{"id":1114489,"date":"2025-01-08T17:57:46","date_gmt":"2025-01-08T09:57:46","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1114489.html"},"modified":"2025-01-08T17:57:52","modified_gmt":"2025-01-08T09:57:52","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e4%b8%a4%e5%bc%a0%e5%9b%be%e5%8f%a0%e5%8a%a0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1114489.html","title":{"rendered":"python\u5982\u4f55\u5c06\u4e24\u5f20\u56fe\u53e0\u52a0"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25075639\/a419f4a8-9bc7-4173-bf92-fa08baf20d80.webp\" alt=\"python\u5982\u4f55\u5c06\u4e24\u5f20\u56fe\u53e0\u52a0\" \/><\/p>\n<p><p> <strong>\u8981\u5c06\u4e24\u5f20\u56fe\u50cf\u53e0\u52a0\u5728\u4e00\u8d77\uff0c\u53ef\u4ee5\u4f7f\u7528 Python \u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982 OpenCV \u6216 Pillow\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u56fe\u50cf\u53e0\u52a0\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u900f\u660e\u5ea6\uff08Alpha\u901a\u9053\uff09\u8fdb\u884c\u53e0\u52a0\u3001\u76f4\u63a5\u50cf\u7d20\u76f8\u52a0\u3001\u56fe\u50cf\u6df7\u5408\u7b49\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u4f7f\u7528\u900f\u660e\u5ea6\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<p><p><strong>\u4f7f\u7528\u900f\u660e\u5ea6\uff08Alpha\u901a\u9053\uff09\u8fdb\u884c\u53e0\u52a0\uff1a<\/strong> \u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u4f60\u5c06\u4e00\u5f20\u56fe\u50cf\u53e0\u52a0\u5728\u53e6\u4e00\u5f20\u56fe\u50cf\u4e0a\uff0c\u5e76\u4e14\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u900f\u660e\u5ea6\u6765\u63a7\u5236\u53e0\u52a0\u6548\u679c\u3002\u900f\u660e\u5ea6\u503c\u901a\u5e38\u5728 0 \u5230 1 \u4e4b\u95f4\uff0c0 \u8868\u793a\u5b8c\u5168\u900f\u660e\uff0c1 \u8868\u793a\u5b8c\u5168\u4e0d\u900f\u660e\u3002\u901a\u8fc7\u8c03\u6574\u900f\u660e\u5ea6\u503c\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5e73\u6ed1\u8fc7\u6e21\u548c\u6df7\u5408\u6548\u679c\u3002<\/p>\n<\/p>\n<p><h3>\u4f7f\u7528 OpenCV \u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/h3>\n<\/p>\n<p><p><strong>\u4e00\u3001\u5b89\u88c5\u5e76\u5bfc\u5165\u6240\u9700\u5e93<\/strong><\/p>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165 OpenCV \u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\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>\u7136\u540e\u5728\u4f60\u7684 Python \u811a\u672c\u4e2d\u5bfc\u5165\u6240\u9700\u7684\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>import numpy as np<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u4e8c\u3001\u8bfb\u53d6\u56fe\u50cf<\/strong><\/p>\n<\/p>\n<p><p>\u4f7f\u7528 OpenCV \u7684 <code>cv2.imread<\/code> \u65b9\u6cd5\u8bfb\u53d6\u4e24\u5f20\u56fe\u50cf\uff0c\u5e76\u786e\u4fdd\u5b83\u4eec\u7684\u5927\u5c0f\u76f8\u540c\u3002\u5982\u679c\u56fe\u50cf\u5927\u5c0f\u4e0d\u540c\uff0c\u53ef\u4ee5\u4f7f\u7528 <code>cv2.resize<\/code> \u65b9\u6cd5\u8fdb\u884c\u8c03\u6574\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">image1 = cv2.imread(&#39;path_to_image1.jpg&#39;)<\/p>\n<p>image2 = cv2.imread(&#39;path_to_image2.jpg&#39;)<\/p>\n<h2><strong>\u786e\u4fdd\u56fe\u50cf\u5927\u5c0f\u76f8\u540c<\/strong><\/h2>\n<p>image1 = cv2.resize(image1, (image2.shape[1], image2.shape[0]))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u4e09\u3001\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/strong><\/p>\n<\/p>\n<p><p>\u4f7f\u7528 <code>cv2.addWeighted<\/code> \u65b9\u6cd5\u5c06\u4e24\u5f20\u56fe\u50cf\u53e0\u52a0\u5728\u4e00\u8d77\u3002\u8fd9\u4e2a\u65b9\u6cd5\u5141\u8bb8\u4f60\u4e3a\u6bcf\u5f20\u56fe\u50cf\u8bbe\u7f6e\u4e0d\u540c\u7684\u6743\u91cd\uff0c\u4ece\u800c\u63a7\u5236\u900f\u660e\u5ea6\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">alpha = 0.5  # \u56fe\u50cf1\u7684\u900f\u660e\u5ea6<\/p>\n<p>beta = 1 - alpha  # \u56fe\u50cf2\u7684\u900f\u660e\u5ea6<\/p>\n<p>gamma = 0  # \u53ef\u9009\u7684\u6807\u91cf<\/p>\n<h2><strong>\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/strong><\/h2>\n<p>blended_image = cv2.addWeighted(image1, alpha, image2, beta, gamma)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u56db\u3001\u663e\u793a\u548c\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf<\/strong><\/p>\n<\/p>\n<p><p>\u4f7f\u7528 OpenCV \u7684 <code>cv2.imshow<\/code> \u65b9\u6cd5\u663e\u793a\u53e0\u52a0\u540e\u7684\u56fe\u50cf\uff0c\u5e76\u4f7f\u7528 <code>cv2.imwrite<\/code> \u65b9\u6cd5\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">cv2.imshow(&#39;Blended Image&#39;, blended_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<h2><strong>\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf<\/strong><\/h2>\n<p>cv2.imwrite(&#39;blended_image.jpg&#39;, blended_image)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4f7f\u7528 Pillow \u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/h3>\n<\/p>\n<p><p><strong>\u4e00\u3001\u5b89\u88c5\u5e76\u5bfc\u5165\u6240\u9700\u5e93<\/strong><\/p>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165 Pillow \u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\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>\u7136\u540e\u5728\u4f60\u7684 Python \u811a\u672c\u4e2d\u5bfc\u5165\u6240\u9700\u7684\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u4e8c\u3001\u8bfb\u53d6\u56fe\u50cf<\/strong><\/p>\n<\/p>\n<p><p>\u4f7f\u7528 Pillow \u7684 <code>Image.open<\/code> \u65b9\u6cd5\u8bfb\u53d6\u4e24\u5f20\u56fe\u50cf\uff0c\u5e76\u786e\u4fdd\u5b83\u4eec\u7684\u5927\u5c0f\u76f8\u540c\u3002\u5982\u679c\u56fe\u50cf\u5927\u5c0f\u4e0d\u540c\uff0c\u53ef\u4ee5\u4f7f\u7528 <code>Image.resize<\/code> \u65b9\u6cd5\u8fdb\u884c\u8c03\u6574\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">image1 = Image.open(&#39;path_to_image1.png&#39;).convert(&quot;RGBA&quot;)<\/p>\n<p>image2 = Image.open(&#39;path_to_image2.png&#39;).convert(&quot;RGBA&quot;)<\/p>\n<h2><strong>\u786e\u4fdd\u56fe\u50cf\u5927\u5c0f\u76f8\u540c<\/strong><\/h2>\n<p>image1 = image1.resize(image2.size)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u4e09\u3001\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/strong><\/p>\n<\/p>\n<p><p>\u4f7f\u7528 Pillow \u7684 <code>Image.blend<\/code> \u65b9\u6cd5\u5c06\u4e24\u5f20\u56fe\u50cf\u53e0\u52a0\u5728\u4e00\u8d77\u3002\u8fd9\u4e2a\u65b9\u6cd5\u5141\u8bb8\u4f60\u4e3a\u6bcf\u5f20\u56fe\u50cf\u8bbe\u7f6e\u4e0d\u540c\u7684\u900f\u660e\u5ea6\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">alpha = 0.5  # \u56fe\u50cf1\u7684\u900f\u660e\u5ea6<\/p>\n<h2><strong>\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/strong><\/h2>\n<p>blended_image = Image.blend(image1, image2, alpha)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u56db\u3001\u663e\u793a\u548c\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf<\/strong><\/p>\n<\/p>\n<p><p>\u4f7f\u7528 Pillow \u7684 <code>show<\/code> \u65b9\u6cd5\u663e\u793a\u53e0\u52a0\u540e\u7684\u56fe\u50cf\uff0c\u5e76\u4f7f\u7528 <code>save<\/code> \u65b9\u6cd5\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">blended_image.show()<\/p>\n<h2><strong>\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf<\/strong><\/h2>\n<p>blended_image.save(&#39;blended_image.png&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u8be6\u7ec6\u6b65\u9aa4\u8bf4\u660e<\/h3>\n<\/p>\n<p><p><strong>\u4e00\u3001\u56fe\u50cf\u8bfb\u53d6\u548c\u9884\u5904\u7406<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u4e2d\uff0c\u56fe\u50cf\u7684\u8bfb\u53d6\u548c\u9884\u5904\u7406\u662f\u81f3\u5173\u91cd\u8981\u7684\u7b2c\u4e00\u6b65\u3002\u65e0\u8bba\u662f\u4f7f\u7528 OpenCV \u8fd8\u662f Pillow\uff0c\u8fd9\u4e00\u8fc7\u7a0b\u90fd\u6d89\u53ca\u5c06\u56fe\u50cf\u6587\u4ef6\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\uff0c\u5e76\u786e\u4fdd\u56fe\u50cf\u7684\u5c3a\u5bf8\u4e00\u81f4\u3002<\/p>\n<\/p>\n<p><p>\u5bf9\u4e8e OpenCV\uff0c<code>cv2.imread<\/code> \u65b9\u6cd5\u53ef\u4ee5\u8bfb\u53d6\u5404\u79cd\u683c\u5f0f\u7684\u56fe\u50cf\u6587\u4ef6\uff08\u5982 JPG\u3001PNG \u7b49\uff09\uff0c\u800c <code>cv2.resize<\/code> \u65b9\u6cd5\u53ef\u4ee5\u8c03\u6574\u56fe\u50cf\u7684\u5c3a\u5bf8\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">image1 = cv2.imread(&#39;path_to_image1.jpg&#39;)<\/p>\n<p>image2 = cv2.imread(&#39;path_to_image2.jpg&#39;)<\/p>\n<h2><strong>\u786e\u4fdd\u56fe\u50cf\u5927\u5c0f\u76f8\u540c<\/strong><\/h2>\n<p>image1 = cv2.resize(image1, (image2.shape[1], image2.shape[0]))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5bf9\u4e8e Pillow\uff0c<code>Image.open<\/code> \u65b9\u6cd5\u7528\u4e8e\u8bfb\u53d6\u56fe\u50cf\u6587\u4ef6\uff0c\u5e76\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a RGBA \u6a21\u5f0f\uff08\u5305\u542b\u900f\u660e\u5ea6\u901a\u9053\uff09\u3002<code>resize<\/code> \u65b9\u6cd5\u7528\u4e8e\u8c03\u6574\u56fe\u50cf\u5c3a\u5bf8\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">image1 = Image.open(&#39;path_to_image1.png&#39;).convert(&quot;RGBA&quot;)<\/p>\n<p>image2 = Image.open(&#39;path_to_image2.png&#39;).convert(&quot;RGBA&quot;)<\/p>\n<h2><strong>\u786e\u4fdd\u56fe\u50cf\u5927\u5c0f\u76f8\u540c<\/strong><\/h2>\n<p>image1 = image1.resize(image2.size)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u4e8c\u3001\u56fe\u50cf\u53e0\u52a0<\/strong><\/p>\n<\/p>\n<p><p>\u56fe\u50cf\u53e0\u52a0\u662f\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u5173\u952e\u6b65\u9aa4\u3002\u901a\u8fc7\u8c03\u6574\u900f\u660e\u5ea6\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5404\u79cd\u4e0d\u540c\u7684\u53e0\u52a0\u6548\u679c\u3002\u5728 