File size: 1,023 Bytes
75ea7e6 d77453d ab657c0 75ea7e6 ab657c0 75ea7e6 ab657c0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 |
import gradio as gr
import subprocess
import os
import numpy as np
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/'
def run_front_detection(input_img):
input_img.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('$nnUNet_raw_data_base', 'nnUNet_raw_data/Task500_Glacier_zonefronts/imagesTs/'),
'-o', os.path.join('$RESULTS_FOLDER', 'fold_0'),
'-t', '500','-m','2d','-f','0','-p', 'nnUNetPlansv2.1', '-tr','nnUNetTrainerV2', '-model_folder_name',
'$model'
]
#subprocess.run(cmd)
#TODO remove files
demo = gr.Interface(run_front_detection, gr.Image(type='pil'), "image")
demo.launch() |