基于头肩部检测的过线客流统计

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 ...