1.前期工作準備

1.首先從GitHub上下載models 網址：https://github.com/tensorflow/models，將object detection文件夾整個復制到python安裝目錄中的python\python3.5.2\Lib\site-packages下（目的是為了防止之后的代碼發生找不到包的問題）

2.protobuf下載，我下載的是protoc-3.3.0-win32.zip 網址：https://github.com/google/protobuf/releases

3.將其解壓到第一步下載的models文件夾中的research文件夾下，CMD cd到research文件夾下輸入

protoc ./object_detection/protos/*.proto --python_out=.

我在這里發生了報錯：（這里轉載并參考了https://blog.csdn.net/xingwei_09/article/details/79161221的解決方法）

解決方法就是在Lib\site-packages文件夾下（無論是python還是anaconda），找到tensorflow_model.pth文件，沒有就新建一個，輸入下方路徑：

之后就Ok啦！

2.首先準備好圖片以及xml文件

（說明：這里的XML文件是需要使用labelImg軟件進行批注，但是我的數據中原來已經完成批注，所以這里不再解釋）

3.創建文件目錄

說明：其中data文件夾中存放的是之后要進行生成的train.csv,eval.csv , train.record,eval.record等文件；

? ? ? ? ? ?1.output文件夾存放訓練后的輸出路徑；

? ? ? ? ? ?2.pretrained_model中存放的是從GitHub上下載解壓的ssd_mobilenet_v1_coco_2018_01_28文件中的內容，下載網址：? ? ? ? ? ? ? ? ?https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

? ? ? ? ? ?3.train與test分別存放訓練與檢測的圖片與XML文件；

? ? ? ? ? ?4.training中存放從object_detection文件夾中復制來的ssd_mobilenet_v1_coco.config；

下面用到的文件說明（未說到的沒用）：

? ? ? ? ? ? 1.xml_to_cvs.py是將XML文件轉換為CSV文件的代碼；

? ? ? ? ? ? 2.cvs_to_tfrecord.py、generate_tfrecord.py分別是使用pycharm、CMD兩種方法將CSV轉換為TFRECORD文件的代碼

4.進行XML到CSV文件的轉換

代碼：

import os import glob import pandas as pd import xml.etree.ElementTree as ETdef xml_to_csv(path):xml_list = []# 讀取注釋文件for xml_file in glob.glob(path + '/*.xml'):tree = ET.parse(xml_file)root = tree.getroot()for member in root.findall('object'):# value=(root.find('filename').test + '.jpg',#這里要注意，如果生成的filename中沒有.jpg文件，就在這里修改value = (root.find('filename').text,int(root.find('size')[0].text),int(root.find('size')[1].text),member[0].text,int(member[4][0].text),int(member[4][1].text),int(member[4][2].text),int(member[4][3].text))xml_list.append(value)column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']# 將所有數據分為樣本集和驗證集，一般按照3:1的比例train_list = xml_list[0: int(len(xml_list) * 0.67)]eval_list = xml_list[int(len(xml_list) * 0.67) + 1: ]# 保存為CSV格式train_df = pd.DataFrame(train_list, columns=column_name)eval_df = pd.DataFrame(eval_list, columns=column_name)train_df.to_csv('D:\\software\\pycharm\\pycharmproject\\imagerecognition\\models-master\\models-master\\research\\securityhattest\\data\\train.csv', index=None)eval_df.to_csv('D:\\software\\pycharm\\pycharmproject\\imagerecognition\\models-master\\models-master\\research\\securityhattest\\data\\eval.csv', index=None)def main():path = 'D:\\software\\pycharm\\pycharmproject\\imagerecognition\\models-master\\models-master\\research\\securityhattest\\data\\train'xml_to_csv(path)print('Successfully converted xml to csv.')main()

