@jakaskerl
More examples of the new script point cloud, some improvements are noticeable. But from the side again everything is not fully aligned.

@jakaskerl
With the pictures I attached above somewhat explains my side of the project as well. Without the fully, formed point cloud my team and I will not be able to view our material/parts in a 3d(point cloud) format. This results are with me undistorting the RGB camera. So I am still unsure if I can make much more progress on my side since the issue is being fixed internally by you and your team.

    gdeanrexroth The correct angle displays perfect alignment, but as a i rotate the .ply file you can see the gaps and holes.

    That is expected and is a result of occlusion.. This can't be fixed at all since information is missing.

    gdeanrexroth So I am still unsure if I can make much more progress on my side since the issue is being fixed internally by you and your team.

    The only fix on our part is include the RGB undistortion that was missed in the original release..

    Thanks,
    Jaka

        jakaskerl

        Undistorting the rgb doesn't help for me

        Without rgb undistort :

        With rgb undistort:

        It's probably because I'm using fisheye lenses.
        Using cv2.fisheye.initUndistortRectifyMap(...) doesn't help

            jakaskerl
            With undistorting not working properly are there any steps you recommend us to take? Or do we have to wait until depthai v3?

            I doubt that this is my issue but can incorrect calibration of misalignment? I have tried undistorting but it seemed to not work. I am new to the computer vision world so I am unaware of few terms. This is my result of using undistorting

                gdeanrexroth With undistorting not working properly are there any steps you recommend us to take? Or do we have to wait until depthai v3?

                Should be working properly, just on on fisheye lenses.

                gdeanrexroth This is my result of using undistorting

                Did you use the ImageAlign node to align the tof and RGB? The RGB looks ok at first glance, so either extrinsics/intrinsics are incorrect or there is a mistake in aligning.

                Thanks,
                Jaka

                      jakaskerl
                      Yes I did use the image aligned node within my script. I used the tof-rgb alignment script that you provided, I added a key function that will allow me to capture and save the tof-rgb depth as a ply file. There are other ways that I can approach this however, I simply wanted to see how the rgb would look.
                      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

                      # Define intrinsic parameters

                      RGB_INTRINSICS = np.array([

                      [494.3519287109375, 0.0, 321.8478088378906],

[0.0, 499.4835205078125, 258.3044128417969],

[0.0, 0.0, 1.0]

                      ])

                      TOF_INTRINSICS = np.array([

                      [842.6837768554688, 0.0, 673.1340942382812],

[0.0, 851.867431640625, 412.4818115234375],

[0.0, 0.0, 1.0]

                      ])

                      # Define camera resolutions

                      RGB_WIDTH, RGB_HEIGHT = 640, 480

                      TOF_WIDTH, TOF_HEIGHT = 1280, 800

                      class FPSCounter:

                      def __init__(self):

    self.frameTimes = []

def tick(self):

    # record the current time for the FPS calculation

    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 based on the recorded frame times

    return (len(self.frameTimes) - 1) / (self.frameTimes[-1] - self.frameTimes[0])

                      def save_ply(rgb_image, depth_image, filename):

                      # Get dimensions of the RGB image

height, width, _ = rgb_image.shape

# Save as PLY file

with open(filename, 'w') as ply_file:

    #write ply header

    ply_file.write('ply\\n')

    ply_file.write('format ascii 1.0\\n')

    ply_file.write(f'element vertex {height \* width}\\n')

    ply_file.write('property float x\\n')

    ply_file.write('property float y\\n')

    ply_file.write('property float z\\n')

    ply_file.write('property uchar red\\n')

    ply_file.write('property uchar green\\n')

    ply_file.write('property uchar blue\\n')

    ply_file.write('end_header\\n')

    

    # Convert depth image to point cloud using ToF intrinsics

    fx = TOF_INTRINSICS[0, 0]

    fy = TOF_INTRINSICS[1, 1]

    cx = TOF_INTRINSICS[0, 2]

    cy = TOF_INTRINSICS[1, 2]

    

    for v in range(height):

        for u in range(width):

            z = depth_image[v, u]  # Depth value

            if z > 0:  # Ignore zero 

                # calculate 3d coordinates

                x = (u - cx) \* z / fx

                y = (v - cy) \* z / fy

                r, g, b = rgb_image[v, u]  # RGB color

                #write the vertex data to PLY file

                ply_file.write(f'{x} {y} {z} {r} {g} {b}\\n')

                      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, 1)

                      # defines image sizes

                      depthSize = (TOF_WIDTH, TOF_HEIGHT)

                      rgbSize = camRgb.getIspSize()

                      # set output stream name

                      out.setStreamName("out")

                      # configure synchronization threshold

                      sync.setSyncThreshold(timedelta(seconds=0.5 / FPS))

                      # Linking

                      camRgb.isp.link(sync.inputs["rgb"]) #link RGB camera output to sync node

                      camTof.raw.link(tof.input) #link TOF camera raw output to TOF node

                      tof.depth.link(align.input) #link TOF depth output to align node

                      align.outputAligned.link(sync.inputs["depth_aligned"]) #link aligned depth to sync node

                      sync.inputs["rgb"].setBlocking(False) #set rgb input as non-blocking

                      camRgb.isp.link(align.inputAlignTo) #link sync output to XLinkOut node

                      sync.out.link(out.input)

                      def colorizeDepth(frameDepth):

                      invalidMask = frameDepth == 0

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)

    logDepth = np.clip(logDepth, logMinDepth, logMaxDepth)

