from promise2012.Vnet.CNN2 import Vnet3dModule_64 as CNN3D_2 from promise2012.Vnet.CNN3 import Vnet3dModule_64 as CNN3D_3 from promise2012.Vnet.CNN1 import Vnet3dModule_64 as CNN3D_1 # from promise2012.Vnet.util import convertMetaModelToPbModel import numpy as np import pandas as pd import cv2 import os import tensorflow as tf import keras.backend.tensorflow_backend as KTF import SimpleITK as sitk from nipype.interfaces.ants import N4BiasFieldCorrection import dicom import gc os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "0" config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) KTF.set_session(sess) # 设置session def train(): ''' Preprocessing for dataset ''' # Read data set (Train data from CSV file) csvmaskdata = pd.read_csv('promise12Vnet3dMask_train_group1.csv') csvimagedata = pd.read_csv('promise12Vnet3dImage_train_group1.csv') maskdata = csvmaskdata.iloc[:, :].values imagedata = csvimagedata.iloc[:, :].values # 验证数据 csvmaskdata_val = pd.read_csv('promise12Vnet3dMask_val.csv') csvimagedata_val = pd.read_csv('promise12Vnet3dImage_val.csv') maskdata_val = csvmaskdata_val.iloc[:, :].values imagedata_val = csvimagedata_val.iloc[:, :].values # shuffle imagedata and maskdata together perm = np.arange(len(csvimagedata)) np.random.shuffle(perm) imagedata = imagedata[perm] maskdata = maskdata[perm] perm_val = np.arange(len(csvimagedata_val)) np.random.shuffle(perm_val) imagedata_val = imagedata_val[perm_val] maskdata_val = maskdata_val[perm_val] CNN3d = CNN3D_1(96, 96, 16, channels=1, costname="dice coefficient") CNN3d.train(imagedata, maskdata, imagedata_val, maskdata_val, "model\\cross_paper3/CNN1/1/CNN1.pd", "log\\", 0.001, 0.5, 100000, 1) def predict(): CNN3d = CNN3D_1(96, 96, 16, inference=True, model_path="model\\cross_paper3/CNN1/1" "/CNN1.pd") # CNN3d = CNN3D_2(96, 96, 16, inference=True, # model_path="model\\cross_paper3/CNN2/1" # "/CNN2.pd") # CNN3d = CNN3D_3(96, 96, 16, inference=True, # model_path="model\\cross_paper3/CNN3/1" # "/CNN3.pd") src_range = [50, 350] # normalization setting dst_range = [0, 255] # arr = [23, 45] # arr = [22, 37, 48] # arr = [6, 8, 35, 43] # arr = [12, 20, 36, 38, 51] # arr = [19, 33, 40, 47, 49] # arr = [15, 17, 21, 24, 27, 29, 31, 32, 39, 41, 42, 44, 46, 53] arr = [23, 45, 46, 47, 48, 22, 37, 6, 8, 35, 43, 12, 20, 36, 38, 51, 19, 33, 40, 49, 15, 17, 21, 24, 27, 29, 31, 32, 39, 41, 42, 44, 53] for i_ in range(0, len(arr)): if i_ == 0: i = arr[i_] input = np.fromfile('D:/Ktest/' + str(i) + '/delay_isorotopic' + str(i) + '.raw', dtype=np.int16) # DICOM image bone = np.fromfile('D:/Ktest/' + str(i) + '/0228OLDBoneIsoNew' + str(i) + '.raw', dtype=np.uint8) # bone input_w = np.copy(input) slope = (float(dst_range[1]) - float(dst_range[0])) \ / (float(src_range[1]) - float(src_range[0])) print(input_w.shape, 'case------', str(i), '---------start') bone[bone == 0] = 1 bone[bone == 255] = 0 input_w = input_w * bone input_w = np.reshape(input_w, (bone.shape[0]//262144, 512, 512)) output = np.zeros(shape=(input_w.shape[0], input_w.shape[1], input_w.shape[2]), dtype=np.uint8) for a in range(output.shape[0]//16): for b in range(512 // 96): for c in range(512 // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16): ((a + 1) * 16), (b * 96): ((b + 1) * 96), (c * 96): ((c + 1) * 96)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'NNN-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + index, (b * 96): ((b + 1) * 96), (c * 96): ((c + 1) * 96)] += result_1ch del result del result_1ch del batch_xs for b in range(512 // 96): for c in range((512-48) // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16): ((a + 1) * 16), (b * 96): ((b + 1) * 96), (c * 96+48): ((c + 1) * 96+48)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'NNY-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + