\u673a\u5668\u5b66\u4e60<\/a>\u65b9\u6cd5\uff0c\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u96c6\u3002\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u662f\u6df1\u5ea6\u5b66\u4e60\u4e2d\u5904\u7406\u56fe\u50cf\u7684\u4e3b\u8981\u67b6\u6784\u3002\u5176\u7279\u70b9\u662f\u901a\u8fc7\u5377\u79ef\u5c42\u63d0\u53d6\u56fe\u50cf\u7684\u7a7a\u95f4\u7279\u5f81\uff0c\u7136\u540e\u901a\u8fc7\u5168\u8fde\u63a5\u5c42\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n<\/p>\n1. \u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09<\/h4>\n<\/p>\n
\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u662f\u7531\u591a\u4e2a\u5377\u79ef\u5c42\u3001\u6c60\u5316\u5c42\u548c\u5168\u8fde\u63a5\u5c42\u7ec4\u6210\u7684\u3002\u5377\u79ef\u5c42\u8d1f\u8d23\u63d0\u53d6\u56fe\u50cf\u7684\u7279\u5f81\uff0c\u6c60\u5316\u5c42\u7528\u4e8e\u964d\u4f4e\u7279\u5f81\u56fe\u7684\u7ef4\u5ea6\uff0c\u800c\u5168\u8fde\u63a5\u5c42\u5219\u7528\u4e8e\u6700\u7ec8\u7684\u5206\u7c7b\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5728\u4e0d\u540c\u7684\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3CNN\u6a21\u578b\uff0c\u4f7f\u5176\u80fd\u591f\u8bc6\u522b\u7279\u5b9a\u7684\u56fe\u50cf\u7279\u5f81\u3002<\/p>\n<\/p>\n
2. \u6a21\u578b\u8bad\u7ec3<\/h4>\n<\/p>\n
\u4e3a\u4e86\u68c0\u6d4b\u53e3\u7f69\uff0c\u6211\u4eec\u9700\u8981\u4e00\u4e2a\u5305\u542b\u4e24\u7c7b\u6807\u7b7e\u7684\u6570\u636e\u96c6\uff1a\u4f69\u6234\u53e3\u7f69\u548c\u672a\u4f69\u6234\u53e3\u7f69\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Kaggle\u7b49\u5e73\u53f0\u4e0a\u63d0\u4f9b\u7684\u516c\u5f00\u6570\u636e\u96c6\uff0c\u6216\u8005\u81ea\u5df1\u6536\u96c6\u6570\u636e\u3002\u7136\u540e\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u6bd4\u5982\u8c03\u6574\u56fe\u50cf\u5927\u5c0f\u3001\u5f52\u4e00\u5316\u7b49\u64cd\u4f5c\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528TensorFlow\u6216PyTorch\u7b49\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u8bad\u7ec3CNN\u6a21\u578b\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u5904\u7406<\/h3>\n<\/p>\n
OpenCV\u662f\u4e00\u4e2a\u6d41\u884c\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8fdb\u884c\u5b9e\u65f6\u53e3\u7f69\u68c0\u6d4b\u3002<\/p>\n<\/p>\n
1. \u56fe\u50cf\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u53e3\u7f69\u68c0\u6d4b\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u8f93\u5165\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\u3002\u5e38\u89c1\u7684\u5904\u7406\u64cd\u4f5c\u5305\u62ec\u7070\u5ea6\u5316\u3001\u8c03\u6574\u5927\u5c0f\u548c\u76f4\u65b9\u56fe\u5747\u8861\u5316\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u64cd\u4f5c\uff0c\u6211\u4eec\u53ef\u4ee5\u589e\u5f3a\u56fe\u50cf\u7684\u5bf9\u6bd4\u5ea6\u548c\u6e05\u6670\u5ea6\uff0c\u4ece\u800c\u63d0\u9ad8\u68c0\u6d4b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
2. \u4eba\u8138\u68c0\u6d4b<\/h4>\n<\/p>\n
\u5728\u68c0\u6d4b\u53e3\u7f69\u4e4b\u524d\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u68c0\u6d4b\u4eba\u8138\u3002OpenCV\u63d0\u4f9b\u4e86\u591a\u79cd\u4eba\u8138\u68c0\u6d4b\u65b9\u6cd5\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fHaar\u7ea7\u8054\u5206\u7c7b\u5668\u548cDlib\u5e93\u3002Haar\u7ea7\u8054\u5206\u7c7b\u5668\u662f\u4e00\u79cd\u57fa\u4e8e\u7279\u5f81\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u800cDlib\u5e93\u5219\u4f7f\u7528\u4e86\u66f4\u4e3a\u5148\u8fdb\u7684HOG\uff08Histogram of Oriented Gradients\uff09\u7279\u5f81\u548c\u7ebf\u6027SVM\u5206\u7c7b\u5668\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7ed3\u5408TensorFlow\u6216PyTorch\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u548c\u63a8\u7406<\/h3>\n<\/p>\n
