#!/usr/bin/python3.5?? #?-*-?coding:?utf-8?-*-???? ?? import?sys?? import?os?? import?time?? import?random?? ?? import?numpy?as?np?? import?tensorflow?as?tf?? ?? from?PIL?import?Image,ImageFilter ?? ?? SIZE?=?1280?? WIDTH?=?32?? HEIGHT?=?40?? NUM_CLASSES?=?6?? iterations?=?300?? ?? SAVER_DIR?=?"D/train-saver/province/"?? ?? PROVINCES?=?("京","閩","粵","蘇","滬","浙")?? nProvinceIndex?=?0?? ?? time_begin?=?time.time()?? ?? ?? #?定義輸入節(jié)點，對應(yīng)于圖片像素值矩陣集合和圖片標(biāo)簽(即所代表的數(shù)字)?? x?=?tf.placeholder(tf.float32,?shape=[None,?SIZE])?? y_?=?tf.placeholder(tf.float32,?shape=[None,?NUM_CLASSES])?? ?? x_image?=?tf.reshape(x,?[-1,?WIDTH,?HEIGHT,?1])?? ?? ?? #?定義卷積函數(shù)?? def?conv_layer(inputs,?W,?b,?conv_strides,?kernel_size,?pool_strides,?padding):?? ????L1_conv?=?tf.nn.conv2d(inputs,?W,?strides=conv_strides,?padding=padding)?? ????L1_relu?=?tf.nn.relu(L1_conv?+?b)?? ????return?tf.nn.max_pool(L1_relu,?ksize=kernel_size,?strides=pool_strides,?padding='SAME')?? ?? #?定義全連接層函數(shù)?? def?full_connect(inputs,?W,?b):?? ????return?tf.nn.relu(tf.matmul(inputs,?W)?+?b)?? ?? ?? if?__name__?=='__main__'?and?sys.argv[1]=='train':?? ????#?第一次遍歷圖片目錄是為了獲取圖片總數(shù)?? ????input_count?=?0?? ????for?i?in?range(0,NUM_CLASSES):?? ????????dir?=?'D:/tf_car_license_dataset/train_images/training-set/chinese-characters/%s/'?%?i???????????#?這里可以改成你自己的圖片目錄，i為分類標(biāo)簽?? ????????for?rt,?dirs,?files?in?os.walk(dir):?? ????????????for?filename?in?files:?? ????????????????input_count?+=?1?? ?? ????#?定義對應(yīng)維數(shù)和各維長度的數(shù)組?? ????input_images?=?np.array([[0]*SIZE?for?i?in?range(input_count)])?? ????input_labels?=?np.array([[0]*NUM_CLASSES?for?i?in?range(input_count)])?? ?? ????#?第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標(biāo)簽?? ????index?=?0?? ????for?i?in?range(0,NUM_CLASSES):?? ????????dir?=?'D:/tf_car_license_dataset/train_images/training-set/chinese-characters/%s/'?%?i??????????#?這里可以改成你自己的圖片目錄，i為分類標(biāo)簽?? ????????for?rt,?dirs,?files?in?os.walk(dir):?? ????????????for?filename?in?files:?? ????????????????filename?=?dir?+?filename?? ????????????????img?=?Image.open(filename)?? ????????????????width?=?img.size[0]?? ????????????????height?=?img.size[1]?? ????????????????for?h?in?range(0,?height):?? ????????????????????for?w?in?range(0,?width):?? ????????????????????????#?通過這樣的處理，使數(shù)字的線條變細(xì)，有利于提高識別準(zhǔn)確率?? ????????????????????????if?img.getpixel((w,?h))?>?230:?? ????????????????????????????input_images[index][w+h*width]?=?0?? ????????????????????????else:?? ????????????????????????????input_images[index][w+h*width]?=?1?? ????????????????input_labels[index][i]?=?1?? ????????????????index?+=?1?? ?? ????#?第一次遍歷圖片目錄是為了獲取圖片總數(shù)?? ????val_count?=?0?? ????for?i?in?range(0,NUM_CLASSES):?? ????????dir?=?'D:/tf_car_license_dataset/train_images/validation-set/chinese-characters/%s/'?%?i???????????#?這里可以改成你自己的圖片目錄，i為分類標(biāo)簽?? ????????for?rt,?dirs,?files?in?os.walk(dir):?? ????????????for?filename?in?files:?? ????????????????val_count?