Deep Hough Transform for Conveyor Detection

Useful Links:

Kaggle Notebook with usage example

Dataset on Kaggle

Pretrained on the conveyor-detection-dataset model (based on ResNet50-FPN): link

Dependecies

Install requirements.txt:

pip3 install -r requirements.txt

To install deep-hough, run the following commands.

cd deep-hough-transform
cd model/_cdht
python setup.py build 
python setup.py install --user

Prepare training data

Locate your dataset to data/ folder in the following format:

• data/dht_images folder - for .jpg images
• data/dht_data folder - for .txt files in the following format: x1 y1 x2 y2 H W - for each line on the image, where x1 y1 x2 y2 - two dots representing line, H and W - image height and weight

Then run python script to generate parametric space label.

./prepare_data.sh

After, run python script to generate train test split.

./generate_train_test_split.sh

Training

Following the default config file 'config.yml', you can arbitrarily modify hyperparameters. Then, run the following command.

python train.py