line

EHM (Extreme Heatpoints Model) for lines

The model is based on the code of HRNet semantic segmentation with GCCPM pose estimation model keypoints refinemen stage. The core of the model used in the challenge is HRNetV2-w48 backbone.

The model predicts 23 heatpoint pairs for lines, from 23 heatmap layers. semi-circles and circles are ignored. The target tensor consisted of a 2D heatmap for each point pairs, where Gaussian peaks were positioned at the keypoint locations.

Since there are two point from each heatmap, we extract the first extreme point with the highest probability. After that a Gaussian kernel was used to mask the first extreme point area, followed by a similar process to find the second extreme point.

Prepare the dataset

The dataset shall be organised in the following structure.

|-- soccernet-calibration-sportlight
    |-- data
        |-- dataset
        |   |   |-- valid
        |   |   |   |-- 00000.jpg
        |   |   |   |-- 00000.json
        |   |   |   | ...
        |   |   |-- train
        |   |   |   |-- 00000.jpg
        |   |   |   |-- 00000.json
        |   |   |   | ...

Visualise extreme points

In some annotations, there are more than 2 points, an algorithm is designed to find extreme points for lines. User can run the following demo code:

python src/datatools/line.py

The following plot will be generated for visual inspection.

Train the extreme heatpoint model (EHM)

• The training config file for EHM is src/models/line/train_config.yaml
• The EHM is based on HRNetV2-w48 backbone, it is possible to use a pretraind weight in /src/models/line/model_config/hrnet_w48.yaml
• Run python src/models/line/train.py to start training.

Assemble line model result

The result of the line model could be saved for each image from a folder, in a pickle format. run:

python src/utils/export_line_result.py \
--image-folder /workdir/data/dataset/test \
--result-file /workdir/data/result/line_model_result.pkl \
--model line_model_pytorch.pth \
--device cuda:0  \
--sigma 3.0 \
--prob-thre 0. \
--no-vis

Uncomment no-vis and increase prob-thre to see the visualisation on images.

Here set prob-thre to save all result, a probability search might be conducted in later stage when combining model result with other model results.