    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)

    depthFrameColor[invalidMask] = 0

except IndexError:

    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):

                      global depthWeight

global rgbWeight

rgbWeight = float(percentRgb) / 100.0

depthWeight = 1.0 - rgbWeight

                      # Connect to device and start pipeline

                      remapping = True

                      save_ply_flag = False

                      with dai.Device(pipeline) as device:

                      queue = device.getOutputQueue("out", 8, False)

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))

    # Use the predefined intrinsics if available, otherwise use the calibration data

    if TOF_INTRINSICS is not None:

        M1 = TOF_INTRINSICS

    if RGB_INTRINSICS is not None:

        M2 = RGB_INTRINSICS

    try:

        T = np.array(calibData.getCameraTranslationVector(ALIGN_SOCKET, RGB_SOCKET, False)) \* 10

    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

    

    if frameDepth is not None:

        rgbFrame = frameRgb.getCvFrame()

        alignedDepthColorized = colorizeDepth(frameDepth.getFrame())

        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"):

            remapping = not remapping

            print(f"Remap turned {'ON' if remapping else 'OFF'}.")

        elif key == ord('s'):

            save_ply_flag = 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)

    if save_ply_flag:

        rgb_frame = cv2.cvtColor(rgbFrame, cv2.COLOR_BGR2RGB)

        save_ply(rgb_frame, frameDepth.getFrame(), 'george_rgb1.ply')

        print("PLY file saved as 'george_rgb.ply'")

        save_ply_flag = False

    if key == ord("q"):

        break

                      cv2.destroyAllWindows()

                      I ran this script :https://docs.luxonis.com/software/depthai/examples/calibration_reader/
                      Here are the results from it, however if its extrinsic. Then I may have to recalibrate the camera with the method you recommended to me :
                      RGB Camera Default intrinsics...

                      [[494.3519287109375, 0.0, 321.8478088378906], [0.0, 499.4835205078125, 258.3044128417969], [0.0, 0.0, 1.0]]

                      640

                      480

                      RGB Camera Default intrinsics...

                      [[494.3519287109375, 0.0, 321.8478088378906], [0.0, 499.4835205078125, 258.3044128417969], [0.0, 0.0, 1.0]]

                      640

                      480

                      RGB Camera resized intrinsics... 3840 x 2160

                      [[2.96611157e+03 0.00000000e+00 1.93108691e+03]

                      [0.00000000e+00 2.99690112e+03 1.18982642e+03]

                      [0.00000000e+00 0.00000000e+00 1.00000000e+00]]

                      RGB Camera resized intrinsics... 4056 x 3040

                      [[3.13295532e+03 0.00000000e+00 2.03971057e+03]

                      [0.00000000e+00 3.16547681e+03 1.63600427e+03]

                      [0.00000000e+00 0.00000000e+00 1.00000000e+00]]

                      LEFT Camera Default intrinsics...

                      [[842.6837768554688, 0.0, 673.1340942382812], [0.0, 851.867431640625, 412.4818115234375], [0.0, 0.0, 1.0]]

                      1280

                      800

                      LEFT Camera resized intrinsics... 1280 x 720

                      [[842.68377686 0. 673.13409424]

                      [ 0. 851.86743164 372.48181152]

                      [ 0. 0. 1. ]]

                      RIGHT Camera resized intrinsics... 1280 x 720

                      [[836.24615479 0. 656.42828369]

                      [ 0. 845.62658691 399.05911255]

                      [ 0. 0. 1. ]]

                      LEFT Distortion Coefficients...

                      k1: -9.116254806518555

                      k2: 262.5550842285156

                      p1: 0.007134947460144758

                      p2: -0.0009857615223154426

                      k3: 1347.274169921875

                      k4: -9.195904731750488

                      k5: 260.98687744140625

                      k6: 1308.9786376953125

                      s1: 0.0

                      s2: 0.0

                      s3: 0.0

                      s4: 0.0

                      τx: 0.0

                      τy: 0.0

                      RIGHT Distortion Coefficients...

                      k1: -5.861973762512207

                      k2: 5.3061065673828125

                      p1: 0.005871884059160948

                      p2: 0.00142634566873312

                      k3: 88.46317291259766

                      k4: -6.072614669799805

                      k5: 7.037742614746094

                      k6: 83.25321960449219

                      s1: 0.0

                      s2: 0.0

                      s3: 0.0

                      s4: 0.0

                      τx: 0.0

                      τy: 0.0

                      RGB FOV 71.86000061035156, Mono FOV 71.86000061035156

                      LEFT Camera stereo rectification matrix...

                      [[ 9.94211665e-01 8.86633717e-03 -1.81476751e+01]

                      [-4.90145546e-03 9.94452611e-01 2.86947005e+01]

                      [ 2.85166444e-06 4.52051103e-06 9.96386328e-01]]

                      RIGHT Camera stereo rectification matrix...

                      [[ 1.00186534e+00 8.93177200e-03 -6.82440986e+00]

                      [-4.93918803e-03 1.00179181e+00 -7.21055399e-01]

                      [ 2.87361723e-06 4.55387305e-06 9.96286100e-01]]

                      Transformation matrix of where left Camera is W.R.T right Camera's optical center

                      [[ 9.99597728e-01 -7.52320397e-04 -2.83513945e-02 -3.98795390e+00]

                      [ 5.31902653e-04 9.99969602e-01 -7.78123224e-03 -2.61692833e-02]

                      [ 2.83563845e-02 7.76302209e-03 9.99567747e-01 -1.03633568e-01]

                      [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]

                      Transformation matrix of where left Camera is W.R.T RGB Camera's optical center

                      [[ 9.99843597e-01 -8.23507272e-03 1.56521089e-02 -7.51402760e+00]

                      [ 8.25367495e-03 9.99965310e-01 -1.12427305e-03 -1.49354547e-01]

                      [-1.56423096e-02 1.25328498e-03 9.99876857e-01 4.59043831e-01]

                      [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]

                        @jakaskerl

                        Screenshots of the same point cloud but from different angles. This is using the updated script that you gave me, along with the using the image align node.

