| import random
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| from PIL import Image, ImageEnhance
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| import numpy as np
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| import cv2
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|
|
|
|
| def refine_foreground(image, mask, r=90):
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| if mask.size != image.size:
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| mask = mask.resize(image.size)
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| image = np.array(image) / 255.0
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| mask = np.array(mask) / 255.0
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| estimated_foreground = FB_blur_fusion_foreground_estimator_2(image, mask, r=r)
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| image_masked = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8))
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| return image_masked
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|
|
|
|
| def FB_blur_fusion_foreground_estimator_2(image, alpha, r=90):
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|
|
| alpha = alpha[:, :, None]
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| F, blur_B = FB_blur_fusion_foreground_estimator(
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| image, image, image, alpha, r)
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| return FB_blur_fusion_foreground_estimator(image, F, blur_B, alpha, r=6)[0]
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|
|
|
|
| def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90):
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| if isinstance(image, Image.Image):
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| image = np.array(image) / 255.0
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| blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]
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|
|
| blurred_FA = cv2.blur(F * alpha, (r, r))
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| blurred_F = blurred_FA / (blurred_alpha + 1e-5)
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|
|
| blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
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| blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
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| F = blurred_F + alpha * \
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| (image - alpha * blurred_F - (1 - alpha) * blurred_B)
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| F = np.clip(F, 0, 1)
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| return F, blurred_B
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|
|
|
|
| def preproc(image, label, preproc_methods=['flip']):
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| if 'flip' in preproc_methods:
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| image, label = cv_random_flip(image, label)
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| if 'crop' in preproc_methods:
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| image, label = random_crop(image, label)
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| if 'rotate' in preproc_methods:
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| image, label = random_rotate(image, label)
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| if 'enhance' in preproc_methods:
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| image = color_enhance(image)
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| if 'pepper' in preproc_methods:
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| label = random_pepper(label)
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| return image, label
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|
|
|
|
| def cv_random_flip(img, label):
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| if random.random() > 0.5:
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| img = img.transpose(Image.FLIP_LEFT_RIGHT)
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| label = label.transpose(Image.FLIP_LEFT_RIGHT)
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| return img, label
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|
|
|
|
| def random_crop(image, label):
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| border = 30
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| image_width = image.size[0]
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| image_height = image.size[1]
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| border = int(min(image_width, image_height) * 0.1)
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| crop_win_width = np.random.randint(image_width - border, image_width)
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| crop_win_height = np.random.randint(image_height - border, image_height)
|
| random_region = (
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| (image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
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| (image_height + crop_win_height) >> 1)
|
| return image.crop(random_region), label.crop(random_region)
|
|
|
|
|
| def random_rotate(image, label, angle=15):
|
| mode = Image.BICUBIC
|
| if random.random() > 0.8:
|
| random_angle = np.random.randint(-angle, angle)
|
| image = image.rotate(random_angle, mode)
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| label = label.rotate(random_angle, mode)
|
| return image, label
|
|
|
|
|
| def color_enhance(image):
|
| bright_intensity = random.randint(5, 15) / 10.0
|
| image = ImageEnhance.Brightness(image).enhance(bright_intensity)
|
| contrast_intensity = random.randint(5, 15) / 10.0
|
| image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
|
| color_intensity = random.randint(0, 20) / 10.0
|
| image = ImageEnhance.Color(image).enhance(color_intensity)
|
| sharp_intensity = random.randint(0, 30) / 10.0
|
| image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
|
| return image
|
|
|
|
|
| def random_gaussian(image, mean=0.1, sigma=0.35):
|
| def gaussianNoisy(im, mean=mean, sigma=sigma):
|
| for _i in range(len(im)):
|
| im[_i] += random.gauss(mean, sigma)
|
| return im
|
|
|
| img = np.asarray(image)
|
| width, height = img.shape
|
| img = gaussianNoisy(img[:].flatten(), mean, sigma)
|
| img = img.reshape([width, height])
|
| return Image.fromarray(np.uint8(img))
|
|
|
|
|
| def random_pepper(img, N=0.0015):
|
| img = np.array(img)
|
| noiseNum = int(N * img.shape[0] * img.shape[1])
|
| for i in range(noiseNum):
|
| randX = random.randint(0, img.shape[0] - 1)
|
| randY = random.randint(0, img.shape[1] - 1)
|
| if random.randint(0, 1) == 0:
|
| img[randX, randY] = 0
|
| else:
|
| img[randX, randY] = 255
|
| return Image.fromarray(img)
|
|
|
