tensorflow 物体检测（检测限速标志）
- 环境配置
使用protobuf来配置模型和训练参数，所以API正常使用必须先编译protobuf库，这里可以下载直接编译好的pb库（https://github.com/google/protobuf/releases ），解压压缩包后，把protoc加入到环境变量中：
$ cd tensorflow/models
$ protoc object_detection/protos/*.proto --python_out=. #注意： *在这里有时会报错，找不到文件，可以手动添加文件命名
（我是把protoc加到环境变量中，遇到找不到*.proto文件的报错，后来把protoc.exe放到models/object_detection目录下，重新执行才可以）
然后将models和slim（tf高级框架）加入python环境变量：
PYTHONPATH=$PYTHONPATH:/your/path/to/tensorflow/models:/your/path/to/tensorflow/models/slim
2.数据准备
自己制作了限速标志的数据，总共250张，训练200，测试50.有了数据以后我们需要给他们打标签。我们需要手动在每一张图中框出限速标志的位置。一个比较好的打标工具是LabelImg，标签：sign,生成VOC格式的数据。制作VOC格式数据文件夹形式：my_images
__VOCdevkit
__VOC2012
__Annotations(文件名：2007_0000)#xml格式的标签
__JPEGImages(文件名：2007_0000)#jpg图像
__ImageSets
__Main
__train
__val
import os import randompt="/tensorflow/model/research/object_detection/my_images/VOCdevkit/VOC2012/JPEGImages" image_name=os.listdir(pt) for temp in image_name:if temp.endswith(".jpg"):print (temp.replace('.jpg',''))以上代码可以生成train和val列表
将VOC数据转化成tf.recoord数据 ：参考 dataset_tools/create_pascal_tf_record.py 根据自己路径文件格式做出适当修改，我的如下；
# 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"""Convert raw PASCAL dataset to TFRecord for object_detection.Example usage:python object_detection/dataset_tools/create_pascal_tf_record.py \--data_dir=/home/user/VOCdevkit \--year=VOC2012 \--output_path=/home/user/pascal.record """ from __future__ import absolute_import from __future__ import division from __future__ import print_functionimport hashlib import io import logging import osfrom lxml import etree import PIL.Image import tensorflow as tffrom object_detection.utils import dataset_util from object_detection.utils import label_map_utilflags = tf.app.flags flags.DEFINE_string('data_dir', '', 'Root directory to raw PASCAL VOC dataset.') flags.DEFINE_string('set', 'train', 'Convert training set, validation set or ''merged set.') flags.DEFINE_string('annotations_dir', 'Annotations','(Relative) path to annotations directory.') flags.DEFINE_string('year', 'VOC2007', 'Desired challenge year.') flags.DEFINE_string('output_path', '', 'Path to output TFRecord') flags.DEFINE_string('label_map_path', 'data/pascal_label_map.pbtxt','Path to label map proto') #此处修改自己标签 flags.DEFINE_boolean('ignore_difficult_instances', False, 'Whether to ignore ''difficult instances') FLAGS = flags.FLAGSSETS = ['train', 'val', 'trainval', 'test'] YEARS = ['VOC2007', 'VOC2012', 'merged']def dict_to_tf_example(data,dataset_directory,label_map_dict,ignore_difficult_instances=False,image_subdirectory='JPEGImages'):"""Convert XML derived dict to tf.Example proto.Notice that this function normalizes the bounding box coordinates providedby the raw data.Args:data: dict holding PASCAL XML fields for a single image (obtained byrunning dataset_util.recursive_parse_xml_to_dict)dataset_directory: Path to root directory holding PASCAL datasetlabel_map_dict: A map from string label names to integers ids.ignore_difficult_instances: Whether to skip difficult instances in thedataset (default: False).image_subdirectory: String specifying subdirectory within thePASCAL dataset directory holding the actual image data.Returns:example: The converted tf.Example.Raises:ValueError: if the image pointed to by data['filename'] is not a valid JPEG"""img_path = os.path.join(image_subdirectory,data['filename'])#image_subdirectory=JPEGImages 此处修改了year1='VOC2012'#此处修改full_path = os.path.join(dataset_directory,year1, img_path)#此处修改with tf.gfile.GFile(full_path, 'rb') as fid:encoded_jpg = fid.read()encoded_jpg_io = io.BytesIO(encoded_jpg)image = PIL.Image.open(encoded_jpg_io)if image.format != 'JPEG':raise ValueError('Image format not JPEG')key = hashlib.sha256(encoded_jpg).hexdigest()width = int(data['size']['width'])height = int(data['size']['height'])xmin = []ymin = []xmax = []ymax = []classes = []classes_text = []truncated = []poses = []difficult_obj = []if 'object' in data:for obj in data['object']:difficult = bool(int(obj['difficult']))if ignore_difficult_instances and difficult:continuedifficult_obj.append(int(difficult))xmin.append(float(obj['bndbox']['xmin']) / width)ymin.append(float(obj['bndbox']['ymin']) / height)xmax.append(float(obj['bndbox']['xmax']) / width)ymax.append(float(obj['bndbox']['ymax']) / height)classes_text.append(obj['name'].encode('utf8'))classes.append(label_map_dict[obj['name']])truncated.append(int(obj['truncated']))poses.append(obj['pose'].encode('utf8'))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(data['filename'].encode('utf8')),'image/source_id': dataset_util.bytes_feature(data['filename'].encode('utf8')),'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),'image/encoded': dataset_util.bytes_feature(encoded_jpg),'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),'image/object/class/text': dataset_util.bytes_list_feature(classes_text),'image/object/class/label': dataset_util.int64_list_feature(classes),'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),'image/object/truncated': dataset_util.int64_list_feature(truncated),'image/object/view': dataset_util.bytes_list_feature(poses),}))return exampledef main(_):if FLAGS.set not in SETS:raise ValueError('set must be in : {}'.format(SETS))if FLAGS.year not in YEARS:raise ValueError('year must be in : {}'.format(YEARS))data_dir = FLAGS.data_diryears = ['VOC2007', 'VOC2012']if FLAGS.year != 'merged':years = [FLAGS.year]writer = tf.python_io.TFRecordWriter(FLAGS.output_path)label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path)for year in years:logging.info('Reading from PASCAL %s dataset.', year)examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main',FLAGS.set + '.txt')annotations_dir = os.path.join(data_dir, year, FLAGS.annotations_dir)examples_list = dataset_util.read_examples_list(examples_path)for idx, example in enumerate(examples_list):if idx % 100 == 0:logging.info('On image %d of %d', idx, len(examples_list))path = os.path.join(annotations_dir, example + '.xml')#此处修改with tf.gfile.GFile(path, 'r') as fid:xml_str = fid.read()xml = etree.fromstring(xml_str)data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict,FLAGS.ignore_difficult_instances)writer.write(tf_example.SerializeToString())writer.close()if __name__ == '__main__':tf.app.run()修改data/pascal_label_map.pbtxt 改成自己的标签数据，我只有一类，从id:1开始:
item {
id: 1
name: 'sign'
}生成训练数据:pascal_train.record
python dataset_tools/create_pascal_tf_record.py --data_dir=my_images/VOCdevkit/ --year=VOC2012 --output_path=my_images/VOCdevkit/pascal_train.record --set=train
生成测试数据：pascal_val.record
python dataset_tools/create_pascal_tf_record.py --data_dir=my_images/VOCdevkit/ --year=VOC2012 --output_path=my_images/VOCdevkit/pascal_val.record --set=val
