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()
