Tensorflow object detection API 搭建属于自己的物体识别模型
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一、下载Tensorflow object detection API工程源码
网址：https://github.com/tensorflow/models，可通过Git下载，打开Git Bash，输入git clone https://github.com/tensorflow/models.git进行下载。
二、标记需要训练的图片
①、在第一步下载的工程文件models\\research\\object_detection目录下，建立一个my_test_images用来放测试test和训练train的文件夹。将需要识别的图片放在test和train中进行训练和测试。
②、到https://tzutalin.github.io/labelImg/下载labelImg工具，打开labelImg.exe，点击open dir，打开models\\research\\object_detection\\my_test_images\\test和train，对里面的所有照片标注完成，标注完成后保存为与图片名字一样的.xml文件。
③、在models\\research\\object_detection\\my_test_images文件夹下新建名字为xml_to_csv文件夹，在xml_to_csv文件夹下新建test_xml_to_csv.py和train_xml_to_csv.py文件。
test_xml_to_csv.py代码如下：
1 # -*- coding: utf-8 -*- 2 """ 3 Created on Wed Mar 13 21:50:27 2019 4 5 @author: CFF 6 """ 7 8 import os 9 import glob 10 import pandas as pd 11 import xml.etree.ElementTree as ET 12 13 os.chdir(\'C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\\\\my_test_images\\\\test\') 14 path = \'C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\\\\my_test_images\\\\test\' 15 16 def xml_to_csv(path): 17 xml_list = [] 18 for xml_file in glob.glob(path + \'/*.xml\'): 19 tree = ET.parse(xml_file) 20 root = tree.getroot() 21 for member in root.findall(\'object\'): 22 value = (root.find(\'filename\').text, 23 int(root.find(\'size\')[0].text), 24 int(root.find(\'size\')[1].text), 25 member[0].text, 26 int(member[4][0].text), 27 int(member[4][1].text), 28 int(member[4][2].text), 29 int(member[4][3].text) 30 ) 31 xml_list.append(value) 32 column_name = [\'filename\', \'width\', \'height\', \'class\', \'xmin\', \'ymin\', \'xmax\', \'ymax\'] 33 xml_df = pd.DataFrame(xml_list, columns=column_name) 34 return xml_df 35 def main(): 36 image_path = path 37 xml_df = xml_to_csv(image_path) 38 xml_df.to_csv(\'cat_test.csv\', index=None)#cat_test.csv可以改为自己的文件名 39 print(\'Successfully converted xml to csv.\') 40 main()
用Spyder打开test_xml_to_csv.py，点击编译，在C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\\\\my_test_images\\\\test目录下生成一个cat_test.csv文件，可用Excel打开。
同理，train_xml_to_csv.py代码如下：
1 # -*- coding: utf-8 -*- 2 """ 3 Created on Wed Mar 13 21:48:33 2019 4 5 @author: CFF 6 """ 7 import os 8 import glob 9 import pandas as pd 10 import xml.etree.ElementTree as ET 11 12 os.chdir(\'C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\\\\my_test_images\\\\train\') 13 path = \'C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\\\\my_test_images\\\\train\' 14 15 def xml_to_csv(path): 16 xml_list = [] 17 for xml_file in glob.glob(path + \'/*.xml\'): 18 tree = ET.parse(xml_file) 19 root = tree.getroot() 20 for member in root.findall(\'object\'): 21 value = (root.find(\'filename\').text, 22 int(root.find(\'size\')[0].text), 23 int(root.find(\'size\')[1].text), 24 member[0].text, 25 int(member[4][0].text), 26 int(member[4][1].text), 27 int(member[4][2].text), 28 int(member[4][3].text) 29 ) 30 xml_list.append(value) 31 column_name = [\'filename\', \'width\', \'height\', \'class\', \'xmin\', \'ymin\', \'xmax\', \'ymax\'] 32 xml_df = pd.DataFrame(xml_list, columns=column_name) 33 return xml_df 34 def main(): 35 image_path = path 36 xml_df = xml_to_csv(image_path) 37 xml_df.to_csv(\'cat_train.csv\', index=None) 38 print(\'Successfully converted xml to csv.\') 39 main()
用Spyder打开train_xml_to_csv.py，点击编译，在C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\\\\my_test_images\\\\train目录下生成一个cat_train.csv文件，可用Excel打开。
