Hi, The pipeline I have includes two color cameras, encoding nodes and detection networks for both cameras. Both cameras should run at 15 FPS. When I run the code, sometimes the pipeline will launch correctly, all FPS are as expected. See the following pipeline graph [upl-image-preview url=https://discuss.luxonis.com/assets/files/2023-12-05/1701760162-911509-screenshot-from-2023-12-05-00-51-34.png] But sometimes one camera will be totally block. No data can go through in that path. And for the other camera, the FPS is not even correct (in the following pipeline graph, see 18 FPS instead of 15 FPS). [upl-image-preview url=https://discuss.luxonis.com/assets/files/2023-12-05/1701760275-872493-screenshot-from-2023-12-05-00-52-21.png] The chance for each case is almost 50 percent. The issue only happens during launching. Once it launched correctly, it won't have problem running for hours. Here is the system log when the pipeline is running correctly. All CMX is used. Will it cause this problem? [upl-image-preview url=https://discuss.luxonis.com/assets/files/2023-12-05/1701760521-951208-screenshot-from-2023-12-05-00-56-23.png] Thanks Lincoln

Hi @"lincolnxlw"#p14933 This is very strange, could you send the minimal reproducible example so I can try to debug it? Are you using any multiprocessing/threading that could cause a race condition or something similar? Perhaps there is a bug somewhere on our side. Thanks, Jaka

@"jakaskerl"#p14958 Thanks for the reply. I will prepare something for you. Lincoln

Hi @"jakaskerl"#p14958 The [link](https://public.3.basecamp.com/p/jGEVkEJP3PgjHBSSFSv6aLnz) should contain the MRE for this issue. Hardware * OAK FFC-3P with 2 IMX477 modules running in 1080P in CAM B and CAM C sockets The MRE is an minimum ROS package depends on [depthai-ros noetic](https://github.com/luxonis/depthai-ros/tree/noetic). `root@83d31607bd1d:/workspaces/depthai_catkin_ws/src/minimum_image_publisher# tree . -h` `.` `├── [1.2K] CMakeLists.txt` `├── [4.0K] config` `│ └── [1.4K] yolov6n.yaml` `├── [4.0K] include` `│ └── [4.0K] minimum_image_publisher` `├── [4.0K] models` `│ └── [8.9M] yolov6n_openvino_2022.1_6shave.blob` `├── [2.0K] package.xml` `└── [4.0K] src` ` └── [ 11K] image_publisher.cpp` `5 directories, 5 files` There are also instructions inside the link for how to reproduce the problem. Attached below are two screenshots, one when the pipeline is running correctly, and one when the left side camera path is totally blocked. [upl-image-preview url=https://discuss.luxonis.com/assets/files/2023-12-12/1702423547-364730-screenshot-from-2023-12-12-16-51-26.png] [upl-image-preview url=https://discuss.luxonis.com/assets/files/2023-12-12/1702423550-647636-screenshot-from-2023-12-12-16-49-45.png] Let me know what do you think. Thanks Lincoln

