Inaccurate depth while tracking vehicles

erik · Feb 25, 2023

Hi AnirbanRaha ,
Could you also share a video of rgb/depth together with the bounding boxes? That would help us spot the issue.
I would also suggest using latest develop of depthai library (on depthai-python checkout to develop, and run python examples/install_requirements.py), as we have changed depth averaging algo to median (see PR here), which, in general, improves spatial detections. Thoughts?
Thanks, Erik

AnirbanRaha · Feb 25, 2023

Hey erik . Thanks for the quick response!
Here is the link to the rgb video with bounding boxes.:
[

Please let me know if you need anything else. I will also try this with the latest develop of depthai library tomorrow during daytime and let you know here.

[edit] Another important thing I forgot to mention is that the Oak D is mounted upside down. I am using a rotate 180deg on the RGB frame at the beginning of my code to correct the orientation. Don't know if this causes the issue.

Thanks!

erik · Feb 25, 2023

Hi AnirbanRaha , Could you share the source code?

AnirbanRaha · Feb 26, 2023

erik It's in the first post. The code is based on the Kalman filter example in depthai-experiments repo. Although I have disabled the Kalmann filter output as it's even worse than the object tracker output.

erik · Feb 27, 2023

Hi AnirbanRaha ,
Sorry my bad, I wanted to ask if you could provide MRE (much shorter code), so our development team can spot the issue and debug it.
Thanks, Erik

AnirbanRaha · Feb 27, 2023

erik Here is the minimum amount of code to produce the video preview similar to the video I had posted in the previous message. Please let me know if this is what you wanted.

import depthai as dai
import blobconverter
import cv2
import numpy as np
import time

label_map = ['unknown','vehicle', 'pedestrian']

# Create pipeline
pipeline = dai.Pipeline()

cam_rgb = pipeline.create(dai.node.ColorCamera)
detection_network = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
mono_left = pipeline.create(dai.node.MonoCamera)
mono_right = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
object_tracker = pipeline.create(dai.node.ObjectTracker)

xout_rgb = pipeline.create(dai.node.XLinkOut)
tracker_out = pipeline.create(dai.node.XLinkOut)

xout_rgb.setStreamName('rgb')
tracker_out.setStreamName('tracklets')

#rotating the camera because it's mounted upside down
cam_rgb.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
cam_rgb.setPreviewSize(672, 384)
cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
cam_rgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
cam_rgb.setInterleaved(False)
cam_rgb .setPreviewNumFramesPool(15)

mono_left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
mono_left.setBoardSocket(dai.CameraBoardSocket.LEFT)
mono_right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
mono_right.setBoardSocket(dai.CameraBoardSocket.RIGHT)

mono_left.setFps(10)
mono_right.setFps(10)
cam_rgb.setFps(10)

stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
stereo.setDepthAlign(dai.CameraBoardSocket.RGB)

detection_network.setBlobPath('./pedestrian-and-vehicle-detector-adas-0001.blob')
detection_network.setConfidenceThreshold(0.5)
detection_network.input.setBlocking(False)
detection_network.setBoundingBoxScaleFactor(0.7)
detection_network.setNumInferenceThreads(2)
detection_network.setDepthLowerThreshold(100)
detection_network.setDepthUpperThreshold(15000)

object_tracker.setDetectionLabelsToTrack([1,2])  # track only person and car
object_tracker.setTrackerType(dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM)
object_tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID)

mono_left.out.link(stereo.left)
mono_right.out.link(stereo.right)

cam_rgb.preview.link(detection_network.input)
object_tracker.passthroughTrackerFrame.link(xout_rgb.input)
object_tracker.out.link(tracker_out.input)

detection_network.passthrough.link(object_tracker.inputTrackerFrame)
detection_network.passthrough.link(object_tracker.inputDetectionFrame)
detection_network.out.link(object_tracker.inputDetections)
stereo.depth.link(detection_network.inputDepth)


# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    calibration_handler = device.readCalibration()
    baseline = calibration_handler.getBaselineDistance() * 10
    focal_length = calibration_handler.getCameraIntrinsics(dai.CameraBoardSocket.RIGHT, 640, 400)[0][0]

    q_rgb  = device.getOutputQueue(name='rgb', maxSize=10, blocking=False)
    q_tracklets = device.getOutputQueue(name='tracklets', maxSize=10, blocking=False)
    
    startTime = time.monotonic()
    counter = 0
    fps = 0
    color = (255, 255, 255)
   
    while(True):
        frame = q_rgb.get().getCvFrame()
        tracklets = q_tracklets.get()
        current_time = tracklets.getTimestamp()
        
        h, w = frame.shape[:2]

        for t in tracklets.tracklets:
            roi = t.roi.denormalize(frame.shape[1], frame.shape[0])
            x1 = int(roi.topLeft().x)
            y1 = int(roi.topLeft().y)
            x2 = int(roi.bottomRight().x)
            y2 = int(roi.bottomRight().y)

            x_space = t.spatialCoordinates.x
            y_space = t.spatialCoordinates.y
            z_space = t.spatialCoordinates.z

            try:
                label = label_map[t.label]
            except:
                label = t.label

            cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f'ID: {[t.id]}', (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, t.status.name, (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0))

            cv2.putText(frame, f'X: {int(x_space)} mm', (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f'Y: {int(y_space)} mm', (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f'Z: {int(z_space)} mm', (x1 + 10, y1 + 95), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            
        counter+=1
        current_time = time.monotonic()
        if (current_time - startTime) > 1 :
            fps = counter / (current_time - startTime)
            counter = 0
            startTime = current_time
        cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color)
        cv2.imshow('tracker', frame)

        if cv2.waitKey(1) == ord('q'):
            break
        
    cv2.destroyAllWindows()

GGergelySzabolcs · Feb 27, 2023

180 degree rotation is not supported by SpatialDetectionNetwork unless all cameras are rotated the same way. (E.g. depth map is also 180 rotated. For that you need to rotate LEFT and RIGHT cameras 180 degrees, then link left cam to right stereo input, right cam to left stereo input and latest develop branch/library. @AnirbanRaha

AnirbanRaha · Mar 4, 2023

@GergelySzabolcs @erik Hi. I don't want to go into the complications of reversing the image at this stage and so I found a way to mount the camera in the upright position. I also did some extensive testing both indoor and outdoor (on a bright sunny day). I did this with both the latest "develop" branch of depthai-python and the latest stable release 2.20.2.0.

What I found was that indoor, the spatial detection works very well. I am getting accurate results that are good enough for me. However, outdoor, the thing still won't work properly. The only differences in the indoor and outdoor setups are as follows:

Indoor - Raspberry Pi is being powered by the official 3A power supply
Outdoor - Raspberry Pi is being powered by a Quick charge 3.0 (3A) USB port that is wired to my car's battery
Indoor - Lighting is low
Outdoor - It's very bright and somehow objects don't appear as clear as in indoor lighting conditions

Another difference in both conditions is that the OAK-D lite is placed behind my car's windshield. However, I also tried putting a thick piece of transparent glass in front of the camera when indoors and it still performs just as well. So I don't think this may be the cause but who knows!

I'm attaching the link to both the outdoor and indoor video setup. Also, the MRE for this setup is almost the same as before but I will still post it:

import depthai as dai
import blobconverter
import cv2
import numpy as np
import time

labelMap = ['unknown','vehicle', 'pedestrian']

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
objectTracker = pipeline.create(dai.node.ObjectTracker)

xoutRgb = pipeline.create(dai.node.XLinkOut)
trackerOut = pipeline.create(dai.node.XLinkOut)

xoutRgb.setStreamName("preview")
trackerOut.setStreamName("tracklets")

# Properties
#camRgb.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
camRgb.setPreviewSize(672, 384)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)

#monoLeft.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)

#monoRight.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)

monoLeft.setFps(10)
monoRight.setFps(10)
camRgb.setFps(10)

