How to set, Sensor Field of view (FOV) and resolution ?

I am using the following code for depth calculation...
I want to know FOV and resolution of the output window of this code
please also tell me what FOV and resolution should I set for RGB, in order to show the same FOV as the depth cameras are outputting in the following code!

#!/usr/bin/env python3

import cv2
import depthai as dai

stepSize = 0.05

newConfig = False

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
spatialLocationCalculator = pipeline.create(dai.node.SpatialLocationCalculator)

xoutDepth = pipeline.create(dai.node.XLinkOut)
xoutSpatialData = pipeline.create(dai.node.XLinkOut)
xinSpatialCalcConfig = pipeline.create(dai.node.XLinkIn)

xoutDepth.setStreamName("depth")
xoutSpatialData.setStreamName("spatialData")
xinSpatialCalcConfig.setStreamName("spatialCalcConfig")

# Properties
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)

lrcheck = False
subpixel = False

stereo.initialConfig.setConfidenceThreshold(255)
stereo.setLeftRightCheck(lrcheck)
stereo.setSubpixel(subpixel)

# Config
topLeft = dai.Point2f(0.45, 0.45)
bottomRight = dai.Point2f(0.5, 0.5)

config = dai.SpatialLocationCalculatorConfigData()
config.depthThresholds.lowerThreshold = 100
config.depthThresholds.upperThreshold = 10000
config.roi = dai.Rect(topLeft, bottomRight)

spatialLocationCalculator.setWaitForConfigInput(False)
spatialLocationCalculator.initialConfig.addROI(config)

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

spatialLocationCalculator.passthroughDepth.link(xoutDepth.input)
stereo.depth.link(spatialLocationCalculator.inputDepth)

spatialLocationCalculator.out.link(xoutSpatialData.input)
xinSpatialCalcConfig.out.link(spatialLocationCalculator.inputConfig)

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

    # Output queue will be used to get the depth frames from the outputs defined above
    depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)
    spatialCalcQueue = device.getOutputQueue(name="spatialData", maxSize=4, blocking=False)
    spatialCalcConfigInQueue = device.getInputQueue("spatialCalcConfig")

    color = (255, 255, 255)

    print("Use WASD keys to move ROI!")

    while True:
        inDepth = depthQueue.get() # Blocking call, will wait until a new data has arrived

        depthFrame = inDepth.getFrame()
        depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
        depthFrameColor = cv2.equalizeHist(depthFrameColor)
        depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)

        spatialData = spatialCalcQueue.get().getSpatialLocations()
        for depthData in spatialData:
            roi = depthData.config.roi
            roi = roi.denormalize(width=depthFrameColor.shape[1], height=depthFrameColor.shape[0])
            xmin = int(roi.topLeft().x)
            ymin = int(roi.topLeft().y)
            xmax = int(roi.bottomRight().x)
            ymax = int(roi.bottomRight().y)

            depthMin = depthData.depthMin
            depthMax = depthData.depthMax

            fontType = cv2.FONT_HERSHEY_TRIPLEX
            cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
            cv2.putText(depthFrameColor, f"X: {int(depthData.spatialCoordinates.x)} mm", (xmin + 10, ymin + 20), fontType, 0.5, 255)
            cv2.putText(depthFrameColor, f"Y: {int(depthData.spatialCoordinates.y)} mm", (xmin + 10, ymin + 35), fontType, 0.5, 255)
            cv2.putText(depthFrameColor, f"Z: {int(depthData.spatialCoordinates.z)} mm", (xmin + 10, ymin + 50), fontType, 0.5, 255)
        # Show the frame
        cv2.imshow("depth", depthFrameColor)

        key = cv2.waitKey(1)
        if key == ord('q'):
            break
        elif key == ord('w'):
            if topLeft.y - stepSize >= 0:
                topLeft.y -= stepSize
                bottomRight.y -= stepSize
                newConfig = True
        elif key == ord('a'):
            if topLeft.x - stepSize >= 0:
                topLeft.x -= stepSize
                bottomRight.x -= stepSize
                newConfig = True
        elif key == ord('s'):
            if bottomRight.y + stepSize <= 1:
                topLeft.y += stepSize
                bottomRight.y += stepSize
                newConfig = True
        elif key == ord('d'):
            if bottomRight.x + stepSize <= 1:
                topLeft.x += stepSize
                bottomRight.x += stepSize
                newConfig = True

        if newConfig:
            config.roi = dai.Rect(topLeft, bottomRight)
            config.calculationAlgorithm = dai.SpatialLocationCalculatorAlgorithm.AVERAGE
            cfg = dai.SpatialLocationCalculatorConfig()
            cfg.addROI(config)
            spatialCalcConfigInQueue.send(cfg)
            newConfig = False

Hi Eric,
In my code, the FOV of the depth image is bigger than FOV of RGB image, I want both FOVs to be exactly same.

It looks like even if my RGB camera is reading (1920,1080- which is full FOV of RGB), its still less than the FOV of the combined frame formed by both the mono-cameras.

As you can see in the following image, the RGB can not see the FOV as big as (mono_cameras) combined.
And I know I can crop the depth frame and resize it to match with RGB, but I want to keep the accuracy as high as it is theoretically possible!

As RGB's FOV is smaller than the depth camera's FOV, I am thinking of cropping depth frame instead of RGB frame.

Can you please guide me with exact numbers, to crop the depth frame to match with RGB frame. As I am unable to know the total resolution and proportion of the depth frame. Though mono-cameras are set to 400P in my code, I still don't know the specifications of the combined image formed by both the mono cameras...