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import cv2
import matplotlib.pyplot as plt
import nibabel as nib
import os
import glob
# from scipy.ndimage import zoom
import numpy as np
import skimage.transform
import torch.optim
from skimage import transform
from scipy.ndimage import binary_fill_holes,zoom
from scipy.ndimage import map_coordinates
#todo:先裁剪出bounding box，在resize成统一大小
# from imblearn.over_sampling import SMOTE
# image_paths=glob.glob('./train/label/*.png')
# cnt_mp={0:0,1:0,2:0,3:0,4:0,5:0,6:0,7:0}
# for path in image_paths:
# image=cv2.imread(path)[:,:,0]
# for i in cnt_mp:
# cnt_mp[i]+=np.sum(image==i)
#
# # break
# print(cnt_mp)
# cnt_mp.pop(0)
# cnt=0
# for i in cnt_mp:
# cnt+=cnt_mp[i]
# for i in cnt_mp:
# print(i,(cnt/len(cnt_mp)/cnt_mp[i]))
# image_paths=glob.glob('./mr_train/*_image.nii.gz')
# for path in image_paths:
# # print(path)
#
# filename=path.split('\\')[-1].split('.')[0]
#
# print(filename)
# image=nib.load(path).dataobj
# image=np.floor((image-np.min(image))/(np.max(image)-np.min(image))*255)
# # image=zoom(image,[256/image.shape[0],256/image.shape[1],128/image.shape[2]],order=0)
# # print(image.dtype)
#
# for i in range(image.shape[-1]):
#
# cv2.imwrite(os.path.join('train/image',f'{filename}_{i}.png'),image[:,:,i])
#
# # break
# #
# os.path.join('')
# image_paths=glob.glob('./ct_train/*_image.nii.gz')
# for path in image_paths:
# data_info=nib.load(path)
# h_,w_,d_=data_info.header['pixdim'][1:4]
# h,w,d=data_info.shape
# data=data_info.get_fdata()
#
# print(data.shape)
# print('实际大小',int(h*h_),int(w*w_),int(d*d_))
# # part_image=data[data!=-1024]
# # data=(data-np.mean(data))/np.std(data)
# # data=cv2.imread('../xm12/train_image/01001.png')
# # data=(data-np.mean(part_image))/np.std(part_image)
# print('取值范围', data.min(), data.max())
# # plt.imshow(data[:, :, 0],cmap='gray')
# # plt.show()
#
# # nonzero_mask = np.zeros(data.shape[1:], dtype=bool)
# # for c in range(data.shape[0]):
# # this_mask = data[c] != 0
# # nonzero_mask = nonzero_mask | this_mask
# # nonzero_mask = binary_fill_holes(nonzero_mask)
# break
def handle_image_and_label():
cnt=0
image_paths=glob.glob(r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\Task02_Heart\labelsTr\*.nii.gz')
for path in image_paths:
# print(path)
# if cnt<16:
# folder='mri_train_2d'
# else:
# folder='mri_test_2d'
folder='mri_train_2d'
filename=path.split('\\')[-1].split('.')[0].replace('label','image')
print(filename)
image=nib.load(path).dataobj
image=np.array(image,dtype=np.int8)
# print(image.shape)
# print(np.unique(image))
'''
label_map = [0, 1]
for i, v in enumerate(label_map):
image = np.where(image == v, i, image)
image = np.where(image == 421, 2, image)
# 能用的label-resize
rows, cols, dim = 256,256,image.shape[-1]
orig_rows, orig_cols, orig_dim = image.shape
row_scale = float(orig_rows) / rows
col_scale = float(orig_cols) / cols
dim_scale = float(orig_dim) / dim
map_rows, map_cols, map_dims = np.mgrid[:rows, :cols, :dim]
map_rows = row_scale * (map_rows + 0.5) - 0.5
map_cols = col_scale * (map_cols + 0.5) - 0.5
map_dims = dim_scale * (map_dims + 0.5) - 0.5
coord_map = np.array([map_rows, map_cols, map_dims])
image=map_coordinates(image, coord_map, order=1)
'''
