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1-1-1_sample.cpp
1-1-1_sample.cpp
 
 
1-1-1_tritonserver_sample.cpp
1-1-1_tritonserver_sample.cpp
 
 
1-1-1_yolo_tritonserver_sample.cpp
1-1-1_yolo_tritonserver_sample.cpp
 
 
1-1-N_sample.cpp
1-1-N_sample.cpp
 
 
1-N-1_sample.cpp
1-N-1_sample.cpp
 
 
1-N-1_sample2.cpp
1-N-1_sample2.cpp
 
 
1-N-1_sample3.cpp
1-N-1_sample3.cpp
 
 
1-N-N_sample.cpp
1-N-N_sample.cpp
 
 
CMakeLists.txt
CMakeLists.txt
 
 
N-1-N_sample.cpp
N-1-N_sample.cpp
 
 
N-N_sample.cpp
N-N_sample.cpp
 
 
README.md
README.md
 
 
app_des_sample.cpp
app_des_sample.cpp
 
 
app_src_des_sample.cpp
app_src_des_sample.cpp
 
 
app_src_sample.cpp
app_src_sample.cpp
 
 
ba_crossline_sample.cpp
ba_crossline_sample.cpp
 
 
ba_jam_sample.cpp
ba_jam_sample.cpp
 
 
ba_stop_sample.cpp
ba_stop_sample.cpp
 
 
body_scan_and_plate_detect_sample.cpp
body_scan_and_plate_detect_sample.cpp
 
 
dynamic_pipeline_sample.cpp
dynamic_pipeline_sample.cpp
 
 
dynamic_pipeline_sample2.cpp
dynamic_pipeline_sample2.cpp
 
 
enet_seg_sample.cpp
enet_seg_sample.cpp
 
 
face_swap_sample.cpp
face_swap_sample.cpp
 
 
face_tracking_sample.cpp
face_tracking_sample.cpp
 
 
firesmoke_detect_sample.cpp
firesmoke_detect_sample.cpp
 
 
frame_fusion_sample.cpp
frame_fusion_sample.cpp
 
 
image_des_sample.cpp
image_des_sample.cpp
 
 
image_src_sample.cpp
image_src_sample.cpp
 
 
interaction_with_pipe_sample.cpp
interaction_with_pipe_sample.cpp
 
 
lane_detect_sample.cpp
lane_detect_sample.cpp
 
 
mask_rcnn_sample.cpp
mask_rcnn_sample.cpp
 
 
message_broker_kafka_sample.cpp
message_broker_kafka_sample.cpp
 
 
message_broker_sample.cpp
message_broker_sample.cpp
 
 
message_broker_sample2.cpp
message_broker_sample2.cpp
 
 
multi_detectors_and_classifiers_sample.cpp
multi_detectors_and_classifiers_sample.cpp
 
 
multi_detectors_sample.cpp
multi_detectors_sample.cpp
 
 
multi_trt_infer_nodes_sample.cpp
multi_trt_infer_nodes_sample.cpp
 
 
nv_hard_codec_sample.cpp
nv_hard_codec_sample.cpp
 
 
obstacle_detect_sample.cpp
obstacle_detect_sample.cpp
 
 
openpose_sample.cpp
openpose_sample.cpp
 
 
paddle_infer_sample.cpp
paddle_infer_sample.cpp
 
 
plate_recognize_sample.cpp
plate_recognize_sample.cpp
 
 
record_sample.cpp
record_sample.cpp
 
 
rtsp_des_sample.cpp
rtsp_des_sample.cpp
 
 
skip_sample.cpp
skip_sample.cpp
 
 
src_des_sample.cpp
src_des_sample.cpp
 
 
triton_infer_sample.cpp
triton_infer_sample.cpp
 
 
triton_infer_video_sample.cpp
triton_infer_video_sample.cpp
 
 
trt_infer_sample.cpp
trt_infer_sample.cpp
 
 
trt_yolov8_sample.cpp
trt_yolov8_sample.cpp
 
 
trt_yolov8_sample2.cpp
trt_yolov8_sample2.cpp
 
 
vehicle_body_scan_sample.cpp
vehicle_body_scan_sample.cpp
 
 
vehicle_cluster_based_on_classify_encoding_sample.cpp
vehicle_cluster_based_on_classify_encoding_sample.cpp
 
 
vehicle_tracking_sample.cpp
vehicle_tracking_sample.cpp
 
 
video_restoration_sample.cpp
video_restoration_sample.cpp
 
 
vp_logger_sample.cpp
vp_logger_sample.cpp
 
 
vp_test.cpp
vp_test.cpp
 
 

README.md

1-1-1_sample

1 video input, 1 infer task, and 1 output.

1-1-N_sample

1 video input, 1 infer task, and 2 outputs.

1-N-N_sample

1 video input and then split into 2 branches for different infer tasks, then 2 total outputs.

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.

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)

paddle_infer_sample

ocr based on paddle (install paddle_inference first!), 1 video input and 2 outputs (screen, rtmp)

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)

trt_infer_sample

vehicle and plate detector based on tensorrt (install tensorrt first!), 1 video input and 3 outputs (screen, file, rtmp)

vp_logger_sample

show how vp_logger works.

face_tracking_sample

tracking for multi faces.

vehicle_tracking_sample

tracking for multi vehicles.

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.

mask_rcnn_sample

show image segmentation by mask-rcnn.

openpose_sample

show pose estimation by openpose network.

enet_seg_sample

show semantic segmentation by enet network.

multi_detectors_and_classifiers_sample

show multi infer node work together.

image_des_sample

show save/push image to local file or remote via udp.

image_src_sample

show read/receive image from local file or remote via udp.

rtsp_des_sample

show push video stream via rtsp, no rtsp server needed, you can visit it directly.

ba_crossline_sample

count for vehicle based on tracking, the simplest one of behaviour analysis.

plate_recognize_sample

vehicle plate detect and recognize on the whole frame (no need to detect vechile first)

vehicle_body_scan_sample

detect parts of vehicle based on side view of body

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

app_src_sample

send data to pipeline from host coda using app_src_node

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)

ba_stop_sample

vehicle stop behaviour analysis

similiarity search

flask demo for vehicle and face similiarity search

behaviour analysis

flask demo for crossline and stop

property and similiarity search

flask demo for vehicle search by similiarity and properties

ba_jam_sample

traffic jam behaviour analysis

face recognize

flask demo for face recognize

license plate recognize

flask demo for license plate recognize

for more samples