def load_nii_file(self, path):
img = self.__read_nii_file(path) #读取3D源文件
# img = self.__cut_nii_file(img)#去除文件周围无意义的区域 3D去黑边
#缩放到目标大小 最近邻插值
if len(self.target_size) == 2:
# temp_targer_size = self.target_size[:]+[self.target_size[-1]]
temp_targer_size = self.target_size[:] + [155]
else:
temp_targer_size = self.target_size[:]
# ratio = [temp_targer_size[x]/img.shape[x] for x in range(3)]
# resize_image = ndimage.interpolation.zoom(img,ratio, mode='nearest')
# assert resize_image.shape==tuple(temp_targer_size)
# resize_image[resize_image<0]=0#去除插值后出现的负像素
resize_image = CutPadding.center_crop_3D(img=img,
target_size=temp_targer_size)
if self.dims == 3:
resize_image = resize_image
elif self.dims == 2:
resize_image = resize_image[:, :, temp_targer_size[-1] // 2]
else:
raise ValueError
img_norm = self.__normalize(resize_image, dtype=np.float32) #归一化
img_saved = self.__save_nii2npy(img_norm,
path) #保存 并且返回保存的文件 将对2D 3D区别对待
return img_saved
def __read_nii_file(self,path):
temp_path = path[:-3]+"npy"
if os.path.exists(temp_path)==True:
if self.update==False:
self.get_saved = True
return np.load(temp_path)
img = nib.load(path)
img = np.array(img.dataobj[:,:,:])
# print("*****")
# print(img.shape,img.dtype)
# print(img.min(),img.max())
# print("*****")
img = CutPadding.cut_img_3D(img)
# print("*****")
# print(img.shape,img.dtype)
# print(img.min(),img.max())
# print("*****")
return img
def __cut_nii_file(self, img):
return CutPadding.cut_img_3D(img)