diff --git a/photo_mask/auto_photo_mask.py b/photo_mask/auto_photo_mask.py index eea755e..e82685b 100644 --- a/photo_mask/auto_photo_mask.py +++ b/photo_mask/auto_photo_mask.py @@ -62,7 +62,7 @@ def find_boxes(content, layout, offset=0, length=None, improve=False, image_path captured_image, offset_x, offset_y = image_util.expand_to_a4_size(captured_image) cv2.imwrite(temp_file.name, captured_image) try: - layouts = util.get_ocr_layout(OCR, temp_file.name) + layouts, _ = util.get_ocr_layout(OCR, temp_file.name) except TypeError: # 如果是类型错误，大概率是没识别到文字 layouts = [] @@ -100,7 +100,7 @@ def get_mask_layout(image, name, id_card_num): result = [] try: try: - layouts = util.get_ocr_layout(OCR, temp_file.name) + layouts, _ = util.get_ocr_layout(OCR, temp_file.name) # layouts = OCR.parse({"doc": temp_file.name})["layout"] except TypeError: # 如果是类型错误，大概率是没识别到文字 @@ -198,7 +198,7 @@ def mask_photo(img_url, name, id_card_num, color=(255, 255, 255)): return do_mask, i # 打开图片 - image = image_util.read(img_url) + image, _ = image_util.read(img_url) if image is None: return False, image original_image = image diff --git a/photo_mask/photo_mask_error_check.py b/photo_mask/photo_mask_error_check.py index ca5ecc8..fc5e4f7 100644 --- a/photo_mask/photo_mask_error_check.py +++ b/photo_mask/photo_mask_error_check.py @@ -23,7 +23,7 @@ def check_error(error_ocr): image = mask_photo(img_url, name, id_card_num, (0, 0, 0))[1] final_img_url = ufile.get_private_url(error_ocr.cfjaddress, "drg100") - final_image = image_util.read(final_img_url) + final_image, _ = image_util.read(final_img_url) return image_util.combined(final_image, image) diff --git a/photo_review.py b/photo_review.py index 232376a..ef05ef3 100644 --- a/photo_review.py +++ b/photo_review.py @@ -13,14 +13,14 @@ from photo_review import auto_photo_review, SEND_ERROR_EMAIL # 项目必须从此处启动，否则代码中的相对路径可能导致错误的发生 if __name__ == '__main__': - program_name = '照片审核自动识别脚本' + program_name = "照片审核自动识别脚本" logging.config.dictConfig(LOGGING_CONFIG) parser = argparse.ArgumentParser() parser.add_argument("--clean", default=False, type=bool, help="是否将识别中的案子改为待识别状态") args = parser.parse_args() if args.clean: - # 主要用于启动时，清除仍在涂抹中的案子 + # 主要用于启动时，清除仍在识别中的案子 session = MysqlSession() update_flag = (update(ZxPhhd).where(ZxPhhd.exsuccess_flag == "2").values(exsuccess_flag="1")) session.execute(update_flag) @@ -34,7 +34,7 @@ if __name__ == '__main__': logging.info(f"【{program_name}】开始运行") auto_photo_review.main() except Exception as e: - error_logger = logging.getLogger('error') + error_logger = logging.getLogger("error") error_logger.error(traceback.format_exc()) if SEND_ERROR_EMAIL: send_error_email(program_name, repr(e), traceback.format_exc()) diff --git a/photo_review/__init__.py b/photo_review/__init__.py index 5b8be9f..e35c6d0 100644 --- a/photo_review/__init__.py +++ b/photo_review/__init__.py @@ -2,9 +2,9 @@ import jieba from paddlenlp import Taskflow from paddleocr import PaddleOCR -''' +""" 项目配置 -''' +""" # 每次从数据库获取的案子数量 PHHD_BATCH_SIZE = 10 # 没有查询到案子的等待时间(分钟) @@ -18,35 +18,35 @@ LAYOUT_ANALYSIS = False 信息抽取关键词配置 """ # 患者姓名 -PATIENT_NAME = ['患者姓名'] +PATIENT_NAME = ["患者姓名"] # 入院日期 -ADMISSION_DATE = ['入院日期'] +ADMISSION_DATE = ["入院日期"] # 出院日期 -DISCHARGE_DATE = ['出院日期'] +DISCHARGE_DATE = ["出院日期"] # 发生医疗费 -MEDICAL_EXPENSES = ['费用总额'] +MEDICAL_EXPENSES = ["费用总额"] # 个人现金支付 -PERSONAL_CASH_PAYMENT = ['个人现金支付'] +PERSONAL_CASH_PAYMENT = ["个人现金支付"] # 个人账户支付 -PERSONAL_ACCOUNT_PAYMENT = ['个人账户支付'] +PERSONAL_ACCOUNT_PAYMENT = ["个人账户支付"] # 个人自费金额 -PERSONAL_FUNDED_AMOUNT = ['自费金额', '个人自费'] +PERSONAL_FUNDED_AMOUNT = ["自费金额", "个人自费"] # 医保类别 -MEDICAL_INSURANCE_TYPE = ['医保类型'] +MEDICAL_INSURANCE_TYPE = ["医保类型"] # 就诊医院 -HOSPITAL = ['医院'] +HOSPITAL = ["医院"] # 就诊科室 -DEPARTMENT = ['科室'] +DEPARTMENT = ["科室"] # 主治医生 -DOCTOR = ['主治医生'] +DOCTOR = ["主治医生"] # 住院号 -ADMISSION_ID = ['住院号'] +ADMISSION_ID = ["住院号"] # 医保结算单号码 -SETTLEMENT_ID = ['医保结算单号码'] +SETTLEMENT_ID = ["医保结算单号码"] # 年龄 -AGE = ['年龄'] +AGE = ["年龄"] # 大写总额 -UPPERCASE_MEDICAL_EXPENSES = ['大写总额'] +UPPERCASE_MEDICAL_EXPENSES = ["大写总额"] SETTLEMENT_LIST_SCHEMA = \ (PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + MEDICAL_EXPENSES + PERSONAL_CASH_PAYMENT @@ -58,57 +58,55 @@ DISCHARGE_RECORD_SCHEMA = \ COST_LIST_SCHEMA = PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + MEDICAL_EXPENSES -''' +""" 别名配置 -''' +""" # 使用别名中的value替换key。考虑到效率问题，只会替换第一个匹配到的key。 HOSPITAL_ALIAS = { - '沐阳': ['沭阳'], - '连水': ['涟水'], - '唯宁': ['睢宁'], # 雕宁 - '九〇四': ['904'], - '漂水': ['溧水'], + "沐阳": ["沭阳"], + "连水": ["涟水"], + "唯宁": ["睢宁"], # 雕宁 + "九〇四": ["904"], + "漂水": ["溧水"], } DEPARTMENT_ALIAS = { - '耳鼻喉': ['耳鼻咽喉'], - '急症': ['急诊'], + "耳鼻喉": ["耳鼻咽喉"], + "急症": ["急诊"], } -''' +""" 搜索过滤配置 -''' +""" # 默认会过滤单字 -HOSPITAL_FILTER = ['医院', '人民', '第一', '第二', '第三', '大学', '附属'] +HOSPITAL_FILTER = ["医院", "人民", "第一", "第二", "第三", "大学", "附属"] -DEPARTMENT_FILTER = ['医', '伤', '西', '新'] +DEPARTMENT_FILTER = ["医", "伤", "西", "新"] -''' +""" 分词配置 -''' -jieba.suggest_freq(('肿瘤', '医院'), True) -jieba.suggest_freq(('骨', '伤'), True) -jieba.suggest_freq(('感染', '性'), True) -jieba.suggest_freq(('胆', '道'), True) -jieba.suggest_freq(('脾', '胃'), True) +""" +jieba.suggest_freq(("肿瘤", "医院"), True) +jieba.suggest_freq(("骨", "伤"), True) +jieba.suggest_freq(("感染", "性"), True) +jieba.suggest_freq(("胆", "道"), True) +jieba.suggest_freq(("脾", "胃"), True) -''' +""" 模型配置 -''' -SETTLEMENT_IE = Taskflow('information_extraction', schema=SETTLEMENT_LIST_SCHEMA, model='uie-x-base', - task_path='model/settlement_list_model', layout_analysis=LAYOUT_ANALYSIS, precision='fp16') -DISCHARGE_IE = Taskflow('information_extraction', schema=DISCHARGE_RECORD_SCHEMA, model='uie-x-base', - task_path='model/discharge_record_model', layout_analysis=LAYOUT_ANALYSIS, precision='fp16') -COST_IE = Taskflow('information_extraction', schema=COST_LIST_SCHEMA, model='uie-x-base', device_id=1, - task_path='model/cost_list_model', layout_analysis=LAYOUT_ANALYSIS, precision='fp16') +""" +SETTLEMENT_IE = Taskflow("information_extraction", schema=SETTLEMENT_LIST_SCHEMA, model="uie-x-base", + task_path="model/settlement_list_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16") +DISCHARGE_IE = Taskflow("information_extraction", schema=DISCHARGE_RECORD_SCHEMA, model="uie-x-base", + task_path="model/discharge_record_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16") +COST_IE = Taskflow("information_extraction", schema=COST_LIST_SCHEMA, model="uie-x-base", device_id=1, + task_path="model/cost_list_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16") OCR = PaddleOCR( - gpu_id=1, - use_angle_cls=False, - show_log=False, - det_db_thresh=0.1, - det_db_box_thresh=0.3, - det_limit_side_len=1248, - drop_score=0.3, - rec_model_dir='model/ocr/openatom_rec_repsvtr_ch_infer', - rec_algorithm='SVTR_LCNet', -) + device="gpu:0", + ocr_version="PP-OCRv4", + use_textline_orientation=False, + # 检测像素阈值，输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点 + text_det_thresh=0.1, + # 检测框阈值，检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域 + text_det_box_thresh=0.3, + ) \ No newline at end of file diff --git a/photo_review/auto_photo_review.py b/photo_review/auto_photo_review.py index 3aac8d4..dca8699 100644 --- a/photo_review/auto_photo_review.py +++ b/photo_review/auto_photo_review.py @@ -36,14 +36,15 @@ def merge_result(result1, result2): return result1 -def ie_temp_image(ie, ocr, image): +def ie_temp_image(ie, ocr, image, is_screenshot=False): with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file: cv2.imwrite(temp_file.name, image) ie_result = [] ocr_pure_text = '' + angle = '0' try: - layout = util.get_ocr_layout(ocr, temp_file.name) + layout, angle = util.get_ocr_layout(ocr, temp_file.name, is_screenshot) if not layout: # 无识别结果 ie_result = [] @@ -61,7 +62,7 @@ def ie_temp_image(ie, ocr, image): os.remove(temp_file.name) except Exception as e: logging.info(f"删除临时文件 {temp_file.name} 时出错", exc_info=e) - return ie_result, ocr_pure_text + return ie_result, ocr_pure_text, angle # 关键信息提取 @@ -159,7 +160,7 @@ def information_extraction(ie, phrecs, identity): if not img_path: continue - image = image_util.read(img_path) + image, exif_data = image_util.read(img_path) if image is None: # 图片可能因为某些原因获取不到 continue @@ -175,7 +176,7 @@ def information_extraction(ie, phrecs, identity): if text: info_extract = ie(text)[0] else: - info_extract = ie_temp_image(ie, OCR, image)[0] + info_extract = ie_temp_image(ie, OCR, image, True)[0] ie_result = {'result': info_extract, 'angle': '0'} now = util.get_default_datetime() @@ -193,27 +194,20 @@ def information_extraction(ie, phrecs, identity): result = merge_result(result, ie_result['result']) else: + is_screenshot = image_util.is_screenshot(image, exif_data) target_images = [] # target_images += detector.request_book_areas(image) # 识别文档区域并裁剪 if not target_images: target_images.append(image) # 识别失败 angle_count = defaultdict(int, {'0': 0}) # 分割后图片的最优角度统计 for target_image in target_images: - # dewarped_image = dewarp.dewarp_image(target_image) # 去扭曲 - dewarped_image = target_image - angles = image_util.parse_rotation_angles(dewarped_image) - - split_results = image_util.split(dewarped_image) + split_results = image_util.split(target_image) for split_result in split_results: if split_result['img'] is None or split_result['img'].size == 0: continue - rotated_img = image_util.rotate(split_result['img'], int(angles[0])) - ie_temp_result = ie_temp_image(ie, OCR, rotated_img) + ie_temp_result = ie_temp_image(ie, OCR, split_result['img'], is_screenshot) ocr_text += ie_temp_result[1] - ie_results = [{'result': ie_temp_result[0], 'angle': angles[0]}] - if not ie_results[0]['result'] or len(ie_results[0]['result']) < len(ie.kwargs.get('schema')): - rotated_img = image_util.rotate(split_result['img'], int(angles[1])) - ie_results.append({'result': ie_temp_image(ie, OCR, rotated_img)[0], 'angle': angles[1]}) + ie_results = [{'result': ie_temp_result[0], 'angle': ie_temp_result[2]}] now = util.get_default_datetime() best_angle = ['0', 0] for ie_result in ie_results: diff --git a/requirements.txt b/requirements.txt index 6697c5e..cbdcec4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,16 +1,11 @@ -numpy==1.26.4 -onnxconverter-common==1.14.0 +aistudio_sdk==0.2.6 +onnxconverter-common==1.15.0 +onnxruntime-gpu==1.22.0 OpenCC==1.1.6 -opencv-python==4.6.0.66 paddle2onnx==1.2.3 -paddleclas==2.5.2 -paddlenlp==2.6.1 -paddleocr==2.7.3 -pillow==10.4.0 +paddlenlp==3.0.0b4 +paddleocr==3.1.1 +PyMuPDF==1.26.3 pymysql==1.1.1 -requests==2.32.3 -sqlacodegen==2.3.0.post1 -sqlalchemy==1.4.52 -tenacity==8.5.0 -ufile==3.2.9 -zxing-cpp==2.2.0 \ No newline at end of file +ufile==3.2.11 +zxing-cpp==2.3.0 \ No newline at end of file diff --git a/util/image_util.py b/util/image_util.py index f1949cc..c0f1c7b 100644 --- a/util/image_util.py +++ b/util/image_util.py @@ -1,9 +1,12 @@ import logging import math import urllib.request +from io import BytesIO import cv2 import numpy +from PIL import Image +from PIL.ExifTags import TAGS from