3. For rtsp stream, use --rtsp RTSP [RTSP ...] parameter to specify one or more rtsp streams. Separate the multiple addresses with a space, or replace the video address directly after the video_file with the rtsp stream address), examples as follows
3. rtsp push/pull stream
- rtsp pull stream
For rtsp pull stream, use `--rtsp RTSP [RTSP ...]` parameter to specify one or more rtsp streams. Separate the multiple addresses with a space, or replace the video address directly after the video_file with the rtsp stream address), examples as follows
```
# Example: Single video stream for pedestrian attribute recognition
For rtsp push stream, use `--pushurl rtsp:[IP]` parameter to push stream to a IP set, and you can visualize the output video by [VLC Player](https://vlc.onl/) with the `open network` funciton. the whole url path is `rtsp:[IP]/videoname`, the videoname here is the basename of the video file to infer, and the default of videoname is `output` when the video come from local camera and there is no video name.
```
# Example：Pedestrian attribute recognition，in this example the whole url path is: rtsp://[YOUR_SERVER_IP]:8554/test_video
1. rtsp push stream is based on [rtsp-simple-server](https://github.com/aler9/rtsp-simple-server), please enable this serving first.
2. the output visualize will be frozen frequently if the model cost too much time, we suggest to use faster model like ppyoloe_s in tracking, this is simply replace mot_ppyoloe_l_36e_pipeline.zip with mot_ppyoloe_s_36e_pipeline.zip in model config yaml file.
### Jetson Deployment
Due to the large gap in computing power of the Jetson platform compared to the server, we suggest:
...
...
@@ -155,6 +171,8 @@ With this recommended configuration, it is possible to achieve higher speeds on
| --rtsp | Option | rtsp video stream address, supports one or more simultaneous streams input |
| --camera_id | Option | The camera ID for prediction, default is -1 ( for no camera prediction, can be set to 0 - (number of cameras - 1) ), press `q` in the visualization interface during the prediction process to output the prediction result to: output/output.mp4 |
| --device | Option | Running device, options include `CPU/GPU/XPU`, and the default is `CPU`. |
| --pushurl | Option | push the output video to rtsp stream, normaly start with `rtsp://`; this has higher priority than local video save, while this is set, pipeline will not save local visualize video, the default is "", means this will not work now.
|
| --output_dir | Option | The root directory for the visualization results, and the default is output/ |
| --run_mode | Option | For GPU, the default is paddle, with (paddle/trt_fp32/trt_fp16/trt_int8) as optional |
| --enable_mkldnn | Option | Whether to enable MKLDNN acceleration in CPU prediction, the default is False |
3. For rtsp stream, use --rtsp RTSP [RTSP ...] parameter to specify one or more rtsp streams. Separate the multiple addresses with a space, or replace the video address directly after the video_file with the rtsp stream address), examples as follows
3. rtsp push/pull stream
- rtsp pull stream
For rtsp pull stream, use --rtsp RTSP [RTSP ...] parameter to specify one or more rtsp streams. Separate the multiple addresses with a space, or replace the video address directly after the video_file with the rtsp stream address), examples as follows
```
# Example: Single video stream for pedestrian attribute recognition
For rtsp push stream, use --pushurl rtsp:[IP] parameter to push stream to a IP set, and you can visualize the output video by [VLC Player](https://vlc.onl/) with the `open network` funciton. the whole url path is `rtsp:[IP]/videoname`, the videoname here is the basename of the video file to infer, and the default of videoname is `output` when the video come from local camera and there is no video name.
```
# Example：license plate recognition，in this example the whole url path is: rtsp://[YOUR_SERVER_IP]:8554/test_video
1. rtsp push stream is based on [rtsp-simple-server](https://github.com/aler9/rtsp-simple-server), please enable this serving first.
2. the output visualize will be frozen frequently if the model cost too much time, we suggest to use faster model like ppyoloe_s in tracking, this is simply replace mot_ppyoloe_l_36e_pipeline.zip with mot_ppyoloe_s_36e_pipeline.zip in model config yaml file.
### Jetson Deployment
Due to the large gap in computing power of the Jetson platform compared to the server, we suggest:
...
...
@@ -161,6 +176,8 @@ With this recommended configuration, it is possible to achieve higher speeds on
| --rtsp | Option | rtsp video stream address, supports one or more simultaneous streams input |
| --camera_id | Option | The camera ID for prediction, default is -1 ( for no camera prediction, can be set to 0 - (number of cameras - 1) ), press `q` in the visualization interface during the prediction process to output the prediction result to: output/output.mp4 |
| --device | Option | Running device, options include `CPU/GPU/XPU`, and the default is `CPU`. |
| --pushurl | Option | push the output video to rtsp stream, normaly start with `rtsp://`; this has higher priority than local video save, while this is set, pipeline will not save local visualize video, the default is "", means this will not work now.
|
| --output_dir | Option | The root directory for the visualization results, and the default is output/ |
| --run_mode | Option | For GPU, the default is paddle, with (paddle/trt_fp32/trt_fp16/trt_int8) as optional |
| --enable_mkldnn | Option | Whether to enable MKLDNN acceleration in CPU prediction, the default is False |
