Skip to content

Extend Tensorflow Object Detection API with results exporter for exporting annotated images, ROI, and bounding box data as XML. Utilizing the open-source Tensorflow Object Detection API by Google, with no code modification.

License

Notifications You must be signed in to change notification settings

catptype/TensorFlow-Object-Detection-API-Results-Exporter

Repository files navigation

TensorFlow Object Detection API Results Exporter

Introduction

TensorFlow Object Detection API Results Exporter is a Python project designed to address a specific need in the field of object detection. It extends the functionality of the Tensorflow Object Detection API by providing the capability to export images with Region of Interest (ROI) annotations and boundary box detections as XML data.

It utilizes portions of Tensorflow Object Detection API, an open-source project developed by the TensorFlow team at Google, which is licensed under the Apache License Version 2.0. I have not modified code and have only imported modules directly for the purpose of object detection.

For more information about Tensorflow Object Detection API and its license, please visit here.

Table of Contents

Imported modules

Usage

Follow these steps to use the TensorFlow Object Detection API Results Exporter:

  1. Put your .jpg and .png files into the Image directory.
  2. Place your Object Detection API model in the Model directory.
  3. Add a label_map.pbtxt file to the corresponding Object Detection API model directory.
  4. Run code in TensorFlow Object Detection API Results Exporter.ipynb notebook.

After completing the third step, your directory structure should resemble the following:

.
├── Image
│   ├── *.jpg
│   ├── *.png
│   └── ...
├── Model
│   ├── Object Detection API model
│   │   ├── checkpoint
│   │   │   ├── checkpoint
│   │   │   ├── ckpt-0.data-00000-of-00001
│   │   │   └── ckpt-0.index
│   │   ├── saved_model
│   │   │   ├── fingerprint.pb
│   │   │   └── saved_model.pb
│   │   ├── label_map.pbtxt
│   │   └── pipeline.config
...

Output

After running the code in the notebook, the Image directory will contain export_result, object_found, and object_not_found subdirectories.
In the export_result subdirectory, images with ROI and their corresponding XML annotations will be saved here.
In the object_found subdirectory, all detected images will be moved here.
In the object_not_found subdirectory, all images that were not detected will be moved here.

.
├── Images
│   ├── export_result
│   │     ├── *.jpg
│   │     ├── *.png
│   │     ├── *.xml
│   │     └── ...
│   ├── object_found
│   │     ├── *.jpg
│   │     ├── *.png
│   │     └── ...
│   ├── object_not_found
│   │     ├── *.jpg
│   │     ├── *.png
│   │     └── ...
...

License

This project is also licensed under the Apache License Version 2.0. See the LICENSE file for details.

About

Extend Tensorflow Object Detection API with results exporter for exporting annotated images, ROI, and bounding box data as XML. Utilizing the open-source Tensorflow Object Detection API by Google, with no code modification.

Topics

Resources

License

Stars

Watchers

Forks

Languages