Anno-Mage: A Semi Automatic Image Annotation Tool

Semi Automatic Image Annotation Toolbox with tensorflow and keras object detection models.

Installation

1. Clone this repository.

2. In the repository, execute pip install -r requirements.txt. Note that due to inconsistencies with how tensorflow should be installed, this package does not define a dependency on tensorflow as it will try to install that (which at least on Arch Linux results in an incorrect installation). Please make sure tensorflow is installed as per your systems requirements. Also, make sure Keras 2.1.3 or higher and OpenCV 3.x is installed.

3. a) For Keras model - Download the pretrained weights and save it in /snapshots/keras.

   b) For tensorflow model get the desired model from here and extract it in /sanpshots/tensorfow

   c) You can even save custom pre trained model in the respective directory.

Dependencies

1. Tensorflow >= 1.7.0

2. OpenCV = 3.x

3. Keras >= 2.1.3

For, Python >= 3.5

Instructions

1. Select the COCO object classes for which you need suggestions from the drop-down menu and add them. Or simply click on Add all classes .

2. Select the desired model and click on Add model.

3. Click on detect button.

4. When annotating manually, select the object class from the List and while keep it selected, select the BBox.

5. The final annotations can be found in the file annotations.csv in ./annotations/ . Also a xml file will saved.

Usage

For MSCOCO dataset

python main.py

For any other dataset-

First change the labels in config.py (for keras model) or in tf_config.py( for tensorflow model). Then run:

python main.py

Tested on:

1. Windows 10

2. Linux 16.04

3. macOS High Sierra

Join the developers channel for contributions

Slack: https://join.slack.com/t/annomage/shared_invite/zt-dh4ca9du-4VOcwUMCSNA6lmyG~tNUPg

Acknowledgments

1. Meditab Software Inc.

2. Keras implementation of RetinaNet object detection

3. Computer Vision Group, L.D. College of Engineering