openrec/preprocess/abinet_aug.py

"""This code is refer from:

https://github.com/FangShancheng/ABINet/blob/main/transforms.py
"""
import math
import numbers
import random

import cv2
import numpy as np
from PIL import Image
from torchvision.transforms import ColorJitter, Compose


def sample_asym(magnitude, size=None):
    return np.random.beta(1, 4, size) * magnitude


def sample_sym(magnitude, size=None):
    return (np.random.beta(4, 4, size=size) - 0.5) * 2 * magnitude


def sample_uniform(low, high, size=None):
    return np.random.uniform(low, high, size=size)


def get_interpolation(type='random'):
    if type == 'random':
        choice = [
            cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC,
            cv2.INTER_AREA
        ]
        interpolation = choice[random.randint(0, len(choice) - 1)]
    elif type == 'nearest':
        interpolation = cv2.INTER_NEAREST
    elif type == 'linear':
        interpolation = cv2.INTER_LINEAR
    elif type == 'cubic':
        interpolation = cv2.INTER_CUBIC
    elif type == 'area':
        interpolation = cv2.INTER_AREA
    else:
        raise TypeError(
            'Interpolation types only nearest, linear, cubic, area are supported!'
        )
    return interpolation


class CVRandomRotation(object):

    def __init__(self, degrees=15):
        assert isinstance(degrees,
                          numbers.Number), 'degree should be a single number.'
        assert degrees >= 0, 'degree must be positive.'
        self.degrees = degrees

    @staticmethod
    def get_params(degrees):
        return sample_sym(degrees)

    def __call__(self, img):
        angle = self.get_params(self.degrees)
        src_h, src_w = img.shape[:2]
        M = cv2.getRotationMatrix2D(center=(src_w / 2, src_h / 2),
                                    angle=angle,
                                    scale=1.0)
        abs_cos, abs_sin = abs(M[0, 0]), abs(M[0, 1])
        dst_w = int(src_h * abs_sin + src_w * abs_cos)
        dst_h = int(src_h * abs_cos + src_w * abs_sin)
        M[0, 2] += (dst_w - src_w) / 2
        M[1, 2] += (dst_h - src_h) / 2

        flags = get_interpolation()
        return cv2.warpAffine(img,
                              M, (dst_w, dst_h),
                              flags=flags,
                              borderMode=cv2.BORDER_REPLICATE)


class CVRandomAffine(object):

    def __init__(self, degrees, translate=None, scale=None, shear=None):
        assert isinstance(degrees,
                          numbers.Number), 'degree should be a single number.'
        assert degrees >= 0, 'degree must be positive.'
        self.degrees = degrees

        if translate is not None:
            assert (
                isinstance(translate, (tuple, list)) and len(translate) == 2
            ), 'translate should be a list or tuple and it must be of length 2.'
            for t in translate:
                if not (0.0 <= t <= 1.0):
                    raise ValueError(
                        'translation values should be between 0 and 1')
        self.translate = translate

        if scale is not None:
            assert (
                isinstance(scale, (tuple, list)) and len(scale) == 2
            ), 'scale should be a list or tuple and it must be of length 2.'
            for s in scale:
                if s <= 0:
                    raise ValueError('scale values should be positive')
        self.scale = scale

        if shear is not None:
            if isinstance(shear, numbers.Number):
                if shear < 0:
                    raise ValueError(
                        'If shear is a single number, it must be positive.')
                self.shear = [shear]
            else:
                assert isinstance(shear, (tuple, list)) and (
                    len(shear) == 2
                ), 'shear should be a list or tuple and it must be of length 2.'
                self.shear = shear
        else:
            self.shear = shear

    def _get_inverse_affine_matrix(self, center, angle, translate, scale,
                                   shear):
        # https://github.com/pytorch/vision/blob/v0.4.0/torchvision/transforms/functional.py#L717
        from numpy import cos, sin, tan

        if isinstance(shear, numbers.Number):
            shear = [shear, 0]

        if not isinstance(shear, (tuple, list)) and len(shear) == 2:
            raise ValueError(
                'Shear should be a single value or a tuple/list containing ' +
                'two values. Got {}'.format(shear))

        rot = math.radians(angle)
        sx, sy = [math.radians(s) for s in shear]

        cx, cy = center
        tx, ty = translate

        # RSS without scaling
        a = cos(rot - sy) / cos(sy)
        b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
        c = sin(rot - sy) / cos(sy)
        d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)

        # Inverted rotation matrix with scale and shear
        # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
        M = [d, -b, 0, -c, a, 0]
        M = [x / scale for x in M]

        # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
        M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
        M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)

