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# Copyright (C) 2021-2024, Mindee.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
import cv2
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import torch
from doctr.io import DocumentFile
from doctr.utils.visualization import visualize_page
from backend.pytorch import DET_ARCHS, RECO_ARCHS, forward_image, load_predictor
forward_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def main(det_archs, reco_archs):
"""Build a streamlit layout"""
# Wide mode
st.set_page_config(layout="wide")
# Designing the interface
st.title("docTR: Document Text Recognition")
# 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.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")
# 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())
else:
doc = DocumentFile.from_images(uploaded_file.read())
page_idx = st.sidebar.selectbox("Page selection", [idx + 1 for idx in range(len(doc))]) - 1
page = doc[page_idx]
cols[0].image(page)
# 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")
# Only straight pages or possible rotation
st.sidebar.title("Parameters")
assume_straight_pages = st.sidebar.checkbox("Assume straight pages", value=True)
st.sidebar.write("\n")
# Straighten pages
straighten_pages = st.sidebar.checkbox("Straighten pages", value=False)
st.sidebar.write("\n")
# Binarization threshold
bin_thresh = st.sidebar.slider("Binarization threshold", min_value=0.1, max_value=0.9, value=0.3, step=0.1)
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 = load_predictor(
det_arch, reco_arch, assume_straight_pages, straighten_pages, bin_thresh, forward_device
)
with st.spinner("Analyzing..."):
# Forward the image to the model
seg_map = forward_image(predictor, page, forward_device)
seg_map = np.squeeze(seg_map)
seg_map = cv2.resize(seg_map, (page.shape[1], page.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([page])
fig = visualize_page(out.pages[0].export(), out.pages[0].page, interactive=False, add_labels=False)
cols[2].pyplot(fig)
# Page reconsitution under input page
page_export = out.pages[0].export()
if assume_straight_pages or (not assume_straight_pages and straighten_pages):
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, expanded=False)
if __name__ == "__main__":
main(DET_ARCHS, RECO_ARCHS)
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