# setting node configs
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
# Align depth map to the perspective of RGB camera, on which inference is done
stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())

spatialDetectionNetwork.setBlobPath('./pedestrian-and-vehicle-detector-adas-0001.blob')
spatialDetectionNetwork.setConfidenceThreshold(0.5)
spatialDetectionNetwork.input.setBlocking(True)
spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5)
spatialDetectionNetwork.setDepthLowerThreshold(100)
spatialDetectionNetwork.setDepthUpperThreshold(15000)

objectTracker.setDetectionLabelsToTrack([1,2])  # track only person
# possible tracking types: ZERO_TERM_COLOR_HISTOGRAM, ZERO_TERM_IMAGELESS, SHORT_TERM_IMAGELESS, SHORT_TERM_KCF
objectTracker.setTrackerType(dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM)
# take the smallest ID when new object is tracked, possible options: SMALLEST_ID, UNIQUE_ID
objectTracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.SMALLEST_ID)

# Linking
monoLeft.out.link(stereo.left)
monoRight.out.link(stereo.right)

camRgb.preview.link(spatialDetectionNetwork.input)
objectTracker.passthroughTrackerFrame.link(xoutRgb.input)
objectTracker.out.link(trackerOut.input)

# if fullFrameTracking:
#     camRgb.setPreviewKeepAspectRatio(False)
#     camRgb.video.link(objectTracker.inputTrackerFrame)
#     objectTracker.inputTrackerFrame.setBlocking(False)
#     # do not block the pipeline if it's too slow on full frame
#     objectTracker.inputTrackerFrame.setQueueSize(2)
# else:
spatialDetectionNetwork.passthrough.link(objectTracker.inputTrackerFrame)

spatialDetectionNetwork.passthrough.link(objectTracker.inputDetectionFrame)
spatialDetectionNetwork.out.link(objectTracker.inputDetections)
stereo.depth.link(spatialDetectionNetwork.inputDepth)
    
# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    preview = device.getOutputQueue("preview", 4, False)
    tracklets = device.getOutputQueue("tracklets", 4, False)

    startTime = time.monotonic()
    counter = 0
    fps = 0
    color = (255, 255, 255)

    while(True):
        imgFrame = preview.get()
        track = tracklets.get()

        counter+=1
        current_time = time.monotonic()
        if (current_time - startTime) > 1 :
            fps = counter / (current_time - startTime)
            counter = 0
            startTime = current_time

        frame = imgFrame.getCvFrame()
        trackletsData = track.tracklets
        for t in trackletsData:
            roi = t.roi.denormalize(frame.shape[1], frame.shape[0])
            x1 = int(roi.topLeft().x)
            y1 = int(roi.topLeft().y)
            x2 = int(roi.bottomRight().x)
            y2 = int(roi.bottomRight().y)

            try:
                label = labelMap[t.label]
            except:
                label = t.label

            cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.4, 255)
            cv2.putText(frame, f"ID: {[t.id]}", (x1 + 10, y1 + 30), cv2.FONT_HERSHEY_TRIPLEX, 0.2, 255)
            cv2.putText(frame, t.status.name, (x1 + 10, y1 + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.2, 255)
            cv2.rectangle(frame, (x1, y1), (x2, y2), color, cv2.FONT_HERSHEY_SIMPLEX)

            cv2.putText(frame, f"X: {int(t.spatialCoordinates.x)} mm", (x1 + 10, y1 + 60), cv2.FONT_HERSHEY_TRIPLEX, 0.4, 255)
            #cv2.putText(frame, f"Y: {int(t.spatialCoordinates.y)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f"Z: {int(t.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.4, 255)

        cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color)

        cv2.imshow("tracker", frame)
        
      
        if cv2.waitKey(1) == ord('q'):
            break
        
    cv2.destroyAllWindows()

Outdoor video: [
Indoor video: [

Can you please give any hints as to how I can reproduce the indoor results outside in daylight?

GGergelySzabolcs · Mar 4, 2023

AnirbanRaha

Increase depth threshold for outside, currently it's set to max 15 meters in your pipeline:
setDepthUpperThreshold(15000)
Set mono cameras to 720p, which is more suitable for lang range
Enable stereo subpixel mode, better for long range
Use latest develop, spatial locations are more stable

AnirbanRaha · Mar 5, 2023

GergelySzabolcs

GergelySzabolcs Increase depth threshold for outside, currently it's set to max 15 meters in your pipeline:
setDepthUpperThreshold(15000)

I've set depth to 30000 now.