3，下载模型：ssd_mobilenet_v1_coco
下载链接https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
下载ssd_mobilenet_v1_coco完后，将其解压到my_images文件夹下,将model.ckpt 3个文件放在VOC2012下面
接下来呢，新建配置文件，samples/configs/文件夹下有一些示例文件，我们就模仿它们配置，参考faster_rcnn_inception_resnet_v2_atrous_coco.config文件，将其复制在VOC2012下面
文件名：ssd_mobilenet_v1.config
model { ssd { num_classes: 1 box_coder { faster_rcnn_box_coder { y_scale: 10.0 x_scale: 10.0 height_scale: 5.0 width_scale: 5.0 } } matcher { argmax_matcher { matched_threshold: 0.5 unmatched_threshold: 0.5 ignore_thresholds: false negatives_lower_than_unmatched: true force_match_for_each_row: true } } similarity_calculator { iou_similarity { } } anchor_generator { ssd_anchor_generator { num_layers: 6 min_scale: 0.2 max_scale: 0.95 aspect_ratios: 1.0 aspect_ratios: 2.0 aspect_ratios: 0.5 aspect_ratios: 3.0 aspect_ratios: 0.3333 } } image_resizer { fixed_shape_resizer { height: 300 width: 300 } } box_predictor { convolutional_box_predictor { min_depth: 0 max_depth: 0 num_layers_before_predictor: 0 use_dropout: false dropout_keep_probability: 0.8 kernel_size: 1 box_code_size: 4 apply_sigmoid_to_scores: false conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } } feature_extractor { type: 'ssd_mobilenet_v1' min_depth: 16 depth_multiplier: 1.0 conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 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: 3000 iou_threshold: 0.99 loss_type: CLASSIFICATION max_negatives_per_positive: 3 min_negatives_per_image: 0 } classification_weight: 1.0 localization_weight: 1.0 } normalize_loss_by_num_matches: true post_processing { batch_non_max_suppression { score_threshold: 1e-8 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 100 } score_converter: SIGMOID } } }train_config: { batch_size: 24 optimizer { rms_prop_optimizer: { learning_rate: { exponential_decay_learning_rate { initial_learning_rate: 0.004 decay_steps: 800720 decay_factor: 0.95 } } momentum_optimizer_value: 0.9 decay: 0.9 epsilon: 1.0 } } fine_tune_checkpoint: "my_images/VOCdevkit/VOC2012/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: 20000 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } }train_input_reader: { tf_record_input_reader { input_path: "my_images/pascal_train.record" } label_map_path: "data/pascal_label_map.pbtxt" }eval_config: { num_examples: 50 # 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: "my_images/pascal_val.record" } label_map_path: "data/pascal_label_map.pbtxt" shuffle: false num_readers: 1 }
4，训练模型
在object_detection下执行:创建检查点文件在my_images/train
python legacy/train.py --train_dir=my_images/train/ --pipeline_config_path=my_images/VOCdevkit/VOC2012/ssd_mobilenet_v1.config
my_images目录下新建一个eval目录,用于保存eval的文件。另开终端，执行如下命令
/tensorflow/models/research/object_detection$ python legacy/eval.py
--logtostderr \
--pipeline_config_path=my_images/VOCdevkit/VOC2012/ssd_mobilenet_v1_raccoon.config \
--checkpoint_dir=my_images/train \
--eval_dir=my_images/eval
特别提醒：建议使用legacy/train.py 用object_detection/model_main.py本人没成功过。可能还会遇到GPU内存方面的错误，建议指定GPU训练。训练了20k steps.效果测试精确度在94%,97%左右，数据太少每次测试有变化。但是效果还是不错的，贴几张效果图如下：
下一篇：tensorflow 训练完模型的导出和测试模型
tensorflow 物体检测（检测限速标志）相关推荐
- Google发布新的TensorFlow物体检测API
\\ Google发布TensorFlow物体检测API,帮助开发人员和研究人员识别图片中的物体.Google专注于提高API的易用性和性能,新的模型于6月16号发布,在基准测试中表现出良好的性能,并 ...
