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| import os | |
| import streamlit as st | |
| import matplotlib.pyplot as plt | |
| from doctr.io import DocumentFile | |
| from doctr.models import ocr_predictor | |
| from doctr.utils.visualization import visualize_page | |
| DET_ARCHS = ['db_resnet50', 'db_mobilenet_v3_large', 'linknet16'] | |
| RECO_ARCHS = ["crnn_vgg16_bn", "master", "sar_resnet31"] | |
| def main(): | |
| # Wide mode | |
| st.set_page_config(layout="wide") | |
| # Designing the interface | |
| st.title("DocTR: Document Text Recognition") | |
| # For newline | |
| st.write('\n') | |
| # | |
| st.write('Find more info at: https://github.com/mindee/doctr') | |
| # For newline | |
| st.write('\n') | |
| # Instructions | |
| st.markdown("*Hint: click on the top-right corner of an image to enlarge it!*") | |
| # Set the columns | |
| cols = st.beta_columns((1, 1, 1, 1)) | |
| cols[0].subheader("Input page") | |
| cols[1].subheader("Segmentation heatmap") | |
| cols[2].subheader("OCR output") | |
| cols[3].subheader("Page reconstitution") | |
| # Sidebar | |
| # File selection | |
| st.sidebar.title("Document selection") | |
| # Disabling warning | |
| st.set_option('deprecation.showfileUploaderEncoding', False) | |
| # Choose your own image | |
| uploaded_file = st.sidebar.file_uploader("Upload files", type=['pdf', 'png', 'jpeg', 'jpg']) | |
| if uploaded_file is not None: | |
| if uploaded_file.name.endswith('.pdf'): | |
| doc = DocumentFile.from_pdf(uploaded_file.read()).as_images() | |
| else: | |
| doc = DocumentFile.from_images(uploaded_file.read()) | |
| page_idx = st.sidebar.selectbox("Page selection", [idx + 1 for idx in range(len(doc))]) - 1 | |
| cols[0].image(doc[page_idx]) | |
| # Model selection | |
| st.sidebar.title("Model selection") | |
| det_arch = st.sidebar.selectbox("Text detection model", DET_ARCHS) | |
| reco_arch = st.sidebar.selectbox("Text recognition model", RECO_ARCHS) | |
| # For newline | |
| st.sidebar.write('\n') | |
| if st.sidebar.button("Analyze page"): | |
| if uploaded_file is None: | |
| st.sidebar.write("Please upload a document") | |
| else: | |
| with st.spinner('Loading model...'): | |
| predictor = ocr_predictor(det_arch, reco_arch, pretrained=True) | |
| with st.spinner('Analyzing...'): | |
| # Forward the image to the model | |
| processed_batches = predictor.det_predictor.pre_processor([doc[page_idx]]) | |
| out = predictor.det_predictor.model(processed_batches[0], return_model_output=True) | |
| seg_map = out["out_map"] | |
| seg_map = tf.squeeze(seg_map[0, ...], axis=[2]) | |
| seg_map = cv2.resize(seg_map.numpy(), (doc[page_idx].shape[1], doc[page_idx].shape[0]), | |
| interpolation=cv2.INTER_LINEAR) | |
| # Plot the raw heatmap | |
| fig, ax = plt.subplots() | |
| ax.imshow(seg_map) | |
| ax.axis('off') | |
| cols[1].pyplot(fig) | |
| # Plot OCR output | |
| out = predictor([doc[page_idx]]) | |
| fig = visualize_page(out.pages[0].export(), doc[page_idx], interactive=False) | |
| cols[2].pyplot(fig) | |
| # Page reconsitution under input page | |
| page_export = out.pages[0].export() | |
| img = out.pages[0].synthesize() | |
| cols[3].image(img, clamp=True) | |
| # Display JSON | |
| st.markdown("\nHere are your analysis results in JSON format:") | |
| st.json(page_export) | |
| if __name__ == '__main__': | |
| main() |