三、将cat_train.csv和cat_test.csv文件转换为train.record和test.record数据集
①、先将cat_train.csv和cat_test.csv文件放在C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\\\\data文件夹下。
②、在C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection文件夹下新建一个images文件夹，放入训练和测试的图片。
③、用Spyder在C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection文件夹下新建一个generate_tfrecord.py文件，generate_tfrecord.py代码如下：
1 # -*- coding: utf-8 -*- 2 """ 3 Created on Wed Mar 13 21:56:20 2019 4 5 @author: CFF 6 """ 7 8 """ 9 Usage: 10 # From tensorflow/models/ 11 # Create train data: 12 python generate_tfrecord.py --csv_input=data/cat_train.csv --output_path=data/train.record 13 # Create test data: 14 python generate_tfrecord.py --csv_input=data/cat_test.csv --output_path=data/test.record 15 """ 16 17 import os 18 import io 19 import pandas as pd 20 import tensorflow as tf 21 22 from PIL import Image 23 from object_detection.utils import dataset_util 24 from collections import namedtuple, OrderedDict 25 26 os.chdir(\'C:\\\\Users\\\\CFF\\\\Desktop\\\\models\\\\research\\\\object_detection\') 27 28 flags = tf.app.flags 29 flags.DEFINE_string(\'csv_input\', \'\', \'Path to the CSV input\') 30 flags.DEFINE_string(\'output_path\', \'\', \'Path to output TFRecord\') 31 FLAGS = flags.FLAGS 32 33 34 # TO-DO replace this with label map 35 def class_text_to_int(row_label):#标签类型，根据实际情况写 36 if row_label == \'cat\': 37 return 1 38 else: 39 None 40 41 42 def split(df, group): 43 data = namedtuple(\'data\', [\'filename\', \'object\']) 44 gb = df.groupby(group) 45 return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)] 46 47 48 def create_tf_example(group, path): 49 with tf.gfile.GFile(os.path.join(path, \'{}\'.format(group.filename)), \'rb\') as fid: 50 encoded_jpg = fid.read() 51 encoded_jpg_io = io.BytesIO(encoded_jpg) 52 image = Image.open(encoded_jpg_io) 53 width, height = image.size 54 55 filename = group.filename.encode(\'utf8\') 56 image_format = b\'jpg\' 57 xmins = [] 58 xmaxs = [] 59 ymins = [] 60 ymaxs = [] 61 classes_text = [] 62 classes = [] 63 64 for index, row in group.object.iterrows(): 65 xmins.append(row[\'xmin\'] / width) 66 xmaxs.append(row[\'xmax\'] / width) 67 ymins.append(row[\'ymin\'] / height) 68 ymaxs.append(row[\'ymax\'] / height) 69 classes_text.append(row[\'class\'].encode(\'utf8\')) 70 classes.append(class_text_to_int(row[\'class\'])) 71 72 tf_example = tf.train.Example(features=tf.train.Features(feature={ 73 \'image/height\': dataset_util.int64_feature(height), 74 \'image/width\': dataset_util.int64_feature(width), 75 \'image/filename\': dataset_util.bytes_feature(filename), 76 \'image/source_id\': dataset_util.bytes_feature(filename), 77 \'image/encoded\': dataset_util.bytes_feature(encoded_jpg), 78 \'image/format\': dataset_util.bytes_feature(image_format), 79 \'image/object/bbox/xmin\': dataset_util.float_list_feature(xmins), 80 \'image/object/bbox/xmax\': dataset_util.float_list_feature(xmaxs), 81 \'image/object/bbox/ymin\': dataset_util.float_list_feature(ymins), 82 \'image/object/bbox/ymax\': dataset_util.float_list_feature(ymaxs), 83 \'image/object/class/text\': dataset_util.bytes_list_feature(classes_text), 84 \'image/object/class/label\': dataset_util.int64_list_feature(classes), 85 })) 86 return tf_example 87 def main(_): 88 writer = tf.python_io.TFRecordWriter(FLAGS.output_path) 89 path = os.path.join(os.getcwd(), \'images\') 90 examples = pd.read_csv(FLAGS.csv_input) 91 grouped = split(examples, \'filename\') 92 for group in grouped: 93 tf_example = create_tf_example(group, path) 94 writer.write(tf_example.SerializeToString()) 95 writer.close() 96 output_path = os.path.join(os.getcwd(), FLAGS.output_path) 97 print(\'Successfully created the TFRecords: {}\'.format(output_path)) 98 if __name__ == \'__main__\': 99 tf.app.run()
