AnimationManager/matting-server/BiRefNet/image_proc.py

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)