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from data_augs.aug_utils import transform_torch
from data_augs.aug_utils import transform_cv2
from PIL import Image
from PIL import ImageDraw
import torch
from typing import Tuple, Dict
from torch import Tensor
from torchvision.transforms import functional as F
import imgaug.augmenters as iaa
import cv2
import albumentations as A
from torchvision import transforms
class GridSampleAugmenter():
'''
GridSampleAugmenter:
This class is used to augment the input image while keeping track of the corresponding theta for grid sampling.
Output is (image, theta) where theta can be used as
>>>from torchvision.transforms import ToTensor
>>>image_tensor = ToTensor()(image_pil).unsqueeze(0)
>>>align_input_theta = theta.unsqueeze(0)
>>>b, c, h, w = image_tensor.shape
>>>sample_grid = torch.nn.functional.affine_grid(align_input_theta, [b, c, h, w], align_corners=True)
>>>image_tensor_aug = torch.nn.functional.grid_sample(image_tensor, sample_grid, align_corners=True)
'''
def __init__(self, aug_params, input_size=112):
print('GridSampleAugmenter')
self.aug_params = aug_params
self.input_size = input_size
self.photo_aug = PhotometricRandAugment(num_ops=self.aug_params['photometric_num_ops'],
magnitude=self.aug_params['photometric_magnitude'],
magnitude_offset=self.aug_params['photometric_magnitude_offset'],
num_magnitude_bins=self.aug_params['photometric_num_magnitude_bins'])
self.blur_aug = BlurAugmenter(magnitude=self.aug_params['blur_magnitude'], prob=self.aug_params['blur_prob'])
self.cutout = CutoutAugment(aug_params['cutout_prob'])
def augment(self, sample):
image_np = np.array(sample)
# augment
params = transform_torch.sample_param(
scale_min=self.aug_params['scale_min'],
scale_max=self.aug_params['scale_max'],
rot_prob=self.aug_params['rot_prob'],
max_rot=self.aug_params['max_rot'],
hflip_prob=self.aug_params['hflip_prob'],
extra_offset=self.aug_params['extra_offset'],
)
mat = transform_cv2.generate_transform_cv2(image_np, self.input_size, self.input_size, **params)
aug_sample = transform_cv2.augment_cv2_deterministic(image_np, mat, self.input_size, self.input_size)
# corresponding theta
align_input_theta = transform_torch.generate_transform_torch(image_np, self.input_size, self.input_size, **params)
align_input_theta = align_input_theta.squeeze(0)
# cutout
aug_sample = self.cutout.augment(aug_sample)
# blur
blur_params = self.blur_aug.sample_param()
aug_sample = self.blur_aug.augment(aug_sample, param=blur_params)
# photometric
photo_params = self.photo_aug.sample_param()
aug_sample = self.photo_aug.augment(aug_sample, param=photo_params)
return aug_sample, align_input_theta
class CutoutAugment():
def __init__(self, cutout_prob):
self.cutout_prob = cutout_prob
self.dropout = A.CoarseDropout(max_holes=20, # Maximum number of regions to zero out. (default: 8)
max_height=16, # Maximum height of the hole. (default: 8)
max_width=16, # Maximum width of the hole. (default: 8)
min_holes=12, # Maximum number of regions to zero out. (default: None, which equals max_holes)
min_height=None, # Maximum height of the hole. (default: None, which equals max_height)
min_width=None, # Maximum width of the hole. (default: None, which equals max_width)
fill_value=0, # value for dropped pixels.
mask_fill_value=None, # fill value for dropped pixels in mask.
always_apply=False,
p=1.0
)
self.random_resized_crop = transforms.RandomResizedCrop(size=(112, 112),
scale=(0.2, 1.0),
ratio=(0.75, 1.3333333333333333))
def augment(self, sample):
if np.random.random() < self.cutout_prob:
if np.random.random() < 0.05:
# not too natural
return Image.fromarray(self.dropout(image=np.array(sample))['image'])
else:
new = np.zeros_like(np.array(sample))
i, j, h, w = self.random_resized_crop.get_params(sample,
self.random_resized_crop.scale,
self.random_resized_crop.ratio)
cropped = F.crop(sample, i, j, h, w)
new[i:i+h,j:j+w, :] = np.array(cropped)
sample = Image.fromarray(new.astype(np.uint8))
return sample
else:
return sample
class PhotometricRandAugment():
def __init__(self,
num_ops: int = 2,
magnitude: int = 9,
magnitude_offset: int = 4,
num_magnitude_bins: int = 31) -> None:
self.num_ops = num_ops
self.magnitude = magnitude
self.magnitude_offset = magnitude_offset
self.num_magnitude_bins = num_magnitude_bins
self.op_names = list(self._augmentation_space(self.num_magnitude_bins).keys())
self.op_meta = self._augmentation_space(self.num_magnitude_bins)
def _augmentation_space(self, num_bins: int) -> Dict[str, Tuple[Tensor, bool]]:
return {
# op_name: (magnitudes, signed)
"Identity": (torch.tensor(0.0), False),
"Brightness": (torch.linspace(0.0, 0.9, num_bins), True),
"Saturate": (torch.linspace(0.0, 0.9, num_bins), True),
"Contrast": (torch.linspace(0.0, 0.9, num_bins), True),
"Sharpness": (torch.linspace(0.0, 0.9, num_bins), True),
"Equalize": (torch.tensor(0.0), False),
"Grayscale": (torch.tensor(0.0), False),
}
def apply_op(self, img: Tensor, op_name: str, magnitude: float):
if op_name == "Brightness":
img = F.adjust_brightness(img, 1.0 + magnitude)
elif op_name == "Saturate":
img = F.adjust_saturation(img, 1.0 + magnitude)
elif op_name == "Contrast":
img = F.adjust_contrast(img, 1.0 + magnitude)
elif op_name == "Sharpness":
img = F.adjust_sharpness(img, 1.0 + magnitude)
elif op_name == "Equalize":
img = F.equalize(img)
elif op_name == 'Grayscale':
img = F.to_grayscale(img, num_output_channels=3)
elif op_name == "Identity":
pass
else:
raise ValueError("The provided operator {} is not recognized.".format(op_name))
return img
def sample_param(self):
ops = []
for _ in range(self.num_ops):
# random sample op
op_name = np.random.choice(self.op_names)
# reduce probability of these two ops
if op_name in ['Equalize', 'Grayscale']:
op_name = np.random.choice(self.op_names)
if op_name in ['Equalize', 'Grayscale']:
op_name = np.random.choice(self.op_names)
magnitudes, signed = self.op_meta[op_name]
# random sample magnitude
magnitude_idx = np.random.randint(self.magnitude-self.magnitude_offset,
self.magnitude+self.magnitude_offset)
magnitude_idx = np.clip(magnitude_idx, 0, self.num_magnitude_bins-1)
if magnitudes.ndim > 0:
magnitude = float(magnitudes[magnitude_idx].item())
else:
magnitude = 0.0
if signed and torch.randint(2, (1,)):
magnitude *= -1.0
ops.append((op_name, magnitude))
return ops
def augment(self, img: Tensor, param=None) -> Tensor:
"""
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: Transformed image.
"""
if param is None:
param = self.sample_param()
for op_name, magnitude in param:
img = self.apply_op(img, op_name, magnitude)
return img
class BlurAugmenter():
def __init__(self, magnitude=0.5, prob=0.2):
self.magnitude = magnitude
self.prob = prob
def sample_param(self):
if np.random.random() < self.prob:
blur_method = np.random.choice(['avg', 'gaussian',
'resize', 'resize', 'resize', 'resize',
'resize', 'resize', 'resize', 'resize']) # more resizing aug, no motion
if blur_method == 'avg':
k = np.random.randint(1, int(10 * self.magnitude))
param = [blur_method, k]
elif blur_method == 'gaussian':
sigma = np.random.random() * 4 * self.magnitude
param = [blur_method, sigma]
elif blur_method == 'motion':
k = np.random.randint(5, max(int(10 * self.magnitude), 6))
angle = np.random.randint(-45, 45)
direction = np.random.random() * 2 - 1
param = [blur_method, k, angle, direction]
elif blur_method == 'resize':
side_ratio = np.random.uniform(1.0 - 0.8 * self.magnitude, 1.0)
