PS ███> .\solution.bat -l
...
Solutions name:
mask_det_yolo3
...
passenger_flow_head_det_yolo7

PS ███> .\solution.bat -s passenger_flow_head_det_yolo7
...

PS ███> .\create.bat -t server -n passenger_flow -s passenger_flow_head_det_yolo7
sdk version is modelbox-xxx
success: create passenger_flow in ███\modelbox\workspace

passenger_flow
|--bin
│  |--main.bat：应用执行入口
│  |--mock_task.toml：应用在本地执行时的输入输出配置，此应用默认使用本地视频文件为输入源，最终结果输出到另一本地视频文件，可根据需要修改
|--CMake：存放一些自定义CMake函数
|--data：存放应用运行所需要的图片、视频、文本、配置等数据
│  |--passenger_flow.mp4：客流统计测试用视频文件
│  |--simsun.ttc：中文字体库
|--dependence
│  |--modelbox_requirements.txt：应用运行依赖的外部库在此文件定义，本应用依赖pillow工具包
|--etc
│  |--flowunit：应用所需的功能单元存放在此目录
│  │  |--cpp：存放C++功能单元编译后的动态链接库，此应用没有C++功能单元
│  │  |--draw_passenger_bbox：客流画图功能单元
│  │  |--object_tracker：目标跟踪功能单元
│  │  |--yolov7_post：头肩部检测使用的是YOLO7模型，此处即为后处理功能单元
|--flowunit_cpp：存放C++功能单元的源代码，此应用没有C++功能单元
|--graph：存放流程图
│  |--passenger_flow.toml：默认流程图，使用本地视频文件作为输入源
│  |--modelbox.conf：modelbox相关配置
|--hilens_data_dir：存放应用输出的结果文件、日志、性能统计信息
|--model：推理功能单元目录
│  |--head_detection：头肩部检测推理功能单元
│  │  |--head_detection.toml：头肩部检测推理功能单元的配置文件
│  │  |--head_det_yolo7_lite_224x352.onnx：头肩部检测onnx模型
|--build_project.sh：应用构建脚本
|--CMakeLists.txt
|--rpm：打包rpm时生成的目录，将存放rpm包所需数据
|--rpm_copyothers.sh：rpm打包时的辅助脚本

[driver]
# 功能单元的扫描路径，包含在[]中，多个路径使用,分隔
# ${HILENS_APP_ROOT} 表示当前应用的实际路径
# ${HILENS_MB_SDK_PATH} 表示ModelBox核心库的实际路径
dir = [
 "${HILENS_APP_ROOT}/etc/flowunit",
 "${HILENS_APP_ROOT}/etc/flowunit/cpp",
 "${HILENS_APP_ROOT}/model",
 "${HILENS_MB_SDK_PATH}/flowunit",
]
skip-default = true
[profile]
# 通过配置profile和trace开关启用应用的性能统计
profile = false                       # 是否记录profile信息，每隔60s记录一次统计信息
trace = false                         # 是否记录trace信息，在任务执行过程中和结束时，输出统计信息
dir = "${HILENS_DATA_DIR}/mb_profile" # profile/trace信息的保存位置
[flow]
desc = "passenger detection using head-shoulder detection with yolov7 for local video or rtsp video stream" # 应用的简单描述
[graph]
format = "graphviz" # 流程图的格式，当前仅支持graphviz
graphconf = """digraph passenger_flow {
    node [shape=Mrecord]
 queue_size = 4
 batch_size = 1
    # 定义节点，即功能单元及其属性
    input1[type=input,flowunit=input,device=cpu,deviceid=0]
 data_source_parser[type=flowunit, flowunit=data_source_parser, device=cpu, deviceid=0]
 video_demuxer[type=flowunit, flowunit=video_demuxer, device=cpu, deviceid=0]
 video_decoder[type=flowunit, flowunit=video_decoder, device=cpu, deviceid=0, pix_fmt="rgb"]
 resize[type=flowunit flowunit=resize device=cpu deviceid="0" image_width=352, image_height=224]
 color_transpose[type=flowunit flowunit=packed_planar_transpose device=cpu deviceid="0"]
 normalize[type=flowunit flowunit=normalize device=cpu deviceid="0" standard_deviation_inverse="0.0039215686,0.0039215686,0.0039215686"]
 head_detection[type=flowunit flowunit=head_detection device=cpu deviceid="0"]
    yolov7_post[type=flowunit flowunit=yolov7_post device=cpu deviceid="0"]
 object_tracker[type=flowunit, flowunit=object_tracker, device=cpu, deviceid=0]
 draw_passenger_bbox[type=flowunit, flowunit=draw_passenger_bbox, device=cpu, deviceid=0]
 video_out[type=flowunit flowunit=video_out device=cpu deviceid="0"]
    # 定义边，即功能间的数据传递关系
    input1:input -> data_source_parser:in_data
 data_source_parser:out_video_url -> video_demuxer:in_video_url
 video_demuxer:out_video_packet -> video_decoder:in_video_packet
 video_decoder:out_video_frame -> resize:in_image
 resize:out_image -> color_transpose:in_image
 color_transpose:out_image -> normalize:in_data
 normalize:out_data -> head_detection:input
 head_detection:output -> yolov7_post:in_feat
    yolov7_post:out_data -> object_tracker:in_bbox
 object_tracker:out_track -> draw_passenger_bbox:in_track
 video_decoder:out_video_frame -> draw_passenger_bbox:in_image
 draw_passenger_bbox:out_image -> video_out:in_video_frame
}"""

  def get_tracking_objects(self, line_y):
 '''从跟踪器中获取跟踪目标，保存到结构化数据中'''
        def _is_pass_line(bbox, line_y):
 '''根据检测框的中心点与线段的水平位置关系判断是否过线'''
 center_y = (bbox[1] + bbox[3]) / 2
 return center_y > line_y
 tracking_objects = []  # 所有跟踪目标
 for track in self.tracker.tracks:
            # 只记录CONFIRMED状态的跟踪目标
 if track.state != EasyTracker.TrackingState.CONFIRMED:
 continue
 tracking_obj = {}  # 使用字典保存跟踪目标
 tracking_obj["id"] = track.track_id  # 跟踪id
 tracking_obj["bbox"] = track.det  # 跟踪框
 if not track.passline and _is_pass_line(track.det, line_y):  # 刚好过线
 track.passline = True
 self.flow_count += 1
 tracking_obj["passline"] = track.passline  # 记录过线信息
 tracking_objects.append(tracking_obj)
 return tracking_objects

[input]
type = "url"
url = "${HILENS_APP_ROOT}/data/passenger_flow.mp4"  # 表示输入源为本地视频文件
[output]
type = "local"
url = "${HILENS_APP_ROOT}/hilens_data_dir/passenger_flow_result.mp4"  # 表示输出为本地视频文件

PS ███> .\build_project.sh
...
PS ███>

PS ███> .\bin\main.bat
...