rewrited README

README.md

 # TensorFlow Object Detection API Tutorial
 
-This repository has the code from my [O'Reilly article](https://www.oreilly.com/ideas/object-detection-with-tensorflow)  published on October 25, 2017.
-
-## Required Packages
-* [TensorFlow v1.3](http://www.tensorflow.org/)
-* [Jupyter](http://jupyter.org/)
-* [NumPy](http://www.numpy.org/)
-* [Scipy](https://www.scipy.org/)
-* [Matplotlib](http://matplotlib.org/)
-* [Scikit-Image](http://scikit-image.org/)
-* [Pandas](http://pandas.pydata.org/)
-* [lxml](http://lxml.de/)
-* [protobuf](https://github.com/google/protobuf)
-
-There are two ways you can install these packages: by using Docker or by using native Python 3.5.
-
-### Using Native Python 3
-
-1. Install system requirements
-
-```bash
-sudo apt-get install -y git-core wget protobuf-compiler 
-```
-2. Download and unzip [this entire repo from GitHub](https://github.com/wagonhelm/TF_ObjectDetection_API), either interactively, or by entering
-
-```bash
-git clone https://github.com/wagonhelm/TF_ObjectDetection_API.git
-```
-
-3. Install Python Requirement
-
-```bash
-cd TF_ObjectDetection_API
-# Requires sudo if not in a virtual environment
-pip3 install -r requirements.txt
-pip3 install tensorflow jupyter
-```
-4. Clone TensorFlow Models Into Repository Directory and Install Object Detection API
-
-```bash
-cd TF_ObjectDetection_API
-git clone https://github.com/tensorflow/models.git
-```
-
-You will have to run this command every time you close your terminal unless you add the the path to slim to your `.bashrc` file
-
-```bash
-cd models/research/
-protoc object_detection/protos/*.proto --python_out=.
-export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
-cd ..
-cd ..
-```
-
-4. Launch Jupyter
-```bash
-jupyter notebook
-```
-5. Launch Tensorboard In New Terminal
-```bash
-tensorboard --logdir='data'
-```
-Once both jupyter and tensorboard are running, using your browser, navigate to the URLs shown in the terminal output if those don't work  try http://localhost:8888/ for Jupyter Notebook and http://localhost:6006/ for Tensorboard.
-
 ### Work with TensorFlow Object Detection API and Google PubLayNet dataset
     
 1 Структура датасета PubLayNet
 ```bash
 Датасет размещен по https://dax-assets-dev.s3.us-south.cloud-object-storage.appdomain.cloud/dax-publaynet/1.0.0/PubLayNet.html
 Датасет PubLayNet помещен в директорию DataSet/:
-DataSet/jsons - скачанный json (train-<number>.json, test.json)
-DataSet/csv - генерированные csv из json (train-<number>.csv, test.csv)
-DataSet/images - скачанные изображения в подпапках ("train-<number>/", "test/", "examples/", "examples_res/"), где
+PubLayNet/jsons - скачанный json (COCO-формат) (train-<number>.json, test.json)
+PubLayNet/csv - генерированные csv из json (train-<number>.csv, test.csv)
+PubLayNet/images - скачанные изображения в подпапках ("train-<number>/", "test/", "examples/", "examples_res/"), где
     "examples/" - изображения для тестирования скаченные из https://github.com/ibm-aur-nlp/PubLayNet/tree/master/examples
     "examples_res/" - результат тестирования
 
-DataSet/labeled - изображения с нарисованным GT, генерируются из DataSet/draw_GT.py
+PubLayNet/labeled - изображения с нарисованным GT, генерируются из DataSet/draw_GT.py
 ```
 
 2 Конвертация json в csv
@@ -101,11 +38,39 @@ python3 models/research/object_detection/legacy/train.py --logtostderr --train_d
 ```bash
 rm -rf object_detection_graph
 python3 models/research/object_detection/export_inference_graph.py     --input_type image_tensor     --pipeline_config_path configs/faster_rcnn_resnet101_coco.config     --trained_checkpoint_prefix training/data_PubNet_frcnn/model.ckpt-<number_need_checkpoint> --output_directory object_detection_graph
-
 ```
 
-6 Тестирование модели
+6 Тестирование модели (predict)
 ```bash
 python test_model.py
 ```
 
+7 Тестирование модели на тестовом наборе и вычисление точности по coco-api
+```bash
+python evaluate_model.py
+```
+8 Преобразователи форматов GT в utils_convert_draw_format/
+* convert_csv_to_coco.py - csv в COCO format
+* convert_json_to_csv.py - COCO format в csv
+* convert_pascal_to_csv.py - Pascal формат в csv
+* draw_GT_coco.py - нарисовать GT COCO формата
+
+9 Скрипты для создания tfrecords из изображений для тренинга и их GT расположены в папках датасетов PubLayNet и CLAWDATA:
+* PubLayNet/generate_tfrecords_publaynet.py
+* CLAWDATA/generate_tfrecords_claw_data.py
+
+You will have to run this command every time you close your terminal unless you add the the path to slim to your `.bashrc` file
+```bash
+cd models/research/
+protoc object_detection/protos/*.proto --python_out=.
+export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
+cd ..
+cd ..
+```
+
+7 Launch Tensorboard In New Terminal
+```bash
+tensorboard --logdir='data'
+```
+
+Once both jupyter and tensorboard are running, using your browser, navigate to the URLs shown in the terminal output if those don't work  try http://localhost:8888/ for Jupyter Notebook and http://localhost:6006/ for Tensorboard.