{"id":1145992,"date":"2025-01-08T23:13:02","date_gmt":"2025-01-08T15:13:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1145992.html"},"modified":"2025-01-08T23:13:05","modified_gmt":"2025-01-08T15:13:05","slug":"python%e5%a6%82%e4%bd%95%e5%b0%864%e9%80%9a%e9%81%93%e5%8f%98%e4%b8%ba1%e9%80%9a%e9%81%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1145992.html","title":{"rendered":"python\u5982\u4f55\u5c064\u901a\u9053\u53d8\u4e3a1\u901a\u9053"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24182302\/6db376d7-e5cf-4e1b-aff4-0f64109e50dc.webp\" alt=\"python\u5982\u4f55\u5c064\u901a\u9053\u53d8\u4e3a1\u901a\u9053\" \/><\/p>\n<p><p> <strong>Python\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\u56fe\u50cf\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u53d6\u7070\u5ea6\u503c\u3001\u53d6\u5e73\u5747\u503c\u3001\u53d6\u6700\u5927\u503c\u3001\u53d6\u6700\u5c0f\u503c\u3002<\/strong> \u5176\u4e2d\uff0c\u6700\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u70b9\u7684\u52a0\u6743\u5e73\u5747\u503c\u6765\u5b9e\u73b0\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u8be6\u7ec6\u7684\u4ee3\u7801\u793a\u4f8b\u548c\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u7070\u5ea6\u5316\u5904\u7406<\/p>\n<\/p>\n<p><p>\u7070\u5ea6\u5316\u662f\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u7684\u8fc7\u7a0b\u3002\u7070\u5ea6\u56fe\u50cf\u53ea\u6709\u4e00\u4e2a\u901a\u9053\uff0c\u6bcf\u4e2a\u50cf\u7d20\u503c\u8868\u793a\u8be5\u50cf\u7d20\u7684\u4eae\u5ea6\u3002\u7070\u5ea6\u5316\u901a\u5e38\u4f7f\u7528\u52a0\u6743\u5e73\u5747\u7684\u65b9\u6cd5\uff0c\u5176\u4e2d\u7ea2\u8272\u3001\u7eff\u8272\u548c\u84dd\u8272\u901a\u9053\u7684\u6743\u91cd\u5206\u522b\u4e3a0.299\u30010.587\u548c0.114\u3002\u8fd9\u4e9b\u6743\u91cd\u662f\u6839\u636e\u4eba\u773c\u5bf9\u4e0d\u540c\u989c\u8272\u7684\u611f\u77e5\u654f\u611f\u5ea6\u786e\u5b9a\u7684\u3002<\/p>\n<\/p>\n<p><h3>1.1\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u7070\u5ea6\u5316<\/h3>\n<\/p>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3002\u4f7f\u7528OpenCV\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;image_with_alpha.png&#39;, cv2.IMREAD_UNCHANGED)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u4e3a4\u901a\u9053<\/strong><\/h2>\n<p>if image.shape[2] == 4:<\/p>\n<p>    # \u4ec5\u53d6\u524d\u4e09\u4e2a\u901a\u9053\uff08RGB\uff09<\/p>\n<p>    rgb_image = image[:, :, :3]<\/p>\n<p>    # \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    gray_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2GRAY)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    cv2.imwrite(&#39;gray_image.png&#39;, gray_image)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>1.2\u3001\u4f7f\u7528Pillow\u8fdb\u884c\u7070\u5ea6\u5316<\/h3>\n<\/p>\n<p><p>Pillow\u662fPython Imaging Library (PIL) \u7684\u4e00\u4e2a\u5206\u652f\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u53cb\u597d\u7684\u56fe\u50cf\u5904\u7406\u63a5\u53e3\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;image_with_alpha.png&#39;)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u6a21\u5f0f\u662f\u5426\u4e3aRGBA<\/strong><\/h2>\n<p>if image.mode == &#39;RGBA&#39;:<\/p>\n<p>    # \u8f6c\u6362\u4e3aRGB\u6a21\u5f0f<\/p>\n<p>    rgb_image = image.convert(&#39;RGB&#39;)<\/p>\n<p>    # \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    gray_image = rgb_image.convert(&#39;L&#39;)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    gray_image.save(&#39;gray_image.png&#39;)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e8c\u3001\u53d6\u5e73\u5747\u503c<\/p>\n<\/p>\n<p><p>\u5c06\u6bcf\u4e2a\u50cf\u7d20\u7684\u56db\u4e2a\u901a\u9053\u7684\u503c\u53d6\u5e73\u5747\u503c\uff0c\u4e5f\u53ef\u4ee5\u5b9e\u73b0\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\u56fe\u50cf\u3002\u8fd9\u4e2a\u65b9\u6cd5\u76f8\u5bf9\u7b80\u5355\uff0c\u4f46\u6548\u679c\u53ef\u80fd\u4e0d\u5982\u7070\u5ea6\u5316\u65b9\u6cd5\u597d\u3002<\/p>\n<\/p>\n<p><h3>2.1\u3001\u4f7f\u7528NumPy\u53d6\u5e73\u5747\u503c<\/h3>\n<\/p>\n<p><p>NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u6570\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>from