Loading... ## 0x00 环境 OS: Ubuntu 1810 x64 Anaconda: 4.6.12 Python: 3.6.8 TensorFlow: 1.13.1 OpenCV: 3.4.1 ## 0x01 基础环境配置 Anaconda 下载地址: [Anaconda-4.6.12-Linux](https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh) 本文中安装位置为 ```/usr/local/anaconda3``` 修改默认的 python 版本为 3.6 ``` conda install python=3.6 ``` 安装 OpenCV 3.4.1 ``` conda install opencv=3.4.1 ``` 安装 TensorFlow 1.13.1 ``` conda install tensorflow=1.13.1 ``` ## 0x02 TensorFlow Models 下载地址: [Github - TensorFlow Models](https://github.com/tensorflow/models) 下载后得到一个 models-master.zip 文件，解压后移动到 ```/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow``` 文件夹下，并重命名为 models ``` unzip models-master.zip mv models /usr/local/anaconda3/lib/python3.6/site-packages/tensorflow ``` 进入 ```models/research/``` 目录，并编译 protobuf ``` cd /usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research protoc object_detection/protos/*.proto --python_out=. ``` 安装 object_detection 库 ``` python setup.py build python setup.py install ``` 设置 PYTHONPATH ``` export PYTHONPATH=$PYTHONPATH:/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research export PYTHONPATH=$PYTHONPATH:/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/slim ``` 直接执行以上命令只会在当前终端生效，将以上命令写入 ```~/.bashrc``` 并执行如下命令可以永久保存 ``` source ~/.bashrc ``` 测试 object_detection 库是否安装成功 ``` python object_detection/builders/model_builder_test.py ``` 进入 ```object_detection/``` 目录并启动 jupyter-notebook，测试目标检测 ``` cd object_detection/ jupyter-notebook ``` 在浏览器中打开 ```http://localhost:8888/```，进入 jupyter-notebook 控制台，打开 object_detection_tutorial.ipynb 文件并执行，待模型下载完成并检测完成后会在页面底部出现两张标注后的图片 ## 0x03 训练 下载 VOC 2012 数据集: [VOCtrainval_11-May-2012.tar](http://cvlab.postech.ac.kr/~mooyeol/pascal_voc_2012/VOCtrainval_11-May-2012.tar) 在 ```object_detection/``` 目录下创建目录 ```ssd_model```，并解压数据集至 ```object_detection/ssd_model``` ``` mkdir ssd_model/ cd ssd_model tar xvf VOCtrainval_11-May-2012.tar ``` 返回 ```research/``` 目录，执行 train 和 val 脚本 ``` cd ../.. python ./object_detection/dataset_tools/create_pascal_tf_record.py --label_map_path=./object_detection/data/pascal_label_map.pbtxt --data_dir=object_detection/ssd_model/VOCdevkit/ --year=VOC2012 --set=train --output_path=./object_detection/ssd_model/pascal_train.record python ./object_detection/dataset_tools/create_pascal_tf_record.py --label_map_path=./object_detection/data/pascal_label_map.pbtxt --data_dir=./object_detection/ssd_model/VOCdevkit/ --year=VOC2012 --set=val --output_path=./object_detection/ssd_model/pascal_val.record ``` 这两个脚本会在 ```ssd_model/``` 目录下生成 pascal_train.record 和 pascal_val.record 两个文件，各 600M 左右 复制配置文件，在此基础上修改，并训练数据 ``` cp object_detection/data/pascal_label_map.pbtxt object_detection/ssd_model/ cp object_detection/samples/configs/ssd_mobilenet_v1_pets.config object_detection/ssd_model/ ``` pascal_label_map.pbtxt 文件中保存了数据集中有哪些 label 将 ssd_mobilenet_v1_pets.config 中的 num_classes 改为 pascal_label_map.pbtxt 中列出的文件数量，这里是 20，并修改迭代次数 num_steps，并将配置文件末尾的路径按照如下格式修改 ``` train_input_reader: { tf_record_input_reader { input_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_train.record" } label_map_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_label_map.pbtxt" } eval_input_reader: { tf_record_input_reader { input_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_val.record" } label_map_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_label_map.pbtxt" shuffle: false num_readers: 1 } ``` 下载 ssd_mobilenet 至 ```ssd_model/``` 目录下，解压并重命名为 ssd_mobilenet ssd_mobilenet: [ssd_mobilenet_v1_coco_11_06_2017.tar.gz](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz) ``` tar zxvf ssd_mobilenet_v1_coco_11_06_2017.tar.gz mv ssd_mobilenet_v1_coco_11_06_2017 ssd_mobilenet ``` 将 ssd_mobilenet_v1_pets.config 中 fine_tune_checkpoint 修改为如下格式的路径 ``` fine_tune_checkpoint: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/ssd_mobilenet/model.ckpt" ``` 使用 train.py 脚本训练模型 **注意：脚本可能位于 ```object_detection/``` 或 ```object_detection/legacy/``` 目录下** 这里位于 ```object_detection/legacy/``` 目录 ``` python ./object_detection/legacy/train.py --train_dir ./object_detection/legacy/train/ --pipeline_config_path ./object_detection/ssd_model/ssd_mobilenet_v1_pets.config ``` 运行 export_inference_graph.py 脚本将训练出的模型固化成 TensorFlow 的 .pb 模型，其中 trained_checkpoint_prefix 要设置成 model.ckpt-[step]，其中 step 要与训练迭代次数相同 ``` python ./object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path ./object_detection/ssd_model/ssd_mobilenet_v1_pets.config --trained_checkpoint_prefix ./object_detection/legacy/train/model.ckpt-9000 --output_directory ./object_detection/ssd_model/model/ ``` 转换后生成的 .pb 模型位于 ```object_detection/ssd_model/model/``` 目录下 将 pascal_label_map.pbtxt 作为 label 文件 最后修改：2022 年 01 月 03 日 © 允许规范转载