{"id":81466,"date":"2024-10-16T16:02:23","date_gmt":"2024-10-16T08:02:23","guid":{"rendered":"https:\/\/server.hk\/cnblog\/81466\/"},"modified":"2024-10-16T16:02:23","modified_gmt":"2024-10-16T08:02:23","slug":"opencv%e5%af%a6%e7%8f%be%e6%89%8b%e5%af%ab%e6%95%b8%e5%ad%97%e8%ad%98%e5%88%a5%ef%bc%9a%e4%bd%bf%e7%94%a8mnist%e6%95%b8%e6%93%9a%e5%ba%ab-opencv-mnist%e6%95%b8%e6%93%9a%e5%ba%ab","status":"publish","type":"post","link":"https:\/\/server.hk\/cnblog\/81466\/","title":{"rendered":"OpenCV\u5be6\u73fe\u624b\u5beb\u6578\u5b57\u8b58\u5225\uff1a\u4f7f\u7528MNIST\u6578\u64da\u5eab (OpenCV MNIST\u6578\u64da\u5eab)"},"content":{"rendered":"<h1 id=\"opencv%e5%af%a6%e7%8f%be%e6%89%8b%e5%af%ab%e6%95%b8%e5%ad%97%e8%ad%98%e5%88%a5%ef%bc%9a%e4%bd%bf%e7%94%a8mnist%e6%95%b8%e6%93%9a%e5%ba%ab-iHfzOOHjoc\">OpenCV\u5be6\u73fe\u624b\u5beb\u6578\u5b57\u8b58\u5225\uff1a\u4f7f\u7528MNIST\u6578\u64da\u5eab<\/h1>\n<p>\u624b\u5beb\u6578\u5b57\u8b58\u5225\u662f\u8a08\u7b97\u6a5f\u8996\u89ba\u548c\u6a5f\u5668\u5b78\u7fd2\u9818\u57df\u4e2d\u7684\u4e00\u500b\u91cd\u8981\u61c9\u7528\u3002MNIST\u6578\u64da\u5eab\u662f\u9019\u4e00\u9818\u57df\u4e2d\u6700\u5e38\u7528\u7684\u6578\u64da\u96c6\u4e4b\u4e00\uff0c\u5305\u542b\u4e86\u5927\u91cf\u7684\u624b\u5beb\u6578\u5b57\u6a23\u672c\u3002\u672c\u6587\u5c07\u63a2\u8a0e\u5982\u4f55\u4f7f\u7528OpenCV\u548cMNIST\u6578\u64da\u5eab\u4f86\u5be6\u73fe\u624b\u5beb\u6578\u5b57\u8b58\u5225\uff0c\u4e26\u63d0\u4f9b\u76f8\u61c9\u7684\u4ee3\u78bc\u793a\u4f8b\u3002<\/p>\n<h2 id=\"%e4%bb%80%e9%ba%bc%e6%98%afmnist%e6%95%b8%e6%93%9a%e5%ba%ab%ef%bc%9f-iHfzOOHjoc\">\u4ec0\u9ebc\u662fMNIST\u6578\u64da\u5eab\uff1f<\/h2>\n<p>MNIST\uff08Modified National Institute of Standards and Technology\uff09\u6578\u64da\u5eab\u662f\u4e00\u500b\u5305\u542b70,000\u500b\u624b\u5beb\u6578\u5b57\u7684\u6578\u64da\u96c6\uff0c\u5176\u4e2d60,000\u500b\u7528\u65bc\u8a13\u7df4\uff0c10,000\u500b\u7528\u65bc\u6e2c\u8a66\u3002\u6bcf\u500b\u6578\u5b57\u90fd\u662f28&#215;28\u50cf\u7d20\u7684\u7070\u5ea6\u5716\u50cf\uff0c\u6a19\u7c64\u70ba0\u52309\u7684\u6578\u5b57\u3002\u9019\u500b\u6578\u64da\u96c6\u7684\u5ee3\u6cdb\u4f7f\u7528\u4f7f\u5176\u6210\u70ba\u6a5f\u5668\u5b78\u7fd2\u548c\u6df1\u5ea6\u5b78\u7fd2\u7814\u7a76\u7684\u57fa\u6e96\u3002<\/p>\n<h2 id=\"opencv%e7%b0%a1%e4%bb%8b-iHfzOOHjoc\">OpenCV\u7c21\u4ecb<\/h2>\n<p>OpenCV\uff08Open Source Computer Vision Library\uff09\u662f\u4e00\u500b\u958b\u6e90\u7684\u8a08\u7b97\u6a5f\u8996\u89ba\u5eab\uff0c\u63d0\u4f9b\u4e86\u591a\u7a2e\u5716\u50cf\u8655\u7406\u548c\u8a08\u7b97\u6a5f\u8996\u89ba\u7684\u529f\u80fd\u3002\u5b83\u652f\u6301\u591a\u7a2e\u7de8\u7a0b\u8a9e\u8a00\uff0c\u5305\u62ecC++\u3001Python\u548cJava\uff0c\u4e26\u4e14\u5728\u5404\u7a2e\u5e73\u53f0\u4e0a\u5747\u53ef\u904b\u884c\u3002OpenCV\u7684\u5f37\u5927\u529f\u80fd\u4f7f\u5176\u6210\u70ba\u5be6\u73fe\u624b\u5beb\u6578\u5b57\u8b58\u5225\u7684\u7406\u60f3\u9078\u64c7\u3002<\/p>\n<h2 