@"jakaskerl"#1112 Just to follow up, any questions regarding the MRE I provided. Also, I created the exact same pipeline in python running with [depthai-library docker image](https://hub.docker.com/layers/luxonis/depthai-library/latest/images/sha256-be59799f694ef7cc5c60519bd462ac2daa6bef27bd2462e60ed37791121d3c29?context=explore), and I will run 10 times without any problem. I attach the python script below. ``` #!/usr/bin/env python3 from pathlib import Path import cv2 import depthai as dai import numpy as np import argparse import time import errno import os import sys import json parser = argparse.ArgumentParser() parser.add_argument('-nn', '--nn_model', help='select model path for inference', default='/workspaces/yolov6n_openvino_2022.1_6shave.blob', type=str) parser.add_argument('-c', '--config', help='Provide config path for inference', default='/workspaces/yolov6n.json', type=str) parser.add_argument('-fps', '--fps', type=float, help='Frame rate of camera capturing', default=15) args = parser.parse_args() nn_path = Path(args.nn_model) config_path = Path(args.config) config_fps = args.fps if not nn_path.is_file(): sys.exit('NN not found!') if not config_path.is_file(): sys.exit('Config not found!') with config_path.open() as f: config = json.load(f) nn_config = config.get("nn_config", {}) if "input_size" in nn_config: nn_width, nn_height = tuple(map(int, nn_config.get("input_size").split('x'))) metadata = nn_config.get("NN_specific_metadata", {}) classes = metadata.get("classes", {}) coordinates = metadata.get("coordinates", {}) anchors = metadata.get("anchors", {}) anchor_masks = metadata.get("anchor_masks", {}) iou_threshold = metadata.get("iou_threshold", {}) confidence_threshold = metadata.get("confidence_threshold", {}) print(metadata) nn_mappings = config.get("mappings", {}) labels = nn_mappings.get("labels", {}) # pipeline pipeline = dai.Pipeline() camera_l = pipeline.create(dai.node.ColorCamera) detection_nn_l = pipeline.create(dai.node.YoloDetectionNetwork) xout_rgb_l = pipeline.create(dai.node.XLinkOut) xout_nn_l = pipeline.create(dai.node.XLinkOut) camera_r = pipeline.create(dai.node.ColorCamera) detection_nn_r = pipeline.create(dai.node.YoloDetectionNetwork) xout_rgb_r = pipeline.create(dai.node.XLinkOut) xout_nn_r = pipeline.create(dai.node.XLinkOut) xout_rgb_l.setStreamName("rgb_l") xout_nn_l.setStreamName("nn_l") xout_rgb_r.setStreamName("rgb_r") xout_nn_r.setStreamName("nn_r") camera_l.setPreviewSize(nn_width, nn_height) camera_l.setBoardSocket(dai.CameraBoardSocket.CAM_B) camera_l.setPreviewKeepAspectRatio(False) camera_l.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) camera_l.setInterleaved(False) camera_l.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR) camera_l.setFps(config_fps) camera_r.setPreviewSize(nn_width, nn_height) camera_r.setBoardSocket(dai.CameraBoardSocket.CAM_C) camera_r.setPreviewKeepAspectRatio(False) camera_r.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) camera_r.setInterleaved(False) camera_r.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR) camera_r.setFps(config_fps) detection_nn_l.setConfidenceThreshold(confidence_threshold) detection_nn_l.setNumClasses(classes) detection_nn_l.setCoordinateSize(coordinates) detection_nn_l.setAnchors(anchors) detection_nn_l.setAnchorMasks(anchor_masks) detection_nn_l.setIouThreshold(iou_threshold) detection_nn_l.setBlobPath(str(nn_path)) detection_nn_l.setNumInferenceThreads(1) detection_nn_l.setNumNCEPerInferenceThread(1) detection_nn_l.input.setBlocking(False) detection_nn_r.setConfidenceThreshold(confidence_threshold) detection_nn_r.setNumClasses(classes) detection_nn_r.setCoordinateSize(coordinates) detection_nn_r.setAnchors(anchors) detection_nn_r.setAnchorMasks(anchor_masks) detection_nn_r.setIouThreshold(iou_threshold) detection_nn_r.setBlobPath(str(nn_path)) detection_nn_r.setNumInferenceThreads(1) detection_nn_r.setNumNCEPerInferenceThread(1) detection_nn_r.input.setBlocking(False) # linking camera_l.preview.link(xout_rgb_l.input) camera_l.preview.link(detection_nn_l.input) detection_nn_l.out.link(xout_nn_l.input) camera_r.preview.link(xout_rgb_r.input) camera_r.preview.link(detection_nn_r.input) detection_nn_r.out.link(xout_nn_r.input) with dai.Device(pipeline) as device: print('Device name:', device.getDeviceName()) if device.getBootloaderVersion() is not None: print('Bootloader version:', device.getBootloaderVersion()) print('Usb speed:', device.getUsbSpeed().name) print('Connected cameras:', device.getConnectedCameraFeatures()) device_info = device.getDeviceInfo() print('Device mixid:', device_info.getMxId()) q_rgb_l = device.getOutputQueue(name="rgb_l", maxSize=4, blocking=False) q_nn_l = device.getOutputQueue(name="nn_l", maxSize=4, blocking=False) q_rgb_r = device.getOutputQueue(name="rgb_r", maxSize=4, blocking=False) q_nn_r = device.getOutputQueue(name="nn_r", maxSize=4, blocking=False) start_time = time.monotonic() counter_l = 0 detections_l = [] fps_l = 0 frame_l = None counter_r = 0 detections_r = [] fps_r = 0 frame_r = None text_color = (0, 0, 255) def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray: return cv2.resize(arr, shape).transpose(2, 0, 1).flatten() def frameNorm(frame, bbox): normVals = np.full(len(bbox), frame.shape[0]) normVals[::2] = frame.shape[1] return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int) def displayFrame(name, frame, detections): color = (0, 0, 255) for detection in detections: bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) cv2.putText(frame, labels[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color) cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color) cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2) cv2.imshow(name, frame) while True: in_nn_l = q_nn_l.tryGet() in_rgb_l = q_rgb_l.tryGet() in_nn_r = q_nn_r.tryGet() in_rgb_r = q_rgb_r.tryGet() if in_rgb_l is not None: frame_l = in_rgb_l.getCvFrame() fps_l = counter_l / (time.monotonic() - start_time) cv2.putText(frame_l, "NN fps: {:.2f}".format(fps_l), (2, frame_l.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.5, text_color) if in_rgb_r is not None: frame_r = in_rgb_r.getCvFrame() fps_r = counter_r / (time.monotonic() - start_time) cv2.putText(frame_r, "NN fps: {:.2f}".format(fps_r), (2, frame_r.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.5, text_color) if in_nn_l is not None: detections_l = in_nn_l.detections counter_l += 1 if in_nn_r is not None: detections_r = in_nn_r.detections counter_r += 1 if frame_l is not None: displayFrame("left_camera", frame_l, detections_l) if frame_r is not None: displayFrame("right_camera", frame_r, detections_r) if cv2.waitKey(1) == ord('q'): break ``` The pipeline graph for the python version is the same [upl-image-preview url=https://discuss.luxonis.com/assets/files/2023-12-14/1702595965-120644-screenshot-from-2023-12-14-17-12-19.png] This [link](https://public.3.basecamp.com/p/SXDBt446Pz14wkLaXapEkuSt) contains the code and model to run this python MRE Thanks Lincoln

