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Updated DepthSimulatedBlur. #421

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Dec 25, 2023
Merged

Updated DepthSimulatedBlur. #421

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52 changes: 36 additions & 16 deletions augraphy/augmentations/depthsimulatedblur.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,10 @@ class DepthSimulatedBlur(Augmentation):
: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
"""
Expand All @@ -24,16 +28,18 @@ def __init__(
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

# 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}, p={self.p})"
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})"

# Applies the Augmentation to input data.
def __call__(self, image, layer=None, mask=None, keypoints=None, bounding_boxes=None, force=False):
Expand All @@ -53,32 +59,50 @@ def __call__(self, image, layer=None, mask=None, keypoints=None, bounding_boxes=

ysize, xsize = image.shape[:2]

# initial gaussian kernel value, will be incremented per iteration
kernel_value = 3
gaussian_kernel = [kernel_value, kernel_value]

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])

min_x = int(xsize / 5)
min_y = int(ysize / 5)
max_x = xsize - min_x
max_y = ysize - min_y

if self.blur_center == "random":
center_x = random.randint(0, xsize)
center_y = random.randint(0, ysize)
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]

step = 10
decremental_value = 10
angle = random.randint(0, 360) # Angle of rotation (in degrees)
step = random.randint(self.blur_iteration_range[0], self.blur_iteration_range[1])

# decremental value per step
decremental_value = int(max(1, np.ceil(min(axes_major, axes_minor) / step)))

# 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

# Angle of rotation (in degrees)
angle = random.randint(0, 360)
# Center of ellipse
center_coordinates = (center_x, center_y)
color = (255, 255, 255) # BGR color (here, blue)
thickness = -1 # Line thickness
# BGR color
color = (255, 255, 255)
# fill ellipse
thickness = -1

image_output = image.copy()

# it still run slow now, need further optimization
for i in range(step):

gaussian_kernel = (int(gaussian_kernels[i]), int(gaussian_kernels[i]))

image_ellipse = np.zeros_like(image, dtype="uint8")

axes_length = (axes_major, axes_minor) # Major and minor axes lengths
Expand All @@ -98,10 +122,6 @@ def __call__(self, image, layer=None, mask=None, keypoints=None, bounding_boxes=
cv2.NORMAL_CLONE,
)

# increase gaussian kernel size
gaussian_kernel[0] += 4
gaussian_kernel[1] += 4

# 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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