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:

                          gdeanrexroth

                          gdeanrexroth 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.

                          I'm not entirely sure what you mean. How do you want to enable it and where should it show up?

                          Thanks,
                          Jaka

                            jakaskerl
                            Maybe I misunderstood your previous response. Whenever you mentioned that I could add the point cloud node into the updated undistorted script to visualize it. I am attempting to that but I am unsuccessful in creating the point cloud node.

                            On another note, I have tried to use depthai viewer to see if the point cloud looks any different. However it updates or installs everytime i run the command "python -m depthai_viewer". it successfully installs yet it does not recognize the camera. I will try to update my depthai-viewer and follow instructions again but it was working two months. I haven't used it since then.