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import streamlit as st
import openvino as ov
from pathlib import Path
import numpy as np
from PIL import Image
from src.config import (
DICT_DIR,
IMAGE_TYPES,
IMAGE_EXAMPLE,
MODEL_DIR,
DEVICE,
)
from src.image_processing import recognize
# hide deprication warnings which directly don't affect the working of the application
import warnings
warnings.filterwarnings("ignore")
# Setting custom Page Title and Icon with changed layout and sidebar state
st.set_page_config(
page_title="日本語手書き認識",
page_icon="",
layout="centered",
initial_sidebar_state="auto",
)
# hide the part of the code, as this is just for adding some custom CSS styling but not a part of the main idea
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True) # hide the CSS code from the screen as they are embedded in markdown text. Also, allow streamlit to unsafely process as HTML
@st.cache_resource
def init():
# Load models
core = ov.Core()
model = core.read_model(model=Path(MODEL_DIR))
print("[INFO] Loaded recognition model")
# Select device (CPU or GPU)
compiled_model = core.compile_model(model=model, device_name=DEVICE)
# Fetch Information About Input and Output Layers
recognition_input_layer = compiled_model.input(0)
recognition_output_layer = compiled_model.output(0)
print("[INFO] Fetched recognition model")
# In JA model, there should be blank symbol added at index 0 of each charlist.
blank_char = "~"
with Path(DICT_DIR).open(mode="r", encoding="utf-8") as charlist:
letters = blank_char + "".join(line.strip() for line in charlist)
print("[INFO] Loaded dictionary")
return [compiled_model, recognition_input_layer, recognition_output_layer, letters]
def display_text(bounds):
text = []
for x in bounds:
t = x[1]
text.append(t)
text = ' '.join(text)
return text
HWRmodel = init()
st.set_option('deprecation.showfileUploaderEncoding',False)
st.title('日本語手書き認識')
st.subheader('Tokyo Teachies (DEMO)')
st.subheader('注意：画像には1行のテキストしか含まれていません。')
#st.text('Select source Language from the Sidebar.')
image_file = st.file_uploader("画像をアップロード…",type=IMAGE_TYPES)
if image_file is not None:
st.subheader('アップロードした画像')
st.image(image_file,width=450)
else:
st.subheader('例：')
image_file = IMAGE_EXAMPLE
st.image(image_file, use_column_width=450)
if st.button("Recognize"):
if image_file is not None:
img = Image.open(image_file).convert('L')
img = np.array(img)
with st.spinner('テキストの抽出...'):
recognized_text = recognize(img, HWRmodel[0], HWRmodel[1], HWRmodel[2], HWRmodel[3])
#st.subheader('Extracted text is ...')
#text = display_text(recognized_text)
st.write("".join(recognized_text))
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