PIL import Image<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;image_with_alpha.png&#39;)<\/p>\n<h2><strong>\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n<p>image_array = np.array(image)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u4e3a4\u901a\u9053<\/strong><\/h2>\n<p>if image_array.shape[2] == 4:<\/p>\n<p>    # \u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u5e73\u5747\u503c<\/p>\n<p>    gray_array = np.mean(image_array, axis=2).astype(np.uint8)<\/p>\n<p>    # \u8f6c\u6362\u4e3a\u56fe\u50cf<\/p>\n<p>    gray_image = Image.fromarray(gray_array)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    gray_image.save(&#39;gray_image_average.png&#39;)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2.2\u3001\u4f7f\u7528OpenCV\u53d6\u5e73\u5747\u503c<\/h3>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;image_with_alpha.png&#39;, cv2.IMREAD_UNCHANGED)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u4e3a4\u901a\u9053<\/strong><\/h2>\n<p>if image.shape[2] == 4:<\/p>\n<p>    # \u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u5e73\u5747\u503c<\/p>\n<p>    gray_image = np.mean(image, axis=2).astype(np.uint8)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    cv2.imwrite(&#39;gray_image_average.png&#39;, gray_image)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e09\u3001\u53d6\u6700\u5927\u503c<\/p>\n<\/p>\n<p><p>\u5728\u67d0\u4e9b\u5e94\u7528\u573a\u666f\u4e2d\uff0c\u53d6\u6bcf\u4e2a\u50cf\u7d20\u7684\u56db\u4e2a\u901a\u9053\u503c\u4e2d\u7684\u6700\u5927\u503c\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\u56fe\u50cf\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u7a81\u51fa\u56fe\u50cf\u4e2d\u7684\u4eae\u70b9\u3002<\/p>\n<\/p>\n<p><h3>3.1\u3001\u4f7f\u7528NumPy\u53d6\u6700\u5927\u503c<\/h3>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>from PIL import Image<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;image_with_alpha.png&#39;)<\/p>\n<h2><strong>\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n<p>image_array = np.array(image)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u4e3a4\u901a\u9053<\/strong><\/h2>\n<p>if image_array.shape[2] == 4:<\/p>\n<p>    # \u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u6700\u5927\u503c<\/p>\n<p>    gray_array = np.max(image_array, axis=2).astype(np.uint8)<\/p>\n<p>    # \u8f6c\u6362\u4e3a\u56fe\u50cf<\/p>\n<p>    gray_image = Image.fromarray(gray_array)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    gray_image.save(&#39;gray_image_max.png&#39;)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3.2\u3001\u4f7f\u7528OpenCV\u53d6\u6700\u5927\u503c<\/h3>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;image_with_alpha.png&#39;, cv2.IMREAD_UNCHANGED)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u4e3a4\u901a\u9053<\/strong><\/h2>\n<p>if image.shape[2] == 4:<\/p>\n<p>    # \u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u6700\u5927\u503c<\/p>\n<p>    gray_image = np.max(image, axis=2).astype(np.uint8)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    cv2.imwrite(&#39;gray_image_max.png&#39;, gray_image)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u56db\u3001\u53d6\u6700\u5c0f\u503c<\/p>\n<\/p>\n<p><p>\u7c7b\u4f3c\u4e8e\u53d6\u6700\u5927\u503c\u7684\u65b9\u6cd5\uff0c\u53d6\u6bcf\u4e2a\u50cf\u7d20\u7684\u56db\u4e2a\u901a\u9053\u503c\u4e2d\u7684\u6700\u5c0f\u503c\u53ef\u4ee5\u7528\u4e8e\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\u56fe\u50cf\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u7a81\u51fa\u56fe\u50cf\u4e2d\u7684\u6697\u70b9\u3002<\/p>\n<\/p>\n<p><h3>4.1\u3001\u4f7f\u7528NumPy\u53d6\u6700\u5c0f\u503c<\/h3>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>from