灾害数据输出上行下行逻辑调整，共享目录识别，异步更新任务状态为已结束

predict/predict_yolo11seg.py

@@ -16,6 +16,7 @@ import matplotlib.pyplot as plt
 
 from ultralytics import YOLO
 
+from middleware.recognition_task import RecognitionTaskDAO, RecognitionTask
 from middleware.minio_util import upload_file, downFile, check_zip_size, upload_folder
 from util.yolo2pix_new import *
 from util.smb_tool import *
@@ -643,7 +644,7 @@ class YOLOSegmentationInference:
         InferenceResult]:
         for input_dir in input_dir_list :
             self.process_image_directory_share_dir(task_id,current_time,input_dir,user_name,pwd,output_dir,conf_threshold,iou_threshold,save_mask,save_label,show,result_save)
-
+        del_file_shutil(output_dir)
 
     def process_image_directory_share_dir(self, task_id, current_time, input_dir, user_name, pwd, output_dir: Optional[str] = None,
                                 conf_threshold: float = 0.25, iou_threshold: float = 0.5,
@@ -666,7 +667,9 @@ class YOLOSegmentationInference:
             推理结果列表
         """
         results = []
-        print(f"正在处理共享目录: {input_dir} ")
+        tmp_output_dir = output_dir + "\\" + datetime.now().strftime("%Y%m%d%H%M%S")
+        print(f"正在处理共享目录: {input_dir} - {tmp_output_dir}")
+
         try:
             # 检查目录是否存在
             config = get_conf(zip_url=input_dir, user_name=user_name, pwd=pwd)
@@ -690,7 +693,7 @@ class YOLOSegmentationInference:
                     image_path=image_path,
                     user_name=user_name,
                     pwd=pwd,
-                    output_dir=output_dir,
+                    output_dir=tmp_output_dir,
                     conf_threshold=conf_threshold,
                     iou_threshold=iou_threshold,
                     save_mask=save_mask,
@@ -704,14 +707,27 @@ class YOLOSegmentationInference:
             tmpConfig = get_conf(input_dir, user_name, pwd)
             pile_dict = get_pile_dict(input_dir, user_name, pwd)
             road_dict = get_road_dict(f"{tmpConfig['ip']}/{tmpConfig['share']}/{tmpConfig['excel_dir']}", user_name, pwd)
-            process_dir(road_dict, pile_dict, output_dir)
+            process_dir(road_dict, pile_dict, tmp_output_dir)
             # 识别生成的文件压缩成zip
-            zip_folder_shutil(output_dir)
+            zip_folder_shutil(tmp_output_dir)
-            move_file_shutil(output_dir+".zip", output_dir)
+            move_file_shutil(tmp_output_dir+".zip", tmp_output_dir + f"/{task_id}.zip")
             remote_dir = f"{tmpConfig['dir']}_识别/{task_id}/{current_time}"
-            scanner.upload_directory(output_dir, config['share'], remote_dir=remote_dir)
+            scanner.upload_directory(tmp_output_dir, config['share'], remote_dir=remote_dir)
-            del_file_shutil(output_dir)
+            del_file_shutil(tmp_output_dir)
-
+            # 更新识别任务状态为已结束
+            DB_CONFIG = {
+                "dbname": "postgres",
+                "user": "admin",
+                "password": "admin.123",
+                "host": "127.0.0.1",
+                "port": "5432"
+            }
+            dao = RecognitionTaskDAO(DB_CONFIG)
+            recognition_task = RecognitionTask(
+                task_id=task_id,
+                status=2
+            )
+            dao.update_recognition_task(recognition_task)
             return results
 
         except PermissionError:

util/yolo2pix_new.py

@@ -95,17 +95,37 @@ def detect_road_type_from_content(label_file):
         if kw in content: return "gravel"
     return "gravel"
 
-def get_road_info(road_dict, pile_dict, img_file_name):
+def get_road_info(summary_data, road_dict, pile_dict, img_file_name):
     """获取路线信息"""
     parts = pile_dict.get(img_file_name)
     if parts :
         road_code = parts[0]
         road_info = road_dict.get(road_code)
         if road_info :
-            pile_no = convert_special_format(parts[1]) * 1000
+            pile_no_min = convert_special_format(summary_data[0][0]) * 1000
+            pile_no_max = convert_special_format(summary_data[len(summary_data)-1][0]) * 1000
+            if pile_no_max < pile_no_min : #上行下行
+                tmp = pile_no_max
+                pile_no_max = pile_no_min
+                pile_no_min = tmp
+            final_excel_start_pile_no = pile_no_min
+            final_excel_end_pile_no = pile_no_max
             for road in road_info :
-                data = road['data']
+                data = road.get("data")
-                if data.get('起桩号（米）',0) > pile_no and pile_no > data.get('止桩号（米）', 0) or data.get('起桩号（米）', 0) < pile_no and pile_no < data.get('止桩号（米）', 0) :
+                excel_start_pile_no = data.get('起桩号（米）',0)
+                excel_end_pile_no = data.get('止桩号（米）',0)
+                if excel_start_pile_no < final_excel_start_pile_no :
+                    final_excel_start_pile_no = excel_start_pile_no
+                if excel_end_pile_no > final_excel_end_pile_no :
+                    final_excel_end_pile_no = excel_end_pile_no
+                # up_down = data.get("方向(上行/下行)")
+                if excel_start_pile_no <= pile_no_min and excel_end_pile_no >= pile_no_max:
+                    return data
+
+            # 起始结束桩号不匹配
+            data = road_info[0].get("data")
+            data['起桩号（米）'] = final_excel_start_pile_no
+            data['止桩号（米）'] = final_excel_end_pile_no
             return data
 
