app.py

import cv2
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
from flask import Flask, request, jsonify, render_template, send_from_directory
import warnings
warnings.filterwarnings("ignore")

# Clone repository and setup (only run once)
if not os.path.exists("DIS"):
    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 *

# Setup device
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Download official weights if not exists
if not os.path.exists("saved_models"):
    os.mkdir("saved_models")
    if not os.path.exists("saved_models/isnet.pth"):
        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
hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
hypar["seed"] = 0
hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution
hypar["input_size"] = [1024, 1024] ## model input spatial size
hypar["crop_size"] = [1024, 1024] ## random crop size from the input
hypar["model"] = ISNetDIS()

# Build Model
net = build_model(hypar, device)

# Flask app
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'uploads'
app.config['RESULT_FOLDER'] = 'results'

os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
os.makedirs(app.config['RESULT_FOLDER'], exist_ok=True)

@app.route('/', methods=['GET'])
def index():
    return render_template('index.html')

@app.route('/api/remove_bg', methods=['POST'])
def remove_background():
    if 'image' not in request.files:
        return jsonify({'error': 'No image provided'}), 400
    
    file = request.files['image']
    if file.filename == '':
        return jsonify({'error': 'No image selected'}), 400
    
    # Save uploaded file
    upload_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
    file.save(upload_path)
    
    try:
        # Process image
        image_tensor, orig_size = load_image(upload_path, hypar) 
        mask = predict(net, image_tensor, orig_size, hypar, device)
        
        # Create results
        pil_mask = Image.fromarray(mask).convert('L')
        im_rgb = Image.open(upload_path).convert("RGB")
        im_rgba = im_rgb.copy()
        im_rgba.putalpha(pil_mask)
        
        # Save results
        result_rgba_path = os.path.join(app.config['RESULT_FOLDER'], f"rgba_{file.filename}")
        result_mask_path = os.path.join(app.config['RESULT_FOLDER'], f"mask_{file.filename}")
        
        im_rgba.save(result_rgba_path, format="PNG")
        pil_mask.save(result_mask_path, format="PNG")
        
        return jsonify({
            'rgba_image': f"/results/rgba_{file.filename}",
            'mask_image': f"/results/mask_{file.filename}"
        })
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/results/<filename>')
def serve_result(filename):
    return send_from_directory(app.config['RESULT_FOLDER'], filename)

@app.route('/uploads/<filename>')
def serve_upload(filename):
    return send_from_directory(app.config['UPLOAD_FOLDER'], filename)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)