| import gradio as gr | |
| import subprocess | |
| import os | |
| import numpy as np | |
| from PIL import Image | |
| os.environ['data_raw'] = 'data_raw/' | |
| os.environ['nnUNet_raw_data_base'] = 'nnUNet_raw_data_base/' | |
| os.environ['nnUNet_preprocessed'] = 'nnUNet_preprocessed/' | |
| os.environ['RESULTS_FOLDER'] = 'calvingfronts/' | |
| model_path = 'Task500_Glacier_zonefronts' | |
| def run_front_detection(input_img): | |
| image_gray = input_img.convert("L") | |
| image_gray.save('data_raw/test.png') | |
| subprocess.run( | |
| ['python3', 'nnunet/dataset_conversion/Task500_Glacier_inference.py', '-data_percentage', '100', '-base', | |
| os.environ['data_raw']]) | |
| cmd = [ | |
| 'python3', 'nnunet/inference/predict_simple.py', | |
| '-i', os.path.join(os.environ['nnUNet_raw_data_base'], 'nnUNet_raw_data/Task500_Glacier_zonefronts/imagesTs/'), | |
| '-o', os.path.join(os.environ['RESULTS_FOLDER'], 'fold_0'), | |
| '-t', '500','-m','2d','-f','0','-p', 'nnUNetPlansv2.1', '-tr','nnUNetTrainerV2', '-model_folder_name', | |
| model_path | |
| ] | |
| subprocess.run(cmd) | |
| subprocess.run(['python3', 'nnunet/dataset_conversion/Task500_Glacier_reverse.py', '-i', | |
| os.path.join(os.environ['RESULTS_FOLDER'], 'fold_0')]) | |
| os.remove('data_raw/test.png') | |
| os.remove('nnUNet_raw_data_base/nnUNet_raw_data/Task500_Glacier_zonefronts/imagesTs/test_0000.nii.gz') | |
| front = Image.open(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_front.png')) | |
| os.remove(os.path.join(os.environ['RESULTS_FOLDER'],'fold_0/test.nii.gz')) | |
| os.remove(os.path.join(os.environ['RESULTS_FOLDER'], 'tifs/test_front.png')) | |
| return front | |
| demo = gr.Interface(run_front_detection, gr.Image(type='pil'), "image",interface_size=(1000, 1000)) | |
| demo.launch() |