PIL import Image<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;image_with_alpha.png&#39;)<\/p>\n<h2><strong>\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n<p>image_array = np.array(image)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u4e3a4\u901a\u9053<\/strong><\/h2>\n<p>if image_array.shape[2] == 4:<\/p>\n<p>    # \u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u6700\u5c0f\u503c<\/p>\n<p>    gray_array = np.min(image_array, axis=2).astype(np.uint8)<\/p>\n<p>    # \u8f6c\u6362\u4e3a\u56fe\u50cf<\/p>\n<p>    gray_image = Image.fromarray(gray_array)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    gray_image.save(&#39;gray_image_min.png&#39;)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>4.2\u3001\u4f7f\u7528OpenCV\u53d6\u6700\u5c0f\u503c<\/h3>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u8bfb\u53d64\u901a\u9053\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;image_with_alpha.png&#39;, cv2.IMREAD_UNCHANGED)<\/p>\n<h2><strong>\u68c0\u67e5\u56fe\u50cf\u662f\u5426\u4e3a4\u901a\u9053<\/strong><\/h2>\n<p>if image.shape[2] == 4:<\/p>\n<p>    # \u8ba1\u7b97\u6bcf\u4e2a\u50cf\u7d20\u7684\u6700\u5c0f\u503c<\/p>\n<p>    gray_image = np.min(image, axis=2).astype(np.uint8)<\/p>\n<p>    # \u4fdd\u5b58\u7070\u5ea6\u56fe\u50cf<\/p>\n<p>    cv2.imwrite(&#39;gray_image_min.png&#39;, gray_image)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f4\u901a\u9053&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e94\u3001\u5e94\u7528\u573a\u666f\u548c\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n<p><h3>5.1\u3001\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n<ol>\n<li><strong>\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790<\/strong>\uff1a\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\u56fe\u50cf\u662f\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u4efb\u52a1\u7684\u57fa\u7840\u6b65\u9aa4\u3002\u7070\u5ea6\u56fe\u50cf\u66f4\u6613\u4e8e\u5904\u7406\u548c\u5206\u6790\uff0c\u4f8b\u5982\u8fb9\u7f18\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u5272\u7b49\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u673a\u89c6\u89c9<\/strong>\uff1a\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u4e2d\uff0c\u7070\u5ea6\u56fe\u50cf\u901a\u5e38\u7528\u4e8e\u7279\u5f81\u63d0\u53d6\u548c\u6a21\u5f0f\u8bc6\u522b\u3002\u4f8b\u5982\uff0c\u5728\u4eba\u8138\u8bc6\u522b\u4e2d\uff0c\u7070\u5ea6\u56fe\u50cf\u53ef\u4ee5\u964d\u4f4e\u8ba1\u7b97\u590d\u6742\u5ea6\u3002<\/li>\n<li><strong>\u56fe\u50cf\u538b\u7f29\u548c\u4f20\u8f93<\/strong>\uff1a\u7070\u5ea6\u56fe\u50cf\u7684\u6570\u636e\u91cf\u8f83\u5c0f\uff0c\u9002\u7528\u4e8e\u56fe\u50cf\u538b\u7f29\u548c\u4f20\u8f93\uff0c\u7279\u522b\u662f\u5728\u5e26\u5bbd\u6709\u9650\u7684\u60c5\u51b5\u4e0b\u3002<\/li>\n<\/ol>\n<p><h3>5.2\u3001\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n<ol>\n<li><strong>\u56fe\u50cf\u683c\u5f0f<\/strong>\uff1a\u5728\u8bfb\u53d6\u548c\u4fdd\u5b58\u56fe\u50cf\u65f6\uff0c\u8981\u6ce8\u610f\u56fe\u50cf\u683c\u5f0f\u7684\u517c\u5bb9\u6027\u3002\u67d0\u4e9b\u56fe\u50cf\u683c\u5f0f\u53ef\u80fd\u4e0d\u652f\u63014\u901a\u9053\u6216\u7070\u5ea6\u56fe\u50cf\u3002<\/li>\n<li><strong>\u6570\u636e\u7c7b\u578b<\/strong>\uff1a\u5728\u5904\u7406\u56fe\u50cf\u6570\u636e\u65f6\uff0c\u8981\u6ce8\u610f\u6570\u636e\u7c7b\u578b\u7684\u8f6c\u6362\u3002\u4f8b\u5982\uff0c\u5728\u4f7f\u7528NumPy\u8fdb\u884c\u8ba1\u7b97\u65f6\uff0c\u901a\u5e38\u9700\u8981\u5c06\u7ed3\u679c\u8f6c\u6362\u4e3auint8\u7c7b\u578b\u3002<\/li>\n<li><strong>\u989c\u8272\u7a7a\u95f4<\/strong>\uff1a\u4e0d\u540c\u7684\u989c\u8272\u7a7a\u95f4\uff08\u