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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")
# MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn" # IS-Net
MODEL_PATH_URL = "https://drive.google.com/uc?id=1nV57qKuy--d5u1yvkng9aXW1KS4sOpOi" # IS-Net General Use
gdown.download(MODEL_PATH_URL, "saved_models/isnet.pth", use_cookies=False)
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):
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)
# Apply Background
bg_color = 0xFF9AC400
w = im_rgba.size[0]
h = im_rgba.size[1]
im_colored = Image.new("RGBA", (w,h), bg_color)
im_colored.alpha_composite(im_rgba)
return [im_colored, im_rgba]
title = "Background Replacer :)"
description = "Based on 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://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', label="Image")
],
outputs=[
gr.Image(label="Full Background"),
gr.Image(label="Transparent Background")
],
examples=[['robot.png'], ['ship.png']],
title=title,
description=description,
article=article,
allow_flagging='never',
theme="default",
cache_examples=True,
).launch(enable_queue=True, debug=True)
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