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data.py
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data.py
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from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import numpy as np
import os
import glob
#import cv2
#from libtiff import TIFF
class myAugmentation(object):
"""
A class used to augmentate image
Firstly, read train image and label seperately, and then merge them together for the next process
Secondly, use keras preprocessing to augmentate image
Finally, seperate augmentated image apart into train image and label
"""
def __init__(self, train_path="train", label_path="label", merge_path="merge", aug_merge_path="aug_merge", aug_train_path="aug_train", aug_label_path="aug_label", img_type="tif"):
"""
Using glob to get all .img_type form path
"""
self.train_imgs = glob.glob(train_path+"/*."+img_type)
self.label_imgs = glob.glob(label_path+"/*."+img_type)
self.train_path = train_path
self.label_path = label_path
self.merge_path = merge_path
self.img_type = img_type
self.aug_merge_path = aug_merge_path
self.aug_train_path = aug_train_path
self.aug_label_path = aug_label_path
self.slices = len(self.train_imgs)
self.datagen = ImageDataGenerator(
rotation_range=0.2,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.05,
zoom_range=0.05,
horizontal_flip=True,
fill_mode='nearest')
def Augmentation(self):
"""
Start augmentation.....
"""
trains = self.train_imgs
labels = self.label_imgs
path_train = self.train_path
path_label = self.label_path
path_merge = self.merge_path
imgtype = self.img_type
path_aug_merge = self.aug_merge_path
if len(trains) != len(labels) or len(trains) == 0 or len(trains) == 0:
print("trains can't match labels")
return 0
for i in range(len(trains)):
img_t = load_img(path_train+"/"+str(i)+"."+imgtype)
img_l = load_img(path_label+"/"+str(i)+"."+imgtype)
x_t = img_to_array(img_t)
x_l = img_to_array(img_l)
x_t[:,:,2] = x_l[:,:,0]
img_tmp = array_to_img(x_t)
img_tmp.save(path_merge+"/"+str(i)+"."+imgtype)
img = x_t
img = img.reshape((1,) + img.shape)
savedir = path_aug_merge + "/" + str(i)
if not os.path.lexists(savedir):
os.mkdir(savedir)
self.doAugmentate(img, savedir, str(i))
def doAugmentate(self, img, save_to_dir, save_prefix, batch_size=1, save_format='tif', imgnum=30):
"""
augmentate one image
"""
datagen = self.datagen
i = 0
for batch in datagen.flow(img,
batch_size=batch_size,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format):
i += 1
if i > imgnum:
break
def splitMerge(self):
"""
split merged image apart
"""
path_merge = self.aug_merge_path
path_train = self.aug_train_path
path_label = self.aug_label_path
for i in range(self.slices):
path = path_merge + "/" + str(i)
train_imgs = glob.glob(path+"/*."+self.img_type)
savedir = path_train + "/" + str(i)
if not os.path.lexists(savedir):
os.mkdir(savedir)
savedir = path_label + "/" + str(i)
if not os.path.lexists(savedir):
os.mkdir(savedir)
for imgname in train_imgs:
midname = imgname[imgname.rindex("/")+1:imgname.rindex("."+self.img_type)]
img = cv2.imread(imgname)
img_train = img[:,:,2]#cv2 read image rgb->bgr
img_label = img[:,:,0]
cv2.imwrite(path_train+"/"+str(i)+"/"+midname+"_train"+"."+self.img_type,img_train)
cv2.imwrite(path_label+"/"+str(i)+"/"+midname+"_label"+"."+self.img_type,img_label)
def splitTransform(self):
"""
split perspective transform images
"""
#path_merge = "transform"
#path_train = "transform/data/"
#path_label = "transform/label/"
path_merge = "deform/deform_norm2"
path_train = "deform/train/"
path_label = "deform/label/"
train_imgs = glob.glob(path_merge+"/*."+self.img_type)
for imgname in train_imgs:
midname = imgname[imgname.rindex("/")+1:imgname.rindex("."+self.img_type)]
img = cv2.imread(imgname)
img_train = img[:,:,2]#cv2 read image rgb->bgr
img_label = img[:,:,0]
cv2.imwrite(path_train+midname+"."+self.img_type,img_train)
cv2.imwrite(path_label+midname+"."+self.img_type,img_label)
class dataProcess(object):
#def __init__(self, out_rows, out_cols, data_path = "../deform/train", label_path = "../deform/label", test_path = "../test", npy_path = "../npydata", img_type = "tif"):
def __init__(self, out_rows, out_cols, data_path = "./data/train/image", label_path = "./data/train/label", test_path = "./data/test", npy_path = "../npydata", img_type = "tif"):
"""
"""
self.out_rows = out_rows
self.out_cols = out_cols
self.data_path = data_path
self.label_path = label_path
self.img_type = img_type
self.test_path = test_path
self.npy_path = npy_path
def create_train_data(self):
i = 0
print('-'*30)
print('Creating training images...')
