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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
from dataloader_iam import Batch
from model import Model, DecoderType
from preprocessor import Preprocessor
from streamlit_drawable_canvas import st_canvas
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 main():
#Website properties
st.set_page_config(
page_title = "HTR App",
page_icon = ":pencil:",
layout = "centered",
initial_sidebar_state = "auto",
)
st.title('HTR Simple Application')
st.markdown("""
Streamlit Web Interface for Handwritten Text Recognition (HTR), 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:
- [Model 1](https://www.dropbox.com/s/mya8hw6jyzqm0a3/word-model.zip?dl=1) (Trained on Single Word Images)
- [Model 2](https://www.dropbox.com/s/7xwkcilho10rthn/line-model.zip?dl=1) (Trained on Text Line Images)
""", 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'])
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,
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"])
#Infer Button
inferBool = st.button("Recognize Word")
#We start infering once we have the user input and he presses the Infer button.
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()
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