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patchgenerator.py
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patchgenerator.py
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from __future__ import print_function, division
import SimpleITK as sitk
import numpy as np
import cv2
import os
trainImage = "/storage/research/Intern19_v2/KidneyTumorSegmentationChallenge/data/scratch/Train/Images"
trainMask = "/storage/research/Intern19_v2/KidneyTumorSegmentationChallenge/data/scratch/Train/Masks"
def getRangImageDepth(image):
"""
:param image:
:return:rangofimage depth
"""
fistflag = True
startposition = 0
endposition = 0
for z in range(image.shape[0]):
notzeroflag = np.max(image[z])
if notzeroflag and fistflag:
startposition = z
fistflag = False
if notzeroflag:
endposition = z
return startposition, endposition
def subimage_generator(image, patch_block_size, numberxy, numberz):
"""
generate the sub images with patch_block_size
:param image:
:param patch_block_size:
:param stride:
:return:
"""
width = np.shape(image)[1]
height = np.shape(image)[2]
imagez = np.shape(image)[0]
block_width = np.array(patch_block_size)[1]
block_height = np.array(patch_block_size)[2]
blockz = np.array(patch_block_size)[0]
stridewidth = (width - block_width) // (numberxy - 1)
strideheight = (height - block_height) // (numberxy - 1)
stridez = (imagez - blockz) // numberz
# step 1:if image size of z is smaller than blockz,return zeros samples
if imagez < blockz:
nb_sub_images = numberxy * numberxy * 1
hr_samples = np.zeros(shape=(nb_sub_images, blockz, block_width, block_height), dtype=np.float32)
return hr_samples
# step 2:if stridez is bigger 1,return numberxy * numberxy * numberz samples
if stridez >= 1:
nb_sub_images = numberxy * numberxy * numberz
hr_samples = np.empty(shape=(nb_sub_images, blockz, block_width, block_height), dtype=np.float32)
indx = 0
for z in range(0, numberz * stridez, stridez):
for x in range(0, width - block_width + 1, stridewidth):
for y in range(0, height - block_height + 1, strideheight):
hr_samples[indx, :, :, :] = image[z:z + blockz, x:x + block_width, y:y + block_height]
indx += 1
if (indx != nb_sub_images):
print("error sub number image")
return hr_samples
# step3: if stridez==imagez,return numberxy * numberxy * 1 samples,one is [0:blockz,:,:]
if imagez == blockz:
nb_sub_images = numberxy * numberxy * 1
hr_samples = np.empty(shape=(nb_sub_images, blockz, block_width, block_height), dtype=np.float32)
indx = 0
for x in range(0, width - block_width + 1, stridewidth):
for y in range(0, height - block_height + 1, strideheight):
hr_samples[indx, :, :, :] = image[:, x:x + block_width, y:y + block_height]
indx += 1
if (indx != nb_sub_images):
print("error sub number image")
print(indx)
print(nb_sub_images)
return hr_samples
# step4: if stridez==0,return numberxy * numberxy * 2 samples,one is [0:blockz,:,:],two is [-blockz-1:-1,:,:]
if stridez == 0:
nb_sub_images = numberxy * numberxy * 2
hr_samples = np.empty(shape=(nb_sub_images, blockz, block_width, block_height), dtype=np.float32)
indx = 0
for x in range(0, width - block_width + 1, stridewidth):
for y in range(0, height - block_height + 1, strideheight):
hr_samples[indx, :, :, :] = image[0:blockz, x:x + block_width, y:y + block_height]
indx += 1
hr_samples[indx, :, :, :] = image[-blockz - 1:-1, x:x + block_width, y:y + block_height]
indx += 1
if (indx != nb_sub_images):
print("error sub number image")
return hr_samples
def make_patch(image, patch_block_size, numberxy, numberz, startpostion, endpostion):
"""
make number patch
:param image:[depth,512,512]
:param patch_block: such as[64,128,128]
:return:[samples,64,128,128]
expand the dimension z range the subimage:[startpostion-blockz//2:endpostion+blockz//2,:,:]
"""
blockz = np.array(patch_block_size)[0]
imagezsrc = np.shape(image)[0]
subimage_startpostion = startpostion - blockz // 2
subimage_endpostion = endpostion + blockz // 2
if subimage_startpostion < 0:
subimage_startpostion = 0
if subimage_endpostion > imagezsrc:
subimage_endpostion = imagezsrc
if (subimage_endpostion - subimage_startpostion) < blockz:
subimage_startpostion = 0
subimage_endpostion = imagezsrc
imageroi = image[subimage_startpostion:subimage_endpostion, :, :]
image_subsample = subimage_generator(image=imageroi, patch_block_size=patch_block_size, numberxy=numberxy,
numberz=numberz)
return image_subsample
'''
This funciton reads a '.mhd' file using SimpleITK and return the image array, origin and spacing of the image.
