OpenCV installation

sudo pip install opencv-python

Prepare an image

(Example) 1. Card illustration blueeyes2.jpg 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 blueeyes1.jpg Source: Adere

Write a string in the upper left of the image

Use OpenCV putTxt

When displaying in the upper left

`sample.py`


import PIL
from PIL import Image
from PIL import ExifTags
import cv2 as cv
import numpy as np

def write_text(image_path ,x ,y):
    str = "ATK:3000, DEF:2500"
    str_height = 25
    str_width = 340

    img = cv.imread(image_path)

    #Add a black background
    cv.rectangle(img, (x, y), (x+str_width, y-str_height), (0, 0, 0), cv.FILLED, cv.LINE_AA)

    #Add a line
    cv.rectangle(img, (x, y), (x+str_width, y-str_height), (0, 0, 0), 2, cv.LINE_AA)

    #Add letters
    cv.putText(img, str, (x, y),
               cv.FONT_HERSHEY_PLAIN, 2.0,
               (255, 255, 255), 2, cv.LINE_AA)

    cv.imwrite(image_path + '.new.jpg', img)

if __name__ == '__main__':
    write_text('blueeyes1.jpg', 10, 30)

Run

python sample.py

blueeyes1.jpg.new.jpg

When combining pattern matching

Combine with the code below

Find card illustrations from images using feature point matching

`sample.py`


import cv2
import numpy as np

fname_img1='blueeyes1.jpg'
fname_img2='blueeyes2.jpg'

img1 = cv2.imread(fname_img1)
img2 = cv2.imread(fname_img2)

def write_text(img ,x ,y):
    str = "ATK:3000, DEF:2500"
    str_height = 25
    str_width = 340

    #Add a black background
    cv2.rectangle(img, (x, y), (x+str_width, y-str_height), (0, 0, 0), cv2.FILLED, cv2.LINE_AA)

    #Add a line
    cv2.rectangle(img, (x, y), (x+str_width, y-str_height), (0, 0, 0), 2, cv2.LINE_AA)

    #Add letters
    cv2.putText(img, str, (x, y),
               cv2.FONT_HERSHEY_PLAIN, 2.0,
               (255, 255, 255), 2, cv2.LINE_AA)
    cv2.imwrite(fname_img1 + '.new.jpg', img)

def detect(img1, 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)
    
    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)
        
    write_text(img1 ,top_left[0] ,top_left[1])

detect(img1, img2)

Run

$ $ python sample.py
[[ 6.43984577e-01 -4.58173428e-02  1.61203033e+02]
 [ 2.85230902e-02  4.12312421e-01  1.50286636e+02]
 [ 4.54533377e-04 -5.75705548e-04  1.00000000e+00]]
161.2030333928222
150.28663624674783