                          gdeanrexroth
                          Try this:

                          import os
import time
import json
import cv2
import depthai as dai
import numpy as np
from datetime import timedelta
print(dai.__version__)
from numba import jit, prange

@jit(nopython=True, parallel=True)
def reprojection(depth_image, depth_camera_intrinsics, camera_extrinsics, color_camera_intrinsics, depth_image_show = None):
    height = len(depth_image)
    width = len(depth_image[0])
    if depth_image_show is not None:
        image = np.zeros((height, width), np.uint8)
    else:
        image = np.zeros((height, width), np.uint16)
    if(camera_extrinsics[0][3] > 0):
        sign = 1
    else:
        sign = -1
    for i in prange(0, height):
        for j in prange(0, width):
            if sign == 1:
                # Reverse the order of the pixels
                j = width - j - 1
            d = depth_image[i][j]
            if(d == 0):
                continue
            # Convert pixel to 3d point
            x = (j - depth_camera_intrinsics[0][2]) * d / depth_camera_intrinsics[0][0]
            y = (i - depth_camera_intrinsics[1][2]) * d / depth_camera_intrinsics[1][1]
            z = d

            # Move the point to the camera frame
            x1 = camera_extrinsics[0][0] * x + camera_extrinsics[0][1] * y + camera_extrinsics[0][2] * z + camera_extrinsics[0][3]
            y1 = camera_extrinsics[1][0] * x + camera_extrinsics[1][1] * y + camera_extrinsics[1][2] * z + camera_extrinsics[1][3]
            z1 = camera_extrinsics[2][0] * x + camera_extrinsics[2][1] * y + camera_extrinsics[2][2] * z + camera_extrinsics[2][3]

            u = color_camera_intrinsics[0][0] * (x1  / z1) + color_camera_intrinsics[0][2]
            v = color_camera_intrinsics[1][1] * (y1  / z1) + color_camera_intrinsics[1][2]
            int_u = round(u)
            int_v = round(v)
            if(int_v != i):
                print(f'v -> {v} and i -> {i}') # This should never be printed
            if int_u >= 0 and int_u < (len(image[0]) - 1) and int_v >= 0 and int_v < len(image):
                if depth_image_show is not None:
                    image[int_v][int_u] = depth_image_show[i][j][0]
                    image[int_v][int_u + sign] = depth_image_show[i][j][0]
                else:
                    image[int_v][int_u] = z1
                    image[int_v][int_u + sign] = z1
    return image

def create_pipeline(ALIGN_SOCKET):
    pipeline = dai.Pipeline()

    # Create ToF node
    tof = pipeline.create(dai.node.ToF)
    tof.setNumShaves(4)

    # Configure the ToF node
    tofConfig = tof.initialConfig.get()
    tofConfig.enableFPPNCorrection = True
    tofConfig.enableOpticalCorrection = True
    tofConfig.enableWiggleCorrection = True
    tofConfig.enableTemperatureCorrection = True
    tofConfig.phaseUnwrappingLevel = 4
    tof.initialConfig.set(tofConfig)

    # Input for ToF configuration
    xinTofConfig = pipeline.create(dai.node.XLinkIn)
    xinTofConfig.setStreamName("tofConfig")
    xinTofConfig.out.link(tof.inputConfig)

    # Create ToF camera node
    cam_tof = pipeline.create(dai.node.Camera)
    cam_tof.setFps(20)
    cam_tof.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
    cam_tof.setBoardSocket(dai.CameraBoardSocket.CAM_A)
    cam_tof.raw.link(tof.input)

    # Create ColorCamera nodes for stereo pair
    colorLeft = pipeline.create(dai.node.ColorCamera)
    colorRight = pipeline.create(dai.node.ColorCamera)


    # Configure ColorCameras
    colorLeft.setBoardSocket(dai.CameraBoardSocket.CAM_B)
    colorRight.setBoardSocket(dai.CameraBoardSocket.CAM_C)
    colorLeft.setResolution(dai.ColorCameraProperties.SensorResolution.THE_800_P)
    colorRight.setResolution(dai.ColorCameraProperties.SensorResolution.THE_800_P)
    colorLeft.setFps(20)
    colorRight.setFps(20)
    colorLeft.setInterleaved(False)
    colorRight.setInterleaved(False)
    colorLeft.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
    colorRight.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
    #colorLeft.setImageOrientation(dai.CameraImageOrientation.NORMAL)
    #colorRight.setImageOrientation(dai.CameraImageOrientation.NORMAL)

    #colorLeft.setIspScale(2, 2)  # Corrected line
    #colorRight.setIspScale(2, 1)  # Corrected line


    # Create StereoDepth node
    stereo = pipeline.create(dai.node.StereoDepth)
    stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
    stereo.initialConfig.setMedianFilter(dai.MedianFilter.MEDIAN_OFF)
    stereo.setLeftRightCheck(True)
    stereo.setExtendedDisparity(False)
    stereo.setSubpixel(False)
    stereo.setDepthAlign(ALIGN_SOCKET)

