MISP channels enabled in defaults : 0
MISP channels enabled in config file: 0
Setting MISP AWB to Auto
MISP channels enabled in defaults : 0
MISP channels enabled in config file: 0
max request queue depth: 6
=================================================================
configuration for 2 cameras:

cam #0
    name:                boson
    type:                boson
    bayer_type:          0
    enabled:             0
    camera_id:           0
    camera_id_second:    -1
    fps:                 30
    en_rotate:           0
    en_rotate2:          0

    en_preview:          1
    en_raw_preview:      1
    preview_width:       640
    preview_height:      512

    en_misp:             1
    misp_width:          512
    misp_height:         640

    en_small_video:      0
    small_video_width:   640
    small_video_height:  480

    en_large_video:      0
    large_video_width:   -1
    large_video_height:  -1

    en_snapshot:         0
    snap_width:          -1
    snap_height:         -1
    exif_focal_length:   0.000000
    exif_focal_len_35mm_format:0
    exif_fnumber:        0.000000

    ae_mode:             off
    msv_exposure_min_us: 20
    msv_exposure_max_us: 33000
    gain_min           : 100
    gain_max           : 100
    standby_enabled:     0
    decimator:           1
    independent_exposure:0

cam #1
    name:                hires
    type:                ov64b
    bayer_type:          1
    enabled:             1
    camera_id:           1
    camera_id_second:    -1
    fps:                 30
    en_rotate:           0
    en_rotate2:          0

    en_preview:          1
    en_raw_preview:      1
    preview_width:       9216
    preview_height:      6944

    en_misp:             1
    misp_width:          9216
    misp_height:         6944

    en_small_video:      0
    small_video_width:   1024
    small_video_height:  768

    en_large_video:      0
    large_video_width:   3840
    large_video_height:  2160

    en_snapshot:         0
    snap_width:          3840
    snap_height:         2160
    exif_focal_length:   3.100000
    exif_focal_len_35mm_format:17
    exif_fnumber:        1.240000

    ae_mode:             isp
    msv_exposure_min_us: 20
    msv_exposure_max_us: 33000
    gain_min           : 100
    gain_max           : 800
    standby_enabled:     0
    decimator:           1
    independent_exposure:0

fsync_en:            0
fsync_gpio:          109
=================================================================
thread is locked to cores: 4 5 6 7
connected to mavlink pipe
Connected to cpu-monitor
Skipping Camera: boson, configuration marked disabled
Starting Camera: hires (id #1)
ModalExposureMSV: initializing for camera name  type 
Estimated imu dt = 0.000976s
gbm_create_device(156): Info: backend name is: msm_drm
MISP Initializing for camera hires
 Detected 1 platform(s)
 Detected 1 GPU device(s)
ERROR in json_from_yaml, failed to open file
MISP: Loading intrinsics cal file hires_intrinsics.yml for camera hires, ret -1
WARNING: Lens calibration for camera hires is missing (hires_intrinsics.yml). Using defaults:

------ voxl-camera-server: Started 1 of 2 cameras

------ voxl-camera-server: Camera server is now running
ERROR:   Received "Result" error from camera: hires, frame number 0
ERROR:   Received "Buffer" error from camera: hires, frame number 0
WARNING: Pushed buffers back to pools for frame: 0
ERROR:   Received "Result" error from camera: hires, frame number 1
ERROR:   Received "Buffer" error from camera: hires, frame number 1
WARNING: Pushed buffers back to pools for frame: 1
ERROR:   Received "Result" error from camera: hires, frame number 2
ERROR:   Received "Buffer" error from camera: hires, frame number 2
WARNING: Pushed buffers back to pools for frame: 2
ERROR:   Received "Result" error from camera: hires, frame number 3
ERROR:   Received "Buffer" error from camera: hires, frame number 3
WARNING: Pushed buffers back to pools for frame: 3
ERROR:   Received "Result" error from camera: hires, frame number 4
ERROR:   Received "Buffer" error from camera: hires, frame number 4
WARNING: Pushed buffers back to pools for frame: 4
ERROR:   Received "Request" error from camera: hires, frame number 5
WARNING: Pushed buffers back to pools for frame: 5
^C
received SIGINT Ctrl-C

------ voxl-camera-server: Camera server is now stopping
------------------------------
Stats For Camera hires:
	 Received Frame Count: 12
	 Request  Error Count: 1
	 Buffer   Error Count: 5
	 Result   Error Count: 5