paddleclas import PaddleClas from tenacity import retry, stop_after_attempt, wait_random @@ -14,20 +17,36 @@ def read(image_path): """ 从网络或本地读取图片 :param image_path: 网络或本地路径 - :return: NumPy数组形式的图片 + :return: NumPy数组形式的图片, EXIF数据 """ if image_path.startswith("http"): # 发送HTTP请求并获取图像数据 resp = urllib.request.urlopen(image_path, timeout=60) # 将数据读取为字节流 image_data = resp.read() - # 将字节流转换为NumPy数组 - image_np = numpy.frombuffer(image_data, numpy.uint8) - # 解码NumPy数组为OpenCV图像格式 - image = cv2.imdecode(image_np, cv2.IMREAD_COLOR) else: - image = cv2.imread(image_path) - return image + with open(image_path, "rb") as f: + image_data = f.read() + + # 解析EXIF信息（基于原始字节流） + exif_data = {} + try: + # 用PIL打开原始字节流 + with Image.open(BytesIO(image_data)) as img: + # 获取EXIF字典 + exif_info = img._getexif() + if exif_info: + # 将EXIF标签的数字ID转换为可读名称（如36867对应"DateTimeOriginal"） + for tag_id, value in exif_info.items(): + tag_name = TAGS.get(tag_id, tag_id) + exif_data[tag_name] = value + except Exception as e: + logging.error("解析EXIF信息失败", exc_info=e) + # 将字节流转换为NumPy数组 + image_np = numpy.frombuffer(image_data, numpy.uint8) + # 解码NumPy数组为OpenCV图像格式 + image = cv2.imdecode(image_np, cv2.IMREAD_COLOR) + return image, exif_data def capture(image, rectangle): @@ -61,7 +80,7 @@ def split(image, ratio=1.414, overlap=0.05, x_compensation=3): """ split_result = [] if isinstance(image, str): - image = read(image) + image, _ = read(image) height, width = image.shape[:2] hw_ratio = height / width wh_ratio = width / height diff --git a/util/util.py b/util/util.py index 29b9737..01a6fe6 100644 --- a/util/util.py +++ b/util/util.py @@ -12,9 +12,10 @@ def get_default_datetime(): return datetime.now().strftime('%Y-%m-%d %H:%M:%S') -def get_ocr_layout(ocr, img_path): +def get_ocr_layout(ocr, img_path, is_screenshot=False): """ 获取ocr识别的结果，转为合适的layout形式 + :param is_screenshot: 是否是截图 :param ocr: ocr模型 :param img_path: 图片本地路径 :return: @@ -36,18 +37,18 @@ def get_ocr_layout(ocr, img_path): return True layout = [] - ocr_result = ocr.ocr(img_path, cls=False) - ocr_result = ocr_result[0] + ocr_result = ocr.predict(input=img_path, use_doc_orientation_classify=not is_screenshot, use_doc_unwarping=not is_screenshot) + ocr_result = next(ocr_result) if not ocr_result: - return layout - for segment in ocr_result: - box = segment[0] + return layout, "0" + angle = ocr_result.get("doc_preprocessor_res", {}).get("angle", "0") + for i in range(len(ocr_result.get('rec_texts'))): + box = ocr_result.get("rec_polys")[i].tolist() box = _get_box(box) if not _normal_box(box): continue - text = segment[1][0] - layout.append((box, text)) - return layout + layout.append((box, ocr_result.get("rec_texts")[i])) + return layout, str(angle) def delete_temp_file(temp_files): diff --git a/visual_model_test/visual_model_test.py b/visual_model_test/visual_model_test.py index cef670a..1a33974 100644 --- a/visual_model_test/visual_model_test.py +++ b/visual_model_test/visual_model_test.py @@ -24,7 +24,7 @@ def write_visual_result(image, angle=0, layout=None, result=None): img_name = img[:last_dot_index] img_type = img[last_dot_index + 1:] - img_array = image_util.read(image) + img_array, _ = image_util.read(image) if angle != 0: img_array = image_util.rotate(img_array, angle) with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file: @@ -63,7 +63,7 @@ def visual_model_test(model_type, test_img, task_path, schema): img["y_offset"] -= offset_y temp_files_paths.append(temp_file.name) - parsed_doc = util.get_ocr_layout( + parsed_doc, _ = util.get_ocr_layout( PaddleOCR(det_db_box_thresh=0.3, det_db_thresh=0.1, det_limit_side_len=1248, drop_score=0.3, save_crop_res=False), temp_file.name)