import cv2
from matplotlib import pyplot as plt
from numpy import floor

method = cv2.TM_SQDIFF_NORMED

# Read the images from the file
energy_image = cv2.imread('glyphs-a-energy.png', cv2.IMREAD_GRAYSCALE)
large_image = cv2.imread('radofdispear.png', cv2.IMREAD_GRAYSCALE)

orb = cv2.ORB_create()

# screenshot = cv2.resize(large_image, down_points, interpolation= cv2.INTER_LINEAR)


for i in range(64):
    mod8 = i % 8

    x1 = (int(floor(i / 8))) * 256
    y1 = (int(mod8)) * 192

    x2 = x1 + 256
    y2 = y1 + 192

    # print(x1, y1, x2, y2)
    small_image = energy_image[y1:y2, x1:x2]
    # print(small_image)
    plt.imshow(small_image)
    plt.show()

    kp1, des1 = orb.detectAndCompute(small_image, None)
    kp2, des2 = orb.detectAndCompute(large_image, None)

    img1 = small_image
    img2 = large_image

    # result = cv2.matchTemplate(small_image, large_image, method)

    # print(result)
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    # Match descriptors.
    matches = bf.match(des1, des2)
    # Sort them in the order of their distance.
    matches = sorted(matches, key=lambda x: x.distance)
    # Draw first 10 matches.
    img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

    img3 = cv2.resize(img3, dsize=(int(3840 / 2), int(2180 / 2)))

    cv2.imshow('output', img3)
    cv2.waitKey(0)
    break
    # break
    # # We want the minimum squared difference
    # mn, _, mnLoc, _ = cv2.minMaxLoc(result)
    # #
    # # Draw the rectangle:
    # # Extract the coordinates of our best match
    # MPx, MPy = mnLoc
    #
    # print(MPx, MPy)
    # #
    # # # Step 2: Get the size of the template. This is the same size as the match.
    # # trows, tcols = small_image.shape[:2]
    # #
    # # # Step 3: Draw the rectangle on large_image
    # # cv2.rectangle(large_image, (MPx, MPy), (MPx + tcols, MPy + trows), (0, 0, 255), 2)
    # cv2.imshow('output', small_image)
    # cv2.waitKey(0)
    # break
# # Display the original image with the rectangle around the match.
# cv2.imshow('output', large_image)
#
# # The image is only displayed if we call this
#