Deep-Template-Matching

Learning Accurate Template Matching with Differentiable Coarse-to-fine Correspondence Refinement

Official implementation of Deep-Template-Matching (Learning Accurate Template Matching with Differentiable Coarse-to-fine Correspondence Refinement) using pytorch (pytorch-lightning) This paper has accepted by CVMJ 2023 and can be founded on arxiv.

Abstract

Template matching is a fundamental task in computer vision and has been studied for decades. It plays an essential role in the manufacturing industry for estimating the poses of different parts, facilitating downstream tasks such as robotic grasping. Existing works fail when the template and source images are in different modalities, cluttered backgrounds or weak textures. They also rarely consider geometric transformations via homographies, which commonly existed even for planar industrial parts. To tackle the challenges, we propose an accurate template matching method based on differentiable coarse-to-fine correspondence refinement. Considering the domain gap between the mask template and the grayscale image, we leverage an edge-aware module to eliminate the difference for robust matching. Based on coarse correspondences with novel structure-aware information by transformers, an initial warping transformation is estimated and performed as a preliminary result. After the initial alignment, we execute a refinement network on reference and aligned images to obtain sub-pixel level correspondences and thus obtain the final geometric transformation. Comprehensive evaluations show that our method significantly outperforms state-of-the-art methods and baselines, with good generalization abilities and visually plausible results even on unseen real data.

Introduction

we propose an accurate template matching method based on differentiable coarse-to-fine correspondence refinement. Considering the domain gap between the mask template and the grayscale image, we leverage an edge-aware module to eliminate the difference for robust matching. Based on coarse correspondences with novel structure-aware information by transformers, an initial warping transformation is estimated and performed as a preliminary result. After the initial alignment, we execute a refinement network on reference and aligned images to obtain sub-pixel level correspondences and thus obtain the final geometric transformation.

Installation

# For full pytorch-lightning trainer features (recommended)
conda env create -f environment.yaml
conda activate tm
pip install torch einops yacs kornia

We provide the datasets used in our paper. Download link to

• Assembled hole dataset
• Steel dataset

Run Deep-Template-Matching demo

Match image pairs

An example is given in notebooks/demo_single_pair.ipynb. The pretraind weight is here

Training(./train.py or ./scripts/train.sh)

We use a two-stage training method.（Modify configuration parameters in ./src/config/default.py）

1. main training steps

In the coarse stage, we only train the coarse network until convergence(about 10-20 epochs):

_CN.TM.MATCH_COARSE.TRAIN_STAGE = 'only_coarse'

and then modify the ckpt_path in train.py

parser.add_argument(
        '--ckpt_path', type=str, default='', # the path of coarse ckpt
        help='pretrained checkpoint path')

In the fine stage, we train the whole network until convergence(about 10-20 epochs)::

_CN.TM.MATCH_COARSE.TRAIN_STAGE = 'whole'

The detail files of training are saved in the ./logs folder

2. Use edge detetion

• If the edge of the test data is easy to detect, we recommend

  _CN.TM.MATCH_COARSE.USE_EDGE = True   #better generalization
  

  otherwise

  _CN.TM.MATCH_COARSE.USE_EDGE = False
  

3. Additional description of other configuration parameters in ./src/config/default.py

• Use online data augmentation:

  _CN.DATASET.AUGMENTATION_TYPE = 'None'
  

  otherwise

  _CN.DATASET.AUGMENTATION_TYPE = 'mobile_myself'
  

• Use online data augmentation:

  _CN.DATASET.AUGMENTATION_TYPE = 'None'
  

  otherwise

  _CN.DATASET.AUGMENTATION_TYPE = 'mobile_myself'
  

• Save Plots of matching images to the training file using tensorboard:

  _CN.TRAINER.SAVE_PLOTS_VAL = True
  _CN.TRAINER.SAVE_PLOTS_TRAIN = False
  

  otherwise

   _CN.TRAINER.SAVE_PLOTS_VAL = False
  _CN.TRAINER.SAVE_PLOTS_TRAIN = False
  

4. image resize

• All images are resized to [512, 512] (h,w), and we set the max number of query points is 128.
• If you want change the size of images,please change Resize = [512, 512] # h,w in ./src/lightning/data.py.
• The image size is not recommended to be too small, otherwise the matching pair will decline seriously. [480,640] is a good option.

Multiple test samples (./test.py)

Data preparation

Please modify the paths to your dataset in the files(./config/Synthetic_train.py and ) ./config/Synthetic_test.py ). And we have prepared a standard data format in the folder(./own_dataset)

Synthetic_train.py:

   TRAIN_BASE_PATH = './own_dataset'

Synthetic_test.py:

   TEST_BASE_PATH = './own_dataset'

Demos

Single-object demo

Multi-objects demo

Acknowledgements

• LoFTR
• SuperGlue
• pidinet
• PointDSC