OpenCV \u4e2d\uff0c<code>cv2.addWeighted<\/code> \u65b9\u6cd5\u5141\u8bb8\u7528\u6237\u4e3a\u6bcf\u5f20\u56fe\u50cf\u8bbe\u7f6e\u4e0d\u540c\u7684\u6743\u91cd\uff0c\u4ece\u800c\u63a7\u5236\u900f\u660e\u5ea6\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">alpha = 0.5  # \u56fe\u50cf1\u7684\u900f\u660e\u5ea6<\/p>\n<p>beta = 1 - alpha  # \u56fe\u50cf2\u7684\u900f\u660e\u5ea6<\/p>\n<p>gamma = 0  # \u53ef\u9009\u7684\u6807\u91cf<\/p>\n<h2><strong>\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/strong><\/h2>\n<p>blended_image = cv2.addWeighted(image1, alpha, image2, beta, gamma)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728 Pillow \u4e2d\uff0c<code>Image.blend<\/code> \u65b9\u6cd5\u7528\u4e8e\u5c06\u4e24\u5f20\u56fe\u50cf\u53e0\u52a0\u5728\u4e00\u8d77\uff0c\u5e76\u901a\u8fc7\u900f\u660e\u5ea6\u53c2\u6570\u63a7\u5236\u53e0\u52a0\u6548\u679c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">alpha = 0.5  # \u56fe\u50cf1\u7684\u900f\u660e\u5ea6<\/p>\n<h2><strong>\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0<\/strong><\/h2>\n<p>blended_image = Image.blend(image1, image2, alpha)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u4e09\u3001\u663e\u793a\u548c\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf<\/strong><\/p>\n<\/p>\n<p><p>\u5904\u7406\u5b8c\u56fe\u50cf\u540e\uff0c\u4e0b\u4e00\u6b65\u662f\u663e\u793a\u548c\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf\u3002\u5728 OpenCV \u4e2d\uff0c<code>cv2.imshow<\/code> \u65b9\u6cd5\u7528\u4e8e\u663e\u793a\u56fe\u50cf\uff0c<code>cv2.imwrite<\/code> \u65b9\u6cd5\u7528\u4e8e\u4fdd\u5b58\u56fe\u50cf\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">cv2.imshow(&#39;Blended Image&#39;, blended_image)<\/p>\n<p>cv2.waitKey(0)<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<h2><strong>\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf<\/strong><\/h2>\n<p>cv2.imwrite(&#39;blended_image.jpg&#39;, blended_image)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728 Pillow \u4e2d\uff0c<code>show<\/code> \u65b9\u6cd5\u7528\u4e8e\u663e\u793a\u56fe\u50cf\uff0c<code>save<\/code> \u65b9\u6cd5\u7528\u4e8e\u4fdd\u5b58\u56fe\u50cf\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">blended_image.show()<\/p>\n<h2><strong>\u4fdd\u5b58\u7ed3\u679c\u56fe\u50cf<\/strong><\/h2>\n<p>blended_image.save(&#39;blended_image.png&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u5176\u4ed6\u56fe\u50cf\u53e0\u52a0\u65b9\u6cd5<\/h3>\n<\/p>\n<p><p>\u9664\u4e86\u4f7f\u7528\u900f\u660e\u5ea6\u8fdb\u884c\u56fe\u50cf\u53e0\u52a0\u5916\uff0c\u8fd8\u6709\u5176\u4ed6\u51e0\u79cd\u5e38\u89c1\u7684\u56fe\u50cf\u53e0\u52a0\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<p><p><strong>1\u3001\u76f4\u63a5\u50cf\u7d20\u76f8\u52a0<\/strong><\/p>\n<\/p>\n<p><p>\u8fd9\u79cd\u65b9\u6cd5\u5c06\u4e24\u5f20\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u76f4\u63a5\u76f8\u52a0\u3002\u867d\u7136\u7b80\u5355\uff0c\u4f46\u53ef\u80fd\u4f1a\u5bfc\u81f4\u50cf\u7d20\u503c\u6ea2\u51fa\uff0c\u4ece\u800c\u4ea7\u751f\u4e0d\u6b63\u786e\u7684\u989c\u8272\u7ed3\u679c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">added_image = cv2.add(image1, image2)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>2\u3001\u56fe\u50cf\u6df7\u5408<\/strong><\/p>\n<\/p>\n<p><p>\u56fe\u50cf\u6df7\u5408\u662f\u4e00\u79cd\u66f4\u590d\u6742\u7684\u53e0\u52a0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u6df7\u5408\u6a21\u5f0f\uff08\u5982\u4e58\u6cd5\u3001\u5c4f\u5e55\u3001\u53e0\u52a0\u7b49\uff09\u6765\u5b9e\u73b0\u5404\u79cd\u6548\u679c\u3002OpenCV \u63d0\u4f9b\u4e86\u591a\u79cd\u6df7\u5408\u6a21\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u7528 <code>cv2.multiply<\/code>\u3001<code>cv2.add<\/code> \u7b49\u51fd\u6570\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u793a\u4f8b\uff1a\u4e58\u6cd5\u6df7\u5408<\/p>\n<p>multiplied_image = cv2.multiply(image1, image2)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>3\u3001\u63a9\u6a21\u53e0\u52a0<\/strong><\/p>\n<\/p>\n<p><p>\u63a9\u6a21\u53e0\u52a0\u65b9\u6cd5\u4f7f\u7528\u4e00\u5f20\u63a9\u6a21\u56fe\u50cf\u6765\u63a7\u5236\u54ea\u4e9b\u90e8\u5206\u8fdb\u884c\u53e0\u52a0\u3002\u8fd9\u79cd\u65b9\u6cd5\u901a\u5e38\u7528\u4e8e\u56fe\u50cf\u5408\u6210\u548c\u7279\u6548\u5904\u7406\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">mask = cv2.imread(&#39;path_to_mask.png&#39;, 0)<\/p>\n<p>masked_image = cv2.bitwise_and(image1, image1, mask=mask)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u5b9e\u9645\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n<p><p>\u56fe\u50cf\u53e0\u52a0\u6280\u672f\u5728\u8bb8\u591a\u5b9e\u9645\u5e94\u7528\u4e2d\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n<p><p><strong>1\u3001\u56fe\u50cf\u6c34\u5370<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u56fe\u50cf\u4e0a\u6dfb\u52a0\u6c34\u5370\u662f\u56fe\u50cf\u53e0\u52a0\u7684\u4e00\u79cd\u5e38\u89c1\u5e94\u7528\u3002\u53ef\u4ee5\u4f7f\u7528\u900f\u660e\u5ea6\u63a7\u5236\u6c34\u5370\u7684\u663e\u8457\u6027\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">watermark = Image.open(&#39;path_to_watermark.png&#39;).convert(&quot;RGBA&quot;)<\/p>\n<p>watermark = watermark.resize(image1.size)<\/p>\n<p>watermarked_image = Image.alpha_composite(image1, watermark)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>2\u3001\u56fe\u50cf\u5408\u6210<\/strong><\/p>\n<\/p>\n<p><p>\u56fe\u50cf\u5408\u6210\u6d89\u53ca\u5c06\u591a\u4e2a\u56fe\u50cf\u5408\u5e76\u6210\u4e00\u4e2a\uff0c\u4ee5\u521b\u5efa\u65b0\u7684\u89c6\u89c9\u6548\u679c\u3002\u4f8b\u5982\uff0c\u7535\u5f71\u7279\u6548\u3001\u5e7f\u544a\u8bbe\u8ba1\u7b49\u9886\u57df\u90fd\u5e7f\u6cdb\u4f7f\u7528\u56fe\u50cf\u5408\u6210\u6280\u672f\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">background = Image.open(&#39;path_to_background.jpg&#39;).convert(&quot;RGBA&quot;)<\/p>\n<p>foreground = Image.open(&#39;path_to_foreground.png&#39;).convert(&quot;RGBA&quot;)<\/p>\n<p>composite_image = Image.alpha_composite(background, foreground)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>3\u3001\u6570\u636e\u589e\u5f3a<\/strong><\/p>\n<\/p>\n<p><p>\u5728<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\uff0c\u56fe\u50cf\u53e0\u52a0\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u589e\u5f3a\uff0c\u4ece\u800c\u751f\u6210\u66f4\u591a\u7684\u8bad\u7ec3\u6570\u636e\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5c06\u4e0d\u540c\u7684\u7269\u4f53\u53e0\u52a0\u5728\u80cc\u666f\u56fe\u50cf\u4e0a\uff0c\u4ee5\u521b\u5efa\u66f4\u591a\u6837\u7684\u8bad\u7ec3\u6837\u672c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">augmented_image = Image.blend(background, foreground, alpha=0.7)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u56fe\u50cf\u53e0\u52a0\u662f\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u57fa\u672c\u64cd\u4f5c\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u79cd\u9886\u57df\u3002\u901a\u8fc7\u8c03\u6574\u900f\u660e\u5ea6\u548c\u4f7f\u7528\u4e0d\u540c\u7684\u53e0\u52a0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e30\u5bcc\u7684\u89c6\u89c9\u6548\u679c\u3002\u65e0\u8bba\u662f\u4f7f\u7528 OpenCV \u8fd8\u662f Pillow\uff0c\u56fe\u50cf\u53e0\u52a0\u90fd\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\uff0c\u5e76\u4e14\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u8c03\u6574\u548c\u4f18\u5316\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u8be6\u7ec6\u4ecb\u7ecd\uff0c\u60a8\u80fd\u591f\u6df1\u5165\u7406\u89e3\u5e76\u638c\u63e1\u56fe\u50cf\u53e0\u52a0\u7684\u5404\u79cd\u65b9\u6cd5\u548c\u5e94\u7528\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u53e0\u52a0\u4e24\u5f20\u56fe\u50cf\uff1f<\/strong><\/p>\n<p>\u53e0\u52a0\u56fe\u50cf\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u5e93\u6765\u5b9e\u73b0\uff0c\u4f8b\u5982OpenCV\u3001PIL\uff08Pillow\uff09\u6216Matplotlib\u3002\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u4e24\u5f20\u56fe\u50cf\u53e0\u52a0\u5728\u4e00\u8d77\uff0c\u521b\u5efa\u51fa\u65b0\u7684\u89c6\u89c9\u6548\u679c\u3002\u901a\u5e38\u6b65\u9aa4\u5305\u62ec\u8bfb\u53d6\u56fe\u50cf\u3001\u8c03\u6574\u56fe\u50cf\u5927\u5c0f\uff08\u5982\u679c\u9700\u8981\uff09\uff0c\u5e76\u4f7f\u7528\u52a0\u6743\u6216\u900f\u660e\u5ea6\u6765\u53e0\u52a0\u56fe\u50cf\u3002<\/p>\n<p><strong>\u4f7f\u7528OpenCV\u53e0\u52a0\u56fe\u50cf\u7684\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong><\/p>\n<p>\u5728OpenCV\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528<code>cv2.addWeighted()<\/code>\u51fd\u6570\u6765\u53e0\u52a0\u56fe\u50cf\u3002\u9996\u5148\uff0c\u5bfc\u5165OpenCV\u5e93\u5e76\u8bfb\u53d6\u4e24\u5f20\u56fe\u50cf\u3002\u7136\u540e\uff0c\u60a8\u53ef\u4ee5\u6307\u5b9a\u6bcf\u5f20\u56fe\u50cf\u7684\u6743\u91cd\u548c\u900f\u660e\u5ea6\uff0c\u6700\u540e\u8f93\u51fa\u53e0\u52a0\u540e\u7684\u56fe\u50cf\u3002\u4f8b\u5982\uff1a<\/p>\n<pre><code class=\"language-python\">import cv2\n\n# \u8bfb\u53d6\u56fe\u50cf\nimg1 = cv2.imread(&#39;image1.jpg&#39;)\nimg2 = cv2.imread(&#39;image2.jpg&#39;)\n\n# \u786e\u4fdd\u4e24\u5f20\u56fe\u50cf\u5927\u5c0f\u76f8\u540c\nimg2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))\n\n# \u53e0\u52a0\u56fe\u50cf\nalpha = 0.5  # \u56fe\u50cf1\u7684\u6743\u91cd\nbeta = 0.5   # \u56fe\u50cf2\u7684\u6743\u91cd\nresult = cv2.addWeighted(img1, alpha, img2, beta, 0)\n\n# \u663e\u793a\u7ed3\u679c\ncv2.imshow(&#39;Blended Image&#39;, result)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n<\/code><\/pre>\n<p><strong>PIL\u5e93\u662f\u5426\u4e5f\u53ef\u4ee5\u7528\u4e8e\u53e0\u52a0\u56fe\u50cf\uff1f<\/strong><\/p>\n<p>\u662f\u7684\uff0cPIL\uff08Pillow\uff09\u5e93\u540c\u6837\u53ef\u4ee5\u5b9e\u73b0\u56fe\u50cf\u53e0\u52a0\u3002\u901a\u8fc7\u4f7f\u7528<code>Image<\/code>\u6a21\u5757\u7684<code>blend()<\/code>\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u6307\u5b9a\u56fe\u50cf\u7684\u900f\u660e\u5ea6\u6765\u5b9e\u73b0\u53e0\u52a0\u6548\u679c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<pre><code class=\"language-python\">from PIL import Image\n\n# \u6253\u5f00\u56fe\u50cf\nimg1 = Image.open(&#39;image1.png&#39;)\nimg2 = Image.open(&#39;image2.png&#39;)\n\n# \u786e\u4fdd\u4e24\u5f20\u56fe\u50cf\u5927\u5c0f\u76f8\u540c\nimg2 = img2.resize(img1.size)\n\n# \u53e0\u52a0\u56fe\u50cf\nresult = Image.blend(img1, img2, alpha=0.5)\n\n# \u4fdd\u5b58\u548c\u663e\u793a\u7ed3\u679c\nresult.show()\nresult.save(&#39;blended_image.png&#39;)\n<\/code><\/pre>\n<p><strong>\u5728\u53e0\u52a0\u56fe\u50cf\u65f6\uff0c\u5982\u4f55\u5904\u7406\u4e0d\u540c\u5c3a\u5bf8\u7684\u56fe\u50cf\uff1f<\/strong><\/p>\n<p>\u5904\u7406\u4e0d\u540c\u5c3a\u5bf8\u7684\u56fe\u50cf\u65f6\uff0c\u60a8\u901a\u5e38\u9700\u8981\u5148\u8c03\u6574\u5b83\u4eec\u7684\u5927\u5c0f\u4ee5\u786e\u4fdd\u5b83\u4eec\u5339\u914d\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684<code>cv2.resize()<\/code>\u6216PIL\u7684<code>resize()<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u9009\u62e9\u5408\u9002\u7684\u7f29\u653e\u65b9\u6cd5\u5f88\u91cd\u8981\uff0c\u4ee5\u907f\u514d\u5931\u771f\u6216\u56fe\u50cf\u8d28\u91cf\u4e0b\u964d\u3002\u5728\u8c03\u6574\u5c3a\u5bf8\u65f6\uff0c\u786e\u4fdd\u4fdd\u6301\u56fe\u50cf\u7684\u6bd4\u4f8b\uff0c\u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u56fe\u50cf\u53d8\u5f62\u3002<\/p>\n<p>\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u8f7b\u677e\u5b9e\u73b0\u56fe\u50cf\u53e0\u52a0\uff0c\u521b\u9020\u51fa\u4ee4\u4eba\u60ca\u53f9\u7684\u89c6\u89c9\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5c06\u4e24\u5f20\u56fe\u50cf\u53e0\u52a0\u5728\u4e00\u8d77\uff0c\u53ef\u4ee5\u4f7f\u7528 Python \u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982 OpenCV \u6216 Pillow\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86 [&hellip;]","protected":false},"author":3,"featured_media":1114505,"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\/1114489"}],"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=1114489"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1114489\/revisions"}],"predecessor-version":[{"id":1114507,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1114489\/revisions\/1114507"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1114505"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1114489"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1114489"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1114489"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}