import os import numpy as np """ Describe: 根据传过来的中心点对数据进行切割 Parameters: data: 所有的切面数据 crop_start: 在切面中选择出来的大小为(48, 256, 256)区域的三个维度的中心点的坐标 """ def get_crop_data(data, crop_start, crop_size, mode='constant', constant_values=-1000): if data is None: return data data_shape = np.array(data.shape) crop_data = data[ max(crop_start[0], 0):min(crop_start[0] + crop_size[0], data_shape[0]), max(crop_start[1], 0):min(crop_start[1] + crop_size[1], data_shape[1]), max(crop_start[2], 0):min(crop_start[2] + crop_size[2], data_shape[2])] # pad = np.zeros((3, 2), np.int) pad = np.zeros((3, 2), np.int32) #np.maximum(x1, x2) 用于逐个比较其中元素的大小，最后返回的是较大的形成的数据 #往前需要填充多少数据 pad[:, 0] = np.maximum(-crop_start, 0) pad[:, 1] = np.maximum(crop_start + crop_size - data_shape, 0) if not np.all(pad == 0): #pad表示在每个维度上要填充的长度 if mode == 'edge': crop_data = np.pad(crop_data, pad, mode=mode) else: crop_data = np.pad(crop_data, pad, mode=mode, constant_values=constant_values) return crop_data def get_crop_data_padding_2d_opt(ct_data=None, select_box=None, crop_size=None, mode='constant', constant_values=-1000): # 获取原始数据 data = ct_data.get_raw_image() data_shape = np.array(data.shape) crop_size = np.array(crop_size) crop_start = np.int0((select_box[:, 0] + select_box[:, 1])//2 - crop_size // 2) crop_data = get_crop_data(data=data, crop_start=crop_start, crop_size=crop_size, mode=mode, constant_values=constant_values) return crop_data """ 只保留一层，将该层数据放置中心点，别的层都填充为-1000 crop_size=[48, 256, 256] """ def get_crop_data_padding(ct_data=None, select_box=None, crop_size=None, mode='constant', constant_values=-1000): data = ct_data.get_raw_image() data_shape = np.array(data.shape) crop_size = np.array(crop_size) z_index = int(select_box[0][0]) crop_start = np.int0((select_box[1:, 0] + select_box[1:, 1])//2 - crop_size[1:] // 2) crop_data = data[z_index:z_index+1, max(crop_start[0], 0) : min(crop_start[0] + crop_size[1], data_shape[1]), max(crop_start[1], 0) : min(crop_start[1] + crop_size[2], data_shape[2])] # pad = np.zeros((3, 2), np.int) pad = np.zeros((3, 2), np.int32) pad[1:, 0] = np.maximum(-crop_start, 0) pad[1:, 1] = np.maximum(crop_start + crop_size[1:] - data_shape[1:], 0) if not np.all(pad == 0): crop_data = np.pad(crop_data, pad, mode=mode, constant_values=constant_values) result_data = np.full(crop_size, -1000) result_data[crop_size[0]//2] = crop_data return result_data #从当前(1024, 1024)的图片数据中获取到指定坐标区域的数据，如果所在区域不够（256， 256），则需要相对应的补充-1000 #select_box=[[z_index, z_index], [y_min, y_max], [x_min, x_max]] #crop_size=[256. 256] def get_crop_data_2d(data=None, select_box=None, crop_size=None, mode='constant', constant_values=-1000): data_shape = np.array(data.shape) crop_size = np.array(crop_size) crop_start = np.int0((select_box[1:, 0] + select_box[1:, 1])//2 - crop_size[:]//2) crop_data = data[max(crop_start[0], 0) : min(crop_start[0] + crop_size[0], data_shape[0]), max(crop_start[1], 0) : min(crop_start[1] + crop_size[1], data_shape[1])] # pad = np.zeros((2, 2), np.int) pad = np.zeros((2, 2), np.int32) pad[:, 0] = np.maximum(-crop_start, 0) pad[:, 1] = np.maximum(crop_start + crop_size[:] - data_shape[:], 0) if not np.all(pad == 0): crop_data = np.pad(crop_data, pad, mode=mode, constant_values=constant_values) """result_data = np.full(crop_size, -1000) result_data[crop_size[0]//2] = crop_data""" result_data = crop_data return result_data ''' #将对切面数据继续处理，返回(48, 256, 256)大小的数据 def crop_ct_data(ct_data=None, select_box=None, crop_size=None): data = ct_data.get_raw_image() #print(data.shape) spacing = ct_data.get_raw_spacing() z_center = int((select_box[0, 0] + select_box[0, 1]) // 2) crop_size = np.array(crop_size) #以下操作包含原box的情况下，并z_center处于中间位置 #z_diff=crop_size[0] - (select_box[0, 1] - select_box[0, 0]) z_diff = crop_size[0] - (select_box[0, 1] - select_box[0, 0]) - 1 #所选择的层数小于48 if z_diff > 0: #新增前面层数 add_front_z = 0 #新增后面层数 add_behind_z = 0 #select_box所对应的中间层距离z_min相差的层数 z_front_num = z_center - select_box[0, 0] #select_box所对应的中间层距离z_max相差的层数 z_behind_num = select_box[0, 1] - z_center #这里add_front_z为1或0 add_front_z = min(z_behind_num - z_front_num, z_diff) #当z_diff != 1 时 if add_front_z < z_diff: add_front_z = add_front_z + (z_diff - add_front_z) // 2 add_behind_z = z_diff - add_front_z else: add_front_z = add_front_z add_behind_z = 0 select_box[0, 0] = select_box[0, 0] - add_front_z select_box[0, 1] = select_box[0, 1] + add_behind_z scale = (1, 1, 1) #scale = (1, 1, 1) if spacing[0] > 0.5 else (2, 1, 1) crop_size = np.int0(crop_size * scale) crop_start = np.int0((select_box[:, 0] + select_box[:, 1])//2 - crop_size // 2) #根据尺寸大小对数据进行切割 original_data = get_crop_data(data=data, crop_start=crop_start, crop_size=crop_size) return original_data ''' def crop_ct_data(ct_data=None, select_box=None, crop_size=None): data = ct_data.get_raw_image() #print(data.shape) spacing = ct_data.get_raw_spacing() z_center = int((select_box[0, 0] + select_box[0, 1]) // 2) crop_size = np.array(crop_size) #以下操作包含原box的情况下，并z_center处于中间位置 #z_diff=crop_size[0] - (select_box[0, 1] - select_box[0, 0]) z_diff = crop_size[0] - (select_box[0, 1] - select_box[0, 0]) #所选择的层数小于48 if z_diff > 0: #新增前面层数 add_front_z = 0 #新增后面层数 add_behind_z = 0 #select_box所对应的中间层距离z_min相差的层数 z_front_num = z_center - select_box[0, 0] #select_box所对应的中间层距离z_max相差的层数 z_behind_num = select_box[0, 1] - z_center if z_behind_num >= z_front_num > 0: add_front_z = min(z_behind_num - z_front_num, z_diff) if add_front_z < z_diff: add_front_z = add_front_z + (z_diff - add_front_z) // 2 add_behind_z = z_diff - add_front_z elif z_front_num > z_behind_num > 0: add_behind_z = min(z_front_num - z_behind_num, z_diff) if add_behind_z < z_diff: add_behind_z = add_behind_z + (z_diff - add_behind_z) // 2 add_front_z = z_diff - add_behind_z select_box[0, 0] = select_box[0, 0] - add_front_z select_box[0, 1] = select_box[0, 1] + add_behind_z scale = (1, 1, 1) #scale = (1, 1, 1) if spacing[0] > 0.5 else (2, 1, 1) crop_size = np.int0(crop_size * scale) crop_start = np.int0((select_box[:, 0] + select_box[:, 1])//2 - crop_size // 2) #根据尺寸大小对数据进行切割 original_data = get_crop_data(data=data, crop_start=crop_start, crop_size=crop_size) return original_data #将自动分割出来的数据都将其每个切面都作为中心面，然后其余地方进行填充成(48, 256, 256)的数据进行预测