generated from zhangwei/Train_Identify
166 lines
5.0 KiB
YAML
166 lines
5.0 KiB
YAML
init_deviceid: "ALL" #例： 0; 0,1; 2,3; ALL
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# 基础控制参数
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base:
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# 股道名称
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track_name: "1"
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# 测试模式
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test_model: false
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# Api 监听端口
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api_port: 7070
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# 是否上传识别结果
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up_result: false
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# 是否启用socket-server
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use_socket_server: false
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# 日志文件目录
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log_path: "./logs"
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# 识别结果目录
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result_path: "./result"
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# 调试结果目录
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debug_result_path: "./debug_result"
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# 最优识别目录
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best_result_path: "./best_result"
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# 结果存储天数
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result_save_days: 2
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# 日志参数
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log:
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# 输出日志级别[DEBUG, INFO, WARN, ERROR, FATAL]
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out_level: "DEBUG"
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# 保存日志级别
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save_level: "DEBUG"
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#识别数据来源参数配置
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data_source:
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#url: "rtsp://admin:sgt12345@10.27.119.13:554/h264/ch1/main/av_stream"
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url: "./vedio/buertai2.mp4"
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# 跳帧数
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skip_interval: 3
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# 识别目标
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target: "NUM"
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# 行驶方向 0-自动识别 1-向左 2-向右 （与“首位信息”成对存在，形成例如向左就编号在前，向右就属性在前的对应）
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direction: 0
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# 0-向左编号在前 1-向左属性在前 (向右行驶的情况：2-向右编号在前 3-向右属性在前)
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left_first: 0
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# (向左行驶的情况：0-向左编号在前 1-向左属性在前) 2-向右编号在前 3-向右属性在前
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right_first: 3
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# (ltx,lty,rbx,rby)
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identify_areas: "120, 0, 1800, 1080"
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# 大框的最小高度(为屏蔽远股道识别到的信息)
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classid_minheight: "1:90, 2:120, 3:120, 9:240, 10:240, 18:120"
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# 识别参数
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identify:
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# 硬件解码
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hardware_decode: true
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# 运行方式
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run_mode: "always" #[always; command]
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# 是否开启动态检测
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need_move_detect_flag: true
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# 识别方向 [LEFT,RIGHT,ALL]
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identify_direction: "LEFT"
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# 大框帧跨度（比一个大框从出现到消失的跨度稍大一点, 跟跳帧有关系）
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partition_frame_span: 20
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# 大框帧跨度的位置像素差异
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split_frame_span_px: 200
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# 每帧大框位置差异最小值 (持续小于此值，则可能停车)
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chkstop_px: 15
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# 持续X次续位置差异小于chkstop_px，则判断为停车。
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chkstop_count: 10
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# 过滤最小大框高度（不需要的话就写个很小的值）
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num_frame_height: 150
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pro_frame_height: 120
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# 过滤最大框宽度（不需要的话就写个很大的值）
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space_frame_width: 500
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# 是否识别车头
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train_heard_detect: true
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# 选优 0-频率优先 1-长度优先
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select_best_mode: 0
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# 保存图片质量（1~100 越高越清晰）
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save_pic_quality: 50
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#是否实时推流-用于直播
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#gc_push_actual_flag: false
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# 模型参数
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model:
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# 来车检测
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MoveEngine:
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path: "./model/step0/step0.engine"
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model_info_path: "./model/step0/move_modelinfo.txt"
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score_threshold: 0.9
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nms_threshold: 0.3
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# 关键区域识别
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TrainStepOneEngine:
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path: "./model/step1/step1.engine"
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model_info_path: "./model/step1/train_step1_modelinfo.txt"
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score_threshold: 0.6
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nms_threshold: 0.3
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# 字符识别
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TrainStepTwoEngine:
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path: "./model/step2/step2.engine"
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model_info_path: "./model/step2/train_step2_modelinfo.txt"
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score_threshold: 0.7
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nms_threshold: 0.3
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# 定检期关键区域识别
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ChkDateStepOneEngine:
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path: "./model/chkDate_step1/step1.engine"
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model_info_path: "./model/chkDate_step1/chkdate_step1_modelinfo.txt"
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score_threshold: 0.6
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nms_threshold: 0.3
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# 定检期字符识别
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ChkDateStepTwoEngine:
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path: "./model/chkDate_step2/step2.engine"
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model_info_path: "./model/chkDate_step2/chkdate_step2_modelinfo.txt"
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score_threshold: 0.7
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nms_threshold: 0.3
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# 集装箱关键区域识别
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StepOneContainerEngine:
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path: "./model/container_step1/con1.engine"
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model_info_path: "./model/container_step1/container_step1_modelinfo.txt"
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score_threshold: 0.6
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nms_threshold: 0.3
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# 集装箱字符识别
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StepTwoContainerEngine:
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path: "./model/container_step2/con2.engine"
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model_info_path: "./model/container_step2/container_step2_modelinfo.txt"
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score_threshold: 0.7
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nms_threshold: 0.3
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# http 接口
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http_client:
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# 服务器IP
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http_ip: 192.168.2.108
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# 通讯端口
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http_port: 20004
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# 获取接口授权地址
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token_path: "/api/blade-auth/oauth/token"
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# 识别结果上传地址
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up_result_path: "/api/train-carriage/identification/rfid-save"
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# 设备状态上传地址
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device_status_url: "/api/blade-train/deviceInfo/save"
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# 接口用户名
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username: "guest_01"
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# 接口密码
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password: "d55b0f642e817eea24725d2f2a31dd08"
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# 上传图片的地址
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image_srv: "http://192.168.0.121:9010/"
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# websocket_server 的服务端参数
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wsocket_server:
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port: 7071
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max_queue_len: 10
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#sftp用户名、密码、地址
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ftp:
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type: "ftp" #可选 ftp 或 sftp
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ip: "192.168.2.138"
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port: 21 # ftp默认21 sftp默认22
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username: "nvidia"
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password: "nvidia"
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image_path: ""
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quit_time: 10 #无上传任务延迟XXX秒断开FTP连接
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gc_space_minrbx_imgpercent: 0 #间隔框最低点不应小于画面某个高度值(该值为画面百分比) [主要为屏蔽远股道间隔框，若不需要屏蔽则配置为0]
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