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> 

#### **Linux**
- 照[官方的說明](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md)，安裝相關套件。
```
pip install protobuf             (版本 3.12.0)
sudo apt-get install python3-tk  (版本 8.6)
pip install pillow               (版本 7.1.2)
pip install lxml                 (版本 4.5.0)
pip install tf-slim              (版本 1.1.0)
pip install matplotlib           (版本 3.2.1)
pip install Cython               (版本 0.29.17)
pip install contextlib2          (版本 0.6.0.post1)
pip install pyqt5                (版本 5.15.0)
pip install opencv-python        (版本 4.2.0)
pip install numpy                (版本 1.17，原本anaconda太新會有問題)
pip install scipy                (版本 1.5.0)
pip install labelme              (版本 4.5.6)
```

- Linux
```
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
```

- Linux
    - **直接編譯**，在 ./models/research/ 目錄下執行以下指令：
    ```
    protoc object_detection/protos/*.proto --python_out=.
    ```
    若有錯誤訊息，請使用以下手動編譯。
    - **手動編譯**，下載 protobuf.zip，解壓縮後於 ./models/research/ 執行。
    ```
    wget -O protobuf.zip https://github.com/google/protobuf/releases/download/v3.0.0/protoc-3.0.0-linux-x86_64.zip
    unzip protobuf.zip
    ./bin/protoc object_detection/protos/*.proto --python_out=.
    ```
    
- **Linux**
```
export PYTHONPATH=$PYTHONPATH:<path to research>:<path to research>/slim
```
> pwd : 為路徑 
> 

以下為實際運行指令：
```
# Training set
python create_labelme_tf_record.py \
	--images_dir=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/ITRI_dataset/train/train_img
	--annotations_json_dir=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/ITRI_dataset/train/train_json
	--label_map_path=/home/<virtual env folder>/research/object_detection/myMaskrcnn/wrench_label.pbtxt
	--output_path=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/

# Validation set
python create_labelme_tf_record.py \
	--images_dir=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/ITRI_dataset/val/val_img
	--annotations_json_dir=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/ITRI_dataset/val/val_json
	--label_map_path=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/unknown_label.pbtxt
	--output_path=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/
```

:::warning
##### 若無法使用GPU訓練資料，請確認是否無法順利開啟CUDA相關檔案，若無法開啟並產生以下訊息，請設定環境變數，便能順利開啟檔案使用GPU運行 (以下為Linux使用情境): 
- ++錯誤訊息++
`Cannot dlopen some GPU libraries. Skipping registering GPU devices`
- ++設定環境變數++
```
export PATH=$PATH:/usr/local/cuda-10.0/bin
export CUDADIR=/usr/local/cuda-10.0
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.0/lib64
```
:::

以下為實際運行指令：
```
python export_inference_graph.py \
    --pipeline_config_path=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/wrench_itri/pipeline.config
    --trained_checkpoint_prefix=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/wrench_itri/model.ckpt-25503
    --output_directory=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/test2.0.0
```


以下為實際運行指令：
```
python legacy/train.py \
    --train_dir=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/wrench_itri
    --pipeline_config_path=/home/<virtual env folder>/models/research/object_detection/myMaskrcnn/mask_rcnn_inception_v2_coco_2018_01_28/pipeline.config
```

```
python mo_tf.py \
    --input_model=C:/Users/<username>/Desktop/test2.0.0/frozen_inference_graph.pb 
    --model_name=maskrcnn_v3 
    --output_dir=C:/Users/<username>/Desktop/test2.0.0
    --tensorflow_use_custom_operations_config=C:/"Program Files (x86)"/IntelSWTools/openvino_2020.1.033/deployment_tools/model_optimizer/extensions/front/tf/mask_rcnn_support_api_v1.15.json
    --tensorflow_object_detection_api_pipeline_config=C:/Users/<username>/Desktop/test2.0.0/pipeline.config 
    --log_level=ERROR
```


#### **Linux**
- **Virtualenv (Linux 建議使用)**
    Virtualenv 屬於Python套件，直接於Terminal執行。
    - 安裝Virtualenv
    `pip install vitrualenv`
    - 建立虛擬環境
    `virtualenv -p python3.7 myenv`
        >-p : 選擇Python版本
        >環境名稱之命名

```
python m.py \
    --train_dir=path to train direction          
    --pipeline_config=path to configuration file   (檔案名稱 *.config)
```
> train_dir : 訓練產生檔案的位置。
> pipeline_config : 參數組態檔案。

以下為實際運行指令：
![](https://i.imgur.com/8vzbMEH.png)


### [5] [COCO API](https://github.com/cocodataset/cocoapi) 安裝
```

```