188 lines
7.6 KiB
Python
188 lines
7.6 KiB
Python
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import json
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from rapidocr_onnxruntime import RapidOCR
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from rapidocr_paddle import RapidOCR as RapidOCR_paddle
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from ocr_utils import split_ocr_results, save_ocr_result, is_ocr_result_exist, load_ocr_result, re_search, \
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draw_ocr_results, remove_ocr_result
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from wired_table_rec import WiredTableRecognition
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from wired_table_rec.table_line_rec import TableLineRecognition
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from wired_table_rec.utils import LoadImage
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import os
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current_directory = os.getcwd()
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upload_dir = os.path.join(current_directory, "data\\")
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def get_ocr(image_path,num_threshold = 15,is_gpu=False, json_save_path = upload_dir + "json_data"):
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if is_gpu:
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ocr_engine = RapidOCR_paddle(det_use_cuda=True, cls_use_cuda=True, rec_use_cuda=True)
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else:
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ocr_engine = RapidOCR(det_model_path='../Models/ch_PP-OCRv4_det_infer.onnx',
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rec_model_path='../Models/ch_PP-OCRv4_rec_infer.onnx', box_thresh=0.3, return_word_box=True)
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ocr_result, _ = ocr_engine(image_path, box_thresh=0.3, return_word_box=True)
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if ocr_result == None:
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ocr_result = []
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for res in ocr_result:
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# 删除最后一个元素
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res.pop()
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# 将ocr识别结果保存为json
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save_ocr_result(ocr_result, image_path, save_path=json_save_path)
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# 文字识别结果切分
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ocr_result = split_ocr_results(ocr_result, num_threshold)
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# 识别表格
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wired_engine = WiredTableRecognition()
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html, elasp, table_result, logic_points, ocr_res, cell_box_det_map, not_match_orc_boxes = wired_engine(image_path, ocr_result=ocr_result)
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if cell_box_det_map == None:
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return [[item[1]] for item in ocr_result]
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fin_list = list(cell_box_det_map.values()) + list(not_match_orc_boxes.values())
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# 对每个子列表，获取第二个元素
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output = [item[1] for item in fin_list]
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print("ocr result:", output)
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return output
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# print('output:', output)
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def get_ocr_list(image_path_list,num_threshold = 15, is_gpu=False, json_save_path = upload_dir + "json_data"):
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"""
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获取多个图片的ocr结果
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:param image_path_list: 图片地址列表
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:param num_threshold: 相邻数字字符之间的分割阈值
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:param is_gpu: 是否使用gpu
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:param json_save_path: json保存地址
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:return: [ocr_result1,ocr_result2,...]
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ocr_result1: [box1,box2,...]
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"""
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# 如果是单个图片地址
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if type(image_path_list) == str:
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return [get_ocr(image_path_list,num_threshold)]
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output = []
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for image_path in image_path_list:
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output.append(get_ocr(image_path,num_threshold,is_gpu,json_save_path=json_save_path))
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return output
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def get_ocr_image(image_path, img_json, json_save_path, img_save_path, is_gpu = False):
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"""
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获取ocr标记图片地址
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:param image_path: 图片地址
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:param img_json: 图片json
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:return: 标记图片地址
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"""
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# 需要标注的方框坐标
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boxes = []
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# 读取图片ocr结果
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if is_ocr_result_exist(image_path, save_path=json_save_path):
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ocr_result = load_ocr_result(image_path, save_path=json_save_path)
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else:
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get_ocr(image_path, is_gpu=is_gpu, json_save_path=json_save_path)
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ocr_result = load_ocr_result(image_path, save_path=json_save_path)
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# print("ocr_result:",ocr_result)
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ocr_result_only_str = [item[1] for item in ocr_result]
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# print("ocr_result_only_str:",ocr_result_only_str)
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img_json = json.loads(img_json)
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# 遍历json
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for key, value in img_json.items():
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# print("key:", key, "value:", value)
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if value == "":
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continue
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search_result = re_search(value, ocr_result_only_str)
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if search_result != []:
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box = []
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for item in search_result:
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txt_index, start_index, end_index = item
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# 获取方框坐标
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# print("ocr_result_box:", box)
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box.append(ocr_result[txt_index][3][start_index][0] + ocr_result[txt_index][3][end_index-1][2])
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boxes.append(box)
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# 绘制保存ocr结果
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ocr_results_path = draw_ocr_results(image_path, boxes, save_path=img_save_path)
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# 删掉ocr的json文件
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remove_ocr_result(image_path, save_path=json_save_path)
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return ocr_results_path
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# 返回ocr标记图片地址
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def get_ocr_image_list(image_path_list, img_json, json_save_path = upload_dir + "json_data", img_save_path = upload_dir + "ocr_mark_data"):
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"""
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获取ocr标记图片地址
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:param image_path_list: 图片地址列表
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:param img_json: 图片json
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:param json_save_path: json保存地址
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:param img_save_path: 图片保存地址
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:return: 标记图片地址[ocr_img1,ocr_img2,...]
