def build_transforms(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
divide_by=255.0,
scale_limit=0.0,
shear_limit=0,
rotate_limit=0,
brightness_limit=0.0,
contrast_limit=0.0,
clahe_p=0.0,
blur_p=0.0,
autoaugment=False,
):
norm = Normalize(mean=mean, std=std, max_pixel_value=divide_by)
if autoaugment:
train_transform = Compose([
torch_custom.AutoAugmentWrapper(p=1.0),
torch_custom.RandomCropThenScaleToOriginalSize(limit=scale_limit, p=1.0),
norm,
])
else:
train_transform = Compose([
IAAAffine(rotate=(-rotate_limit, rotate_limit), shear=(-shear_limit, shear_limit), mode='constant'),
RandomBrightnessContrast(brightness_limit=brightness_limit, contrast_limit=contrast_limit),
MotionBlur(p=blur_p),
CLAHE(p=clahe_p),
torch_custom.RandomCropThenScaleToOriginalSize(limit=scale_limit, p=1.0),
norm,
])
eval_transform = Compose([norm])
return train_transform, eval_transform
def empty_aug2():
return [
HorizontalFlip(p=0.001),
IAAPiecewiseAffine(p=1.0),
OneOf([
# OpticalDistortion(p=0.1),
# GridDistortion(p=0.1),
# IAAPerspective(p=1.0),
IAAAffine(p=1.0),
# IAAPiecewiseAffine(p=1.0),
], p=0.0)
]
def shiftscalerotate_aug():
augs_list = [
OneOf([
ShiftScaleRotate(scale_limit=.15,
rotate_limit=15,
border_mode=cv2.BORDER_REPLICATE,
p=0.5),
IAAAffine(shear=20, p=0.5),
IAAPerspective(p=0.5),
],
p=0.5),
Normalize(),
ToTensorV2()
]
return Compose(augs_list, p=1)
def aug():
return Compose(
[
HorizontalFlip(p=0.5), # applied
VerticalFlip(p=0.5), # applied
ShiftScaleRotate(
shift_limit=(
0.1,
0.1), # width_shift_range=0.1,# height_shift_range=0.1,
# zoom_range=[0.9,1.25]
scale_limit=(0.9, 1.25),
rotate_limit=20,
p=0.5), # rotation_range=20,
RandomBrightnessContrast(brightness_limit=(0.4, 1.5),
p=0.5), # brightness_range=[0.4,1.5]
# shear_range=0.01,fill_mode='reflect'
IAAAffine(shear=0.01, mode='reflect', p=0.5)
],
p=1)
def __init__(self,
basic=True,
elastic_transform=False,
shift_scale_rotate=False):
transforms = []
if basic:
transforms.append(Flip(p=0.5))
transforms.append(
Rotate(p=0.5, border_mode=Config.BORDER_MODE, limit=45))
if elastic_transform:
transforms.append(ElasticTransform(p=0.2))
if shift_scale_rotate:
transforms.append(ShiftScaleRotate(p=0.2))
transforms.append(IAAAffine(p=1, shear=0.2, mode="constant"))
self.transforms = transforms
def strong_aug(p=0.5):
return Compose([
RandomGridShuffle((2, 2), p=0.75),
OneOf([
ShiftScaleRotate(shift_limit=0.125),
Transpose(),
RandomRotate90(),
VerticalFlip(),
HorizontalFlip(),
IAAAffine(shear=0.1)
]),
OneOf([GaussNoise(),
GaussianBlur(),
MedianBlur(),
MotionBlur()]),
OneOf([RandomBrightnessContrast(),
CLAHE(), IAASharpen()]),
Cutout(10, 2, 2, 127),
],
p=p)
def transform(image, mask, image_name, mask_name):
x, y = image, mask
rand = random.uniform(0, 1)
if (rand > 0.5):
images_name = [f"{image_name}"]
masks_name = [f"{mask_name}"]
images_aug = [x]
masks_aug = [y]
it = iter(images_name)
it2 = iter(images_aug)
imagedict = dict(zip(it, it2))
it = iter(masks_name)
it2 = iter(masks_aug)
