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* Refactor blur * Refactor blur * Fix imports * Fix tests import * Fix readme * Fix mypy * Fix mypy * Fix mypy Co-authored-by: Vladimir Iglovikov <ternaus@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
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| from .functional import * | ||
| from .transforms import * |
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| from itertools import product | ||
| from math import ceil | ||
| from typing import Sequence, Union | ||
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| import cv2 | ||
| import numpy as np | ||
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| from albumentations.augmentations.functional import convolve | ||
| from albumentations.augmentations.geometric.functional import scale | ||
| from albumentations.augmentations.utils import ( | ||
| _maybe_process_in_chunks, | ||
| clipped, | ||
| preserve_shape, | ||
| ) | ||
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| __all__ = ["blur", "median_blur", "gaussian_blur", "glass_blur"] | ||
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| @preserve_shape | ||
| def blur(img: np.ndarray, ksize: int) -> np.ndarray: | ||
| blur_fn = _maybe_process_in_chunks(cv2.blur, ksize=(ksize, ksize)) | ||
| return blur_fn(img) | ||
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| @preserve_shape | ||
| def median_blur(img: np.ndarray, ksize: int) -> np.ndarray: | ||
| if img.dtype == np.float32 and ksize not in {3, 5}: | ||
| raise ValueError(f"Invalid ksize value {ksize}. For a float32 image the only valid ksize values are 3 and 5") | ||
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| blur_fn = _maybe_process_in_chunks(cv2.medianBlur, ksize=ksize) | ||
| return blur_fn(img) | ||
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| @preserve_shape | ||
| def gaussian_blur(img: np.ndarray, ksize: int, sigma: float = 0) -> np.ndarray: | ||
| # When sigma=0, it is computed as `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8` | ||
| blur_fn = _maybe_process_in_chunks(cv2.GaussianBlur, ksize=(ksize, ksize), sigmaX=sigma) | ||
| return blur_fn(img) | ||
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| @preserve_shape | ||
| def glass_blur( | ||
| img: np.ndarray, sigma: float, max_delta: int, iterations: int, dxy: np.ndarray, mode: str | ||
| ) -> np.ndarray: | ||
| x = cv2.GaussianBlur(np.array(img), sigmaX=sigma, ksize=(0, 0)) | ||
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| if mode == "fast": | ||
| hs = np.arange(img.shape[0] - max_delta, max_delta, -1) | ||
| ws = np.arange(img.shape[1] - max_delta, max_delta, -1) | ||
| h: Union[int, np.ndarray] = np.tile(hs, ws.shape[0]) | ||
| w: Union[int, np.ndarray] = np.repeat(ws, hs.shape[0]) | ||
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| for i in range(iterations): | ||
| dy = dxy[:, i, 0] | ||
| dx = dxy[:, i, 1] | ||
| x[h, w], x[h + dy, w + dx] = x[h + dy, w + dx], x[h, w] | ||
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| elif mode == "exact": | ||
| for ind, (i, h, w) in enumerate( | ||
| product( | ||
| range(iterations), | ||
| range(img.shape[0] - max_delta, max_delta, -1), | ||
| range(img.shape[1] - max_delta, max_delta, -1), | ||
| ) | ||
| ): | ||
| ind = ind if ind < len(dxy) else ind % len(dxy) | ||
| dy = dxy[ind, i, 0] | ||
| dx = dxy[ind, i, 1] | ||
| x[h, w], x[h + dy, w + dx] = x[h + dy, w + dx], x[h, w] | ||
| else: | ||
| ValueError(f"Unsupported mode `{mode}`. Supports only `fast` and `exact`.") | ||
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| return cv2.GaussianBlur(x, sigmaX=sigma, ksize=(0, 0)) | ||
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| def defocus(img: np.ndarray, radius: int, alias_blur: float) -> np.ndarray: | ||
| length = np.arange(-max(8, radius), max(8, radius) + 1) | ||
| ksize = 3 if radius <= 8 else 5 | ||
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| x, y = np.meshgrid(length, length) | ||
| aliased_disk = np.array((x**2 + y**2) <= radius**2, dtype=np.float32) | ||
| aliased_disk /= np.sum(aliased_disk) | ||
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| kernel = gaussian_blur(aliased_disk, ksize, sigma=alias_blur) | ||
| return convolve(img, kernel=kernel) | ||
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| def central_zoom(img: np.ndarray, zoom_factor: int) -> np.ndarray: | ||
| h, w = img.shape[:2] | ||
| h_ch, w_ch = ceil(h / zoom_factor), ceil(w / zoom_factor) | ||
| h_top, w_top = (h - h_ch) // 2, (w - w_ch) // 2 | ||
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| img = scale(img[h_top : h_top + h_ch, w_top : w_top + w_ch], zoom_factor, cv2.INTER_LINEAR) | ||
| h_trim_top, w_trim_top = (img.shape[0] - h) // 2, (img.shape[1] - w) // 2 | ||
| return img[h_trim_top : h_trim_top + h, w_trim_top : w_trim_top + w] | ||
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| @clipped | ||
| def zoom_blur(img: np.ndarray, zoom_factors: Union[np.ndarray, Sequence[int]]) -> np.ndarray: | ||
| out = np.zeros_like(img, dtype=np.float32) | ||
| for zoom_factor in zoom_factors: | ||
| out += central_zoom(img, zoom_factor) | ||
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| img = ((img + out) / (len(zoom_factors) + 1)).astype(img.dtype) | ||
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| return img |
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