{"id":1099569,"date":"2025-01-08T15:31:58","date_gmt":"2025-01-08T07:31:58","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1099569.html"},"modified":"2025-01-08T15:32:00","modified_gmt":"2025-01-08T07:32:00","slug":"python%e5%a6%82%e4%bd%95%e9%99%8d%e4%bd%8e%e5%9b%be%e5%83%8f%e7%9a%84%e5%88%86%e8%be%a8%e7%8e%87-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1099569.html","title":{"rendered":"python\u5982\u4f55\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25063226\/0ab203fa-c181-446a-8b40-5a0cbe5b7a8b.webp\" alt=\"python\u5982\u4f55\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u5f0f\u6765\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\uff0c\u6bd4\u5982\u4f7f\u7528PIL\u5e93\u3001OpenCV\u5e93\u3001scikit-image\u5e93\u7b49<\/strong>\u3002\u8fd9\u4e9b\u5e93\u90fd\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u65b9\u6cd5\u6765\u5904\u7406\u56fe\u50cf\uff0c\u5305\u62ec\u8c03\u6574\u56fe\u50cf\u7684\u5927\u5c0f\u3001\u4fee\u6539\u56fe\u50cf\u5206\u8fa8\u7387\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528PIL\u5e93\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u4f7f\u7528PIL\u5e93\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387<\/h3>\n<\/p>\n<p><p>PIL\uff08Python Imaging Library\uff09\u662fPython\u4e2d\u5e38\u7528\u7684\u56fe\u50cf\u5904\u7406\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u5305\u62ec\u8c03\u6574\u56fe\u50cf\u5927\u5c0f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528PIL\u5e93\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u6253\u5f00\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n<p>image = Image.open(&#39;example.jpg&#39;)<\/p>\n<h2><strong>\u6253\u5370\u539f\u59cb\u56fe\u50cf\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>print(&quot;Original size:&quot;, image.size)<\/p>\n<h2><strong>\u8bbe\u7f6e\u65b0\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>new_size = (image.size[0] \/\/ 2, image.size[1] \/\/ 2)<\/p>\n<h2><strong>\u8c03\u6574\u56fe\u50cf\u5927\u5c0f<\/strong><\/h2>\n<p>resized_image = image.resize(new_size, Image.ANTIALIAS)<\/p>\n<h2><strong>\u6253\u5370\u8c03\u6574\u540e\u56fe\u50cf\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>print(&quot;Resized size:&quot;, resized_image.size)<\/p>\n<h2><strong>\u4fdd\u5b58\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>resized_image.save(&#39;resized_example.jpg&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u6253\u5f00\u4e86\u4e00\u4e2a\u540d\u4e3a <code>example.jpg<\/code> \u7684\u56fe\u50cf\u6587\u4ef6\uff0c\u7136\u540e\u6253\u5370\u4e86\u539f\u59cb\u56fe\u50cf\u7684\u5c3a\u5bf8\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u56fe\u50cf\u7684\u5c3a\u5bf8\u7f29\u5c0f\u4e3a\u539f\u6765\u7684\u4e00\u534a\uff0c\u5e76\u4f7f\u7528 <code>resize<\/code> \u65b9\u6cd5\u8c03\u6574\u56fe\u50cf\u7684\u5927\u5c0f\u3002\u8c03\u6574\u540e\u7684\u56fe\u50cf\u88ab\u4fdd\u5b58\u4e3a <code>resized_example.jpg<\/code>\u3002<\/p>\n<\/p>\n<p><p><strong>\u8be6\u7ec6\u63cf\u8ff0<\/strong>\uff1a<\/p>\n<p>\u4f7f\u7528PIL\u5e93\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u7684\u5173\u952e\u662f\u7406\u89e3 <code>resize<\/code> \u65b9\u6cd5\u4e2d\u7684\u53c2\u6570\u3002<code>resize<\/code> \u65b9\u6cd5\u63a5\u53d7\u4e24\u4e2a\u53c2\u6570\uff1a\u65b0\u7684\u5c3a\u5bf8\u548c\u4e00\u4e2a\u53ef\u9009\u7684\u91c7\u6837\u8fc7\u6ee4\u5668\u3002<code>Image.ANTIALIAS<\/code> \u662f\u4e00\u79cd\u9ad8\u8d28\u91cf\u7684\u4e0b\u91c7\u6837\u8fc7\u6ee4\u5668\uff0c\u4f7f\u7528\u5b83\u53ef\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u56fe\u50cf\u8d28\u91cf\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u4f7f\u7528OpenCV\u5e93\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387<\/h3>\n<\/p>\n<p><p>OpenCV\uff08Open Source Computer Vision Library\uff09\u662f\u4e00\u4e2a\u5f00\u6e90\u8ba1\u7b97\u673a\u89c6\u89c9\u548c<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u8f6f\u4ef6\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCV\u5e93\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u7684\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n<p>image = cv2.imread(&#39;example.jpg&#39;)<\/p>\n<h2><strong>\u6253\u5370\u539f\u59cb\u56fe\u50cf\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>print(&quot;Original size:&quot;, image.shape)<\/p>\n<h2><strong>\u8bbe\u7f6e\u65b0\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>new_size = (image.shape[1] \/\/ 2, image.shape[0] \/\/ 2)<\/p>\n<h2><strong>\u8c03\u6574\u56fe\u50cf\u5927\u5c0f<\/strong><\/h2>\n<p>resized_image = cv2.resize(image, new_size, interpolation=cv2.INTER_AREA)<\/p>\n<h2><strong>\u6253\u5370\u8c03\u6574\u540e\u56fe\u50cf\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>print(&quot;Resized size:&quot;, resized_image.shape)<\/p>\n<h2><strong>\u4fdd\u5b58\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>cv2.imwrite(&#39;resized_example.jpg&#39;, resized_image)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528 <code>cv2.imread<\/code> \u65b9\u6cd5\u8bfb\u53d6\u4e86\u4e00\u4e2a\u540d\u4e3a <code>example.jpg<\/code> \u7684\u56fe\u50cf\u6587\u4ef6\uff0c\u5e76\u6253\u5370\u4e86\u539f\u59cb\u56fe\u50cf\u7684\u5c3a\u5bf8\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u56fe\u50cf\u7684\u5c3a\u5bf8\u7f29\u5c0f\u4e3a\u539f\u6765\u7684\u4e00\u534a\uff0c\u5e76\u4f7f\u7528 <code>cv2.resize<\/code> \u65b9\u6cd5\u8c03\u6574\u56fe\u50cf\u7684\u5927\u5c0f\u3002\u8c03\u6574\u540e\u7684\u56fe\u50cf\u88ab\u4fdd\u5b58\u4e3a <code>resized_example.jpg<\/code>\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u4f7f\u7528scikit-image\u5e93\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387<\/h3>\n<\/p>\n<p><p>scikit-image \u662f\u4e00\u4e2a\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u7684Python\u5e93\uff0c\u57fa\u4e8eSciPy\u6784\u5efa\u3002\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u51fd\u6570\uff0c\u5305\u62ec\u8c03\u6574\u56fe\u50cf\u5927\u5c0f\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528scikit-image\u5e93\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u7684\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from skimage import io, transform<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n<p>image = io.imread(&#39;example.jpg&#39;)<\/p>\n<h2><strong>\u6253\u5370\u539f\u59cb\u56fe\u50cf\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>print(&quot;Original size:&quot;, image.shape)<\/p>\n<h2><strong>\u8bbe\u7f6e\u65b0\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>new_size = (image.shape[0] \/\/ 2, image.shape[1] \/\/ 