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https://github.com/virajmavani/semi-auto-image-annotation-tool
Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model
https://github.com/virajmavani/semi-auto-image-annotation-tool
automation deep-learning hackoctoberfest hacktoberfest2020 image-annotation image-labeling keras tensorflow
Last synced: about 2 months ago
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Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model
- Host: GitHub
- URL: https://github.com/virajmavani/semi-auto-image-annotation-tool
- Owner: virajmavani
- License: apache-2.0
- Created: 2018-05-17T08:55:29.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-04-27T19:09:13.000Z (over 1 year ago)
- Last Synced: 2024-07-24T23:02:28.233Z (about 2 months ago)
- Topics: automation, deep-learning, hackoctoberfest, hacktoberfest2020, image-annotation, image-labeling, keras, tensorflow
- Language: Python
- Homepage: https://www.virajmavani.com/saiat
- Size: 8.1 MB
- Stars: 571
- Watchers: 18
- Forks: 127
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-dataset-tools - Anno-Mage - Helps you in annotating images by suggesting you annotations for 80 object classes (Labeling Tools / Images)
README
# 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](https://github.com/fizyr/keras-retinanet/releases/download/0.3.1/resnet50_coco_best_v2.1.0.h5) and save it in /snapshots/keras.
b) For tensorflow model get the desired model from [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) 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.](https://www.meditab.com/)
2) [Keras implementation of RetinaNet object detection](https://github.com/fizyr/keras-retinanet)
3) [Computer Vision Group](https://cvgldce.github.io/), L.D. College of Engineering