Hi @"lincolnxlw"#1387 , Thank you for the MRE. Just to confirm, are you using latest depthai version (2.24)?

Hi @"erik"#p15213 For the python example, I am using this docker image that release yesterday https://hub.docker.com/layers/luxonis/depthai-library/latest/images/sha256-be59799f694ef7cc5c60519bd462ac2daa6bef27bd2462e60ed37791121d3c29?context=explore It looks like it is using "'2.23.0.0.dev0+dev'" For the ROS example, I rebuild it with this [docker file](https://github.com/luxonis/depthai-ros/blob/noetic/Dockerfile) yesterday and still having the issue. Let me know what do you think Lincoln

Hi @"lincolnxlw"#1387 I tried to reproduce with normal OAK-D (so 2x OV9282), but it worked as expected every time (out of 20x) running latest depthai. @"jakaskerl"#1112 , could you perhaps try to repro with FFC-3P and 2x IMX477 cam modules? I'd be surprised that would make a difference though. Perhaps the camera that fails doesn't have perfect connection, and it sometimes doesn't get enumerated, so it doesn't stream?

Hi @"lincolnxlw"#p15214 Tried with FFC-3P (r3) and two IMX477 connected to cam_L and cam_R. > @"lincolnxlw"#p15204 This [link](https://public.3.basecamp.com/p/SXDBt446Pz14wkLaXapEkuSt) contains the code and model to run this python MRE This link/code works without issues on my machine. Versions: depthai==2.24.0.0. Tried running 20 times, no errors/crashes. Couldn't setup the ROS version due to USB passthrough issues on M1 macs. @"Luxonis-Adam"#919 Can you check with ROS noetic please, so we can pinpoint the issue to a source? link: > @"lincolnxlw"#p15147 The [link](https://public.3.basecamp.com/p/jGEVkEJP3PgjHBSSFSv6aLnz) should contain the MRE for this issue. Thanks, Jaka

Hi @"jakaskerl"#1112 and @"erik"#175 Just want to make sure we are on the same page. I don't have issue running the python MRE, like I mentioned earlier > Also, I created the exact same pipeline in python running with **[depthai-library docker image](https://hub.docker.com/layers/luxonis/depthai-library/latest/images/sha256-be59799f694ef7cc5c60519bd462ac2daa6bef27bd2462e60ed37791121d3c29?context=explore)**, and I will run 10 times without any problem. I attach the python script below. The one has blocking issue is the ROS c++ MRE. It is weird because the pipeline is pretty much the same. Thanks Lincoln

Hi, regarding ROS Noetic on Mac, I don't it is officially supported, but you could use Docker images to get the latest version running. In general, if it's possible to connect to the camera with basic DepthAI libraries then it should also work for ROS. Could you share your ROS setup?

Pipeline sometimes not being launched as expected

lincolnxlw

Hi Luxonis-Adam

Just want to follow up on this. Were you able to reproduce the pipeline blocking issue with the provided MRE?

Thanks

Lincoln

lincolnxlw

Hi @Luxonis-Adam, @jakaskerl, @erik,

I added another file image_publisher_ros.cpp in the MRE folder for comparison.

root@0b6870f3b150:/workspaces/depthai_catkin_ws/src/station_depthai/minimum_image_publisher# tree .
.
├── CMakeLists.txt
├── README.md
├── config
│   └── yolov6n.yaml
├── include
│   └── minimum_image_publisher
├── models
│   └── yolov6n_openvino_2022.1_6shave.blob
├── package.xml
└── src
    ├── image_publisher.cpp
    └── image_publisher_ros.cpp

In image_publisher_ros, it doesn't try to get the image from the queues directly but use dai::rosBridge::BridgePublisher to publish the image data. And the blocking issue is much worse than image_publisher.cpp, almost half of the times are failure. I attached here the screen recording of 10 of my run.

The depthai library I am using is 2.24.0, which is the latest depthai-ros noetic is using. Let me know what else I can provide to help figure out the issue.

Library information - version: 2.24.0, commit: 6628488ef8956f73f1c7bf4c8f1da218ad327a6f from 2023-12-13 14:45:09 +0100, build: 2023-12-13 23:26:25 +0000, libusb enabled: true

Thanks!

Lincoln

Luxonis-Adam

Hi @lincolnxlw, so far I've been unable to replicate the issue. Could you try setting export DEPTHAI_DEBUG=1 before running the example? Also, is this also happening if you run one or none detection networks?

lincolnxlw

Hi Luxonis-Adam

Thanks for responding. I set up the debug and here are my test videos.

With image_publisher_ros, it failed 2 times out of 5 runs. Here is the video.

I created a node with only one color camera with detection network. It worked 10 times out of 10 runs. Here is the video.

Also, when I run the code without pipeline_graph consuming the terminal output. I noticed some red debug messages. But it shows those messages regardless success or failure. And regardless of two detection network or just one.