5982RGB\u3001BGR\uff09\u5728\u5904\u7406\u56fe\u50cf\u65f6\u53ef\u80fd\u4f1a\u6709\u4e0d\u540c\u7684\u6548\u679c\u3002\u8981\u786e\u4fdd\u5904\u7406\u8fc7\u7a0b\u4e2d\u7684\u989c\u8272\u7a7a\u95f4\u4e00\u81f4\u6027\u3002<\/li>\n<\/ol>\n<p><p>\u603b\u7ed3\u6765\u8bf4\uff0c\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\u56fe\u50cf\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u7070\u5ea6\u5316\u3001\u53d6\u5e73\u5747\u503c\u3001\u53d6\u6700\u5927\u503c\u3001\u53d6\u6700\u5c0f\u503c\u3002\u4e0d\u540c\u7684\u65b9\u6cd5\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u63d0\u9ad8\u56fe\u50cf\u5904\u7406\u7684\u6548\u679c\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\uff1f<\/strong><br \/>\u5c064\u901a\u9053\u56fe\u50cf\uff08\u5982RGBA\u683c\u5f0f\uff09\u8f6c\u6362\u4e3a1\u901a\u9053\u901a\u5e38\u6d89\u53ca\u5230\u9009\u62e9\u5408\u9002\u7684\u901a\u9053\u6216\u8fdb\u884c\u67d0\u79cd\u5f62\u5f0f\u7684\u5408\u6210\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u63d0\u53d6Alpha\u901a\u9053\u3001\u4f7f\u7528\u7070\u5ea6\u5316\u516c\u5f0f\u6216\u5c06\u591a\u4e2a\u901a\u9053\u7ed3\u5408\u6210\u4e00\u4e2a\u65b0\u7684\u901a\u9053\u3002\u4f7f\u7528Python\u4e2d\u7684PIL\u6216OpenCV\u5e93\u90fd\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u8fd9\u4e00\u8fc7\u7a0b\u3002<\/p>\n<p><strong>\u4f7f\u7528Python\u5e93\u8f6c\u6362\u901a\u9053\u65f6\uff0c\u6211\u8be5\u9009\u62e9\u54ea\u79cd\u5e93\uff1f<\/strong><br \/>Python\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u5904\u7406\u56fe\u50cf\u901a\u9053\u7684\u8f6c\u6362\uff0c\u5176\u4e2dPIL\uff08Pillow\uff09\u548cOpenCV\u662f\u6700\u6d41\u884c\u7684\u9009\u62e9\u3002PIL\u9002\u5408\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\uff0c\u5177\u6709\u53cb\u597d\u7684\u63a5\u53e3\uff0c\u800cOpenCV\u5219\u5728\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u65b9\u9762\u66f4\u4e3a\u5f3a\u5927\uff0c\u9002\u5408\u590d\u6742\u7684\u64cd\u4f5c\u3002\u6839\u636e\u9879\u76ee\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n<p><strong>\u5728\u8fdb\u884c\u901a\u9053\u8f6c\u6362\u65f6\uff0c\u6709\u4ec0\u4e48\u6ce8\u610f\u4e8b\u9879\uff1f<\/strong><br \/>\u5728\u8fdb\u884c\u901a\u9053\u8f6c\u6362\u65f6\uff0c\u9700\u8981\u8003\u8651\u56fe\u50cf\u7684\u8d28\u91cf\u548c\u4fe1\u606f\u635f\u5931\u3002\u4f8b\u5982\uff0c\u7b80\u5355\u7684\u901a\u9053\u63d0\u53d6\u53ef\u80fd\u4f1a\u5bfc\u81f4\u56fe\u50cf\u7ec6\u8282\u7684\u4e22\u5931\u3002\u6b64\u5916\uff0c\u786e\u4fdd\u8f6c\u6362\u540e\u7684\u56fe\u50cf\u683c\u5f0f\u548c\u6570\u636e\u7c7b\u578b\u7b26\u5408\u540e\u7eed\u5904\u7406\u7684\u8981\u6c42\uff0c\u907f\u514d\u5728\u540e\u7eed\u5904\u7406\u4e2d\u51fa\u73b0\u95ee\u9898\u3002\u5efa\u8bae\u5728\u8f6c\u6362\u524d\u5bf9\u539f\u59cb\u56fe\u50cf\u8fdb\u884c\u5907\u4efd\uff0c\u4ee5\u9632\u610f\u5916\u6570\u636e\u4e22\u5931\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5c064\u901a\u9053\u56fe\u50cf\u8f6c\u6362\u4e3a1\u901a\u9053\u56fe\u50cf\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u53d6\u7070\u5ea6\u503c\u3001\u53d6\u5e73\u5747\u503c\u3001\u53d6\u6700\u5927\u503c\u3001\u53d6\u6700\u5c0f\u503c\u3002 [&hellip;]","protected":false},"author":3,"featured_media":1146004,"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\/1145992"}],"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=1145992"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1145992\/revisions"}],"predecessor-version":[{"id":1146006,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1145992\/revisions\/1146006"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1146004"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1145992"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1145992"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1145992"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}