\u5728\u5b8c\u6210\u56fe\u50cf\u9884\u5904\u7406\u548c\u4eba\u8138\u68c0\u6d4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u8fdb\u884c\u53e3\u7f69\u68c0\u6d4b\u3002<\/p>\n<\/p>\n
1. TensorFlow\u5b9e\u73b0<\/h4>\n<\/p>\n
TensorFlow\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u90e8\u7f72\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528TensorFlow\u8bad\u7ec3\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u6765\u68c0\u6d4b\u53e3\u7f69\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5c06\u6a21\u578b\u4fdd\u5b58\u4e3aTensorFlow\u6a21\u578b\u6587\u4ef6\uff0c\u5e76\u5728\u5e94\u7528\u7a0b\u5e8f\u4e2d\u52a0\u8f7d\u8fdb\u884c\u5b9e\u65f6\u63a8\u7406\u3002<\/p>\n<\/p>\n
2. PyTorch\u5b9e\u73b0<\/h4>\n<\/p>\n
PyTorch\u662f\u53e6\u4e00\u4e2a\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u4e0eTensorFlow\u76f8\u6bd4\uff0cPyTorch\u66f4\u5177\u7075\u6d3b\u6027\u548c\u6613\u7528\u6027\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528PyTorch\u5b9a\u4e49\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u7136\u540e\u4f7f\u7528\u9884\u5904\u7406\u540e\u7684\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5c06\u6a21\u578b\u4fdd\u5b58\u4e3aPyTorch\u6a21\u578b\u6587\u4ef6\uff0c\u5e76\u5728\u5e94\u7528\u7a0b\u5e8f\u4e2d\u52a0\u8f7d\u8fdb\u884c\u5b9e\u65f6\u63a8\u7406\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5b9e\u73b0\u5b9e\u65f6\u53e3\u7f69\u68c0\u6d4b<\/h3>\n<\/p>\n
\u5728\u5b8c\u6210\u6a21\u578b\u8bad\u7ec3\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u5176\u5e94\u7528\u4e8e\u5b9e\u65f6\u53e3\u7f69\u68c0\u6d4b\u3002\u901a\u8fc7\u6444\u50cf\u5934\u6355\u83b7\u5b9e\u65f6\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528OpenCV\u8fdb\u884c\u9884\u5904\u7406\u548c\u4eba\u8138\u68c0\u6d4b\uff0c\u6700\u540e\u5c06\u68c0\u6d4b\u5230\u7684\u4eba\u8138\u56fe\u50cf\u8f93\u5165\u5230\u8bad\u7ec3\u597d\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4e2d\u8fdb\u884c\u53e3\u7f69\u68c0\u6d4b\u3002<\/p>\n<\/p>\n
1. \u6444\u50cf\u5934\u6355\u83b7<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528OpenCV\u6355\u83b7\u6444\u50cf\u5934\u7684\u5b9e\u65f6\u56fe\u50cf\u3002OpenCV\u63d0\u4f9b\u4e86VideoCapture\u7c7b\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u6444\u50cf\u5934\u8bfb\u53d6\u5e27\u6570\u636e\u3002<\/p>\n<\/p>\n
2. \u5b9e\u65f6\u63a8\u7406<\/h4>\n<\/p>\n
\u5728\u6355\u83b7\u5230\u5b9e\u65f6\u56fe\u50cf\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u63a8\u7406\u3002\u9996\u5148\u5bf9\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\uff0c\u7136\u540e\u4f7f\u7528OpenCV\u68c0\u6d4b\u4eba\u8138\uff0c\u6700\u540e\u5c06\u4eba\u8138\u56fe\u50cf\u8f93\u5165\u5230\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4e2d\u8fdb\u884c\u5206\u7c7b\u3002\u6839\u636e\u6a21\u578b\u7684\u8f93\u51fa\uff0c\u6211\u4eec\u53ef\u4ee5\u5224\u65ad\u4eba\u8138\u662f\u5426\u4f69\u6234\u4e86\u53e3\u7f69\uff0c\u5e76\u5728\u56fe\u50cf\u4e0a\u6807\u8bb0\u7ed3\u679c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f18\u5316\u548c\u63d0\u5347\u68c0\u6d4b\u6548\u679c<\/h3>\n<\/p>\n
