import json

import cv2 as cv
import numpy as np


# Load source and target points from JSON
with open('source_points.json', 'r') as f:
    source_points = np.array(json.load(f))

with open('target_points.json', 'r') as f:
    target_points = np.array(json.load(f))

# Estimate homography from source to target using RANSAC with a specified point to point distance threshold
# Use the "index with None trick" to unsqueeze one axis to fit OpenCV shape requirements
point_to_point_distance_threshold = 5
homography, mask = cv.findHomography(source_points[:, None, :], target_points[:, None, :], cv.RANSAC, point_to_point_distance_threshold)

# Extract the points that passed in the RANSAC step using the mask
# These points should be within the point to point distance threshold specified earlier
mask = mask.astype(bool).squeeze()
predicted_points = cv.perspectiveTransform(source_points[:, None, :], homography).squeeze(1)
point_to_point_distances = np.linalg.norm(predicted_points[mask] - target_points[mask], axis=-1)

# The maximum error is around 410 pixels, which is way bigger than the specified threshold of 5 pixels
assert point_to_point_distances.max() <= point_to_point_distance_threshold, point_to_point_distances.max()