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()