    # Link the RAW outputs of the ColorCameras to the StereoDepth node
    colorLeft.isp.link(stereo.left)
    colorRight.isp.link(stereo.right)


    # Create Sync node
    sync = pipeline.create(dai.node.Sync)
    sync.setSyncThreshold(timedelta(milliseconds=50))

    # Link outputs to Sync node with specified input names
    tof.depth.link(sync.inputs["depth_tof"])
    stereo.depth.link(sync.inputs["depth_stereo"])
    stereo.rectifiedLeft.link(sync.inputs["left_img"])
    stereo.rectifiedRight.link(sync.inputs["right_img"])
    colorLeft.isp.link(sync.inputs["rgb_img"])  # Corrected line

    # Create XLinkOut node
    xout = pipeline.create(dai.node.XLinkOut)
    xout.setStreamName("sync_out")
    sync.out.link(xout.input)

    return pipeline

def get_calib(RGB_SOCKET, ALIGN_SOCKET, depthSize, rgbSize):
    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:
        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:
        R = np.eye(3)
    TARGET_MATRIX = M1
    lensPosition = calibData.getLensPosition(RGB_SOCKET)
    return M1, D1, M2, D2, T, R, TARGET_MATRIX

def getAlignedDepth(frameDepth, M1, D1, M2, D2, T, R, TARGET_MATRIX, depthSize,rgbSize):
    R1, R2, _, _, _, _, _ = cv2.stereoRectify(M1, D1, M2, D2, (100, 100), R, T)  # The (100,100) doesn't matter as it is not used for calculating the rotation matrices
    leftMapX, leftMapY = cv2.initUndistortRectifyMap(M1, None, R1, TARGET_MATRIX, depthSize, cv2.CV_32FC1)
    depthRect = cv2.remap(frameDepth, leftMapX, leftMapY, cv2.INTER_NEAREST)
    newR = np.dot(R2, np.dot(R, R1.T))  # Should be very close to identity
    newT = np.dot(R2, T)
    combinedExtrinsics = np.eye(4)
    combinedExtrinsics[0:3, 0:3] = newR
    combinedExtrinsics[0:3, 3] = newT
    depthAligned = reprojection(depthRect, TARGET_MATRIX, combinedExtrinsics, TARGET_MATRIX)
    # Rotate the depth to the RGB frame
    R_back = R2.T
    mapX, mapY = cv2.initUndistortRectifyMap(TARGET_MATRIX, None, R_back, M2, rgbSize, cv2.CV_32FC1)
    outputAligned = cv2.remap(depthAligned, mapX, mapY, cv2.INTER_NEAREST)
    return outputAligned

MIN_DEPTH = 500  # mm
MAX_DEPTH = 10000  # mm
def colorizeDepth(frameDepth, minDepth=MIN_DEPTH, maxDepth=MAX_DEPTH):
    invalidMask = frameDepth == 0
    # Log the depth, minDepth and maxDepth
    logDepth = np.log(frameDepth, where=frameDepth != 0)
    logMinDepth = np.log(minDepth)
    logMaxDepth = np.log(maxDepth)
    depthFrameColor = np.interp(logDepth, (logMinDepth, logMaxDepth), (0, 255)).astype(
        np.uint8
    )
    depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_JET)
    # Set invalid depth pixels to black
    depthFrameColor[invalidMask] = 0
    return depthFrameColor

RGB_SOCKET = dai.CameraBoardSocket.RGB
TOF_SOCKET = dai.CameraBoardSocket.CAM_A
LEFT_SOCKET = dai.CameraBoardSocket.LEFT
RIGHT_SOCKET = dai.CameraBoardSocket.RIGHT
ALIGN_SOCKET = RIGHT_SOCKET

COLOR_RESOLUTION = dai.ColorCameraProperties.SensorResolution.THE_1080_P
LEFT_RIGHT_RESOLUTION = dai.MonoCameraProperties.SensorResolution.THE_800_P
toFSize =  (640, 480)
rgbSize = (1280, 800)
rgbWeight = 0.4
depthWeight = 0.6

def updateBlendWeights(percent_rgb):
    """
    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(percent_rgb) / 100.0
    depthWeight = 1.0 - rgbWeight

if __name__ == '__main__':
    pipeline = create_pipeline(ALIGN_SOCKET)
    rgb_depth_window_name = "rgb-depth"

    with dai.Device(pipeline) as device:
        cv2.namedWindow(rgb_depth_window_name)
        cv2.createTrackbar(
        "RGB Weight %",
        rgb_depth_window_name,
        int(rgbWeight * 100),
        100,
        updateBlendWeights,
        )
        # Create output queue
        q_sync = device.getOutputQueue(name="sync_out", maxSize=4, blocking=False)
        try:
            M1, D1, M2, D2, T, R, TARGET_MATRIX = get_calib(RGB_SOCKET, ALIGN_SOCKET, toFSize, rgbSize)
        except:
            raise
        # Read calibration data
        calibration_handler = device.readCalibration()
        camera_names = {
            dai.CameraBoardSocket.CAM_B: 'cam_b',
            dai.CameraBoardSocket.CAM_C: 'cam_c',
            dai.CameraBoardSocket.CAM_A: 'tof'
        }

