{"id":958825,"date":"2024-12-27T03:34:54","date_gmt":"2024-12-26T19:34:54","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/958825.html"},"modified":"2024-12-27T03:34:56","modified_gmt":"2024-12-26T19:34:56","slug":"%e5%a6%82%e4%bd%95%e5%ba%94%e7%94%a8python%e6%b1%82%e9%98%88%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/958825.html","title":{"rendered":"\u5982\u4f55\u5e94\u7528python\u6c42\u9608\u503c"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25101806\/98228366-b558-48c1-83a4-c10f642e9d64.webp\" alt=\"\u5982\u4f55\u5e94\u7528python\u6c42\u9608\u503c\" \/><\/p>\n<p><p> <strong>\u5e94\u7528Python\u6c42\u9608\u503c\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u76f4\u65b9\u56fe\u5206\u6790\u3001\u4f18\u5316\u7b97\u6cd5\u548c<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u7b49\u3002\u76f4\u65b9\u56fe\u5206\u6790\u901a\u8fc7\u89c2\u5bdf\u6570\u636e\u5206\u5e03\u6765\u786e\u5b9a\u5408\u9002\u7684\u9608\u503c\uff0c\u4f18\u5316\u7b97\u6cd5\u5982Otsu\u6cd5\u5219\u53ef\u4ee5\u81ea\u52a8\u8ba1\u7b97\u6700\u4f73\u9608\u503c\uff0c\u673a\u5668\u5b66\u4e60\u6a21\u578b\u5219\u901a\u8fc7\u8bad\u7ec3\u6570\u636e\u6765\u9884\u6d4b\u9608\u503c\u3002\u5177\u4f53\u65b9\u6cd5\u7684\u9009\u62e9\u5e94\u4f9d\u636e\u6570\u636e\u7279\u6027\u548c\u5e94\u7528\u573a\u666f\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u5177\u4f53\u5e94\u7528\u4e2d\uff0c\u76f4\u65b9\u56fe\u5206\u6790\u662f\u57fa\u7840\u4e14\u76f4\u89c2\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u7ed8\u5236\u6570\u636e\u7684\u76f4\u65b9\u56fe\uff0c\u53ef\u4ee5\u89c2\u5bdf\u6570\u636e\u5206\u5e03\u7684\u5cf0\u548c\u8c37\uff0c\u624b\u52a8\u9009\u62e9\u9608\u503c\u3002\u4f8b\u5982\uff0c\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u53ef\u4ee5\u901a\u8fc7\u76f4\u65b9\u56fe\u627e\u51fa\u524d\u666f\u4e0e\u80cc\u666f\u4e4b\u95f4\u7684\u5206\u754c\u7ebf\u3002Otsu\u6cd5\u5219\u662f\u4e00\u79cd\u81ea\u52a8\u5316\u7684\u56fe\u50cf\u5206\u5272\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u6700\u5927\u5316\u7c7b\u95f4\u65b9\u5dee\u6765\u786e\u5b9a\u5168\u5c40\u9608\u503c\uff0c\u65e0\u9700\u4eba\u5de5\u5e72\u9884\u3002\u673a\u5668\u5b66\u4e60\u6a21\u578b\u5219\u66f4\u9002\u5408\u4e8e\u590d\u6742\u6570\u636e\u548c\u52a8\u6001\u9608\u503c\u7684\u9700\u6c42\uff0c\u901a\u8fc7\u8bad\u7ec3\u6570\u636e\uff0c\u6a21\u578b\u53ef\u4ee5\u5b66\u4e60\u4e0d\u540c\u60c5\u51b5\u4e0b\u7684\u9608\u503c\u9009\u62e9\uff0c\u4ece\u800c\u63d0\u9ad8\u7cbe\u5ea6\u548c\u81ea\u52a8\u5316\u7a0b\u5ea6\u3002<\/p>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5728Python\u4e2d\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u6c42\u53d6\u9608\u503c\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u76f4\u65b9\u56fe\u5206\u6790<\/h3>\n<\/p>\n<p><p>\u76f4\u65b9\u56fe\u5206\u6790\u662f\u6570\u636e\u5206\u6790\u4e2d\u4e00\u79cd\u7b80\u5355\u800c\u76f4\u89c2\u7684\u65b9\u6cd5\uff0c\u7528\u4e8e\u89c2\u5bdf\u6570\u636e\u5206\u5e03\u60c5\u51b5\uff0c\u4ece\u800c\u9009\u62e9\u5408\u9002\u7684\u9608\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u5c24\u5176\u9002\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u7b80\u5355\u7684\u6570\u636e\u5206\u5272\u4efb\u52a1\u3002<\/p>\n<\/p>\n<p><h4>1. \u76f4\u65b9\u56fe\u7684\u6784\u5efa<\/h4>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528<code>matplotlib<\/code>\u5e93\u6765\u7ed8\u5236\u76f4\u65b9\u56fe\u3002\u5bf9\u4e8e\u7070\u5ea6\u56fe\u50cf\uff0c\u76f4\u65b9\u56fe\u8868\u793a\u6bcf\u79cd\u7070\u5ea6\u7ea7\u522b\u7684\u50cf\u7d20\u6570\u91cf\u3002\u901a\u8fc7\u5206\u6790\u76f4\u65b9\u56fe\u7684\u5cf0\u548c\u8c37\uff0c\u53ef\u4ee5\u8bc6\u522b\u51fa\u524d\u666f\u548c\u80cc\u666f\u7684\u5206\u754c\u7ebf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe<\/strong><\/h2>\n<p>image = cv2.imread(&#39;image.jpg&#39;, 0)<\/p>\n<h2><strong>\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n<p>plt.hist(image.ravel(), 256, [0, 256])<\/p>\n<p>plt.title(&#39;Histogram for gray scale image&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>2. \u9608\u503c\u9009\u62e9<\/h4>\n<\/p>\n<p><p>\u901a\u8fc7\u89c2\u5bdf\u76f4\u65b9\u56fe\uff0c\u624b\u52a8\u9009\u62e9\u4e00\u4e2a\u9608\u503c\u6765\u5206\u5272\u6570\u636e\u3002\u5728\u76f4\u65b9\u56fe\u4e2d\uff0c\u5cf0\u503c\u901a\u5e38\u5bf9\u5e94\u4e8e\u80cc\u666f\u6216\u524d\u666f\uff0c\u800c\u8c37\u503c\u5219\u662f\u7406\u60f3\u7684\u9608\u503c\u9009\u62e9\u70b9\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001Otsu\u6cd5\u5219<\/h3>\n<\/p>\n<p><p>Otsu\u6cd5\u5219\u662f\u4e00\u79cd\u7528\u4e8e\u81ea\u52a8\u6c42\u53d6\u56fe\u50cf\u9608\u503c\u7684\u7b97\u6cd5\uff0c\u5b83\u901a\u8fc7\u6700\u5927\u5316\u7c7b\u95f4\u65b9\u5dee\u6765\u786e\u5b9a\u6700\u4f73\u9608\u503c\u3002<\/p>\n<\/p>\n<p><h4>1. Otsu\u6cd5\u5219\u7684\u57fa\u672c\u539f\u7406<\/h4>\n<\/p>\n<p><p>Otsu\u6cd5\u5219\u7684\u57fa\u672c\u601d\u60f3\u662f\u5c06\u56fe\u50cf\u50cf\u7d20\u5206\u4e3a\u4e24\u7c7b\uff0c\u4f7f\u5f97\u7c7b\u5185\u65b9\u5dee\u6700\u5c0f\u5316\uff0c\u7c7b\u95f4\u65b9\u5dee\u6700\u5927\u5316\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u53ef\u4ee5\u81ea\u52a8\u786e\u5b9a\u4e00\u4e2a\u5168\u5c40\u9608\u503c\uff0c\u9002\u7528\u4e8e\u7070\u5ea6\u56fe\u50cf\u7684\u4e8c\u503c\u5316\u3002<\/p>\n<\/p>\n<p><h4>2. \u5728Python\u4e2d\u7684\u5b9e\u73b0<\/h4>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528<code>cv2.threshold<\/code>\u51fd\u6570\u5b9e\u73b0Otsu\u6cd5\u5219\u3002\u8be5\u51fd\u6570\u53ef\u4ee5\u81ea\u52a8\u8ba1\u7b97\u5e76\u8fd4\u56de\u6700\u4f73\u9608\u503c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;image.jpg&#39;, 0)<\/p>\n<h2><strong>\u5e94\u7528Otsu\u6cd5\u5219<\/strong><\/h2>\n<p>ret, thresh_img = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)<\/p>\n<h2><strong>\u663e\u793a\u7ed3\u679c<\/strong><\/h2>\n<p>cv2.imshow(&#39;Otsu Thresholding&#39;, thresh_img)<\/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<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/h3>\n<\/p>\n<p><p>\u5728\u4e00\u4e9b\u590d\u6742\u573a\u666f\u4e0b\uff0c\u7279\u522b\u662f\u52a8\u6001\u6570\u636e\u548c\u590d\u6742\u80cc\u666f\u4e0b\uff0c\u673a\u5668\u5b66\u4e60\u6a21\u578b\u53ef\u4ee5\u901a\u8fc7\u8bad\u7ec3\u6570\u636e\u6765\u9884\u6d4b\u9608\u503c\u3002<\/p>\n<\/p>\n<p><h4>1. \u6570\u636e\u51c6\u5907\u4e0e\u7279\u5f81\u9009\u62e9<\/h4>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u51c6\u5907\u4e00\u4e2a\u5e26\u6807\u7b7e\u7684\u6570\u636e\u96c6\uff0c\u5305\u542b\u8f93\u5165\u6570\