道路灾害识别更换最新模型，道路信息excel模板，识别宽度提取模板调整
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liyubo
parent
f349efaa56
commit
085e1f4044
@ -95,7 +95,7 @@ class InferenceResult:
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class YOLOSegmentationInference:
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"""YOLO分割模型推理工具"""
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def __init__(self, model_path: str, device: Optional[str] = None):
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def __init__(self, model_path: Optional[str] = None, device: Optional[str] = None):
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"""
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初始化推理工具
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@ -105,6 +105,7 @@ class YOLOSegmentationInference:
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"""
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self.model_path = model_path
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self.device = device
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self.conf_threshold = 0.25
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self.model = None
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self.class_names = []
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self.clean_memory_per_count = 500
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@ -128,6 +129,11 @@ class YOLOSegmentationInference:
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(64, 64, 64), # 浅灰色
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]
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def set_model_info(self, model_path, device, conf_threshold) :
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self.model_path = model_path
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self.device = device
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self.conf_threshold = conf_threshold
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def load_model(self) -> bool:
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"""
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加载YOLO分割模型
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@ -136,7 +142,7 @@ class YOLOSegmentationInference:
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加载成功返回True，否则返回False
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"""
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try:
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print(f"正在加载模型: {self.model_path}")
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print(f"正在加载模型: {self.model_path} 置信度：{self.conf_threshold}")
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self.model = YOLO(self.model_path)
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# 设置设备
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@ -638,7 +644,33 @@ class YOLOSegmentationInference:
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except Exception as e:
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print(f"处理目录失败: {e}")
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return results
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def get_model_info(self, pile_dict, road_dict, image_path) :
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tmp_image_path = smb_tool.standardized_path(image_path)
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img_file_name = tmp_image_path.split('/')[-1]
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road_data = get_road_info(road_dict, pile_dict, img_file_name)
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print(f"road_data={road_data}")
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# 获取当前脚本文件所在的目录
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script_dir = os.path.dirname(__file__)
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# 构建模型文件的绝对路径
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asphalt_model_path = os.path.abspath(os.path.join(script_dir, '..', 'pt_save', 'asphalt20260212_0.391.pt'))
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cream_model_path = os.path.abspath(os.path.join(script_dir, '..', 'pt_save', 'asphalt20260212_0.391.pt'))
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if road_data :
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road_type_cn = road_data['路面类型（沥青/水泥/砂石）']
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if road_type_cn == '沥青' :
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road_type = "asphalt"
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return 0.391, asphalt_model_path
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elif road_type_cn == '水泥' :
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road_type = "cement"
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return 0.1, cream_model_path
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elif road_type_cn == '砾石' :
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road_type = "gravel"
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return 0.391, asphalt_model_path
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return 0.391, asphalt_model_path
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def process_image_directory_share_dir_circle(self, task_id, current_time, input_dir_list, user_name, pwd, output_dir: Optional[str] = None,
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conf_threshold: float = 0.25, iou_threshold: float = 0.5,
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save_mask: bool = False, save_label: bool = False, show: bool = False,
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@ -685,15 +717,32 @@ class YOLOSegmentationInference:
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print(f"找到 {len(image_files)} 个图片文件")
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# 推送识别数据到共享目录
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tmpConfig = get_conf(input_dir, user_name, pwd)
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pile_dict = get_pile_dict(input_dir, user_name, pwd)
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road_dict = get_road_dict(f"{tmpConfig['ip']}/{tmpConfig['share']}/{tmpConfig['excel_dir']}", user_name, pwd)
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# 处理每张图片
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for idx, image_path in enumerate(image_files):
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# for image_path in image_files:
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# 检查路面类型，切换模型
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if not self.model :
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model_conf_threshold, model_path = self.get_model_info(pile_dict, road_dict, image_path)
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try:
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"设备情况：{device}")
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except Exception as e:
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print(f"获取设备失败: {e}")
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self.set_model_info(model_path, device, model_conf_threshold)
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# 加载模型
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if not self.load_model():
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return
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result = self.process_single_image_share_dir(
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image_path=image_path,
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user_name=user_name,
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pwd=pwd,
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output_dir=tmp_output_dir,
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conf_threshold=conf_threshold,
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conf_threshold=self.conf_threshold,
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iou_threshold=iou_threshold,
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save_mask=save_mask,
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save_label=save_label,
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@ -715,18 +764,17 @@ class YOLOSegmentationInference:
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memory_info = process.memory_info()
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mem_usage = memory_info.rss / 1024 / 1024 # 返回MB
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print(f"清理后，内存使用: {mem_usage:.2f} MB")
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# 推送识别数据到共享目录
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tmpConfig = get_conf(input_dir, user_name, pwd)
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pile_dict = get_pile_dict(input_dir, user_name, pwd)
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road_dict = get_road_dict(f"{tmpConfig['ip']}/{tmpConfig['share']}/{tmpConfig['excel_dir']}", user_name, pwd)
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# 处理输出目录的文件格式
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process_dir(road_dict, pile_dict, tmp_output_dir)
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# 识别生成的文件压缩成zip
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zip_folder_shutil(tmp_output_dir)
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move_file_shutil(tmp_output_dir+".zip", tmp_output_dir + f"/{task_id}.zip")
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remote_dir = f"{tmpConfig['dir']}_识别/{task_id}/{current_time}"
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scanner.upload_directory(tmp_output_dir, config['share'], remote_dir=remote_dir)
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del_file_shutil(tmp_output_dir)
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# del_file_shutil(tmp_output_dir)
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# 更新识别任务状态为已结束
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DB_CONFIG = {
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"dbname": "postgres",
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@ -896,33 +944,8 @@ def predict_images(pt_name, zip_url, output_dir="predictions", conf_threshold=0.
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def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_dir="predictions", conf_threshold=0.25, save_json=False):
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# 本地测试模式 - 请根据实际情况修改以下路径
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# local_model_path = r"D:\project\verification\ultralytics-main\model\script\seg\pt\test.pttest.pt"
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local_model_path = r"../pt_save/road_crack.pt"
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local_output_dir = output_dir
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# zip_url = "meta_data/ai_train_platform/train.zip"
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try:
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# 加载模型
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print(f"正在加载模型: {local_model_path}")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"模型已加载到 {device}")
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except Exception as e:
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print(f"处理目录失败: {e}")
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inference = YOLOSegmentationInference(