------ voxl-camera-server: Camera server exited gracefully, returning 0

Here is the output of voxl-version on the non-working voxl:

────────────────────────────────────────────────────────────────────────────────
system-image: 1.8.06-M0054-14.1a-perf
kernel:       #1 SMP PREEMPT Wed Oct 22 03:21:50 UTC 2025 4.19.125
────────────────────────────────────────────────────────────────────────────────
hw platform:  M0054
mach.var:     1.0.0
SKU:          MCCA-M0054-C11-T0-M0-X0
────────────────────────────────────────────────────────────────────────────────
voxl-suite:   1.6.3
────────────────────────────────────────────────────────────────────────────────
Packages:
Repo:  http://voxl-packages.modalai.com/ ./dists/qrb5165/sdk-1.6/binary-arm64/
Last Updated: 2023-03-02 13:58:13
List:
libfc-sensor                   1.0.9
libmodal-cv                    0.6.0
libmodal-exposure              0.1.4
libmodal-flow                  1.0.3
libmodal-journal               0.2.7
libmodal-json                  0.4.8
libmodal-pipe                  2.14.11
libqrb5165-io                  0.6.3
libvoxl-cci-direct             0.3.3
libvoxl-codec                  0.0.2
libvoxl-cutils                 0.1.6
modalai-slpi                   1.2.2
mv-voxl                        0.1-r0
qrb5165-bind                   0.1-r0
qrb5165-dfs-server             0.2.0
qrb5165-mini-tof-server        0.2.2
qrb5165-rangefinder-server     0.1.6
qrb5165-slpi-test-sig          01-r0
qrb5165-tflite                 2.17.2
voxl-bind-spektrum             0.1.1
voxl-camera-calibration        0.6.1
voxl-camera-server             2.2.23-202604170806
voxl-ceres-solver              2:2.0.0-2
voxl-configurator              1.1.5
voxl-cpu-monitor               0.7.7
voxl-docker-support            1.3.1
voxl-elrs                      1.1.0
voxl-esc                       1.5.7
voxl-esptool                   0.2.0
voxl-feature-tracker           0.5.2
voxl-flow-server               0.3.6
voxl-gphoto2-server            0.0.10
voxl-imu-server                2.0.1
voxl-io-server                 0.0.8
voxl-jpeg-turbo                2.1.3-7
voxl-lepton-server             1.3.3
voxl-lepton-tracker            0.0.4
voxl-libgeographic             1.0.0
voxl-libgphoto2                0.0.4
voxl-libuvc                    1.0.7
voxl-logger                    0.6.1
voxl-mavcam-manager            0.6.0
voxl-mavlink                   0.1.6
voxl-mavlink-server            1.4.14
voxl-modem                     1.2.3
voxl-mongoose                  7.19.0
voxl-mpa-to-ros                0.3.9
voxl-mpa-tools                 1.5.8
voxl-nano-tracker              0.1.7
voxl-open-vins-server          0.6.0
voxl-opencv                    4.5.5-3
voxl-osd                       0.3.8
voxl-portal                    0.8.7
voxl-px4                       1.14.0-2.0.133
voxl-px4-params                0.9.0
voxl-qvio-server               1.2.3
voxl-remote-id                 0.0.9
voxl-reset-slpi                0.0.1
voxl-state-estimator           0.0.6
voxl-streamer                  0.8.0
voxl-suite                     1.6.3
voxl-tag-detector              0.1.0
voxl-tflite-server             0.5.1
voxl-utils                     2.0.2
voxl-uvc-server                0.1.7
voxl-vision-hub                1.9.21
voxl-vtx                       2.0.2
voxl-wavemux                   0.0.3
voxl2-io                       0.0.3
voxl2-security-hardening-utls  1.0-r0
voxl2-system-image             1.8.06-r0
voxl2-wlan                     1.0-r0
────────────────────────────────────────────────────────────────────────────────

And here are the versions for the working voxl:

────────────────────────────────────────────────────────────────────────────────
system-image: 1.8.06-M0054-14.1a-perf
kernel:       #1 SMP PREEMPT Wed Oct 22 04:13:18 UTC 2025 4.19.125
────────────────────────────────────────────────────────────────────────────────
hw platform:  M0054
mach.var:     1.0.1
SKU:          TF-M0054-4-V1-C11
────────────────────────────────────────────────────────────────────────────────
voxl-suite:   1.6.3
────────────────────────────────────────────────────────────────────────────────
Packages:
Repo:  http://voxl-packages.modalai.com/ ./dists/qrb5165/sdk-1.6/binary-arm64/
Last Updated: 2026-05-07 16:00:59
List:
libfc-sensor                   1.0.9
libmodal-cv                    0.6.0
libmodal-exposure              0.1.4
libmodal-flow                  1.0.3
libmodal-journal               0.2.7
libmodal-json                  0.4.8
libmodal-pipe                  2.14.11
libqrb5165-io                  0.6.3
libvoxl-cci-direct             0.3.3
libvoxl-codec                  0.0.2
libvoxl-cutils                 0.1.6
modalai-slpi                   1.2.2
mv-voxl                        0.1-r0
qrb5165-bind                   0.1-r0
qrb5165-dfs-server             0.2.0
qrb5165-mini-tof-server        0.2.2
qrb5165-rangefinder-server     0.1.6
qrb5165-slpi-test-sig          01-r0
qrb5165-tflite                 2.17.2
voxl-ardupilot                 4.8.0-8ed7441c
voxl-bind-spektrum             0.1.1
voxl-camera-calibration        0.6.1
voxl-camera-server             2.2.23-202604170806
voxl-ceres-solver              2:2.0.0-2
voxl-configurator              1.1.5
voxl-cpu-monitor               0.7.7
voxl-docker-support            1.3.1
voxl-elrs                      1.1.0
voxl-esc                       1.5.7
voxl-esptool                   0.2.0
voxl-feature-tracker           0.5.2
voxl-flow-server               0.3.6
voxl-gphoto2-server            0.0.10
voxl-imu-server                2.0.1
voxl-io-server                 0.0.8
voxl-jpeg-turbo                2.1.3-7
voxl-lepton-server             1.3.3
voxl-lepton-tracker            0.0.4
voxl-libgeographic             1.0.0
voxl-libgphoto2                0.0.4
voxl-libuvc                    1.0.7
voxl-logger                    0.6.1
voxl-mavcam-manager            0.6.0
voxl-mavlink                   0.1.6
voxl-mavlink-server            1.4.14
voxl-microdds-agent            3.0.0-0
voxl-modem                     1.2.3
voxl-mongoose                  7.19.0
voxl-mpa-to-ros                0.3.9
voxl-mpa-to-ros2               0.0.7
voxl-mpa-tools                 1.5.8
voxl-nano-tracker              0.1.7
voxl-open-vins-server          0.6.0
voxl-opencv                    4.5.5-3
voxl-osd                       0.3.8
voxl-portal                    0.8.7
voxl-px4                       1.14.0-2.0.133
voxl-px4-params                0.9.0
voxl-qvio-server               1.2.3
voxl-remote-id                 0.0.9
voxl-reset-slpi                0.0.1
voxl-ros2-foxy                 0.0.1
voxl-state-estimator           0.0.6
voxl-streamer                  0.8.0
voxl-suite                     1.6.3
voxl-tag-detector              0.1.0
voxl-tflite-server             0.5.1
voxl-utils                     2.0.2
voxl-uvc-server                0.1.7
voxl-vision-hub                1.9.21
voxl-vtx                       2.0.2
voxl-wavemux                   0.0.3
voxl2-io                       0.0.3
voxl2-security-hardening-utls  1.0-r0
voxl2-system-image             1.8.06-r0
voxl2-wlan                     1.0-r0
────────────────────────────────────────────────────────────────────────────────

The versions look to be the same for the most part, which is confusing us.

We also have opencl code that runs offline pretty quick on an nvidia GPU, but it does not have as many tuning knobs.

I guess it all depends on whether you are using the images right on the voxl2 or just collecting and analyzing offline at a later time.

Alex

I don't know why the ISP is outputting the jpeg with the issue - that is outside of our area of expertise. It could be a bug in the ISP or JPEG encoder.

I will get together some scripts i have been using for testing. I need add the LSC (lens shading correction) otherwise the colors look wrong and also image gets darker towards the edges. In order to apply LSC correction, we need a map (either look-up table or a poly fit) of each channel's response as a function of pixel coordinate (or radius) -- this needs to be calibrated (not for each camera module, but for each camera type + lens type). So after the calibration, my results should apply to your camera as well.

I will follow up early next week.

Alex

Here is the raw image:

And here is the snapshot:

Also is the most recent camera server here the version with the auto-auto exposure? If so, what is the parameter to use to activate that?