打开Anaconda Prompt，分别输入python generate_tfrecord.py --csv_input=data/cat_train.csv --output_path=data/train.record和python generate_tfrecord.py --csv_input=data/cat_test.csv --output_path=data/test.record，在data文件夹下将生成train.record和test.record文件。（注意：出现tensorflow object detection API 验证时报No module named \'object_detection\'时，在安装路径Anaconda3\\Lib\\site-packages下，新建tensorflow_model.pth文件，内容为模型文件路径：如C:\\Users\\CFF\\Desktop\\mymodels\\research 和C:\\Users\\CFF\\Desktop\\mymodels\\research\\slim）
③、在data文件夹下，新建一个cat_label_map.pbtxt文件，用Spyder打开，内容为：
1 item { 2 id: 1 3 name: \'cat\' 4 }
可根据分类数量进行修改。
四、在C:\\Users\\CFF\\Desktop\\models\\research\\object_detection文件夹下，建立一个training文件夹。
到https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs下载ssd_mobilenet_v1_coco.config模型，在training文件夹下新建一个文本文档，命名为ssd_mobilenet_v1_coco.config，内容如下：
1 # SSD with Mobilenet v1 configuration for MSCOCO Dataset. 2 # Users should configure the fine_tune_checkpoint field in the train config as 3 # well as the label_map_path and input_path fields in the train_input_reader and 4 # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that 5 # should be configured. 6 7 model { 8 ssd { 9 num_classes: 1 #根据实际情况填写分类数量 10 box_coder { 11 faster_rcnn_box_coder { 12 y_scale: 10.0 13 x_scale: 10.0 14 height_scale: 5.0 15 width_scale: 5.0 16 } 17 } 18 matcher { 19 argmax_matcher { 20 matched_threshold: 0.5 21 unmatched_threshold: 0.5 22 ignore_thresholds: false 23 negatives_lower_than_unmatched: true 24 force_match_for_each_row: true 25 } 26 } 27 similarity_calculator { 28 iou_similarity { 29 } 30 } 31 anchor_generator { 32 ssd_anchor_generator { 33 num_layers: 6 34 min_scale: 0.2 35 max_scale: 0.95 36 aspect_ratios: 1.0 37 aspect_ratios: 2.0 38 aspect_ratios: 0.5 39 aspect_ratios: 3.0 40 aspect_ratios: 0.3333 41 } 42 } 43 image_resizer { 44 fixed_shape_resizer { 45 height: 300 46 width: 300 47 } 48 } 49 box_predictor { 50 convolutional_box_predictor { 51 min_depth: 0 52 max_depth: 0 53 num_layers_before_predictor: 0 54 use_dropout: false 55 dropout_keep_probability: 0.8 56 kernel_size: 1 57 box_code_size: 4 58 apply_sigmoid_to_scores: false 59 conv_hyperparams { 60 activation: RELU_6, 61 regularizer { 62 l2_regularizer { 63 weight: 0.00004 64 } 65 } 66 initializer { 67 truncated_normal_initializer { 68 stddev: 0.03 69 mean: 0.0 70 } 71 } 72 batch_norm { 73 train: true, 74 scale: true, 75 center: true, 76 decay: 0.9997, 77 epsilon: 0.001, 78 } 79 } 80 } 81 } 82 feature_extractor { 83 type: \'ssd_mobilenet_v1\' 84 min_depth: 16 85 depth_multiplier: 1.0 86 conv_hyperparams { 87 activation: RELU_6, 88 regularizer { 89 l2_regularizer { 90 weight: 0.00004 91 } 92 } 93 initializer { 94 truncated_normal_initializer { 95 stddev: 0.03 96 mean: 0.0 97 } 98 } 99 batch_norm { 100 train: true, 101 scale: true, 102 center: true, 103 decay: 0.9997, 104 epsilon: 0.001, 105 } 106 } 107 } 108 loss { 109 classification_loss { 110 weighted_sigmoid { 111 } 112 } 113 localization_loss { 114 weighted_smooth_l1 { 115 } 116 } 117 hard_example_miner { 118 num_hard_examples: 3000 119 iou_threshold: 0.99 120 loss_type: CLASSIFICATION 121 max_negatives_per_positive: 3 122 min_negatives_per_image: 0 