From cdd9a63517a3958c0f2a73030185a5290e7727c7 Mon Sep 17 00:00:00 2001 From: Atlas Shen Date: Fri, 14 Aug 2020 06:33:26 +0800 Subject: [PATCH] to submit cnn related stuff --- .../area_model/area.data-00000-of-00001 | Bin 0 -> 1725440 bytes .../area_model/area.index | Bin 0 -> 1174 bytes .../area_model/area.meta | Bin 0 -> 92282 bytes .../area_model/checkpoint | 2 + .../cnn_model.py | 96 ++++++++++++++++ .../dataset/map.py | 102 +++++++++++++++++ .../dataset/test/1.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076083_954_2.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076083_954_3.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076083_955_5.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076083_956_7.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076148_823_1.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076148_824_3.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076148_825_4.bmp | Bin 0 -> 2358 bytes .../dataset/test/1510076148_825_5.bmp | Bin 0 -> 2358 bytes 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668 bytes .../24/1509819720_744_2_new_warped2.jpg | Bin 0 -> 706 bytes .../24/1509819720_744_2_new_warped3.jpg | Bin 0 -> 674 bytes .../24/1509819720_744_2_new_warped4.jpg | Bin 0 -> 721 bytes .../val/letter/24/1509819761_294_2.jpg | Bin 0 -> 765 bytes .../24/1509819761_294_2_new_smaller.jpg | Bin 0 -> 710 bytes .../24/1509819761_294_2_new_warped1.jpg | Bin 0 -> 677 bytes .../24/1509819761_294_2_new_warped2.jpg | Bin 0 -> 731 bytes .../24/1509819761_294_2_new_warped3.jpg | Bin 0 -> 678 bytes .../24/1509819761_294_2_new_warped4.jpg | Bin 0 -> 755 bytes .../val/letter/24/1509819776_103_2.jpg | Bin 0 -> 778 bytes .../24/1509819776_103_2_new_smaller.jpg | Bin 0 -> 725 bytes .../24/1509819776_103_2_new_warped1.jpg | Bin 0 -> 695 bytes .../24/1509819776_103_2_new_warped2.jpg | Bin 0 -> 731 bytes .../24/1509819776_103_2_new_warped3.jpg | Bin 0 -> 678 bytes .../24/1509819776_103_2_new_warped4.jpg | Bin 0 -> 744 bytes .../dataset/val/letter/24/1509819781_21_2.jpg | Bin 0 -> 765 bytes 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bytes .../24/1509819787_938_6_new_warped4.jpg | Bin 0 -> 754 bytes .../dataset/val/letter/24/1509819803_85_2.jpg | Bin 0 -> 768 bytes .../letter/24/1509819803_85_2_new_smaller.jpg | Bin 0 -> 699 bytes .../letter/24/1509819803_85_2_new_warped1.jpg | Bin 0 -> 685 bytes .../letter/24/1509819803_85_2_new_warped2.jpg | Bin 0 -> 725 bytes .../letter/24/1509819803_85_2_new_warped3.jpg | Bin 0 -> 662 bytes .../letter/24/1509819803_85_2_new_warped4.jpg | Bin 0 -> 739 bytes .../dataset/val/letter/24/1509819813_15_2.jpg | Bin 0 -> 787 bytes .../letter/24/1509819813_15_2_new_smaller.jpg | Bin 0 -> 700 bytes .../letter/24/1509819813_15_2_new_warped1.jpg | Bin 0 -> 679 bytes .../letter/24/1509819813_15_2_new_warped2.jpg | Bin 0 -> 737 bytes .../letter/24/1509819813_15_2_new_warped3.jpg | Bin 0 -> 663 bytes .../letter/24/1509819813_15_2_new_warped4.jpg | Bin 0 -> 736 bytes .../val/letter/24/1509819834_172_2.jpg | Bin 0 -> 785 bytes .../24/1509819834_172_2_new_smaller.jpg | Bin 0 -> 724 bytes 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781 bytes .../25/1509809627_768_3_new_warped1.jpg | Bin 0 -> 773 bytes .../25/1509809627_768_3_new_warped2.jpg | Bin 0 -> 777 bytes .../25/1509809627_768_3_new_warped3.jpg | Bin 0 -> 766 bytes .../25/1509809627_768_3_new_warped4.jpg | Bin 0 -> 