import cv2 as cv
import numpy as np
import os, sys

key_and_desc_cache = {}


def getKeyAndDesc(img_path1):
    global key_and_desc_cache

    key = img_path1
    if key in key_and_desc_cache:
        pass
    else:
        detector = cv.SIFT_create()
        img1 = cv.imread(img_path1)
        key_and_desc_cache[key] = detector.detectAndCompute(img1, None)

    return key_and_desc_cache[key]


def SIFT(img_path1, img_path2):
    try:
        img1 = cv.imread(img_path1)
        img2 = cv.imread(img_path2)

        if img1 is None or img2 is None:
            print("Error loading images!")
            exit(0)

    except:
        print("Error loading images!")
        exit(0)

    # -- Step 1: Detect the keypoints using SIFT Detector, compute the descriptors
    minHessian = 400
    keypoints1, descriptors1 = getKeyAndDesc(img_path1)
    keypoints2, descriptors2 = getKeyAndDesc(img_path2)

    # -- Step 2: Matching descriptor vectors with a brute force matcher
    matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE)
    matches = matcher.match(descriptors1, descriptors2)
    # -- Draw matches
    # img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1], 3), dtype=np.uint8)
    # cv.drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches)
    # # -- Show detected matches
    # cv.imshow("Matches: SIFT (Python)", img_matches)
    # cv.waitKey()

    # draw good matches
    matches = sorted(matches, key=lambda x: x.distance)
    min_dist = matches[0].distance
    good_matches = tuple(filter(lambda x: x.distance <= 1.1 * min_dist, matches))

    # img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1], 3), dtype=np.uint8)
    # cv.drawMatches(
    #     img1, keypoints1, img2, keypoints2, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS
    # )

    return len(good_matches)

    # -- Show detected matches
    # cv.imshow("Good Matches: SIFT (Python)", img_matches)
    # cv.waitKey()