app.py

import os
import cv2
import numpy as np
import torch
import gradio as gr
import spaces

from PIL import Image, ImageOps
from transformers import AutoModelForImageSegmentation
from torchvision import transforms

torch.set_float32_matmul_precision('high')
torch.jit.script = lambda f: f

device = "cuda" if torch.cuda.is_available() else "cpu"

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):
    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

class ImagePreprocessor():
    def __init__(self, resolution=(1024, 1024)) -> None:
        self.transform_image = transforms.Compose([
            transforms.Resize(resolution),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ])

    def proc(self, image: Image.Image) -> torch.Tensor:
        image = self.transform_image(image)
        return image

birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet-matting', trust_remote_code=True)
birefnet.to(device)
birefnet.eval()

@spaces.GPU
def remove_background(image):
    if image is None:
        raise gr.Error("Please upload an image.")

    image_ori = Image.fromarray(image).convert('RGB')
    original_size = image_ori.size
    
    # Preprocess the image
    image_preprocessor = ImagePreprocessor(resolution=(1024, 1024))
    image_proc = image_preprocessor.proc(image_ori)
    image_proc = image_proc.unsqueeze(0)

    # Prediction
    with torch.no_grad():
        preds = birefnet(image_proc.to(device))[-1].sigmoid().cpu()
    pred = preds[0].squeeze()

    # Process Results
    pred_pil = transforms.ToPILImage()(pred)
    pred_pil = pred_pil.resize(original_size, Image.BICUBIC)  # Resize mask to original size
    
    # Create reverse mask
    reverse_mask = Image.new('L', original_size)
    reverse_mask.paste(ImageOps.invert(pred_pil))

    # Create foreground image (object with transparent background)
    foreground = image_ori.copy()
    foreground.putalpha(pred_pil)

    # Create background image
    background = image_ori.copy()
    background.putalpha(reverse_mask)

    torch.cuda.empty_cache()

    # Save all images
    mask_path = "mask.png"
    pred_pil.save(mask_path)

    reverse_mask_path = "reverse_mask.png"
    reverse_mask.save(reverse_mask_path)

    foreground_path = "foreground.png"
    foreground.save(foreground_path)

    background_path = "background.png"
    background.save(background_path)

    return mask_path, reverse_mask_path, foreground_path, background_path

license_text = """
MIT License

Copyright (c) 2024 ZhengPeng

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

css = """
body {
    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
}
.gradio-container {
    background: white;
}
#component-0 button {
    font-family: inherit !important;
    font-size: 16px !important;
    font-weight: bold !important;
    color: #000000 !important;
    background: linear-gradient(
        135deg,
        #e0f7fa, #e8f5e9, #fff9c4, #ffebee,
        #f3e5f5, #e1f5fe, #fff3e0, #e8eaf6
    ) !important;
    background-size: 400% 400% !important;
    animation: gradient-animation 15s ease infinite !important;
    border: 2px solid black !important;
    border-radius: 10px !important;
}
#component-0 button:hover {
    background: linear-gradient(
        135deg,
        #b2ebf2, #c8e6c9, #fff176, #ffcdd2,
        #e1bee7, #b3e5fc, #ffe0b2, #c5cae9
    ) !important;
    background-size: 400% 400% !important;
    animation: gradient-animation 15s ease infinite !important;
}
@keyframes gradient-animation {
    0% { background-position: 0% 50%; }
    50% { background-position: 100% 50%; }
    100% { background-position: 0% 50%; }
}
footer {
    text-align: center;
    margin-top: 20px;
}
.license-link {
    color: #007bff;
    text-decoration: none;
    cursor: pointer;
}
.license-link:hover {
    text-decoration: underline;
}
.modal {
    display: none;
    position: fixed;
    z-index: 1000;
    left: 0;
    top: 0;
    width: 100%;
    height: 100%;
    overflow: auto;
    background-color: rgba(0,0,0,0.4);
}
.modal-content {
    background-color: #fefefe;
    margin: 15% auto;
    padding: 20px;
    border: 1px solid #888;
    width: 80%;
    max-width: 600px;
}
.close {
    color: #aaa;
    float: right;
    font-size: 28px;
    font-weight: bold;
}
.close:hover,
.close:focus {
    color: black;
    text-decoration: none;
    cursor: pointer;
}
"""

js = """
function setupLicenseModal() {
    var modal = document.createElement('div');
    modal.className = 'modal';
    modal.innerHTML = `
        <div class="modal-content">
            <span class="close">&times;</span>
            <h2>License</h2>
            <pre>${license_text}</pre>
        </div>
    `;
    document.body.appendChild(modal);

    var link = document.createElement('a');
    link.href = '#';
    link.className = 'license-link';
    link.textContent = 'License';
    link.onclick = function(e) {
        e.preventDefault();
        modal.style.display = 'block';
    };

    var footer = document.createElement('footer');
    footer.appendChild(link);
    document.body.appendChild(footer);

    var span = modal.querySelector('.close');
    span.onclick = function() {
        modal.style.display = 'none';
    };

    window.onclick = function(event) {
        if (event.target == modal) {
            modal.style.display = 'none';
        }
    };
}

if (window.gradio_config.version.startsWith('3')) {
    setupLicenseModal();
} else {
    document.addEventListener('DOMContentLoaded', setupLicenseModal);
}
"""

iface = gr.Interface(
    fn=remove_background,
    inputs=gr.Image(type="numpy"),
    outputs=[
        gr.Image(type="filepath", label="Mask"),
        gr.Image(type="filepath", label="Reverse Mask"),
        gr.Image(type="filepath", label="Foreground"),
        gr.Image(type="filepath", label="Background")
    ],
    allow_flagging="never",
    css=css,
    js=js,
    elem_id="remove-background"
)

if __name__ == "__main__":
    iface.launch(debug=True)