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Update app.py
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app.py
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@@ -2,19 +2,12 @@ import numpy as np
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import torch
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import torch.nn.functional as F
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from torchvision.transforms.functional import normalize
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# from foo import hello
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import gradio as gr
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from gradio_imageslider import ImageSlider
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from briarmbg import BriaRMBG
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import PIL
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from PIL import Image
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from typing import Tuple
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# import git # pip install gitpython
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# hello()
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# git.Git(".").clone("https://huggingface.co/briaai/RMBG-1.4")
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# git.Git(".").clone("git@hf.co:briaai/RMBG-1.4")
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net=BriaRMBG()
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model_path = "./model.pth"
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@@ -54,13 +47,9 @@ def resize_image(image):
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def process(image):
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# prepare input
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print(type(image))
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print(image.shape)
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orig_image = Image.fromarray(image)
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# return [orig_image,orig_image]
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w,h = orig_im_size = orig_image.size
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image = resize_image(orig_image)
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print("process debug1")
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im_np = np.array(image)
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im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
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im_tensor = torch.unsqueeze(im_tensor,0)
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if torch.cuda.is_available():
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im_tensor=im_tensor.cuda()
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print("process debug2")
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#inference
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result=net(im_tensor)
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print("process debug3")
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# post process
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result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
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ma = torch.max(result)
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mi = torch.min(result)
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result = (result-mi)/(ma-mi)
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print("process debug4")
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# image to pil
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im_array = (result*255).cpu().data.numpy().astype(np.uint8)
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pil_im = Image.fromarray(np.squeeze(im_array))
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# block.launch(debug = True)
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examples = [['./input.jpg'],]
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output = ImageSlider(position=0.5,label='Image without background
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demo = gr.Interface(fn=process,inputs="
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if __name__ == "__main__":
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demo.launch(share=False)
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import torch
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import torch.nn.functional as F
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from torchvision.transforms.functional import normalize
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import gradio as gr
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from gradio_imageslider import ImageSlider
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from briarmbg import BriaRMBG
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import PIL
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from PIL import Image
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from typing import Tuple
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net=BriaRMBG()
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model_path = "./model.pth"
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def process(image):
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# prepare input
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orig_image = Image.fromarray(image)
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w,h = orig_im_size = orig_image.size
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image = resize_image(orig_image)
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im_np = np.array(image)
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im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
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im_tensor = torch.unsqueeze(im_tensor,0)
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if torch.cuda.is_available():
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im_tensor=im_tensor.cuda()
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#inference
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result=net(im_tensor)
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# post process
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result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
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ma = torch.max(result)
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mi = torch.min(result)
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result = (result-mi)/(ma-mi)
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# image to pil
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im_array = (result*255).cpu().data.numpy().astype(np.uint8)
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pil_im = Image.fromarray(np.squeeze(im_array))
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# block.launch(debug = True)
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# block = gr.Blocks().queue()
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gr.Markdown("## BRIA RMBG 1.4")
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gr.HTML('''
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<p style="margin-bottom: 10px; font-size: 94%">
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This is a demo for BRIA RMBG 1.4 that using
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<a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone.
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</p>
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''')
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title = "Background Removal"
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description = "Remove Image Background"
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examples = [['./input.jpg'],]
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output = ImageSlider(position=0.5,label='Image without background', type="pil", show_download_button=True)
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demo = gr.Interface(fn=process,inputs="Image", outputs=output, examples=examples, title=title, description=description)
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if __name__ == "__main__":
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demo.launch(share=False)
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