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import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import json
from doctr.file_utils import is_tf_available
from doctr.io import DocumentFile
from doctr.utils.visualization import visualize_page
from doctr.models import ocr_predictor
import torch
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 extract_text_data(ocr_result, page_idx):
"""Extracts and formats text data from OCR results, with the correct page index."""
page_data = {"page_idx": page_idx, "text": ""}
for block in ocr_result.pages[0].blocks:
for line in block.lines:
for word in line.words:
page_data["text"] += word.value + " "
page_data["text"] += "\n "
return page_data
def main(det_archs, reco_archs):
"""Build a Streamlit layout"""
st.set_page_config(layout="wide")
st.title("docTR: Document Text Recognition")
st.write("\n")
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")
st.sidebar.title("Document selection")
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())
cols[0].image(doc)
st.sidebar.title("Model selection")
st.sidebar.markdown("*Backend*: " + ("TensorFlow" if is_tf_available() else "PyTorch"))
det_arch = st.sidebar.selectbox("Text detection model", det_archs)
reco_arch = st.sidebar.selectbox("Text recognition model", reco_archs)
assume_straight_pages = True
straighten_pages = False
bin_thresh = 0.3
box_thresh = 0.1
if st.sidebar.button("Analyze document"):
with st.spinner("Loading model..."):
predictor = load_predictor(
det_arch, reco_arch, assume_straight_pages, straighten_pages, bin_thresh, box_thresh, forward_device
)
with st.spinner("Analyzing..."):
all_pages_export = []
all_pages_text = [] # Store text data from all pages
for page_idx, page in enumerate(doc):
st.write(f"Processing page {page_idx + 1}/{len(doc)}...")
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)
fig, ax = plt.subplots()
ax.imshow(seg_map)
ax.axis("off")
cols[1].pyplot(fig)
out = predictor([page])
fig = visualize_page(out.pages[0].export(), out.pages[0].page, interactive=False, add_labels=False)
cols[2].pyplot(fig)
page_export = out.pages[0].export()
all_pages_export.append(page_export)
img = out.pages[0].synthesize()
cols[3].image(img, clamp=True)
page_text = extract_text_data(out, page_idx) # Extract text data for the current page
all_pages_text.append(page_text) # Add the current page's text data to the overall list
st.markdown("\nHere are your analysis results in JSON format for all pages:")
st.json(all_pages_text, expanded=False)
st.markdown("\nDownload here:")
st.download_button(label="Download JSON", data=json.dumps(all_pages_text), file_name="OCR.json", mime="application/json")
# client = pymongo.MongoClient("mongodb://localhost:27017/")
# db = client["OCRoutputs"]
# collection = db["jsonDatas"]
# collection.insert_one({"document": all_pages_text})
if __name__ == "__main__":
main(DET_ARCHS, RECO_ARCHS)
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