OriLib commited on
Commit
c530952
1 Parent(s): 70974c3

Update app.py

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Files changed (1) hide show
  1. app.py +12 -18
app.py CHANGED
@@ -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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-
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- # hello()
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-
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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"
@@ -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)
@@ -69,16 +58,13 @@ def process(image):
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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))
@@ -112,12 +98,20 @@ def process(image):
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  # block.launch(debug = True)
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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 slider-view', type="pil")
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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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  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)