2)<\/p>\n<h2><strong>\u8c03\u6574\u56fe\u50cf\u5927\u5c0f<\/strong><\/h2>\n<p>resized_image = transform.resize(image, new_size, anti_aliasing=True)<\/p>\n<h2><strong>\u6253\u5370\u8c03\u6574\u540e\u56fe\u50cf\u7684\u5c3a\u5bf8<\/strong><\/h2>\n<p>print(&quot;Resized size:&quot;, resized_image.shape)<\/p>\n<h2><strong>\u4fdd\u5b58\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>io.imsave(&#39;resized_example.jpg&#39;, (resized_image * 255).astype(&#39;uint8&#39;))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528 <code>io.imread<\/code> \u65b9\u6cd5\u8bfb\u53d6\u4e86\u4e00\u4e2a\u540d\u4e3a <code>example.jpg<\/code> \u7684\u56fe\u50cf\u6587\u4ef6\uff0c\u5e76\u6253\u5370\u4e86\u539f\u59cb\u56fe\u50cf\u7684\u5c3a\u5bf8\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u56fe\u50cf\u7684\u5c3a\u5bf8\u7f29\u5c0f\u4e3a\u539f\u6765\u7684\u4e00\u534a\uff0c\u5e76\u4f7f\u7528 <code>transform.resize<\/code> \u65b9\u6cd5\u8c03\u6574\u56fe\u50cf\u7684\u5927\u5c0f\u3002\u8c03\u6574\u540e\u7684\u56fe\u50cf\u88ab\u4fdd\u5b58\u4e3a <code>resized_example.jpg<\/code>\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u7684\u5b9e\u9645\u5e94\u7528<\/h3>\n<\/p>\n<p><p>\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u6709\u5f88\u591a\u7528\u9014\u3002\u4f8b\u5982\uff0c\u5728\u7f51\u9875\u5f00\u53d1\u4e2d\uff0c\u7f29\u5c0f\u56fe\u50cf\u7684\u5927\u5c0f\u53ef\u4ee5\u52a0\u5feb\u7f51\u9875\u52a0\u8f7d\u901f\u5ea6\uff0c\u63d0\u9ad8\u7528\u6237\u4f53\u9a8c\u3002\u5728\u673a\u5668\u5b66\u4e60\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\uff0c\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u53ef\u4ee5\u51cf\u5c11\u8ba1\u7b97\u8d44\u6e90\u7684\u6d88\u8017\uff0c\u52a0\u5feb\u6a21\u578b\u8bad\u7ec3\u548c\u63a8\u7406\u7684\u901f\u5ea6\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5728\u7f51\u9875\u5f00\u53d1\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n<p><p>\u5728\u7f51\u9875\u5f00\u53d1\u4e2d\uff0c\u56fe\u50cf\u901a\u5e38\u662f\u7f51\u9875\u52a0\u8f7d\u901f\u5ea6\u7684\u91cd\u8981\u5f71\u54cd\u56e0\u7d20\u3002\u9ad8\u5206\u8fa8\u7387\u7684\u56fe\u50cf\u867d\u7136\u53ef\u4ee5\u63d0\u4f9b\u66f4\u597d\u7684\u89c6\u89c9\u6548\u679c\uff0c\u4f46\u4e5f\u4f1a\u589e\u52a0\u7f51\u9875\u7684\u52a0\u8f7d\u65f6\u95f4\uff0c\u5c24\u5176\u662f\u5728\u79fb\u52a8\u8bbe\u5907\u4e0a\u3002\u56e0\u6b64\uff0c\u901a\u8fc7\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u7f51\u9875\u7684\u52a0\u8f7d\u901f\u5ea6\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u5e94\u7528<\/h4>\n<\/p>\n<p><p>\u5728\u673a\u5668\u5b66\u4e60\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\uff0c\u56fe\u50cf\u5904\u7406\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u9ad8\u5206\u8fa8\u7387\u7684\u56fe\u50cf\u867d\u7136\u53ef\u4ee5\u63d0\u4f9b\u66f4\u591a\u7684\u7ec6\u8282\u4fe1\u606f\uff0c\u4f46\u4e5f\u4f1a\u589e\u52a0\u6a21\u578b\u7684\u8ba1\u7b97\u590d\u6742\u5ea6\u548c\u8bad\u7ec3\u65f6\u95f4\u3002\u56e0\u6b64\uff0c\u901a\u8fc7\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\uff0c\u53ef\u4ee5\u51cf\u5c11\u8ba1\u7b97\u8d44\u6e90\u7684\u6d88\u8017\uff0c\u52a0\u5feb\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u63a8\u7406\u901f\u5ea6\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u7684\