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| import math | |
| import numbers | |
| import random | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| from torchvision import transforms | |
| from torchvision.transforms import 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 | |
| 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 sin, cos, 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 | |
| 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.] | |
| 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.int) | |
| 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.int) | |
| offset_w = sample_asym(distortion * width / 2, size=4).astype(dtype=np.int) | |
| 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.int) | |
| 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.), | |
| 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: | |
| img = np.array(img) | |
| return Image.fromarray(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: | |
| img = np.array(img) | |
| return Image.fromarray(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 = transforms.ColorJitter(brightness=brightness, contrast=contrast, | |
| saturation=saturation, hue=hue) | |
| def __call__(self, img): | |
| if random.random() < self.p: return self.transforms(img) | |
| else: return img | |