def get_batch():
ran = random.randint(600, data_size)
#print(ran)
image = []
label = []
label_0 = []
n_pic = ran
# print(n_pic)
for i in range(batch_size * n_steps):
frame_0 = cv2.imread('./cropedoriginalPixel2/%d.jpg' % (n_pic+i), 0)
frame_0 = cv2.resize(frame_0, (LONGITUDE, LONGITUDE))
frame_0 = np.array(frame_0).reshape(-1)
image.append(frame_0)
#print(np.shape(image))
for i in range(batch_size):
frame_1 = cv2.imread('./cropedoriginalPixel2/%d.jpg' % (n_pic + batch_size * (i+1) ), 0)
frame_1 = cv2.resize(frame_1, (LONGITUDE, LONGITUDE))
frame_1 = np.array(frame_1).reshape(-1)
label.append(frame_1)
for i in range(batch_size):
frame_2 = cv2.imread('./cropedoriginalUS2/%d.jpg' % (n_pic + batch_size * (i+1) ), 0)
frame_2 = cv2.resize(frame_2, (LONGITUDE, LONGITUDE))
frame_2 = np.array(frame_2).reshape(-1)
label_0.append(frame_2)
return image , label , label_0
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