123 } 124 classification_weight: 1.0 125 localization_weight: 1.0 126 } 127 normalize_loss_by_num_matches: true 128 post_processing { 129 batch_non_max_suppression { 130 score_threshold: 1e-8 131 iou_threshold: 0.6 132 max_detections_per_class: 100 133 max_total_detections: 100 134 } 135 score_converter: SIGMOID 136 } 137 } 138 } 139 140 train_config: { 141 batch_size: 1 142 optimizer { 143 rms_prop_optimizer: { 144 learning_rate: { 145 exponential_decay_learning_rate { 146 initial_learning_rate: 0.004 147 decay_steps: 800720 148 decay_factor: 0.95 149 } 150 } 151 momentum_optimizer_value: 0.9 152 decay: 0.9 153 epsilon: 1.0 154 } 155 } 156 # fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt" 157 # from_detection_checkpoint: true 158 # Note: The below line limits the training process to 200K steps, which we 159 # empirically found to be sufficient enough to train the pets dataset. This 160 # effectively bypasses the learning rate schedule (the learning rate will 161 # never decay). Remove the below line to train indefinitely. 162 num_steps: 200000 163 data_augmentation_options { 164 random_horizontal_flip { 165 } 166 } 167 data_augmentation_options { 168 ssd_random_crop { 169 } 170 } 171 } 172 173 train_input_reader: { 174 tf_record_input_reader { 175 input_path:"data/train.record" 176 } 177 label_map_path:"data/cat_label_map.pbtxt" 178 } 179 180 eval_config: { 181 num_examples: 8000 182 # Note: The below line limits the evaluation process to 10 evaluations. 183 # Remove the below line to evaluate indefinitely. 184 max_evals: 10 185 } 186 187 eval_input_reader: { 188 tf_record_input_reader { 189 input_path:"data/test.record" 190 } 191 label_map_path:"data/cat_label_map.pbtxt" 192 shuffle: false 193 num_readers: 1 194 }
其中，num_classes: 1 是根据实际情况填写分类数量，input_path:"data/train.record"和input_path:"data/test.record"为之前在data文件加下生成的train.record文件和tets.record文件。label_map_path:"data/cat_label_map.pbtxt"也是之前在data中生成的文件。
五、训练模型
①、在models/research路径下，输入protoc object_detection/protos/*.proto --python_out=.命令，将所有的.proto文件生成.py文件。
②、打开Anaconda Prompt，通过命令cd C:\\Users\\CFF\\Desktop\\models\\research\\object_detection到该目录下，运行以下命令：
1 python model_main.py --pipeline_config_path=training/ssd_mobilenet_v1_coco.config \\ --model_dir=training \\ --num_train_steps=50000 \\ --num_eval_steps=2000 \\
开始训练。训练一段时间后，可以在C:\\Users\\CFF\\Desktop\\models\\research\\object_detection，通过tensorboard --logdir=training命令，根据返回的网址在浏览器中打开，可以看到最新的图表。
六、测试自己的图片
①、在C:\\Users\\CFF\\Desktop\\models\\research\\object_detection\\test_images文件夹下放需要识别的图片，用image1-imageN命名。
②、打开Anaconda Prompt，通过命令cd C:\\Users\\CFF\\Desktop\\models\\research\\object_detection到该目录下，输入python export_inference_graph.py \\ --input_type image_tensor \\ --pipeline_config_path training/ssd_mobilement_v1_coco.config \\ --trained_checkpoint_prefix training/model.ckpt-9278 \\ --output_directory cat_detection。其中model.ckpt-9278为训练的最后步数，可在training文件下看到。在cat_detection
下生成以下文件。
③、打开Anaconda Prompt，通过命令cd C:\\Users\\CFF\\Desktop\\models\\research\\object_detection到该目录下，输入jupyter notebook回车，打开交互环境。下载对应的Python文件object_detection_tutorial.py到本地。
④、用Spyder打开object_detection_tutorial.py文件，代码如下：
1 # coding: utf-8 2 3 # # Object Detection Demo 4 # Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start. 5 6 # # Imports 7 8 # In[1]: 9 10 11 import numpy as np 12 import os 如何安装 TensorFlow 2 和 object_detection 模块？TensorFlow Object Detection API
TensorFlow object_detection 使用
TensorFlow object detection API