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import gradio as gr
import openvino as ov
from pathlib import Path
import numpy as np
from PIL import Image
import cv2
from src.config import (
DICT_DIR,
IMAGE_TYPES,
IMAGE_EXAMPLE,
MODEL_DIR,
DEVICE,
)
from src.image_processing import recognize
# 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")
def do_ocr(inp):
#img = Image.open(inp).convert('L')
#img = np.array(img)
print(f"input: {inp}")
print(type(inp))
#img = cv2.imread(inp, cv2.IMREAD_GRAYSCALE)
img = cv2.cvtColor(inp, cv2.COLOR_BGR2GRAY)
recognized_text = recognize(img,
compiled_model,
recognition_input_layer,
recognition_output_layer,
letters,
)
return "".join(recognized_text)
input = gr.Image()
output = gr.Textbox()
title = "日本語手書き認識"
description = "DEMO by TOKYO TECHIES (注意：画像には1行のテキストしか含まれていません。)"
examples=[['data/in_1.png'],['data/sample_1_1.png']]
gr.Interface(fn=do_ocr,
inputs=input,
outputs=output,
title=title,
description=description,
examples=examples).launch()
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