print('-'*30)
imgs = glob.glob(self.data_path+"/*."+self.img_type)
print(len(imgs))
imgdatas = np.ndarray((len(imgs),self.out_rows,self.out_cols,1), dtype=np.uint8)
imglabels = np.ndarray((len(imgs),self.out_rows,self.out_cols,1), dtype=np.uint8)
for imgname in imgs:
midname = imgname[imgname.rindex("/")+1:]
img = load_img(self.data_path + "/" + midname,grayscale = True)
label = load_img(self.label_path + "/" + midname,grayscale = True)
img = img_to_array(img)
label = img_to_array(label)
#img = cv2.imread(self.data_path + "/" + midname,cv2.IMREAD_GRAYSCALE)
#label = cv2.imread(self.label_path + "/" + midname,cv2.IMREAD_GRAYSCALE)
#img = np.array([img])
#label = np.array([label])
imgdatas[i] = img
imglabels[i] = label
if i % 100 == 0:
print('Done: {0}/{1} images'.format(i, len(imgs)))
i += 1
print('loading done')
np.save(self.npy_path + '/imgs_train.npy', imgdatas)
np.save(self.npy_path + '/imgs_mask_train.npy', imglabels)
print('Saving to .npy files done.')
def create_test_data(self):
i = 0
print('-'*30)
print('Creating test images...')
print('-'*30)
imgs = glob.glob(self.test_path+"/*."+self.img_type)
print(len(imgs))
imgdatas = np.ndarray((len(imgs),self.out_rows,self.out_cols,1), dtype=np.uint8)
for imgname in imgs:
midname = imgname[imgname.rindex("/")+1:]
img = load_img(self.test_path + "/" + midname,grayscale = True)
img = img_to_array(img)
#img = cv2.imread(self.test_path + "/" + midname,cv2.IMREAD_GRAYSCALE)
#img = np.array([img])
imgdatas[i] = img
i += 1
print('loading done')
np.save(self.npy_path + '/imgs_test.npy', imgdatas)
print('Saving to imgs_test.npy files done.')
def load_train_data(self):
print('-'*30)
print('load train images...')
print('-'*30)
imgs_train = np.load(self.npy_path+"/imgs_train.npy")
imgs_mask_train = np.load(self.npy_path+"/imgs_mask_train.npy")
imgs_train = imgs_train.astype('float32')
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_train /= 255
#mean = imgs_train.mean(axis = 0)
#imgs_train -= mean
imgs_mask_train /= 255
imgs_mask_train[imgs_mask_train > 0.5] = 1
imgs_mask_train[imgs_mask_train <= 0.5] = 0
return imgs_train,imgs_mask_train
def load_test_data(self):
print('-'*30)
print('load test images...')
print('-'*30)
imgs_test = np.load(self.npy_path+"/imgs_test.npy")
imgs_test = imgs_test.astype('float32')
imgs_test /= 255
#mean = imgs_test.mean(axis = 0)
#imgs_test -= mean
return imgs_test
if __name__ == "__main__":
#aug = myAugmentation()
#aug.Augmentation()
#aug.splitMerge()
#aug.splitTransform()
mydata = dataProcess(512,512)
mydata.create_train_data()
mydata.create_test_data()
#imgs_train,imgs_mask_train = mydata.load_train_data()
#print imgs_train.shape,imgs_mask_train.shape