71 lines
2.0 KiB
Python
Executable File
71 lines
2.0 KiB
Python
Executable File
from traceback import FrameSummary
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import numpy as np
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import SimpleITK as sitk
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from os import listdir
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from pip import main
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from scipy.fftpack import fftshift, ifftshift, ifftn
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from umcglib.utils import apply_parallel
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import time
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import h5py
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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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files = [f for f in listdir(mypath)]
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dict = {
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'background': [],
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'femoral_cartilage': [],
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'tibial_cartilage': [],
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'patellar_cartilage': [],
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'femur': [],
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'tibia': [],
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'patella': []
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}
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for file_idx in range(300):
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seg = []
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patient_id = files[file_idx]
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seg = np.load(f'{mypath}/{patient_id}/{patient_id}_seg.npy')
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# # 0: background; 1: femoral cartilage; 2: tibial cartilage; 3: patellar cartilage; 4: femur; 5: tibia; 6: patella.
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dict['background'].append(np.sum(seg == 0))
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dict['femoral_cartilage'].append(np.sum(seg == 1))
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dict['tibial_cartilage'].append(np.sum(seg == 2))
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dict['patellar_cartilage'].append(np.sum(seg == 3))
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dict['femur'].append(np.sum(seg == 4))
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dict['tibia'].append(np.sum(seg == 5))
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dict['patella'].append(np.sum(seg == 6))
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print('done:',file_idx,' ',f'{mypath}/{patient_id}/{patient_id}_seg.npy')
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np.save('../dict.npy',dict)
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for keys in dict:
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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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# print(f.keys())
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# print(np.shape(f['us_mask'][()]))
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# print(type(f['us_mask'][()]))
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# quit()
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# seg = sitk.GetImageFromArray(np.squeeze(seg))
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# sitk.WriteImage(seg, f"../test_seg_2.nii.gz")
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# img_s = sitk.GetImageFromArray(np.squeeze(image))
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# # img_s.CopyInformation(seg)
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# sitk.WriteImage(img_s, f"../test_image_3.nii.gz")
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# img_s = sitk.GetImageFromArray(np.squeeze(coil_image))
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# # img_s.CopyInformation(seg)
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# sitk.WriteImage(img_s, f"../test_last_coil_image_2.nii.gz") |