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f_load_data.py
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import os
import glob
import numpy as np
import cv2
from f_normalize_images import normalize_img
def load_data(self, args):
def get_img(img):
if self.ch == 1:
image = cv2.imread(img,0)
image = normalize_img(image)
img = np.zeros(self.image_size*self.ch)
img[0:self.image_size] = np.reshape(image,[self.image_size])
else:
image = cv2.imread(img)
image = normalize_img(image)
img = np.zeros(self.image_size*self.ch)
img[0:self.image_size] = np.reshape(image[:,:,0],[self.image_size])
img[self.image_size:self.image_size*2] = np.reshape(image[:,:,1],[self.image_size])
img[self.image_size*2:self.image_size*3] = np.reshape(image[:,:,2],[self.image_size])
return img
if args.mode == 'test_inthewild':
in_path = args.inthewild_dataset_dir
imgA=glob.glob(os.path.join(in_path, '*.png'))
print('Number of images in '+str(args.mode)+':',len(imgA))
count = 0
for img in imgA:
img_name = os.path.basename(img)
filename, ext = os.path.splitext(img_name)
self.names.append(filename)
img = get_img(img)
self.imagesA[count] = np.array(img)
count = count + 1
return self.imagesA
else:
if args.mode == 'train':
in_path = args.train_dataset_dir
else:
in_path = args.test_dataset_dir
imgA=glob.glob(os.path.join(in_path, str(args.mode)+'A/*.png'))
print('Number of images in '+str(args.mode)+'A:',len(imgA))
imgB=glob.glob(os.path.join(in_path, str(args.mode)+'B/*.png'))
print('Number of images in '+str(args.mode)+'B:',len(imgB))
for img in imgA:
img_name = os.path.basename(img)
filename, ext = os.path.splitext(img_name)
num = filename.split('_')[1]
self.names.append(filename)
img = get_img(img)
self.imagesA[int(num)-1] = np.array(img)
for img in imgB:
img_name = os.path.basename(img)
filename, ext = os.path.splitext(img_name)
num = filename.split('_')[1]
img = get_img(img)
self.imagesB[int(num)-1] = np.array(img)
return self.imagesA, self.imagesB
def load_x_t(self, p,ch,imagesA,imagesB):
if ch==0:
x = imagesA[p,0:self.image_size]
t = imagesB[p,0:self.image_size]
if ch==1:
x = imagesA[p,self.image_size:self.image_size*2]
t = imagesB[p,self.image_size:self.image_size*2]
if ch==2:
x = imagesA[p,self.image_size*2:self.image_size*3]
t = imagesB[p,self.image_size*2:self.image_size*3]
xt = np.ones(self.size_n)
xt[0:self.image_size] = x
return xt,t
def load_x(self, p, ch):
if ch==0:
x = self.imagesA[p,0:self.image_size]
if ch==1:
x = self.imagesA[p,self.image_size:self.image_size*2]
if ch==2:
x = self.imagesA[p,self.image_size*2:self.image_size*3]
xt = np.ones(self.size_n)
xt[0:self.image_size] = x
return xt
def create_design_response_matrices(self, M,ch, imagesA, imagesB):
M1 = np.ones((self.n, self.size_n))
M2 = np.zeros((self.n, self.image_size))
r=0
for p in range(self.n):
x, t = load_x_t(self, p,ch,imagesA,imagesB)
if len(x) or len(t) != 0:
x = x.T
tt = np.reshape(t,[1,self.image_size])
M1[r,:] = x
M2[r,:] = tt
r = r+1
return M1, M2