5.將生成的CSV文件轉換為tfrecord文件

方法一：在pycharm中直接運行?cvs_to_tfrecord.py

from __future__ import division from __future__ import print_function from __future__ import absolute_importimport os import io import pandas as pd import tensorflow as tffrom PIL import Image # from object_detection.utils import dataset_util from object_detection.utils import dataset_util from collections import namedtuple, OrderedDictflags = tf.app.flags flags.DEFINE_string('csv_input', '', 'Path to the CSV input') flags.DEFINE_string('output_path', '', 'Path to output TFRecord') FLAGS = flags.FLAGS# 將分類名稱轉成ID號 #這里要修改成你的分類ID，打開之前的CVS文件可以看到 def class_text_to_int(row_label):if row_label == 'hat':return 1elif row_label == 'person':return 2else:print('NONE: ' + row_label)# Nonedef split(df, group):data = namedtuple('data', ['filename', 'object'])gb = df.groupby(group)return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]def create_tf_example(group, path):print(os.path.join(path, '{}'.format(group.filename)))with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:encoded_jpg = fid.read()encoded_jpg_io = io.BytesIO(encoded_jpg)image = Image.open(encoded_jpg_io)width, height = image.sizefilename = (group.filename + '.jpg').encode('utf8')#這里要注意，很多網上的代碼都是filename =group.filename.encode('utf8')，我直接運行會產生段錯誤image_format = b'jpg'xmins = []xmaxs = []ymins = []ymaxs = []classes_text = []classes = []for index, row in group.object.iterrows():xmins.append(row['xmin'] / width)xmaxs.append(row['xmax'] / width)ymins.append(row['ymin'] / height)ymaxs.append(row['ymax'] / height)classes_text.append(row['class'].encode('utf8'))classes.append(class_text_to_int(row['class']))tf_example = tf.train.Example(features=tf.train.Features(feature={'image/height': dataset_util.int64_feature(height),'image/width': dataset_util.int64_feature(width),'image/filename': dataset_util.bytes_feature(filename),'image/source_id': dataset_util.bytes_feature(filename),'image/encoded': dataset_util.bytes_feature(encoded_jpg),'image/format': dataset_util.bytes_feature(image_format),'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),'image/object/class/text': dataset_util.bytes_list_feature(classes_text),'image/object/class/label': dataset_util.int64_list_feature(classes),}))return tf_exampledef main(csv_input, output_path, imgPath):writer = tf.python_io.TFRecordWriter(output_path)path = imgPathexamples = pd.read_csv(csv_input)grouped = split(examples, 'filename')for group in grouped:tf_example = create_tf_example(group, path)writer.write(tf_example.SerializeToString())writer.close()print('Successfully created the TFRecords: {}'.format(output_path))if __name__ == '__main__':# imgPath = 'E:\data\Images'存放的圖片的路徑imgPath = r'D:\software\pycharm\pycharmproject\imagerecognition\models-master\models-master\research\securityhattest\images\train'# 生成train.record文件output_path = 'data/train.tfrecord'#輸出路徑csv_input = 'data/train.csv'#存放的csv文件路徑main(csv_input, output_path, imgPath)# 生成驗證文件 eval.recordoutput_path = 'data/eval.tfrecord'csv_input = 'data/eval.csv'main(csv_input, output_path, imgPath)

方法二：在CMD中CD到該項目文件夾下，我這里是images文件夾下，輸入下列命令，運行generate_tfrecord.py：

python generate_tfrecord.py --csv_input=data/train.csv --output_path=data/train.record python generate_tfrecord.py --csv_input=data/eval.csv --output_path=eval.record

?輸出Successfully created the TFRecords即表示完成。

（小小提醒：開始沒發現，我的數據集中XML文件中的圖片名稱有的是JPEG格式，而對應的圖片全是JPG格式，導致PY代碼中出現段錯誤，提醒大家多多注意這中坑…………）

