{"id":1089976,"date":"2025-01-08T13:58:22","date_gmt":"2025-01-08T05:58:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1089976.html"},"modified":"2025-01-08T13:58:25","modified_gmt":"2025-01-08T05:58:25","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e5%9b%be%e7%89%87%e9%99%8d%e4%bd%8e%e6%9b%9d%e5%85%89%e5%ba%a6-3","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1089976.html","title":{"rendered":"Python\u5982\u4f55\u5c06\u56fe\u7247\u964d\u4f4e\u66dd\u5149\u5ea6"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24202638\/3b63a9ab-8061-42fd-9391-fb39e8049cda.webp\" alt=\"Python\u5982\u4f55\u5c06\u56fe\u7247\u964d\u4f4e\u66dd\u5149\u5ea6\" \/><\/p>\n<p><p> <strong>Python\u5c06\u56fe\u7247\u964d\u4f4e\u66dd\u5149\u5ea6\u7684\u65b9\u6cd5<\/strong>\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Pillow\u5e93\u3001OpenCV\u5e93\u4ee5\u53caNumPy\u5e93\u7b49\u3002<strong>\u5e38\u89c1\u7684\u65b9\u6cd5\u6709\u8c03\u6574\u50cf\u7d20\u503c\u3001\u4f7f\u7528\u6ee4\u955c\u3001\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6<\/strong>\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u4f7f\u7528Pillow\u5e93\u964d\u4f4e\u66dd\u5149\u5ea6<\/h3>\n<\/p>\n<p><p>Pillow\u662fPython\u7684\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4e0b\u9762\u662f\u4f7f\u7528Pillow\u5e93\u964d\u4f4e\u56fe\u7247\u66dd\u5149\u5ea6\u7684\u5177\u4f53\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5b89\u88c5Pillow\u5e93<\/h4>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Pillow\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install pillow<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u52a0\u8f7d\u56fe\u50cf<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u4f7f\u7528Pillow\u5e93\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&quot;example.jpg&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6<\/h4>\n<\/p>\n<p><p>Pillow\u63d0\u4f9b\u4e86\u4e00\u4e2aImageEnhance\u6a21\u5757\uff0c\u53ef\u4ee5\u7528\u6765\u8c03\u6574\u56fe\u50cf\u7684\u4eae\u5ea6\u3001\u5bf9\u6bd4\u5ea6\u3001\u989c\u8272\u548c\u9510\u5ea6\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528ImageEnhance.Brightness\u7c7b\u6765\u964d\u4f4e\u56fe\u50cf\u7684\u4eae\u5ea6\uff0c\u4ece\u800c\u964d\u4f4e\u66dd\u5149\u5ea6\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import ImageEnhance<\/p>\n<h2><strong>\u521b\u5efa\u4eae\u5ea6\u589e\u5f3a\u5668<\/strong><\/h2>\n<p>enhancer = ImageEnhance.Brightness(image)<\/p>\n<h2><strong>\u964d\u4f4e\u4eae\u5ea6\uff0cfactor\u503c\u5c0f\u4e8e1\u8868\u793a\u964d\u4f4e\u4eae\u5ea6<\/strong><\/h2>\n<p>image_darkened = enhancer.enhance(0.5)<\/p>\n<h2><strong>\u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>image_darkened.save(&quot;darkened_example.jpg&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c<code>enhance<\/code>\u65b9\u6cd5\u7684\u53c2\u6570<code>factor<\/code>\u51b3\u5b9a\u4e86\u4eae\u5ea6\u7684\u8c03\u6574\u7a0b\u5ea6\u3002<code>factor<\/code>\u503c\u5c0f\u4e8e1\u8868\u793a\u964d\u4f4e\u4eae\u5ea6\uff0c\u503c\u8d8a\u5c0f\uff0c\u56fe\u50cf\u8d8a\u6697\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u4f7f\u7528OpenCV\u5e93\u964d\u4f4e\u66dd\u5149\u5ea6<\/h3>\n<\/p>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u3002\u4e0b\u9762\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528OpenCV\u5e93\u964d\u4f4e\u56fe\u7247\u7684\u66dd\u5149\u5ea6\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5b89\u88c5OpenCV\u5e93<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5OpenCV\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install opencv-python<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u52a0\u8f7d\u56fe\u50cf<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528OpenCV\u5e93\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&quot;example.jpg&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6<\/h4>\n<\/p>\n<p><p>\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u6765\u964d\u4f4e\u66dd\u5149\u5ea6\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u7c7b\u578b<\/strong><\/h2>\n<p>image_float = image.astype(np.float64)<\/p>\n<h2><strong>\u964d\u4f4e\u66dd\u5149\u5ea6\uff0c\u51cf\u5c0f\u50cf\u7d20\u503c<\/strong><\/h2>\n<p>factor = 0.5<\/p>\n<p>image_darkened = image_float * factor<\/p>\n<h2><strong>\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u65e0\u7b26\u53f78\u4f4d\u6574\u6570\u7c7b\u578b<\/strong><\/h2>\n<p>image_darkened = np.clip(image_darkened, 0, 