Purpose

This is a memorandum for finding card illustrations from images using feature point matching.

Preparation

1. Card illustration
Image with card illustration
1. Code to find the card illustration from the image containing the card illustration

(Example) 1. Card illustration test2.png Source: [[All of Blue-Eyes White Dragon] The locus of the legendary dragon with the title of Yu-Gi-Oh!](Https://yu-gi-oh.xyz/ Blue-Eyes White Dragon / post- 49725 /)

(Example) 2. Image including card illustration test1.png Source: Adere

3. 3. Code to find the card illustration from the image containing the card illustration

Code that calculates feature points and extracts card illustrations from images Reference: Detect Mickey by feature point matching

The feature point uses AKAZE.

`sample.py`


import cv2
import numpy as np

fname_img1='test1.png'
fname_img2='test2.png'

img1 = cv2.imread(fname_img1)
img2 = cv2.imread(fname_img2)
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
akaze = cv2.AKAZE_create()
kp1, des1 = akaze.detectAndCompute(gray1, None)
kp2, des2 = akaze.detectAndCompute(gray2, None)

#img1_sift = cv2.drawKeypoints(gray1, kp1, None, flags=4)
#img2_sift = cv2.drawKeypoints(gray2, kp2, None, flags=4)
#cv2.imwrite('out1.png', img1_sift)
#cv2.imwrite('out2.png', img2_sift)

bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key = lambda x:x.distance)
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=2)
cv2.imwrite('out-match.png', img3)

#homography
good_match_rate = 0.15;
good = matches[:int(len(matches) * good_match_rate)]

min_match=10
if len(good) > min_match:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
    # Find homography
    M, mask = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC)
    matchesMask = mask.ravel().tolist()

    print(M)
    print(M[0][2])
    print(M[1][2])

    height = img2.shape[0]
    width = img2.shape[1]
    top_left = (int(M[0][2] +0.5), int(M[1][2] +0.5)); #tx,ty
    bottom_right = (top_left[0] + width, top_left[1] + height)

#result
result = cv2.imread(fname_img1)
cv2.rectangle(result,top_left, bottom_right, (255, 0, 0), 10)
cv2.imwrite("result.png ", result)

test

Execution result

$ python sample.py
[[ 6.43984577e-01 -4.58173428e-02  1.61203033e+02]
 [ 2.85![result.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/344880/23ae3f02-409a-6a3a-5e8b-673a20afa502.png)
230902e-02  4.12312421e-01  1.50286636e+02]
 [ 4.54533377e-04 -5.75705548e-04  1.00000000e+00]]
161.2030333928222
150.28663624674783

Calculate and match feature points

Detect frame

The frame is slightly larger because the size change of the card cannot be taken into consideration.

For webcam images (1 card)

It seems that one sheet can be detected.

Frame deviation needs adjustment (currently fixed value)

For webcam images (multiple cards)

Multiple sheets are strict.

Task

--Supports multiple cards --Video support --Frame misalignment adjustment

It may be used for Yu-Gi-Oh's remote duel.

Coding Error measures

nothing special

reference

[[All about Blue-Eyes White Dragon] The locus of the legendary dragon with the title of Yu-Gi-Oh!](Https://yu-gi-oh.xyz/ Blue-Eyes White Dragon / post-49725 /) Adere Detect Mickey by feature point matching AKAZE

Find card illustrations from images using feature point matching