        # Create timestamped directory
        timestamp = time.strftime("%Y%m%d_%H%M%S")
        save_dir = os.path.join("data", timestamp)
        os.makedirs(save_dir, exist_ok=True)

        extrinsics_coeffs_tof_cam_b = calibration_handler.getCameraExtrinsics(dai.CameraBoardSocket.CAM_A, dai.CameraBoardSocket.CAM_B)
        extrinsics_coeffs_cam_b_tof = calibration_handler.getCameraExtrinsics(dai.CameraBoardSocket.CAM_B, dai.CameraBoardSocket.CAM_A)
        extrinsics_coeffs_tof_cam_c = calibration_handler.getCameraExtrinsics(dai.CameraBoardSocket.CAM_A, dai.CameraBoardSocket.CAM_C)

        # Save calibration data into the folder
        calib_data = {}
        for camera_socket, camera_name in camera_names.items():
            intrinsics = calibration_handler.getCameraIntrinsics(camera_socket)
            dist_coeffs = calibration_handler.getDistortionCoefficients(camera_socket)
            calib_data[camera_name] = {
                'intrinsics': intrinsics,
                'distortion_coefficients': dist_coeffs
            }

        calib_data['extrinsics'] = {
            'tof_cam_b': extrinsics_coeffs_tof_cam_b,
            'cam_b_tof': extrinsics_coeffs_cam_b_tof,
            'tof_cam_c': extrinsics_coeffs_tof_cam_c

        }
        calibration_file = os.path.join(save_dir, "calibration.json")
        with open(calibration_file, 'w') as f:
            json.dump(calib_data, f, indent=4)

        frame_counter = 0
        while True:
            # Get synchronized messages
            msgGrp = q_sync.get()

            frames = {}
            for name, msg in msgGrp:
                frames[name] = msg.getCvFrame()

            if len(frames) == 5:
                # Process the frames
                depth_tof_frame = frames['depth_tof']
                depth_stereo_frame = frames['depth_stereo']
                left_frame = frames['left_img']
                right_frame = frames['right_img']
                rgb_frame = frames['rgb_img']

                # Save the frames
                depth_tof_filename = os.path.join(save_dir, f"depth_tof_{frame_counter:06d}.npy")
                depth_stereo_filename = os.path.join(save_dir, f"depth_stereo_{frame_counter:06d}.npy")
                left_filename = os.path.join(save_dir, f"left_img_{frame_counter:06d}.png")
                right_filename = os.path.join(save_dir, f"right_img_{frame_counter:06d}.png")
                rgb_filename = os.path.join(save_dir, f"rgb_img_{frame_counter:06d}.png")

                np.save(depth_tof_filename, depth_tof_frame)
                np.save(depth_stereo_filename, depth_stereo_frame)
                cv2.imwrite(left_filename, left_frame)
                cv2.imwrite(right_filename, right_frame)
                cv2.imwrite(rgb_filename, rgb_frame)

                # Optional: Display the images and depth maps
                # Normalize and colorize depth maps for visualization
                depth_tof_display = cv2.normalize(depth_tof_frame, None, 0, 255, cv2.NORM_MINMAX)
                depth_tof_display = np.uint8(depth_tof_display)
                depth_tof_display = cv2.applyColorMap(depth_tof_display, cv2.COLORMAP_JET)
                cv2.imshow("Depth ToF", depth_tof_display)

                depth_stereo_display = cv2.normalize(depth_stereo_frame, None, 0, 255, cv2.NORM_MINMAX)
                depth_stereo_display = np.uint8(depth_stereo_display)
                depth_stereo_display = cv2.applyColorMap(depth_stereo_display, cv2.COLORMAP_JET)
                cv2.imshow("Depth Stereo", depth_stereo_display)

                cv2.imshow("Left Image", left_frame)
                cv2.imshow("Right Image", right_frame)
                cv2.imshow("RGB Image", rgb_frame)
                h, w = depth_stereo_frame.shape[:2]
                M1_r, D1_r, M2_r, D2_r, T_r, R_r, TARGET_MATRIX_r = get_calib(RIGHT_SOCKET, TOF_SOCKET, toFSize, (w, h))
                alignedFrame = depth_stereo_frame
                alignedDepth = getAlignedDepth(depth_tof_frame, M1_r, D1_r, M2_r, D2_r, T_r, R_r, TARGET_MATRIX_r, toFSize,(w, h))
                frame_counter += 1

                alignedDepthColorized = colorizeDepth(alignedDepth)
                alignedFrame = colorizeDepth(alignedFrame)
                #mapX, mapY = cv2.initUndistortRectifyMap(
                #    M2_r, D2_r, None, M2_r, (w,h), cv2.CV_32FC1
                #)
                #alignedFrame = cv2.remap(alignedFrame, mapX, mapY, cv2.INTER_LINEAR)
                cv2.imshow("Aligned Image", alignedFrame)
                cv2.imshow("Aligned depth Image", alignedDepthColorized)
                #cv2.waitKey(0)

                blended = cv2.addWeighted(alignedFrame, rgbWeight, alignedDepthColorized, depthWeight, 0)
                cv2.imshow(rgb_depth_window_name, blended)
                # Exit condition
                if cv2.waitKey(1) == ord('q'):
                    break

        device.close()
        print('Data collection complete.')

                          The script aims to align TOF to depth.

                          LMK if it works. If it doesn't, either intrinsics or extrinsics are bad.

