start working on simulating averages & the miccai challenge: h5.gz > npy, build hists in parrallel
This commit is contained in:
Stefan
parent
18cceb9791
commit
03bd157664
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import numpy as np
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import SimpleITK as sitk
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#paths
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# path_b800 = '/data/pca-rad/sfransen/UMCG_009.nii.gz'
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# path_seg_noise = '/data/pca-rad/sfransen/seg_umcg_0003.nii.gz'
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path_b800 = '/data/pca-rad/sfransen/b800_003.nii.gz'
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path_seg_noise = '/data/pca-rad/sfransen/segment_003.nii.gz'
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#load image/seg_signal/seg_noise
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b800_img = sitk.ReadImage(path_b800, sitk.sitkFloat32)
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b800_arr = sitk.GetArrayFromImage(b800_img)
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noise_img = sitk.ReadImage(path_seg_noise, sitk.sitkFloat32)
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noise_arr = sitk.GetArrayFromImage(noise_img)
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noise_list = np.matrix.flatten(np.multiply(noise_arr,b800_arr))
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#calculate standard deviation
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values = [i for i in np.matrix.flatten(np.multiply(noise_arr,b800_arr)) if i > 0]
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std_old = np.std(values)
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print('std old',std_old)
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A1 = 12
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A2 = 4
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#calculate new std
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std_new = np.multiply(std_old,np.divide(np.sqrt(A1),np.sqrt(A2)))
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print('std new',std_new)
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# std_2 = np.divide(std_1,np.sqrt(2))
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std_noise = np.sqrt(np.abs(np.power(std_old,2)-np.power(std_new,2)))
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# std_noise = np.multiply(std_1,std_2)
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print('std_noise',std_noise)
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noise = np.random.normal(0,std_noise,np.shape(b800_arr))
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b800_arr_noise = np.add(b800_arr,noise)
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values_check = [i for i in np.matrix.flatten(np.multiply(noise_arr,b800_arr_noise)) if i > 0]
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std_new_check = np.std(values_check)
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print('std new check',std_new_check)
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b800_img_2 = sitk.GetImageFromArray(b800_arr_noise)
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b800_img_2.CopyInformation(b800_img)
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sitk.WriteImage(b800_img_2, f"../test_4.nii.gz")
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b800_img = sitk.ReadImage(path_b800, sitk.sitkFloat32)
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sitk.WriteImage(b800_img, f"../test_true.nii.gz")
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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")
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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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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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# save_file = files[1]
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# seg = np.load(f'{mypath}/{save_file}/{save_file}_seg.npy')
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# kspace = np.load(f'{mypath}/{save_file}/{save_file}_ksp.npy')
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def k2p_to_img(kspace_single_coil):
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kspace_padded = np.pad(np.squeeze(kspace_single_coil[:,:,:]),[(128,128),(128,128),(0,0)],'constant')
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coil_image = np.abs(fftshift(fftshift(ifftn(ifftshift(kspace_padded)),axes=0),axes=1))
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return coil_image
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def img_from_ksp(
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kspace: np.array):
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kspace = np.transpose(kspace, (2, 0, 1, 3))
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coil_images = apply_parallel(kspace, k2p_to_img, 12)
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image = np.round(np.sqrt(np.sum(np.power(coil_images,2),0)))
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image = image[:,:,2:-2]
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image = image[:,:,::-1]
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return image
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for file_idx in range(167,300):
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image_recon = []
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kspace = []
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save_file = []
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save_file = files[file_idx]
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kspace = np.load(f'{mypath}/{save_file}/{save_file}_ksp.npy')
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image_recon = img_from_ksp(kspace)
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np.save(f'{mypath}/{save_file}/{save_file}_rss_recon.npy',image_recon)
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print('done:',file_idx,' ',f'{mypath}/{save_file}/{save_file}_rss_recon.npy')
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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")
