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# Draw the keypoint matches between both pictures
# Still based on: https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html
draw_params = dict(matchColor=(0, 255, 0),
                   singlePointColor=(255, 0, 0),
                   matchesMask=matchesMask[300:500],
                   flags=cv.DrawMatchesFlags_DEFAULT)

keypoint_matches = cv.drawMatchesKnn(
    img1, kp1, img2, kp2, matches[300:500], None, **draw_params)
cv.imshow("Keypoint matches", keypoint_matches)

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# Match keypoints in both images
# Based on: https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)   # or pass empty dictionary
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)

# Keep good matches: calculate distinctive image features
# Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94

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# Visualize keypoints
imgSift = cv.drawKeypoints(
    img1, kp1, None, flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv.imshow("SIFT Keypoints", imgSift)

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# 1. Detect keypoints and their descriptors
# Based on: https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html

# Initiate SIFT detector
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

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import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt

# Read both images and convert to grayscale
img1 = cv.imread('left_img.png', cv.IMREAD_GRAYSCALE)
img2 = cv.imread('right_img.png', cv.IMREAD_GRAYSCALE)

# ------------------------------------------------------------
# PREPROCESSING

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LightEstimate estimate = Frame.LightEstimate;
// Updates directional light
DirectionalLight.transform.rotation = estimate.DirectionalLightRotation;
DirectionalLight.color = estimate.DirectionalLightColor;
// Set ambient probe
RenderSettings.ambientMode = AmbientMode.Skybox;
RenderSettings.ambientProbe = estimate.AmbientProbe;
// Set reflection probe (if activated)
RenderSettings.defaultReflectionMode = DefaultReflectionMode.Custom;
RenderSettings.customReflection = estimate.ReflectionProbe; 

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// Import Sumerian scripts
import * as s from 'module://sumerian-common/api';
import * as si from 'module://sumerian-common/internal';

export default class AnchorPositioning extends s.Action {

	static get PROPERTIES() {
		return {
			// Optional property to set the owner of the AR anchor
			anchorEntity: {

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// Listen for the world event "StartPlaceWorld". This will be sent
// by another component in the scene when the placement mode should
// be active. When that event is received, this script sets placeMode to true,
// which actually allows this script to react to touch events.
this.startPlaceWorldEvent = ctx.world.event('StartPlaceWorld');
this.startPlaceWorldEvent.listen(ctx, data => this.placeMode.set(true));

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// Anchor registration callback. Sets the anchor ID of the entity's
// ArAnchorComponent. The engine's ArSystem will automatically update
// the world position and orientation of entities with a valid anchor ID.
registerAnchorCallback(anchorId) {
	if (anchorId) {
		this.internalAnchorEntity.getComponent('ArAnchorComponent').anchorId = anchorId;
		console.log("Registered AR anchor: " + anchorId);
		this.placeMode.set(false);
		this.worldPlacedEvent.emit();
		console.log("Quit place mode and sent WorldPlaced event");

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// Hit test callback. If the hit test was successful (i.e., detected a
// point in the real world), registers an anchor with that point.
hitTestCallback(anchorTransform) {
	if (anchorTransform) {
		console.log("Transform from hit test: " + anchorTransform + ". Registering anchor...");
		this.arSystem.registerAnchor(anchorTransform, (anchorId) => this.registerAnchorCallback(anchorId));
	} else {
		console.log("No transform received from hit test");
	}
}