QATM:Quality-Aware Template Matching For Deep Learning

arxiv: https://arxiv.org/abs/1903.07254
original code (tensorflow+keras): https://github.com/cplusx/QATM

I used code from https://github.com/kamata1729/QATM_pytorch and modified it to my purpose.

For base code credit to @https://github.com/kamata1729.

Dependencies

• torch(1.0.0)
• torchvision(0.2.1)
• cv2
• seaborn
• sklearn
• pathlib

Installing the right version of PyTorch

This project is updated to be compatible with pytorch 1.0.1 and requires python 3.6

You can find other project requirements in requirements.txt , which you can install using pip install -r requirements.txt

Inference

• Run : inference.py
• For custum datasets : inference_custom.py

template1_1.png to template1_4.png are contained in sample1.jpg, however, template1_dummy.png is a dummy and not contained

template1_1.png	template1_2.png	template1_3.png	template1_4.png	template1_dummy.png

Usage

See qatm_pytorch.ipynb

or

python qatm.py -s sample/sample1.jpg -t template --cuda

• Add --cuda option to use GPU
• Add -s/--sample_image to specify sample image
  only single sample image can be specified in this present implementation
• Add -t/--template_images_dir to specify template image dir

[notice] If neither -s nor -t is specified, the demo program will be executed, which is the same as:

python qatm.py -s sample/sample1.jpg -t template

• --thresh_csv and --alpha option can also be added