app.py

# -*- encoding: utf-8 -*-
# @Author: SWHL
# @Contact: liekkaskono@163.com
import time
from pathlib import Path

import cv2
import numpy as np
import pandas as pd
import streamlit as st
from PIL import Image
from rapidocr_onnxruntime import RapidOCR
from streamlit_image_select import image_select

from utils import visualize

font_dict = {
    "ch": "chinese_cht.ttf",
    "japan": "japan.ttc",
    "korean": "korean.ttf",
    "en": "chinese_cht.ttf",
}


def init_sidebar():
    st.session_state["params"] = {}

    st.sidebar.markdown(
        "### [🛠️ Parameter Settings](https://github.com/RapidAI/RapidOCR/wiki/config_parameter)"
    )
    box_thresh = st.sidebar.slider(
        "box_thresh",
        min_value=0.0,
        max_value=1.0,
        value=0.5,
        step=0.1,
        help="检测到的框是文本的概率，值越大，框中是文本的概率就越大。存在漏检时，调低该值。取值范围：[0, 1.0]，默认值为0.5",
    )
    st.session_state["params"]["box_thresh"] = box_thresh

    unclip_ratio = st.sidebar.slider(
        "unclip_ratio",
        min_value=1.5,
        max_value=2.0,
        value=1.6,
        step=0.1,
        help="控制文本检测框的大小，值越大，检测框整体越大。在出现框截断文字的情况，调大该值。取值范围：[1.5, 2.0]，默认值为1.6",
    )
    st.session_state["params"]["unclip_ratio"] = unclip_ratio

    text_score = st.sidebar.slider(
        "text_score",
        min_value=0.0,
        max_value=1.0,
        value=0.5,
        step=0.1,
        help="文本识别结果是正确的置信度，值越大，显示出的识别结果更准确。存在漏检时，调低该值。取值范围：[0, 1.0]，默认值为0.5",
    )
    st.session_state["params"]["text_score"] = text_score

    img_file_buffer = st.sidebar.file_uploader(
        "Upload an image",
        accept_multiple_files=False,
        label_visibility="visible",
        type=["png", "jpg", "jpeg", "bmp"],
    )
    if img_file_buffer:
        image = Image.open(img_file_buffer)
        img = np.array(image)

    with st.sidebar.container():
        img_path = image_select(
            label="Examples（click to select）:",
            images=examples,
            key="equation_default",
            use_container_width=True,
        )
        img = cv2.imread(img_path)
    st.session_state["img"] = img


def inference(
    text_det=None,
    text_rec=None,
):
    img = st.session_state.get("img")
    box_thresh = st.session_state["params"].get("box_thresh")
    unclip_ratio = st.session_state["params"].get("unclip_ratio")
    text_score = st.session_state["params"].get("text_score")

    det_model_path = str(Path("models") / "text_det" / text_det)
    rec_model_path = str(Path("models") / "text_rec" / text_rec)
    if "v2" in rec_model_path:
        rec_image_shape = [3, 32, 320]
    else:
        rec_image_shape = [3, 48, 320]

    rapid_ocr = RapidOCR(
        det_model_path=det_model_path,
        rec_model_path=rec_model_path,
        rec_img_shape=rec_image_shape,
    )

    if "ch" in rec_model_path or "en" in rec_model_path:
        lan_name = "ch"
    elif "japan" in rec_model_path:
        lan_name = "japan"
    elif "korean" in rec_model_path:
        lan_name = "korean"
    else:
        lan_name = "ch"

    ocr_result, infer_elapse = rapid_ocr(
        img, box_thresh=box_thresh, unclip_ratio=unclip_ratio, text_score=text_score
    )
    if not ocr_result or not infer_elapse:
        return None, None, None, None

    det_cost, cls_cost, rec_cost = infer_elapse
    elapse = f"- `det cost`: {det_cost:.5f}\n - `cls cost`: {cls_cost:.5f}\n - `rec cost`: {rec_cost:.5f}"
    dt_boxes, rec_res, scores = list(zip(*ocr_result))
    font_path = Path("fonts") / font_dict.get(lan_name)
    vis_img = visualize(
        Image.fromarray(img), dt_boxes, rec_res, scores, font_path=str(font_path)
    )
    out_df = pd.DataFrame(
        [[rec, score] for rec, score in zip(rec_res, scores)],
        columns=("Rec", "Score"),
    )
    return vis_img, out_df, elapse, rec_res


def tips(txt: str, wait_time: int = 2, icon: str = "🎉"):
    st.toast(txt, icon=icon)
    time.sleep(wait_time)


if __name__ == "__main__":
    st.markdown(
        "<h1 style='text-align: center;'><a href='https://github.com/RapidAI/RapidOCR' style='text-decoration: none'>Rapid⚡OCR</a></h1>",
        unsafe_allow_html=True,
    )
    st.markdown(
        """
    <p align="left">
        <a href=""><img src="https://img.shields.io/badge/Python->=3.6,<3.12-aff.svg"></a>
        <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a>
        <a href="https://pepy.tech/project/rapidocr_onnxruntime"><img src="https://static.pepy.tech/personalized-badge/rapidocr_onnxruntime?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads%20Ort"></a>
        <a href="https://pypi.org/project/rapidocr-onnxruntime/"><img alt="PyPI" src="https://img.shields.io/pypi/v/rapidocr-onnxruntime"></a>
        <a href='https://rapidocr.readthedocs.io/en/latest/?badge=latest'>
            <img src='https://readthedocs.org/projects/rapidocr/badge/?version=latest' alt='Documentation Status' />
    </p>
    """,
        unsafe_allow_html=True,
    )

    examples = [
        "images/1.jpg",
        "images/ch_en_num.jpg",
        "images/air_ticket.jpg",
        "images/car_plate.jpeg",
        "images/train_ticket.jpeg",
        "images/japan_2.jpg",
        "images/korean_1.jpg",
    ]

    init_sidebar()

    menu_det, menu_rec = st.columns([1, 1])
    det_models = [
        "ch_PP-OCRv4_det_infer.onnx",
        "ch_PP-OCRv3_det_infer.onnx",
        "ch_PP-OCRv2_det_infer.onnx",
        "ch_ppocr_server_v2.0_det_infer.onnx",
    ]
    select_det = menu_det.selectbox("Det model:", det_models)

    rec_models = [
        "ch_PP-OCRv4_rec_infer.onnx",
        "ch_PP-OCRv3_rec_infer.onnx",
        "ch_PP-OCRv2_rec_infer.onnx",
        "ch_ppocr_server_v2.0_rec_infer.onnx",
        "en_PP-OCRv3_rec_infer.onnx",
        "en_number_mobile_v2.0_rec_infer.onnx",
        "korean_mobile_v2.0_rec_infer.onnx",
        "japan_rec_crnn_v2.onnx",
    ]
    select_rec = menu_rec.selectbox("Rec model:", rec_models)

    out_img, out_json, elapse, only_txts = inference(select_det, select_rec)
    if all(v is not None for v in [out_img, out_json, elapse]):
        st.markdown("#### Visualize:")
        st.image(out_img)

        st.markdown("### Rec Result:")
        st.markdown(elapse)
        st.dataframe(out_json, use_container_width=True)

        st.markdown("### Only Txts")
        st.code(only_txts)
    else:
        tips("识别结果为空", wait_time=5, icon="⚠️")