import threading import time import cv2 import gradio as gr from lineless_table_rec import LinelessTableRecognition from paddleocr import PPStructure from rapid_table import RapidTable from rapidocr_onnxruntime import RapidOCR from table_cls import TableCls from wired_table_rec import WiredTableRecognition from utils import plot_rec_box, LoadImage, format_html, box_4_2_poly_to_box_4_1 img_loader = LoadImage() table_rec_path = "models/table_rec/ch_ppstructure_mobile_v2_SLANet.onnx" det_model_dir = { "mobile_det": "models/ocr/ch_PP-OCRv4_det_infer.onnx", } rec_model_dir = { "mobile_rec": "models/ocr/ch_PP-OCRv4_rec_infer.onnx", } table_engine_list = [ "auto", "RapidTable(SLANet)", "RapidTable(SLANet-plus)", "wired_table_v2", "pp_table", "wired_table_v1", "lineless_table" ] # 示例图片路径 example_images = [ "images/wired1.png", "images/wired2.png", "images/wired3.png", "images/lineless1.png", "images/wired4.jpg", "images/lineless2.png", "images/wired5.jpg", "images/lineless3.jpg", "images/wired6.jpg", ] rapid_table_engine = RapidTable(model_path=table_rec_path) SLANet_plus_table_Engine = RapidTable() wired_table_engine_v1 = WiredTableRecognition(version="v1") wired_table_engine_v2 = WiredTableRecognition(version="v2") lineless_table_engine = LinelessTableRecognition() table_cls = TableCls() ocr_engine_dict = {} pp_engine_dict = {} for det_model in det_model_dir.keys(): for rec_model in rec_model_dir.keys(): det_model_path = det_model_dir[det_model] rec_model_path = rec_model_dir[rec_model] key = f"{det_model}_{rec_model}" ocr_engine_dict[key] = RapidOCR(det_model_path=det_model_path, rec_model_path=rec_model_path) pp_engine_dict[key] = PPStructure( layout=False, show_log=False, table=True, use_onnx=True, table_model_dir=table_rec_path, det_model_dir=det_model_path, rec_model_dir=rec_model_path ) def select_ocr_model(det_model, rec_model): return ocr_engine_dict[f"{det_model}_{rec_model}"] def select_table_model(img, table_engine_type, det_model, rec_model): if table_engine_type == "RapidTable(SLANet)": return rapid_table_engine, table_engine_type elif table_engine_type == "RapidTable(SLANet-plus)": return SLANet_plus_table_Engine, table_engine_type elif table_engine_type == "wired_table_v1": return wired_table_engine_v1, table_engine_type elif table_engine_type == "wired_table_v2": print("使用v2 wired table") return wired_table_engine_v2, table_engine_type elif table_engine_type == "lineless_table": return lineless_table_engine, table_engine_type elif table_engine_type == "pp_table": return pp_engine_dict[f"{det_model}_{rec_model}"], 0 elif table_engine_type == "auto": cls, elasp = table_cls(img) if cls == 'wired': table_engine = wired_table_engine_v2 return table_engine, "wired_table_v2" return lineless_table_engine, "lineless_table" def process_image(img, table_engine_type, det_model, rec_model): img = img_loader(img) start = time.time() table_engine, talbe_type = select_table_model(img, table_engine_type, det_model, rec_model) ocr_engine = select_ocr_model(det_model, rec_model) if isinstance(table_engine, PPStructure): result = table_engine(img, return_ocr_result_in_table=True) html = result[0]['res']['html'] polygons = result[0]['res']['cell_bbox'] polygons = [[polygon[0], polygon[1], polygon[4], polygon[5]] for polygon in polygons] ocr_boxes = result[0]['res']['boxes'] all_elapse = f"- `table all cost: {time.time() - start:.5f}`" else: ocr_res, ocr_infer_elapse = ocr_engine(img) det_cost, cls_cost, rec_cost = ocr_infer_elapse ocr_boxes = [box_4_2_poly_to_box_4_1(ori_ocr[0]) for ori_ocr in ocr_res] if isinstance(table_engine, RapidTable): html, polygons, table_rec_elapse = table_engine(img, ocr_result=ocr_res) polygons = [[polygon[0], polygon[1], polygon[4], polygon[5]] for polygon in polygons] elif isinstance(table_engine, (WiredTableRecognition, LinelessTableRecognition)): html, table_rec_elapse, polygons, _, _ = table_engine(img, ocr_result=ocr_res) sum_elapse = time.time() - start all_elapse = f"- table_type: {talbe_type}\n table all cost: {sum_elapse:.5f}\n - table rec cost: {table_rec_elapse:.5f}\n - ocr cost: {det_cost + cls_cost + rec_cost:.5f}" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) table_boxes_img = plot_rec_box(img.copy(), polygons) ocr_boxes_img = plot_rec_box(img.copy(), ocr_boxes) complete_html = format_html(html) return complete_html, table_boxes_img, ocr_boxes_img, all_elapse def main(): det_models_labels = list(det_model_dir.keys()) rec_models_labels = list(rec_model_dir.keys()) with gr.Blocks(css=""" .scrollable-container { overflow-x: auto; white-space: nowrap; } """) as demo: with gr.Row(): # 两列布局 with gr.Tab("Options"): with gr.Column(variant="panel", scale=1): # 侧边栏，宽度比例为1 img_input = gr.Image(label="Upload or Select Image", sources="upload", value="images/lineless3.jpg") # 示例图片选择器 examples = gr.Examples( examples=example_images, inputs=img_input, fn=lambda x: x, # 简单返回图片路径 outputs=img_input, cache_examples=True ) table_engine_type = gr.Dropdown(table_engine_list, label="Select Recognition Table Engine", value=table_engine_list[0]) det_model = gr.Dropdown(det_models_labels, label="Select OCR Detection Model", value=det_models_labels[0]) rec_model = gr.Dropdown(rec_models_labels, label="Select OCR Recognition Model", value=rec_models_labels[0]) run_button = gr.Button("Run") gr.Markdown("# Elapsed Time") elapse_text = gr.Text(label="") # 使用 `gr.Text` 组件展示字符串 with gr.Column(scale=2): # 右边列 # 使用 Markdown 标题分隔各个组件 gr.Markdown("# Html Render") html_output = gr.HTML(label="", elem_classes="scrollable-container") gr.Markdown("# Table Boxes") table_boxes_output = gr.Image(label="") gr.Markdown("# OCR Boxes") ocr_boxes_output = gr.Image(label="") run_button.click( fn=process_image, inputs=[img_input, table_engine_type, det_model, rec_model], outputs=[html_output, table_boxes_output, ocr_boxes_output, elapse_text] ) demo.launch() if __name__ == '__main__': main()