Unet Semantic Segmentation for Cracks
Real time Crack Segmentation using PyTorch, OpenCV, ONNX runtime
Dependencies:
Pytorch
OpenCV
ONNX runtime
CUDA >= 9.0
Instructions:
1.Train model with your datatset and save model weights (.pt file) using unet_train.py on supervisely.ly
2.Convert model weights to ONNX format using pytorch_to_onnx.py
3.Obtain real time inference using crack_det_new.py
Crack segmentation model files can be downloaded by clicking this link
Commands
Usage: Used to inference on images available in a folder on GPU
python crack_inference_folder.py -c "class file" -l "color file" -idir "dataset directory" -odir "output directory" -m model file
python crack_inference_folder.py -c unet_classes.txt -l unet_colors.txt -idir "Dataset/sample dataset/" -odir "output_test/" -m model_files/model.pt
Usage: Used to inference on images available in a folder on CPU
python crack_det_new.py -c "class file" -l "color file" -i "input video" -o "output video" -m model file
python crack_det_new.py -c unet_classes.txt -l unet_colors.txt -i "input_vdo.mp4 -odir "output_vdo.mp4"
Results:
Graphs:
