Upload app (15).py

app (15).py

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+import numpy as np
+import torch
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+from huggingface_hub import hf_hub_download
+import gradio as gr
+from gradio_imageslider import ImageSlider
+from briarmbg import BriaRMBG
+import PIL
+from PIL import Image
+from typing import Tuple
+
+net=BriaRMBG()
+# model_path = "./model1.pth"
+model_path = hf_hub_download("briaai/RMBG-1.4", 'model.pth')
+if torch.cuda.is_available():
+    net.load_state_dict(torch.load(model_path))
+    net=net.cuda()
+else:
+    net.load_state_dict(torch.load(model_path,map_location="cpu"))
+net.eval() 
+
+    
+def resize_image(image):
+    image = image.convert('RGB')
+    model_input_size = (1024, 1024)
+    image = image.resize(model_input_size, Image.BILINEAR)
+    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')
+
+    return new_im
+    # return [new_orig_image, new_im]
+
+
+# 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'],]
+# 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)