6.新建自己的pbtxt文件

在自己項目中的data文件夾中新建label_map.pbtxt文件，我的是hatlabel_map.pbtxt，輸入：

數字要與cvs_to_tfrecord.py、generate_tfrecord.py中的class_text_to_int函數一致對應

7.修改自己的ssd_mobilenet_v1_coco.config文件

代碼：

# SSD with Mobilenet v1 configuration for MSCOCO Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that # should be configured.model {ssd {num_classes: 2#這里是你的種類數box_coder {faster_rcnn_box_coder {y_scale: 10.0x_scale: 10.0height_scale: 5.0width_scale: 5.0}}matcher {argmax_matcher {matched_threshold: 0.5unmatched_threshold: 0.5ignore_thresholds: falsenegatives_lower_than_unmatched: trueforce_match_for_each_row: true}}similarity_calculator {iou_similarity {}}anchor_generator {ssd_anchor_generator {num_layers: 6min_scale: 0.2max_scale: 0.95aspect_ratios: 1.0aspect_ratios: 2.0aspect_ratios: 0.5aspect_ratios: 3.0aspect_ratios: 0.3333}}image_resizer {fixed_shape_resizer {height: 300width: 300}}box_predictor {convolutional_box_predictor {min_depth: 0max_depth: 0num_layers_before_predictor: 0use_dropout: falsedropout_keep_probability: 0.8kernel_size: 1box_code_size: 4apply_sigmoid_to_scores: falseconv_hyperparams {activation: RELU_6,regularizer {l2_regularizer {weight: 0.00004}}initializer {truncated_normal_initializer {stddev: 0.03mean: 0.0}}batch_norm {train: true,scale: true,center: true,decay: 0.9997,epsilon: 0.001,}}}}feature_extractor {type: 'ssd_mobilenet_v1'min_depth: 16depth_multiplier: 1.0conv_hyperparams {activation: RELU_6,regularizer {l2_regularizer {weight: 0.00004}}initializer {truncated_normal_initializer {stddev: 0.03mean: 0.0}}batch_norm {train: true,scale: true,center: true,decay: 0.9997,epsilon: 0.001,}}}loss {classification_loss {weighted_sigmoid {}}localization_loss {weighted_smooth_l1 {}}hard_example_miner {num_hard_examples: 3000iou_threshold: 0.99loss_type: CLASSIFICATIONmax_negatives_per_positive: 3min_negatives_per_image: 0}classification_weight: 1.0localization_weight: 1.0}normalize_loss_by_num_matches: truepost_processing {batch_non_max_suppression {score_threshold: 1e-8iou_threshold: 0.6max_detections_per_class: 100max_total_detections: 100}score_converter: SIGMOID}} }train_config: {batch_size: 2optimizer {rms_prop_optimizer: {learning_rate: {exponential_decay_learning_rate {initial_learning_rate: 0.004decay_steps: 800720decay_factor: 0.95}}momentum_optimizer_value: 0.9decay: 0.9epsilon: 1.0}} #這里修改fine_tune_checkpoint: "D:/software/pycharm/pycharmproject/imagerecognition/models-master/models-master/research/securityhattest/images/pretrained_model/model.ckpt"from_detection_checkpoint: true# Note: The below line limits the training process to 200K steps, which we# empirically found to be sufficient enough to train the pets dataset. This# effectively bypasses the learning rate schedule (the learning rate will# never decay). Remove the below line to train indefinitely.num_steps: 40000data_augmentation_options {random_horizontal_flip {}}data_augmentation_options {ssd_random_crop {}} } #下方4個路徑修改 train_input_reader: {tf_record_input_reader {input_path: "D:/software/pycharm/pycharmproject/imagerecognition/models-master/models-master/research/securityhattest/images/data/train.record"}label_map_path: "D:/software/pycharm/pycharmproject/imagerecognition/models-master/models-master/research/securityhattest/images/data/hatlabel_map.pbtxt" }eval_config: {num_examples: 48#這里要修改# Note: The below line limits the evaluation process to 10 evaluations.# Remove the below line to evaluate indefinitely.max_evals: 10 }eval_input_reader: {tf_record_input_reader {input_path: "D:/software/pycharm/pycharmproject/imagerecognition/models-master/models-master/research/securityhattest/images/data/eval.record"}label_map_path: "D:/software/pycharm/pycharmproject/imagerecognition/models-master/models-master/research/securityhattest/images/data/label_map.pbtxt"shuffle: falsenum_readers: 1 }

