Google Colab Notebook

https://colab.research.google.com/drive/1QyamrJYnwBIfmFKzW6_ODSQyu4ENGklN?usp=sharing

Set environment

For now, you can set the environment by typing

git clone [email protected]:bioscan-ml/BioScan-croptool.git
cd BioScan-croptool
conda create -n BioScan-croptool python=3.10
conda activate BioScan-croptool
pip install -r requirements.txt

in the terminal. However, based on your GPU version, you may have to modify the torch version and install other packages manually in difference version.

Data preparation

Please download the annotated data and images by wget https://aspis.cmpt.sfu.ca/projects/bioscan/BIOUG_1k_images_resized.zip. Then unzip it in /data. You can split and prepare the data by

python scripts/complete_coco_json.py --input_dir data/resized
python scripts/split_data.py --input_dir data/resized --dataset_name data/insect

If you want to annotate more data for the training part, you can check Toronto Annotation suite(https://aidemos.cs.toronto.edu/toras). Note that some of the information is missing from their coco annotation file, that is why the complete_coco_json.py exist. (However, this scripts use boudning box area to replace the mask area, but it is not affecting the cropping tool too much).

Train and eval

python scripts/train.py --data_dir data/insect --output_dir insect_detection_ckpt

Here is a checkpoint for you to use, so you can skip the training. wget https://aspis.cmpt.sfu.ca/projects/bioscan/ckpt_for_pined_images.ckpt If you want to use it, you can put it into your project folder ,and change the checkpoint_path in following commands. (To --checkpoint_path ckpt_for_pined_images.ckpt)

Evaluation

To evaluate the checkpoint:

python scripts/evaluate.py --data_dir data/insect --checkpoint_path insect_detection_ckpt/lightning_logs/version_0/checkpoints/epoch=11-step=300.ckpt

Visualization

To visualize the predicted bounding box

python scripts/visualization.py --data_dir data/insect --checkpoint_path insect_detection_ckpt/lightning_logs/version_0/checkpoints/epoch=11-step=300.ckpt

Crop image

You can put the insect images that need to be cropped in a folder (Maybe call original_images), then type

python scripts/crop_images.py --input_dir original_images --checkpoint_path insect_detection_ckpt/lightning_logs/version_0/checkpoints/epoch=11-step=300.ckpt --crop_ratio 1.4

in the terminal. Note that by setting --crop_ratio 1.4, the cropped image is 1.4 scaled than the predicted boudning box. If you want to check the origional bounding box, you can add --show_bbox at the end of the command.

To crop BIOSCAN-6M images:

python copy_to_local_then_crop_images_6M.py

If you want the cropped image in 4:3 ratio, you can add --fix_ratio to the command. Here is an example:

python scripts/crop_images.py --input_dir Part_2 --output_dir Part_2_cropped --checkpoint_path epoch=11-step=600_trained_on_part_1.ckpt --crop_ratio 1.4 --show_bbox --fix_ratio

Acknowledgement

This repo is built upon Fine_tuning_DetrForObjectDetection_on_custom_dataset.