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Merge pull request #425 from kwcckw/rel
Updated version to 8.2.6.
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| Original file line number | Diff line number | Diff line change |
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| from augraphy.default import * | ||
| from augraphy.utilities import * | ||
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| __version__ = "8.2.5" | ||
| __version__ = "8.2.6" | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,145 @@ | ||
| import random | ||
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| import cv2 | ||
| import numpy as np | ||
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| from augraphy.base.augmentation import Augmentation | ||
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| class DepthSimulatedBlur(Augmentation): | ||
| """Creates a depth-simulated blur effect from a camera by blurring a small elliptical region of image. | ||
| :param blur_centerr: Center (x,y) of blur effect. Use "random" for random location. | ||
| :type blur_center: tuple or string, optional | ||
| :param blur_major_axes_length_range: Pair of ints determining the value of major axis in the blurring ellipse. | ||
| :type blur_major_axes_length_range: tuple, optional | ||
| :param blur_minor_axes_length_range: Pair of ints determining the value of minor axis in the blurring ellipse. | ||
| :type blur_minor_axes_length_range: tuple, optional | ||
| :param blur_iteration_range: Pair of ints determining the value of number of blurring iterations. | ||
| The higher the iteration number, the smoother the transition of blurring area to non blurring area. | ||
| However, it runs slower with higher iterations number. | ||
| :type blur_iteration_range: tuple, optional | ||
| :param p: The probability this Augmentation will be applied. | ||
| :type p: float, optional | ||
| """ | ||
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| def __init__( | ||
| self, | ||
| blur_center="random", | ||
| blur_major_axes_length_range=(120, 200), | ||
| blur_minor_axes_length_range=(120, 200), | ||
| blur_iteration_range=(8, 10), | ||
| p=1, | ||
| ): | ||
| super().__init__(p=p) | ||
| self.blur_center = blur_center | ||
| self.blur_major_axes_length_range = blur_major_axes_length_range | ||
| self.blur_minor_axes_length_range = blur_minor_axes_length_range | ||
| self.blur_iteration_range = blur_iteration_range | ||
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| # Constructs a string representation of this Augmentation. | ||
| def __repr__(self): | ||
| return f"DepthSimulatedBlur(blur_center={self.blur_center}, blur_major_axes_length_range={self.blur_major_axes_length_range}, blur_minor_axes_length_range={self.blur_minor_axes_length_range}, blur_iteration_range={self.blur_iteration_range}, p={self.p})" | ||
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| # Applies the Augmentation to input data. | ||
| def __call__(self, image, layer=None, mask=None, keypoints=None, bounding_boxes=None, force=False): | ||
| if force or self.should_run(): | ||
| image = image.copy() | ||
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| # check and convert image into BGR format | ||
| has_alpha = 0 | ||
| if len(image.shape) > 2: | ||
| is_gray = 0 | ||
| if image.shape[2] == 4: | ||
| has_alpha = 1 | ||
| image, image_alpha = image[:, :, :3], image[:, :, 3] | ||
| else: | ||
| is_gray = 1 | ||
| image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) | ||
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| ysize, xsize = image.shape[:2] | ||
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| axes_major = random.randint(self.blur_major_axes_length_range[0], self.blur_major_axes_length_range[1]) | ||
| axes_minor = random.randint(self.blur_minor_axes_length_range[0], self.blur_minor_axes_length_range[1]) | ||
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| min_x = int(xsize / 5) | ||
| min_y = int(ysize / 5) | ||
| max_x = xsize - min_x | ||
| max_y = ysize - min_y | ||
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| if self.blur_center == "random": | ||
| center_x = random.randint(min_x, max_x) | ||
| center_y = random.randint(min_y, max_y) | ||
| else: | ||
| center_x = self.blur_center[0] | ||
| center_y = self.blur_center[0] | ||
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| step = random.randint(self.blur_iteration_range[0], self.blur_iteration_range[1]) | ||
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| # decremental value per step | ||
| decremental_value = int(max(1, np.ceil(min(axes_major, axes_minor) / step))) | ||
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| # gaussian kernel incremental value per step | ||
| gaussian_kernels = np.linspace(3, random.randint(15, 21), step) | ||
| for i, gaussian_kernel in enumerate(gaussian_kernels): | ||
| gaussian_kernel = np.ceil(gaussian_kernel) | ||
| if not gaussian_kernel % 2: | ||
| gaussian_kernel += 1 | ||
| gaussian_kernels[i] = gaussian_kernel | ||
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| # Angle of rotation (in degrees) | ||
| angle = random.randint(0, 360) | ||
| # Center of ellipse | ||
| center_coordinates = (center_x, center_y) | ||
| # BGR color | ||
| color = (255, 255, 255) | ||
| # fill ellipse | ||
| thickness = -1 | ||
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| image_output = image.copy() | ||
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| # it still run slow now, need further optimization | ||
| for i in range(step): | ||
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| gaussian_kernel = (int(gaussian_kernels[i]), int(gaussian_kernels[i])) | ||
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| image_ellipse = np.zeros_like(image, dtype="uint8") | ||
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| axes_length = (axes_major, axes_minor) # Major and minor axes lengths | ||
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| # Draw the oval on the image | ||
| cv2.ellipse(image_ellipse, center_coordinates, axes_length, angle, 0, 360, color, thickness) | ||
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| # blur image | ||
| image_blur = cv2.GaussianBlur(image, gaussian_kernel, 0) | ||
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| # blend blur region into image | ||
| image_output = cv2.seamlessClone( | ||
| image_output, | ||
| image_blur, | ||
| 255 - image_ellipse, | ||
| (int(xsize / 2), int(ysize / 2)), | ||
| cv2.NORMAL_CLONE, | ||
| ) | ||
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| # increase major and minor length | ||
| axes_major = max(axes_major - decremental_value, 1) | ||
| axes_minor = max(axes_minor - decremental_value, 1) | ||
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| # return image follows the input image color channel | ||
| if is_gray: | ||
| image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2GRAY) | ||
| if has_alpha: | ||
| image_output = np.dstack((image_output, image_alpha)) | ||
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| # check for additional output of mask, keypoints and bounding boxes | ||
| outputs_extra = [] | ||
| if mask is not None or keypoints is not None or bounding_boxes is not None: | ||
| outputs_extra = [mask, keypoints, bounding_boxes] | ||
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| # returns additional mask, keypoints and bounding boxes if there is additional input | ||
| if outputs_extra: | ||
| # returns in the format of [image, mask, keypoints, bounding_boxes] | ||
| return [image_output] + outputs_extra | ||
| else: | ||
| return image_output |
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