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import random
import numpy as np
def mod_crop(img, scale):
"""Mod crop images, used during testing.
Args:
img (ndarray): Input image.
scale (int): Scale factor.
Returns:
ndarray: Result image.
"""
img = img.copy()
if img.ndim in (2, 3):
h, w = img.shape[0], img.shape[1]
h_remainder, w_remainder = h % scale, w % scale
img = img[:h - h_remainder, :w - w_remainder, ...]
else:
raise ValueError(f'Wrong img ndim: {img.ndim}.')
return img
def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
"""Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).
We use vertical flip and transpose for rotation implementation.
All the images in the list use the same augmentation.
Args:
imgs (list[ndarray] | ndarray): Images to be augmented. If the input
is an ndarray, it will be transformed to a list.
hflip (bool): Horizontal flip. Default: True.
rotation (bool): Ratotation. Default: True.
flows (list[ndarray]: Flows to be augmented. If the input is an
ndarray, it will be transformed to a list.
Dimension is (h, w, 2). Default: None.
return_status (bool): Return the status of flip and rotation.
Default: False.
Returns:
list[ndarray] | ndarray: Augmented images and flows. If returned
results only have one element, just return ndarray.
"""
hflip = hflip and random.random() < 0.5
vflip = rotation and random.random() < 0.5
rot90 = rotation and random.random() < 0.5
def _augment(img):
if hflip: # horizontal
cv2.flip(img, 1, img)
if vflip: # vertical
cv2.flip(img, 0, img)
if rot90:
img = img.transpose(1, 0, 2)
return img
def _augment_flow(flow):
if hflip: # horizontal
cv2.flip(flow, 1, flow)
flow[:, :, 0] *= -1
if vflip: # vertical
cv2.flip(flow, 0, flow)
flow[:, :, 1] *= -1
if rot90:
flow = flow.transpose(1, 0, 2)
flow = flow[:, :, [1, 0]]
return flow
if not isinstance(imgs, list):
imgs = [imgs]
imgs = [_augment(img) for img in imgs]
if len(imgs) == 1:
imgs = imgs[0]
if flows is not None:
if not isinstance(flows, list):
flows = [flows]
flows = [_augment_flow(flow) for flow in flows]
if len(flows) == 1:
flows = flows[0]
return imgs, flows
else:
if return_status:
return imgs, (hflip, vflip, rot90)
else:
return imgs
def img_rotate(img, angle, center=None, scale=1.0):
"""Rotate image.
Args:
img (ndarray): Image to be rotated.
angle (float): Rotation angle in degrees. Positive values mean
counter-clockwise rotation.
center (tuple[int]): Rotation center. If the center is None,
initialize it as the center of the image. Default: None.
scale (float): Isotropic scale factor. Default: 1.0.
"""
(h, w) = img.shape[:2]
if center is None:
center = (w // 2, h // 2)
matrix = cv2.getRotationMatrix2D(center, angle, scale)
rotated_img = cv2.warpAffine(img, matrix, (w, h))
return rotated_img
def data_augmentation(image, mode):
"""
Performs data augmentation of the input image
Input:
image: a cv2 (OpenCV) image
mode: int. Choice of transformation to apply to the image
0 - no transformation
1 - flip up and down
2 - rotate counterwise 90 degree
3 - rotate 90 degree and flip up and down
4 - rotate 180 degree
5 - rotate 180 degree and flip
6 - rotate 270 degree
7 - rotate 270 degree and flip
"""
if mode == 0:
# original
out = image
elif mode == 1:
# flip up and down
out = np.flipud(image)
elif mode == 2:
# rotate counterwise 90 degree
out = np.rot90(image)
elif mode == 3:
# rotate 90 degree and flip up and down
out = np.rot90(image)
out = np.flipud(out)
elif mode == 4:
# rotate 180 degree
out = np.rot90(image, k=2)
elif mode == 5:
# rotate 180 degree and flip
out = np.rot90(image, k=2)
out = np.flipud(out)
elif mode == 6:
# rotate 270 degree
out = np.rot90(image, k=3)
elif mode == 7:
# rotate 270 degree and flip
out = np.rot90(image, k=3)
out = np.flipud(out)
else:
raise Exception('Invalid choice of image transformation')
return out
def random_augmentation(*args):
out = []
flag_aug = random.randint(0,7)
for data in args:
if type(data) == list:
out.append([data_augmentation(_data, flag_aug).copy() for _data in data])
else:
out.append(data_augmentation(data, flag_aug).copy())
return out
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