95 lines
3.6 KiB
Python
Executable File
95 lines
3.6 KiB
Python
Executable File
from multiprocessing import set_start_method
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import numpy as np
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from umcglib.utils import apply_parallel,print_stats_np
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from os import listdir
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import matplotlib.pyplot as plt
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# mypath = f'/data/pca-rad/datasets/miccai_2022/K2S_MICCAI2022_GRP/train/data/TBrecon1/train/untarred'
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# patient_id = 'pat00123'
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# seg = np.load(f'{mypath}/{patient_id}/{patient_id}_seg.npy').astype(int)
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# print(np.shape(seg))
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# print_stats_np(seg,'seg_sdgsd')
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def load_seg(file,mypath):
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patient_id = file
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print(patient_id)
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seg = np.load(f'{mypath}/{patient_id}/{patient_id}_seg.npy').astype(int)
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background = np.sum(seg == 0)
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femoral_cartilage = np.sum(seg == 1)
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tibial_cartilage = np.sum(seg == 2)
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patellar_cartilage = np.sum(seg == 3)
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femur = np.sum(seg == 4)
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tibia = np.sum(seg == 5)
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patella = np.sum(seg == 6)
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output = [background,femoral_cartilage,tibial_cartilage,patellar_cartilage,femur,tibia,patella]
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# print('background',background)
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return output
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def calculate_hist(segs):
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background = np.sum(segs == 0)
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femoral_cartilage = np.sum(segs == 1)
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tibial_cartilage = np.sum(segs == 2)
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patellar_cartilage = np.sum(segs == 3)
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femur = np.sum(segs == 4)
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tibia = np.sum(segs == 5)
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patella = np.sum(segs == 6)
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output = [background,femoral_cartilage,tibial_cartilage,patellar_cartilage,femur,tibia,patella]
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# print('background',background)
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return output
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if __name__ == '__main__':
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path = f'/data/pca-rad/datasets/miccai_2022/K2S_MICCAI2022_GRP/train/data/TBrecon1/train/untarred'
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files = [f for f in listdir(path)]
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print('files length',len(files))
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# For multiprocessing
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set_start_method("spawn")
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print('spawn done')
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# segs_list = apply_parallel(files, load_seg, 4, mypath = path)
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segs_list = apply_parallel(files, load_seg, 12, mypath = path)
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print('done loading 50')
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# segs_list_100 = apply_parallel(files[50:100], load_seg, 4, mypath = path)
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# print('done loading 100')
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# segs_list_150 = apply_parallel(files[100:150], load_seg, 4, mypath = path)
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# print('done loading 150')
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# segs_list_200 = apply_parallel(files[150:200], load_seg, 4, mypath = path)
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# print('done loading 200')
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# segs_list_250 = apply_parallel(files[200:250], load_seg, 4, mypath = path)
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# print('done loading 250')
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# segs_list_300 = apply_parallel(files[250:300], load_seg, 4, mypath = path)
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print('done loading 300')
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# segs_list.extend(segs_list_100)
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# segs_list.extend(segs_list_150)
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# segs_list.extend(segs_list_200)
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# segs_list.extend(segs_list_250)
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# segs_list.extend(segs_list_300)
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print('segs_list is done')
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output = np.stack(segs_list, axis=0)
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# print('load done',np.shape(segs_n))
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# output = apply_parallel(segs_list,calculate_hist,4)
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print('hist calc done',np.shape(output))
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# print(np.stack(output)[:,0])
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dict = {
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'background': np.stack(output)[:,0],
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'femoral_cartilage': np.stack(output)[:,1],
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'tibial_cartilage': np.stack(output)[:,2],
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'patellar_cartilage': np.stack(output)[:,3],
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'femur': np.stack(output)[:,4],
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'tibia': np.stack(output)[:,5],
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'patella': np.stack(output)[:,6]
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}
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np.save('../dict.npy',dict)
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for keys in dict:
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plt.figure()
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data = dict[f'{keys}']
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plt.hist(data)
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plt.title(f"historgram of {keys}")
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plt.savefig(f"../{keys}.png", dpi=300)
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print('done ',keys)
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