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import gradio as gr |
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import subprocess |
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import os |
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import numpy as np |
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os.environ['data_raw'] = 'data_raw/' |
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os.environ['nnUNet_raw_data_base'] = 'nnUNet_raw_data_base/' |
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os.environ['nnUNet_preprocessed'] = 'nnUNet_preprocessed/' |
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os.environ['RESULTS_FOLDER'] = 'calvingfronts/' |
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def run_front_detection(input_img): |
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input_img.save('data_raw/test.png') |
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subprocess.run( |
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['python3', 'nnunet/dataset_conversion/Task500_Glacier_inference.py', '-data_percentage', '100', '-base', |
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os.environ['data_raw']]) |
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cmd = [ |
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'python3', 'nnunet/inference/predict_simple.py', |
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'-i', os.path.join('$nnUNet_raw_data_base', 'nnUNet_raw_data/Task500_Glacier_zonefronts/imagesTs/'), |
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'-o', os.path.join('$RESULTS_FOLDER', 'fold_0'), |
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'-t', '500','-m','2d','-f','0','-p', 'nnUNetPlansv2.1', '-tr','nnUNetTrainerV2', '-model_folder_name', |
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'$model' |
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] |
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demo = gr.Interface(run_front_detection, gr.Image(type='pil'), "image") |
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demo.launch() |