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n<p><p>\u867d\u7136\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u6709\u8bb8\u591a\u597d\u5904\uff0c\u4f46\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u4e5f\u9700\u8981\u6ce8\u610f\u4e00\u4e9b\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u53ef\u80fd\u4f1a\u5bfc\u81f4\u56fe\u50cf\u8d28\u91cf\u7684\u4e0b\u964d\uff0c\u5c24\u5176\u662f\u5728\u7ec6\u8282\u8f83\u591a\u7684\u56fe\u50cf\u4e2d\u3002\u56e0\u6b64\uff0c\u5728\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u65f6\uff0c\u9700\u8981\u5728\u56fe\u50cf\u8d28\u91cf\u548c\u5206\u8fa8\u7387\u4e4b\u95f4\u627e\u5230\u4e00\u4e2a\u5e73\u8861\u70b9\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u56fe\u50cf\u8d28\u91cf\u7684\u4e0b\u964d<\/h4>\n<\/p>\n<p><p>\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u901a\u5e38\u4f1a\u5bfc\u81f4\u56fe\u50cf\u8d28\u91cf\u7684\u4e0b\u964d\uff0c\u5c24\u5176\u662f\u5728\u7ec6\u8282\u8f83\u591a\u7684\u56fe\u50cf\u4e2d\u3002\u56e0\u6b64\uff0c\u5728\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u65f6\uff0c\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u91c7\u6837\u8fc7\u6ee4\u5668\uff0c\u4ee5\u5c3d\u91cf\u51cf\u5c11\u56fe\u50cf\u8d28\u91cf\u7684\u4e0b\u964d\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528PIL\u5e93\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9 <code>Image.ANTIALIAS<\/code> \u8fc7\u6ee4\u5668\uff0c\u5728\u4f7f\u7528OpenCV\u5e93\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9 <code>cv2.INTER_AREA<\/code> \u8fc7\u6ee4\u5668\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u5206\u8fa8\u7387\u548c\u8d28\u91cf\u7684\u5e73\u8861<\/h4>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u9700\u6c42\u5728\u56fe\u50cf\u8d28\u91cf\u548c\u5206\u8fa8\u7387\u4e4b\u95f4\u627e\u5230\u4e00\u4e2a\u5e73\u8861\u70b9\u3002\u4f8b\u5982\uff0c\u5728\u7f51\u9875\u5f00\u53d1\u4e2d\uff0c\u5982\u679c\u7f51\u9875\u7684\u52a0\u8f7d\u901f\u5ea6\u975e\u5e38\u91cd\u8981\uff0c\u53ef\u4ee5\u9002\u5f53\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\uff0c\u4ee5\u63d0\u9ad8\u52a0\u8f7d\u901f\u5ea6\u3002\u800c\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u5982\u679c\u6a21\u578b\u7684\u8ba1\u7b97\u8d44\u6e90\u975e\u5e38\u6709\u9650\uff0c\u53ef\u4ee5\u9002\u5f53\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\uff0c\u4ee5\u51cf\u5c11\u8ba1\u7b97\u8d44\u6e90\u7684\u6d88\u8017\u3002<\/p>\n<\/p>\n<p><h3>\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u603b\u7684\u6765\u8bf4\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528PIL\u5e93\u3001OpenCV\u5e93\u548cscikit-image\u5e93\u3002\u8fd9\u4e9b\u5e93\u90fd\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u65b9\u6cd5\u6765\u5904\u7406\u56fe\u50cf\uff0c\u5305\u62ec\u8c03\u6574\u56fe\u50cf\u7684\u5927\u5c0f\u3001\u4fee\u6539\u56fe\u50cf\u5206\u8fa8\u7387\u7b49\u3002\u901a\u8fc7\u5408\u7406\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\uff0c\u53ef\u4ee5\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u83b7\u5f97\u8bb8\u591a\u597d\u5904\uff0c\u4f8b\u5982\u63d0\u9ad8\u7f51\u9875\u52a0\u8f7d\u901f\u5ea6\u3001\u51cf\u5c11\u8ba1\u7b97\u8d44\u6e90\u7684\u6d88\u8017\u7b49\u3002\u7136\u800c\uff0c\u5728\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u65f6\uff0c\u4e5f\u9700\u8