更新OCR版本，Bata版，还不能上线

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

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)
 
 

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())

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'],
+    "沐阳": ["沭阳"],
-    '连水': ['涟水'],
+    "连水": ["涟水"],
-    '唯宁': ['睢宁'],  # 雕宁
+    "唯宁": ["睢宁"],  # 雕宁
-    '九〇四': ['904'],
+    "九〇四": ["904"],
-    '漂水': ['溧水'],
+    "漂水": ["溧水"],
 }
 DEPARTMENT_ALIAS = {
-    '耳鼻喉': ['耳鼻咽喉'],
+    "耳鼻喉": ["耳鼻咽喉"],
-    '急症': ['急诊'],
+    "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(("肿瘤", "医院"), True)
-jieba.suggest_freq(('骨', '伤'), True)
+jieba.suggest_freq(("骨", "伤"), True)
-jieba.suggest_freq(('感染', '性'), True)
+jieba.suggest_freq(("感染", "性"), True)
-jieba.suggest_freq(('胆', '道'), True)
+jieba.suggest_freq(("胆", "道"), True)
-jieba.suggest_freq(('脾', '胃'), True)
+jieba.suggest_freq(("脾", "胃"), True)
 
-'''
+"""
 模型配置
-'''
+"""
-SETTLEMENT_IE = Taskflow('information_extraction', schema=SETTLEMENT_LIST_SCHEMA, model='uie-x-base',
+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')
+                         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',
+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')
+                        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,
+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')
+                   task_path="model/cost_list_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16")
 
 OCR = PaddleOCR(
-    gpu_id=1,
+    device="gpu:0",
-    use_angle_cls=False,
+    ocr_version="PP-OCRv4",
-    show_log=False,
+    use_textline_orientation=False,
-    det_db_thresh=0.1,
+    # 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
-    det_db_box_thresh=0.3,
+    text_det_thresh=0.1,
-    det_limit_side_len=1248,
+    # 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
-    drop_score=0.3,
+    text_det_box_thresh=0.3,
-    rec_model_dir='model/ocr/openatom_rec_repsvtr_ch_infer',
+    )
-    rec_algorithm='SVTR_LCNet',
-)

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
+                split_results = image_util.split(target_image)
-                dewarped_image = target_image
-                angles = image_util.parse_rotation_angles(dewarped_image)
-
-                split_results = image_util.split(dewarped_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, split_result['img'], is_screenshot)
-                    ie_temp_result = ie_temp_image(ie, OCR, rotated_img)
                     ocr_text += ie_temp_result[1]
-                    ie_results = [{'result': ie_temp_result[0], 'angle': angles[0]}]
+                    ie_results = [{'result': ie_temp_result[0], 'angle': ie_temp_result[2]}]
-                    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]})
                     now = util.get_default_datetime()
                     best_angle = ['0', 0]
                     for ie_result in ie_results:

requirements.txt

@@ -1,16 +1,11 @@
-numpy==1.26.4
+aistudio_sdk==0.2.6
-onnxconverter-common==1.14.0
+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==3.0.0b4
-paddlenlp==2.6.1
+paddleocr==3.1.1
-paddleocr==2.7.3
+PyMuPDF==1.26.3
-pillow==10.4.0
 pymysql==1.1.1
-requests==2.32.3
+ufile==3.2.11
-sqlacodegen==2.3.0.post1
+zxing-cpp==2.3.0
-sqlalchemy==1.4.52
-tenacity==8.5.0
-ufile==3.2.9
-zxing-cpp==2.2.0

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()
+    else:
+        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)
-    else:
+    return image, exif_data
-        image = cv2.imread(image_path)
-    return image
 
 
 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

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.predict(input=img_path, use_doc_orientation_classify=not is_screenshot, use_doc_unwarping=not is_screenshot)
-    ocr_result = ocr_result[0]
+    ocr_result = next(ocr_result)
     if not ocr_result:
-        return layout
+        return layout, "0"
-    for segment in ocr_result:
+    angle = ocr_result.get("doc_preprocessor_res", {}).get("angle", "0")
-        box = segment[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, ocr_result.get("rec_texts")[i]))
-        layout.append((box, text))
+    return layout, str(angle)
-    return layout
 
 
 def delete_temp_file(temp_files):

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)