     return {}
@@ -307,7 +327,7 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
     process_info_data = []
     if summary_data:
         img_file_name = os.path.basename(image_path)
-        road_data = get_road_info(road_dict, pile_dict, img_file_name)
+        road_data = get_road_info(summary_data, road_dict, pile_dict, img_file_name)
         identify_width = road_data.get('识别宽度（米）', '3.6')
         up_or_down = road_data.get('方向(上行/下行)', '上行')
         road_code = pile_dict.get(img_file_name)[0]
@@ -322,7 +342,7 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
             group_list = summary_data
             list(reversed(group_list))
             increment = float(-0.001) * interval
-            tmp_pile_no = first_pile_no
+            tmp_pile_no = last_pile_no
             pile_no_start = tmp_pile_no
             if tmp_pile_no % increment != 0 :
                 pile_no_end = tmp_pile_no - (tmp_pile_no % (-increment))
@@ -340,11 +360,32 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
         tmp_start = pile_no_start
         tmp_end = pile_no_end
         while True :
-            if tmp_end < last_pile_no :
+            # 上行
+            if (tmp_start < last_pile_no and tmp_end > last_pile_no) :
                 tmp_end = last_pile_no
+            if tmp_start >= last_pile_no and tmp_end >= last_pile_no :
+                break
+            # 下行
+            if (tmp_start > first_pile_no and tmp_end < first_pile_no) :
+                tmp_end = first_pile_no
+            if tmp_start <= first_pile_no and tmp_end <= first_pile_no :
+                break
+ 
             print(f"process_info:tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
             if index >= len(group_list) :
-                break
+                # 无灾害数据直接输出无病害数据
+                pile_no_s = format_number_to_k_code(tmp_start)
+                pile_no_e = format_number_to_k_code(tmp_end)
+                up_or_down_code = "B" if up_or_down == '下行' else "A"
+                row = [road_code, pile_no_s,pile_no_e,up_or_down_code,up_or_down,road_level,f"{road_type_cn}路面",'','',f"{0:.2f}",'','','','','','','','','','','','','','','']
+                if road_type=="asphalt":
+                    keys = list(CLASS_MAP_ASPHALT.keys())
+                elif road_type=="cement":
+                    keys = list(CLASS_MAP_CEMENT.keys())
+                else:
+                    keys = list(CLASS_MAP_GRAVEL.keys())
+                process_info_data.append(row)
+            else :
                 # 每10m一个区间，在区间内进行灾害计算
                 pile_no, DR, counts, road_type = group_list[index]
                 cur_pile_no = convert_special_format(pile_no)
@@ -471,6 +512,12 @@ def create_multiple_sheets_with_multiple_headers(file_path, excel_data):
     wb.save(file_path)
     print(f"文件已保存为 {file_path}")
 
+def format_number_three_decimal_places(number) :
+    """
+    将数字转换为 三位小数字符串
+    """
+    return f"{number:0.3f}"
+
 def format_number_to_k_code(number):
     """
     将数字转换为 K0000+000 格式
@@ -531,10 +578,54 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 summary_data.append((pile_no, DR, counts, road_type))
 