interpolation1 = np.random.choice([cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_AREA,
cv2.INTER_CUBIC, cv2.INTER_LANCZOS4])
interpolation2 = np.random.choice([cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_AREA,
cv2.INTER_CUBIC, cv2.INTER_LANCZOS4])
param = [blur_method, side_ratio, [interpolation1, interpolation2]]
else:
raise ValueError('not a correct blur')
else:
param = ['skip']
return param
def augment(self, sample, param=None):
if param is None:
param = self.sample_param()
blur_method = param[0]
if blur_method == 'skip':
return sample
if blur_method == 'avg':
blur_method, k = param
avg_blur = iaa.AverageBlur(k=k) # max 10
blurred = avg_blur(image=np.array(sample))
elif blur_method == 'gaussian':
blur_method, sigma = param
gaussian_blur = iaa.GaussianBlur(sigma=sigma) # 4 is max
blurred = gaussian_blur(image=np.array(sample))
elif blur_method == 'motion':
blur_method, k, angle, direction = param
motion_blur = iaa.MotionBlur(k=k, angle=angle, direction=direction) # k 20 max angle:-45 45, dir:-1 1
blurred = motion_blur(image=np.array(sample))
elif blur_method == 'resize':
blur_method, side_ratio, interpolation = param
blurred = self.low_res_augmentation(np.array(sample), side_ratio, interpolation)
else:
raise ValueError('not a correct blur')
sample = Image.fromarray(blurred.astype(np.uint8))
return sample
def low_res_augmentation(self, img, side_ratio, interpolation):
# resize the image to a small size and enlarge it back
img_shape = img.shape
small_side = int(side_ratio * img_shape[0])
small_img = cv2.resize(img, (small_side, small_side), interpolation=interpolation[0])
aug_img = cv2.resize(small_img, (img_shape[1], img_shape[0]), interpolation=interpolation[1])
return aug_img
def main():
image = Image.open('/data/data/faces/ms1mv2_subset_images/84946/5770863.jpg')
# draw a square box on the image
image_draw = ImageDraw.Draw(image)
image_draw.rectangle((10, 10, 110, 110), outline='red')
image_draw.rectangle((0, 0, 120, 120), outline='blue')
scale_min = 0.7
scale_max = 2.0
rot_prob = 0.2
max_rot = 30
hflip_prob = 0.5
extra_offset = 0.15
photometric_num_ops = 2
photometric_magnitude = 14
photometric_magnitude_offset = 9
photometric_num_magnitude_bins = 31
blur_magnitude = 1.0
blur_prob = 0.3
cutout_prob = 0.2
aug_params = {
'scale_min': scale_min,
'scale_max': scale_max,
'rot_prob': rot_prob,
'max_rot': max_rot,
'hflip_prob': hflip_prob,
'extra_offset': extra_offset,
'photometric_num_ops': photometric_num_ops,
'photometric_magnitude': photometric_magnitude,
'photometric_magnitude_offset': photometric_magnitude_offset,
'photometric_num_magnitude_bins': photometric_num_magnitude_bins,
'blur_magnitude': blur_magnitude,
'blur_prob': blur_prob,
'cutout_prob': cutout_prob
}
align_input_size = 112
augmenter = GridSampleAugmenter(aug_params, align_input_size)
# make a grid 10x10
grids = []
grids_theta = []
for i in range(10):
grid = []
grid_theta = []
for j in range(10):
align_input_sample, align_input_theta = augmenter.augment(image)
grid.append(align_input_sample)
from torchvision.transforms import ToTensor
image_tensor = ToTensor()(image).unsqueeze(0)
align_input_theta = align_input_theta.unsqueeze(0)
b, c, h, w = image_tensor.shape
sample_grid = torch.nn.functional.affine_grid(align_input_theta, [b, c, h, w], align_corners=True)
image_tensor_aug = torch.nn.functional.grid_sample(image_tensor, sample_grid, align_corners=True)
from general_utils.img_utils import tensor_to_pil
grid_theta.append(tensor_to_pil(image_tensor_aug)[0])
grids.append(grid)
grids_theta.append(grid_theta)
# save the grid
grid_image = Image.new('RGB', (1120, 1120))
for i in range(10):
for j in range(10):
grid_image.paste(grids[i][j], (112 * j, 112 * i))
grid_image.save(f'/mckim/temp/GridSampleAugmenter.jpg')
grid_theta_image = Image.new('RGB', (1120, 1120))
for i in range(10):
for j in range(10):
grid_theta_image.paste(grids_theta[i][j], (112 * j, 112 * i))
grid_theta_image.save(f'/mckim/temp/GridSampleAugmenter_by_theta.jpg')
if __name__ == '__main__':
main()
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