{"id":935773,"date":"2024-12-26T19:00:03","date_gmt":"2024-12-26T11:00:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/935773.html"},"modified":"2024-12-26T19:00:04","modified_gmt":"2024-12-26T11:00:04","slug":"python%e5%a6%82%e4%bd%95%e5%90%88%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/935773.html","title":{"rendered":"python\u5982\u4f55\u5408\u56fe"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25072306\/785e181c-087b-4803-904e-3fe63d552b9d.webp\" alt=\"python\u5982\u4f55\u5408\u56fe\" \/><\/p>\n<p><p> \u5f00\u5934\u6bb5\u843d\uff1a<br \/><strong>Python\u5408\u5e76\u56fe\u50cf\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pillow\u5e93\u3001OpenCV\u5e93\u548cMatplotlib\u5e93<\/strong>\u3002\u5176\u4e2d\uff0cPillow\u5e93\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u65b9\u6cd5\u6765\u5408\u5e76\u56fe\u50cf\uff1bOpenCV\u5e93\u5219\u66f4\u9002\u5408\u4e8e\u5904\u7406\u9700\u8981\u590d\u6742\u56fe\u50cf\u64cd\u4f5c\u7684\u4efb\u52a1\uff1b\u800cMatplotlib\u5e93\u867d\u7136\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u4f46\u4e5f\u53ef\u4ee5\u7528\u6765\u5408\u5e76\u7b80\u5355\u7684\u56fe\u50cf\u3002\u4f7f\u7528Pillow\u5e93\u5408\u5e76\u56fe\u50cf\u7684\u57fa\u672c\u6b65\u9aa4\u662f\uff1a\u9996\u5148\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u50cf\u5bf9\u8c61\u6765\u5bb9\u7eb3\u5408\u5e76\u540e\u7684\u56fe\u50cf\uff0c\u6700\u540e\u5c06\u5404\u4e2a\u56fe\u50cf\u7c98\u8d34\u5230\u65b0\u56fe\u50cf\u5bf9\u8c61\u7684\u6307\u5b9a\u4f4d\u7f6e\u3002\u8fd9\u79cd\u65b9\u6cd5\u975e\u5e38\u76f4\u89c2\u4e14\u6613\u4e8e\u64cd\u4f5c\uff0c\u9002\u5408\u521d\u5b66\u8005\u4f7f\u7528\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001PILLOW\u5e93\u5408\u5e76\u56fe\u50cf<\/p>\n<\/p>\n<p><p>Pillow\u662fPython Imaging Library\uff08PIL\uff09\u7684\u4e00\u4e2a\u5206\u652f\uff0c\u4e13\u6ce8\u4e8e\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002\u5b83\u975e\u5e38\u9002\u5408\u521d\u5b66\u8005\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u65b9\u6cd5\u6765\u52a0\u8f7d\u3001\u4fee\u6539\u548c\u4fdd\u5b58\u56fe\u50cf\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5b89\u88c5\u4e0e\u52a0\u8f7dPillow\u5e93<\/li>\n<\/ol>\n<p><p>\u8981\u4f7f\u7528Pillow\u5e93\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install pillow<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Pillow\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u5408\u5e76\u56fe\u50cf\u7684\u57fa\u672c\u6b65\u9aa4<\/li>\n<\/ol>\n<p><p>\u4f7f\u7528Pillow\u5e93\u5408\u5e76\u56fe\u50cf\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<ul>\n<li>\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\uff1a\u4f7f\u7528<code>Image.open()<\/code>\u65b9\u6cd5\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\u3002<\/li>\n<li>\u521b\u5efa\u65b0\u7684\u56fe\u50cf\u5bf9\u8c61\uff1a\u4f7f\u7528<code>Image.new()<\/code>\u65b9\u6cd5\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u50cf\u5bf9\u8c61\u4ee5\u5bb9\u7eb3\u5408\u5e76\u540e\u7684\u56fe\u50cf\u3002<\/li>\n<li>\u7c98\u8d34\u56fe\u50cf\uff1a\u4f7f\u7528<code>paste()<\/code>\u65b9\u6cd5\u5c06\u5404\u4e2a\u56fe\u50cf\u7c98\u8d34\u5230\u65b0\u56fe\u50cf\u5bf9\u8c61\u7684\u6307\u5b9a\u4f4d\u7f6e\u3002<\/li>\n<\/ul>\n<p><p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Pillow\u5408\u5e76\u4e24\u5f20\u56fe\u50cf\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u6253\u5f00\u4e24\u5f20\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = Image.open(&#39;image1.jpg&#39;)<\/p>\n<p>image2 = Image.open(&#39;image2.jpg&#39;)<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u50cf\uff0c\u5176\u5927\u5c0f\u4e3a\u4e24\u5f20\u56fe\u50cf\u5bbd\u5ea6\u4e4b\u548c\uff0c\u9ad8\u5ea6\u53d6\u8f83\u9ad8\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>new_image = Image.new(&#39;RGB&#39;, (image1.width + image2.width, max(image1.height, image2.height)))<\/p>\n<h2><strong>\u7c98\u8d34\u56fe\u50cf<\/strong><\/h2>\n<p>new_image.paste(image1, (0, 0))<\/p>\n<p>new_image.paste(image2, (image1.width, 0))<\/p>\n<h2><strong>\u4fdd\u5b58\u5408\u5e76\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>new_image.save(&#39;merged_image.jpg&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e8c\u3001OPENCV\u5e93\u5408\u5e76\u56fe\u50cf<\/p>\n<\/p>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u9002\u7528\u4e8e\u9700\u8981\u590d\u6742\u56fe\u50cf\u5904\u7406\u64cd\u4f5c\u7684\u4efb\u52a1\u3002\u5c3d\u7ba1\u5b83\u901a\u5e38\u7528\u4e8e\u5b9e\u65f6\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\uff0c\u4f46\u4e5f\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u5408\u5e76\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5b89\u88c5\u4e0e\u52a0\u8f7dOpenCV\u5e93<\/li>\n<\/ol>\n<p><p>\u8981\u4f7f\u7528OpenCV\u5e93\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install opencv-python<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165OpenCV\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u5408\u5e76\u56fe\u50cf\u7684\u57fa\u672c\u6b65\u9aa4<\/li>\n<\/ol>\n<p><p>\u4f7f\u7528OpenCV\u5e93\u5408\u5e76\u56fe\u50cf\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<ul>\n<li>\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\uff1a\u4f7f\u7528<code>cv2.imread()<\/code>\u65b9\u6cd5\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\u3002<\/li>\n<li>\u521b\u5efa\u65b0\u7684\u56fe\u50cf\u5bf9\u8c61\uff1a\u901a\u8fc7\u8c03\u6574NumPy\u6570\u7ec4\u7684\u5f62\u72b6\u6765\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u56fe\u50cf\u5bf9\u8c61\u3002<\/li>\n<li>\u62fc\u63a5\u56fe\u50cf\uff1a\u4f7f\u7528<code>cv2.hconcat()<\/code>\u6216<code>cv2.vconcat()<\/code>\u65b9\u6cd5\u5c06\u56fe\u50cf\u6c34\u5e73\u6216\u5782\u76f4\u62fc\u63a5\u3002<\/li>\n<\/ul>\n<p><p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528OpenCV\u6c34\u5e73\u5408\u5e76\u4e24\u5f20\u56fe\u50cf\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = cv2.imread(&#39;image1.jpg&#39;)<\/p>\n<p>image2 = cv2.imread(&#39;image2.jpg&#39;)<\/p>\n<h2><strong>\u6c34\u5e73\u62fc\u63a5\u56fe\u50cf<\/strong><\/h2>\n<p>merged_image = cv2.hconcat([image1, image2])<\/p>\n<h2><strong>\u4fdd\u5b58\u5408\u5e76\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>cv2.imwrite(&#39;merged_image.jpg&#39;, merged_image)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e09\u3001MATPLOTLIB\u5e93\u5408\u5e76\u56fe\u50cf<\/p>\n<\/p>\n<p><p>Matplotlib\u4e3b\u8981\u7528\u4e8e\u7ed8\u5236\u56fe\u8868\u548c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u4f46\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\uff0c\u4f8b\u5982\u5408\u5e76\u56fe\u50cf\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5b89\u88c5\u4e0e\u52a0\u8f7dMatplotlib\u5e93<\/li>\n<\/ol>\n<p><p>\u8981\u4f7f\u7528Matplotlib\u5e93\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install