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1-1-1_sample

1 video input, 1 infer task, and 1 output.
![](../doc/p10.png)

1-1-N_sample

1 video input, 1 infer task, and 2 outputs.
![](../doc/p11.png)

1-N-N_sample

1 video input and then split into 2 branches for different infer tasks, then 2 total outputs.
![](../doc/p12.png)

N-1-N_sample

2 video input and merge into 1 branch automatically for 1 infer task, then resume to 2 branches for outputs again.
![](../doc/p13.png)

N-N_sample

multi pipe exist separately and each pipe is 1-1-1 (can be any structure like 1-1-N, 1-N-N)
![](../doc/p14.png)

paddle_infer_sample

ocr based on paddle (install paddle_inference first!), 1 video input and 2 outputs (screen, rtmp)
![](../doc/p15.png)

src_des_sample

show how src nodes and des nodes work.
3 (file, rtsp, udp) input and merge into 1 infer task, then resume to 3 branches for outputs (screen, rtmp, fake)
![](../doc/p16.png)

trt_infer_sample

vehicle and plate detector based on tensorrt (install tensorrt first!), 1 video input and 3 outputs (screen, file, rtmp)
![](../doc/p17.png)

vp_logger_sample

show how vp_logger works.

face_tracking_sample

tracking for multi faces.
![](../doc/p18.png)

vehicle_tracking_sample

tracking for multi vehicles.
![](../doc/p22.png)

interaction_with_pipe_sample

show how to interact with pipe, such as start/stop channel by calling api.

record_sample

show how vp_record_node works.

message_broker_sample & message_broker_sample2

show how message broker nodes work.
![](../doc/p20.png)
![](../doc/p21.png)

mask_rcnn_sample

show image segmentation by mask-rcnn.
![](../doc/p30.png)

openpose_sample

show pose estimation by openpose network.
![](../doc/p31.png)

enet_seg_sample

show semantic segmentation by enet network.
![](../doc/p32.png)

multi_detectors_and_classifiers_sample

show multi infer node work together.
![](../doc/p33.png)

image_des_sample

show save/push image to local file or remote via udp.
![](../doc/p34.png)

image_src_sample

show read/receive image from local file or remote via udp.
![](../doc/p35.png)

rtsp_des_sample

show push video stream via rtsp, no rtsp server needed, you can visit it directly.
![](../doc/p36.png)

ba_crossline_sample

count for vehicle based on tracking, the simplest one of behaviour analysis.
![](../doc/p37.png)

plate_recognize_sample

vehicle plate detect and recognize on the whole frame (no need to detect vechile first)
![](../doc/p38.png)

vehicle_body_scan_sample

detect parts of vehicle based on side view of body
![](../doc/p40.png)

body_scan_and_plate_detect_sample

2 channels to detect parts of vehicle and detect vehicle plate, you can do something like data fusion later
![](../doc/p39.png)

app_src_sample

send data to pipeline from host coda using app_src_node
![](../doc/p41.png)

vehicle_cluster_based_on_classify_encoding_sample

vehicle cluster based on labels(classify) and encoding(feature extract), pipeline would display 3 windows (cluster by t-SNE, cluster by labels, detect result)
![](../doc/p42.png)

ba_stop_sample

vehicle stop behaviour analysis
![](../doc/p49.png)

similiarity search

flask demo for vehicle and face similiarity search

![](../doc/p44.png)![](../doc/p43.png)![](../doc/p45.png)

behaviour analysis

flask demo for crossline and stop

![](../doc/p48.png)

property and similiarity search

flask demo for vehicle search by similiarity and properties

![](../doc/p46.png)![](../doc/p47.png)

ba_jam_sample

traffic jam behaviour analysis

![](../doc/p50.png)

face recognize

flask demo for face recognize

![](../doc/p51.png)

license plate recognize

flask demo for license plate recognize

![](../doc/p52.png)

for more samples