https://github.com/mdhmz1/Auto-Annotate
Auto-Annotate - Automatically annotate your entire image directory by a single command. As simple as saying - "Annotate all the street sign (label) in the autonomous car dataset (directory)" and BAM! DONE. Each and every image with a street sign in the diverse dataset directory containing images of all sorts which have a street sign are filtered and the segmentation annotation is performed in a single command. The Auto-Annotate tool provides auto annotation of segmentation masks for the objects in the images inside some directory based on the labels. Auto-Annotate is able to provide automated annotations for the labels defined in the COCO Dataset and also supports Custom Labels.
https://github.com/mdhmz1/Auto-Annotate
deep-learning hacktoberfest image-processing machine-learning object-detection
Last synced: about 1 year ago
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Auto-Annotate - Automatically annotate your entire image directory by a single command. As simple as saying - "Annotate all the street sign (label) in the autonomous car dataset (directory)" and BAM! DONE. Each and every image with a street sign in the diverse dataset directory containing images of all sorts which have a street sign are filtered and the segmentation annotation is performed in a single command. The Auto-Annotate tool provides auto annotation of segmentation masks for the objects in the images inside some directory based on the labels. Auto-Annotate is able to provide automated annotations for the labels defined in the COCO Dataset and also supports Custom Labels.
- Host: GitHub
- URL: https://github.com/mdhmz1/Auto-Annotate
- Owner: mdhmz1
- License: mit
- Created: 2020-10-10T18:35:06.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2022-10-07T23:01:37.000Z (over 3 years ago)
- Last Synced: 2024-11-06T02:38:08.498Z (over 1 year ago)
- Topics: deep-learning, hacktoberfest, image-processing, machine-learning, object-detection
- Language: Python
- Homepage:
- Size: 3.74 MB
- Stars: 182
- Watchers: 2
- Forks: 29
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-auto-annotation - mdhmz1/Auto-Annotate
README
Auto-Annotate
Automatically annotate your entire image directory by a single command.
For a more detailed explaination and code usage, please refer to this [medium article](https://medium.com/analytics-vidhya/automated-image-annotation-using-auto-annotate-tool-f8fff8ea4900).
**As simple as saying - "Annotate all the street sign (label) in the autonomous car dataset (directory)" and BAM! DONE.**
Each and every image with a street sign in the diverse dataset directory containing images of all sorts which have a street sign are filtered and the segmentation annotation is performed in a single command.
The Auto-Annotate tool provides auto annotation of segmentation masks for the objects in the images inside some directory based on the labels. Auto-Annotate is able to provide automated annotations for the labels defined in the COCO Dataset and also supports Custom Labels. This tool is built on top of [Mask R-CNN](https://github.com/matterport/Mask_RCNN) to support auto annotation for each instance of an object segment in the image.
The Auto-Annotate tool works on two modes
* **COCO Label Annotation** - NO TRAINING REQUIRED. Just use the weights of the the Coco dataset. Point to the directory and the annotations are ready.
* **Custom Label Annotation** - Train the model for the custom label. Use the weights and annotate.
NOTE: Please refer to [knownIssues.md](knownIssues.md) file in the repo for known issues and their resolution. Please feel free to contribute in case of any errors/issues arising during the installation and usage of the tool.
## JSON Annotation Format
### SAMPLE JSON:
```json
[
{
"filename": "bird_house_in_lawn.jpg",
"id": 1,
"label": "Bird House",
"bbox": [ 111.5, 122.5, 73, 66 ],
"segmentation": [
[ 167, 188.5, 174, 185.5, 177.5, 181, 177.5, 157, 183.5, 154, 184.5, 149, 159, 124.5, 150, 122.5,
142, 124.5, 131, 131.5, 111.5, 150, 111.5, 156, 116.5, 162, 116.5, 184, 121, 188.5, 167, 188.5
]
]
}
]
```
### JSON FORMAT:
```
[
{
"filename": image_file_name,
"id": id_of_the_image,
"label": label_to_search_and_annotate,
"bbox": [ x, y, w, h ], -- x,y coordinate of top left point of bounding box
-- w,h width and height of the bounding box
Format correspond to coco json response format for bounding box
"segmentation": [
[ x1, y1, x2, y2,...] -- For X,Y belonging to the pixel location of the mask segment
-- Format correspond to coco json response format for segmentation
]
}
]
```
###
ORIGINAL IMAGE | MASKED IMAGE
:-------------------------:|:-------------------------:
 | 
## Installation
1. Clone this repository
2. Install dependencies
```bash
pip3 install -r requirements.txt
```
3. **If annotating objects supported by COCO Dataset**
Download pre-trained COCO weights (mask_rcnn_coco.h5) from the [releases page](https://github.com/matterport/Mask_RCNN/releases) and store it in the root directory.
**If annotating objects Custom Objects**
Train Mask RCNN and use those weights.
4. Run Commands as below based on the mode.
```bash
python3 annotate.py annotateCoco --image_directory=/path/to/the/image/directory/ --label=object_label_to_annotate --weights=/path/to/weights.h5 --displayMaskedImages=False
```
```bash
python3 annotate.py annotateCustom --image_directory=/path/to/the/image/directory/ --label=object_label_to_annotate --weights=/path/to/weights.h5 --displayMaskedImages=False
```
5. Find the annotations in the directory - /path/to/the/image/directory/ specified above
## Annotating on MS COCO
Use pre-trained weights for MS COCO. After finishing with the installation, we
can run it directly from the command line as such:
```
# Annotate label defined by COCO
python3 annotate.py annotateCoco --image_directory=/path/to/the/image/directory/ --label=object_label_to_annotate --weights=/path/to/weights.h5 --displayMaskedImages=False
```
Note: --label=object_label_to_annotate should be in accordance to the COCO dataset labels.
Refer [COCO Dataset](https://cocodataset.org/) for more details.
## Annotating on Custom Images
Use pre-trained weights for the custom label. After finishing with the installation, we
can run it directly from the command line as such:
```
# Annotate Custom
python3 annotate.py annotateCustom --image_directory=/path/to/the/image/directory/ --label=object_label_to_annotate --weights=/path/to/weights.h5 --displayMaskedImages=False
```
Note: --label=object_label_to_annotate should be a label for which the weights are provided.
## Training on Your Own Dataset
Read the original post by Waleed Abdulla [blog post about the balloon color splash sample](https://engineering.matterport.com/splash-of-color-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46) where he explained the process starting from annotating images to training to using the results in a sample application.
The use train.py which is a modified version of balloon.py written by Waleed to support only the training part.
```
# Train a new model starting from pre-trained COCO weights
python3 customTrain.py train --dataset=/path/to/custom/dataset --weights=coco
# Resume training a model that you had trained earlier
python3 customTrain.py train --dataset=/path/to/custom/dataset --weights=last
```
## :clap: Supporters
### :star: Stargazers
[](https://github.com/mdhmz1/Auto-Annotate/stargazers)
### :twisted_rightwards_arrows: Forkers
[](https://github.com/mdhmz1/Auto-Annotate/network/members)
##
[🤝 CONNECT WITH ME ON LINKEDIN](https://www.linkedin.com/in/mdhmz1/)
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