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"""
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# 读取图片ocr结果
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ocr_result_list = []
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for i in range(len(image_path_list)):
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img_json = img_json
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image_path = image_path_list[i]
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ocr_img = get_ocr_image(image_path, img_json, json_save_path, img_save_path)
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ocr_result_list.append(ocr_img)
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return ocr_result_list
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if __name__ == '__main__':
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# print(get_ocr("images/1/ivc-591248001.jpg",is_gpu=True))
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# with open("ocr_res.json", "w", encoding="utf-8") as file:
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# json.dump(get_ocr_list("images/1/ivc-591248001.jpg",is_gpu=True), file, ensure_ascii=False, indent=4)
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# heton_json = {
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# "合同名称":"广东电网有限责任公司2024年数字服务工单项目（营销系统工单）开发实施框架合同之（电能表参数配置等）工 单委托函",
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# "合同编号":"0375002024030102YG00046",
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# "项目名称":"2024年数字服务工单项目",
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# "项目编号":"037800HK23120061",
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# "起止时间":"2024年6月至2024年11月",
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# "工作内容及完成情况":"项目已于2024年11月4日完成初步验收。",
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# "结论及意见":"广东电网有限责任公 司2024年数字服务工单项目（营销系统工单)开发实施框架合同之(电能表参数配置等)工单委托函签订后,已完成初步验收工作。",
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# "委托 函的约定情况":"依据委托函约定，“本委托函暂定价款为人民币含税价￥3,922,900.00元（大写：叁佰玖拾贰万贰仟玖佰元整），根据框架合同约定，单项工单费用计算方式：工单服务费=甲方评审后的单项工单概算费用×中标费率。所以本委托函结算价=评审后的单项工单概算费用×中标费率93.6%。”本委托函评审后的单项工单概算费用=开发费3482390元+实施费413049元=3895439元，故本委托函结算价=评审后的单项工单概算费用3895439元×中标费率93.6%=3646130.90元。",
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# "支付方式":"2、初验款，项目通过甲方组织【初步验收】合格， 并取得乙方开具的相应金额的符合国家规定的正规发票后45个工作日内，甲方向乙方支付至本委托函结算价[70]%的条款。委托函结算价 为￥3,646,130.90元，初验款金额=委托函结算价3646130.90元*70%-首付款1176870元=￥1,375,421.63元。该项目已达到初验款支付条件，金额为￥1,375,421.63元，即人民币壹佰叁拾柒万伍仟肆佰贰拾壹元陆角叁分。",
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# "是否达到支付条件":"是",
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# "支付金额":"1375421.63",
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# "日期":"2024：11.12"
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# }
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# get_ocr_image("images/1/ivc-591248004.jpg", heton_json)
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print(get_ocr("/root/autodl-tmp/LLMServe/data/img/广东电网有限责任公司信息中心2024年客户服务平台（网级95598语音平台V1page_4.png"))
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# out = get_ocr("images/1/ivc-591248004.jpg")
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#
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# # 保存到文件
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# with open("ocr_res.json", "w", encoding="utf-8") as file:
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# json.dump(out, file, ensure_ascii=False, indent=4)
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