Mattral's picture
Update app.py
26dede0 verified
raw
history blame
No virus
8.81 kB
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()