782 bytes .../val/letter/25/1509814289_233_3.jpg | Bin 0 -> 806 bytes .../25/1509814289_233_3_new_smaller.jpg | Bin 0 -> 771 bytes .../25/1509814289_233_3_new_warped1.jpg | Bin 0 -> 728 bytes .../25/1509814289_233_3_new_warped2.jpg | Bin 0 -> 768 bytes .../25/1509814289_233_3_new_warped3.jpg | Bin 0 -> 737 bytes .../25/1509814289_233_3_new_warped4.jpg | Bin 0 -> 776 bytes .../val/letter/25/1509814299_707_7.jpg | Bin 0 -> 812 bytes .../25/1509814299_707_7_new_smaller.jpg | Bin 0 -> 798 bytes .../25/1509814299_707_7_new_warped1.jpg | Bin 0 -> 784 bytes .../25/1509814299_707_7_new_warped2.jpg | Bin 0 -> 776 bytes .../25/1509814299_707_7_new_warped3.jpg | Bin 0 -> 792 bytes .../25/1509814299_707_7_new_warped4.jpg | Bin 0 -> 792 bytes 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shR_=50oNlNS5gC^l(!`{JhxpiC^g)VGkxTphMf2xkks&%?xM#32g{B4xBvhE literal 0 HcmV?d00001 diff --git a/cnn_for_license_plate_recognition/area_model/checkpoint b/cnn_for_license_plate_recognition/area_model/checkpoint new file mode 100644 index 00000000..b9bdf662 --- /dev/null +++ b/cnn_for_license_plate_recognition/area_model/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "area" +all_model_checkpoint_paths: "area" diff --git a/cnn_for_license_plate_recognition/cnn_model.py b/cnn_for_license_plate_recognition/cnn_model.py new file mode 100644 index 00000000..8437d3cf --- /dev/null +++ b/cnn_for_license_plate_recognition/cnn_model.py @@ -0,0 +1,96 @@ +import tensorflow as tf +from process_data import DataProcessor + +batch_size = 128 +learning_rate = 1e-4 +aspect = 'province' +data_processor = DataProcessor(aspect) +dir = './' +model_name = aspect + +input_x = tf.placeholder(dtype=tf.float32, shape=[None, 20, 20], name='input_x') +input_y = tf.placeholder(dtype=tf.float32, shape=[None, 34], name='input_y') + +with tf.name_scope('conv1'): + W_C1 = tf.Variable(tf.truncated_normal(shape=[3, 3, 1, 32], stddev=0.1)) + b_C1 = tf.Variable(tf.constant(0.1, tf.float32, shape=[32])) + X = tf.reshape(input_x, [-1, 20, 20, 1]) + featureMap_C1 = tf.nn.relu(tf.nn.conv2d(X, W_C1, strides=[1, 1, 1, 1], padding='SAME') + b_C1) + +with tf.name_scope('conv2'): + W_C2 = tf.Variable(tf.truncated_normal(shape=[3, 3, 32, 64], stddev=0.1)) + b_C2 = tf.Variable(tf.constant(0.1, tf.float32, shape=[64])) + featureMap_C2 = tf.nn.relu(tf.nn.conv2d(featureMap_C1, W_C2, strides=[1, 1, 1, 1], padding='SAME') + b_C2) + +with tf.name_scope('pooling2'): + featureMap_S2 = tf.nn.max_pool(featureMap_C2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + +with tf.name_scope('conv3'): + W_C3 = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 8], stddev=0.1)) + b_C3 = tf.Variable(tf.constant(0.1, shape=[8], dtype=tf.float32)) + featureMap_C3 = tf.nn.relu(tf.nn.conv2d(featureMap_S2, filter=W_C3, strides=[1, 1, 1, 1], padding='SAME') + b_C3) + +with tf.name_scope('pooling3'): + featureMap_S3 = tf.nn.max_pool(featureMap_C3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + +with tf.name_scope('fulnet'): + featureMap_flatten = tf.reshape(featureMap_S3, [-1, 5*5*8]) + W_F4 = tf.Variable(tf.truncated_normal(shape=[5*5*8, 512], stddev=0.1)) + b_F4 = tf.Variable(tf.constant(0.1, shape=[512], dtype=tf.float32)) + out_F4 = tf.nn.relu(tf.matmul(featureMap_flatten, W_F4) + b_F4) + out_F4 = tf.nn.dropout(out_F4, keep_prob=0.5) + +with tf.name_scope('output'): + W_OUTPUT = tf.Variable(tf.truncated_normal(shape=[512, 