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    void Update()
    {
        if (OcclusionManager.TryAcquireEnvironmentDepthCpuImage(out XRCpuImage image))
        {
            using (image)
            {
                // Use the texture.
                UpdateRawImage(_rawImage, image);
            }
        }

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public class DepthImageVisualizer : MonoBehaviour
{
    public ARCameraManager CameraManager
    {
        get => _cameraManager;
        set => _cameraManager = value;
    }

    [SerializeField]
    [Tooltip("The ARCameraManager which will produce camera frame events.")]

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fragment_output frag (v2f i)
{
    UNITY_SETUP_STEREO_EYE_INDEX_POST_VERTEX(i);

    // Sample the environment depth (in meters).
    float envDistance = SAMPLE_TEXTURE2D(_MainTex, sampler_MainTex, i.texcoord).r;

    real lerpFactor = (envDistance - _MinDistance) / (_MaxDistance - _MinDistance);
    real hue = lerp(0.70h, -0.15h, saturate(lerpFactor));
    if (hue < 0.0h)

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plt.imshow(disparity_SGBM, cmap='plasma')
plt.colorbar()
plt.show()

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# ------------------------------------------------------------
# CALCULATE DISPARITY (DEPTH MAP)
# Adapted from: https://github.com/opencv/opencv/blob/master/samples/python/stereo_match.py
# and: https://docs.opencv.org/master/dd/d53/tutorial_py_depthmap.html

# StereoSGBM Parameter explanations:
# https://docs.opencv.org/4.5.0/d2/d85/classcv_1_1StereoSGBM.html

# Matched block size. It must be an odd number >=1 . Normally, it should be somewhere in the 3..11 range.
block_size = 11

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# Draw the rectified images
fig, axes = plt.subplots(1, 2, figsize=(15, 10))
axes[0].imshow(img1_rectified, cmap="gray")
axes[1].imshow(img2_rectified, cmap="gray")
axes[0].axhline(250)
axes[1].axhline(250)
axes[0].axhline(450)
axes[1].axhline(450)
plt.suptitle("Rectified images")
plt.savefig("rectified_images.png")

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# Undistort (rectify) the images and save them
# Adapted from: https://stackoverflow.com/a/62607343
img1_rectified = cv.warpPerspective(img1, H1, (w1, h1))
img2_rectified = cv.warpPerspective(img2, H2, (w2, h2))
cv.imwrite("rectified_1.png", img1_rectified)
cv.imwrite("rectified_2.png", img2_rectified)

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# Stereo rectification (uncalibrated variant)
# Adapted from: https://stackoverflow.com/a/62607343
h1, w1 = img1.shape
h2, w2 = img2.shape
_, H1, H2 = cv.stereoRectifyUncalibrated(
    np.float32(pts1), np.float32(pts2), fundamental_matrix, imgSize=(w1, h1)
)

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# Visualize epilines
# Adapted from: https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html
def drawlines(img1src, img2src, lines, pts1src, pts2src):
    ''' img1 - image on which we draw the epilines for the points in img2
        lines - corresponding epilines '''
    r, c = img1src.shape
    img1color = cv.cvtColor(img1src, cv.COLOR_GRAY2BGR)
    img2color = cv.cvtColor(img2src, cv.COLOR_GRAY2BGR)
    # Edit: use the same random seed so that two images are comparable!
    np.random.seed(0)

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# ------------------------------------------------------------
# STEREO RECTIFICATION

# Calculate the fundamental matrix for the cameras
# https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html
pts1 = np.int32(pts1)
pts2 = np.int32(pts2)
fundamental_matrix, inliers = cv.findFundamentalMat(pts1, pts2, cv.FM_RANSAC)

# We select only inlier points