- 使用SSD网络模型进行Tensorflow物体检测（V1.1摄像头检测）
使用SSD网络模型进行Tensorflow物体检测?(V1.1摄像头检测) 文章目录 使用SSD网络模型进行Tensorflow物体检测?(V1.1摄像头检测) 1.模型的加载和utils库环境的配置 ...
- 使用SSD网络模型进行Tensorflow物体检测（V1.2视频检测）
使用SSD网络模型进行Tensorflow物体检测?(V1.2视频检测) 文章目录 使用SSD网络模型进行Tensorflow物体检测?(V1.2视频检测) 1.模型的加载和utils库环境的配置? ...
- vuforia的物体识别能识别大物体吗_衢州sensopart 物体识别检测视觉-灵测信息
首页 > 新闻列表 > 浏览文章 发布时间:2020-11-03 12:07:00 浏览量: 6 导读:灵测信息为您提供衢州sensopart 物体识别检测视觉的相关知识与详情: 本文的关 ...
- YOLOv3物体/目标检测之实战篇（Windows系统、Python3、TensorFlow2版本）
前言 基于YOLO进行物体检测.对象识别,在搭建好开发环境后,先和大家进行实践应用中,体验YOLOv3物体/目标检测效果和魅力:同时逐步了解YOLOv3的不足和优化思路. 开发环境参数 系统:Wind ...
- 人车目标检测、交通标志识别…云测数据喊你参加第九届CCF大数据与计算智能大赛啦
2021年大数据与AI领域年度盛事--第九届CCF大数据与计算智能大赛已全面开赛! 云测数据携手OneFlow一流科技,发布基于自动驾驶场景下的"人车目标检测.交通标志分类识别.交通灯识别. ...
- 线扫相机——机器视觉中无限制物体的检测
点击上方"小白学视觉",选择加"星标"或"置顶" 重磅干货,第一时间送达 在机器视觉中,在检测连续物体或者滚动物体时,线扫相机是最佳的解决方 ...
- 利用SIFT和RANSAC算法（openCV框架）实现物体的检测与定位，并求出变换矩阵（findFundamentalMat和findHomography的比较）
本文目标是通过使用SIFT和RANSAC算法,完成特征点的正确匹配,并求出变换矩阵,通过变换矩阵计算出要识别物体的边界(文章中有部分源码,整个工程我也上传了,请点击这里). SIFT算法是目前公认的效 ...
- 【自动驾驶】鸽了很久的小物体目标检测代码【小物体目标检测】
鸽了很久的小物体目标检测代码 https://github.com/LT1st/SmallObstacleDetection/tree/main/code#readme Pytorch: Small ...
最新文章
- win10怎么设置开机启动项目_苹果mac开机启动项怎么设置
- 关于Virtual-Hosting的理解
- latex中怎样使公式居中_LaTeX_多行公式对齐居中的同时选择性的加编号
- accept和select的区别
- mybatis那些事~
- ruby gem 安装mysql2_Ruby gem mysql2安装错误
- 【Calcite】Calcite入门
- 一个系统管理员的自白
- 蚂蚁上市，身边又多了一堆千万富翁！
- arcpy 实现列举目录下的要素类与描述矢量数据要素类
- 561. 数组拆分 I
- 最简单的WIN7内核PE系统的U盘安装方法+WIN7密码破解
- 什么是静态测试、动态测试、黑盒测试、白盒测试、α测试 β测试？
- [历朝通俗演义-蔡东藩-前汉]第011回 降真龙光韬泗水 斩大蛇夜走丰乡
- python中pixels函数_Python的PIL库中getpixel方法的使用
- 如何关闭WPS烦人的广告推送
- 「Ubuntu」ubuntu18.04键盘输入卡顿、延迟输入
- 【从0到1搭建LoRa物联网】15、LoRa连接到The Things Network
- 设计模式之 Composite(组合)通俗理解
- [2016 版] 常见操作性能对比