back to og

app.py

@@ -3,87 +3,98 @@ 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)
-    return BytesIO(response.content)
 
-def load_image(image_source):
-    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)
-    return image
+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')
 
-def process(image_source):
-    try:
-        # 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()
+    return new_im
+    # return [new_orig_image, new_im]
 
-        # 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)
 
-        return new_im
-    except Exception as e:
-        print(f"Error during processing: {e}")
-        return None
+# block = gr.Blocks().queue()
 
+# 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'],]
-
-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
-)
+# 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)
 
 if __name__ == "__main__":
     demo.launch(share=False)