8.開始訓練

我是直接打開research\object_detection\legacy下的train.py文件，修改后直接運行

代碼：

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================r"""Training executable for detection models.This executable is used to train DetectionModels. There are two ways of configuring the training job:1) A single pipeline_pb2.TrainEvalPipelineConfig configuration file can be specified by --pipeline_config_path.Example usage:./train \--logtostderr \--train_dir=path/to/train_dir \--pipeline_config_path=pipeline_config.pbtxt2) Three configuration files can be provided: a model_pb2.DetectionModel configuration file to define what type of DetectionModel is being trained, an input_reader_pb2.InputReader file to specify what training data will be used and a train_pb2.TrainConfig file to configure training parameters.Example usage:./train \--logtostderr \--train_dir=path/to/train_dir \--model_config_path=model_config.pbtxt \--train_config_path=train_config.pbtxt \--input_config_path=train_input_config.pbtxt """import functools import json import os import tensorflow as tf from tensorflow.contrib import framework as contrib_frameworkfrom object_detection.builders import dataset_builder from object_detection.builders import graph_rewriter_builder from object_detection.builders import model_builder from object_detection.legacy import trainer from object_detection.utils import config_utiltf.logging.set_verbosity(tf.logging.INFO)flags = tf.app.flags flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.') flags.DEFINE_integer('task', 0, 'task id') flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.') flags.DEFINE_boolean('clone_on_cpu', False,'Force clones to be deployed on CPU. Note that even if ''set to False (allowing ops to run on gpu), some ops may ''still be run on the CPU if they have no GPU kernel.') flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer ''replicas.') flags.DEFINE_integer('ps_tasks', 0,'Number of parameter server tasks. If None, does not use ''a parameter server.') flags.DEFINE_string('train_dir', r'D:\software\pycharm\pycharmproject\imagerecognition\models-master\models-master\research\securityhattest\images\output','Directory to save the checkpoints and training summaries.')flags.DEFINE_string('pipeline_config_path', r'D:\software\pycharm\pycharmproject\imagerecognition\models-master\models-master\research\securityhattest\images\training\ssd_mobilenet_v1_coco.config','Path to a pipeline_pb2.TrainEvalPipelineConfig config ''file. If provided, other configs are ignored')flags.DEFINE_string('train_config_path', '','Path to a train_pb2.TrainConfig config file.') flags.DEFINE_string('input_config_path', '','Path to an input_reader_pb2.InputReader config file.') flags.DEFINE_string('model_config_path', '','Path to a model_pb2.DetectionModel config file.')FLAGS = flags.FLAGS@contrib_framework.deprecated(None, 'Use object_detection/model_main.py.') def main(_):assert FLAGS.train_dir, '`train_dir` is missing.'if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)if FLAGS.pipeline_config_path:configs = config_util.get_configs_from_pipeline_file(FLAGS.pipeline_config_path)if FLAGS.task == 0:tf.gfile.Copy(FLAGS.pipeline_config_path,os.path.join(FLAGS.train_dir, 'pipeline.config'),overwrite=True)else:configs = config_util.get_configs_from_multiple_files(model_config_path=FLAGS.model_config_path,train_config_path=FLAGS.train_config_path,train_input_config_path=FLAGS.input_config_path)if FLAGS.task == 0:for name, config in [('model.config', FLAGS.model_config_path),('train.config', FLAGS.train_config_path),('input.config', FLAGS.input_config_path)]:tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),overwrite=True)model_config = configs['model']train_config = configs['train_config']input_config = configs['train_input_config']model_fn = functools.partial(model_builder.build,model_config=model_config,is_training=True)def get_next(config):return dataset_builder.make_initializable_iterator(dataset_builder.build(config)).get_next()create_input_dict_fn = functools.partial(get_next, input_config)env = json.loads(os.environ.get('TF_CONFIG', '{}'))cluster_data = env.get('cluster', None)cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else Nonetask_data = env.get('task', None) or {'type': 'master', 'index': 0}task_info = type('TaskSpec', (object,), task_data)# Parameters for a single worker.ps_tasks = 0worker_replicas = 1worker_job_name = 'lonely_worker'task = 0is_chief = Truemaster = ''if cluster_data and 'worker' in cluster_data:# Number of total worker replicas include "worker"s and the "master".worker_replicas = len(cluster_data['worker']) + 1if cluster_data and 'ps' in cluster_data:ps_tasks = len(cluster_data['ps'])if worker_replicas > 1 and ps_tasks < 1:raise ValueError('At least 1 ps task is needed for distributed training.')if worker_replicas >= 1 and ps_tasks > 0:# Set up distributed training.server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',job_name=task_info.type,task_index=task_info.index)if task_info.type == 'ps':server.join()returnworker_job_name = '%s/task:%d' % (task_info.type, task_info.index)task = task_info.indexis_chief = (task_info.type == 'master')master = server.targetgraph_rewriter_fn = Noneif 'graph_rewriter_config' in configs:graph_rewriter_fn = graph_rewriter_builder.build(configs['graph_rewriter_config'], is_training=True)trainer.train(create_input_dict_fn,model_fn,train_config,master,task,FLAGS.num_clones,worker_replicas,FLAGS.clone_on_cpu,ps_tasks,worker_job_name,is_chief,FLAGS.train_dir,graph_hook_fn=graph_rewriter_fn)if __name__ == '__main__':tf.app.run()

修改內容：只是添加了train_dir、pipeline_config_path的路徑

?