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model_path=local_model_path,
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device=device
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)
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# 加载模型
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if not inference.load_model():
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return
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# zip_url = r"D:\project\verification\ultralytics-main\model\script\seg\test_seg_pic"
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inference = YOLOSegmentationInference()
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result_save = []
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conf_threshold = 0.25
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iou_threshold = 0.5
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@ -1050,7 +1050,7 @@ def main():
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# for i, path in enumerate(imgPaths, 1):
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# print(f"{i}. {path}")
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# # 读取excel
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# 读取excel
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# full_path = scanner.build_full_path(share_path=config['share'], file_path='西南计算机\\AA县\\每公里指标明细表（北碚）.xls')
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# df = scanner.read_excel(full_path)
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# rows = scanner.process_all_rows(df)
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@ -1099,23 +1099,25 @@ def main():
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# str = '\\\\192.168.110.114\\share_File\\西南计算机/北碚报送数据/3.25/图像类\\C071500109B\\Images_识别/77/20251120172316/77.zip'
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# print(f"standardized_path(str)={standardized_path(str)}")
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road_dict_for_width = {}
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df = pd.read_excel('D:/devForBdzlWork/ai-train_platform/predictions/C005500155A/nl_yy_glzb (2)_识别宽度数据读取.xls')
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rows = scanner.process_all_rows(df, columns=['线路编码','区划代码','方向(上行/下行)','识别宽度（米）'])
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for i, row in enumerate(rows, 1):
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data = row['data']
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if pd.notna(data['线路编码']) :
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up_or_down = 'A'
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if data['方向(上行/下行)'] == '下行' :
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up_or_down = 'B'
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key = f"{data['线路编码']}{str(int(data['区划代码']))}{up_or_down}"
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width = data.get('识别宽度（米）')
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if width :
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road_dict_for_width[key] = width
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print('读取excel')
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# road_dict_for_width = {}
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# df = pd.read_excel('D:/devForBdzlWork/ai-train_platform/predictions/C005500155A/nl_yy_glzb (2)_识别宽度数据读取.xls')
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# rows = scanner.process_all_rows(df, columns=['线路编码','区划代码','方向(上行/下行)','识别宽度（米）'])
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# for i, row in enumerate(rows, 1):
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# data = row['data']
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# if pd.notna(data['线路编码']) :
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# up_or_down = 'A'
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# if data['方向(上行/下行)'] == '下行' :
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# up_or_down = 'B'
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# key = f"{data['线路编码']}{str(int(data['区划代码']))}{up_or_down}"
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# width = data.get('识别宽度（米）')
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# if width :
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# road_dict_for_width[key] = width
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# print('读取excel')
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road_dict = {}
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df = pd.read_excel('D:/devForBdzlWork/ai-train_platform/predictions/C005500155A/每公里指标明细表-农村公路技术状况评定结果表-500155.xls')
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# df = pd.read_excel('D:/devForBdzlWork/ai-train_platform/predictions/C005500155A/每公里指标明细表-农村公路技术状况评定结果表-500155.xls')
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# df = pd.read_csv('D:/devForBdzlWork/ai-train_platform/predictions/jlp/20260227090742/公里指标数据导出-jlp.csv')
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df = pd.read_excel('D:/devForBdzlWork/ai-train_platform/predictions/jlp/20260227090742/每公里指标明细表-jlp.xlsx')
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rows = scanner.process_all_rows(df, columns=['线路编码','区划代码','识别宽度（米）','方向(上行/下行)','技术等级','路面类型（沥青/水泥/砂石）','起桩号（米）','止桩号（米）'])
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for i, row in enumerate(rows, 1):
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data = row['data']
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@ -1124,14 +1126,14 @@ def main():
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if data['方向(上行/下行)'] == '下行' :