     current_time = datetime.now().strftime("%Y%m%d%H%M%S")
+
     # 写桩号问题列表.txt
+    process_damage_detail_txt(road_dict, pile_dict, dir, summary_data, image_path, current_time)
+
+    # 灾害数据.txt
+    process_damage_txt(road_dict, pile_dict, dir, summary_data, image_path, current_time)
+
+    # 病害明细列表.xlsx
+    img_file_path = process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, summary_data, image_path)
+
+    # 综合明细表.xlsx
+    process_damage_composite_excel(road_dict, pile_dict, summary_data, image_path, current_time, img_file_path)
+
+
+def process_damage_composite_excel(road_dict, pile_dict, summary_data, image_path, current_time, img_file_path):
+    print("输出:综合明细表.xlsx")
+    heads = ['路线编码','起点','终点','车道编码','上下行','公路等级','路面类型','PQI','PQI等级','DR(%)','PCI','PCI等级','IRI','RQI','RQI等级','RD','RDI','RDI等级','SMTD','PBI','PBI等级','WR','PWI','PWI等级','备注']
+    data1 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,10)
+    data2 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,100)
+    data3 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,1000)
+
+    excel_data = [
+        {
+            'name': '综合明细十米',
+            'headers': [[['综合明细十米','A','Y']]],
+            'columns': heads,
+            'data': data1
+        },
+        {
+            'name': '综合明细百米',
+            'headers': [[['综合明细百米','A','Y']]],
+            'columns': heads,
+            'data': data2
+        },
+        {
+            'name': '综合明细千米',
+            'headers': [[['综合明细千米','A','Y']]],
+            'columns': heads,
+            'data': data3
+        }
+    ]
+    create_multiple_sheets_with_multiple_headers(img_file_path + '/excel/综合明细表.xlsx', excel_data)
+
+
+def process_damage_detail_txt(road_dict, pile_dict, dir, summary_data, image_path, current_time):
     if summary_data:
         img_file_name = os.path.basename(image_path)
-        road_data = get_road_info(road_dict, pile_dict, img_file_name)
+        road_data = get_road_info(summary_data, road_dict, pile_dict, img_file_name)
         road_code = pile_dict.get(img_file_name)[0]
         road_type = summary_data[0][3]
 
@@ -558,10 +649,35 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 f.write(','.join(row)+'\n')
         print(f"输出完成: {out_file}")
 
-    # 灾害数据.txt
+
+def process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, summary_data, image_path):
+    print("输出:病害明细列表.xlsx")
+    os.makedirs(f"{dir}/excel", exist_ok=True)
+    headers = ['序号','路线编码','方向','桩号','路面类型','病害名称','程度','长度(m)',' 宽度(m)',' 面积(㎡)',' 横向位置','备注']
+    data_list = []
+    if summary_data:
+        img_file_path = os.path.dirname(image_path)
+        img_file_name = os.path.basename(image_path)
+        road_data = get_road_info(summary_data, road_dict, pile_dict, img_file_name)
+        road_code, pile_no, road_type = detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name)
+        identify_width = road_data.get('识别宽度（米）', '3.6')
+        up_or_down = road_data.get('方向(上行/下行)', '上行')
+        excel_index = 1
+        for data in summary_data:
+            damage_data = data[2]
+            for attr_name, attr_value in damage_data.items():
+                excel_data = [excel_index, road_code, up_or_down, f"K000{data[0]}", ROAD_TYPE_EN_TO_CN.get(road_type), attr_name, '', attr_value[1]*cell_area, attr_value[2]*cell_area, attr_value[0], '', '']
+                data_list.append(excel_data)
+
+    all_data = [headers] + data_list
+    smb_tool.write_to_excel_pandas(all_data, img_file_path + '/excel/病害明细列表.xlsx')
+    return img_file_path
+
+
+def process_damage_txt(road_dict, pile_dict, dir, summary_data, image_path, current_time):
     if summary_data:
         img_file_name = os.path.basename(image_path)
-        road_data = get_road_info(road_dict, pile_dict, img_file_name)
+        road_data = get_road_info(summary_data, road_dict, pile_dict, img_file_name)
         identify_width = road_data.get('识别宽度（米）', '3.6')
         up_or_down = road_data.get('方向(上行/下行)', '上行')
         road_code = pile_dict.get(img_file_name)[0]
@@ -584,7 +700,7 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 group_list = group
                 list(reversed(group_list))
                 increment = float(-0.010)
-                tmp_pile_no = first_pile_no
+                tmp_pile_no = last_pile_no
                 pile_no_start = tmp_pile_no
                 if tmp_pile_no % increment != 0 :
                     pile_no_end = tmp_pile_no - (tmp_pile_no % (-increment))
@@ -604,18 +720,38 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 tmp_start = pile_no_start
                 tmp_end = pile_no_end
                 while True :
-                    if tmp_end < last_pile_no :
+                    # 上行
+                    if (tmp_start < last_pile_no and tmp_end > last_pile_no) :
                         tmp_end = last_pile_no
+                    if tmp_start >= last_pile_no and tmp_end >= last_pile_no :
+                        break
+                    # 下行
+                    if (tmp_start > first_pile_no and tmp_end < first_pile_no) :
+                        tmp_end = first_pile_no
+                    if tmp_start <= first_pile_no and tmp_end <= first_pile_no :
+                        break
                     print(f"process_dir:tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
                     if index >= len(group_list) :
-                        break
+                        # 无灾害直接输出无病害数据
+                        pile_no = format_number_three_decimal_places(tmp_start)
+                        row = [pile_no,f"{identify_width}",f"{0:.2f}"]
+                        if road_type=="asphalt":
+                            keys = list(CLASS_MAP_ASPHALT.keys())
+                        elif road_type=="cement":
+                            keys = list(CLASS_MAP_CEMENT.keys())
+                        else:
+                            keys = list(CLASS_MAP_GRAVEL.keys())
+                        for k in keys:
+                            row.append(f"{0:.2f}")
+                        f.write(','.join(row)+'\n')
+                    else :
                         # 每10m一个区间，在区间内进行灾害计算
                         pile_no, DR, counts, road_type = group_list[index]
                         cur_pile_no = convert_special_format(pile_no)
                         in_interval_val = in_interval(increment, cur_pile_no, tmp_start, tmp_end)
                         if in_interval_val == 0 :
                             # 没在刻度内直接输出无病害数据
-                        pile_no = format_number_to_k_code(tmp_start)
+                            pile_no = format_number_three_decimal_places(tmp_start)
                             row = [pile_no,f"{identify_width}",f"{0:.2f}"]
                             if road_type=="asphalt":
                                 keys = list(CLASS_MAP_ASPHALT.keys())
@@ -627,7 +763,7 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                                 row.append(f"{0:.2f}")
                             f.write(','.join(row)+'\n')
                         elif in_interval_val == 1 :
-                        row = [format_number_to_k_code(tmp_start), f"{identify_width}"]
+                            row = [format_number_three_decimal_places(tmp_start), f"{identify_width}"]
                             subRows = []
                             while index < len(group_list):
                                 pile_no, DR, counts, road_type = group_list[index]
@@ -662,57 +798,6 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
         