GergelySzabolcs Set mono cameras to 720p, which is more suitable for lang range

I've got the Oak D Lite version. Don't think it has 720p resolution on the mono cameras (throws an error if I set the mono resolution to 720p and defaults to 480p).

GergelySzabolcs Enable stereo subpixel mode, better for long range

Enabled it with: stereo.setSubpixel(True)

GergelySzabolcs Use latest develop, spatial locations are more stable

Yes using the latest "develop" branch of depthai-python. As I've mentioned before, the readings are quite stable for me indoors.

[Edit]: I have measured distances with a measuring tape. I'm somehow getting twice the actual distance on the Z coordinate?!

`Please have a look at this video, I'm standing at the end of the 5meter long tape and getting around 10meter. The reading is always roughly around 2x of what I should actually be getting:

[

GGergelySzabolcs · Mar 6, 2023

AnirbanRaha Can you post the output of: https://github.com/luxonis/depthai-python/blob/main/examples/calibration/calibration_dump.py
e.g. running python3 calibration_dump.py > dump.jsoj

AnirbanRaha · Mar 6, 2023

Here it is. Says something about "No factory calibration".
Is EEPROM available: True

User calibration: {
  "batchName": "",
  "batchTime": 0,
  "boardConf": "",
  "boardCustom": "",
  "boardName": "OAK-D-LITE",
  "boardOptions": 0,
  "boardRev": "R1M1E3",
  "cameraData": [
    [
      2,
      {
        "cameraType": 0,
        "distortionCoeff": [
          -3.5793373584747314,
          -27.937454223632812,
          -0.0013723287265747786,
          -0.00048459265963174403,
          181.11610412597656,
          -3.565078020095825,
          -28.067947387695312,
          181.3641357421875,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0
        ],
        "extrinsics": {
          "rotationMatrix": [
            [
              0.9998652338981628,
              0.016339778900146484,
              0.0015999042661860585
            ],
            [
              -0.016334136947989464,
              0.9998605251312256,
              -0.0034779615234583616
            ],
            [
              -0.0016565101686865091,
              0.0034513596910983324,
              0.9999926686286926
            ]
          ],
          "specTranslation": {
            "x": 3.75,
            "y": 0.0,
            "z": 0.0
          },
          "toCameraSocket": 0,
          "translation": {
            "x": 3.729553699493408,
            "y": 0.03537023440003395,
            "z": -0.4103076159954071
          }
        },
        "height": 480,
        "intrinsicMatrix": [
          [
            452.56829833984375,
            0.0,
            314.34759521484375
          ],
          [
            0.0,
            452.56829833984375,
            237.3134765625
          ],
          [
            0.0,
            0.0,
            1.0
          ]
        ],
        "lensPosition": 0,
        "specHfovDeg": 72.9000015258789,
        "width": 640
      }
    ],
    [
      1,
      {
        "cameraType": 0,
        "distortionCoeff": [
          -19.380821228027344,
          145.0615997314453,
          -0.0009900459554046392,
          0.0008708707173354924,
          -169.3114013671875,
          -19.37105941772461,
          144.98361206054688,
          -169.1844940185547,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0
        ],
        "extrinsics": {
          "rotationMatrix": [
            [
              0.9994993805885315,
              -0.015042693354189396,
              -0.027834275737404823
            ],
            [
              0.015119130723178387,
              0.9998824596405029,
              0.002537747146561742
            ],
            [
              0.027792830020189285,
              -0.002957306569442153,
              0.9996093511581421
            ]
          ],
          "specTranslation": {
            "x": -7.5,
            "y": 0.0,
            "z": 0.0
          },
          "toCameraSocket": 2,
          "translation": {
            "x": -7.491415500640869,
            "y": -0.11638329923152924,
            "z": -0.08934982120990753
          }
        },
        "height": 480,
        "intrinsicMatrix": [
          [
            452.8463439941406,
            0.0,
            298.63897705078125
          ],
          [
            0.0,
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            216.44290161132812
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          [
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            1.0
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        ],
        "lensPosition": 0,
        "specHfovDeg": 72.9000015258789,
        "width": 640
      }
    ],
    [
      0,
      {
        "cameraType": 0,
        "distortionCoeff": [
          -2.4185500144958496,
          -2.238746404647827,
          0.0005566917243413627,
          0.0017487785080447793,
          9.495307922363281,
          -2.5591840744018555,
          -1.5969901084899902,
          8.690184593200684,
          0.0,
          0.0,
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        ],
        "extrinsics": {
          "rotationMatrix": [
            [
              0.0,
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            ],
            [
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            ],
            [
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            ]
          ],
          "specTranslation": {
            "x": -0.0,
            "y": -0.0,
            "z": -0.0
          },
          "toCameraSocket": -1,
          "translation": {
            "x": 0.0,
            "y": 0.0,
            "z": 0.0
          }
        },
        "height": 2160,
        "intrinsicMatrix": [
          [
            2958.43115234375,
            0.0,
            1867.47021484375
          ],
          [
            0.0,
            2958.43115234375,
            1093.8828125
          ],
          [
            0.0,
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        ],
        "lensPosition": 0,
        "specHfovDeg": 68.7938003540039,
        "width": 3840
      }
    ]
  ],
  "hardwareConf": "",
  "imuExtrinsics": {
    "rotationMatrix": [
      [
        0.0,
        0.0,
        0.0
      ],
      [
        0.0,
        0.0,
        0.0
      ],
      [
        0.0,
        0.0,
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    ],
    "specTranslation": {
      "x": 0.0,
      "y": 0.0,
      "z": 0.0
    },
    "toCameraSocket": -1,
    "translation": {
      "x": 0.0,
      "y": 0.0,
      "z": 0.0
    }
  },
  "miscellaneousData": [],
  "productName": "",
  "stereoRectificationData": {
    "leftCameraSocket": 1,
    "rectifiedRotationLeft": [
      [
        0.9998739957809448,
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        -0.01586950570344925
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      [
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    "rectifiedRotationRight": [
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        0.9999276995658875
      ]
    ],
    "rightCameraSocket": 2
  },
  "version": 6
}
No factory calibration: No or invalid EEPROM configuration flashed, error: EEPROM_INVALID_DATA
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GergelySzabolcs