981\u6ce8\u610f\u56fe\u50cf\u8d28\u91cf\u7684\u4e0b\u964d\uff0c\u5e76\u5728\u56fe\u50cf\u8d28\u91cf\u548c\u5206\u8fa8\u7387\u4e4b\u95f4\u627e\u5230\u4e00\u4e2a\u5e73\u8861\u70b9\u3002\u5e0c\u671b\u672c\u6587\u7684\u4ecb\u7ecd\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528Python\u6765\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\u5982PIL\uff08Pillow\uff09\u6216OpenCV\u6765\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u3002Pillow\u5e93\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\uff0c\u901a\u8fc7<code>resize()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u8c03\u6574\u56fe\u50cf\u5c3a\u5bf8\u3002\u800cOpenCV\u5219\u901a\u8fc7<code>cv2.resize()<\/code>\u51fd\u6570\u5b9e\u73b0\u7c7b\u4f3c\u529f\u80fd\uff0c\u652f\u6301\u591a\u79cd\u63d2\u503c\u65b9\u6cd5\u4ee5\u786e\u4fdd\u56fe\u50cf\u8d28\u91cf\u3002<\/p>\n<p><strong>\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u4f1a\u5f71\u54cd\u56fe\u50cf\u8d28\u91cf\u5417\uff1f<\/strong><br \/>\u662f\u7684\uff0c\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u901a\u5e38\u4f1a\u5bfc\u81f4\u7ec6\u8282\u7684\u4e22\u5931\u548c\u6a21\u7cca\u611f\u7684\u589e\u52a0\u3002\u56fe\u50cf\u4e2d\u7684\u9ad8\u9891\u7ec6\u8282\u53ef\u80fd\u4f1a\u88ab\u538b\u7f29\u6216\u6d88\u5931\uff0c\u56e0\u6b64\u5728\u5904\u7406\u65f6\u9700\u8c28\u614e\u9009\u62e9\u76ee\u6807\u5206\u8fa8\u7387\u3002\u5982\u679c\u9700\u8981\u4fdd\u6301\u67d0\u4e9b\u7ec6\u8282\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u9002\u5f53\u7684\u63d2\u503c\u65b9\u6cd5\uff0c\u6bd4\u5982\u53cc\u7ebf\u6027\u63d2\u503c\u6216\u7acb\u65b9\u63d2\u503c\u3002<\/p>\n<p><strong>\u964d\u4f4e\u56fe\u50cf\u5206\u8fa8\u7387\u540e\uff0c\u5982\u4f55\u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u50cf\uff1f<\/strong><br \/>\u5728\u4f7f\u7528Pillow\u5904\u7406\u56fe\u50cf\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528<code>save()<\/code>\u65b9\u6cd5\u5c06\u5904\u7406\u540e\u7684\u56fe\u50cf\u4fdd\u5b58\u4e3a\u65b0\u6587\u4ef6\u3002\u6307\u5b9a\u6587\u4ef6\u540d\u53ca\u683c\u5f0f\u540e\uff0c\u56fe\u50cf\u5c06\u4ee5\u65b0\u7684\u5206\u8fa8\u7387\u4fdd\u5b58\u5728\u6307\u5b9a\u4f4d\u7f6e\u3002\u4f7f\u7528OpenCV\u65f6\uff0c<code>cv2.imwrite()<\/code>\u51fd\u6570\u540c\u6837\u53ef\u4ee5\u5b9e\u73b0\u4fdd\u5b58\u529f\u80fd\uff0c\u786e\u4fdd\u63d0\u4f9b\u5408\u9002\u7684\u6587\u4ef6\u8def\u5f84\u548c\u683c\u5f0f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u5f0f\u6765\u964d\u4f4e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\uff0c\u6bd4\u5982\u4f7f\u7528PIL\u5e93\u3001OpenCV\u5e93\u3001scikit-imag [&hellip;]","protected":false},"author":3,"featured_media":1099579,"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\/1099569"}],"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=1099569"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1099569\/revisions"}],"predecessor-version":[{"id":1099584,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1099569\/revisions\/1099584"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1099579"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1099569"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1099569"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1099569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}