masksdict = dict(zip(it, it2))
return imagedict, masksdict
mask_density = np.count_nonzero(y)
## Augmenting only images with Gloms
if (mask_density > 0):
try:
h, w, c = x.shape
except Exception as e:
image = image[:-1]
x, y = image, mask
h, w, c = x.shape
aug = Blur(p=1, blur_limit=3)
augmented = aug(image=x, mask=y)
x0 = augmented['image']
y0 = augmented['mask']
# aug = CenterCrop(p=1, height=32, width=32)
# augmented = aug(image=x, mask=y)
# x1 = augmented['image']
# y1 = augmented['mask']
## Horizontal Flip
aug = HorizontalFlip(p=1)
augmented = aug(image=x, mask=y)
x2 = augmented['image']
y2 = augmented['mask']
aug = VerticalFlip(p=1)
augmented = aug(image=x, mask=y)
x3 = augmented['image']
y3 = augmented['mask']
# aug = Normalize(p=1)
# augmented = aug(image=x, mask=y)
# x4 = augmented['image']
# y4 = augmented['mask']
aug = Transpose(p=1)
augmented = aug(image=x, mask=y)
x5 = augmented['image']
y5 = augmented['mask']
aug = RandomGamma(p=1)
augmented = aug(image=x, mask=y)
x6 = augmented['image']
y6 = augmented['mask']
## Optical Distortion
aug = OpticalDistortion(p=1, distort_limit=2, shift_limit=0.5)
augmented = aug(image=x, mask=y)
x7 = augmented['image']
y7 = augmented['mask']
## Grid Distortion
aug = GridDistortion(p=1)
augmented = aug(image=x, mask=y)
x8 = augmented['image']
y8 = augmented['mask']
aug = RandomGridShuffle(p=1)
augmented = aug(image=x, mask=y)
x9 = augmented['image']
y9 = augmented['mask']
aug = HueSaturationValue(p=1)
augmented = aug(image=x, mask=y)
x10 = augmented['image']
y10 = augmented['mask']
# aug = PadIfNeeded(p=1)
# augmented = aug(image=x, mask=y)
# x11 = augmented['image']
# y11 = augmented['mask']
aug = RGBShift(p=1)
augmented = aug(image=x, mask=y)
x12 = augmented['image']
y12 = augmented['mask']
## Random Brightness
aug = RandomBrightness(p=1)
augmented = aug(image=x, mask=y)
x13 = augmented['image']
y13 = augmented['mask']
## Random Contrast
aug = RandomContrast(p=1)
augmented = aug(image=x, mask=y)
x14 = augmented['image']
y14 = augmented['mask']
#aug = MotionBlur(p=1)
#augmented = aug(image=x, mask=y)
# x15 = augmented['image']
# y15 = augmented['mask']
aug = MedianBlur(p=1, blur_limit=5)
augmented = aug(image=x, mask=y)
x16 = augmented['image']
y16 = augmented['mask']
aug = GaussianBlur(p=1, blur_limit=3)
augmented = aug(image=x, mask=y)
x17 = augmented['image']
y17 = augmented['mask']
aug = GaussNoise(p=1)
augmented = aug(image=x, mask=y)
x18 = augmented['image']
y18 = augmented['mask']
aug = GlassBlur(p=1)
augmented = aug(image=x, mask=y)
x19 = augmented['image']
y19 = augmented['mask']
aug = CLAHE(clip_limit=1.0,
tile_grid_size=(8, 8),
always_apply=False,
p=1)
augmented = aug(image=x, mask=y)
x20 = augmented['image']
y20 = augmented['mask']
aug = ChannelShuffle(p=1)
augmented = aug(image=x, mask=y)
x21 = augmented['image']
y21 = augmented['mask']
aug = ToGray(p=1)
augmented = aug(image=x, mask=y)
x22 = augmented['image']
y22 = augmented['mask']
aug = ToSepia(p=1)
augmented = aug(image=x, mask=y)
