Spaces:
Sleeping
Sleeping
File size: 3,528 Bytes
44c05f0 1d07711 2ef412f a044f10 2ef412f a044f10 cae2f22 2ef412f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
import os
import cv2
import streamlit as st
import tensorflow as tf
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() |