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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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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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KSPACE_U_PATH = f'/data/pca-rad/datasets/miccai_2022/K2S_MICCAI2022_GRP/train/data/TBrecon1/train/R_8_mask.h5'
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f = h5py.File(KSPACE_U_PATH,'r')
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kspace_mask = f['us_mask'][()]
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def k2p_to_img(kspace_single_coil):
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kspace_padded = np.pad(np.squeeze(kspace_single_coil[:,:,:]),[(128,128),(128,128),(0,0)],'constant')
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coil_image = np.abs(fftshift(fftshift(ifftn(ifftshift(kspace_padded)),axes=0),axes=1))
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return coil_image
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def img_from_ksp(
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kspace: np.array):
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kspace_masked = np.multiply(kspace, kspace_mask)
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kspace_masked = np.transpose(kspace_masked, (2, 0, 1, 3))
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coil_images = apply_parallel(kspace_masked, k2p_to_img, 12)
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image = np.round(np.sqrt(np.sum(np.power(coil_images,2),0)))
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image = image[:,:,2:-2]
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image = image[:,:,::-1]
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return image.astype(np.float32)
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for file_idx in range(187,300):
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image_recon = []
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kspace = []
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save_file = []
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save_file = files[file_idx]
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kspace = np.load(f'{mypath}/{save_file}/{save_file}_ksp.npy')
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image_recon = img_from_ksp(kspace)
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np.save(f'{mypath}/{save_file}/{save_file}_rss_recon_usampled.npy',image_recon)
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print('done:',file_idx,' ',f'{mypath}/{save_file}/{save_file}_rss_recon_usampled.npy')
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# img_s = sitk.GetImageFromArray(np.squeeze(image_recon))
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# sitk.WriteImage(img_s, f"../job_test_undersampled_{save_file}.nii.gz")
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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")
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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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199
scripts/tmp.py
199
scripts/tmp.py
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from scipy import stats
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from traceback import FrameSummary
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import numpy as np
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# siemens_froc = [1.68,1.81,1.44,1.55]
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# b400_froc = [3.4,3.93,2.82,]
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# b800_froc = [1.58,1.99,1.36,1.6]
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# siemens_roc = [0.782, 0.732, 0.775, 0.854]
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b400_roc = [0.746, 0.814, 0.789, 0.763]
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b800_roc = [0.786, 0.731, 0.67, 0.782]
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# stat_test = stats.wilcoxon(siemens_froc,b800_froc,alternative='less')
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# print('froc stats siemens > b400',stat_test)
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# print(' Mean and std siemens froc:', np.mean(siemens_froc),'+-',np.std(siemens_froc))
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# print(' Mean and std b400 froc:', np.mean(b400_froc),'+-',np.std(b400_froc))
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# print(' Mean and std b800 froc:', np.mean(b800_froc),'+-',np.std(b800_froc))
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# print(' Mean and std siemens roc:', np.mean(siemens_roc),'+-',np.std(siemens_roc))
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print(' Mean and std b400 roc:', np.mean(b400_roc),'+-',np.std(b400_roc))
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print(' Mean and std b800 roc:', np.mean(b800_roc),'+-',np.std(b800_roc))
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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, print_stats_np
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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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from multiprocessing import set_start_method
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import gzip
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import os
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# The test has been introduced in [4]. Given n independent samples (xi, yi) from a bivariate distribution
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# (i.e. paired samples), it computes the differences di = xi - yi. One assumption of the test is that the
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# differences are symmetric, see [2]. The two-sided test has the null hypothesis that the median of the
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# differences is zero against the alternative that it is different from zero. The one-sided test has the
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# null hypothesis that the median is positive against the alternative that it is negative
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# (alternative == 'less'), or vice versa (alternative == 'greater.').