                          Thanks,
                          Jaka

                              jakaskerl
                              This is the error I ran into:
                              line 219, in <module>

                              **pipeline = create_pipeline(ALIGN_SOCKET)**

           **^^^^^^^^^^^^^^^^^^^^^^^^^^^^^**

                              File "s:\DEPT\SVM4\Shared\Crossfunctional_Work\Projects\DepthCameras\LuxonisDepthAI\test_run\jimmy.py", line 61, in create_pipeline

                              **tof.setNumShaves(4)**

**^^^^^^^^^^^^^^^^**

                              AttributeError: 'depthai.node.ToF' object has no attribute 'setNumShaves'

                                  jakaskerl
                                  Thank you for your response. I updated depthai within my conda environment, i was on version 2.25.1.0 and now I am at 2.28.0.0. I then proceed to run the code that you gave me and it successfully ran. However it was extremely lagging, I can only assume that the behavior occurred due to the multiple streams that were outputed…left,right image, rgb, depth tof, stereo and etc.

                                  This is the results of what were outputted. Is this expected? Or do I still have to recalibrate my camera? In my opinion the rgb depth seems to improved once again, but since the multiple streams caused lag. I was unable to fully conclude much. Is it possible for me to edit this script to capture the rgb-depth ,stereo depth and tof depth and save them as .ply files to view them as a point cloud? I proved the real image so you can understand the environment that I am in and also see the objects that I am capturing. If I have said anything that is incorrect, please correct me 🙂.

                                      @jakaskerl
                                      One last thing here is the data from the calibration.json, which was created from the script along with the .npy files. With the new script that you gave me, do I have to add in my intrinsic and extrinsic parameters as I did before in a format like this? I asked a similar question before and I remember you saying that I should add it to the/a script that captures the pcd. However this files generates .npy files for stereo,tof,left,right and rgb. I am able to convert that .npy file into a point cloud, the results will be pasted at the bottom.
                                      :
                                      "

                                      RGB_INTRINSICS = np.array([

                                      [836.2461547851562, 0.0, 656.4282836914062],

                                      [0.0, 845.6265869140625, 439.0591125488281],

                                      [0.0, 0.0, 1.0]
                                      I add the above parameters to a both the script you gave me and a script that convers .npy and depth png to a point cloud.

                                      ])

                                      **
                                      TOF_INTRINSICS = np.array([    [494.3519287109375, 0.0, 321.8478088378906],    [0.0, 499.4835205078125, 258.3044128417969],    [0.0, 0.0, 1.0]])**

                                      "
                                      {

                                      "cam_b": {

                                      "intrinsics": [

                                      [

                                      842.6837768554688,

                                      0.0,

                                      673.1340942382812

                                      ],

                                      [

                                      0.0,

                                      851.867431640625,

                                      412.4818115234375

                                      ],

                                      [

                                      0.0,

                                      0.0,

                                      1.0

                                      ]

                                      ],

                                      "distortion_coefficients": [

                                      -9.116254806518555,

                                      262.5550842285156,

                                      0.007134947460144758,

                                      -0.0009857615223154426,

                                      1347.274169921875,

                                      -9.195904731750488,

                                      260.98687744140625,

                                      1308.9786376953125,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0

                                      ]

                                      },

                                      "cam_c": {

                                      "intrinsics": [

                                      [

                                      836.2461547851562,

                                      0.0,

                                      656.4282836914062

                                      ],

                                      [

                                      0.0,

                                      845.6265869140625,

                                      439.0591125488281

                                      ],

                                      [

                                      0.0,

                                      0.0,

                                      1.0

                                      ]

                                      ],

                                      "distortion_coefficients": [

                                      -5.861973762512207,

                                      5.3061065673828125,

                                      0.005871884059160948,

                                      0.00142634566873312,

                                      88.46317291259766,

                                      -6.072614669799805,

                                      7.037742614746094,

                                      83.25321960449219,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0

                                      ]

                                      },

                                      "tof": {

                                      "intrinsics": [

                                      [

                                      494.3519287109375,

                                      0.0,

                                      321.8478088378906

                                      ],

                                      [

                                      0.0,

                                      499.4835205078125,

                                      258.3044128417969

                                      ],

                                      [

                                      0.0,

                                      0.0,

                                      1.0

                                      ]

                                      ],

                                      "distortion_coefficients": [

                                      -8.41947078704834,

                                      64.94961547851562,

                                      0.006374209653586149,

                                      0.0031096169259399176,

                                      -82.0173110961914,

                                      -8.491059303283691,

                                      65.28081512451172,

                                      -83.71215057373047,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0,

                                      0.0

                                      ]

                                      },

                                      "extrinsics": {

                                      "tof_cam_b": [

                                      [

                                      0.9998435974121094,

                                      0.008253674954175949,

                                      -0.015642309561371803,

                                      7.521265506744385

                                      ],

                                      [

                                      -0.00823507271707058,

                                      0.9999653100967407,

                                      0.0012532849796116352,

                                      0.08689548820257187

                                      ],

                                      [

                                      0.015652108937501907,

                                      -0.0011242730543017387,

                                      0.999876856803894,

                                      -0.34154483675956726

                                      ],

                                      [

                                      0.0,

                                      0.0,

                                      0.0,

                                      1.0

                                      ]

                                      ],

                                      "cam_b_tof": [

                                      [

                                      0.9998435974121094,

                                      -0.00823507271707058,

                                      0.015652108937501907,

                                      -7.5140275955200195

                                      ],

                                      [

                                      0.008253674954175949,

                                      0.9999653100967407,

                                      -0.0011242730543017387,

                                      -0.14935454726219177

                                      ],

                                      [

                                      -0.015642309561371803,

                                      0.0012532849796116352,

                                      0.999876856803894,

                                      0.45904383063316345

                                      ],

                                      [

                                      0.0,

                                      0.0,

                                      0.0,

                                      1.0

                                      ]