read_Image_mask fucntion get image and mask
'''
def load_itk(filename):
"""
load mhd files and normalization 0-255
:param filename:
:return:
"""
rescalFilt = sitk.RescaleIntensityImageFilter()
rescalFilt.SetOutputMaximum(255)
rescalFilt.SetOutputMinimum(0)
# Reads the image using SimpleITK
itkimage = rescalFilt.Execute(sitk.Cast(sitk.ReadImage(filename), sitk.sitkFloat32))
return itkimage
def gen_image_mask(srcimg, seg_image, index, shape, numberxy, numberz):
# step 1 get mask effective range(startpostion:endpostion)
startpostion, endpostion = getRangImageDepth(seg_image)
# step 2 get subimages (numberxy*numberxy*numberz,16, 256, 256)
sub_srcimages = make_patch(srcimg, patch_block_size=shape, numberxy=numberxy, numberz=numberz,
startpostion=startpostion,
endpostion=endpostion)
sub_liverimages = make_patch(seg_image, patch_block_size=shape, numberxy=numberxy, numberz=numberz,
startpostion=startpostion,
endpostion=endpostion)
# step 3 only save subimages (numberxy*numberxy*numberz,16, 256, 256)
samples, imagez = np.shape(sub_srcimages)[0], np.shape(sub_srcimages)[1]
for j in range(samples):
sub_masks = sub_liverimages.astype(np.float32)
sub_masks = np.clip(sub_masks, 0, 255).astype('uint8')
if np.max(sub_masks[j, :, :, :]) == 255:
filepath = trainImage + "/" + str(index) + "_" + str(j) + "/"
filepath2 = trainMask + "/" + str(index) + "_" + str(j) + "/"
if not os.path.exists(filepath) and not os.path.exists(filepath2):
os.makedirs(filepath)
os.makedirs(filepath2)
for z in range(imagez):
image = sub_srcimages[j, z, :, :]
image = image.astype(np.float32)
image = np.clip(image, 0, 255).astype('uint8')
cv2.imwrite(filepath + str(z) + ".bmp", image)
cv2.imwrite(filepath2 + str(z) + ".bmp", sub_masks[j, z, :, :])
import glob
def preparetraindata():
img_path = glob.glob("/storage/research/Intern19_v2/KidneyTumorSegmentationChallenge/scratch/data/case_ (*)/imaging.nii")
mask_path = glob.glob("/storage/research/Intern19_v2/KidneyTumorSegmentationChallenge/scratch/data/case_ (*)/segmentation.nii")
for i in range(1, 210, 1):
seg = sitk.ReadImage(img_path[i], sitk.sitkUInt8)
segimg = sitk.GetArrayFromImage(seg)
src = load_itk(mask_path[i])
srcimg = sitk.GetArrayFromImage(src)
seg_image = segimg.copy()
seg_image[segimg > 0] = 255
seg_tumorimage = segimg.copy()
seg_tumorimage[segimg == 1] = 0
seg_tumorimage[segimg == 2] = 255
gen_image_mask(srcimg, seg_image, i, shape=(16, 256, 256), numberxy=5, numberz=10)
gen_image_mask(srcimg, seg_tumorimage, i, shape=(16, 256, 256), numberxy=5, numberz=10)
preparetraindata()