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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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for file_idx in range(188):
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image_recon = []
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||||
save_file = []
|
||||
save_file = files[file_idx]
|
||||
image_recon = np.load(f'{mypath}/{save_file}/{save_file}_rss_recon_usampled.npy').astype(np.float32)
|
||||
np.save(f'{mypath}/{save_file}/{save_file}_rss_recon_usampled.npy',image_recon)
|
||||
print('done:',file_idx,' ',f'{mypath}/{save_file}/{save_file}_rss_recon_usampled.npy')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
quit()
|
||||
mypath = f'/data/pca-rad/datasets/miccai_2022/K2S_MICCAI2022_GRP/train/data/TBrecon1/train/untarred'
|
||||
patient_id = 'pat00335'
|
||||
seg = np.load(f'{mypath}/{patient_id}/{patient_id}_seg.npy')
|
||||
print_stats_np(seg,'seg_')
|
||||
image = np.load(f'{mypath}/{patient_id}/{patient_id}_rss_recon.npy')
|
||||
print_stats_np(image,'image_')
|
||||
image_u = np.load(f'{mypath}/{patient_id}/{patient_id}_rss_recon_usampled.npy')
|
||||
print_stats_np(image_u,'image_u_')
|
||||
|
||||
seg = sitk.GetImageFromArray(np.squeeze(seg))
|
||||
sitk.WriteImage(seg, f"../test_seg_335.nii.gz")
|
||||
image = sitk.GetImageFromArray(np.squeeze(image))
|
||||
sitk.WriteImage(image, f"../test_image_335.nii.gz")
|
||||
image_u = sitk.GetImageFromArray(np.squeeze(image_u))
|
||||
sitk.WriteImage(image_u, f"../test_image_u_335.nii.gz")
|
||||
print(np.shape(seg))
|
||||
|
||||
|
||||
|
||||
|
||||
# ROOT_H5_GZ = r'../../../../../scratch/p290820/MICCAI/TBrecon-01-02-00335.h5.gz'
|
||||
# SAVE_TO_DIR = r'/data/pca-rad/datasets/miccai_2022/K2S_MICCAI2022_GRP/train/data/TBrecon1/train/untarred/pat00335'
|
||||
# unzipped = gzip.open(ROOT_H5_GZ,'rb')
|
||||
# h5_file = h5py.File(unzipped,'r') # Keys: kspace, kspace_info, seg, seg_info
|
||||
|
||||
# # ksp_n = h5_file['kspace'][()] #np.complex64
|
||||
# seg_n = h5_file['seg'][()].astype(np.float32) #np.float32
|
||||
|
||||
# # fname_ksp = os.path.join(patient_path, f"pat{pat_num}_ksp.npy")
|
||||
# fname_seg = os.path.join(SAVE_TO_DIR, f"pat00335_seg_test.npy")
|
||||
# # np.save(fname_ksp, ksp_n)
|
||||
# np.save(fname_seg, seg_n)
|
||||
|
||||
|
||||
|
||||
quit()
|
||||
|
||||
def inladen(patient_id,mypath):
|
||||
seg = np.load(f'{mypath}/{patient_id}/{patient_id}_seg.npy').astype(int)
|
||||
return seg
|
||||
|
||||
|
||||
def load_seg(file,mypath):
|
||||
patient_id = file
|
||||
print(patient_id)
|
||||
seg = inladen(patient_id,mypath)
|
||||
return seg
|
||||
|
||||
def calculate_hist(segs):
|
||||
background = np.sum(segs == 0)
|
||||
femoral_cartilage = np.sum(segs == 1)
|
||||
tibial_cartilage = np.sum(segs == 2)
|
||||
patellar_cartilage = np.sum(segs == 3)
|
||||
femur = np.sum(segs == 4)
|
||||
tibia = np.sum(segs == 5)
|
||||
patella = np.sum(segs == 6)
|
||||
output = [background,femoral_cartilage,tibial_cartilage,patellar_cartilage,femur,tibia,patella]
|
||||