Also, i was wondering if you decided that the ISP snapshot is good enough for you or you want to explore saving the RAW bayer and processing offline? We have been experimenting with some approaches for offline processing and I can share some scripts, which have some flexibility on how much to de-noise , sharpen, etc. I am also going to add the LSC (lens shading correction) for the offline processing (and later into real-time misp pipeline) to correct for those artifacts that you saw where the colors change across the image.

Alex

Ah yes that seems to be the issue, we are using MISP, preview, and snapshot. It would be useful to have the AE run without us viewing the stream. Here is the conf file:

{
	"version":	0.1,
	"fsync_en":	false,
	"fsync_gpio":	109,
	"cameras":	[{
			"type":	"boson",
			"name":	"boson",
			"enabled":	true,
			"camera_id":	0,
			"fps":	30,
			"en_preview":	true,
			"en_misp":	true,
			"preview_width":	640,
			"preview_height":	512,
			"en_raw_preview":	true,
			"en_small_video":	false,
			"en_large_video":	false,
			"ae_mode":	"off",
			"en_rotate":	false,
			"misp_width":	512,
			"misp_height":	640,
			"misp_venc_enable":	true,
			"misp_venc_mode":	"h264",
			"misp_venc_br_ctrl":	"cqp",
			"misp_venc_Qfixed":	30,
			"misp_venc_Qmin":	15,
			"misp_venc_Qmax":	50,
			"misp_venc_nPframes":	29,
			"misp_venc_mbps":	2,
			"misp_venc_osd":	false,
			"misp_awb":	"off",
			"misp_gamma":	1,
			"misp_zoom":	1,
			"gain_min":	100,
			"gain_max":	100
		}, {
			"type":	"ov64b",
			"name":	"hires",
			"enabled":	true,
			"camera_id":	1,
			"fps":	30,
			"en_preview":	true,
			"en_misp":	true,
			"preview_width":	9216,
			"preview_height":	6912,
			"en_raw_preview":	true,
			"en_small_video":	false,
			"en_large_video":	false,
			"en_snapshot":	true,
			"ae_mode":	"isp",
			"gain_min":	100,
			"gain_max":	100,
			"misp_width":	9216,
			"misp_height":	6912,
			"misp_venc_enable":	false,
			"misp_venc_mode":	"h265",
			"misp_venc_br_ctrl":	"cqp",
			"misp_venc_Qfixed":	38,
			"misp_venc_Qmin":	15,
			"misp_venc_Qmax":	50,
			"misp_venc_nPframes":	29,
			"misp_venc_mbps":	30,
			"misp_venc_osd":	false,
			"misp_awb":	"auto",
			"misp_gamma":	1,
			"misp_zoom":	1,
			"ae_desired_msv":	75,
			"exposure_min_us":	1000,
			"exposure_max_us":	1001,
			"exposure_soft_min_us":	5000,
			"ae_filter_alpha":	0.6,
			"ae_ignore_fraction":	0.2,
			"ae_slope":	0.1,
			"ae_exposure_period":	1,
			"ae_gain_period":	1,
			"max_request_queue_depth":	6,
			"en_snapshot_width":	9216,
			"en_snapshot_height":	6912,
			"exif_focal_length":	3.1,
			"exif_focal_length_in_35mm_format":	17,
			"exif_fnumber":	1.24,
			"snapshot_jpeg_quality":	90
		}]
}

We can add the jpeg meta to the jpeg file saved from voxl-record-raw-image. which fields specifically are good to have?

Also, regarding the exposure settling - can you please provide the exact configuration you are running? specifically, which are enabled (misp, small_video, snapshot) and is auto exposure set to "isp" or not -- maybe provide a camera server config? When using MISP, i believe there is a case that if none of the streams are being used, the AE won't run, but we can fix this for the case of enabled snapshot.

Alex

Also noticed the auto exposure doesn't seem to settle unless the camera's feed is opened on the voxl-portal. Here are two images I took - voxl-camera-server was running for a considerable time before taking either one.

Before looking at the live stream in the portal:

After looking at the stream in the portal for a few seconds (I could see the exposure settle down)

Didn't mess with voxl-camera-server at all in between taking the two images.

https://gitlab.com/voxl-public/voxl-sdk/utilities/voxl-mpa-tools/-/tree/dev

Can you please clarify what you meant by the following statement in your previous post:

"Also as a side note, the metadata seems to not be recording when using voxl-record-raw-image -j"

Thanks!