matplotlib<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Matplotlib\u5e93\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><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u5408\u5e76\u56fe\u50cf\u7684\u57fa\u672c\u6b65\u9aa4<\/li>\n<\/ol>\n<p><p>\u4f7f\u7528Matplotlib\u5e93\u5408\u5e76\u56fe\u50cf\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n<ul>\n<li>\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\uff1a\u4f7f\u7528<code>mpimg.imread()<\/code>\u65b9\u6cd5\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\u3002<\/li>\n<li>\u521b\u5efa\u5b50\u56fe\uff1a\u4f7f\u7528<code>plt.subplots()<\/code>\u65b9\u6cd5\u521b\u5efa\u591a\u4e2a\u5b50\u56fe\u3002<\/li>\n<li>\u663e\u793a\u56fe\u50cf\uff1a\u4f7f\u7528<code>imshow()<\/code>\u65b9\u6cd5\u5c06\u56fe\u50cf\u663e\u793a\u5728\u6307\u5b9a\u7684\u5b50\u56fe\u4e0a\u3002<\/li>\n<\/ul>\n<p><p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Matplotlib\u5408\u5e76\u5e76\u663e\u793a\u4e24\u5f20\u56fe\u50cf\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<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image1 = mpimg.imread(&#39;image1.jpg&#39;)<\/p>\n<p>image2 = mpimg.imread(&#39;image2.jpg&#39;)<\/p>\n<h2><strong>\u521b\u5efa\u5b50\u56fe<\/strong><\/h2>\n<p>fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))<\/p>\n<h2><strong>\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n<p>ax1.imshow(image1)<\/p>\n<p>ax1.axis(&#39;off&#39;)  # \u5173\u95ed\u5750\u6807\u8f74<\/p>\n<p>ax2.imshow(image2)<\/p>\n<p>ax2.axis(&#39;off&#39;)  # \u5173\u95ed\u5750\u6807\u8f74<\/p>\n<h2><strong>\u663e\u793a\u5408\u5e76\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u56db\u3001\u6ce8\u610f\u4e8b\u9879\u4e0e\u4f18\u5316<\/p>\n<\/p>\n<p><p>\u5728\u5408\u5e76\u56fe\u50cf\u65f6\uff0c\u6709\u4e00\u4e9b\u6ce8\u610f\u4e8b\u9879\u548c\u4f18\u5316\u6280\u5de7\u53ef\u4ee5\u5e2e\u52a9\u4f60\u83b7\u5f97\u66f4\u597d\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n<ol>\n<li>\u56fe\u50cf\u5c3a\u5bf8\u4e0e\u683c\u5f0f<\/li>\n<\/ol>\n<p><p>\u786e\u4fdd\u8981\u5408\u5e76\u7684\u56fe\u50cf\u5177\u6709\u76f8\u540c\u7684\u683c\u5f0f\u548c\u5408\u9002\u7684\u5c3a\u5bf8\u3002\u5982\u679c\u56fe\u50cf\u5c3a\u5bf8\u4e0d\u540c\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u5728\u5408\u5e76\u4e4b\u524d\u8c03\u6574\u56fe\u50cf\u7684\u5c3a\u5bf8\u3002Pillow\u548cOpenCV\u90fd\u63d0\u4f9b\u4e86\u8c03\u6574\u56fe\u50cf\u5c3a\u5bf8\u7684\u65b9\u6cd5\uff0c\u4f8b\u5982<code>resize()<\/code>\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u5904\u7406\u56fe\u50cf\u8d28\u91cf<\/li>\n<\/ol>\n<p><p>\u5408\u5e76\u56fe\u50cf\u65f6\uff0c\u53ef\u80fd\u4f1a\u51fa\u73b0\u56fe\u50cf\u8d28\u91cf\u4e0b\u964d\u7684\u95ee\u9898\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u5728\u4fdd\u5b58\u56fe\u50cf\u65f6\u8c03\u6574\u538b\u7f29\u6bd4\u6216\u5206\u8fa8\u7387\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528Pillow\u4fdd\u5b58\u56fe\u50cf\u65f6\uff0c\u53ef\u4ee5\u6307\u5b9a<code>quality<\/code>\u53c2\u6570\u3002<\/p>\n<\/p>\n<ol