id=\"%e6%89%8b%e5%af%ab%e6%95%b8%e5%ad%97%e8%ad%98%e5%88%a5%e7%9a%84%e6%ad%a5%e9%a9%9f-iHfzOOHjoc\">\u624b\u5beb\u6578\u5b57\u8b58\u5225\u7684\u6b65\u9a5f<\/h2>\n<p>\u5be6\u73fe\u624b\u5beb\u6578\u5b57\u8b58\u5225\u7684\u904e\u7a0b\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u5e7e\u500b\u6b65\u9a5f\uff1a<\/p>\n<ul>\n<li>\u6578\u64da\u9810\u8655\u7406<\/li>\n<li>\u7279\u5fb5\u63d0\u53d6<\/li>\n<li>\u6a21\u578b\u8a13\u7df4<\/li>\n<li>\u6a21\u578b\u8a55\u4f30<\/li>\n<li>\u5be6\u969b\u61c9\u7528<\/li>\n<\/ul>\n<h3 id=\"1-%e6%95%b8%e6%93%9a%e9%a0%90%e8%99%95%e7%90%86-iHfzOOHjoc\">1. \u6578\u64da\u9810\u8655\u7406<\/h3>\n<p>\u5728\u4f7f\u7528MNIST\u6578\u64da\u5eab\u4e4b\u524d\uff0c\u9700\u8981\u5c0d\u6578\u64da\u9032\u884c\u9810\u8655\u7406\u3002\u9019\u5305\u62ec\u5c07\u5716\u50cf\u8f49\u63db\u70ba\u9069\u5408\u6a21\u578b\u8a13\u7df4\u7684\u683c\u5f0f\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Python\u548cOpenCV\u9032\u884c\u6578\u64da\u9810\u8655\u7406\u7684\u793a\u4f8b\u4ee3\u78bc\uff1a<\/p>\n<pre><code>\nimport cv2\nimport numpy as np\nfrom keras.datasets import mnist\n\n# \u8f09\u5165MNIST\u6578\u64da\u96c6\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# \u5c07\u6578\u64da\u8f49\u63db\u70ba\u7070\u5ea6\u5716\u50cf\nx_train = x_train.reshape((x_train.shape[0], 28, 28, 1)).astype('float32') \/ 255\nx_test = x_test.reshape((x_test.shape[0], 28, 28, 1)).astype('float32') \/ 255\n<\/code><\/pre>\n<h3 id=\"2-%e7%89%b9%e5%be%b5%e6%8f%90%e5%8f%96-iHfzOOHjoc\">2. \u7279\u5fb5\u63d0\u53d6<\/h3>\n<p>\u7279\u5fb5\u63d0\u53d6\u662f\u5c07\u5716\u50cf\u8f49\u63db\u70ba\u6578\u5b57\u7279\u5fb5\u7684\u904e\u7a0b\u3002\u5c0d\u65bc\u624b\u5beb\u6578\u5b57\u8b58\u5225\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u908a\u7de3\u6aa2\u6e2c\u548c\u8f2a\u5ed3\u6aa2\u6e2c\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCV\u9032\u884c\u908a\u7de3\u6aa2\u6e2c\u7684\u793a\u4f8b\uff1a<\/p>\n<pre><code>\n# \u4f7f\u7528Canny\u908a\u7de3\u6aa2\u6e2c\nedges = cv2.Canny(image, 100, 200)\n<\/code><\/pre>\n<h3 id=\"3-%e6%a8%a1%e5%9e%8b%e8%a8%93%e7%b7%b4-iHfzOOHjoc\">3. \u6a21\u578b\u8a13\u7df4<\/h3>\n<p>\u5728\u7279\u5fb5\u63d0\u53d6\u5f8c\uff0c\u53ef\u4ee5\u4f7f\u7528\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u9032\u884c\u8a13\u7df4\u3002\u5e38\u7528\u7684\u6a21\u578b\u5305\u62ec\u652f\u6301\u5411\u91cf\u6a5f\uff08SVM\uff09\u3001\u96a8\u6a5f\u68ee\u6797\u548c\u795e\u7d93\u7db2\u7d61\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Keras\u8a13\u7df4\u7c21\u55ae\u795e\u7d93\u7db2\u7d61\u7684\u793a\u4f8b\uff1a<\/p>\n<pre><code>\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten\n\nmodel = Sequential()\nmodel.add(Flatten(input_shape=(28, 28, 1)))\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(10, activation='softmax'))\n\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(x_train, y_train, epochs=5)\n<\/code><\/pre>\n<h3 