• the reason why the resolution 9216x6944 was not accepted for snapshot is that the camera pipeline has a list of allowed resolutions and that resolution was not one of them. I could add the resolution 9216x6944 to the list, but actually another resolution (9216x6912) was already supported, so i updated the ov64b driver to include that. The latest driver is available here : https://storage.googleapis.com/modalai_public/temp/ov64b/20260417/ov64b_20260417.zip

  • this driver has the following resolutions : 9248x6944, 9216x6944 9216x6912 and the other lower resolutions. You can use 9216x6912 since it's compatible with misp and isp snapshot.

• I also added 9216x6912 resolution to the supported misp resolutions in camera server (dev branch) as well as allowed using misp with snapshot (previously it would throw an error since we thought that previously they were not compatible).

• Actually, snapshot does work with raw_preview + misp, but here is a warning that gets printed continuously:

WARNING: preview buffer pool for Cam(hires), Frame(30) has 0 free, skipping request

we can work around this warning by adding the following line to the voxl-camera-server.conf for the ov64b camera. this limits the number of buffers that are queued up for the streams and resolves the warning.

"max_request_queue_depth": 6,

So with these changes, you should be able to use two configurations

• preview + raw_preview + misp + snapshot
• small_video + snapshot

Please use the latest camera server from dev branch.

Right now, if you enable misp + small+video + snapshot, it does not work properly, but i will try it figure it out. Here is an example set up for small video + snapshot, but you can easily change it to use misp by disabling small_video and enabling preview + misp (and set auto exposure to "auto" from "isp" (auto will use misp auto exposure)).

Please try it out. I do agree that the ISP output has better overall image quality than MISP, also related to pretty significant lens shading of this small lens on ov64b. We will work on improvements of misp image processing.

For now, you just need to decide whether you need both RAW bayer and ISP JPG, in which case you cannot use small_video for streaming (until i resolve the issue). This means slightly worse image quality for live streaming. But if you are ok with ISP jpg, you can disable misp and use small_video stream for streaming.

By the way, i did not see any artifacts in the jpeg from ISP. Can you please let me know if the artifact is in every frame and where exactly it is located? or is the occurrence and location random?

{
                        "type":                 "ov64b",
                        "name":                 "hires",
                        "enabled":              true,
                        "camera_id":            1,
                        "fps":                  30,
                        "en_preview":           false,
                        "en_misp":              false,
                        "max_request_queue_depth": 6,
                        "preview_width":        9216,
                        "preview_height":       6912,
                        "en_raw_preview":       true,
                        "en_small_video":       true,
                        "en_large_video":       false,
                        "en_snapshot":          true,
                        "ae_mode":              "isp",
                        "gain_min":             100,
                        "gain_max":             1600,
                        "misp_width":           1600,
                        "misp_height":          1200,
                        "misp_venc_enable":     false,
                        "misp_venc_mode":       "h265",
                        "misp_venc_br_ctrl":    "cqp",
                        "misp_venc_Qfixed":     38,
                        "misp_venc_Qmin":       15,
                        "misp_venc_Qmax":       50,
                        "misp_venc_nPframes":   29,
                        "misp_venc_mbps":       30,
                        "misp_venc_osd":        false,
                        "misp_awb":             "auto",
                        "misp_gamma":           1,
                        "misp_zoom":            1,
                        "ae_desired_msv":       110,
                        "exposure_min_us":      20,
                        "exposure_max_us":      33000,
                        "exposure_soft_min_us": 5000,
                        "ae_filter_alpha":      0.6,
                        "ae_ignore_fraction":   0.2,
                        "ae_slope":     0.05,
                        "ae_exposure_period":   1,
                        "ae_gain_period":       1,
                        "small_video_width":    1600,
                        "small_video_height":   1200,
                        "small_venc_mode":      "h264",
                        "small_venc_br_ctrl":   "cqp",
                        "small_venc_Qfixed":    30,
                        "small_venc_Qmin":      15,
                        "small_venc_Qmax":      40,
                        "small_venc_nPframes":  9,
                        "small_venc_mbps":      2,
                        "en_snapshot_width":    9216,
                        "en_snapshot_height":   6912,
                        "exif_focal_length":    3.1,
                        "exif_focal_length_in_35mm_format":     17,
                        "exif_fnumber": 1.24,
                        "snapshot_jpeg_quality":        75
                }

I am testing OV64b now. I confirmed that snapshot of size 9216x6944 is not possible (i think i can enable it), but 9248x6944 works.