+=?1?? ?? ????#?定義對應(yīng)維數(shù)和各維長度的數(shù)組?? ????val_images?=?np.array([[0]*SIZE?for?i?in?range(val_count)])?? ????val_labels?=?np.array([[0]*NUM_CLASSES?for?i?in?range(val_count)])?? ?? ????#?第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標(biāo)簽?? ????index?=?0?? ????for?i?in?range(0,NUM_CLASSES):?? ????????dir?=?'D:/tf_car_license_dataset/validation-set/chinese-characters/%s/'?%?i??????????#?這里可以改成你自己的圖片目錄，i為分類標(biāo)簽?? ????????for?rt,?dirs,?files?in?os.walk(dir):?? ????????????for?filename?in?files:?? ????????????????filename?=?dir?+?filename?? ????????????????img?=?Image.open(filename)?? ????????????????width?=?img.size[0]?? ????????????????height?=?img.size[1]?? ????????????????for?h?in?range(0,?height):?? ????????????????????for?w?in?range(0,?width):?? ????????????????????????#?通過這樣的處理，使數(shù)字的線條變細(xì)，有利于提高識別準(zhǔn)確率?? ????????????????????????if?img.getpixel((w,?h))?>?230:?? ????????????????????????????val_images[index][w+h*width]?=?0?? ????????????????????????else:?? ????????????????????????????val_images[index][w+h*width]?=?1?? ????????????????val_labels[index][i]?=?1?? ????????????????index?+=?1?? ?????? ????with?tf.Session()?as?sess:?? ????????#?第一個卷積層?? ????????W_conv1?=?tf.Variable(tf.truncated_normal([8,?8,?1,?16],?stddev=0.1),?name="W_conv1")?? ????????b_conv1?=?tf.Variable(tf.constant(0.1,?shape=[16]),?name="b_conv1")?? ????????conv_strides?=?[1,?1,?1,?1]?? ????????kernel_size?=?[1,?2,?2,?1]?? ????????pool_strides?=?[1,?2,?2,?1]?? ????????L1_pool?=?conv_layer(x_image,?W_conv1,?b_conv1,?conv_strides,?kernel_size,?pool_strides,?padding='SAME')?? ?? ????????#?第二個卷積層?? ????????W_conv2?=?tf.Variable(tf.truncated_normal([5,?5,?16,?32],?stddev=0.1),?name="W_conv2")?? ????????b_conv2?=?tf.Variable(tf.constant(0.1,?shape=[32]),?name="b_conv2")?? ????????conv_strides?=?[1,?1,?1,?1]?? ????????kernel_size?=?[1,?1,?1,?1]?? ????????pool_strides?=?[1,?1,?1,?1]?? ????????L2_pool?=?conv_layer(L1_pool,?W_conv2,?b_conv2,?conv_strides,?kernel_size,?pool_strides,?padding='SAME')?? ?? ?? ????????#?全連接層?? ????????W_fc1?=?tf.Variable(tf.truncated_normal([16?*?20?*?32,?512],?stddev=0.1),?name="W_fc1")?? ????????b_fc1?=?tf.Variable(tf.constant(0.1,?shape=[512]),?name="b_fc1")?? ????????h_pool2_flat?=?tf.reshape(L2_pool,?[-1,?16?*?20*32])?? ????????h_fc1?=?full_connect(h_pool2_flat,?W_fc1,?b_fc1)?? ?? ?? ????????#?dropout?? ????????keep_prob?=?tf.placeholder(tf.float32)?? ?? ????????h_fc1_drop?=?tf.nn.dropout(h_fc1,?keep_prob)?? ?? ?? ????????#?readout層?? ????????W_fc2?=?tf.Variable(tf.truncated_normal([512,?NUM_CLASSES],?stddev=0.1),?name="W_fc2")?? ????????b_fc2?=?tf.Variable(tf.constant(0.1,?shape=[NUM_CLASSES]),?name="b_fc2")?? ?? ????????#?定義優(yōu)化器和訓(xùn)練op?? ????????y_conv?=?tf.matmul(h_fc1_drop,?W_fc2)?+?b_fc2?? ????????cross_entropy?=?tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_,?logits=y_conv))?? ????????train_step?