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import random
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import cv2
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import numpy as np
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from albumentations import DualTransform, ImageOnlyTransform
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from albumentations.augmentations.crops.functional import crop
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def isotropically_resize_image(img, size, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC):
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h, w = img.shape[:2]
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if max(w, h) == size:
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return img
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if w > h:
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scale = size / w
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h = h * scale
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w = size
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else:
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scale = size / h
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w = w * scale
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h = size
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interpolation = interpolation_up if scale > 1 else interpolation_down
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resized = cv2.resize(img, (int(w), int(h)), interpolation=interpolation)
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return resized
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class IsotropicResize(DualTransform):
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def __init__(self, max_side, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC,
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always_apply=False, p=1):
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super(IsotropicResize, self).__init__(always_apply, p)
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self.max_side = max_side
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self.interpolation_down = interpolation_down
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self.interpolation_up = interpolation_up
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def apply(self, img, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC, **params):
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return isotropically_resize_image(img, size=self.max_side, interpolation_down=interpolation_down,
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interpolation_up=interpolation_up)
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def apply_to_mask(self, img, **params):
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return self.apply(img, interpolation_down=cv2.INTER_NEAREST, interpolation_up=cv2.INTER_NEAREST, **params)
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def get_transform_init_args_names(self):
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return ("max_side", "interpolation_down", "interpolation_up")
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class Resize4xAndBack(ImageOnlyTransform):
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def __init__(self, always_apply=False, p=0.5):
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super(Resize4xAndBack, self).__init__(always_apply, p)
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def apply(self, img, **params):
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h, w = img.shape[:2]
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scale = random.choice([2, 4])
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img = cv2.resize(img, (w // scale, h // scale), interpolation=cv2.INTER_AREA)
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img = cv2.resize(img, (w, h),
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interpolation=random.choice([cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_NEAREST]))
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return img
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class RandomSizedCropNonEmptyMaskIfExists(DualTransform):
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def __init__(self, min_max_height, w2h_ratio=[0.7, 1.3], always_apply=False, p=0.5):
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super(RandomSizedCropNonEmptyMaskIfExists, self).__init__(always_apply, p)
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self.min_max_height = min_max_height
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self.w2h_ratio = w2h_ratio
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def apply(self, img, x_min=0, x_max=0, y_min=0, y_max=0, **params):
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cropped = crop(img, x_min, y_min, x_max, y_max)
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return cropped
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@property
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def targets_as_params(self):
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return ["mask"]
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def get_params_dependent_on_targets(self, params):
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mask = params["mask"]
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mask_height, mask_width = mask.shape[:2]
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crop_height = int(mask_height * random.uniform(self.min_max_height[0], self.min_max_height[1]))
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w2h_ratio = random.uniform(*self.w2h_ratio)
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crop_width = min(int(crop_height * w2h_ratio), mask_width - 1)
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if mask.sum() == 0:
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x_min = random.randint(0, mask_width - crop_width + 1)
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y_min = random.randint(0, mask_height - crop_height + 1)
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else:
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mask = mask.sum(axis=-1) if mask.ndim == 3 else mask
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non_zero_yx = np.argwhere(mask)
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y, x = random.choice(non_zero_yx)
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x_min = x - random.randint(0, crop_width - 1)
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y_min = y - random.randint(0, crop_height - 1)
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x_min = np.clip(x_min, 0, mask_width - crop_width)
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y_min = np.clip(y_min, 0, mask_height - crop_height)
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x_max = x_min + crop_height
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y_max = y_min + crop_width
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y_max = min(mask_height, y_max)
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x_max = min(mask_width, x_max)
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return {"x_min": x_min, "x_max": x_max, "y_min": y_min, "y_max": y_max}
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def get_transform_init_args_names(self):
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return "min_max_height", "height", "width", "w2h_ratio" |