\u5728\u5b9e\u73b0\u4e86\u57fa\u672c\u7684\u53e3\u7f69\u68c0\u6d4b\u529f\u80fd\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u6765\u4f18\u5316\u548c\u63d0\u5347\u68c0\u6d4b\u6548\u679c\u3002<\/p>\n<\/p>\n
1. \u6570\u636e\u589e\u5f3a<\/h4>\n<\/p>\n
\u6570\u636e\u589e\u5f3a\u662f\u4e00\u79cd\u6709\u6548\u7684\u63d0\u9ad8\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u5bf9\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u7ffb\u8f6c\u7b49\u64cd\u4f5c\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u66f4\u591a\u7684\u6837\u672c\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u9c81\u68d2\u6027\u3002<\/p>\n<\/p>\n
2. \u8d85\u53c2\u6570\u8c03\u6574<\/h4>\n<\/p>\n
\u5728\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u8d85\u53c2\u6570\u7684\u9009\u62e9\u5bf9\u6a21\u578b\u7684\u6027\u80fd\u6709\u5f88\u5927\u5f71\u54cd\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u65b9\u6cd5\u6765\u9009\u62e9\u5408\u9002\u7684\u5b66\u4e60\u7387\u3001\u6279\u91cf\u5927\u5c0f\u7b49\u8d85\u53c2\u6570\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
3. \u6a21\u578b\u4f18\u5316<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u66f4\u4e3a\u5148\u8fdb\u7684\u6a21\u578b\u67b6\u6784\u6765\u63d0\u9ad8\u68c0\u6d4b\u6548\u679c\u3002\u4f8b\u5982\uff0c\u4f7f\u7528MobileNet\u3001ResNet\u7b49\u8f7b\u91cf\u7ea7\u6a21\u578b\u53ef\u4ee5\u5728\u4fdd\u8bc1\u51c6\u786e\u7387\u7684\u540c\u65f6\uff0c\u63d0\u9ad8\u68c0\u6d4b\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u5b9e\u73b0\u4e00\u4e2a\u5b8c\u6574\u7684\u53e3\u7f69\u68c0\u6d4b\u7cfb\u7edf\u3002\u65e0\u8bba\u662f\u5728\u65e5\u5e38\u751f\u6d3b\u4e2d\u7684\u5b89\u5168\u9632\u62a4\uff0c\u8fd8\u662f\u5728\u75ab\u60c5\u9632\u63a7\u7684\u63aa\u65bd\u4e2d\uff0c\u53e3\u7f69\u68c0\u6d4b\u6280\u672f\u90fd\u80fd\u53d1\u6325\u91cd\u8981\u7684\u4f5c\u7528\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u5230\u6709\u9700\u6c42\u7684\u5f00\u53d1\u8005\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u53e3\u7f69\u68c0\u6d4b\uff1f<\/strong>
\u5728Python\u4e2d\u5b9e\u73b0\u53e3\u7f69\u68c0\u6d4b\u901a\u5e38\u9700\u8981\u501f\u52a9\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5982TensorFlow\u6216PyTorch\u3002\u53ef\u4ee5\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u6a21\u578b\uff0c\u9488\u5bf9\u53e3\u7f69\u4e0e\u975e\u53e3\u7f69\u56fe\u50cf\u8fdb\u884c\u8bad\u7ec3\u3002\u6570\u636e\u96c6\u53ef\u4ee5\u901a\u8fc7\u7f51\u7edc\u6536\u96c6\uff0c\u6db5\u76d6\u5404\u79cd\u573a\u666f\u548c\u4eba\u8138\u89d2\u5ea6\u3002\u5b9e\u73b0\u6b65\u9aa4\u5305\u62ec\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u6784\u5efa\u4e0e\u8bad\u7ec3\u3001\u4ee5\u53ca\u6700\u7ec8\u7684\u68c0\u6d4b\u548c\u8bc4\u4f30\u3002<\/p>\n\u6211\u9700\u8981\u54ea\u4e9b\u5e93\u548c\u5de5\u5177\u6765\u5b9e\u73b0\u53e3\u7f69\u68c0\u6d4b\uff1f<\/strong>
\u8fdb\u884c\u53e3\u7f69\u68c0\u6d4b\u65f6\uff0c\u63a8\u8350\u4f7f\u7528\u4ee5\u4e0b\u5e93\u548c\u5de5\u5177\uff1aOpenCV\u7528\u4e8e\u56fe\u50cf\u5904\u7406\uff0cTensorFlow\u6216PyTorch\u7528\u4e8e\u6784\u5efa\u548c\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0cNumPy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0cMatplotlib\u7528\u4e8e\u53ef\u89c6\u5316\u7ed3\u679c\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528Jupyter Notebook\u8fdb\u884c\u5b9e\u9a8c\u548c\u8c03\u8bd5\uff0c\u65b9\u4fbf\u5feb\u901f\u8fed\u4ee3\u3002<\/p>\n\u53e3\u7f69\u68c0\u6d4b\u7684\u51c6\u786e\u7387\u5982\u4f55\u63d0\u9ad8\uff1f<\/strong>
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