Spaces:
Runtime error
Runtime error
File size: 6,836 Bytes
67390a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | import random
from PIL import Image, ImageEnhance
import numpy as np
import cv2
import torch
from torchvision import transforms
## CPU version refinement
def FB_blur_fusion_foreground_estimator_cpu(image, FG, B, alpha, r=90):
if isinstance(image, Image.Image):
image = np.array(image) / 255.0
blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]
blurred_FGA = cv2.blur(FG * alpha, (r, r))
blurred_FG = blurred_FGA / (blurred_alpha + 1e-5)
blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
FG = blurred_FG + alpha * (image - alpha * blurred_FG - (1 - alpha) * blurred_B)
FG = np.clip(FG, 0, 1)
return FG, blurred_B
def FB_blur_fusion_foreground_estimator_cpu_2(image, alpha, r=90):
# Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation
alpha = alpha[:, :, None]
FG, blur_B = FB_blur_fusion_foreground_estimator_cpu(image, image, image, alpha, r)
return FB_blur_fusion_foreground_estimator_cpu(image, FG, blur_B, alpha, r=6)[0]
## GPU version refinement
def mean_blur(x, kernel_size):
"""
equivalent to cv.blur
x: [B, C, H, W]
"""
if kernel_size % 2 == 0:
pad_l = kernel_size // 2 - 1
pad_r = kernel_size // 2
pad_t = kernel_size // 2 - 1
pad_b = kernel_size // 2
else:
pad_l = pad_r = pad_t = pad_b = kernel_size // 2
x_padded = torch.nn.functional.pad(x, (pad_l, pad_r, pad_t, pad_b), mode='replicate')
return torch.nn.functional.avg_pool2d(x_padded, kernel_size=(kernel_size, kernel_size), stride=1, count_include_pad=False)
def FB_blur_fusion_foreground_estimator_gpu(image, FG, B, alpha, r=90):
as_dtype = lambda x, dtype: x.to(dtype) if x.dtype != dtype else x
input_dtype = image.dtype
# convert image to float to avoid overflow
image = as_dtype(image, torch.float32)
FG = as_dtype(FG, torch.float32)
B = as_dtype(B, torch.float32)
alpha = as_dtype(alpha, torch.float32)
blurred_alpha = mean_blur(alpha, kernel_size=r)
blurred_FGA = mean_blur(FG * alpha, kernel_size=r)
blurred_FG = blurred_FGA / (blurred_alpha + 1e-5)
blurred_B1A = mean_blur(B * (1 - alpha), kernel_size=r)
blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
FG_output = blurred_FG + alpha * (image - alpha * blurred_FG - (1 - alpha) * blurred_B)
FG_output = torch.clamp(FG_output, 0, 1)
return as_dtype(FG_output, input_dtype), as_dtype(blurred_B, input_dtype)
def FB_blur_fusion_foreground_estimator_gpu_2(image, alpha, r=90):
# Thanks to the source: https://github.com/ZhengPeng7/BiRefNet/issues/226#issuecomment-3016433728
FG, blur_B = FB_blur_fusion_foreground_estimator_gpu(image, image, image, alpha, r)
return FB_blur_fusion_foreground_estimator_gpu(image, FG, blur_B, alpha, r=6)[0]
def refine_foreground(image, mask, r=90, device='cuda'):
"""both image and mask are in range of [0, 1]"""
if mask.size != image.size:
mask = mask.resize(image.size)
if device == 'cuda':
image = transforms.functional.to_tensor(image).float().cuda()
mask = transforms.functional.to_tensor(mask).float().cuda()
image = image.unsqueeze(0)
mask = mask.unsqueeze(0)
estimated_foreground = FB_blur_fusion_foreground_estimator_gpu_2(image, mask, r=r)
estimated_foreground = estimated_foreground.squeeze()
estimated_foreground = (estimated_foreground.mul(255.0)).to(torch.uint8)
estimated_foreground = estimated_foreground.permute(1, 2, 0).contiguous().cpu().numpy().astype(np.uint8)
else:
image = np.array(image, dtype=np.float32) / 255.0
mask = np.array(mask, dtype=np.float32) / 255.0
estimated_foreground = FB_blur_fusion_foreground_estimator_cpu_2(image, mask, r=r)
estimated_foreground = (estimated_foreground * 255.0).astype(np.uint8)
estimated_foreground = Image.fromarray(np.ascontiguousarray(estimated_foreground))
return estimated_foreground
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)
|