new test

app.py

@@ -3,98 +3,95 @@ import torch
 import torch.nn.functional as F
 from torchvision.transforms.functional import normalize
 import gradio as gr
-from gradio_imageslider import ImageSlider
 from briarmbg import BriaRMBG
 import PIL
 from PIL import Image
 from typing import Tuple
-
+import requests
+from io import BytesIO
 
 net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 net.to(device)
 
-    
 def resize_image(image):
     image = image.convert('RGB')
     model_input_size = (1024, 1024)
     image = image.resize(model_input_size, Image.BILINEAR)
     return image
 
+def get_url_image(url):
+    headers = {'User-Agent': 'gradio-app'}
+    response = requests.get(url, headers=headers)
+    print(f"Response status code: {response.status_code}")
+    response.raise_for_status()  # Raise an error for bad status codes
+    return BytesIO(response.content)
 
-def process(image):
-
-    # prepare input
-    orig_image = Image.fromarray(image)
-    w,h = orig_im_size = orig_image.size
-    image = resize_image(orig_image)
-    im_np = np.array(image)
-    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
-    im_tensor = torch.unsqueeze(im_tensor,0)
-    im_tensor = torch.divide(im_tensor,255.0)
-    im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
-    if torch.cuda.is_available():
-        im_tensor=im_tensor.cuda()
-
-    #inference
-    result=net(im_tensor)
-    # post process
-    result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
-    ma = torch.max(result)
-    mi = torch.min(result)
-    result = (result-mi)/(ma-mi)    
-    # image to pil
-    im_array = (result*255).cpu().data.numpy().astype(np.uint8)
-    pil_im = Image.fromarray(np.squeeze(im_array))
-    # paste the mask on the original image
-    new_im = Image.new("RGBA", pil_im.size, (0,0,0,0))
-    new_im.paste(orig_image, mask=pil_im)
-    # new_orig_image = orig_image.convert('RGBA')
-
-    return new_im
-    # return [new_orig_image, new_im]
+def load_image(image_source):
+    try:
+        if isinstance(image_source, str):  # Check if input is a URL
+            print(f"Loading image from URL: {image_source}")
+            image = Image.open(get_url_image(image_source))
+        else:
+            print("Loading image from file upload")
+            image = Image.fromarray(image_source)
+        print("Image loaded successfully")
+        return image
+    except Exception as e:
+        print(f"Error loading image: {e}")
+        raise
 
+def process(image_source):
+    try:
+        print("Processing image")
+        # Load and prepare input
+        orig_image = load_image(image_source)
+        w, h = orig_im_size = orig_image.size
+        image = resize_image(orig_image)
+        im_np = np.array(image)
+        im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1)
+        im_tensor = torch.unsqueeze(im_tensor, 0)
+        im_tensor = torch.divide(im_tensor, 255.0)
+        im_tensor = normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
+        if torch.cuda.is_available():
+            im_tensor = im_tensor.cuda()
 
-# block = gr.Blocks().queue()
+        # Inference
+        result = net(im_tensor)
+        # Post-process
+        result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode='bilinear'), 0)
+        ma = torch.max(result)
+        mi = torch.min(result)
+        result = (result - mi) / (ma - mi)
+        # Image to PIL
+        im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
+        pil_im = Image.fromarray(np.squeeze(im_array))
+        # Paste the mask on the original image
+        new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
+        new_im.paste(orig_image, mask=pil_im)
+        print("Image processed successfully")
+        return new_im
+    except Exception as e:
+        print(f"Error during processing: {e}")
+        return f"Error: {e}"
 
-# with block:
-#     gr.Markdown("## BRIA RMBG 1.4")
-#     gr.HTML('''
-#       <p style="margin-bottom: 10px; font-size: 94%">
-#         This is a demo for BRIA RMBG 1.4 that using
-#         <a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone. 
-#       </p>
-#     ''')
-#     with gr.Row():
-#         with gr.Column():
-#             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
-#             # input_image = gr.Image(sources=None, type="numpy") # None for upload, ctrl+v and webcam
-#             run_button = gr.Button(value="Run")
-            
-#         with gr.Column():
-#             result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[1], height='auto')
-#     ips = [input_image]
-#     run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
-
-# block.launch(debug = True)
-
-# block = gr.Blocks().queue()
-
-gr.Markdown("## BRIA RMBG 1.4")
-gr.HTML('''
-  <p style="margin-bottom: 10px; font-size: 94%">
-    This is a demo for BRIA RMBG 1.4 that using
-    <a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone. 
-  </p>
-''')
 title = "Background Removal"
 description = r"""Background removal model developed by <a href='https://BRIA.AI' target='_blank'><b>BRIA.AI</b></a>, trained on a carefully selected dataset and is available as an open-source model for non-commercial use.<br> 
 For test upload your image and wait. Read more at model card <a href='https://huggingface.co/briaai/RMBG-1.4' target='_blank'><b>briaai/RMBG-1.4</b></a>.<br>
 """
 examples = [['./input.jpg'],]
-# output = ImageSlider(position=0.5,label='Image without background', type="pil", show_download_button=True)
-# demo = gr.Interface(fn=process,inputs="image", outputs=output, examples=examples, title=title, description=description)
-demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description)
+
+demo = gr.Interface(
+    fn=process,
+    inputs=[
+        gr.Image(type="numpy", label="Upload Image"),
+        gr.Textbox(label="Image URL")
+    ],
+    outputs="image",
+    examples=examples,
+    title=title,
+    description=description
+)
 
 if __name__ == "__main__":
     demo.launch(share=False)