start=\"3\">\n<li>\u56fe\u50cf\u5bf9\u9f50<\/li>\n<\/ol>\n<p><p>\u5728\u5408\u5e76\u56fe\u50cf\u65f6\uff0c\u53ef\u80fd\u9700\u8981\u5bf9\u56fe\u50cf\u8fdb\u884c\u5bf9\u9f50\u64cd\u4f5c\uff0c\u4f8b\u5982\u5c45\u4e2d\u6216\u5bf9\u9f50\u5230\u67d0\u4e2a\u8fb9\u7f18\u3002\u901a\u8fc7\u8ba1\u7b97\u5750\u6807\u504f\u79fb\u91cf\uff0c\u53ef\u4ee5\u5728\u7c98\u8d34\u56fe\u50cf\u65f6\u8fdb\u884c\u5bf9\u9f50\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n<p><p>\u56fe\u50cf\u5408\u5e76\u5728\u8bb8\u591a\u5e94\u7528\u573a\u666f\u4e2d\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5305\u62ec\u56fe\u50cf\u62fc\u63a5\u3001\u6570\u636e\u589e\u5f3a\u548c\u56fe\u50cf\u5904\u7406\u7b49\u3002<\/p>\n<\/p>\n<ol>\n<li>\u56fe\u50cf\u62fc\u63a5<\/li>\n<\/ol>\n<p><p>\u56fe\u50cf\u62fc\u63a5\u662f\u5c06\u591a\u4e2a\u56fe\u50cf\u5408\u5e76\u4e3a\u4e00\u4e2a\u56fe\u50cf\u7684\u8fc7\u7a0b\uff0c\u5e38\u7528\u4e8e\u5168\u666f\u56fe\u7684\u521b\u5efa\u3002\u901a\u8fc7\u5408\u5e76\u56fe\u50cf\uff0c\u53ef\u4ee5\u751f\u6210\u66f4\u5927\u8303\u56f4\u7684\u89c6\u56fe\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u6570\u636e\u589e\u5f3a<\/li>\n<\/ol>\n<p><p>\u5728\u6df1\u5ea6\u5b66\u4e60\u4e2d\uff0c\u5408\u5e76\u56fe\u50cf\u662f\u6570\u636e\u589e\u5f3a\u7684\u4e00\u79cd\u65b9\u6cd5\u3002\u901a\u8fc7\u5408\u5e76\u4e0d\u540c\u7684\u56fe\u50cf\uff0c\u53ef\u4ee5\u751f\u6210\u66f4\u591a\u6837\u5316\u7684\u6570\u636e\u6837\u672c\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/p>\n<ol start=\"3\">\n<li>\u56fe\u50cf\u5904\u7406<\/li>\n<\/ol>\n<p><p>\u5728\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u4e2d\uff0c\u5408\u5e76\u56fe\u50cf\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u591a\u901a\u9053\u56fe\u50cf\u6216\u7279\u5f81\u56fe\uff0c\u4ece\u800c\u652f\u6301\u66f4\u590d\u6742\u7684\u56fe\u50cf\u5206\u6790\u548c\u5904\u7406\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><p>\u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n<p><p>Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u6765\u5b9e\u73b0\u56fe\u50cf\u5408\u5e76\uff0c\u5305\u62ecPillow\u3001OpenCV\u548cMatplotlib\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u9ad8\u6548\u5730\u5b8c\u6210\u56fe\u50cf\u5408\u5e76\u4efb\u52a1\u3002\u5728\u4f7f\u7528\u8fd9\u4e9b\u5e93\u65f6\uff0c\u5e94\u6ce8\u610f\u56fe\u50cf\u683c\u5f0f\u3001\u5c3a\u5bf8\u548c\u8d28\u91cf\uff0c\u4ee5\u786e\u4fdd\u5408\u5e76\u540e\u7684\u56fe\u50cf\u6ee1\u8db3\u671f\u671b\u3002\u6b64\u5916\uff0c\u56fe\u50cf\u5408\u5e76\u5728\u8bb8\u591a\u5b9e\u9645\u5e94\u7528\u4e2d\u90fd\u6709\u5e7f\u6cdb\u7684\u7528\u9014\uff0c\u5982\u56fe\u50cf\u62fc\u63a5\u3001\u6570\u636e\u589e\u5f3a\u548c\u56fe\u50cf\u5904\u7406\u7b49\u3002\u5728\u638c\u63e1\u8fd9\u4e9b\u6280\u672f\u540e\uff0c\u4f60\u53ef\u4ee5\u5728\u9879\u76ee\u4e2d\u7075\u6d3b\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u89e3\u51b3\u5404\u79cd\u56fe\u50cf\u5904\u7406\u95ee\u9898\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u5408\u5e76\u591a\u5f20\u56fe\u7247\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528PIL\uff08Python Imaging