mirror of
https://huggingface.co/spaces/ECCV2022/dis-background-removal
synced 2024-11-26 09:53:17 +01:00
154 lines
5.1 KiB
Python
154 lines
5.1 KiB
Python
import cv2
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import gradio as gr
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import os
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from PIL import Image
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import numpy as np
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import torch
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from torch.autograd import Variable
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from torchvision import transforms
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import torch.nn.functional as F
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import gdown
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import matplotlib.pyplot as plt
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import warnings
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warnings.filterwarnings("ignore")
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os.system("git clone https://github.com/xuebinqin/DIS")
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os.system("mv DIS/IS-Net/* .")
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# project imports
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from data_loader_cache import normalize, im_reader, im_preprocess
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from models import *
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#Helpers
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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# Download official weights
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if not os.path.exists("saved_models"):
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os.mkdir("saved_models")
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os.system("mv isnet.pth saved_models/")
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class GOSNormalize(object):
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'''
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Normalize the Image using torch.transforms
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'''
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def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
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self.mean = mean
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self.std = std
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def __call__(self,image):
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image = normalize(image,self.mean,self.std)
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return image
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transform = transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
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def load_image(im_path, hypar):
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im = im_reader(im_path)
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im, im_shp = im_preprocess(im, hypar["cache_size"])
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im = torch.divide(im,255.0)
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shape = torch.from_numpy(np.array(im_shp))
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return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
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def build_model(hypar,device):
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net = hypar["model"]#GOSNETINC(3,1)
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# convert to half precision
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if(hypar["model_digit"]=="half"):
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net.half()
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for layer in net.modules():
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if isinstance(layer, nn.BatchNorm2d):
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layer.float()
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net.to(device)
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if(hypar["restore_model"]!=""):
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net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
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net.to(device)
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net.eval()
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return net
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def predict(net, inputs_val, shapes_val, hypar, device):
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'''
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Given an Image, predict the mask
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'''
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net.eval()
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if(hypar["model_digit"]=="full"):
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inputs_val = inputs_val.type(torch.FloatTensor)
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else:
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inputs_val = inputs_val.type(torch.HalfTensor)
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inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
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ds_val = net(inputs_val_v)[0] # list of 6 results
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pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W # we want the first one which is the most accurate prediction
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## recover the prediction spatial size to the orignal image size
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pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))
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ma = torch.max(pred_val)
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mi = torch.min(pred_val)
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pred_val = (pred_val-mi)/(ma-mi) # max = 1
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if device == 'cuda': torch.cuda.empty_cache()
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return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
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# Set Parameters
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hypar = {} # paramters for inferencing
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hypar["model_path"] ="./saved_models" ## load trained weights from this path
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hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
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hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
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## choose floating point accuracy --
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hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
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hypar["seed"] = 0
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hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
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## data augmentation parameters ---
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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
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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
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hypar["model"] = ISNetDIS()
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# Build Model
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net = build_model(hypar, device)
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def inference(image):
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image_path = image
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image_tensor, orig_size = load_image(image_path, hypar)
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mask = predict(net, image_tensor, orig_size, hypar, device)
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pil_mask = Image.fromarray(mask).convert('L')
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im_rgb = Image.open(image).convert("RGB")
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im_rgba = im_rgb.copy()
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im_rgba.putalpha(pil_mask)
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return [im_rgba, pil_mask]
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title = "Highly Accurate Dichotomous Image Segmentation"
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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://twitter.com/DoEvent)"
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article = "<div><center><img src='https://visitor-badge.glitch.me/badge?page_id=max_skobeev_dis_cmp_public' alt='visitor badge'></center></div>"
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interface = gr.Interface(
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fn=inference,
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inputs=gr.Image(type='filepath'),
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outputs=["image", "image"],
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examples=[['robot.png'], ['ship.png']],
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title=title,
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description=description,
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article=article,
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allow_flagging='never',
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cache_examples=False,
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).queue(concurrency_count=1, api_open=True).launch(show_api=True, show_error=True)
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