=?tf.train.AdamOptimizer((1e-4)).minimize(cross_entropy)?? ?? ????????correct_prediction?=?tf.equal(tf.argmax(y_conv,?1),?tf.argmax(y_,?1))?? ????????accuracy?=?tf.reduce_mean(tf.cast(correct_prediction,?tf.float32))?? ?? ????????#?初始化saver?? ????????saver?=?tf.train.Saver()?? ?? ????????sess.run(tf.global_variables_initializer())?? ?? ????????time_elapsed?=?time.time()?-?time_begin?? ????????print("讀取圖片文件耗費時間：%d秒"?%?time_elapsed)?? ????????time_begin?=?time.time()?? ?? ????????print?("一共讀取了?%s?個訓(xùn)練圖像，?%s?個標(biāo)簽"?%?(input_count,?input_count))?? ?? ????????#?設(shè)置每次訓(xùn)練op的輸入個數(shù)和迭代次數(shù)，這里為了支持任意圖片總數(shù)，定義了一個余數(shù)remainder，譬如，如果每次訓(xùn)練op的輸入個數(shù)為60，圖片總數(shù)為150張，則前面兩次各輸入60張，最后一次輸入30張（余數(shù)30）?? ????????batch_size?=?60?? ????????iterations?=?iterations?? ????????batches_count?=?int(input_count?/?batch_size)?? ????????remainder?=?input_count?%?batch_size?? ????????print?("訓(xùn)練數(shù)據(jù)集分成?%s?批,?前面每批?%s?個數(shù)據(jù)，最后一批?%s?個數(shù)據(jù)"?%?(batches_count+1,?batch_size,?remainder))?? ?? ????????#?執(zhí)行訓(xùn)練迭代?? ????????for?it?in?range(iterations):?? ????????????#?這里的關(guān)鍵是要把輸入數(shù)組轉(zhuǎn)為np.array?? ????????????for?n?in?range(batches_count):?? ????????????????train_step.run(feed_dict={x:?input_images[n*batch_size:(n+1)*batch_size],?y_:?input_labels[n*batch_size:(n+1)*batch_size],?keep_prob:?0.5})?? ????????????if?remainder?>?0:?? ????????????????start_index?=?batches_count?*?batch_size;?? ????????????????train_step.run(feed_dict={x:?input_images[start_index:input_count-1],?y_:?input_labels[start_index:input_count-1],?keep_prob:?0.5})?? ?? ????????????#?每完成五次迭代，判斷準(zhǔn)確度是否已達(dá)到100%，達(dá)到則退出迭代循環(huán)?? ????????????iterate_accuracy?=?0?? ????????????if?it%5?==?0:?? ????????????????iterate_accuracy?=?accuracy.eval(feed_dict={x:?val_images,?y_:?val_labels,?keep_prob:?1.0})?? ????????????????print?('第?%d?次訓(xùn)練迭代:?準(zhǔn)確率?%0.5f%%'?%?(it,?iterate_accuracy*100))?? ????????????????if?iterate_accuracy?>=?0.9999?and?it?>=?150:?? ????????????????????break;?? ?? ????????print?('完成訓(xùn)練!')?? ????????time_elapsed?=?time.time()?-?time_begin?? ????????print?("訓(xùn)練耗費時間：%d秒"?%?time_elapsed)?? ????????time_begin?=?time.time()?? ?? ????????#?保存訓(xùn)練結(jié)果?? ????????if?not?os.path.exists(SAVER_DIR):?? ????????????print?('不存在訓(xùn)練數(shù)據(jù)保存目錄，現(xiàn)在創(chuàng)建保存目錄')?? ????????????os.makedirs(SAVER_DIR)?? ????????saver_path?=?saver.save(sess,?"%smodel.ckpt"%(SAVER_DIR))?? ?? ?? ?? if?__name__?=='__main__'?and?sys.argv[1]=='predict':?? ????saver?=?tf.train.import_meta_graph("%smodel.ckpt.meta"%(SAVER_DIR))?? ????with?tf.Session()?as?sess:?? ????????model_file=tf.train.latest_checkpoint(SAVER_DIR)?? ????????saver.restore(sess,?model_file)?? ?? ????????#?第一個卷積層?? ????????W_conv1?=?sess.graph.get_tensor_by_name("W_conv1:0")?? ????????b_conv1?=?sess.graph.get_tensor_by_name("b_conv1:0")?? ????????conv_strides?=?[1,?1,?1,?1]?? ????????kernel_size?=?[1,?2,?2,?1]?? ????????pool_strides?=?[1,?2,?2,?1]?? ????????L1_pool?