Delete app.py

app.py

@@ -1,292 +0,0 @@
-# -*- encoding: utf-8 -*-
-# @Author: SWHL
-# @Contact: [email protected]
-from enum import Enum
-from pathlib import Path
-from typing import List, Union
-
-import gradio as gr
-import numpy as np
-from rapidocr import RapidOCR
-
-
-class InferEngine(Enum):
-    ort = "ONNXRuntime"
-    vino = "OpenVino"
-    paddle = "PaddlePaddle"
-    torch = "PyTorch"
-
-
-def get_ocr_engine(infer_engine: str, lang_det: str, lang_rec: str) -> RapidOCR:
-    if infer_engine == InferEngine.vino.value:
-        return RapidOCR(
-            params={
-                "Global.with_openvino": True,
-                "Global.lang_det": lang_det,
-                "Global.lang_rec": lang_rec,
-            }
-        )
-
-    if infer_engine == InferEngine.paddle.value:
-        return RapidOCR(
-            params={
-                "Global.with_paddle": True,
-                "Global.lang_det": lang_det,
-                "Global.lang_rec": lang_rec,
-            }
-        )
-
-    if infer_engine == InferEngine.torch.value:
-        return RapidOCR(
-            params={
-                "Global.with_torch": True,
-                "Global.lang_det": lang_det,
-                "Global.lang_rec": lang_rec,
-            }
-        )
-
-    return RapidOCR(
-        params={
-            "Global.with_onnx": True,
-            "Global.lang_det": lang_det,
-            "Global.lang_rec": lang_rec,
-        }
-    )
-
-
-def get_ocr_result(
-    img: np.ndarray,
-    text_score,
-    box_thresh,
-    unclip_ratio,
-    lang_det,
-    lang_rec,
-    infer_engine,
-    is_word: str,
-):
-    return_word_box = True if is_word == "Yes" else False
-
-    ocr_engine = get_ocr_engine(infer_engine, lang_det=lang_det, lang_rec=lang_rec)
-
-    ocr_result = ocr_engine(
-        img,
-        text_score=text_score,
-        box_thresh=box_thresh,
-        unclip_ratio=unclip_ratio,
-        return_word_box=return_word_box,
-    )
-    vis_img = ocr_result.vis()
-    if return_word_box:
-        txts, scores, _ = list(zip(*ocr_result.word_results))
-        ocr_txts = [[i, txt, score] for i, (txt, score) in enumerate(zip(txts, scores))]
-        return vis_img, ocr_txts, ocr_result.elapse
-
-    ocr_txts = [
-        [i, txt, score]
-        for i, (txt, score) in enumerate(zip(ocr_result.txts, ocr_result.scores))
-    ]
-    return vis_img, ocr_txts, ocr_result.elapse
-
-
-def create_examples() -> List[List[Union[str, float]]]:
-    examples = [
-        [
-            "images/ch_en_num.jpg",
-            0.5,
-            0.5,
-            1.6,
-            "ch_mobile",
-            "ch_mobile",
-            "ONNXRuntime",
-            "No",
-        ],
-        [
-            "images/japan.jpg",
-            0.5,
-            0.5,
-            1.6,
-            "multi_mobile",
-            "japan_mobile",
-            "ONNXRuntime",
-            "No",
-        ],
-        [
-            "images/korean.jpg",
-            0.5,
-            0.5,
-            1.6,
-            "multi_mobile",
-            "korean_mobile",
-            "ONNXRuntime",
-            "No",
-        ],
-        [
-            "images/air_ticket.jpg",
-            0.5,
-            0.5,
-            1.6,
-            "ch_mobile",
-            "ch_mobile",
-            "ONNXRuntime",
-            "No",
-        ],
-        [
-            "images/car_plate.jpeg",
-            0.5,
-            0.5,
-            1.6,
-            "ch_mobile",
-            "ch_mobile",
-            "ONNXRuntime",
-            "No",
-        ],
-        [
-            "images/train_ticket.jpeg",
-            0.5,
-            0.5,
-            1.6,
-            "ch_mobile",
-            "ch_mobile",
-            "ONNXRuntime",
-            "No",
-        ],
-    ]
-    return examples
-
-
-infer_engine_list = [InferEngine[v].value for v in InferEngine.__members__]
-
-lang_det_list = ["ch_mobile", "ch_server", "en_mobile", "en_server", "multi_mobile"]
-lang_rec_list = [
-    "ch_mobile",
-    "ch_server",
-    "chinese_cht",
-    "en_mobile",
-    "ar_mobile",
-    "cyrillic_mobile",
-    "devanagari_mobile",
-    "japan_mobile",
-    "ka_mobile",
-    "korean_mobile",
