File size: 1,419 Bytes
eccc6dd 396d5f5 eccc6dd |
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 |
import pandas as pd
import numpy as np
import streamlit as st
import easyocr
import PIL
from PIL import Image, ImageDraw
from matplotlib import pyplot as plt
#Main Title
st.title("Get text from Image with EASYOCR")
#Subtitle
st.markdown('## Easy OCR with streamlit')
#Upload image file
file = st.file_uploader(label = "Upload your image", type=['png', 'jpg', 'jpeg'])
image = Image.open(file)
st.image(image) #display
reader = easyocr.Reader(['en'])
result = reader.readtext(np.array(image))
textdic_easyocr = {}
for idx in range(len(result)):
pred_coor = result[idx][0]
pred_text = result[idx][1]
pred_confidence = result[idx][2]
textdic_easyocr[pred_text] = {}
textdic_easyocr[pred_text]['pred_confidence'] = pred_confidence
# create a dataframe which shows the predicted text and prediction confidence
df = pd.DataFrame.from_dict(textdic_easyocr).T
st.table(df)
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)
|