=?conv_layer(x_image,?W_conv1,?b_conv1,?conv_strides,?kernel_size,?pool_strides,?padding='SAME')?? ?? ????????#?第二個卷積層?? ????????W_conv2?=?sess.graph.get_tensor_by_name("W_conv2:0")?? ????????b_conv2?=?sess.graph.get_tensor_by_name("b_conv2:0")?? ????????conv_strides?=?[1,?1,?1,?1]?? ????????kernel_size?=?[1,?1,?1,?1]?? ????????pool_strides?=?[1,?1,?1,?1]?? ????????L2_pool?=?conv_layer(L1_pool,?W_conv2,?b_conv2,?conv_strides,?kernel_size,?pool_strides,?padding='SAME')?? ?? ?? ????????#?全連接層?? ????????W_fc1?=?sess.graph.get_tensor_by_name("W_fc1:0")?? ????????b_fc1?=?sess.graph.get_tensor_by_name("b_fc1:0")?? ????????h_pool2_flat?=?tf.reshape(L2_pool,?[-1,?16?*?20*32])?? ????????h_fc1?=?full_connect(h_pool2_flat,?W_fc1,?b_fc1)?? ?? ?? ????????#?dropout?? ????????keep_prob?=?tf.placeholder(tf.float32)?? ?? ????????h_fc1_drop?=?tf.nn.dropout(h_fc1,?keep_prob)?? ?? ?? ????????#?readout層?? ????????W_fc2?=?sess.graph.get_tensor_by_name("W_fc2:0")?? ????????b_fc2?=?sess.graph.get_tensor_by_name("b_fc2:0")?? ?? ????????#?定義優(yōu)化器和訓(xùn)練op?? ????????conv?=?tf.nn.softmax(tf.matmul(h_fc1_drop,?W_fc2)?+?b_fc2)?? ?? ????????for?n?in?range(1,2):?? ????????????path?=?"test_images/%s.bmp"?%?(n)?? ????????????img?=?Image.open(path)?? ????????????width?=?img.size[0]?? ????????????height?=?img.size[1]?? ?? ????????????img_data?=?[[0]*SIZE?for?i?in?range(1)]?? ????????????for?h?in?range(0,?height):?? ????????????????for?w?in?range(0,?width):?? ????????????????????if?img.getpixel((w,?h))?<?190:?? ????????????????????????img_data[0][w+h*width]?=?1?? ????????????????????else:?? ????????????????????????img_data[0][w+h*width]?=?0?? ?????????????? ????????????result?=?sess.run(conv,?feed_dict?=?{x:?np.array(img_data),?keep_prob:?1.0})?? ????????????max1?=?0?? ????????????max2?=?0?? ????????????max3?=?0?? ????????????max1_index?=?0?? ????????????max2_index?=?0?? ????????????max3_index?=?0?? ????????????for?j?in?range(NUM_CLASSES):?? ????????????????if?result[0][j]?>?max1:?? ????????????????????max1?=?result[0][j]?? ????????????????????max1_index?=?j?? ????????????????????continue?? ????????????????if?(result[0][j]>max2)?and?(result[0][j]<=max1):?? ????????????????????max2?=?result[0][j]?? ????????????????????max2_index?=?j?? ????????????????????continue?? ????????????????if?(result[0][j]>max3)?and?(result[0][j]<=max2):?? ????????????????????max3?=?result[0][j]?? ????????????????????max3_index?=?j?? ????????????????????continue?? ?????????????? ????????????nProvinceIndex?=?max1_index?? ????????????print?("概率：??[%s?%0.2f%%]????[%s?%0.2f%%]????[%s?%0.2f%%]"?%?(PROVINCES[max1_index],max1*100,?PROVINCES[max2_index],max2*100,?PROVINCES[max3_index],max3*100))?? ?????????????? ????????print?("省份簡稱是:?%s"?%?PROVINCES[nProvinceIndex])執(zhí)行的時候出了以下問題：測試的時候有這句 path = "D:/tf_car_license_dataset/test_images/%s.bmp" % (n)這其中的%s.bmp是什么意思 如果我把它換成代表閩的1.bmp就會一直報錯 如果是%s.bmp就會打開測試集里的京...以下是我的文件夾的圖片 是測試集里圖片的名字問題嗎。。。求問大神原因...做在tensorflow下的車牌識別來著