Also, please see this post, there was a bug that broke snapshot in recent releases, which i just fixed. perhaps that was an issue for you when you switched to latest camera server : https://forum.modalai.com/topic/5166/snapshot-crashes-voxl-camera-server

I am going to see if i can get MISP running together with ISP snapshot, right now it only runs when preview and misp are disabled and i have small_video and snapshot streams enabled and snapshot resolution set to 9248x6944 and small_video resolution to something smaller.

Alex

In general, the pixel noise increases as you the pixel gain is increased. Part of Auto Exposure control is controlling exposure time and gain (both of which contribute to the image brightness), but higher gain has higher pixel noise and higher exposure values will result in motion blur.

The Qualcomm ISP has lots of liters, including noise reduction, which are applied when you use the ISP snapshot (even though they have not been tuned for the particular camera, the ISP output may result in better de-noised image). There are several types of filters, for example spatial (such as bilateral filter) and temporal (TNR - temporal noise reduction), and it's usually a combination of both with the filter weights increasing as the gain increases (more noise requires more noise reduction).

Currently, MISP does not have any de-noise filtering, but we are working on adding some. That is why i was discussing with you the ability to process the image offline (from the original bayer source). You could save the full raw image and perform any filtering you need in post processing. This approach is similar to using a RAW image on a fancy camera and then importing that into image processing software on a laptop / desktop, which can perform a lot more filters / effects directly on the raw image (loss-less).

What i suspect is that you are testing in the low-light environment and you are seeing the effects of high gain (high pixel noise). the MISP auto exposure tries to balance exposure and gain and there are a few parameters for that, but in your tests you should see what exposure and gain values the camera is at when you see the noisy image. You can use voxl-portal to control exposure and gain to see what the difference is. I believe the max gain for ov64b is 16x (1600).

I am about to set up your use case again for testing and i will investigate the noise and the original ISP snapshot artifact and we can also compare the image noise from the ISP snapshot and misp snapshot.

Unfortunately the lens for ov64b in the Hadron unit is very small, which reduces the amount of light that gets into the sensor. The sensor is 8+K resolution which means the pixel size is small, so the amount of light that gets to each pixel is small. To compensate for that, we would typically want a larger lens, but it is probably not possible to change the lens in this specific Hadron unit (we have not tried).

If you want to explore offline image processing, you would need:

• the original raw bayer (which you can already save)
• the gain and exposure used for that frame (i will need to check, i thin we had an option to save the exposure and gain as part of the file name).
• then you can have offline processing that is dependent on exposure / gain and apply the filters of your choice.
• the only down side is that the raw snapshots are huge, but you can probably zip them up if needed to store a lot of them on voxl2 before offloading.

Alex

Also as a side note, the metadata seems to not be recording when using voxl-record-raw-image -j

I was able to get both the full YUV and them in JPEG working. We also noticed that the vertical artifact seemed to disappear in these images. Regarding the resolution we were using when we had the artifacts, we were using 9248 × 6944 on the old drivers.

Long term, our plan is to add snapshot functionality directly into misp (just like you were getting snapshots from the ISP). That is not too difficult to achieve, may be a few weeks away.

Also let me ask you this: would you want to save the raw bayer and get that from voxl2 and convert to jpg / png offline? That would give you maximum control over quality / processing algorithm. Alternatively, de-bayering can be done on voxl2 and saved as lossy or lossless image?

We do have tools to convert bayer -> yuv -> jpeg but not yet in a standalone app.

You you can do for now is the following:

• set misp output resolution equal to the raw resolution
• this will enable misp to publish the de-bayered yuv to the image pipe
• then you can use voxl-record-raw-image to save the yuv. Alternatively, you can test a WIP version of this tool which can save yuv as jpeg : https://gitlab.com/voxl-public/voxl-sdk/utilities/voxl-mpa-tools/-/merge_requests/37
• ideally this logging would be done within camera server to reduce overhead for sending these huge images over the pipe. But the approach i described should work for now.

I will be able to test this again mid next week, meanwhile please let me know if you run into the saving issue. I will double check the issue with snapshot resolution being too large. How large was the isp snapshot that you tested before and had artifacts?

