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import os
import cv2
import numpy as np
from PIL import Image
from path import Path
import streamlit as st
from typing import Tuple
import easyocr # Import EasyOCR
from pathlib import Path
import sys
# Add the 'app' directory to the sys.path
# Assuming 'app' is in the current working directory
sys.path.append(str(Path(__file__).parent / 'app'))
from app.dataloader_iam import Batch
from app.model import Model, DecoderType
from app.preprocessor import Preprocessor
from streamlit_drawable_canvas import st_canvas
# Set page config at the very beginning (only executed once)
st.set_page_config(
page_title="HTR App",
page_icon=":pencil:",
layout="centered",
initial_sidebar_state="auto",
)
ms = st.session_state
if "themes" not in ms:
ms.themes = {"current_theme": "light",
"refreshed": True,
"light": {"theme.base": "dark",
"theme.backgroundColor": "black",
"theme.primaryColor": "#c98bdb",
"theme.secondaryBackgroundColor": "#5591f5",
"theme.textColor": "white",
"theme.textColor": "white",
"button_face": "🌜"},
"dark": {"theme.base": "light",
"theme.backgroundColor": "white",
"theme.primaryColor": "#5591f5",
"theme.secondaryBackgroundColor": "#82E1D7",
"theme.textColor": "#0a1464",
"button_face": "🌞"},
}
def ChangeTheme():
previous_theme = ms.themes["current_theme"]
tdict = ms.themes["light"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]
for vkey, vval in tdict.items():
if vkey.startswith("theme"): st._config.set_option(vkey, vval)
ms.themes["refreshed"] = False
if previous_theme == "dark": ms.themes["current_theme"] = "light"
elif previous_theme == "light": ms.themes["current_theme"] = "dark"
btn_face = ms.themes["light"]["button_face"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]["button_face"]
st.button(btn_face, on_click=ChangeTheme)
if ms.themes["refreshed"] == False:
ms.themes["refreshed"] = True
st.rerun()
def get_img_size(line_mode: bool = False) -> Tuple[int, int]:
"""
Auxiliary method that sets the height and width
Height is fixed while width is set according to the Model used.
"""
if line_mode:
return 256, get_img_height()
return 128, get_img_height()
def get_img_height() -> int:
"""
Auxiliary method that sets the height, which is fixed for the Neural Network.
"""
return 32
def infer(line_mode: bool, model: Model, fn_img: Path) -> None:
"""
Auxiliary method that does inference using the pretrained models:
Recognizes text in an image given its path.
"""
img = cv2.imread(fn_img, cv2.IMREAD_GRAYSCALE)
assert img is not None
preprocessor = Preprocessor(get_img_size(line_mode), dynamic_width=True, padding=16)
img = preprocessor.process_img(img)
batch = Batch([img], None, 1)
recognized, probability = model.infer_batch(batch, True)
return [recognized, probability]
def infer_super_model(image_path) -> None:
reader = easyocr.Reader(['en']) # Initialize EasyOCR reader
result = reader.readtext(image_path)
recognized_texts = [text[1] for text in result] # Extract recognized texts
probabilities = [text[2] for text in result] # Extract probabilities
return recognized_texts, probabilities
def main():
st.title('Extract text from Image Demo')
st.markdown("""
Streamlit Web Interface for Handwritten Text Recognition (HTR), Optical Character Recognition (OCR)
implemented with TensorFlow and trained on the IAM off-line HTR dataset.
The model takes images of single words or text lines (multiple words) as input and outputs the recognized text.
""", unsafe_allow_html=True)
st.markdown("""
Predictions can be made using one of two models:
- Single_Model (Trained on Single Word Images)
- Line_Model (Trained on Text Line Images)
- Super_Model ( Most Robust Option for English )
- Burmese (Link)
""", unsafe_allow_html=True)
st.subheader('Select a Model, Choose the Arguments and Draw in the box below or Upload an Image to obtain a prediction.')
#Selectors for the model and decoder
modelSelect = st.selectbox("Select a Model", ['Single_Model', 'Line_Model', 'Super_Model'])
if modelSelect != 'Super_Model':
decoderSelect = st.selectbox("Select a Decoder", ['Bestpath', 'Beamsearch', 'Wordbeamsearch'])
#Mappings (dictionaries) for the model and decoder. Asigns the directory or the DecoderType of the selected option.
modelMapping = {
"Single_Model": '../model/word-model',
"Line_Model": '../model/line-model'
}
decoderMapping = {
'Bestpath': DecoderType.BestPath,
'Beamsearch': DecoderType.BeamSearch,
'Wordbeamsearch': DecoderType.WordBeamSearch
}
#Slider for pencil width
strokeWidth = st.slider("Stroke Width: ", 1, 25, 6)
#Canvas/Text Box for user input. BackGround Color must be white (#FFFFFF) or else text will not be properly recognised.
inputDrawn = st_canvas(
fill_color="rgba(255, 165, 0, 0.3)",
stroke_width=strokeWidth,
update_streamlit=True,
background_image=None,
height = 200,
width = 400,
drawing_mode='freedraw',
key="canvas",
background_color = '#FFFFFF'
)
#Buffer for user input (images uploaded from the user's device)
inputBuffer = st.file_uploader("Upload an Image", type=["png"])
#Inference Button
inferBool = st.button("Recognize Text")
# After clicking the "Recognize Text" button, check if the model selected is Super_Model
if inferBool:
if modelSelect == 'Super_Model':
inputArray = None # Initialize inputArray to None
# Handling uploaded file
if inputBuffer is not None:
with Image.open(inputBuffer).convert('RGB') as img:
inputArray = np.array(img)
# Handling canvas data
elif inputDrawn.image_data is not None:
# Convert RGBA to RGB
inputArray = cv2.cvtColor(np.array(inputDrawn.image_data, dtype=np.uint8), cv2.COLOR_RGBA2RGB)
# Now check if inputArray has been set
if inputArray is not None:
# Initialize EasyOCR Reader
reader = easyocr.Reader(['en']) # Assuming English language; adjust as necessary
# Perform OCR
results = reader.readtext(inputArray)
# Display results
all_text = ''
for (bbox, text, prob) in results:
all_text += f'{text} (confidence: {prob:.2f})\n'
st.write("**Recognized Texts and their Confidence Scores:**")
st.text(all_text)
else:
st.write("No image data found. Please upload an image or draw on the canvas.")
else:
# Handle other model selections as before
if ((inputDrawn.image_data is not None or inputBuffer is not None) and inferBool == True):
#We turn the input into a numpy array
if inputDrawn.image_data is not None:
inputArray = np.array(inputDrawn.image_data)
if inputBuffer is not None:
inputBufferImage = Image.open(inputBuffer)
inputArray = np.array(inputBufferImage)
#We turn this array into a .png format and save it.
inputImage = Image.fromarray(inputArray.astype('uint8'), 'RGBA')
inputImage.save('userInput.png')
#We obtain the model directory and the decoder type from their mapping
modelDir = modelMapping[modelSelect]
decoderType = decoderMapping[decoderSelect]
#Finally, we call the model with this image as attribute and display the Best Candidate and its probability on the Interface
model = Model(list(open(modelDir + "/charList.txt").read()), modelDir, decoderType, must_restore=True)
inferedText = infer(modelDir == '../model/line-model', model, 'userInput.png')
st.write("**Best Candidate: **", inferedText[0][0])
st.write("**Probability: **", str(inferedText[1][0]*100) + "%")
if __name__ == "__main__":
main() |