app.py

import pandas as pd
import numpy as np
import streamlit as st
import easyocr
import PIL
from PIL import Image, ImageDraw

def rectangle(image, result):
    # https://www.blog.pythonlibrary.org/2021/02/23/drawing-shapes-on-images-with-python-and-pillow/
    """ draw rectangles on image based on predicted coordinates"""
    draw = ImageDraw.Draw(image)
    for res in result:
        top_left = tuple(res[0][0]) # top left coordinates as tuple
        bottom_right = tuple(res[0][2]) # bottom right coordinates as tuple
        draw.rectangle((top_left, bottom_right), outline="blue", width=2)
    #display image on streamlit
    st.image(image)


# main title
st.title("Recognize text and locations from flowchart")

# # subtitle
# st.markdown("## FlowchartOCR")

# upload image file
file = st.file_uploader(label = "Upload Image", type=['png', 'jpg', 'jpeg'])

#read the csv file and display the dataframe
if file is not None:
    image = Image.open(file) # read image with PIL library
    st.image(image) #display

    # it will only detect the English and Turkish part of the image as text
    reader = easyocr.Reader(['en'], gpu=False) 
    result = reader.readtext(np.array(image))  # turn image to numpy array

    
    # collect the results in the dictionary:
    textdic_easyocr = {}
    for idx in range(len(result)):
        pred_coor = result[idx][0]
        pred_text = result[idx][1]
        pred_confidence = result[idx][2]
        if(pred_confidence>0.6):
            textdic_easyocr[pred_text] = {}
            textdic_easyocr[pred_text]['location'] = pred_coor
            textdic_easyocr[pred_text]['pred_confidence'] = pred_confidence

    # create a data frame which shows the predicted text and prediction confidence
    df = pd.DataFrame.from_dict(textdic_easyocr).T
    st.table(df)

    # get boxes on the image 
    rectangle(image, result)

    st.spinner(text="In progress...")
    
else:
    st.write("Upload your image")