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image_proc.py
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image_proc.py
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import random
from PIL import Image, ImageEnhance
import numpy as np
import cv2
def refine_foreground(image, mask, r=90):
if mask.size != image.size:
mask = mask.resize(image.size)
image = np.array(image) / 255.0
mask = np.array(mask) / 255.0
estimated_foreground = FB_blur_fusion_foreground_estimator_2(image, mask, r=r)
image_masked = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8))
return image_masked
def FB_blur_fusion_foreground_estimator_2(image, alpha, r=90):
# Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation
alpha = alpha[:, :, None]
F, blur_B = FB_blur_fusion_foreground_estimator(
image, image, image, alpha, r)
return FB_blur_fusion_foreground_estimator(image, F, blur_B, alpha, r=6)[0]
def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90):
if isinstance(image, Image.Image):
image = np.array(image) / 255.0
blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]
blurred_FA = cv2.blur(F * alpha, (r, r))
blurred_F = blurred_FA / (blurred_alpha + 1e-5)
blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
F = blurred_F + alpha * \
(image - alpha * blurred_F - (1 - alpha) * blurred_B)
F = np.clip(F, 0, 1)
return F, blurred_B
def preproc(image, label, preproc_methods=['flip']):
if 'flip' in preproc_methods:
image, label = cv_random_flip(image, label)
if 'crop' in preproc_methods:
image, label = random_crop(image, label)
if 'rotate' in preproc_methods:
image, label = random_rotate(image, label)
if 'enhance' in preproc_methods:
image = color_enhance(image)
if 'pepper' in preproc_methods:
image = random_pepper(image)
return image, label
def cv_random_flip(img, label):
if random.random() > 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
label = label.transpose(Image.FLIP_LEFT_RIGHT)
return img, label
def random_crop(image, label):
border = 30
image_width = image.size[0]
image_height = image.size[1]
border = int(min(image_width, image_height) * 0.1)
crop_win_width = np.random.randint(image_width - border, image_width)
crop_win_height = np.random.randint(image_height - border, image_height)
random_region = (
(image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
(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)
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)
img[randX, randY] = random.randint(0, 1) * 255
return Image.fromarray(img)