34], stddev=0.1)) + b_OUTPUT = tf.Variable(tf.constant(0.1, shape=[34], dtype=tf.float32)) + logits = tf.matmul(out_F4, W_OUTPUT)+b_OUTPUT + +loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(labels=input_y, logits=logits)) +train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss) +predictY = tf.nn.softmax(logits) +y_pred = tf.arg_max(predictY, 1) +bool_pred = tf.equal(tf.arg_max(input_y, 1), y_pred) +right_rate = tf.reduce_mean(tf.to_float(bool_pred)) + +saver = tf.train.Saver() + + +def load_model(sess, dir, model_name): + ckpt = tf.train.get_checkpoint_state(dir) + if ckpt and ckpt.model_checkpoint_path: + print('*'*30) + print('load latest model......') + saver.restore(sess, dir+'.\\'+model_name) + print('*'*30) + + +def save_model(sess, dir, model_name): + saver.save(sess, dir+model_name) + + +with tf.Session() as sess: + sess.run(tf.initialize_all_variables()) + step = 1 + display_interval = 200 + max_epoch = 500 + epoch = 1 + acc = 0 + load_model(sess, dir=dir, model_name=model_name) + while True: + if step % display_interval == 0: + image_batch, label_batch, epoch = data_processor.next_valid_batch(batch_size) + acc = sess.run(right_rate, feed_dict={input_x: image_batch, input_y: label_batch}) + print({str(epoch)+':'+str(step): acc}) + image_batch, label_batch, epoch = data_processor.next_train_batch(batch_size) + sess.run([loss, train_op], {input_x: image_batch, input_y: label_batch}) + if epoch > max_epoch: + break + step += 1 + while True: + test_img, test_lab, test_epoch = data_processor.next_test_batch(batch_size) + test_acc = sess.run(right_rate, {input_x: test_img, input_y: test_lab}) + acc = test_acc * 0.8 + acc * 0.2 + if test_epoch != epoch: + print({'Test Over..... acc:': acc}) + break + save_model(sess, dir, model_name) diff --git a/cnn_for_license_plate_recognition/dataset/map.py b/cnn_for_license_plate_recognition/dataset/map.py new file mode 100644 index 00000000..aec3f19e --- /dev/null +++ b/cnn_for_license_plate_recognition/dataset/map.py @@ -0,0 +1,102 @@ +maps={ + "province": + { + "0":"çš–", + "1":"沪", + "2":"æ´¥", + "3":"æ¸", + "4":"冀", + "5":"晋", + "6":"è’™", + "7":"è¾½", + "8":"å‰", + "9":"黑", + "10":"è‹", + "11":"æµ™", + "12":"京", + "13":"é—½", + "14":"èµ£", + "15":"é²", + "16":"豫", + "17":"é„‚", + "18":"湘", + "19":"粤", + "20":"æ¡‚", + "21":"ç¼", + "22":"å·", + "23":"è´µ", + "24":"云", + "25":"è—", + "26":"陕", + "27":"甘", + "28":"é’", + "29":"å®", + "30":"æ–°" + }, + "area": + { + "0":"A", + "1":"B", + "2":"C", + "3":"D", + "4":"E", + "5":"F", + "6":"G", + "7":"H", + "8":"I", + "9":"J", + "10":"K", + "11":"L", + "12":"M", + "13":"N", + "14":"O", + "15":"P", + "16":"Q", + "17":"R", + "18":"S", + "19":"T", + "20":"U", + "21":"V", + "22":"W", + "23":"X", + "24":"Y", + "25":"Z" + }, + "letter": + { + "0":"0", + "1":"1", + "2":"2", + "3":"3", + "4":"4", + "5":"5", + "6":"6", + "7":"7", + "8":"8", + "9":"9", + "10":"A", + "11":"B", + "12":"C", + "13":"D", + "14":"E", + "15":"F", + "16":"G", + "17":"H", + "18":"J", + "19":"K", + "20":"L", + "21":"M", + "22":"N", + "23":"P", + "24":"Q", + "25":"R", + "26":"S", + "27":"T", + "28":"U", + "29":"V", + "30":"W", + "31":"X", + "32":"Y", + "33":"Z" + } +} \ No newline at end of file diff --git a/cnn_for_license_plate_recognition/dataset/test/1.bmp b/cnn_for_license_plate_recognition/dataset/test/1.bmp new 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