1. pip install -r requirements.txt
2. Use Image Collection.ipynb to take the images
3. Move them to workspace/images/colectedimages
4. Go to labelImg folder
5. python labelImg.py
6. Use Open Dir and select workspace/images/colectedimages
7. Change Save Dir to workspace/images/colectedimages
8. Annotate the images
9. Go to workspace/images and run prepare-train-test.ipynb

Ctrl + u - Load all of the images from a directory

Ctrl + r - Change the default annotation target dir

Ctrl + s - Save

w - Create a rect box

d - Next image

a - Previous image

del - Delete the selected rect box

Ctrl++ - Zoom in

Ctrl-- - Zoom out

Ctrl + d - Copy the current label and rect box

Space - Flag the current image as verified

↑→↓←Keyboard arrows to move selected rect box

1. Image augmentation done with imgaug
2. Link to augmentation notebook

1. Run pipeline.ipynb in colab env
2. There are a few paths that need to be adjusted to the directory in which you will clone the repo
3. Test it with predictions.ipynb

1. Run predictions.ipynb in Colab env after you completed pipeline.ipynb

1. work out the real time solution in predictions.ipynb -> Done
2. Train on more images -> Done
3. Write documentation overleaf -> Done