With latest depthai, you should just use Camera node, which does undistortion by itself. Demo here: https://github.com/luxonis/depthai-python/blob/main/examples/StereoDepth/rgb_depth_aligned.py

rgb-undistortion-depth-alignment.py

"""
With latest depthai, you should just use Camera node, which does undistortion by itself.
Demo here: https://github.com/luxonis/depthai-python/blob/main/examples/StereoDepth/rgb_depth_aligned.py
"""

#!/usr/bin/env python3

import cv2
import numpy as np
import depthai as dai

# Weights to use when blending depth/rgb image (should equal 1.0)
rgbWeight = 0.4
depthWeight = 0.6


msgs = dict()

def add_msg(msg, name, seq = None):
    if seq is None:
        seq = msg.getSequenceNum()
    seq = str(seq)
    if seq not in msgs:
        msgs[seq] = dict()
    msgs[seq][name] = msg

def get_msgs():
    global msgs
    seq_remove = [] # Arr of sequence numbers to get deleted
    for seq, syncMsgs in msgs.items():
        seq_remove.append(seq) # Will get removed from dict if we find synced msgs pair
        # Check if we have both detections and color frame with this sequence number
        if len(syncMsgs) == 2: # rgb + depth
            for rm in seq_remove:
                del msgs[rm]
            return syncMsgs # Returned synced msgs
    return None

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


# Optional. If set (True), the ColorCamera is downscaled from 1080p to 720p.
# Otherwise (False), the aligned depth is automatically upscaled to 1080p
downscaleColor = True
fps = 30
# The disparity is computed at this resolution, then upscaled to RGB resolution
monoResolution = dai.MonoCameraProperties.SensorResolution.THE_720_P

camSocket = dai.CameraBoardSocket.RGB

def getMesh(calibData, ispSize):
    M1 = np.array(calibData.getCameraIntrinsics(camSocket, ispSize[0], ispSize[1]))
    d1 = np.array(calibData.getDistortionCoefficients(camSocket))
    R1 = np.identity(3)
    mapX, mapY = cv2.initUndistortRectifyMap(M1, d1, R1, M1, ispSize, cv2.CV_32FC1)

    meshCellSize = 16
    mesh0 = []
    # Creates subsampled mesh which will be loaded on to device to undistort the image
    for y in range(mapX.shape[0] + 1): # iterating over height of the image
        if y % meshCellSize == 0:
            rowLeft = []
            for x in range(mapX.shape[1]): # iterating over width of the image
                if x % meshCellSize == 0:
                    if y == mapX.shape[0] and x == mapX.shape[1]:
                        rowLeft.append(mapX[y - 1, x - 1])
                        rowLeft.append(mapY[y - 1, x - 1])
                    elif y == mapX.shape[0]:
                        rowLeft.append(mapX[y - 1, x])
                        rowLeft.append(mapY[y - 1, x])
                    elif x == mapX.shape[1]:
                        rowLeft.append(mapX[y, x - 1])
                        rowLeft.append(mapY[y, x - 1])
                    else:
                        rowLeft.append(mapX[y, x])
                        rowLeft.append(mapY[y, x])
            if (mapX.shape[1] % meshCellSize) % 2 != 0:
                rowLeft.append(0)
                rowLeft.append(0)

            mesh0.append(rowLeft)

    mesh0 = np.array(mesh0)
    meshWidth = mesh0.shape[1] // 2
    meshHeight = mesh0.shape[0]
    mesh0.resize(meshWidth * meshHeight, 2)

    mesh = list(map(tuple, mesh0))

    return mesh, meshWidth, meshHeight

def create_pipeline(calibData):
    # Create pipeline
    pipeline = dai.Pipeline()
    queueNames = []

    # Define sources and outputs
    camRgb = pipeline.create(dai.node.ColorCamera)
    left = pipeline.create(dai.node.MonoCamera)
    right = pipeline.create(dai.node.MonoCamera)
    stereo = pipeline.create(dai.node.StereoDepth)

    rgbOut = pipeline.create(dai.node.XLinkOut)
    disparityOut = pipeline.create(dai.node.XLinkOut)

    rgbOut.setStreamName("rgb")
    queueNames.append("rgb")
    disparityOut.setStreamName("disp")
    queueNames.append("disp")

    #Properties
    camRgb.setBoardSocket(dai.CameraBoardSocket.RGB)
    camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
    camRgb.setFps(fps)
    camRgb.setIspScale(2, 3)

    manip = pipeline.create(dai.node.ImageManip)
    mesh, meshWidth, meshHeight = getMesh(calibData, camRgb.getIspSize())
    manip.setWarpMesh(mesh, meshWidth, meshHeight)
    manip.setMaxOutputFrameSize(camRgb.getIspWidth() * camRgb.getIspHeight() * 3 // 2)
    camRgb.isp.link(manip.inputImage)

    left.setResolution(monoResolution)
    left.setBoardSocket(dai.CameraBoardSocket.LEFT)
    left.setFps(fps)
    right.setResolution(monoResolution)
    right.setBoardSocket(dai.CameraBoardSocket.RIGHT)
    right.setFps(fps)

    stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
    # LR-check is required for depth alignment
    stereo.setLeftRightCheck(True)
    stereo.setDepthAlign(dai.CameraBoardSocket.RGB)

    # Linking
    # manip.out.link(rgbOut.input)
    camRgb.isp.link(rgbOut.input)
    left.out.link(stereo.left)
    right.out.link(stereo.right)
    stereo.disparity.link(disparityOut.input)
    return pipeline

# Connect to device and start pipeline
with dai.Device() as device:
    calibData = device.readCalibration()
    pipeline = create_pipeline(calibData)
    device.startPipeline(pipeline)

    blendedWindowName = "rgb-depth"
    cv2.namedWindow(blendedWindowName)
    cv2.createTrackbar('RGB Weight %', blendedWindowName, int(rgbWeight*100), 100, updateBlendWeights)

    while True:
        for name in ['rgb', 'disp']:
            msg = device.getOutputQueue(name).tryGet()
            if msg is not None:
                add_msg(msg, name)

        synced = get_msgs()
        if synced:
            frameRgb = synced["rgb"].getCvFrame()

            frameDisp = synced["disp"].getFrame()
            maxDisparity = 95
            # Optional, extend range 0..95 -> 0..255, for a better visualisation
            if 1: frameDisp = (frameDisp * 255. / maxDisparity).astype(np.uint8)
            # Optional, apply false colorization
            if 1: frameDisp = cv2.applyColorMap(frameDisp, cv2.COLORMAP_HOT)
            frameDisp = np.ascontiguousarray(frameDisp)

            # Need to have both frames in BGR format before blending
            if len(frameDisp.shape) < 3:
                frameDisp = cv2.cvtColor(frameDisp, cv2.COLOR_GRAY2BGR)
            blended = cv2.addWeighted(frameRgb, rgbWeight, frameDisp, depthWeight, 0)
            cv2.imshow(blendedWindowName, blended)

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