== FSYNC enabled for cam mask 0x0
CAM ID: 1, width: 1920, height: 1080, orientation: 0
CAM ID: 2, width: 1920, height: 1080, orientation: 0
== SW-SYNC: 0, cam mask 0x6
!!! Master Slave config is: single_master_slave !!!
Starting camera 1
[E] app_guzzi_command_callback():173: command->id:1
[E] app_guzzi_command_callback():193: command "1 1" sent

[18443010D1F3F40800] [3.1] [2.609] [system] [warning] PRINT:LeonCss: [E] iq_debug_create():161: iq_debug address 0x88837680
[E] hai_cm_driver_load_dtp():852: Features for camera IMX214R0 (imx214) are received
[E] set_dtp_ids():396: //VIV HAL: Undefined VCM DTP ID 0
[E] set_dtp_ids():405: //VIV HAL: Undefined NVM DTP ID 0
[E] set_dtp_ids():414: //VIV HAL: Undefined lights DTP ID 0
[18443010D1F3F40800] [3.1] [2.619] [system] [warning] PRINT:LeonCss: [E] camera_control_start():347: Camera_id = 1 started.

[E] hai_cm_sensor_select_mode():164: No suitable sensor mode. Selecting default one - 0 for start 1920x1080 at 0x0 fps min 0.000000 max 30.000000
[E] hai_cm_sensor_select_mode():164: No suitable sensor mode. Selecting default one - 0 f
[18443010D1F3F40800] [3.1] [2.581] [DetectionNetwork(2)] [info] Inference thread count: 1, number of shaves allocated per thread: 6, number of Neural Compute Engines (NCE) allocated per thread: 1
[18443010D1F3F40800] [3.1] [2.630] [system] [warning] PRINT:LeonCss: or start 1920x1080 at 0x0 fps min 0.000000 max 30.000000
[18443010D1F3F40800] [3.1] [2.653] [system] [warning] PRINT:LeonCss: [E] vpipe_conv_config():1465: Exit Ok
[E] callback():123: Camera CB START_DONE event.
Starting camera 2
[E] app_guzzi_command_callback():173: command->id:1
[E] app_guzzi_command_callback():193: command "1 2" sent

[18443010D1F3F40800] [3.1] [2.675] [system] [warning] PRINT:LeonCss: [E] iq_debug_create():161: iq_debug address 0x88375cc0
[18443010D1F3F40800] [3.1] [2.686] [system] [warning] PRINT:LeonCss: [E] hai_cm_driver_load_dtp():852: Features for camera IMX214R0 (imx214) are received
[E] set_dtp_ids():396: //VIV HAL: Undefined VCM DTP ID 0
[E] set_dtp_ids():405: //VIV HAL: Undefined NVM DTP ID 0
[E] set_dtp_ids():414: //VIV HAL: Undefined lights DTP ID 0
[E] camera_control_start():347: Camera_id = 2 started.

[18443010D1F3F40800] [3.1] [2.696] [system] [warning] PRINT:LeonCss: [E] hai_cm_sensor_select_mode():164: No suitable sensor mode. Selecting default one - 0 for start 1920x1080 at 0x0 fps min 0.000000 max 30.000000
[E] hai_cm_sensor_select_mode():164: No suitable sensor mode. Selecting default one - 0 for start 1920x1080 at 0x0 fps min 0.000000 max 30.000000
inc_camera_process set exposure and gain
[E] vpipe_conv_config():1465: Exit Ok
[18443010D1F3F40800] [3.1] [2.707] [system] [warning] PRINT:LeonCss: [E] guzzi_event_send():324: Send: Event ID=20003 no registered recipient
[E] guzzi_event_send():324: Send: Event ID=20004 no registered recipient
[E] guzzi_event_send():324: Send: Event ID=20005 no registered recipient
osDrvImx214Control:514: Start stream
[E] callback():123: Camera CB START_DONE event.
inc_camera_process set exposure and gain
osDrvImx214Control:514: Start stream
[18443010D1F3F40800] [3.1] [2.718] [system] [warning] PRINT:LeonCss: AF_TRIGGER on camera 1
[E] app_guzzi_command_callback():173: command->id:5
[E] camera_control_focus_trigger():591: Focus trigger succeeded camera_id = 1.