                                      ],

                                      "tof_cam_c": [

                                      [

                                      0.9990038275718689,

                                      0.007529935333877802,

                                      -0.04398486390709877,

                                      3.5399038791656494

                                      ],

                                      [

                                      -0.007824795320630074,

                                      0.9999480247497559,

                                      -0.006535347551107407,

                                      0.06738178431987762

                                      ],

                                      [

                                      0.04393336549401283,

                                      0.006873009726405144,

                                      0.9990108013153076,

                                      -0.23108027875423431

                                      ],

                                      [

                                      0.0,

                                      0.0,

                                      0.0,

                                      1.0

                                      ]

                                      ]

                                      }

                                      }

                                      The image above is the result one of the .npy tof files that were created.

                                        gdeanrexroth
                                        Alignment looks good! The multiple streams are the cause of lag (displaying them doesn't help either). No need to recalibrate 🙂.

                                        gdeanrexroth With the new script that you gave me, do I have to add in my intrinsic and extrinsic parameters as I did before in a format like this? I asked a similar question before and I remember you saying that I should add it to the/a script that captures the pcd.

                                        The script that creates the pointcloud. If you wish to convert the depth to pointcloud, you need intrinsics. You can also just use the pointcloud node and this will all be done on device.

                                        The layering your image shows, is because your depth is limited to 95 disparity values. If you turn on subpixel, this should be much better since you now have 754(don't quote me on the value) values which results in smoother transition.

                                        Thanks,
                                        Jaka

                                              jakaskerl
                                              Ahh okay, i misunderstood. I will try both methods of adding the node or just converting depth to point cloud. The JSON created a the calibration file after the code you gave me ran. Do I take the intrinsic parameters from there or do I have use the parameters from the calibration.py 🙂 ? Below is an example script that I am using to display my depth color image and my .npy file which then converts the depth image into a point cloud. I am still unfamiliar with .npy files, is it neccesary for me to include into this script:

                                              import matplotlib.image as mpimg

                                              import re

                                              import open3d as o3d

                                              import numpy as np

                                              import matplotlib.pyplot as plt

                                              # Define camera intrinsic parameters for both RGB and TOF

                                              RGB_INTRINSICS = np.array([

                                              [842.6837768554688, 0.0, 673.1340942382812],

                                              [0.0, 851.867431640625, 412.4818115234375],

                                              [0.0, 0.0, 1.0]])

                                              TOF_INTRINSICS = np.array([

                                              [494.3519287109375, 0.0, 321.8478088378906],

[0.0, 499.4835205078125, 258.3044128417969],

[0.0, 0.0, 1.0]

                                              ])

                                              # This is a special function used for reading NYU pgm format

                                              # as it is written in big endian byte order.

                                              def read_nyu_pgm(filename, byteorder='>'):

                                              with open(filename, 'rb') as f:

    buffer = f.read()

try:

    header, width, height, maxval = re.search(

        b"(^P5\\s(?:\\s\*#.\*[\\r\\n])\*"

        b"(\\d+)\\s(?:\\s\*#.\*[\\r\\n])\*"

        b"(\\d+)\\s(?:\\s\*#.\*[\\r\\n])\*"

        b"(\\d+)\\s(?:\\s\*#.\*[\\r\\n]\\s)\*)", buffer).groups()

except AttributeError:

    raise ValueError("Not a raw PGM file: '%s'" % filename)

img = np.frombuffer(buffer,

                    dtype=byteorder + 'u2',

                    count=int(width) \* int(height),

                    offset=len(header)).reshape((int(height), int(width)))

img_out = img.astype('u2')

return img_out

                                              print("Read dataset")

                                              # Use your specified paths for RGB and depth images

                                              color_raw = o3d.io.read_image(r"S:\DEPT\SVM4\Shared\Crossfunctional_Work\Projects\DepthCameras\LuxonisDepthAI\test_run\data\20241023_132728\rgb_img_000005.png")

                                              depth_array = np.load(r"S:\DEPT\SVM4\Shared\Crossfunctional_Work\Projects\DepthCameras\LuxonisDepthAI\test_run\data\20241023_132728\depth_stereo_000004.npy")

                                              # Convert numpy depth array to Open3D image

                                              depth_raw = o3d.geometry.Image(depth_array.astype(np.float32))

                                              color = o3d.geometry.Image(np.asarray(color_raw))

                                              # Create an Open3D RGBD image using your data

                                              rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(

                                              color_raw, depth_raw, convert_rgb_to_intensity=False)

                                              print(rgbd_image)

                                              plt.subplot(1, 2, 1)

                                              plt.title('RGB image')

                                              plt.imshow(rgbd_image.color)

                                              plt.subplot(1, 2, 2)

                                              plt.title('Depth image')

                                              plt.imshow(rgbd_image.depth)

                                              plt.show()

                                              # Create point cloud from RGBD image using custom camera intrinsics

                                              intrinsic = o3d.camera.PinholeCameraIntrinsic(

                                              width=1280,

height=800,

fx=RGB_INTRINSICS[0, 0],

fy=RGB_INTRINSICS[1, 1],

cx=RGB_INTRINSICS[0, 2],

cy=RGB_INTRINSICS[1, 2]

                                              )

                                              pcd = o3d.geometry.PointCloud.create_from_rgbd_image(

                                              rgbd_image,

intrinsic)

                                              # Flip it, otherwise the pointcloud will be upside down

                                              pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])

                                              o3d.visualization.draw_geometries([pcd])

                                              Outcome of script that CREATES the point cloud. And yes this before me trying the point cloud node. Intrinsincs are in but I am unsure if they are placed in the right area or format. Thank you for your reply 🙂 :

                                              10/25/2024 8:17AM
                                              this is my attempt to insert the point cloud while subpixel is set to True.

