Low Latency¶
These tables show what performance you can expect from USB 3.2 Gen 1 (5 Gbps) connection with an OAK camera. XLink chunking was
disabled for these tests (pipeline.setXLinkChunkSize(0)). For an example code, see Latency measurement.
What |
Resolution |
FPS |
FPS set |
Time-to-Host [ms] |
Bandwidth |
Histogram |
|---|---|---|---|---|---|---|
Color (isp) |
1080P |
60 |
60 |
33 |
1.5 Gbps |
|
Color (isp) |
4K |
28.5 |
30 |
150 |
2.8 Gbps |
|
Mono |
720P/800P |
120 |
120 |
24.5 |
442/482 Mbps |
|
Mono |
400P |
120 |
120 |
7.5 |
246 Mbps |
Time-to-Host is measured time between frame timestamp (
imgFrame.getTimestamp()) and host timestamp when the frame is received (dai.Clock.now()).Histogram shows how much Time-to-Host varies frame to frame. Y axis represents number of frame that occured at that time while the X axis represents microseconds.
Bandwidth is calculated bandwidth required to stream specified frames at specified FPS.
Encoded frames¶
What |
Resolution |
FPS |
FPS set |
Time-to-Host [ms] |
Histogram |
|---|---|---|---|---|---|
Color video H.265 |
4K |
28.5 |
30 |
210 |
|
Color video MJPEG |
4K |
30 |
30 |
71 |
|
Color video H.265 |
1080P |
60 |
60 |
42 |
|
Color video MJPEG |
1080P |
60 |
60 |
31 |
|
Mono H.265 |
800P |
60 |
60 |
23.5 |
|
Mono MJPEG |
800P |
60 |
60 |
22.5 |
|
Mono H.265 |
400P |
120 |
120 |
7.5 |
|
Mono MJPEG |
400P |
120 |
120 |
7.5 |
You can also reduce frame latency by using Zero-Copy branch of the DepthAI. This will pass pointers (at XLink level) to cv2.Mat instead of doing memcopy (as it currently does), so performance improvement would depend on the image sizes you are using. (Note: API differs and not all functionality is available as is on the message_zero_copy branch)
Reducing latency when running NN¶
In the examples above we were only streaming frames, without doing anything else on the OAK camera. This section will focus on how to reduce latency when also running NN model on the OAK.
Lowering camera FPS to match NN FPS¶
Lowering FPS to not exceed NN capabilities typically provides the best latency performance, since the NN is able to start the inference as soon as a new frame is available.
For example, with 15 FPS we get a total of about 70 ms latency, measured from capture time (end of exposure and MIPI readout start).
This time includes the following:
MIPI readout
ISP processing
Preview post-processing
NN processing
Streaming to host
And finally, eventual extra latency until it reaches the app
Note: if the FPS is increased slightly more, towards 19..21 FPS, an extra latency of about 10ms appears, that we believe is related to firmware. We are activaly looking for improvements for lower latencies.
NN input queue size and blocking behaviour¶
If the app has detNetwork.input.setBlocking(False), but the queue size doesn’t change, the following adjustment
may help improve latency performance:
By adding detNetwork.input.setQueueSize(1), while setting back the camera FPS to 40, we get about 80.. 105ms latency.
One of the causes of being non-deterministic is that the camera is producing at a different rate (25ms frame-time),
vs. when NN has finished and can accept a new frame to process.
Got questions?
We’re always happy to help with code or other questions you might have.