-    "latin_mobile",
-    "ta_mobile",
-    "te_mobile",
-]
-
-custom_css = """
-    body {font-family: body {font-family: 'Helvetica Neue', Helvetica;}
-    .gr-button {background-color: #4CAF50; color: white; border: none; padding: 10px 20px; border-radius: 5px;}
-    .gr-button:hover {background-color: #45a049;}
-    .gr-textbox {margin-bottom: 15px;}
-    .example-button {background-color: #1E90FF; color: white; border: none; padding: 8px 15px; border-radius: 5px; margin: 5px;}
-    .example-button:hover {background-color: #FF4500;}
-    .tall-radio .gr-radio-item {padding: 15px 0; min-height: 50px; display: flex; align-items: center;}
-    .tall-radio label {font-size: 16px;}
-    .output-image, .input-image, .image-preview {height: 300px !important}
-"""
-
-with gr.Blocks(
-    title="Rapid⚡OCR Demo", css="custom_css", theme=gr.themes.Soft()
-) as demo:
-    gr.Markdown(
-        "<h1 style='text-align: center;'><a href='https://rapidai.github.io/RapidOCRDocs/' style='text-decoration: none;'>Rapid⚡OCR</a></h1>"
-    )
-    gr.HTML(
-        """
-        <div style="display: flex; justify-content: center; gap: 10px;">
-            <a href=""><img src="https://img.shields.io/badge/Python->=3.6-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"><img src="https://static.pepy.tech/personalized-badge/rapidocr?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads%20rapidocr"></a>
-            <a href="https://pypi.org/project/rapidocr/"><img alt="PyPI" src="https://img.shields.io/pypi/v/rapidocr"></a>
-            <a href="https://github.com/RapidAI/RapidOCR"><img src="https://img.shields.io/github/stars/RapidAI/RapidOCR?color=ccf"></a>
-        </div>
-    """
-    )
-    with gr.Row():
-        text_score = gr.Slider(
-            label="text_score",
-            minimum=0,
-            maximum=1.0,
-            value=0.5,
-            step=0.1,
-            info="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",
-        )
-        box_thresh = gr.Slider(
-            label="box_thresh",
-            minimum=0,
-            maximum=1.0,
-            value=0.5,
-            step=0.1,
-            info="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",
-        )
-        unclip_ratio = gr.Slider(
-            label="unclip_ratio",
-            minimum=1.5,
-            maximum=2.0,
-            value=1.6,
-            step=0.1,
-            info="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",
-        )
-
-    with gr.Row():
-        select_infer_engine = gr.Dropdown(
-            choices=infer_engine_list,
-            label="Infer Engine (推理引擎)",
-            value="ONNXRuntime",
-            interactive=True,
-        )
-        lang_det = gr.Dropdown(
-            choices=lang_det_list,
-            label="Det model (文本检测模型)",
-            value=lang_det_list[0],
-            interactive=True,
-        )
-        lang_rec = gr.Dropdown(
-            choices=lang_rec_list,
-            label="Rec model (文本识别模型)",
-            value=lang_rec_list[0],
-            interactive=True,
-        )
-        is_word = gr.Radio(
-            ["Yes", "No"], label="Return word box (返回单字符)", value="No"
-        )
-
-    img_input = gr.Image(label="Upload or Select Image", sources="upload")
-
-    run_btn = gr.Button("Run")
-
-    img_output = gr.Image(label="Output Image")
-    elapse = gr.Textbox(label="Elapse(s)")
-    ocr_results = gr.Dataframe(
-        label="OCR Txts",
-        headers=["Index", "Txt", "Score"],
-        datatype=["number", "str", "number"],
-        show_copy_button=True,
-    )
-
-    ocr_inputs = [
-        img_input,
-        text_score,
-        box_thresh,
-        unclip_ratio,
-        lang_det,
-        lang_rec,
-        select_infer_engine,
-        is_word,
-    ]
-    run_btn.click(
-        get_ocr_result, inputs=ocr_inputs, outputs=[img_output, ocr_results, elapse]
-    )
-
-    examples = gr.Examples(
-        examples=create_examples(),
-        examples_per_page=5,
-        inputs=ocr_inputs,
-        fn=get_ocr_result,
-        outputs=[img_output, ocr_results, elapse],
-        cache_examples=False,
-    )
-
-
-if __name__ == "__main__":
-    demo.launch(debug=True)