AnirbanRaha · Mar 12, 2023

@erik @GergelySzabolcs I tried to calibrate the camera. I followed the steps in this thread: [https://github.com/luxonis/depthai/issues/725#:~:text=git%20checkout%20lite_calibration,python3%20calibrate.py%20...](https://)

I used the command:
python3 calibrate.py -s 2.4 -ms 1.7 -brd OAK-D-LITE

I oriented the charuco board and took 13 images. The calibration process ended with something like "Calibration successful. Error = 0.19..."

After this I again ran the calibration_dump.py and I get the same calibration/invalid configuration error.

Not only this, now I can't even run any of the scripts. I'm getting an "Xlink error: possible device misconfiguration"

~~How can I properly calibrate the OAK D Lite? Please help me through this.~~

[Edit]: I tried the calibration process again. This time I also got a successful calibration message but with a lower error value of 0.15...
Now the scripts are working again and indoor readings look good. Yet to check it outdoors in my car.

However, what I still don't get is why the calibration dump script still shows a configuration error?

AnirbanRaha · Mar 13, 2023

Running with acceptable accuracy outdoors now after recalibration earlier.

Should I be concerned the calibration_dump.py still shows a configuration error?

@erik @GergelySzabolcs

erik · Mar 13, 2023

Hi AnirbanRaha ,
Sounds great that the issue has been resolved! Which error at calib dump are you referring to?
Thanks, Erik

AnirbanRaha · Mar 13, 2023

erik Yes seems so but I'm still getting this error when I run calibration_dump.py:
No factory calibration: No or invalid EEPROM configuration flashed, error: EEPROM_INVALID_DATA

erik · Mar 13, 2023

Hi AnirbanRaha ,
That's because you have an older version of calibration - newer versions have both factory calibration and user calibration - so users can't override their factory calib
But that's not a problem, depthai will just take user calibration.
Thanks, Erik

AnirbanRaha · Mar 14, 2023

I see. Thanks for the explanation @erik ! I really don't mind that error since the distances now look pretty good.