Awesome
Anno-Mage: A Semi Automatic Image Annotation Tool
Semi Automatic Image Annotation Toolbox with tensorflow and keras object detection models.
Installation
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Clone this repository.
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In the repository, execute
pip install -r requirements.txt
. Note that due to inconsistencies with howtensorflow
should be installed, this package does not define a dependency ontensorflow
as it will try to install that (which at least on Arch Linux results in an incorrect installation). Please make suretensorflow
is installed as per your systems requirements. Also, make sure Keras 2.1.3 or higher and OpenCV 3.x is installed. -
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
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Tensorflow >= 1.7.0
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OpenCV = 3.x
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Keras >= 2.1.3
For, Python >= 3.5
Instructions
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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
. -
Select the desired model and click on
Add model
. -
Click on
detect
button. -
When annotating manually, select the object class from the List and while keep it selected, select the BBox.
-
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:
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Windows 10
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Linux 16.04
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macOS High Sierra
Join the developers channel for contributions
Slack: https://join.slack.com/t/annomage/shared_invite/zt-dh4ca9du-4VOcwUMCSNA6lmyG~tNUPg
Acknowledgments
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Computer Vision Group, L.D. College of Engineering