        # Apply center translation: C * RSS^-1 * C^-1 * T^-1
        M[2] += cx
        M[5] += cy
        return M

    @staticmethod
    def get_params(degrees, translate, scale_ranges, shears, height):
        angle = sample_sym(degrees)
        if translate is not None:
            max_dx = translate[0] * height
            max_dy = translate[1] * height
            translations = (np.round(sample_sym(max_dx)),
                            np.round(sample_sym(max_dy)))
        else:
            translations = (0, 0)

        if scale_ranges is not None:
            scale = sample_uniform(scale_ranges[0], scale_ranges[1])
        else:
            scale = 1.0

        if shears is not None:
            if len(shears) == 1:
                shear = [sample_sym(shears[0]), 0.0]
            elif len(shears) == 2:
                shear = [sample_sym(shears[0]), sample_sym(shears[1])]
        else:
            shear = 0.0

        return angle, translations, scale, shear

    def __call__(self, img):
        src_h, src_w = img.shape[:2]
        angle, translate, scale, shear = self.get_params(
            self.degrees, self.translate, self.scale, self.shear, src_h)

        M = self._get_inverse_affine_matrix((src_w / 2, src_h / 2), angle,
                                            (0, 0), scale, shear)
        M = np.array(M).reshape(2, 3)

        startpoints = [(0, 0), (src_w - 1, 0), (src_w - 1, src_h - 1),
                       (0, src_h - 1)]
        project = lambda x, y, a, b, c: int(a * x + b * y + c)
        endpoints = [(project(x, y, *M[0]), project(x, y, *M[1]))
                     for x, y in startpoints]

        rect = cv2.minAreaRect(np.array(endpoints))
        bbox = cv2.boxPoints(rect).astype(dtype=np.int32)
        max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
        min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()

        dst_w = int(max_x - min_x)
        dst_h = int(max_y - min_y)
        M[0, 2] += (dst_w - src_w) / 2
        M[1, 2] += (dst_h - src_h) / 2

        # add translate
        dst_w += int(abs(translate[0]))
        dst_h += int(abs(translate[1]))
        if translate[0] < 0:
            M[0, 2] += abs(translate[0])
        if translate[1] < 0:
            M[1, 2] += abs(translate[1])

        flags = get_interpolation()
        return cv2.warpAffine(img,
                              M, (dst_w, dst_h),
                              flags=flags,
                              borderMode=cv2.BORDER_REPLICATE)


class CVRandomPerspective(object):

    def __init__(self, distortion=0.5):
        self.distortion = distortion

    def get_params(self, width, height, distortion):
        offset_h = sample_asym(distortion * height / 2,
                               size=4).astype(dtype=np.int32)
        offset_w = sample_asym(distortion * width / 2,
                               size=4).astype(dtype=np.int32)
        topleft = (offset_w[0], offset_h[0])
        topright = (width - 1 - offset_w[1], offset_h[1])
        botright = (width - 1 - offset_w[2], height - 1 - offset_h[2])
        botleft = (offset_w[3], height - 1 - offset_h[3])

        startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1),
                       (0, height - 1)]
        endpoints = [topleft, topright, botright, botleft]
        return np.array(startpoints,
                        dtype=np.float32), np.array(endpoints,
                                                    dtype=np.float32)

    def __call__(self, img):
        height, width = img.shape[:2]
        startpoints, endpoints = self.get_params(width, height,
                                                 self.distortion)
        M = cv2.getPerspectiveTransform(startpoints, endpoints)

        # TODO: more robust way to crop image
        rect = cv2.minAreaRect(endpoints)
        bbox = cv2.boxPoints(rect).astype(dtype=np.int32)
        max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
        min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
        min_x, min_y = max(min_x, 0), max(min_y, 0)

        flags = get_interpolation()
        img = cv2.warpPerspective(img,
                                  M, (max_x, max_y),
                                  flags=flags,
                                  borderMode=cv2.BORDER_REPLICATE)
        img = img[min_y:, min_x:]
        return img


class CVRescale(object):

    def __init__(self, factor=4, base_size=(128, 512)):
        """Define image scales using gaussian pyramid and rescale image to
        target scale.

        Args:
            factor: the decayed factor from base size, factor=4 keeps target scale by default.
            base_size: base size the build the bottom layer of pyramid
        """
        if isinstance(factor, numbers.Number):
            self.factor = round(sample_uniform(0, factor))
        elif isinstance(factor, (tuple, list)) and len(factor) == 2:
            self.factor = round(sample_uniform(factor[0], factor[1]))
        else:
            raise Exception('factor must be number or list with length 2')
        # assert factor is valid
        self.base_h, self.base_w = base_size[:2]

    def __call__(self, img):
        if self.factor == 0:
            return img
        src_h, src_w = img.shape[:2]
        cur_w, cur_h = self.base_w, self.base_h
        scale_img = cv2.resize(img, (cur_w, cur_h),
                               interpolation=get_interpolation())
        for _ in range(self.factor):
            scale_img = cv2.pyrDown(scale_img)
        scale_img = cv2.resize(scale_img, (src_w, src_h),
                               interpolation=get_interpolation())
        return scale_img


