# For CPU
pip install tensorflow
# For GPU
pip install tensorflow-gpu

sudo apt-get update
sudo apt-get -y install protobuf-compiler python-pil python-lxml python-tk
pip install --user Cython contextlib2 pillow lxml jupyter matplotlib pandas opencv-python

mkdir ~/Desktop/tensorflow
cd ~/Desktop/tensorflow
git clone https://github.com/tensorflow/models.git

# 最後加上這行
export PYTHONPATH=$PYTHONPATH:~/Desktop/tensorflow/models/research:~/Desktop/tensorflow/models/research/slim

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools ~/Desktop/tensorflow/models/research

1. 先下載並安裝 Visual Studio 2017
   https://visualstudio.microsoft.com/zh-hant/downloads/?rr=https%3A%2F%2Fwww.google.com.tw%2F
2. 因為 Visual Studio 不支援一些參數，所以要修改一下 setup.sh

   # 原
   extra_compile_args=['-Wno-cpp', '-Wno-unused-function', '-std=c99'],
   
   # 改為
   extra_compile_args=['-std=c99'],

3. 到 cocoapi\PythoinAPI 下面執行

   python setup.py install

# From tensorflow/models/research/
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=.

bin\protoc --python_out=. .\object_detection\protos\anchor_generator.proto .\object_detection\protos\argmax_matcher.proto .\object_detection\protos\bipartite_matcher.proto .\object_detection\protos\box_coder.proto .\object_detection\protos\box_predictor.proto .\object_detection\protos\eval.proto .\object_detection\protos\faster_rcnn.proto .\object_detection\protos\faster_rcnn_box_coder.proto .\object_detection\protos\grid_anchor_generator.proto .\object_detection\protos\hyperparams.proto .\object_detection\protos\image_resizer.proto .\object_detection\protos\input_reader.proto .\object_detection\protos\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.proto .\object_detection\protos\optimizer.proto .\object_detection\protos\pipeline.proto .\object_detection\protos\post_processing.proto .\object_detection\protos\preprocessor.proto .\object_detection\protos\region_similarity_calculator.proto .\object_detection\protos\square_box_coder.proto .\object_detection\protos\ssd.proto .\object_detection\protos\ssd_anchor_generator.proto .\object_detection\protos\string_int_label_map.proto .\object_detection\protos\train.proto .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\graph_rewriter.proto

python setup.py build
python setup.py install

# From tensorflow/models/research/
python object_detection/dataset_tools/create_pet_tf_record.py \
    --label_map_path=object_detection/data/pet_label_map.pbtxt \
    --data_dir=`pwd` \
    --output_dir=`pwd`

# From tensorflow/models/research/
mkdir data
cp pet_faces_train.record-* data/
cp pet_faces_val.record-* data/
cp object_detection/data/pet_label_map.pbtxt data/pet_label_map.pbtxt

# From the tensorflow/models/research/ directory
PIPELINE_CONFIG_PATH={path to pipeline config file}
MODEL_DIR={path to model directory}
NUM_TRAIN_STEPS=50000
SAMPLE_1_OF_N_EVAL_EXAMPLES=1

python object_detection/model_main.py \
    --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
    --model_dir=${MODEL_DIR} \
    --num_train_steps=${NUM_TRAIN_STEPS} \
    --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES \
    --alsologtostderr

<annotation>
    <folder>OXIIIT</folder>
    <filename>Abyssinian_100.jpg</filename>
    <source>
        <database>OXFORD-IIIT Pet Dataset</database>
        <annotation>OXIIIT</annotation>
        <image>flickr</image>
    </source>
    <size>
        <width>394</width>
        <height>500</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    <object>
        <name>cat</name>
        <pose>Frontal</pose>
        <truncated>0</truncated>
        <occluded>0</occluded>
        <bndbox>
            <xmin>151</xmin>
            <ymin>71</ymin>
            <xmax>335</xmax>
            <ymax>267</ymax>
        </bndbox>
        <difficult>0</difficult>
    </object>
</annotation>