144 lines
3.8 KiB
Markdown
144 lines
3.8 KiB
Markdown
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## 1-1-1_sample ##
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1 video input, 1 infer task, and 1 output.
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## 1-1-N_sample ##
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1 video input, 1 infer task, and 2 outputs.
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## 1-N-N_sample ##
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1 video input and then split into 2 branches for different infer tasks, then 2 total outputs.
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## N-1-N_sample ##
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2 video input and merge into 1 branch automatically for 1 infer task, then resume to 2 branches for outputs again.
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## N-N_sample ##
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multi pipe exist separately and each pipe is 1-1-1 (can be any structure like 1-1-N, 1-N-N)
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## paddle_infer_sample ##
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ocr based on paddle (install paddle_inference first!), 1 video input and 2 outputs (screen, rtmp)
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## src_des_sample ##
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show how src nodes and des nodes work.
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3 (file, rtsp, udp) input and merge into 1 infer task, then resume to 3 branches for outputs (screen, rtmp, fake)
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## trt_infer_sample ##
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vehicle and plate detector based on tensorrt (install tensorrt first!), 1 video input and 3 outputs (screen, file, rtmp)
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## vp_logger_sample ##
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show how `vp_logger` works.
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## face_tracking_sample ##
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tracking for multi faces.
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## vehicle_tracking_sample ##
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tracking for multi vehicles.
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## interaction_with_pipe_sample ##
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show how to interact with pipe, such as start/stop channel by calling api.
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## record_sample ##
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show how `vp_record_node` works.
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## message_broker_sample & message_broker_sample2 ##
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show how message broker nodes work.
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## mask_rcnn_sample ##
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show image segmentation by mask-rcnn.
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## openpose_sample ##
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show pose estimation by openpose network.
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## enet_seg_sample ##
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show semantic segmentation by enet network.
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## multi_detectors_and_classifiers_sample ##
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show multi infer node work together.
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## image_des_sample ##
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show save/push image to local file or remote via udp.
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## image_src_sample ##
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show read/receive image from local file or remote via udp.
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## rtsp_des_sample ##
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show push video stream via rtsp, no rtsp server needed, you can visit it directly.
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## ba_crossline_sample ##
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count for vehicle based on tracking, the simplest one of behaviour analysis.
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## plate_recognize_sample ##
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vehicle plate detect and recognize on the whole frame (no need to detect vechile first)
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## vehicle_body_scan_sample ##
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detect parts of vehicle based on side view of body
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## body_scan_and_plate_detect_sample ##
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2 channels to detect parts of vehicle and detect vehicle plate, you can do something like data fusion later
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## app_src_sample ##
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send data to pipeline from host coda using app_src_node
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## vehicle_cluster_based_on_classify_encoding_sample ##
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vehicle cluster based on labels(classify) and encoding(feature extract), pipeline would display 3 windows (cluster by t-SNE, cluster by labels, detect result)
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## ba_stop_sample ##
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vehicle stop behaviour analysis
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## similiarity search ##
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flask demo for vehicle and face similiarity search <br/>
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## behaviour analysis ##
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flask demo for crossline and stop<br/>
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## property and similiarity search ##
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flask demo for vehicle search by similiarity and properties<br/>
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## ba_jam_sample ##
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traffic jam behaviour analysis<br/>
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## face recognize ##
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flask demo for face recognize<br/>
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## license plate recognize ##
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flask demo for license plate recognize<br/>
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[for more samples](../SAMPLES.md) |