app.py

import gradio as gr
import os
import cv2
from rapid_table_det.inference import TableDetector
from rapid_table_det.utils.visuallize import img_loader, visuallize, extract_table_img

example_images = [
    "images/doc1.png",
    "images/doc2.jpg",
    "images/doc3.jpg",
    "images/doc4.jpg",
    "images/doc5.jpg",
    "images/real1.jpg",
    "images/real2.jpeg",
    "images/real3.jpg",
    "images/real4.jpg",
    "images/real5.jpg"
]

# 定义模型类型选项
model_type_options = {
    "YOLO 目标检测": ["yolo_obj_det"],
    "Paddle 目标检测": ["paddle_obj_det"],
    "Paddle 目标检测 (量化)": ["paddle_obj_det_s"],
    "YOLO 语义分割": ["yolo_edge_det"],
    "YOLO 语义分割 (小型)": ["yolo_edge_det_s"],
    "Paddle 语义分割": ["paddle_edge_det"],
    "Paddle 语义分割 (量化)": ["paddle_edge_det_s"],
    "Paddle 方向分类": ["paddle_cls_det"]
}

# 预生成所有可能的 TableDetector 实例
preinitialized_detectors = {}

for obj_model_type in model_type_options["YOLO 目标检测"] + model_type_options["Paddle 目标检测"] + model_type_options[
    "Paddle 目标检测 (量化)"]:
    for edge_model_type in model_type_options["YOLO 语义分割"] + model_type_options["YOLO 语义分割 (小型)"] + model_type_options[
        "Paddle 语义分割"] + model_type_options["Paddle 语义分割 (量化)"]:
        for cls_model_type in model_type_options["Paddle 方向分类"]:
            detector_key = (obj_model_type, edge_model_type, cls_model_type)
            preinitialized_detectors[detector_key] = TableDetector(
                obj_model_type=obj_model_type,
                edge_model_type=edge_model_type,
                cls_model_type=cls_model_type,
                obj_model_path=os.path.join("models", f"{obj_model_type}.onnx"),
                edge_model_path=os.path.join("models", f"{edge_model_type}.onnx"),
                cls_model_path=os.path.join("models", f"{cls_model_type}.onnx")
            )


# 定义图片缩放函数
def resize_image(image, max_size=640):
    height, width = image.shape[:2]
    if max(height, width) > max_size:
        scale = max_size / max(height, width)
        new_height = int(height * scale)
        new_width = int(width * scale)
        image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
    return image


# 定义推理函数
def run_inference(img_path, obj_model_type, edge_model_type, cls_model_type, det_accuracy, use_obj_det, use_edge_det,

                  use_cls_det):
    detector_key = (obj_model_type, edge_model_type, cls_model_type)
    table_det = preinitialized_detectors[detector_key]
    result, elapse = table_det(
        img_path,
        det_accuracy=det_accuracy,
        use_obj_det=use_obj_det,
        use_edge_det=use_edge_det,
        use_cls_det=use_cls_det
    )

    # 加载图片
    img = img_loader(img_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    extract_img = img.copy()
    visual_img = img.copy()
    extract_imgs = []

    for i, res in enumerate(result):
        box = res["box"]
        lt, rt, rb, lb = res["lt"], res["rt"], res["rb"], res["lb"]
        # 带识别框和左上角方向位置
        visual_img = visuallize(visual_img, box, lt, rt, rb, lb)
        # 透视变换提取表格图片
        wrapped_img = extract_table_img(extract_img.copy(), lt, rt, rb, lb)
        extract_imgs.append(wrapped_img)
    # 缩放图片
    visual_img = resize_image(visual_img)
    extract_imgs = [resize_image(img) for img in extract_imgs]

    obj_det_elapse, edge_elapse, rotate_det_elapse = elapse
    return visual_img, extract_imgs, f"obj_det_elapse:{obj_det_elapse}, edge_elapse={edge_elapse}, rotate_det_elapse={rotate_det_elapse}"


def update_extract_outputs(visual_img, extract_imgs, time_info):
    if len(extract_imgs) == 1:
        return visual_img, extract_imgs[0], time_info
    else:
        return visual_img, extract_imgs, time_info


# 创建Gradio界面
with gr.Blocks(
        css="""

        .scrollable-container {

            overflow-x: auto;

            white-space: nowrap;

        }

        .header-links {

            text-align: center;

        }

        .header-links a {

            display: inline-block;

            text-align: center;

            margin-right: 10px;  /* 调整间距 */

        }

    """
) as demo:
    gr.HTML(
        "<h1 style='text-align: center;'><a href='https://github.com/RapidAI/RapidTableDetection'>RapidTableDetection</a></h1>"
    )
    gr.HTML('''

                                            <div class="header-links">

                                              <a href=""><img src="https://img.shields.io/badge/Python->=3.8,<3.12-aff.svg"></a>

  <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Mac%2C%20Win-pink.svg"></a>

<a href="https://semver.org/"><img alt="SemVer2.0" src="https://img.shields.io/badge/SemVer-2.0-brightgreen"></a>

  <a href="https://github.com/psf/black"><img src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>

  <a href="https://github.com/RapidAI/TableStructureRec/blob/c41bbd23898cb27a957ed962b0ffee3c74dfeff1/LICENSE"><img alt="GitHub" src="https://img.shields.io/badge/license-Apache 2.0-blue"></a>

                                            </div>

                                            ''')
    with gr.Row():
        with gr.Column(variant="panel", scale=1):
            img_input = gr.Image(label="Upload or Select Image", sources="upload", value="images/real1.jpg")

            # 示例图片选择器
            examples = gr.Examples(
                examples=example_images,
                examples_per_page=len(example_images),
                inputs=img_input,
                fn=lambda x: x,  # 简单返回图片路径
                outputs=img_input,
                cache_examples=False
            )

            obj_model_type = gr.Dropdown(
                choices=model_type_options["YOLO 目标检测"] + model_type_options["Paddle 目标检测"] +
                        model_type_options["Paddle 目标检测 (量化)"],
                value="yolo_obj_det",
                label="obj det model")
            edge_model_type = gr.Dropdown(
                choices=model_type_options["YOLO 语义分割"] + model_type_options["YOLO 语义分割 (小型)"] +
                        model_type_options["Paddle 语义分割"] + model_type_options["Paddle 语义分割 (量化)"],
                value="yolo_edge_det",
                label="edge seg model")
            cls_model_type = gr.Dropdown(choices=model_type_options["Paddle 方向分类"],
                                         value="paddle_cls_det",
                                         label="direction cls model")

            det_accuracy = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="目标检测置信度阈值")
            use_obj_det = gr.Checkbox(value=True, label="use obj det")
            use_edge_det = gr.Checkbox(value=True, label="use edge seg")
            use_cls_det = gr.Checkbox(value=True, label="use direction cls")

            run_button = gr.Button("run")

        with gr.Column(scale=2):
            visual_output = gr.Image(label="output visualize")
            extract_outputs = gr.Gallery(label="extracted images", object_fit="contain", columns=1, preview=True)
            time_output = gr.Textbox(label="elapsed")

    run_button.click(
        fn=run_inference,
        inputs=[img_input, obj_model_type, edge_model_type, cls_model_type, det_accuracy, use_obj_det, use_edge_det,
                use_cls_det],
        outputs=[visual_output, extract_outputs, time_output]
    )

# 启动Gradio应用
demo.launch()