Update app.py

app.py

@@ -1,153 +1,107 @@
-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)
+import cv2
+import gradio as gr
+import os
+from huggingface_hub import hf_hub_download  # Added for Hugging Face download
+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
+
+# Project imports (assumes data_loader_cache.py and models.py are uploaded)
+from data_loader_cache import normalize, im_reader, im_preprocess
+from models import *
+
+# Helpers
+device = 'cpu'  # Free Hugging Face Space uses CPU
+
+# Create directory for model weights
+if not os.path.exists("saved_models"):
+    os.makedirs("saved_models")
+
+# Automatically download isnet.pth from ECCV2022/dis-background-removal if not present
+isnet_path = "saved_models/isnet.pth"
+if not os.path.exists(isnet_path):
+    print("Downloading isnet.pth from ECCV2022/dis-background-removal...")
+    hf_hub_download(
+        repo_id="ECCV2022/dis-background-removal",
+        filename="isnet.pth",
+        local_dir="saved_models",
+        local_dir_use_symlinks=False
+    )
+
+class GOSNormalize(object):
+    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)
+
+def build_model(hypar, device):
+    net = hypar["model"]
+    net.to(device)
+    if hypar["restore_model"]:
+        net.load_state_dict(torch.load(os.path.join(hypar["model_path"], hypar["restore_model"]), map_location=device))
+    net.eval()
+    return net
+
+def predict(net, inputs_val, shapes_val, hypar, device):
+    net.eval()
+    inputs_val = inputs_val.type(torch.FloatTensor).to(device)
+    with torch.no_grad():  # Reduce memory usage
+        inputs_val_v = Variable(inputs_val)
+        ds_val = net(inputs_val_v)[0]
+        pred_val = ds_val[0][0, :, :, :]  # B x 1 x H x W
+        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)  # Normalize to [0, 1]
+        return (pred_val.cpu().numpy() * 255).astype(np.uint8)
+
+# Set Parameters
+hypar = {
+    "model_path": "saved_models",
+    "restore_model": "isnet.pth",
+    "cache_size": [512, 512],  # Optimized for CPU
+    "input_size": [512, 512],
+    "crop_size": [512, 512],
+    "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 = "Dichotomous Image Segmentation"
+description = "Upload an image to remove its background."
+
+interface = gr.Interface(
+    fn=inference,
+    inputs=gr.Image(type='filepath'),
+    outputs=[gr.Image(type='filepath', format="png"), gr.Image(type='filepath', format="png")],
+    title=title,
+    description=description,
+    flagging_mode="never",
+    cache_mode="lazy"
+).launch()