[E] app_guzzi_command_callback():218: command "5 1" sent

AF_TRIGGER on camera 2
[E] app_guzzi_command_callback():173: command->id:5
[E] camera_control_focus_trigger():591: Focus trigger succeeded camera_id = 2.

[E] app_guzzi_command_callback():218: command "5 2" sent

Starting Guzzi command handling loop...

Let me know what do you think

Thanks

Lincoln

Luxonis-Adam

Hi @lincolnxlw, thanks for the information, to narrow it down more, could you try replicating this setup without ROS, using bare depthai-core library?

lincolnxlw

Hi Luxonis-Adam

I switched both cameras from IMX477 to IMX214 and ran the same MRE extensively, and I didn't see any blocking issue. I have 10 of the IMX477 modules and no matter how I pair them up (switch cables as well), I can always see the issue. So I don't think it is an individual camera problem. What do you think? Is this something on firmware side or hardware side? If it is on the hardware side, is it from Luxonis side or ArduCam side?

@erik, @jakaskerl feel free to let me know what you think as well. This issue has been blocking us from releasing our depthai-powered product.

Thanks

Lincoln

erik

Hi Lincoln,
So to confirm, any type of pipeline (just streaming color stream to XLinkOut) with 2x FFC-IMX477 with FFC-3P using latest depthai version there's a chance it will block one camera at the start, and it won't recover? I'm just trying to understand what would be the smallest repro solution, so the engineering team can fix the issue.
Thanks, Erik

lincolnxlw

erik

Yes one camera will be blocked and won't recover. The tricky part is the issue not appear for all the pipelines with IMX477. But as comparison, with IMX214, NO issue for all the pipelines.

In the latest C++ MRE, I have 3 different nodes

image_publisher_node: color camera nodes and detection nodes for CAM_B and CAM_C, no ROS.
image_publisher_ros_node: color camera nodes and detection nodes for CAM_B and CAM_C, also ROS bridges to publish image and detection to ROS.
image_publisher_ros_single_detection_node: color camera nodes for both cameras, only one camera has detection node. ROS publishing image from two cameras and detection from one camera.

In term of severity of the issue

For image_publisher_node, the blocking issue can be seen directly from the screen, it happens around 1 out of 10

For image_publisher_ros_node, we need pipeline_graph to see the FPS. It happens around 2 out of 5

For image_publisher_ros_single_detection_node, we need pipeline_graph to see the FPS, I have NOT see the blocking happen yet

Also, like I mentioned a while ago, the issue rarely happens with the python MRE even with IMX477 (1 out of 30?). But I did see it happen before.

So in summary

The inconsistency of the issue with IMX477 modules in different pipelines, make it looks like it is more on the firmware side.
But absolutely no issue with IMX214 in all pipelines make it looks like IMX477 hardware is the one to blame.

Sorry for the mess. Let me know what do you think.

Thanks

Lincoln

erik

(forgot to cc, @lincolnxlw ^)

erik

Hi @lincolnxlw ,
Thank you for all the details, we will try to reproduce this locally and get back to you asap.

erik

@lincolnxlw , just to confirm, is the hardware absolutely still when you ran these tests, or do you eg. move it even a tiny bit between runs? It might be an improper connection, which is quite common for FFC cameras, and results in camera not being able to stream.

lincolnxlw

Hi erik

The hardware: OAK-FFC-3P with two IMX477 cameras are still during run. The OAK-FFC-3P is powered by the external adapter the came with the product instead of just the USB cable.

Let me know if you have any other questions.

