def create_augimg(self, img, img_info):
"""
Create an augmented image and key points.
Parameters
img: as numpy array
img_info: tuple with label and keypoints
Return
augmented image and keypoints
"""
label, points = img_info
# print(points)
kps_merge = [point for key_points in points for point in key_points]
kps = KeypointsOnImage(kps_merge, shape=img.shape)
# print(kps)
if img.shape != self.dim:
img = ia.imresize_single_image(img, self.dim[0:2])
kps = kps.on(img)
# Augment keypoints and images.
img_aug, kps_aug = seq(image=img, keypoints=kps)
aug_points = [[kp.x, kp.y] for kp in kps_aug.keypoints]
aug_points_dic = {'label': label, 'points': aug_points}
return img_aug, aug_points_dic
def create_multi_augimg(self, img, img_info):
"""
This method create image augmentation of multiple classes per image.
Parameters
img: as numpy array
img_info: tuple with label and keypoints
Return
augmented image and keypoints
"""
labels, points_list = img_info
kps_merge = list()
shapes_dict = list()
index = 0
for label, key_points in zip(labels, points_list):
kps_merge += key_points
dict_aux = {}
dict_aux['label'] = label
dict_aux['index'] = (index, index + len(key_points))
shapes_dict.append(dict_aux)
index += len(key_points)
kps_merge = [
point for key_points in points_list for point in key_points
]
kps_oi = KeypointsOnImage(kps_merge, shape=img.shape)
# Resize image if shapes are not equals
if img.shape != self.dim:
img = ia.imresize_single_image(img, self.dim[0:2])
kps_oi = kps_oi.on(img)
# Augment keypoints and images.
if self.augmentation:
seq_det = seq.to_deterministic()
img_aug = seq_det.augment_images([img])[0]
kps_aug = seq_det.augment_keypoints([kps_oi])[0]
else:
img_aug = img
kps_aug = kps_oi
# Add points to each dictionary
for shape in shapes_dict:
first, last = shape['index']
shape['points'] = [[kp.x, kp.y]
for kp in kps_aug.keypoints[first:last]]
return img_aug, shapes_dict