Library\uff09\u6216\u5176\u5206\u652fPillow\u6765\u5408\u5e76\u591a\u5f20\u56fe\u7247\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Pillow\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4<code>pip install Pillow<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528<code>Image.open()<\/code>\u6253\u5f00\u6bcf\u5f20\u56fe\u7247\uff0c\u5e76\u4f7f\u7528<code>Image.new()<\/code>\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u7a7a\u767d\u56fe\u50cf\uff0c\u7136\u540e\u5c06\u52a0\u8f7d\u7684\u56fe\u7247\u7c98\u8d34\u5230\u8fd9\u4e2a\u7a7a\u767d\u56fe\u50cf\u4e2d\u3002\u6700\u540e\uff0c\u4fdd\u5b58\u5408\u6210\u7684\u56fe\u50cf\u3002<\/p>\n<p><strong>\u5408\u5e76\u56fe\u50cf\u65f6\uff0c\u5982\u4f55\u8bbe\u7f6e\u56fe\u50cf\u7684\u6392\u5217\u65b9\u5f0f\uff1f<\/strong><br \/>\u5728\u5408\u5e76\u56fe\u50cf\u65f6\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u9009\u62e9\u6a2a\u5411\u6216\u7eb5\u5411\u6392\u5217\u3002\u5bf9\u4e8e\u6a2a\u5411\u6392\u5217\uff0c\u9700\u8981\u8ba1\u7b97\u6240\u6709\u56fe\u50cf\u7684\u603b\u5bbd\u5ea6\u548c\u6700\u9ad8\u9ad8\u5ea6\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2a\u65b0\u56fe\u50cf\u5e76\u5c06\u6bcf\u5f20\u56fe\u7247\u4f9d\u6b21\u7c98\u8d34\u5230\u6b63\u786e\u7684\u4f4d\u7f6e\u3002\u5bf9\u4e8e\u7eb5\u5411\u6392\u5217\uff0c\u8ba1\u7b97\u603b\u9ad8\u5ea6\u548c\u6700\u5927\u5bbd\u5ea6\uff0c\u64cd\u4f5c\u65b9\u5f0f\u7c7b\u4f3c\u3002\u901a\u8fc7\u8c03\u6574\u7c98\u8d34\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e0d\u540c\u7684\u6392\u5217\u6548\u679c\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u5408\u6210\u56fe\u50cf\u540e\uff0c\u5982\u4f55\u4f18\u5316\u6700\u7ec8\u56fe\u50cf\u7684\u8d28\u91cf\uff1f<\/strong><br \/>\u5728\u5408\u6210\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u4fdd\u5b58\u65f6\u7684\u53c2\u6570\u6765\u4f18\u5316\u56fe\u50cf\u8d28\u91cf\u3002\u4f8b\u5982\uff0c\u4f7f\u7528<code>save()<\/code>\u65b9\u6cd5\u65f6\uff0c\u53ef\u4ee5\u6307\u5b9a<code>quality<\/code>\u53c2\u6570\uff0c\u8303\u56f4\u4ece1\u5230100\uff0c\u6570\u503c\u8d8a\u5927\u8868\u793a\u8d28\u91cf\u8d8a\u9ad8\u3002\u8fd8\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u4e0d\u540c\u7684\u683c\u5f0f\uff0c\u5982JPEG\u6216PNG\uff0c\u4e0d\u540c\u683c\u5f0f\u5bf9\u56fe\u50cf\u8d28\u91cf\u548c\u6587\u4ef6\u5927\u5c0f\u6709\u4e0d\u540c\u7684\u5f71\u54cd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1aPython\u5408\u5e76\u56fe\u50cf\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pillow\u5e93\u3001OpenCV\u5e93\u548cMatplotlib\u5e93\u3002\u5176\u4e2d\uff0cPi [&hellip;]","protected":false},"author":3,"featured_media":935777,"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\/935773"}],"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=935773"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/935773\/revisions"}],"predecessor-version":[{"id":935780,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/935773\/revisions\/935780"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/935777"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=935773"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=935773"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=935773"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}