Point Cloud Alignment

gdeanrexroth
Yes, the reflective and transparent surfaces are indeed a problem for ToF.

gdeanrexroth But i am trying to create an environment that doesn't allow too much light where the light bounces off the material and the surface that it is on.

That doesn't help. ToF emits it's own light.

The spray bottle you are imaging looks like it has problems due to glare and transparency. if you try to wrap it in something non reflective, does it help?

Thanks,
Jaka

gdeanrexroth
Can't say, we are still figuring it out ourselves.. The refinement will need to be done on our side as well since the borders of the pointcloud are sometimes off by a few pixels (can be seen in your image as well)..

Thanks,
Jaka

gdeanrexroth Does this affect the alignment issues that I have going on right now?

These are the issues (borders are not correctly aligned.

gdeanrexroth Can I also assume that ICP and Global registration would not work in my case due to the material I capturing or it will work with some issues?

The issue is that ICP and global registration use distances between pointclouds to compute the fused pointcloud. As far as I understand you would need custom processing that would allow you to give larger weight to depth pointcloud if there is high reflectivity since stereo generally works better in those circumstances and is more accurate.

Thanks
Jaka

gdeanrexroth
Some efforts were done to improve TOF-RBG alignment.

Script below has some additional undistortion of the RGB, the results were allegedly better. It seems to be an issue of alignment node in FW.

import numpy as np
import cv2
import depthai as dai
import time
from datetime import timedelta

# This example is intended to run unchanged on an OAK-D-SR-PoE camera
FPS = 30.0

RGB_SOCKET = dai.CameraBoardSocket.CAM_C
TOF_SOCKET = dai.CameraBoardSocket.CAM_A
ALIGN_SOCKET = RGB_SOCKET

class FPSCounter:
    def __init__(self):
        self.frameTimes = []

    def tick(self):
        now = time.time()
        self.frameTimes.append(now)
        self.frameTimes = self.frameTimes[-100:]

    def getFps(self):
        if len(self.frameTimes) <= 1:
            return 0
        # Calculate the FPS
        return (len(self.frameTimes) - 1) / (self.frameTimes[-1] - self.frameTimes[0])


pipeline = dai.Pipeline()
# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
tof = pipeline.create(dai.node.ToF)
camTof = pipeline.create(dai.node.Camera)
sync = pipeline.create(dai.node.Sync)
align = pipeline.create(dai.node.ImageAlign)
out = pipeline.create(dai.node.XLinkOut)

# ToF settings
camTof.setFps(FPS)
camTof.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
camTof.setBoardSocket(TOF_SOCKET)

# rgb settings
camRgb.setBoardSocket(RGB_SOCKET)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_800_P)
camRgb.setFps(FPS)
camRgb.setIspScale(1, 2)

depthSize = (1280,800) #PLEASE SET TO BE SIZE OF THE TOF STREAM
rgbSize = camRgb.getIspSize()

out.setStreamName("out")

sync.setSyncThreshold(timedelta(seconds=0.5 / FPS))
rgbSize = camRgb.getIspSize()

# Linking
camRgb.isp.link(sync.inputs["rgb"])
camTof.raw.link(tof.input)
tof.depth.link(align.input)
align.outputAligned.link(sync.inputs["depth_aligned"])
sync.inputs["rgb"].setBlocking(False)
camRgb.isp.link(align.inputAlignTo)
sync.out.link(out.input)

def colorizeDepth(frameDepth):
    invalidMask = frameDepth == 0
    # Log the depth, minDepth and maxDepth
    try:
        minDepth = np.percentile(frameDepth[frameDepth != 0], 3)
        maxDepth = np.percentile(frameDepth[frameDepth != 0], 95)
        logDepth = np.log(frameDepth, where=frameDepth != 0)
        logMinDepth = np.log(minDepth)
        logMaxDepth = np.log(maxDepth)
        np.nan_to_num(logDepth, copy=False, nan=logMinDepth)
        # Clip the values to be in the 0-255 range
        logDepth = np.clip(logDepth, logMinDepth, logMaxDepth)

        # Interpolate only valid logDepth values, setting the rest based on the mask
        depthFrameColor = np.interp(logDepth, (logMinDepth, logMaxDepth), (0, 255))
        depthFrameColor = np.nan_to_num(depthFrameColor)
        depthFrameColor = depthFrameColor.astype(np.uint8)
        depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_JET)
        # Set invalid depth pixels to black
        depthFrameColor[invalidMask] = 0
    except IndexError:
        # Frame is likely empty
        depthFrameColor = np.zeros((frameDepth.shape[0], frameDepth.shape[1], 3), dtype=np.uint8)
    except Exception as e:
        raise e
    return depthFrameColor


rgbWeight = 0.4
depthWeight = 0.6


def updateBlendWeights(percentRgb):
    """
    Update the rgb and depth weights used to blend depth/rgb image

    @param[in] percent_rgb The rgb weight expressed as a percentage (0..100)
    """
    global depthWeight
    global rgbWeight
    rgbWeight = float(percentRgb) / 100.0
    depthWeight = 1.0 - rgbWeight



# Connect to device and start pipeline
remapping = True
with dai.Device(pipeline) as device:
    queue = device.getOutputQueue("out", 8, False)

    # Configure windows; trackbar adjusts blending ratio of rgb/depth
    rgbDepthWindowName = "rgb-depth"

    cv2.namedWindow(rgbDepthWindowName)
    cv2.createTrackbar(
        "RGB Weight %",
        rgbDepthWindowName,
        int(rgbWeight * 100),
        100,
        updateBlendWeights,
    )
    try:
        calibData = device.readCalibration2()
        M1 = np.array(calibData.getCameraIntrinsics(ALIGN_SOCKET, *depthSize))
        D1 = np.array(calibData.getDistortionCoefficients(ALIGN_SOCKET))
        M2 = np.array(calibData.getCameraIntrinsics(RGB_SOCKET, *rgbSize))
        D2 = np.array(calibData.getDistortionCoefficients(RGB_SOCKET))

        try:
            T = (
                np.array(calibData.getCameraTranslationVector(ALIGN_SOCKET, RGB_SOCKET, False))
                * 10
            )  # to mm for matching the depth
        except RuntimeError:
            T = np.array([0.0, 0.0, 0.001])
        try:
            R = np.array(calibData.getCameraExtrinsics(ALIGN_SOCKET, RGB_SOCKET, False))[
                0:3, 0:3
            ]
        except RuntimeError:
            R = np.eye(3)
        TARGET_MATRIX = M1

        lensPosition = calibData.getLensPosition(RGB_SOCKET)
    except:
        raise
    fpsCounter = FPSCounter()
    while True:
        messageGroup = queue.get()
        fpsCounter.tick()
        assert isinstance(messageGroup, dai.MessageGroup)
        frameRgb = messageGroup["rgb"]
        assert isinstance(frameRgb, dai.ImgFrame)
        frameDepth = messageGroup["depth_aligned"]
        assert isinstance(frameDepth, dai.ImgFrame)

        sizeRgb = frameRgb.getData().size
        sizeDepth = frameDepth.getData().size
        # Blend when both received
        if frameDepth is not None:
            rgbFrame = frameRgb.getCvFrame()
            # Colorize the aligned depth
            alignedDepthColorized = colorizeDepth(frameDepth.getFrame())
            # Resize depth to match the rgb frame
            cv2.putText(
                alignedDepthColorized,
                f"FPS: {fpsCounter.getFps():.2f}",
                (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX,
                1,
                (255, 255, 255),
                2,
            )
            cv2.imshow("depth", alignedDepthColorized)
            key = cv2.waitKey(1)
            if key == ord("m"):
                if remapping:
                    print("Remap turned OFF.")
                    remapping = False
                else:
                    print("Remap turned ON.")
                    remapping = True

            if remapping:
                mapX, mapY = cv2.initUndistortRectifyMap(
                    M2, D2, None, M2, rgbSize, cv2.CV_32FC1
                )
                rgbFrame = cv2.remap(rgbFrame, mapX, mapY, cv2.INTER_LINEAR)

            blended = cv2.addWeighted(
                rgbFrame, rgbWeight, alignedDepthColorized, depthWeight, 0
            )
            cv2.imshow(rgbDepthWindowName, blended)

        key = cv2.waitKey(1)
        if key == ord("q"):
            break

Thanks,
Jaka

jakaskerl when can we expect the firmware corrections ?

apirrone Likely with depthai V3

gdeanrexroth Yup.

gdeanrexroth Does the intrinsic or extrinsic value need to be in the script that captures the point cloud(pcd or ply file)? Or does it need to be implemented into the script that does the icp or global registration?

In the pointcloud generation. The idea is to create a colorized pointcloud -- depth needs to be aligned to RGB. This can only be done by knowing the extrinsics from RGB to stereo, and intrinsics of rgb and the rectified stereo frames.

Thanks,
Jaka

jakaskerl Likely with depthai V3

Ok, and any timeline on the release of this version ?

I'm a little confused. If there is a bug in the ImageAlign node in firmware, how did you get such a perfectly aligned point cloud here ?

gdeanrexroth

gdeanrexroth Overall until you guys fix the tof-rgb alignment and release the depthai V3. I can assume that I will not be able to make any more progress on my current project utilizing the ToF senor to generate point cloud. Until the Luxonis team fixes this issue, correct?

The new script I sent aims to fix this issue. We will probably just change the example (not sure what the current idea is for depthai). The only thing different seems to be the RGB undistortion. You can continue to develop the project just make sure you undistort the RGB camera.

apirrone I'm a little confused. If there is a bug in the ImageAlign node in firmware, how did you get such a perfectly aligned point cloud here ?

This was done here iirc.

Thanks,
Jaka