index, (b * 96): ((b + 1) * 96), (c * 96+48): ((c + 1) * 96+48)] += result_1ch del result del result_1ch del batch_xs for b in range((512-48) // 96): for c in range((512-48) // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16): ((a + 1) * 16), (b * 96+48): ((b + 1) * 96+48), (c * 96+48): ((c + 1) * 96+48)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'NYY-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + index, (b * 96+48): ((b + 1) * 96+48), (c * 96+48): ((c + 1) * 96+48)] += result_1ch del result del result_1ch del batch_xs for b in range((512-48) // 96): for c in range(512 // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16): ((a + 1) * 16), (b * 96+48): ((b + 1) * 96+48), (c * 96): ((c + 1) * 96)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'NYN-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + index, (b * 96+48): ((b + 1) * 96+48), (c * 96): ((c + 1) * 96)] += result_1ch del result del result_1ch del batch_xs for a in range((output.shape[0] - 8) // 16): for b in range(512 // 96): for c in range(512 // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16+8): ((a + 1) * 16+8), (b * 96): ((b + 1) * 96), (c * 96): ((c + 1) * 96)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'YNN-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + 8 + index, (b * 96): ((b + 1) * 96), (c * 96): ((c + 1) * 96)] += result_1ch del result del result_1ch del batch_xs for b in range(512 // 96): for c in range((512 - 48) // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16+8): ((a + 1) * 16+8), (b * 96): ((b + 1) * 96), (c * 96 + 48): ((c + 1) * 96 + 48)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'YNY-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + index+8, (b * 96): ((b + 1) * 96), (c * 96 + 48): ((c + 1) * 96 + 48)] += result_1ch del result del result_1ch del batch_xs for b in range((512 - 48) // 96): for c in range((512 - 48) // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16+8): ((a + 1) * 16+8), (b * 96 + 48): ((b + 1) * 96 + 48), (c * 96 + 48): ((c + 1) * 96 + 48)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'YYY-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + index+8, (b * 96 + 48): ((b + 1) * 96 + 48), (c * 96 + 48): ((c + 1) * 96 + 48)] += result_1ch del result del result_1ch del batch_xs for b in range((512 - 48) // 96): for c in range(512 // 96): batch_xs = np.zeros(shape=(16, 96, 96, 1)) # rgb batch_xs[:, :, :, 0] = input_w[(a * 16+8): ((a + 1) * 16+8), (b * 96 + 48): ((b + 1) * 96 + 48), (c * 96): ((c + 1) * 96)] batch_xs = (batch_xs.astype('float') - float(src_range[0])) * slope + float(dst_range[0]) batch_xs[batch_xs < dst_range[0]] = dst_range[0] batch_xs[batch_xs > dst_range[1]] = dst_range[1] batch_xs = batch_xs.astype('uint8') if np.where(batch_xs != 0)[0].shape[0] < 30: print('zero:', a, b, c, '-------', str(np.where(batch_xs != 0)[0].shape[0])) # result = np.zeros(shape=(96, 96, 3), dtype=np.uint8) else: print(a, b, c, 'YYN-case:', str(i)) predictvalue = CNN3d.prediction(batch_xs) for index in range(16): result_1ch = np.zeros(shape=(96, 96), dtype=np.uint8) result = np.zeros(shape=(96, 96), dtype=np.uint8) result = predictvalue[index] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) for m in range(0, 96): for n in range(0, 96): if result[m, n] <= 200: result_1ch[m, n] = 0 else: result_1ch[m, n] = result[m, n] result_1ch = result_1ch // 8 output[a * 16 + index+8, (b * 96 + 48): ((b + 1) * 96 + 48), (c * 96): ((c + 1) * 96)] += result_1ch del result del result_1ch del batch_xs output[output < 100] = 0 output.tofile('D:/Ktest/' + str(i) + '/texture_test.raw') del output del bone del input del input_w gc.collect() train() predict()