x23 = augmented['image']
y23 = augmented['mask']
aug = JpegCompression(p=1)
augmented = aug(image=x, mask=y)
x24 = augmented['image']
y24 = augmented['mask']
aug = ImageCompression(p=1)
augmented = aug(image=x, mask=y)
x25 = augmented['image']
y25 = augmented['mask']
aug = Cutout(p=1)
augmented = aug(image=x, mask=y)
x26 = augmented['image']
y26 = augmented['mask']
# aug = CoarseDropout(p=1, max_holes=8, max_height=32, max_width=32)
# augmented = aug(image=x, mask=y)
# x27 = augmented['image']
# y27 = augmented['mask']
# aug = ToFloat(p=1)
# augmented = aug(image=x, mask=y)
# x28 = augmented['image']
# y28 = augmented['mask']
aug = FromFloat(p=1)
augmented = aug(image=x, mask=y)
x29 = augmented['image']
y29 = augmented['mask']
## Random Brightness and Contrast
aug = RandomBrightnessContrast(p=1)
augmented = aug(image=x, mask=y)
x30 = augmented['image']
y30 = augmented['mask']
aug = RandomSnow(p=1)
augmented = aug(image=x, mask=y)
x31 = augmented['image']
y31 = augmented['mask']
aug = RandomRain(p=1)
augmented = aug(image=x, mask=y)
x32 = augmented['image']
y32 = augmented['mask']
aug = RandomFog(p=1)
augmented = aug(image=x, mask=y)
x33 = augmented['image']
y33 = augmented['mask']
aug = RandomSunFlare(p=1)
augmented = aug(image=x, mask=y)
x34 = augmented['image']
y34 = augmented['mask']
aug = RandomShadow(p=1)
augmented = aug(image=x, mask=y)
x35 = augmented['image']
y35 = augmented['mask']
aug = Lambda(p=1)
augmented = aug(image=x, mask=y)
x36 = augmented['image']
y36 = augmented['mask']
aug = ChannelDropout(p=1)
augmented = aug(image=x, mask=y)
x37 = augmented['image']
y37 = augmented['mask']
aug = ISONoise(p=1)
augmented = aug(image=x, mask=y)
x38 = augmented['image']
y38 = augmented['mask']
aug = Solarize(p=1)
augmented = aug(image=x, mask=y)
x39 = augmented['image']
y39 = augmented['mask']
aug = Equalize(p=1)
augmented = aug(image=x, mask=y)
x40 = augmented['image']
y40 = augmented['mask']
aug = Posterize(p=1)
augmented = aug(image=x, mask=y)
x41 = augmented['image']
y41 = augmented['mask']
aug = Downscale(p=1)
augmented = aug(image=x, mask=y)
x42 = augmented['image']
y42 = augmented['mask']
aug = MultiplicativeNoise(p=1)
augmented = aug(image=x, mask=y)
x43 = augmented['image']
y43 = augmented['mask']
aug = FancyPCA(p=1)
augmented = aug(image=x, mask=y)
x44 = augmented['image']
y44 = augmented['mask']
# aug = MaskDropout(p=1)
# augmented = aug(image=x, mask=y)
# x45 = augmented['image']
# y45 = augmented['mask']
aug = GridDropout(p=1)
augmented = aug(image=x, mask=y)
x46 = augmented['image']
y46 = augmented['mask']
aug = ColorJitter(p=1)
augmented = aug(image=x, mask=y)
x47 = augmented['image']
y47 = augmented['mask']
## ElasticTransform
aug = ElasticTransform(p=1,
alpha=120,
sigma=512 * 0.05,
alpha_affine=512 * 0.03)
augmented = aug(image=x, mask=y)
x50 = augmented['image']
y50 = augmented['mask']
aug = CropNonEmptyMaskIfExists(p=1, height=22, width=32)
augmented = aug(image=x, mask=y)
x51 = augmented['image']