id=\"4-%e6%a8%a1%e5%9e%8b%e8%a9%95%e4%bc%b0-iHfzOOHjoc\">4. \u6a21\u578b\u8a55\u4f30<\/h3>\n<p>\u8a13\u7df4\u5b8c\u6210\u5f8c\uff0c\u9700\u8981\u5c0d\u6a21\u578b\u9032\u884c\u8a55\u4f30\uff0c\u4ee5\u78ba\u4fdd\u5176\u6e96\u78ba\u6027\u3002\u53ef\u4ee5\u4f7f\u7528\u6e2c\u8a66\u6578\u64da\u96c6\u4f86\u8a55\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff1a<\/p>\n<pre><code>\ntest_loss, test_acc = model.evaluate(x_test, y_test)\nprint('Test accuracy:', test_acc)\n<\/code><\/pre>\n<h3 id=\"5-%e5%af%a6%e9%9a%9b%e6%87%89%e7%94%a8-iHfzOOHjoc\">5. \u5be6\u969b\u61c9\u7528<\/h3>\n<p>\u6700\u5f8c\uff0c\u53ef\u4ee5\u5c07\u8a13\u7df4\u597d\u7684\u6a21\u578b\u61c9\u7528\u65bc\u5be6\u969b\u7684\u624b\u5beb\u6578\u5b57\u8b58\u5225\u4efb\u52d9\u4e2d\u3002\u7528\u6236\u53ef\u4ee5\u4e0a\u50b3\u624b\u5beb\u6578\u5b57\u5716\u50cf\uff0c\u7cfb\u7d71\u5c07\u8fd4\u56de\u8b58\u5225\u7d50\u679c\u3002<\/p>\n<h2 id=\"%e7%b8%bd%e7%b5%90-iHfzOOHjoc\">\u7e3d\u7d50<\/h2>\n<p>\u901a\u904e\u4f7f\u7528OpenCV\u548cMNIST\u6578\u64da\u5eab\uff0c\u6211\u5011\u53ef\u4ee5\u8f15\u9b06\u5be6\u73fe\u624b\u5beb\u6578\u5b57\u8b58\u5225\u3002\u9019\u4e00\u904e\u7a0b\u6d89\u53ca\u6578\u64da\u9810\u8655\u7406\u3001\u7279\u5fb5\u63d0\u53d6\u3001\u6a21\u578b\u8a13\u7df4\u548c\u8a55\u4f30\u7b49\u6b65\u9a5f\u3002\u96a8\u8457\u6280\u8853\u7684\u9032\u6b65\uff0c\u624b\u5beb\u6578\u5b57\u8b58\u5225\u7684\u6e96\u78ba\u6027\u548c\u6548\u7387\u5c07\u4e0d\u65b7\u63d0\u9ad8\u3002\u5c0d\u65bc\u9700\u8981\u9ad8\u6548\u8a08\u7b97\u8cc7\u6e90\u7684\u61c9\u7528\uff0c\u9078\u64c7\u5408\u9069\u7684 <a href=\"https:\/\/server.hk\">VPS<\/a> \u89e3\u6c7a\u65b9\u6848\u5c07\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u7121\u8ad6\u662f\u9032\u884c\u6a21\u578b\u8a13\u7df4\u9084\u662f\u5be6\u969b\u61c9\u7528\uff0c\u7a69\u5b9a\u7684 <a href=\"https:\/\/server.hk\">\u9999\u6e2f\u4f3a\u670d\u5668<\/a> \u90fd\u80fd\u63d0\u4f9b\u826f\u597d\u7684\u652f\u6301\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u63a2\u7d22\u5982\u4f55\u4f7f\u7528OpenCV\u5be6\u73fe\u624b\u5beb\u6578\u5b57\u8b58\u5225\uff0c\u5229\u7528MNIST\u6578\u64da\u5eab\u9032\u884c\u9ad8\u6548\u7684\u5716\u50cf\u8655\u7406\u8207\u6a5f\u5668\u5b78\u7fd2\u6280\u8853\u3002<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[101],"tags":[],"class_list":["post-81466","post","type-post","status-publish","format-standard","hentry","category-database"],"_links":{"self":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts\/81466","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/comments?post=81466"}],"version-history":[{"count":1,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts\/81466\/revisions"}],"predecessor-version":[{"id":81467,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/posts\/81466\/revisions\/81467"}],"wp:attachment":[{"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/media?parent=81466"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/categories?post=81466"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/server.hk\/cnblog\/wp-json\/wp\/v2\/tags?post=81466"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}