\u597d\u5730\u8fdb\u884c\u76ee\u6807\u68c0\u6d4b\u548c\u8bc6\u522b\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u5c06\u4f4e\u5149\u7167\u56fe\u50cf\u548c\u9ad8\u5149\u7167\u56fe\u50cf\u8fdb\u884c\u878d\u5408\uff0c\u53ef\u4ee5\u589e\u5f3a\u56fe\u50cf\u7684\u4eae\u5ea6\u548c\u5bf9\u6bd4\u5ea6\uff0c\u4ece\u800c\u63d0\u9ad8\u76ee\u6807\u68c0\u6d4b\u548c\u8bc6\u522b\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n<p><h2>\u4e94\u3001\u603b\u7ed3<\/h2>\n<\/p>\n<p><p>\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u63a2\u8ba8\u4e86Python\u4e2d\u56fe\u50cf\u4e00\u5927\u4e00\u5c0f\u5982\u4f55\u878d\u5408\u7684\u5177\u4f53\u5b9e\u73b0\u65b9\u6cd5\u3002\u6211\u4eec\u9996\u5148\u4ecb\u7ecd\u4e86\u56fe\u50cf\u7f29\u653e\u6280\u672f\uff0c\u5206\u522b\u4f7f\u7528OpenCV\u548cPIL\u5b9e\u73b0\u4e86\u56fe\u50cf\u7684\u7f29\u653e\u64cd\u4f5c\u3002\u63a5\u7740\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u56fe\u50cf\u53e0\u52a0\u6280\u672f\uff0c\u4f7f\u7528OpenCV\u5b9e\u73b0\u4e86\u56fe\u50cf\u53e0\u52a0\u7684\u5177\u4f53\u65b9\u6cd5\u3002\u7136\u540e\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u56fe\u50cf\u6df7\u5408\u6280\u672f\uff0c\u5206\u522b\u4f7f\u7528OpenCV\u548cPIL\u5b9e\u73b0\u4e86\u56fe\u50cf\u6df7\u5408\u7684\u5177\u4f53\u65b9\u6cd5\u3002\u6700\u540e\uff0c\u6211\u4eec\u63a2\u8ba8\u4e86\u56fe\u50cf\u878d\u5408\u6280\u672f\u5728\u533b\u5b66\u56fe\u50cf\u5904\u7406\u3001\u9065\u611f\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u7b49\u9886\u57df\u7684\u5b9e\u9645\u5e94\u7528\u3002<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u672c\u6587\u7684\u5b66\u4e60\uff0c\u76f8\u4fe1\u8bfb\u8005\u5df2\u7ecf\u638c\u63e1\u4e86Python\u4e2d\u56fe\u50cf\u4e00\u5927\u4e00\u5c0f\u5982\u4f55\u878d\u5408\u7684\u57fa\u672c\u65b9\u6cd5\u548c\u5b9e\u9645\u5e94\u7528\uff0c\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5bf9\u8bfb\u8005\u6709\u6240\u5e2e\u52a9\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8bfb\u8005\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u56fe\u50cf\u878d\u5408\u65b9\u6cd5\uff0c\u4ee5\u5b9e\u73b0\u66f4\u597d\u7684\u56fe\u50cf\u5904\u7406\u6548\u679c\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u5c06\u4e24\u5f20\u56fe\u50cf\u8fdb\u884c\u878d\u5408\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u6216PIL\u7b49\u5e93\u6765\u878d\u5408\u4e24\u5f20\u56fe\u50cf\u3002\u901a\u8fc7\u8c03\u6574\u56fe\u50cf\u7684\u900f\u660e\u5ea6\u548c\u5927\u5c0f\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e00\u5927\u4e00\u5c0f\u7684\u56fe\u50cf\u878d\u5408\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u8bfb\u53d6\u56fe\u50cf\u3001\u8c03\u6574\u5927\u5c0f\u3001\u8bbe\u7f6e\u900f\u660e\u5ea6\uff0c\u5e76\u4f7f\u7528\u52a0\u6743\u548c\u65b9\u6cd5\u8fdb\u884c\u878d\u5408\u3002\u8fd9\u6837\u53ef\u4ee5\u5f97\u5230\u7406\u60f3\u7684\u6548\u679c\u3002<\/p>\n<p><strong>\u4f7f\u7528\u54ea\u4e9bPython\u5e93\u53ef\u4ee5\u5b9e\u73b0\u56fe\u50cf\u878d\u5408\uff1f<\/strong><br \/>\u5e38\u7528\u7684Python\u5e93\u5305\u62ecOpenCV\u3001Pillow\uff08PIL\uff09\u3001NumPy\u548cMatplotlib\u7b49\u3002OpenCV\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0cPillow\u5219\u9002\u5408\u7b80\u5355\u7684\u56fe\u50cf\u64cd\u4f5c\u3002NumPy\u53ef\u4ee5\u7528\u4e8e\u6570\u503c\u8fd0\u7b97\uff0c\u5e2e\u52a9\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u878d\u5408\u6548\u679c\uff0c\u800cMatplotlib\u53ef\u4ee5\u7528\u4e8e\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/p>\n<p><strong>\u5728\u56fe\u50cf\u878d\u5408\u8fc7\u7a0b\u4e2d\uff0c\u5982\u4f55\u8c03\u6574\u56fe\u50cf\u7684\u900f\u660e\u5ea6\uff1f<\/strong><br 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[&hellip;]","protected":false},"author":3,"featured_media":1137866,"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\/1137862"}],"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=1137862"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1137862\/revisions"}],"predecessor-version":[{"id":1137868,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1137862\/revisions\/1137868"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1137866"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1137862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1137862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1137862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}