Create app.py

app.py

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+import numpy as np
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+from models import BriaRMBG
+import gradio as gr
+import git  # pip install gitpython
+
+net=BriaRMBG()
+model_path = "./model.pth"
+git.Git(".").clone("https://huggingface.co/spaces/briaai/BRIA-RMBG-1.4")
+
+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 image_size_by_min_resolution(
+    image: Image.Image,
+    resolution: Tuple,
+    resample=None,
+):
+    w, h = image.size  
+
+    image_min = min(w, h)
+    resolution_min = min(resolution)
+
+    scale_factor = image_min / resolution_min
+
+    resize_to: Tuple[int, int] = (
+        int(w // scale_factor),
+        int(h // scale_factor),
+    )
+    return resize_to
+    
+
+def resize_image(image):
+    image = image.convert('RGB')
+    new_image_size = image_size_by_min_resolution(image=image,resolution=(1024, 1024))
+    image = image.resize(new_image_size, Image.BILINEAR)
+    return image
+
+
+def process(input_image):
+
+    # prepare input
+    orig_image = Image.open(im_path)
+    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)    
+
+    # save result
+    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))
+    new_im.paste(orig_image, mask=pil_im)
+
+    return 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=[2], height='auto')
+    ips = [input_image]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+
+block.launch(debug = True)