Duplicate from mcrlc/background-replacer

Co-authored-by: Michael Ehrlich <[email protected]>

.gitattributes

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README.md

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+---
+title: Background Replacer :)
+emoji: 🔥 🌠 🏰
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+sdk_version: 3.15.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: mcrlc/background-replacer
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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://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', 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)

requirements.txt

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+torch
+torchvision
+requests
+gdown
+matplotlib
+opencv-python
+Pillow==8.0.0
+scikit-image==0.15.0