# print('background',background)
|
||||
return output
|
||||
|
||||
if __name__ == '__main__':
|
||||
path = f'/data/pca-rad/datasets/miccai_2022/K2S_MICCAI2022_GRP/train/data/TBrecon1/train/untarred'
|
||||
files = [f for f in listdir(path)]
|
||||
print('files length',len(files))
|
||||
# For multiprocessing
|
||||
set_start_method("spawn")
|
||||
print('spawn done')
|
||||
segs_list = apply_parallel(files[:100], load_seg, 4, mypath = path)
|
||||
print('done loading 100')
|
||||
|
||||
segs_list_200 = apply_parallel(files[100:200], load_seg, 4, mypath = path)
|
||||
print('done loading 200')
|
||||
|
||||
segs_list_300 = apply_parallel(files[200:300], load_seg, 4, mypath = path)
|
||||
print('done loading 300')
|
||||
|
||||
segs_list.extend(segs_list_200)
|
||||
segs_list.extend(segs_list_300)
|
||||
|
||||
segs_n = np.stack(segs_list, axis=0)
|
||||
print('load done',np.shape(segs_n))
|
||||
|
||||
output = apply_parallel(segs_list,calculate_hist,4)
|
||||
print('hist calc done',np.shape(np.stack(output)))
|
||||
# print(np.stack(output)[:,0])
|
||||
|
||||
dict = {
|
||||
'background': np.stack(output)[:,0],
|
||||
'femoral_cartilage': np.stack(output)[:,0],
|
||||
'tibial_cartilage': np.stack(output)[:,0],
|
||||
'patellar_cartilage': np.stack(output)[:,0],
|
||||
'femur': np.stack(output)[:,0],
|
||||
'tibia': np.stack(output)[:,0],
|
||||
'patella': np.stack(output)[:,0]
|
||||
}
|
||||
|
||||
# for file_idx in range(300):
|
||||
# seg = []
|
||||
# patient_id = files[file_idx]
|
||||
# seg = np.load(f'{mypath}/{patient_id}/{patient_id}_seg.npy')
|
||||
# # # 0: background; 1: femoral cartilage; 2: tibial cartilage; 3: patellar cartilage; 4: femur; 5: tibia; 6: patella.
|
||||
|
||||
|
||||
# dict['background'].append(np.sum(seg == 0))
|
||||
# dict['femoral_cartilage'].append(np.sum(seg == 1))
|
||||
# dict['tibial_cartilage'].append(np.sum(seg == 2))
|
||||
# dict['patellar_cartilage'].append(np.sum(seg == 3))
|
||||
# dict['femur'].append(np.sum(seg == 4))
|
||||
# dict['tibia'].append(np.sum(seg == 5))
|
||||
# dict['patella'].append(np.sum(seg == 6))
|
||||
|
||||
# print('done:',file_idx,' ',f'{mypath}/{patient_id}/{patient_id}_seg.npy')
|
||||
|
||||
np.save('../dict.npy',dict)
|
||||
|
||||
for keys in dict:
|
||||
data = dict[f'{keys}']
|
||||
plt.hist(data)
|
||||
plt.title(f"historgram of {keys}")
|
||||
plt.savefig(f"../{keys}.png", dpi=300)
|
||||
print('done ',keys)
|
||||
|
||||
|
||||
|
||||
# print(f.keys())
|
||||
# print(np.shape(f['us_mask'][()]))
|
||||
# print(type(f['us_mask'][()]))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# quit()
|
||||
# seg = sitk.GetImageFromArray(np.squeeze(seg))
|
||||
# sitk.WriteImage(seg, f"../test_seg_2.nii.gz")
|
||||
# img_s = sitk.GetImageFromArray(np.squeeze(image))
|
||||
# # img_s.CopyInformation(seg)
|
||||
# sitk.WriteImage(img_s, f"../test_image_3.nii.gz")
|
||||
# img_s = sitk.GetImageFromArray(np.squeeze(coil_image))
|
||||
# # img_s.CopyInformation(seg)
|
||||
# sitk.WriteImage(img_s, f"../test_last_coil_image_2.nii.gz")
|
||||
Loading…
Reference in New Issue