dis-background-removal

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soiz1 commited on Apr 22

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8df85b7

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1 Parent(s): 53adfc8

Update app.py

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app.py +181 -152

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@@ -1,153 +1,182 @@
-import cv2
-import gradio as gr
-import os
-from PIL import Image
-import numpy as np
-import torch
-from torch.autograd import Variable
-from torchvision import transforms
-import torch.nn.functional as F
-import gdown
-import matplotlib.pyplot as plt
-import warnings
-warnings.filterwarnings("ignore")
-os.system("git clone https://github.com/xuebinqin/DIS")
-os.system("mv DIS/IS-Net/* .")
-# project imports
-from data_loader_cache import normalize, im_reader, im_preprocess
-from models import *
-#Helpers
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-# Download official weights
-if not os.path.exists("saved_models"):
-    os.mkdir("saved_models")
-    os.system("mv isnet.pth saved_models/")
-class GOSNormalize(object):
-    '''
-    Normalize the Image using torch.transforms
-    '''
-    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
-        self.mean = mean
-        self.std = std
-    def __call__(self,image):
-        image = normalize(image,self.mean,self.std)
-        return image
-transform =  transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
-def load_image(im_path, hypar):
-    im = im_reader(im_path)
-    im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im,255.0)
-    shape = torch.from_numpy(np.array(im_shp))
-    return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
-def build_model(hypar,device):
-    net = hypar["model"]#GOSNETINC(3,1)
-    # convert to half precision
-    if(hypar["model_digit"]=="half"):
-        net.half()
-        for layer in net.modules():
-            if isinstance(layer, nn.BatchNorm2d):
-                layer.float()
-    net.to(device)
-    if(hypar["restore_model"]!=""):
-        net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
-        net.to(device)
-    net.eval()
-    return net
-def predict(net,  inputs_val, shapes_val, hypar, device):
-    '''
-    Given an Image, predict the mask
-    '''
-    net.eval()
-    if(hypar["model_digit"]=="full"):
-        inputs_val = inputs_val.type(torch.FloatTensor)
-    else:
-        inputs_val = inputs_val.type(torch.HalfTensor)
-    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
-    ds_val = net(inputs_val_v)[0] # list of 6 results
-    pred_val = 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 orignal image size
-    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))
-    ma = torch.max(pred_val)
-    mi = torch.min(pred_val)
-    pred_val = (pred_val-mi)/(ma-mi) # max = 1
-    if device == 'cuda': torch.cuda.empty_cache()
-    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
-# Set Parameters
-hypar = {} # paramters for inferencing
-hypar["model_path"] ="./saved_models" ## load trained weights from this path
-hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
-hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
-##  choose floating point accuracy --
-hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
-hypar["seed"] = 0
-hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
-## data augmentation parameters ---
-hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-hypar["model"] = ISNetDIS()
- # Build Model
-net = build_model(hypar, device)
-def inference(image):
-  image_path = image
-  image_tensor, orig_size = load_image(image_path, hypar)
-  mask = predict(net, image_tensor, orig_size, hypar, device)
-  pil_mask = Image.fromarray(mask).convert('L')
-  im_rgb = Image.open(image).convert("RGB")
-  im_rgba = im_rgb.copy()
-  im_rgba.putalpha(pil_mask)
-  return [im_rgba, pil_mask]
-title = "Highly Accurate Dichotomous Image Segmentation"
-description = "This is an unofficial demo for DIS, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.<br>GitHub: https://github.com/xuebinqin/DIS<br>Telegram bot: https://t.me/restoration_photo_bot<br>[![](https://img.shields.io/twitter/follow/DoEvent?label=@DoEvent&style=social)](https://twitter.com/DoEvent)"
-article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=max_skobeev_dis_cmp_public' alt='visitor badge'></center></div>"
-interface = gr.Interface(
-    fn=inference,
-    inputs=gr.Image(type='filepath'),
-    outputs=[gr.Image(type='filepath', format="png"), gr.Image(type='filepath', format="png")],
-    examples=[['robot.png'], ['ship.png']],
-    title=title,
-    description=description,
-    article=article,
-    flagging_mode="never",
-    cache_mode="lazy",
-    ).queue(api_open=True).launch(show_error=True, show_api=True)

