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Utility for converting CVAT XML annotation file to TFRECORDS format

Description

Given a CVAT XML and a directory with the image dataset, this script reads the CVAT XML and writes the annotations in tfrecords format into a given directory in addition to the label map required for the tensorflow object detection API.

This implementation supports annotated images only. Make sure to dump the XML annotations and NOT interpolations from CVAT.

Installation

The conversion script depends on the tensorflow object detection API, for installation steps.

1. Install necessary packages (including tensorflow).

sudo apt-get update
sudo apt-get install -y --no-install-recommends python3-pip python3-dev

pip3 install -r requirements.txt

2. Install the tensorflow object detection API

If it's already installed you can check your $PYTHONPATHand move on to the usage section. Here's a quick (unofficial) guide on how to do that. For more details follow the official guide INSTALL TENSORFLOW OBJECT DETECTION API.

# clone the models repository
git clone https://github.com/tensorflow/models.git

# install some dependencies
pip3 install --user Cython
pip3 install --user contextlib2
pip3 install --user pillow
pip3 install --user lxml
pip3 install --user jupyter
pip3 install --user matplotlib

# clone and compile the cocoapi
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools <path_to_models_repo>/models/research/

# Protobuf Compilation
cd <path_to_models_repo>/models/research/
protoc object_detection/protos/*.proto --python_out=.

# setup the PYTHONPATH
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

Usage

Run the script.

$python3 converter.py --cvat-xml </path/to/cvat/xml> --image-dir </path/to/images>\
  --output-dir </path/to/output/directory> --attribute <attribute>

Leave --attribute argument empty if you want the to consider CVAT labels as tfrecords labels, otherwise you can specify a used attribute name like --attribute <attribute>.

Please run python converter.py --help for more details.

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