u636e\u53ca\u5176\u5bf9\u5e94\u7684\u7406\u60f3\u9608\u503c\u3002\u7136\u540e\uff0c\u901a\u8fc7\u7279\u5f81\u5de5\u7a0b\u63d0\u53d6\u6709\u52a9\u4e8e\u9608\u503c\u9884\u6d4b\u7684\u7279\u5f81\u3002<\/p>\n<\/p>\n<p><h4>2. \u6a21\u578b\u8bad\u7ec3\u4e0e\u9884\u6d4b<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u5982\u56de\u5f52\u6a21\u578b\u6216\u795e\u7ecf\u7f51\u7edc\uff0c\u6765\u5b66\u4e60\u6570\u636e\u4e0e\u9608\u503c\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9608\u503c\u9884\u6d4b\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import train_test_split<\/p>\n<p>from sklearn.linear_model import LinearRegression<\/p>\n<h2><strong>\u5047\u8bbeX\u662f\u7279\u5f81\uff0cy\u662f\u9608\u503c<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n<h2><strong>\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<p>model = LinearRegression()<\/p>\n<p>model.fit(X_train, y_train)<\/p>\n<h2><strong>\u9884\u6d4b\u9608\u503c<\/strong><\/h2>\n<p>predicted_thresholds = model.predict(X_test)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u5e94\u7528\u573a\u666f\u4e0e\u5b9e\u4f8b<\/h3>\n<\/p>\n<p><h4>1. \u56fe\u50cf\u5904\u7406<\/h4>\n<\/p>\n<p><p>\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u9608\u503c\u5e94\u7528\u4e8e\u56fe\u50cf\u5206\u5272\u3001\u8fb9\u7f18\u68c0\u6d4b\u7b49\u3002\u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u9608\u503c\uff0c\u53ef\u4ee5\u6709\u6548\u5206\u5272\u524d\u666f\u4e0e\u80cc\u666f\uff0c\u63d0\u9ad8\u56fe\u50cf\u5904\u7406\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n<p><h4>2. \u4fe1\u53f7\u5904\u7406<\/h4>\n<\/p>\n<p><p>\u5728\u4fe1\u53f7\u5904\u7406\u9886\u57df\uff0c\u9608\u503c\u7528\u4e8e\u6ee4\u6ce2\u548c\u566a\u58f0\u6d88\u9664\u3002\u901a\u8fc7\u8bbe\u5b9a\u5408\u9002\u7684\u9608\u503c\uff0c\u53ef\u4ee5\u8fc7\u6ee4\u6389\u4f4e\u5f3a\u5ea6\u7684\u566a\u58f0\u4fe1\u53f7\uff0c\u589e\u5f3a\u4fe1\u53f7\u8d28\u91cf\u3002<\/p>\n<\/p>\n<p><h4>3. \u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u9608\u503c\u7528\u4e8e\u6570\u636e\u5206\u5272\u548c\u5206\u7c7b\u3002\u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u9608\u503c\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b\uff0c\u4ece\u800c\u8fdb\u884c\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n<\/p>\n<p><p>\u5728\u9009\u62e9\u548c\u5e94\u7528\u9608\u503c\u65b9\u6cd5\u65f6\uff0c\u5e94\u6839\u636e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u6570\u636e\u7279\u6027\u8fdb\u884c\u9009\u62e9\u3002\u5bf9\u4e8e\u7b80\u5355\u7684\u4efb\u52a1\uff0c\u76f4\u65b9\u56fe\u5206\u6790\u548cOtsu\u6cd5\u5219\u662f\u826f\u597d\u7684\u9009\u62e9\uff1b\u5bf9\u4e8e\u590d\u6742\u548c\u52a8\u6001\u7684\u4efb\u52a1\uff0c\u673a\u5668\u5b66\u4e60\u6a21\u578b\u80fd\u591f\u63d0\u4f9b\u66f4\u9ad8\u7684\u7075\u6d3b\u6027\u548c\u7cbe\u5ea6\u3002\u5728\u5b9e\u73b0\u8fc7\u7a0b\u4e2d\uff0c\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u5f80\u5f80\u80fd\u83b7\u5f97\u66f4\u597d\u7684\u6548\u679c\u3002\u901a\u8fc7\u4e0d\u65ad\u5730\u5b9e\u9a8c\u548c\u8c03\u6574\uff0c\u53ef\u4ee5\u627e\u5230\u9002\u5408\u7279\u5b9a\u4efb\u52a1\u7684\u6700\u4f73\u9608\u503c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> 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