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up_or_down = 'B'
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key = f"{data['线路编码']}{str(int(data['区划代码']))}{up_or_down}"
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width = road_dict_for_width.get(key)
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width = road_dict.get(key)
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if width :
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data['识别宽度（米）'] = width
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if road_dict.get(key) :
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road_dict[key].append(row)
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else :
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road_dict[key] = [row]
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print('读取excel')
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print('读取excel/csv')
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if __name__ == "__main__":
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main()
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@ -69,9 +69,14 @@ def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGH
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overlay = image.copy()
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for cname, nums in grid_cells.items():
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color = (np.random.randint(64,255),np.random.randint(64,255),np.random.randint(64,255))
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for num in nums[0]:
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x1,y1,x2,y2 = num_to_coord(num, cols, cell_w, cell_h)
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cv2.rectangle(overlay,(x1,y1),(x2,y2),color,-1)
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for cell in nums :
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covered_cells = cell.get("covered_cells")
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for num in covered_cells:
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x1,y1,x2,y2 = num_to_coord(num, cols, cell_w, cell_h)
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cv2.rectangle(overlay,(x1,y1),(x2,y2),color,-1)
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# for num in nums[0]:
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# x1,y1,x2,y2 = num_to_coord(num, cols, cell_w, cell_h)
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# cv2.rectangle(overlay,(x1,y1),(x2,y2),color,-1)
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cv2.addWeighted(overlay,0.4,image,0.6,0,image)
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for i in range(0, w, cell_w):
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cv2.line(image,(i,0),(i,h),(100,100,100),1)
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@ -95,15 +100,17 @@ def detect_road_type_from_content(label_file):
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if kw in content: return "gravel"
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return "gravel"
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def get_road_info(summary_data, road_dict, pile_dict, img_file_name):
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def get_road_info(road_dict, pile_dict, img_file_name):
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"""获取路线信息"""
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parts = pile_dict.get(img_file_name)
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if parts :
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road_code = parts[0]
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road_info = road_dict.get(road_code)
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if road_info :
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pile_no_min = convert_special_format(summary_data[0][0]) * 1000
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pile_no_max = convert_special_format(summary_data[len(summary_data)-1][0]) * 1000
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pile_dict_vals = list(pile_dict.values())
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pile_dict_vals_len = len(pile_dict_vals)
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pile_no_min = convert_special_format(pile_dict_vals[0][1]) * 1000
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pile_no_max = convert_special_format(pile_dict_vals[pile_dict_vals_len-1][1]) * 1000
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if pile_no_max < pile_no_min : #上行下行
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tmp = pile_no_max
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pile_no_max = pile_no_min
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@ -216,9 +223,19 @@ def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,grid_wid
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pile_no_tmp = f"桩号:K000{pile_no}"
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result_lines.append(f"{cname} {pile_no_tmp} {ROAD_TYPE_EN_TO_CN.get(road_type, 'xx')} {ids_str}")
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if cname not in all_class_cells: all_class_cells[cname]=set()
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cell_info.append(covered_cells)
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cell_info.append([max_x - min_x, max_y - min_y])
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# cell_info.append(covered_cells)
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# cell_info.append([max_x - min_x, max_y - min_y])
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# all_class_cells[cname] = cell_info
