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Updated NoiseGenerator. #343

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Aug 11, 2023
Merged

Updated NoiseGenerator. #343

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13 changes: 5 additions & 8 deletions augraphy/augmentations/badphotocopy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -201,6 +201,9 @@ def apply_wave(self, mask, noise_side):
interpolation=cv2.INTER_AREA,
)
mask = 255 - cv2.multiply(img_wave, 255 - mask, scale=1 / 255)
# temporary, will be updated later for new noise side
else:
mask = 255 - cv2.multiply(img_wave, 255 - mask, scale=1 / 255)

return mask.astype("uint8")

Expand All @@ -222,7 +225,7 @@ def apply_augmentation(self, image):
ysize, xsize = image.shape[:2]

if self.noise_side == "random":
noise_side = random.choice(["left", "top", "right", "bottom"])
noise_side = random.choice(["left", "top", "right", "bottom", "none", "all"])
else:
noise_side = self.noise_side

Expand Down Expand Up @@ -252,8 +255,6 @@ def apply_augmentation(self, image):

# rescale to 0 -255
mask = ((mask - np.min(mask)) / (np.max(mask) - np.min(mask))) * 255
if self.noise_value[0] > self.noise_value[1]:
self.noise_value[0] = self.noise_value[1]

# resize back to original size
mask = cv2.resize(mask, (xsize, ysize)).astype("uint8")
Expand All @@ -280,11 +281,7 @@ def apply_augmentation(self, image):
mask[mask > noise_mask] = 255
noise_img = mask

# add blur
gaussian_kernel = (random.choice([3, 5, 7]), random.choice([3, 5, 7]))
blurred = cv2.GaussianBlur(noise_img, gaussian_kernel, 0)
noise_img = cv2.multiply(noise_img, blurred, scale=1 / 255)

# blend noise into image
result = image.copy()
for i in range(3):
result[:, :, i] = cv2.multiply(noise_img, result[:, :, i], scale=1 / 255)
Expand Down
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