Thanks

Lincoln

lincolnxlw

erik

Another information may or may not be relevant. Even though the Luxonis shop is still branding the module as IMX477, but Arducam actually updated the sensor to IMX577, see the description on their own shop for the same module. And the cameras I received and am testing is the updated version with IMX577 sensors. So in order to reproduce the issues, you may need to make sure using the same version.

Thanks

Lincoln

erik

Hi @lincolnxlw ,
We just ordered the exact same cameras (from Arducam), hopefully they will arrive in the next 2 weeks and we'll try to repro then. We tried with the IMX477 many times and didn't encounter the issue.
Thanks, Erik

lincolnxlw

Hi erik,

Thanks for trying to repro the issue. I really appreciate it. Just to make sure I did it the right way. What arguments did you provide to launch the container while reproducing the C++ MRE? Here is my command

docker container run --net=host -it --rm \
    --privileged \
    -e DISPLAY=$DISPLAY \
    -e QT_X11_NO_MITSHM=1 \
    -v /dev/bus/usb:/dev/bus/usb \
    -v /tmp/.X11-unix:/tmp/.X11-unix:rw \
    -v $HOME/.Xauthority:/root/.Xauthority:rw \
    -v $HOME/data/:/data:rw \
    --device /dev/i2c-1 \
    depthai-ros \
    bash

Also, I guess there are no issue with all three nodes while using IMX477?

Thanks

Lincoln

jakaskerl

Hi lincolnxlw
I was not able to setup a ros environment since I am running MacOS, but tried it with python and ran the script 300+ times (had it on script) and encountered no issues. Will try with 577s once I receive them. The sensor should be fully supported (https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/supported_sensors/), but was likely not tested to such an extent.

Thanks,
Jaka

lincolnxlw

Hi jakaskerl

Thanks for the extensive test with python! But see my description in post #21, for some reason the python MRE is the one with the lowest severity comparing to all the other cases. I can rarely produce a failure with it. Did someone in the team try to reproduce with the c++ code?

Thanks

Lincoln

jakaskerl

Hi lincolnxlw
Sorry if I've missed the code somewhere, but afaik the cpp code was for ROS only? The is no cpp code that runs standalone like python right? Please correct me if I've missed something.
The python API are just bindings for cpp so in theory, it should have the same fault occurrence frequency as the cpp examples, unless there is something else going on.

Thanks,
Jaka

lincolnxlw

Hi jakaskerl

Sorry I meant the C++ code for ROS I provided above. Have someone from the team try to run them with IMX477 extensively like you do with the python example? I know we are waiting for IMX577 for further testing according to @erik. Just want to make sure so far we can confirm IMX477 do not have issue with all three ROS nodes I provided (all the modules I purchased turned out to be IMX577). If that is the case, for the time being I can request Arducam to ship us IMX477 specifically so we can release our product without further delay (looks like they have same PCB dimension so our existing plastic mount should still work). Does that make sense?

Thanks

Lincoln

jakaskerl

Hi lincolnxlw
@DaniloPejovic tested your MRE on ROS Noetic as well and encountered no issues. Looks like only the IMX577s are problematic.

Thanks,
Jaka

lincolnxlw

Hi jakaskerl,

Got it. Thanks.

Hi @DaniloPejovic,

thanks again for testing the C++ MRE! Like I mentioned earlier in #27, I want to make sure the arguments I passed to docker run look good to you (the OAK-FFC-3P is connected to an Pi4 running precompiled OS OAK_CM4_POE_V10_64bit). what do you think

docker container run --net=host -it --rm \
    --privileged \
    -e DISPLAY=$DISPLAY \
    -e QT_X11_NO_MITSHM=1 \
    -v /dev/bus/usb:/dev/bus/usb \
    -v /tmp/.X11-unix:/tmp/.X11-unix:rw \
    -v $HOME/.Xauthority:/root/.Xauthority:rw \
    -v $HOME/data/:/data:rw \
    --device /dev/i2c-1 \
    depthai-ros \
    bash

Thanks

Lincoln

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