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if all_class_cells[cname] :
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cell_info = all_class_cells[cname]
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cell_info.append({
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"covered_cells":covered_cells,
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"width": max_x - min_x,
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"height": max_y - min_y
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})
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all_class_cells[cname] = cell_info
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return '\n'.join(result_lines), all_class_cells, road_type, rows * cols
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def yoloseg_to_grid(image_path,label_file,cover_ratio=COVER_RATIO):
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@ -323,11 +340,11 @@ def in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
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return 0
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# 指定间隔区间，输出基本信息+灾害数据
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def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interval=10,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT) :
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def process_info(road_dict,pile_dict,summary_data,current_time,interval=10,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT) :
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process_info_data = []
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if summary_data:
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img_file_name = os.path.basename(image_path)
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road_data = get_road_info(summary_data, road_dict, pile_dict, img_file_name)
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img_file_name = list(pile_dict.keys())[0]
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road_data = get_road_info(road_dict, pile_dict, img_file_name)
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identify_width = road_data.get('识别宽度（米）', '3.6')
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up_or_down = road_data.get('方向(上行/下行)', '上行')
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road_code = pile_dict.get(img_file_name)[0]
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@ -431,7 +448,8 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
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break
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# 同列汇总 10m一个区间--对应5张图
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column_sums = [f"{(sum(column)/(interval / 2)):0.2f}" for column in zip(*subRows)]
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tmp_interval = round((tmp_end - tmp_start) * 1000)
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column_sums = [f"{(sum(column)/(tmp_interval / 2)):0.2f}" for column in zip(*subRows)]
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row += column_sums
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# f.write(','.join(row)+'\n')
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row += ['','','','','','','','','','','','','','','']
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@ -541,6 +559,7 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width=
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# 解压
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# 读取桩号映射
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# 遍历图片
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image_path = None
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summary_data = []
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for root,_,files in os.walk(dir):
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for file in files:
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@ -556,14 +575,17 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width=
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with open(grid_txt_path,'w',encoding='utf-8') as f:
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f.write(out_txt)
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# 生成网格可视化
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# draw_grid_on_image(image_path,class_cells,cell_size=(GRID_WIDTH, GRID_HEIGHT),save_path=os.path.splitext(image_path)[0]+"_grid.jpg")
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draw_grid_on_image(image_path,class_cells,cell_size=(GRID_WIDTH, GRID_HEIGHT),save_path=os.path.splitext(image_path)[0]+"_grid.jpg")
|
||||
# 统计各类面积
|
||||
counts = {k:[len(v[0])*cell_area, v[1][0], v[1][1]] for k,v in class_cells.items()}
|
||||
# counts = {k:[len(v[0])*cell_area, v[1][0], v[1][1]] for k,v in class_cells.items()}
|
||||
counts = {k:[[len(v_child.get("covered_cells"))*cell_area, v_child.get("width"), v_child.get("height")] for v_child in v] for k,v in class_cells.items()}
|
||||
# total_area = sum(counts.values())
|
||||
# 灾害总面积比例
|
||||
merged_set = set([])
|
||||
for k,v in class_cells.items() :
|
||||
merged_set = merged_set.union(v[0])
|
||||
# merged_set = merged_set.union(v[0])
|
||||
for v_child in v:
|
||||
merged_set = merged_set.union(v_child.get("covered_cells"))
|
||||
total_area = len(merged_set)
|
||||
|
||||
# 桩号 路线编号
|
||||
@ -580,24 +602,24 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width=
|
||||
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)
|
||||
process_damage_detail_txt(road_dict, pile_dict, dir, summary_data, current_time)
|
||||
|
||||
# 灾害数据.txt
|
||||
process_damage_txt(road_dict, pile_dict, dir, summary_data, image_path, current_time)
