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SLRNet: Similarity-based Label Reuse for Semantic Segmentation

This is a PyTorch semantic segmentation technique for image datasets with evolving content such as images tracking human decomposition or growing plants.

This repo is built based on MIT repo from the CSAILVision.

Run

CUDA_VISIBLE_DEVICES=3 python3 train.py --gpus 3 --cfg data/bodypart_slrnet_cosin_conv_actV2.yaml

The resulted model would be saved in the 'DIR' specified in the config file.

Inference

More related information can be found at here. Sample python code to do inference:

from seg_utils import *
from PIL import Image

test_img = "/usb/sara_img/2f900410.14.JPG"
cfg_p = "data/bodypart_slrnet_cosin_conv_actV2.yaml"
predict_img = 'predict_2f900410.14.png'

model = load_segmentation_model(config_file=cfg_p)

pred_mask, img_original = predict_segmentation_mask(model, path=test_img, frame=None)

pred_color, org_pred_split = visualize_result(img_original.copy(), pred_mask)

img = Image.fromarray(org_pred_split)
img.save(predict_img)

All Aligned Predictions are located at:

/usb/body_alignment/
    - /img
    - /pred_anno