         print(f"输出完成: {out_file}")
     
-    # 病害明细列表.xlsx
-    print("输出:病害明细列表.xlsx")
-    os.makedirs(f"{dir}/excel", exist_ok=True)
-    headers = ['序号','路线编码','方向','桩号','路面类型','病害名称','程度','长度(m)',' 宽度(m)',' 面积(㎡)',' 横向位置','备注']
-    data_list = []
-    if summary_data:
-        img_file_path = os.path.dirname(image_path)
-        img_file_name = os.path.basename(image_path)
-        road_data = get_road_info(road_dict, pile_dict, img_file_name)
-        road_code, pile_no, road_type = detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name)
-        identify_width = road_data.get('识别宽度（米）', '3.6')
-        up_or_down = road_data.get('方向(上行/下行)', '上行')
-        excel_index = 1
-        for data in summary_data:
-            damage_data = data[2]
-            for attr_name, attr_value in damage_data.items():
-                excel_data = [excel_index, road_code, up_or_down, f"K000{data[0]}", ROAD_TYPE_EN_TO_CN.get(road_type), attr_name, '', attr_value[1]*cell_area, attr_value[2]*cell_area, attr_value[0], '', '']
-                data_list.append(excel_data)
-
-    all_data = [headers] + data_list
-    smb_tool.write_to_excel_pandas(all_data, img_file_path + '/excel/病害明细列表.xlsx')
-
-    # 综合明细表.xlsx
-    print("输出:综合明细表.xlsx")
-    heads = ['路线编码','起点','终点','车道编码','上下行','公路等级','路面类型','PQI','PQI等级','DR(%)','PCI','PCI等级','IRI','RQI','RQI等级','RD','RDI','RDI等级','SMTD','PBI','PBI等级','WR','PWI','PWI等级','备注']
-    data1 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,10)
-    data2 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,100)
-    data3 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,1000)
-
-    excel_data = [
-        {
-            'name': '综合明细十米',
-            'headers': [[['综合明细十米','A','Y']]],
-            'columns': heads,
-            'data': data1
-        },
-        {
-            'name': '综合明细百米',
-            'headers': [[['综合明细百米','A','Y']]],
-            'columns': heads,
-            'data': data2
-        },
-        {
-            'name': '综合明细千米',
-            'headers': [[['综合明细千米','A','Y']]],
-            'columns': heads,
-            'data': data3
-        }
-    ]
-    create_multiple_sheets_with_multiple_headers(img_file_path + '/excel/综合明细表.xlsx', excel_data)
-    
 
 
 
@@ -876,7 +961,7 @@ if __name__=="__main__":
     # calc_cell_area, calc_grid_width, calc_grid_height = calc_grid_param(2048, 4096, 3.6, 2)
     # print(f"calc_cell_area={calc_cell_area}, calc_grid_width={calc_grid_width}, calc_grid_height={calc_grid_height}")
 
-    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/77"
+    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/CV78500155B"
     pile_dict = get_pile_dict(output_dir)
     road_dict = get_road_dict(output_dir)
     process_dir(road_dict, pile_dict, output_dir)