9.打開tensorboard查看訓練過程

1.CMD CD到images文件夾中的output文件夾，輸入tensorboard --logdir ./

10.訓練完成，導出訓練模型

CMD CD到object_detection文件夾，輸入：

python export_inference_graph.py --input_type image_tensor--pipeline_config_path=D://software//pycharm//pycharmproject//imagerecognition//models-master//models-master//research//securityhattest//images//training//ssd_mobilenet_v1_coco.config --trained_checkpoint_prefix=D://software//pycharm//pycharmproject//imagerecognition//models-master//models-master//research//securityhattest//images//output//model.ckpt-10374 --output_directory=D://software//pycharm//pycharmproject//imagerecognition//models-master//models-master//research//securityhattest//images//finished

?報錯：ValueError: The passed save_path is not a valid checkpoint:

原因是項目所在的文件導入路徑的字符太長

將--trained_checkpoint_prefix的路徑改的短一點就可以了

?

之后的模型檢驗借鑒了https://blog.csdn.net/qq_33624802/article/details/82384117

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11.模型檢驗

安裝了anaconda的大家可以使用jupyter打開object_detection中的object_detection_tutorial.ipynb，如果使用的是python可以使用下面的代碼：

# -*- coding: utf-8 -*-import numpy as np import os import pylabimport tensorflow as tf from matplotlib import pyplot as plt from PIL import Image #下面的兩個導入根據你PY文件的路徑進行修改，我的是在object——detection文件夾下 from utils import label_map_utilfrom utils import visualization_utils as vis_util# What model to download.剛才訓練模型的輸出路徑 MODEL_NAME = 'D:\\software\\pycharm\\pycharmproject\\imagerecognition\\models-master\\models-master\\research\\securityhattest\\images\\finished'# Path to frozen detection graph. This is the actual model that is used for the object detection. PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'# List of the strings that is used to add correct label for each box. #PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') #你的label_map.pbtxt文件路徑 PATH_TO_LABELS='D:\\software\\pycharm\\pycharmproject\\imagerecognition\\models-master\\models-master\\research\\securityhattest\\images\\data\\hatlabel_map.pbtxt'NUM_CLASSES = 2#分類個數tf.reset_default_graph()od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:serialized_graph = fid.read()od_graph_def.ParseFromString(serialized_graph)tf.import_graph_def(od_graph_def, name='')label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,use_display_name=True) category_index = label_map_util.create_category_index(categories)def load_image_into_numpy_array(image):(im_width, im_height) = image.sizereturn np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)#存放用于測試的圖片路徑，個數更改的話for i in range(1, 5)也要更改 PATH_TO_TEST_IMAGES_DIR = 'D:\\software\\pycharm\\pycharmproject\\imagerecognition\\models-master\\models-master\\research\\securityhattest\\images\\test' TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 5)]# Size, in inches, of the output images. IMAGE_SIZE = (12, 8)detection_graph = tf.get_default_graph() with tf.Session(graph=detection_graph) as sess:for image_path in TEST_IMAGE_PATHS:image = Image.open(image_path)# the array based representation of the image will be used later in order to prepare the# result image with boxes and labels on it.image_np = load_image_into_numpy_array(image)# Expand dimensions since the model expects images to have shape: [1, None, None, 3]image_np_expanded = np.expand_dims(image_np, axis=0)image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')# Each box represents a part of the image where a particular object was detected.boxes = detection_graph.get_tensor_by_name('detection_boxes:0')# Each score represent how level of confidence for each of the objects.# Score is shown on the result image, together with the class label.scores = detection_graph.get_tensor_by_name('detection_scores:0')classes = detection_graph.get_tensor_by_name('detection_classes:0')num_detections = detection_graph.get_tensor_by_name('num_detections:0')# Actual detection.(boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections],feed_dict={image_tensor: image_np_expanded})# Visualization of the results of a detection.vis_util.visualize_boxes_and_labels_on_image_array(image_np,np.squeeze(boxes),np.squeeze(classes).astype(np.int32),np.squeeze(scores),category_index,use_normalized_coordinates=True,line_thickness=8)plt.figure(figsize=IMAGE_SIZE)plt.imshow(image_np)pylab.show()

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12.效果展示

?

我是使用自己電腦，設置訓練40000次，等不上了10000就停了，而且數據量較小，所以有的圖片識別效果不好，下一步就是擴大數據集，用服務器跑了~

輸出詳細信息：參見https://blog.csdn.net/weixin_43599336/article/details/84112134

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