|
||||
process_damage_txt(road_dict, pile_dict, dir, summary_data, current_time)
|
||||
|
||||
# 病害明细列表.xlsx
|
||||
img_file_path = process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, cell_width, cell_height, summary_data, image_path)
|
||||
process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, cell_width, cell_height, summary_data)
|
||||
|
||||
# 综合明细表.xlsx
|
||||
process_damage_composite_excel(road_dict, pile_dict, summary_data, image_path, current_time, img_file_path)
|
||||
process_damage_composite_excel(road_dict, pile_dict, dir, summary_data, current_time)
|
||||
|
||||
|
||||
def 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, dir, summary_data, current_time):
|
||||
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)
|
||||
data1 = process_info(road_dict,pile_dict,summary_data,current_time,10)
|
||||
data2 = process_info(road_dict,pile_dict,summary_data,current_time,100)
|
||||
data3 = process_info(road_dict,pile_dict,summary_data,current_time,1000)
|
||||
|
||||
excel_data = [
|
||||
{
|
||||
@ -619,13 +641,13 @@ def process_damage_composite_excel(road_dict, pile_dict, summary_data, image_pat
|
||||
'data': data3
|
||||
}
|
||||
]
|
||||
create_multiple_sheets_with_multiple_headers(img_file_path + '/excel/综合明细表.xlsx', excel_data)
|
||||
create_multiple_sheets_with_multiple_headers(f"{dir}/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):
|
||||
def process_damage_detail_txt(road_dict, pile_dict, dir, summary_data, current_time):
|
||||
if summary_data:
|
||||
img_file_name = os.path.basename(image_path)
|
||||
road_data = get_road_info(summary_data, road_dict, pile_dict, img_file_name)
|
||||
img_file_name = list(pile_dict.keys())[0]
|
||||
road_data = get_road_info(road_dict, pile_dict, img_file_name)
|
||||
road_code = pile_dict.get(img_file_name)[0]
|
||||
road_type = summary_data[0][3]
|
||||
|
||||
@ -645,20 +667,22 @@ def process_damage_detail_txt(road_dict, pile_dict, dir, summary_data, image_pat
|
||||
else:
|
||||
keys = list(CLASS_MAP_GRAVEL.keys())
|
||||
for k in keys:
|
||||
row.append(f"{counts.get(k,[0,0,0])[0]:.2f}")
|
||||
# row.append(f"{counts.get(k,[0,0,0])[0]:.2f}")
|
||||
sum_count = sum(count[0] for count in counts.get(k, [[0, 0, 0]]))
|
||||
row.append(f"{sum_count:.2f}")
|
||||
f.write(','.join(row)+'\n')
|
||||
|
||||
print(f"输出完成: {out_file}")
|
||||
|
||||
|
||||
def process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, cell_width, cell_height, summary_data, image_path):
|
||||
def process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, cell_width, cell_height, summary_data):
|
||||
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)
|
||||
img_file_name = list(pile_dict.keys())[0]
|
||||
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')
|
||||
@ -666,18 +690,18 @@ def process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, cell_width
|
||||
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_width, attr_value[2]*cell_height, attr_value[0], '', '']
|
||||
data_list.append(excel_data)
|
||||
for attr_value_child in attr_value :
|
||||
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_child[1]*cell_width, attr_value_child[2]*cell_height, attr_value_child[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
|
||||
smb_tool.write_to_excel_pandas(all_data, f"{dir}/excel/病害明细列表.xlsx")
|
||||
|
||||
|
||||
def process_damage_txt(road_dict, pile_dict, dir, summary_data, image_path, current_time):
|
||||
def process_damage_txt(road_dict, pile_dict, dir, summary_data, current_time):
|
||||
if summary_data:
|
||||
img_file_name = os.path.basename(image_path)
|
||||
road_data = get_road_info(summary_data, road_dict, pile_dict, img_file_name)
|
||||
img_file_name = list(pile_dict.keys())[0]
|
||||
road_data = get_road_info(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]
|
||||
@ -780,14 +804,17 @@ def process_damage_txt(road_dict, pile_dict, dir, summary_data, image_path, curr
|
||||
else:
|
||||
keys = list(CLASS_MAP_GRAVEL.keys())
|
||||
for k in keys:
|
||||
tmp_row.append(counts.get(k, [0,0,0])[0])
|
||||
# tmp_row.append(counts.get(k, [0,0,0])[0])
|
||||
sum_count = sum(count[0] for count in counts.get(k, [[0, 0, 0]]))
|
||||
tmp_row.append(sum_count)
|
||||
subRows.append(tmp_row)
|
||||
index = index + 1
|
||||
else :
|
||||
break
|
||||
|
||||
# 同列汇总 10m一个区间--对应5张图
|
||||
column_sums = [f"{(sum(column)/5):0.2f}" for column in zip(*subRows)]
|
||||
tmp_interval = round((tmp_end - tmp_start) * 1000)
|
||||
column_sums = [f"{(sum(column)/tmp_interval):0.2f}" for column in zip(*subRows)]
|
||||
row += column_sums
|
||||
f.write(','.join(row)+'\n')
|
||||
else :
|
||||
@ -879,10 +906,11 @@ def get_road_dict(local_dir):
|
||||
dict: 路线编码到路线信息的映射字典
|
||||
"""
|
||||
# 查找匹配的Excel文件
|
||||
pattern = os.path.join(local_dir, '每公里指标明细表*.xls')
|
||||
found_paths = glob.glob(pattern)
|
||||
pattern_xls = os.path.join(local_dir, '每公里指标明细表*.xls')
|
||||
pattern_xlsx = os.path.join(local_dir, '每公里指标明细表*.xlsx')
|
||||
found_paths = glob.glob(pattern_xls) + glob.glob(pattern_xlsx)
|
||||
|
||||
print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls' 文件:")
|
||||
print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls/xlsx' 文件:")
|
||||
for i, path in enumerate(found_paths, 1):
|
||||
print(f"{i}. {path}")
|
||||
|
||||
@ -961,7 +989,7 @@ 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/C234500155A"
|
||||
output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/jlp/20260227090742"
|
||||
pile_dict = get_pile_dict(output_dir)
|
||||
road_dict = get_road_dict(output_dir)
|
||||
process_dir(road_dict, pile_dict, output_dir)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user