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| import streamlit as st | |
| from carvekit.api.interface import Interface | |
| from carvekit.ml.wrap.fba_matting import FBAMatting | |
| from carvekit.ml.wrap.tracer_b7 import TracerUniversalB7 | |
| from carvekit.pipelines.postprocessing import MattingMethod | |
| from carvekit.pipelines.preprocessing import PreprocessingStub | |
| from carvekit.trimap.generator import TrimapGenerator | |
| from PIL import Image | |
| # Create Streamlit app title | |
| st.title("Image Background Remover") | |
| # Create a file uploader | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"]) | |
| if uploaded_file is not None: | |
| # Load the image | |
| image = Image.open(uploaded_file) | |
| # Set up ML pipeline | |
| seg_net = TracerUniversalB7(device='cpu', batch_size=1) | |
| fba = FBAMatting(device='cpu', input_tensor_size=2048, batch_size=1) | |
| trimap = TrimapGenerator() | |
| preprocessing = PreprocessingStub() | |
| postprocessing = MattingMethod(matting_module=fba, trimap_generator=trimap, device='cpu') | |
| interface = Interface(pre_pipe=preprocessing, post_pipe=postprocessing, seg_pipe=seg_net) | |
| # Process the image | |
| processed_bg = interface([image])[0] | |
| # Display original and processed images | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.image(image, caption='Original Image', use_column_width=True) | |
| with col2: | |
| st.image(processed_bg, caption='Background Removed', use_column_width=True) | |