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  1. .DS_Store +0 -0
  2. README.md +5 -4
  3. app.py +77 -0
  4. examples/01.jpg +0 -0
  5. examples/02.jpg +0 -0
  6. examples/03.jpg +0 -0
  7. requirements.txt +5 -0
.DS_Store ADDED
Binary file (6.15 kB). View file
 
README.md CHANGED
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  ---
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- title: Removebg Demo
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- emoji: ⚑
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- colorFrom: green
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- colorTo: green
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  sdk: gradio
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  sdk_version: 3.35.2
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  app_file: app.py
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  pinned: false
 
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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  ---
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+ title: ARKA Remove BG
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+ emoji: 🌍
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+ colorFrom: purple
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+ colorTo: indigo
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  sdk: gradio
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  sdk_version: 3.35.2
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  app_file: app.py
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  pinned: false
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+ license: mit
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ import PIL.Image
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+ import gradio as gr
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+ import huggingface_hub
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+ import onnxruntime as rt
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+ import numpy as np
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+ import cv2
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+ from PIL import ImageOps
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+
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+ providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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+ model_path = huggingface_hub.hf_hub_download(
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+ "skytnt/anime-seg", "isnetis.onnx")
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+ rmbg_model = rt.InferenceSession(model_path, providers=providers)
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+
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+
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+ def custom_background(background, foreground):
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+ foreground = ImageOps.contain(foreground, background.size)
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+ x = (background.size[0] - foreground.size[0]) // 2
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+ y = (background.size[1] - foreground.size[1]) // 2
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+ background.paste(foreground, (x, y), foreground)
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+ return background
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+
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+
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+ def get_mask(img, s=1024):
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+ img = (img / 255).astype(np.float32)
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+ h, w = h0, w0 = img.shape[:-1]
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+ h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
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+ ph, pw = s - h, s - w
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+ img_input = np.zeros([s, s, 3], dtype=np.float32)
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+ img_input[ph // 2:ph // 2 + h, pw //
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+ 2:pw // 2 + w] = cv2.resize(img, (w, h))
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+ img_input = np.transpose(img_input, (2, 0, 1))
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+ img_input = img_input[np.newaxis, :]
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+ mask = rmbg_model.run(None, {'img': img_input})[0][0]
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+ mask = np.transpose(mask, (1, 2, 0))
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+ mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
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+ mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
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+ return mask
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+
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+
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+ def predict(image, new_background):
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+ mask = get_mask(image)
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+ image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
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+ mask = (mask * 255).astype(np.uint8)
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+ image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
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+ mask = mask.repeat(3, axis=2)
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+ if new_background is not None:
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+ foreground = PIL.Image.fromarray(image)
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+ return mask, custom_background(new_background, foreground)
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+ return mask, image
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+
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+
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+ footer = r"""
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+ <center>
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+ <b>
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+ DEMO FOR BG REMOVAL
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+ </b>
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+ </center>
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+ """
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+
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+ with gr.Blocks(title="Face Shine") as app:
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+ gr.HTML("<center><h1>ARKA Remove Background</h1></center>")
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+ with gr.Row():
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+ with gr.Column():
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+ input_img = gr.Image(type="numpy", label="Input image")
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+ new_img = gr.Image(type="pil", label="Custom background")
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+ run_btn = gr.Button(variant="primary")
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+ with gr.Column():
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+ with gr.Accordion(label="Image mask", open=False):
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+ output_mask = gr.Image(label="mask")
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+ output_img = gr.Image(type="pil", label="result")
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+
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+ run_btn.click(predict, [input_img, new_img], [output_mask, output_img])
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+
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+ with gr.Row():
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+ gr.HTML(footer)
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+
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+ app.launch(share=False, debug=True, enable_queue=True, show_error=True)
examples/01.jpg ADDED
examples/02.jpg ADDED
examples/03.jpg ADDED
requirements.txt ADDED
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+ onnx
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+ onnxruntime-gpu
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+ opencv-python
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+ numpy
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+ pillow~=9.5.0