255).astype(np.uint8)<\/p>\n<h2><strong>\u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>cv2.imwrite(&quot;darkened_example.jpg&quot;, image_darkened)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u7c7b\u578b\uff0c\u7136\u540e\u4e58\u4ee5\u4e00\u4e2a\u5c0f\u4e8e1\u7684<code>factor<\/code>\u503c\u6765\u964d\u4f4e\u66dd\u5149\u5ea6\u3002\u6700\u540e\uff0c\u5c06\u56fe\u50cf\u8f6c\u6362\u56de\u65e0\u7b26\u53f78\u4f4d\u6574\u6570\u7c7b\u578b\u5e76\u4fdd\u5b58\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u4f7f\u7528NumPy\u5e93\u964d\u4f4e\u66dd\u5149\u5ea6<\/h3>\n<\/p>\n<p><p>NumPy\u662fPython\u7684\u4e00\u4e2a\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u5904\u7406\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u76f4\u63a5\u64cd\u4f5c\u56fe\u50cf\u7684\u50cf\u7d20\u503c\uff0c\u4ece\u800c\u964d\u4f4e\u66dd\u5149\u5ea6\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5b89\u88c5NumPy\u5e93<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5NumPy\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install numpy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2\u3001\u52a0\u8f7d\u56fe\u50cf<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528Pillow\u5e93\u52a0\u8f7d\u56fe\u50cf\u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u52a0\u8f7d\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&quot;example.jpg&quot;)<\/p>\n<h2><strong>\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n<p>image_array = np.array(image)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>3\u3001\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6<\/h4>\n<\/p>\n<p><p>\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574NumPy\u6570\u7ec4\u7684\u503c\u6765\u964d\u4f4e\u66dd\u5149\u5ea6\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u964d\u4f4e\u66dd\u5149\u5ea6\uff0c\u51cf\u5c0f\u50cf\u7d20\u503c<\/p>\n<p>factor = 0.5<\/p>\n<p>image_darkened = image_array * factor<\/p>\n<h2><strong>\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u65e0\u7b26\u53f78\u4f4d\u6574\u6570\u7c7b\u578b<\/strong><\/h2>\n<p>image_darkened = np.clip(image_darkened, 0, 255).astype(np.uint8)<\/p>\n<h2><strong>\u5c06NumPy\u6570\u7ec4\u8f6c\u6362\u56dePillow\u56fe\u50cf<\/strong><\/h2>\n<p>image_darkened = Image.fromarray(image_darkened)<\/p>\n<h2><strong>\u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>image_darkened.save(&quot;darkened_example.jpg&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u4e58\u4ee5\u4e00\u4e2a\u5c0f\u4e8e1\u7684<code>factor<\/code>\u503c\u6765\u964d\u4f4e\u66dd\u5149\u5ea6\u3002\u6700\u540e\uff0c\u5c06NumPy\u6570\u7ec4\u8f6c\u6362\u56dePillow\u56fe\u50cf\u5e76\u4fdd\u5b58\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u4e09\u79cd\u4f7f\u7528Python\u964d\u4f4e\u56fe\u7247\u66dd\u5149\u5ea6\u7684\u65b9\u6cd5\uff0c\u5206\u522b\u662f\u4f7f\u7528Pillow\u5e93\u3001OpenCV\u5e93\u548cNumPy\u5e93\u3002<strong>\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f<\/strong>\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u4e9b\u793a\u4f8b\u4ee3\u7801\uff0c\u8bfb\u8005\u53ef\u4ee5\u8f7b\u677e\u5730\u5b9e\u73b0\u56fe\u50cf\u66dd\u5149\u5ea6\u7684\u8c03\u6574\uff0c\u5e76\u5c06\u5176\u5e94\u7528\u5230\u5b9e\u9645\u9879\u76ee\u4e2d\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u964d\u4f4e\u56fe\u7247\u7684\u66dd\u5149\u5ea6\uff1f<\/strong><br \/>\u8981\u964d\u4f4e\u56fe\u7247\u7684\u66dd\u5149\u5ea6\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684PIL\uff08Pillow\uff09\u5e93\u3002\u901a\u8fc7\u8c03\u6574\u56fe\u7247\u7684\u4eae\u5ea6\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u51cf\u5c11\u66dd\u5149\u5ea6\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u8bfb\u53d6\u56fe\u7247\u3001\u521b\u5efa\u4eae\u5ea6\u8c03\u6574\u5bf9\u8c61\uff0c\u5e76\u8bbe\u7f6e\u9002\u5f53\u7684\u4eae\u5ea6\u56e0\u5b50\u3002\u56e0\u5b50\u5c0f\u4e8e1\u4f1a\u964d\u4f4e\u4eae\u5ea6\uff0c\u4ece\u800c\u51cf\u5c11\u66dd\u5149\u5ea6\u3002<\/p>\n<p><strong>\u4f7f\u7528\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5b9e\u73b0\u56fe\u7247\u66dd\u5149\u5ea6\u7684\u8c03\u6574\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u9664\u4e86Pillow\u5e93\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528OpenCV\u548cMatplotlib\u7b49\u5e93\u3002OpenCV\u63d0\u4f9b\u4e86\u66f4\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u6765\u6539\u53d8\u66dd\u5149\u5ea6\uff1b\u800cMatplotlib\u5219\u9002\u5408\u7528\u4e8e\u53ef\u89c6\u5316\u56fe\u50cf\u5904\u7406\u7ed3\u679c\u3002<\/p>\n<p><strong>\u964d\u4f4e\u66dd\u5149\u5ea6\u5bf9\u56fe\u7247\u8d28\u91cf\u6709\u4ec0\u4e48\u5f71\u54cd\uff1f<\/strong><br 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