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Merge pull request #425 from kwcckw/rel
Updated version to 8.2.6.
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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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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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