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Highly Accurate Dichotomous Image Segmentation</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            max-width: 800px;
+            margin: 0 auto;
+            padding: 20px;
+            line-height: 1.6;
+        }
+        .container {
+            display: flex;
+            flex-direction: column;
+            gap: 20px;
+        }
+        .upload-section {
+            border: 2px dashed #ccc;
+            padding: 20px;
+            text-align: center;
+            border-radius: 5px;
+        }
+        .results {
+            display: flex;
+            gap: 20px;
+            flex-wrap: wrap;
+        }
+        .result-box {
+            flex: 1;
+            min-width: 300px;
+        }
+        img {
+            max-width: 100%;
+            height: auto;
+            border: 1px solid #ddd;
+            border-radius: 4px;
+        }
+        button {
+            background-color: #4CAF50;
+            color: white;
+            padding: 10px 15px;
+            border: none;
+            border-radius: 4px;
+            cursor: pointer;
+            font-size: 16px;
+        }
+        button:hover {
+            background-color: #45a049;
+        }
+        .code-block {
+            background-color: #f5f5f5;
+            padding: 15px;
+            border-radius: 5px;
+            overflow-x: auto;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Highly Accurate Dichotomous Image Segmentation</h1>
+        <p>This is a demo for DIS, a model that can remove the background from a given image.</p>
+        <div class="upload-section">
+            <h2>Upload Image</h2>
+            <input type="file" id="imageInput" accept="image/*">
+            <button onclick="processImage()">Remove Background</button>
+        </div>
+        <div class="results">
+            <div class="result-box">
+                <h3>Original Image</h3>
+                <img id="originalImage" src="" alt="Original image will appear here" style="display: none;">
+            </div>
+            <div class="result-box">
+                <h3>Result (RGBA)</h3>
+                <img id="resultImage" src="" alt="Result will appear here" style="display: none;">
+            </div>
+            <div class="result-box">
+                <h3>Mask</h3>
+                <img id="maskImage" src="" alt="Mask will appear here" style="display: none;">
+            </div>
+        </div>
+        <div>
+            <h2>API Usage Example</h2>
+            <p>You can also use the API directly with this JavaScript code:</p>
+            <div class="code-block">
+                <pre><code>
+async function removeBackground(imageFile) {
+    const formData = new FormData();
+    formData.append('image', imageFile);
+    try {
+        const response = await fetch('/api/remove_bg', {
+            method: 'POST',
+            body: formData
+        });
+        if (!response.ok) {
+            throw new Error(`HTTP error! status: ${response.status}`);
+        }
+        const data = await response.json();
+        console.log('Result:', data);
+        return data;
+    } catch (error) {
+        console.error('Error:', error);
+        throw error;
+    }
+}
+// Usage example:
+// const fileInput = document.querySelector('input[type="file"]');
+// removeBackground(fileInput.files[0])
+//     .then(data => {
+//         // Handle response data
+//         document.getElementById('resultImage').src = data.rgba_url;
+//         document.getElementById('maskImage').src = data.mask_url;
+//     });
+                </code></pre>
+            </div>
+        </div>
+    </div>
+    <script>
+        function processImage() {
+            const fileInput = document.getElementById('imageInput');
+            if (!fileInput.files || fileInput.files.length === 0) {
+                alert('Please select an image first');
+                return;
+            }
+            const file = fileInput.files[0];
+            const reader = new FileReader();
+            reader.onload = function(e) {
+                document.getElementById('originalImage').src = e.target.result;
+                document.getElementById('originalImage').style.display = 'block';
+            };
+            reader.readAsDataURL(file);
+            removeBackground(file)
+                .then(data => {
+                    document.getElementById('resultImage').src = data.rgba_url;
+                    document.getElementById('resultImage').style.display = 'block';
+                    document.getElementById('maskImage').src = data.mask_url;
+                    document.getElementById('maskImage').style.display = 'block';
+                })
+                .catch(error => {
+                    console.error('Error:', error);
+                    alert('An error occurred while processing the image');
+                });
+        }
+        async function removeBackground(imageFile) {
+            const formData = new FormData();
+            formData.append('image', imageFile);
+            try {
+                const response = await fetch('/api/remove_bg', {
+                    method: 'POST',
+                    body: formData
+                });
+                if (!response.ok) {
+                    throw new Error(`HTTP error! status: ${response.status}`);
+                }
+                const data = await response.json();
+                console.log('Result:', data);
+                return data;
+            } catch (error) {
+                console.error('Error:', error);
+                throw error;
+            }
+        }
+    </script>
+</body>
+</html>