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from typing import Tuple |
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import numpy as np |
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import PIL |
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import streamlit as st |
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import torch |
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import torch.nn.functional as F |
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from briarmbg import BriaRMBG |
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from PIL import Image |
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from torchvision.transforms.functional import normalize |
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def resize_image(image): |
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image = image.convert("RGB") |
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model_input_size = (1024, 1024) |
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image = image.resize(model_input_size, Image.BILINEAR) |
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return image |
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def process(image): |
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orig_image = Image.open(image) |
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w, h = 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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im_tensor = torch.divide(im_tensor, 255.0) |
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im_tensor = normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0]) |
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if torch.cuda.is_available(): |
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im_tensor = im_tensor.cuda() |
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net = BriaRMBG.from_pretrained("briaai/RMBG-1.4") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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net.to(device) |
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result = net(im_tensor) |
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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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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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new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0)) |
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new_im.paste(orig_image, mask=pil_im) |
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return new_im |
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def main(): |
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st.set_page_config(page_title="bg-remove", page_icon="⛺️", layout="wide") |
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st.markdown( |
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"""<h1 align="center";>Background Remover</h1>""", |
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unsafe_allow_html=True, |
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) |
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with st.sidebar: |
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img_file = st.file_uploader( |
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label="Upload image", |
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type=["jpg", "png", "jpeg"], |
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key="image_file_uploader", |
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) |
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cols = st.columns(2) |
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with cols[0]: |
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with st.container(border=True, height=600): |
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if img_file: |
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st.image(img_file) |
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else: |
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st.info("Drag and drop the sample image into upload sidebar", icon="💡") |
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sub_btn = st.button("Remove bg", key="sub_btn") |
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with cols[1]: |
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with st.container(border=True, height=600): |
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if sub_btn and img_file: |
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processed_img = process(img_file) |
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st.image(processed_img) |
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else: |
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st.write("Waiting for image...") |
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with st.container(border=True, height=400): |
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st.write("Sample image") |
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st.image("input.jpg") |
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if __name__ == "__main__": |
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main() |
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