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#######################################################################################
#
# MIT License
#
# Copyright (c) [2025] [leonelhs@gmail.com]
#
# 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.
#
#######################################################################################
# This file implements an API endpoint for DIS background image removal system.
#
# Source code is based on or inspired by several projects.
# For more details and proper attribution, please refer to the following resources:
#
# - [DIS] - [https://github.com/xuebinqin/DIS]
import gradio as gr
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from huggingface_hub import hf_hub_download
from torch.autograd import Variable
from torchvision.transforms.functional import normalize
# project imports
from models.isnet import ISNetDIS
REPO_ID = "leonelhs/removators"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
net = ISNetDIS()
model_path = hf_hub_download(repo_id=REPO_ID, filename='isnet.pth')
net.load_state_dict(torch.load(model_path, map_location=device))
net.to(device)
net.eval()
def im_preprocess(im,size):
if len(im.shape) < 3:
im = im[:, :, np.newaxis]
if im.shape[2] == 1:
im = np.repeat(im, 3, axis=2)
im_tensor = torch.tensor(im.copy(), dtype=torch.float32)
im_tensor = torch.transpose(torch.transpose(im_tensor,1,2),0,1)
if len(size)<2:
return im_tensor, im.shape[0:2]
else:
im_tensor = torch.unsqueeze(im_tensor,0)
im_tensor = F.interpolate(im_tensor, size, mode="bilinear")
im_tensor = torch.squeeze(im_tensor,0)
return im_tensor.type(torch.uint8), im.shape[0:2]
def predict(image):
"""
Remove the background from an image.
The function extracts the foreground and generates both a background-removed
image and a binary mask.
Parameters:
image (string): File path to the input image.
Returns:
paths (tuple): paths for background-removed image and cutting mask.
"""
im_tensor, shapes = im_preprocess(image, [1024, 1024])
shapes = torch.from_numpy(np.array(shapes)).unsqueeze(0)
im_tensor = torch.divide(im_tensor, 255.0)
im_tensor = normalize(im_tensor, mean=[0.5, 0.5, 0.5], std=[1.0, 1.0, 1.0]).unsqueeze(0)
im_tensor_v = Variable(im_tensor, requires_grad=False) # wrap inputs in Variable
ds_val = net(im_tensor_v)[0] # list of 6 results
prediction = ds_val[0][0, :, :, :] # B x 1 x H x W # we want the first one which is the most accurate prediction
## recover the prediction spatial size to the original image size
size = (shapes[0][0], shapes[0][1])
prediction = F.interpolate(torch.unsqueeze(prediction, 0), size, mode='bilinear')
prediction = torch.squeeze(prediction)
ma = torch.max(prediction)
mi = torch.min(prediction)
prediction = (prediction - mi) / (ma - mi) # max = 1
torch.cuda.empty_cache()
mask = (prediction.detach().cpu().numpy() * 255).astype(np.uint8) # it is the mask we need
mask = Image.fromarray(mask).convert('L')
image_rgb = Image.fromarray(image).convert("RGB")
image_rgb.putalpha(mask)
return image_rgb, mask
article = "<div><center>Unofficial demo from:<a href='https://github.com/xuebinqin/DIS'>DIS</<></center></div>"
with gr.Blocks(title="DIS") as app:
gr.Markdown("## Dichotomous Image Segmentation")
with gr.Row():
with gr.Column(scale=1):
inp = gr.Image(type="numpy", label="Upload Image")
btn_predict = gr.Button("Remove background")
with gr.Column(scale=2):
with gr.Row():
with gr.Column(scale=1):
out = gr.Image(type="filepath", label="Output image")
with gr.Accordion("See intermediates", open=False):
out_mask = gr.Image(type="filepath", label="Mask")
btn_predict.click(predict, inputs=inp, outputs=[out, out_mask])
gr.HTML(article)
app.launch(share=False, debug=True, show_error=True, mcp_server=True, pwa=True)
app.queue()
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