Alex

MISP channels enabled in defaults : 0
MISP channels enabled in config file: 0
Setting MISP AWB to Auto
MISP channels enabled in defaults : 0
MISP channels enabled in config file: 0
=================================================================
configuration for 2 cameras:

cam #0
    name:                boson
    type:                boson
    bayer_type:          0
    enabled:             1
    camera_id:           0
    camera_id_second:    -1
    fps:                 30
    en_rotate:           0
    en_rotate2:          0

    en_preview:          1
    en_raw_preview:      1
    preview_width:       640
    preview_height:      512

    en_misp:             1
    misp_width:          512
    misp_height:         640

    en_small_video:      0
    small_video_width:   640
    small_video_height:  480

    en_large_video:      0
    large_video_width:   -1
    large_video_height:  -1

    en_snapshot:         0
    snap_width:          -1
    snap_height:         -1
    exif_focal_length:   0.000000
    exif_focal_len_35mm_format:0
    exif_fnumber:        0.000000

    ae_mode:             off
    msv_exposure_min_us: 20
    msv_exposure_max_us: 33000
    gain_min           : 100
    gain_max           : 100
    standby_enabled:     0
    decimator:           1
    independent_exposure:0

cam #1
    name:                hires
    type:                ov64b
    bayer_type:          1
    enabled:             1
    camera_id:           1
    camera_id_second:    -1
    fps:                 30
    en_rotate:           0
    en_rotate2:          0

    en_preview:          1
    en_raw_preview:      0
    preview_width:       9248
    preview_height:      6944

    en_misp:             0
    misp_width:          1280
    misp_height:         720

    en_small_video:      0
    small_video_width:   1024
    small_video_height:  768

    en_large_video:      0
    large_video_width:   3840
    large_video_height:  2160

    en_snapshot:         1
    snap_width:          9248
    snap_height:         6944
    exif_focal_length:   3.100000
    exif_focal_len_35mm_format:17
    exif_fnumber:        1.240000

    ae_mode:             lme_msv
    msv_exposure_min_us: 20
    msv_exposure_max_us: 33000
    gain_min           : 100
    gain_max           : 1600
    standby_enabled:     0
    decimator:           1
    independent_exposure:0

fsync_en:            0
fsync_gpio:          109
=================================================================
thread is locked to cores: 4 5 6 7
connected to mavlink pipe
Connected to cpu-monitor
Starting Camera: boson (id #0)
ModalExposureMSV: initializing for camera name  type 
gbm_create_device(156): Info: backend name is: msm_drm
MISP Initializing for camera boson
 Detected 1 platform(s)
 Detected 1 GPU device(s)
Estimated imu dt = 0.000977s
ERROR in json_from_yaml, failed to open file
MISP: Loading intrinsics cal file boson_intrinsics.yml for camera boson, ret -1
WARNING: Lens calibration for camera boson is missing (boson_intrinsics.yml). Using defaults:
Starting Camera: hires (id #1)
ModalExposureMSV: initializing for camera name hires type ov64b

------ voxl-camera-server: Started 2 of 2 cameras

------ voxl-camera-server: Camera server is now running
Killed

Don't necessarily need to use the snapshot command, but we want some way to get JPG images off the voxl.

ERROR:   MISP: Unexpected frame size for camera hires, width 9216, height 6944, stride 11520, alloc 79994880 bytes, calc frame size 79994880
ERROR:   Could not find frame size from the raw buffer

Same error I was getting last week but now the allocated and calculated values are the same. Running it at 9248x6944 gives a similar error:

ERROR:   MISP: Unexpected frame size for camera hires, width 9248, height 6944, stride 11568, alloc 80330752 bytes, calc frame size 80328192
ERROR:   Could not find frame size from the raw buffer

I do see the boson_bayer pipe, and in the portal the camera feed looks great - I believe we were getting a weird preview with the old drivers, but we didn't look too carefully as we are not using the boson yet. Here is the output of voxl-inspect-cam -a:

|          Pipe Name |  bytes  | wide |  hgt |exp(ms)| gain | frame id |latency(ms)|  fps |  mbps  | format
|              boson |  327680 |  640 |  512 |  0.00 |  800 |      295 |     13.2  | 60.0 |  157.3 | RAW8
|        boson_bayer |  327680 |  640 |  512 |  0.00 |    0 |      295 |     12.7  | 60.0 |  157.3 | RAW8
|        boson_color |  983040 |  640 |  512 |  0.00 |  800 |      295 |     15.6  | 60.0 |  471.9 | RGB
| boson_misp_encoded |      32 |  640 |  512 |  0.00 |  800 |      295 |     17.0  | 60.0 |    0.0 | H264 (P)   
|        hires_bayer |
|   hires_misp_color |
|    hires_misp_grey |

Disabling MISP gives the following warning and output of voxl-inspect-cam -a:

------ voxl-camera-server: Camera server is now running
Received RAW10 frame from camera hires, will be converting to RAW8 on cpu
WARNING: preview buffer pool for Cam(hires), Frame(18) has 0 free, skipping request

|          Pipe Name |  bytes  | wide |  hgt |exp(ms)| gain | frame id |latency(ms)|  fps |  mbps  | format
|              boson |  327680 |  640 |  512 |  0.00 |  800 |      507 |     12.4  | 60.0 |  157.3 | RAW8
|        boson_bayer |  327680 |  640 |  512 |  0.00 |    0 |      507 |     10.9  | 60.0 |  157.3 | RAW8
|        boson_color |  983040 |  640 |  512 |  0.00 |  800 |      507 |     13.3  | 60.0 |  471.9 | RGB
| boson_misp_encoded |      32 |  640 |  512 |  0.00 |  800 |      507 |     14.4  | 60.0 |    0.0 | H264 (P)   
|        hires_bayer |
|        hires_color |
|         hires_grey |64218112 | 9248 | 6944 | 12.06 | 1211 |       16 |   3720.9  |  1.8 |  945.2 | RAW8

Please note that the current debayering algorithm in MISP does add some smoothing / interpolation, so the YUV image is not going to be as crisp as possible. You could compare it to the jpg output of the ISP (even thought it has artifacts). If you are looking for highest possible fidelity, it may be best to perform offline processing on the raw bayer, then you have more options.

Alex

Good news. I got it working, but there were a few things needed to update. I packaged everything into a zip : https://storage.googleapis.com/modalai_public/temp/ov64b/20260401/ov64b_20260401.zip . It contains

• latest sensormodules for Boson and ov64b
• voxl-camera-server conf
• updated default tuning file (to fix the gain scale so that gain 1.0 is 100, not 54)
• updated com.qti.chi.override.so file which contains some pipeline information, now it will allow the large raw resolutions

(I thought the above changes were already in the latest SDK but somehow they did not make it, I will need to double check).

I also made some changes to the ov64b driver:

• updated frame length for the 9248x6944 mode so that it is 10fps (not 9.2) -- this is close to max
• added modes 9216x6944@10fps and 4608x3472@30fps (slightly cropped on the right), so that these buffers can be used by MISP / gpu without doing a copy.

I am able to capture the raw bayer at 9216x6944, 9248x6944 ,4624x3472, 4608x3472 resolutions

voxl-inspect-cam hires_bayer
...
|   Pipe Name |  bytes  | wide |  hgt |exp(ms)| gain | frame id |latency(ms)|  fps |  mbps  | format
| hires_bayer |79994880 | 9216 | 6944 | 33.00 | 1594 |      130 |    157.9  | 10.0 | 6415.5 | RAW10

In order to enable the misp support for the 4624x3472 and 4608x3472 resolutions in camera server, need a small update:
https://gitlab.com/voxl-public/voxl-sdk/services/voxl-camera-server/-/commit/389b1f4628a9b9cc0e53c43a5cf0e457717e1270

You can save the raw bayer using the voxl-record-raw-image tool, for example:

voxl-record-raw-image hires_bayer -d .

You should be able to dump some raw bayer images and de-bayer them offline.

Also, here is the contents of the README that is inside the zip:

Supported Hardware

• voxl2
• Hadron plugged into VOXL2 J8 via M0181 adapter

Instructions

• copy Boson sensormodule to /usr/lib/camera/
• copy ov64b sensormodule with correct id to /usr/lib/camera/
• copy com.qti.tuned.default.bin to /usr/lib/camera to fix the gain scale (1.0x = 100)
• back up /usr/lib/hw/com.qti.chi.override.so and replace it with com.qti.chi.override.so.20260401 (rename to com.qti.chi.override.so)
  • this updated file allows the pipeline to use the highest resolution of the ov64b camera
• copy voxl-camera-server.conf to /etc/modalai

Suported Resolutions

• mode 0 : 9248x6944 10 bit 10 fps
• mode 1 : 9216x6944 10 bit 10 fps (MISP no copy) -- crop 32 pixels on the right
• mode 2 : 4624x3472 10 bit 30 fps
• mode 3 : 4608x3472 10 bit 30 fps (MISP no copy) -- crop 16 pixels on the right
• mode 4 : 3840x2160 10 bit 60 fps (MISP no copy)
• mode 5 : 1920x1080 10 bit 240 fps
• mode 6 : 1920x1080 10 bit 30 fps

Notes

• even thought the 9248x6944 and 9216x6944 modes are 10 fps, you need to specify 30fps in the camera config file. This will be investigated further.