DocumentAIKit/PythonSDK_Auto_Label/auto_label.py

import da_python as dap
import cv2
import argparse
import os
import glob
from PIL import Image
import copy
import json
from tqdm import tqdm

def traverse_folder(path, image_paths):
    # 获取当前文件夹下的所有文件和子文件夹
    for file in os.listdir(path):
        # 获取文件路径
        file_path = os.path.join(path, file)

        # 判断文件类型，如果是图片则加入列表
        if os.path.isfile(file_path) and os.path.splitext(file_path)[1].lower() in allowed_extensions:
            image_paths.append(file_path)
        # 如果是文件夹，则递归调用本函数
        elif os.path.isdir(file_path):
            traverse_folder(file_path, image_paths)

def labeling(model, key, image_paths, set_score, view_result_dir):
    da, create_result = dap.create(key, model)
    if create_result != dap.E_DA_SUCCESS:
        print('create document ai failed:{}'.format(create_result))
        exit()
    detect_engine, a = da.detection()
    # print(a)

    # 存放json信息
    labelme_json = {}
    shape_dic = {}

    for image_path in tqdm(image_paths):
        image = cv2.imread(image_path)
        layout_analysis_result, layout_analysis_result_code = detect_engine.layout_analysis(image)
        if layout_analysis_result_code == dap.E_DA_SUCCESS:
            # 判断是否检测到目标
            if len(layout_analysis_result.boxes):
                # print(dir(layout_analysis_result))
                # print(layout_analysis_result.boxes)
                # print(layout_analysis_result.labels)
                # print(layout_analysis_result.scores)
                # labels_list = layout_analysis_result.labels
                # box_list = layout_analysis_result.boxes
                # score_list = layout_analysis_result.scores
                shapes_list = []
                labelme_json['version'] = '5.0.1'
                labelme_json['flags'] = {}
                # 根据分数进行过滤
                for index, score in enumerate(layout_analysis_result.scores):
                    # print(index, score)
                    if score < set_score:
                        continue
                    # print('检测到')
                    shape_dic['label'] = layout_analysis_result.labels[index]
                    box = layout_analysis_result.boxes[index]
                    x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
                    shape_dic['points'] = [[x1, y1], [x2, y2]]
                    shape_dic['group_id'] = None
                    shape_dic['shape_type'] = 'rectangle'  # 根据需求进行填写
                    shape_dic['flags'] = {}
                    shapes_list.append(copy.deepcopy(shape_dic))
                labelme_json['shapes'] = shapes_list
                labelme_json['imagePath'] = os.path.basename(image_path)
                labelme_json['imageData'] = None
                labelme_json['imageHeight'], labelme_json['imageWidth'] = image.shape[0], image.shape[1]
                json_path = image_path.replace(os.path.splitext(image_path)[1], '.json')
                json.dump(labelme_json, open(json_path, 'w', encoding='utf-8'), ensure_ascii=False, indent=2)
            visualize_im = dap.visualize.detection(image, layout_analysis_result)
            cv2.imwrite(os.path.join(view_result_dir, os.path.basename(image_path)), visualize_im)

        else:
          print('magic color infer failed:{}'.format(layout_analysis_result_code))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', type=str, default='', help='')
    parser.add_argument('--model_licence', type=str, default='', help='')
    parser.add_argument('--score', type=int, default=0.8, help='')
    parser.add_argument('--image_dir', type=str, default='', help='')
    parser.add_argument('--view_result_dir', type=str, default='', help='')
    args = parser.parse_args()

    # 定义允许的图片格式
    allowed_extensions = ['.jpg', '.jpeg', '.png', '.gif']
    # 初始化图片路径列表
    image_paths = []
    # 调用遍历函数
    traverse_folder(args.image_dir, image_paths)

    labeling(args.model, args.model_key, image_paths, args.score, args.view_result_dir)