def gen(data, au=False):
while True:
repeat = 4
index = random.choice(list(range(len(data))), batch_size // repeat)
index = list(map(int, index))
list_images_base = [read_input(data[i][0]) for i in index]
list_gt_base = [read_gt(data[i][1]) for i in index]
list_images = []
list_gt = []
for image, gt in zip(list_images_base, list_gt_base):
for _ in range(repeat):
image_, gt_ = random_crop(image.copy(), gt.copy())
list_images.append(image_)
list_gt.append(gt_)
list_images_aug = []
list_gt_aug = []
for image, gt in zip(list_images, list_gt):
if au:
image, gt = random_augmentation(image, gt)
list_images_aug.append(image)
list_gt_aug.append(gt)
yield tf.squeeze(np.array(list_images_aug)), tf.squeeze(
np.array(list_gt_aug), axis=4)
def gen(data):
while True:
# choose random index in features
# try:
index = random.choice(list(range(len(data))), batch_size)
index = list(map(int, index))
list_images_base = [read_input(data[i][0]) for i in index]
list_gt_base = [read_gt(data[i][1]) for i in index]
list_images_aug = []
list_gt_aug = []
for image_, gt in zip(list_images_base, list_gt_base):
image_aug, gt = random_augmentation(image_, gt) #image_, gt
list_images_aug.append(image_aug)
list_gt_aug.append(gt)
yield np.array(list_images_aug), np.array(list_gt_aug)