class CVGaussianNoise(object):

    def __init__(self, mean=0, var=20):
        self.mean = mean
        if isinstance(var, numbers.Number):
            self.var = max(int(sample_asym(var)), 1)
        elif isinstance(var, (tuple, list)) and len(var) == 2:
            self.var = int(sample_uniform(var[0], var[1]))
        else:
            raise Exception('degree must be number or list with length 2')

    def __call__(self, img):
        noise = np.random.normal(self.mean, self.var**0.5, img.shape)
        img = np.clip(img + noise, 0, 255).astype(np.uint8)
        return img


class CVMotionBlur(object):

    def __init__(self, degrees=12, angle=90):
        if isinstance(degrees, numbers.Number):
            self.degree = max(int(sample_asym(degrees)), 1)
        elif isinstance(degrees, (tuple, list)) and len(degrees) == 2:
            self.degree = int(sample_uniform(degrees[0], degrees[1]))
        else:
            raise Exception('degree must be number or list with length 2')
        self.angle = sample_uniform(-angle, angle)

    def __call__(self, img):
        M = cv2.getRotationMatrix2D((self.degree // 2, self.degree // 2),
                                    self.angle, 1)
        motion_blur_kernel = np.zeros((self.degree, self.degree))
        motion_blur_kernel[self.degree // 2, :] = 1
        motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M,
                                            (self.degree, self.degree))
        motion_blur_kernel = motion_blur_kernel / self.degree
        img = cv2.filter2D(img, -1, motion_blur_kernel)
        img = np.clip(img, 0, 255).astype(np.uint8)
        return img


class CVGeometry(object):

    def __init__(
            self,
            degrees=15,
            translate=(0.3, 0.3),
            scale=(0.5, 2.0),
            shear=(45, 15),
            distortion=0.5,
            p=0.5,
    ):
        self.p = p
        type_p = random.random()
        if type_p < 0.33:
            self.transforms = CVRandomRotation(degrees=degrees)
        elif type_p < 0.66:
            self.transforms = CVRandomAffine(degrees=degrees,
                                             translate=translate,
                                             scale=scale,
                                             shear=shear)
        else:
            self.transforms = CVRandomPerspective(distortion=distortion)

    def __call__(self, img):
        if random.random() < self.p:
            return self.transforms(img)
        else:
            return img


class CVDeterioration(object):

    def __init__(self, var, degrees, factor, p=0.5):
        self.p = p
        transforms = []
        if var is not None:
            transforms.append(CVGaussianNoise(var=var))
        if degrees is not None:
            transforms.append(CVMotionBlur(degrees=degrees))
        if factor is not None:
            transforms.append(CVRescale(factor=factor))

        random.shuffle(transforms)
        transforms = Compose(transforms)
        self.transforms = transforms

    def __call__(self, img):
        if random.random() < self.p:
            return self.transforms(img)
        else:
            return img


class CVColorJitter(object):

    def __init__(self,
                 brightness=0.5,
                 contrast=0.5,
                 saturation=0.5,
                 hue=0.1,
                 p=0.5):
        self.p = p
        self.transforms = ColorJitter(brightness=brightness,
                                      contrast=contrast,
                                      saturation=saturation,
                                      hue=hue)

    def __call__(self, img):
        if random.random() < self.p:
            return np.array(self.transforms(Image.fromarray(img)))
        else:
            return img


class SVTRDeterioration(object):

    def __init__(self, var, degrees, factor, p=0.5):
        self.p = p
        transforms = []
        if var is not None:
            transforms.append(CVGaussianNoise(var=var))
        if degrees is not None:
            transforms.append(CVMotionBlur(degrees=degrees))
        if factor is not None:
            transforms.append(CVRescale(factor=factor))
        self.transforms = transforms

    def __call__(self, img):
        if random.random() < self.p:
            random.shuffle(self.transforms)
            transforms = Compose(self.transforms)
            return transforms(img)
        else:
            return img


class SVTRGeometry(object):

    def __init__(
            self,
            aug_type=0,
            degrees=15,
            translate=(0.3, 0.3),
            scale=(0.5, 2.0),
            shear=(45, 15),
            distortion=0.5,
            p=0.5,
    ):
        self.aug_type = aug_type
        self.p = p
        self.transforms = []
        self.transforms.append(CVRandomRotation(degrees=degrees))
        self.transforms.append(
            CVRandomAffine(degrees=degrees,
                           translate=translate,
                           scale=scale,
                           shear=shear))
        self.transforms.append(CVRandomPerspective(distortion=distortion))

    def __call__(self, img):
        if random.random() < self.p:
            if self.aug_type:
                random.shuffle(self.transforms)
                transforms = Compose(self.transforms[:random.randint(1, 3)])
                img = transforms(img)
            else:
                img = self.transforms[random.randint(0, 2)](img)
            return img
        else:
            return img