# 自己写的label-resize
# print(image.shape,type(image),image.dtype,np.unique(image))
# break
# print(np.unique(image))
# final_index=[]
# # print(np.unique(image,axis=0).shape)
# # print(np.unique(image, axis=1).shape)
# # print(np.unique(image, axis=2).shape)
# temp_index=[]
# for i in range(0,image.shape[0]):
# if len(np.unique(image[i,:,:]))!=1:
# temp_index.append(i)
# break
# for i in range(image.shape[0]-1,0,-1):
# if len(np.unique(image[i,:,:])) != 1:
# temp_index.append(i)
# break
# final_index.append(temp_index)
#
# temp_index = []
# for i in range(0,image.shape[1]):
# if len(np.unique(image[:, i, :])) != 1:
# temp_index.append(i)
# break
# for i in range(image.shape[1] - 1, 0, -1):
# if len(np.unique(image[:, i, :])) != 1:
# temp_index.append(i)
# break
# final_index.append(temp_index)
#
# temp_index = []
# for i in range(0,image.shape[2]):
# if len(np.unique(image[:, :, i])) != 1:
# temp_index.append(i)
# break
# for i in range(image.shape[2] - 1, 0, -1):
# if len(np.unique(image[:, :, i])) != 1:
# temp_index.append(i)
# break
# final_index.append(temp_index)
#
# print(final_index)
# image=image[final_index[0][0]:final_index[0][1],
# final_index[1][0]:final_index[1][1],
# final_index[2][0]:final_index[2][1]]
# 1 2 3
# 注意用保存为图片时，数值类型不要unsigned
image=image.astype(np.int8)
print(np.unique(image),type(image),image.shape,image.dtype)
# print(np.unique(image[:,:,60]))
for i in range(image.shape[-1]):
cv2.imwrite(os.path.join(f'{folder}/label',f'{filename}_{i}.png'),image[:,:,i])
# np.save(os.path.join('./train-3d/label',filename),image)
# if len(np.unique(image>=3)):
# print(image.shape)
# for i in range(image.shape[-1]):
# cv2.imwrite(os.path.join('train/label',f'{filename}_{i}.png'),image[:,:,i])
# filename=filename.replace('label','image')
image = np.array(nib.load(os.path.join('imagesTr', filename + '.nii.gz')).dataobj)
print(np.unique(image))
# f,ax=plt.subplots(2,1)
# ax[0].imshow(image[:,:,0])
# ct处理方式
# image=((image+1024)/4095)*255
# mri处理方式
image=((image-np.min(image))/(np.max(image)-np.min(image)))*255
# print(np.unique(image))
# ax[1].imshow(image[:,:,0])
# plt.show()
# print(image[:,:,0][128])
# image = np.floor(
# (image - np.min(image)) / (np.max(image) - np.min(image)) * 255)
# image = image[final_index[0][0]:final_index[0][1],
# final_index[1][0]:final_index[1][1],
# final_index[2][0]:final_index[2][1]]
# image=(image-np.min(image))/(np.max(image)-np.min(image))*255
# 能用的image-resize
'''
image=skimage.transform.resize(image,(256,256,image.shape[-1]),order=3)
'''
# image=image.astype(np.float32)
# np.save(os.path.join('./train-3d/image',filename),image)
print(image.dtype)
# print(np.unique(image[:,:,60]))
for i in range(image.shape[-1]):
cv2.imwrite(os.path.join(f'{folder}/image',f'{filename}_{i}.png'),image[:,:,i])
cnt+=1
# break
if __name__ == '__main__':
# paths=glob.glob(r'C:\Users\zhang\PycharmProjects\mmsegmentation\data\Task02_Heart\imagesTr\*.nii.gz')
# for p in paths:
#
# img=nib.load(p).dataobj
# print(np.min(img),np.max(img))
handle_image_and_label()
img=cv2.imread('./mri_train_2d/image/la_003_60.png',0)
label=cv2.imread('./mri_train_2d/label/la_003_60.png',0)
print(np.unique(label))
plt.subplot(1,2,1)
plt.imshow(img)
plt.subplot(1,2,2)
plt.imshow(label,cmap='gray',interpolation='none')
plt.show()
# img_3d=nib.load('./mr_train/mr_train_1011_image.nii.gz').dataobj
# label_3d=nib.load('./mr_train/mr_train_1011_label.nii.gz').dataobj
#
# # print(img_3d.shape)
# img_slice=img_3d[:,:,90]
#
# label_slice=label_3d[:,:,90]
# # label_slice=np.where(label_slice==420,2,label_slice)
# # label_slice = np.where(label_slice == 850, 7, label_slice)
# print(np.unique(label_slice))
# fig,ax=plt.subplots(1,2)
# ax[0].imshow(img_slice,cmap='gray')
# ax[1].imshow(label_slice,cmap='CMRmap')
# plt.show() |