y51 = augmented['mask']
aug = IAAAffine(p=1)
augmented = aug(image=x, mask=y)
x52 = augmented['image']
y52 = augmented['mask']
# aug = IAACropAndPad(p=1)
# augmented = aug(image=x, mask=y)
# x53 = augmented['image']
# y53 = augmented['mask']
aug = IAAFliplr(p=1)
augmented = aug(image=x, mask=y)
x54 = augmented['image']
y54 = augmented['mask']
aug = IAAFlipud(p=1)
augmented = aug(image=x, mask=y)
x55 = augmented['image']
y55 = augmented['mask']
aug = IAAPerspective(p=1)
augmented = aug(image=x, mask=y)
x56 = augmented['image']
y56 = augmented['mask']
aug = IAAPiecewiseAffine(p=1)
augmented = aug(image=x, mask=y)
x57 = augmented['image']
y57 = augmented['mask']
aug = LongestMaxSize(p=1)
augmented = aug(image=x, mask=y)
x58 = augmented['image']
y58 = augmented['mask']
aug = NoOp(p=1)
augmented = aug(image=x, mask=y)
x59 = augmented['image']
y59 = augmented['mask']
# aug = RandomCrop(p=1, height=22, width=22)
# augmented = aug(image=x, mask=y)
# x61 = augmented['image']
# y61 = augmented['mask']
# aug = RandomResizedCrop(p=1, height=22, width=20)
# augmented = aug(image=x, mask=y)
# x63 = augmented['image']
# y63 = augmented['mask']
aug = RandomScale(p=1)
augmented = aug(image=x, mask=y)
x64 = augmented['image']
y64 = augmented['mask']
# aug = RandomSizedCrop(p=1, height=22, width=20, min_max_height = [32,32])
# augmented = aug(image=x, mask=y)
# x66 = augmented['image']
# y66 = augmented['mask']
# aug = Resize(p=1, height=22, width=20)
# augmented = aug(image=x, mask=y)
# x67 = augmented['image']
# y67 = augmented['mask']
aug = Rotate(p=1)
augmented = aug(image=x, mask=y)
x68 = augmented['image']
y68 = augmented['mask']
aug = ShiftScaleRotate(p=1)
augmented = aug(image=x, mask=y)
x69 = augmented['image']
y69 = augmented['mask']
aug = SmallestMaxSize(p=1)
augmented = aug(image=x, mask=y)
x70 = augmented['image']
y70 = augmented['mask']
images_aug.extend([
x, x0, x2, x3, x5, x6, x7, x8, x9, x10, x12, x13, x14, x16, x17,
x18, x19, x20, x21, x22, x23, x24, x25, x26, x29, x30, x31, x32,
x33, x34, x35, x36, x37, x38, x39, x40, x41, x42, x43, x44, x46,
x47, x50, x51, x52, x54, x55, x56, x57, x58, x59, x64, x68, x69,
x70
])
masks_aug.extend([
y, y0, y2, y3, y5, y6, y7, y8, y9, y10, y12, y13, y14, y16, y17,
y18, y19, y20, y21, y22, y23, y24, y25, y26, y29, y30, y31, y32,
y33, y34, y35, y36, y37, y38, y39, y40, y41, y42, y43, y44, y46,
y47, y50, y51, y52, y54, y55, y56, y57, y58, y59, y64, y68, y69,
y70
])
idx = -1
images_name = []
masks_name = []
for i, m in zip(images_aug, masks_aug):
if idx == -1:
tmp_image_name = f"{image_name}"
tmp_mask_name = f"{mask_name}"
else:
tmp_image_name = f"{image_name}_{smalllist[idx]}"
tmp_mask_name = f"{mask_name}_{smalllist[idx]}"
images_name.extend(tmp_image_name)
masks_name.extend(tmp_mask_name)
idx += 1
it = iter(images_name)
it2 = iter(images_aug)
imagedict = dict(zip(it, it2))
it = iter(masks_name)
it2 = iter(masks_aug)
masksdict = dict(zip(it, it2))
return imagedict, masksdict
def compose_augmentations(img_height,
img_width,
flip_p=0.5,
translate_p=0.5,
distort_p=0.5,
color_p=0.5,
overlays_p=0.15,
blur_p=0.25,
noise_p=0.25):
# Resize
resize_p = 1 if img_height != 1024 else 0
# Random sized crop
if img_height == 1024:
min_max_height = (896, 960)
elif img_height == 512:
min_max_height = (448, 480)
elif img_height == 256:
min_max_height = (224, 240)
else:
raise NotImplementedError
return Compose([
Resize(p=resize_p, height=img_height, width=img_width),
OneOf([
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
Transpose(p=0.5),
RandomRotate90(p=0.5),
],
p=flip_p),
OneOf([
Rotate(p=0.25, limit=10),
RandomSizedCrop(p=0.5,
min_max_height=min_max_height,
height=img_height,
width=img_width),
OneOrOther(IAAAffine(p=0.1, translate_percent=0.05),
IAAPerspective(p=0.1)),
],
p=translate_p),
OneOf([
ElasticTransform(p=0.5,
alpha=10,
sigma=img_height * 0.05,
alpha_affine=img_height * 0.03,
approximate=True),
GridDistortion(p=0.5),
OpticalDistortion(p=0.5),
IAAPiecewiseAffine(p=0.25, scale=(0.01, 0.03)),
],
p=distort_p),
OneOrOther(
OneOf([
CLAHE(p=0.5),
RandomGamma(p=0.5),
RandomContrast(p=0.5),
RandomBrightness(p=0.5),
RandomBrightnessContrast(p=0.5),
],
p=color_p),
OneOf([IAAEmboss(p=0.1),
IAASharpen(p=0.1),
IAASuperpixels(p=0)],
p=overlays_p)),
OneOrOther(
OneOf([
Blur(p=0.2),
MedianBlur(p=0.1),
MotionBlur(p=0.1),
GaussianBlur(p=0.1),
],
p=blur_p),
OneOf([GaussNoise(p=0.2),
IAAAdditiveGaussianNoise(p=0.1)],
p=noise_p)),
Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
ToTensor(sigmoid=False),
])
def aug_test():
def get_bb_points(msk):
h, w = msk.shape
x0 = 0
x1 = msk.shape[1]
y0 = 0
y1 = msk.shape[0]
for i in range(w):
if msk[:, i].max() > 200:
x0 = i
break
for i in range(w):
if msk[:, msk.shape[1] - i - 1].max() > 200:
x1 = msk.shape[1] - i - 1
break
for i in range(h):
if msk[i, :].max() > 200:
y0 = i
break
for i in range(h):
if msk[msk.shape[0] - i - 1, :].max() > 200:
y1 = msk.shape[0] - i - 1
break
return (x0, y0), (x1, y1)
image_name = '7aea0b3e2.jpg'
p1, p2 = (12, 84), (391, 248)
img = imread(f'../DATA/aug_test/src/{image_name}')
h = 300
alpha, sigma, alpha_affine = h * 2, h * 0.08, h * 0.08
augs = {
'1_IAAAdditiveGaussianNoise':
IAAAdditiveGaussianNoise(scale=(0.01 * 255, 0.05 * 255), p=1.0),
'1_GaussNoise':
GaussNoise(var_limit=(20, 120), p=1.0),
'1_RandomGamma':
RandomGamma(gamma_limit=(80, 120), p=1.0),
'2_RandomBrightnessContrast':
RandomBrightnessContrast(p=1.0),
'2_MotionBlur':
MotionBlur(p=1.0),
'2_MedianBlur':
MedianBlur(blur_limit=6, p=1.0),
'2_Blur':
Blur(blur_limit=9, p=1.0),
'2_IAASharpen':
IAASharpen(p=1.0),
'2_IAAEmboss':
IAAEmboss(p=1.0),
'2_IAASuperpixels':
IAASuperpixels(n_segments=50, p_replace=0.05, p=1.0),
'3_CLAHE':
CLAHE(clip_limit=8, p=1.0),
'3_RGBShift':
RGBShift(p=1.0),
'3_ChannelShuffle':
ChannelShuffle(p=1.0),
'3_HueSaturationValue':
HueSaturationValue(p=1.0),
'3_ToGray':
ToGray(p=1.0),
'4_OpticalDistortion':
OpticalDistortion(border_mode=cv2.BORDER_CONSTANT, p=1.0),
'4_GridDistortion':
GridDistortion(border_mode=cv2.BORDER_CONSTANT, p=1.0),
'4_IAAPiecewiseAffine':
IAAPiecewiseAffine(nb_rows=4, nb_cols=4, p=1.0),
'4_IAAPerspective':
IAAPerspective(p=1.0),
'4_IAAAffine':
IAAAffine(mode='constant', p=1.0),
'4_ElasticTransform':
ElasticTransform(alpha=alpha,
sigma=sigma,
alpha_affine=alpha_affine,
border_mode=cv2.BORDER_CONSTANT,
p=1.0)
}
# im_merge.shape[1] * 2, im_merge.shape[1] * 0.08, im_merge.shape[1] * 0.08
for aug in augs:
mask = np.zeros(img.shape[:2], dtype=np.uint8)
cv2.rectangle(mask, p1, p2, 255, 2)
data = {"image": img.copy(), 'mask': mask}
augmented = augs[aug](**data)
augimg = augmented['image']
draw_shadow_text(augimg, f'{aug}', (5, 15), 0.5, (255, 255, 255), 1)
ap1, ap2 = get_bb_points(augmented['mask'])
cv2.rectangle(augimg, ap1, ap2, (0, 255, 0), 2)
imsave(f'../DATA/aug_test/aug/{aug}-{image_name}', augimg)
def build_databunch(data_dir, img_sz, batch_sz):
# TODO This is to avoid freezing in the middle of the first epoch. Would be nice
# to fix this.
num_workers = 0
train_dir = join(data_dir, 'train')
train_anns = glob.glob(join(train_dir, '*.json'))
valid_dir = join(data_dir, 'valid')
valid_anns = glob.glob(join(valid_dir, '*.json'))
label_names = get_label_names(train_anns[0])
# Augmentations
policy_v3 = [
[
Posterize(p=0.8),
IAAAffine(translate_px=(10, 20), p=1.0),
],
[
RandomCropNearBBox(p=0.2),
IAASharpen(p=0.5),
],
[
Rotate(p=0.6),
Rotate(p=0.8),
],
[
Equalize(p=0.8),
RandomContrast(p=0.2),
],
[
Solarize(p=0.2),
IAAAffine(translate_px=(10, 20), p=0.2),
],
[
IAASharpen(p=0.0),
ToGray(p=0.4),
],
[
Equalize(p=1.0),
IAAAffine(translate_px=(10, 20), p=1.0),
],
[
Posterize(p=0.8),
Rotate(p=0.0),
],
[
RandomContrast(p=0.6),
Rotate(p=1.0),
],
[
Equalize(p=0.0),
Cutout(p=0.8),
],
[
RandomBrightness(p=1.0),
IAAAffine(translate_px=(10, 20), p=1.0),
],
[
RandomContrast(p=0.0),
IAAAffine(shear=60.0, p=0.8),
],
[
RandomContrast(p=0.8),
RandomContrast(p=0.2),
],
[
Rotate(p=1.0),
Cutout(p=1.0),
],
[
Solarize(p=0.8),
Equalize(p=0.8),
],
]
selected_subpolicy = int(np.random.randint(0, 15, 1))
aug_transforms = policy_v3[selected_subpolicy]
standard_transforms = [Resize(img_sz, img_sz), ToTensor()]
aug_transforms.extend(standard_transforms)
bbox_params = BboxParams(format='coco',
min_area=0.,
min_visibility=0.2,
label_fields=['labels'])
aug_transforms = Compose(aug_transforms, bbox_params=bbox_params)
standard_transforms = Compose(standard_transforms, bbox_params=bbox_params)
train_ds = CocoDataset(train_dir, train_anns, transforms=aug_transforms)
valid_ds = CocoDataset(valid_dir,
valid_anns,
transforms=standard_transforms)
train_ds.label_names = label_names
valid_ds.label_names = label_names
train_dl = DataLoader(train_ds,
collate_fn=collate_fn,
shuffle=True,
batch_size=batch_sz,
num_workers=num_workers,
pin_memory=True)
valid_dl = DataLoader(valid_ds,
collate_fn=collate_fn,
batch_size=batch_sz,
num_workers=num_workers,
pin_memory=True)
return DataBunch(train_ds, train_dl, valid_ds, valid_dl, label_names)