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import random
import cv2
import numpy as np
from PIL import Image
from torchvision.transforms import Compose
from .abinet_aug import CVColorJitter, CVDeterioration, CVGeometry, SVTRDeterioration, SVTRGeometry
from .parseq_aug import rand_augment_transform
class PARSeqAugPIL(object):
def __init__(self, **kwargs):
self.transforms = rand_augment_transform()
def __call__(self, data):
img = data['image']
img_aug = self.transforms(img)
data['image'] = img_aug
return data
class PARSeqAug(object):
def __init__(self, **kwargs):
self.transforms = rand_augment_transform()
def __call__(self, data):
img = data['image']
img = np.array(self.transforms(Image.fromarray(img)))
data['image'] = img
return data
class ABINetAug(object):
def __init__(self,
geometry_p=0.5,
deterioration_p=0.25,
colorjitter_p=0.25,
**kwargs):
self.transforms = Compose([
CVGeometry(
degrees=45,
translate=(0.0, 0.0),
scale=(0.5, 2.0),
shear=(45, 15),
distortion=0.5,
p=geometry_p,
),
CVDeterioration(var=20, degrees=6, factor=4, p=deterioration_p),
CVColorJitter(
brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.1,
p=colorjitter_p,
),
])
def __call__(self, data):
img = data['image']
img = self.transforms(img)
data['image'] = img
return data
class SVTRAug(object):
def __init__(self,
aug_type=0,
geometry_p=0.5,
deterioration_p=0.25,
colorjitter_p=0.25,
**kwargs):
self.transforms = Compose([
SVTRGeometry(
aug_type=aug_type,
degrees=45,
translate=(0.0, 0.0),
scale=(0.5, 2.0),
shear=(45, 15),
distortion=0.5,
p=geometry_p,
),
SVTRDeterioration(var=20, degrees=6, factor=4, p=deterioration_p),
CVColorJitter(
brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.1,
p=colorjitter_p,
),
])
def __call__(self, data):
img = data['image']
img = self.transforms(img)
data['image'] = img
return data
class BaseDataAugmentation(object):
def __init__(self,
crop_prob=0.4,
reverse_prob=0.4,
noise_prob=0.4,
jitter_prob=0.4,
blur_prob=0.4,
hsv_aug_prob=0.4,
**kwargs):
self.crop_prob = crop_prob
self.reverse_prob = reverse_prob
self.noise_prob = noise_prob
self.jitter_prob = jitter_prob
self.blur_prob = blur_prob
self.hsv_aug_prob = hsv_aug_prob
# for GaussianBlur
self.fil = cv2.getGaussianKernel(ksize=5, sigma=1, ktype=cv2.CV_32F)
def __call__(self, data):
img = data['image']
h, w, _ = img.shape
if random.random() <= self.crop_prob and h >= 20 and w >= 20:
img = get_crop(img)
if random.random() <= self.blur_prob:
# GaussianBlur
img = cv2.sepFilter2D(img, -1, self.fil, self.fil)
if random.random() <= self.hsv_aug_prob:
img = hsv_aug(img)
if random.random() <= self.jitter_prob:
img = jitter(img)
if random.random() <= self.noise_prob:
img = add_gasuss_noise(img)
if random.random() <= self.reverse_prob:
img = 255 - img
data['image'] = img
return data
def hsv_aug(img):
"""cvtColor."""
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
delta = 0.001 * random.random() * flag()
hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta)
new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return new_img
def blur(img):
"""blur."""
h, w, _ = img.shape
if h > 10 and w > 10:
return cv2.GaussianBlur(img, (5, 5), 1)
else:
return img
def jitter(img):
"""jitter."""
w, h, _ = img.shape
if h > 10 and w > 10:
thres = min(w, h)
s = int(random.random() * thres * 0.01)
src_img = img.copy()
for i in range(s):
img[i:, i:, :] = src_img[:w - i, :h - i, :]
return img
else:
return img
def add_gasuss_noise(image, mean=0, var=0.1):
"""Gasuss noise."""
noise = np.random.normal(mean, var**0.5, image.shape)
out = image + 0.5 * noise
out = np.clip(out, 0, 255)
out = np.uint8(out)
return out
def get_crop(image):
"""random crop."""
h, w, _ = image.shape
top_min = 1
top_max = 8
top_crop = int(random.randint(top_min, top_max))
top_crop = min(top_crop, h - 1)
crop_img = image.copy()
ratio = random.randint(0, 1)
if ratio:
crop_img = crop_img[top_crop:h, :, :]
else:
crop_img = crop_img[0:h - top_crop, :, :]
return crop_img
def flag():
"""flag."""
return 1 if random.random() > 0.5000001 else -1
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