                                              Create StereoDepth node

                                              stereo = pipeline.create(dai.node.StereoDepth)

stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)

stereo.initialConfig.setMedianFilter(dai.MedianFilter.MEDIAN_OFF)

stereo.setLeftRightCheck(True)

stereo.setExtendedDisparity(False)

stereo.setSubpixel(True) # this was False at first but Jaka recommended True to get smoother point cloud

stereo.setDepthAlign(ALIGN_SOCKET)

# Link the RAW outputs of the ColorCameras to the StereoDepth node

colorLeft.isp.link(stereo.left)

colorRight.isp.link(stereo.right)

pc_node = pipeline.create(dai.node.PointCloud)

stereo.depth.link(pc_node.depth)

pc_out = pipeline.create(dai.node.XLinkOut)

pc_out.setStreamName("pc_out")

pc_node.out.link(pc_out.input)

# Create Sync node

sync = pipeline.create(dai.node.Sync)

sync.setSyncThreshold(timedelta(milliseconds=50))

# Link outputs to Sync node with specified input names

tof.depth.link(sync.inputs["depth_tof"])

stereo.depth.link(sync.inputs["depth_stereo"])

stereo.rectifiedLeft.link(sync.inputs["left_img"])

stereo.rectifiedRight.link(sync.inputs["right_img"])

colorLeft.isp.link(sync.inputs["rgb_img"])  # Corrected line

# Create XLinkOut node

xout = pipeline.create(dai.node.XLinkOut)

xout.setStreamName("sync_out")

sync.out.link(xout.input)

return pipeline

                                                  gdeanrexroth Do I take the intrinsic parameters from there or do I have use the parameters from the calibration.py 🙂 ?

                                                  Take intrinsics from eeprom calibration.

                                                  Thanks,
                                                  Jaka

                                                      jakaskerl
                                                      Okay I have been using the those numbers within the file that creates the point cloud. I am still trying to use ICP to stitch the point clouds. My results vary but overall are still giving the similar results.

                                                      I am using a simple script that just displays the .ply file which allow me to crop them. But here is the version of the script that i am using.
                                                      import numpy as np

                                                      import open3d as o3d

                                                      def demo_crop_geometry():

                                                      print("Demo for manual geometry cropping")

print("1) Press 'Y' twice to align geometry with negative direction of y-axis")

print("2) Press 'K' to lock screen and to switch to selection mode")

print("3) Drag for rectangle selection,")

print("   or use ctrl + left click for polygon selection")

print("4) Press 'C' to get a selected geometry")

print("5) Press 'S' to save the selected geometry")

print("6) Press 'F' to switch to freeview mode")



# Load a single point cloud file

# pcd = o3d.io.read_point_cloud(r'S:\\DEPT\\SVM4\\Shared\\Crossfunctional_Work\\Projects\\DepthCameras\\LuxonisDepthAI\\test_run\\Image and PointCloud of Material For AutoStore\\capture_1_2024_10_21_12_06_01\\tof_pointcloud_1.ply')

# pcd = o3d.io.read_point_cloud(r"S:\\DEPT\\SVM4\\Shared\\Crossfunctional_Work\\Projects\\DepthCameras\\LuxonisDepthAI\\test_run\\GT_CUP\\MANUALLY_MODIFIED\\tof_pointcloud_5.ply")

pcd = o3d.io.read_point_cloud(r'S:\\DEPT\\SVM4\\Shared\\Crossfunctional_Work\\Projects\\DepthCameras\\LuxonisDepthAI\\test_run\\GT_CUP\\MANUALLY_MODIFIED\\cropped_cup_5.ply')

# Visualize and edit the point cloud

o3d.visualization.draw_geometries_with_editing([pcd])

                                                      if name == "main":

                                                      demo_crop_geometry()

                                                        @jakaskerl
                                                        overall for my project this is the ending result my team and I are looking for.

                                                        I took this example point cloud from the ICP registration website. But we are still struggling to get a result that resembles that. Even at the bare minimum state I am struggling to display/ distribute a point cloud that resembles the object that is in front of the camera.

                                                        A minor issue that I am experiencing is with the numba library. Every so often I will quit the code so i can view the png's and .npy files. I will run the code again and it will display this error message
                                                        "2.27.0.0

                                                        Traceback (most recent call last):

                                                        File "s:\DEPT\SVM4\Shared\Crossfunctional_Work\Projects\DepthCameras\LuxonisDepthAI\test_run\tree_s.py", line 9, in <module>

                                                        from numba import jit, prange

                                                        ModuleNotFoundError: No module named 'numba'"
                                                        But whenever it does work, it displays the current depthai that I have installed which is 2.28.0.0 and successfully runs the code. I am sure that this conflicting issue is on my end. But I am bringing it to your attention.

                                                        EDIT
                                                        My last struggle is adding the point cloud node to the script that you gave me. I followed the instructions that was listed on the website however I am struggling to enable it within the script correctly.

                                                        Here is another point cloud displayed in meshlab application: