Tagfinder

import cv2
import time
import os
import numpy as np
from matplotlib import pyplot as plt

path = os.path.abspath('C:/Users/Ville/Downloads/WhatsApp Video 2021-05-21 at 22.11.30.mp4')
camera = cv2.VideoCapture(path)

imgPath = os.path.abspath("C:/Users/Ville/Desktop/Opiskelu/Uc71R.jpg")
img = cv2.imread(imgPath,1)

#orb = cv2.ORB_create()
#kp = orb.detect(img,None)
#kp1, des1 = orb.compute(img, kp)

sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img,None)

FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
MIN_MATCH_COUNT = 10

while camera.isOpened():
    ret, frame = camera.read()
    if not ret:
        print("Can't receive frame (stream end?). Exiting ...")
        break
    #kp = orb.detect(frame,None)
    #kp2, des2 = orb.compute(frame, kp)
    
    
    #calculates the "distance" between points and decides if the match is good enough
    kp2, des2 = sift.detectAndCompute(frame,None)
    matches = flann.knnMatch(des1,des2,k=2)
    print(len(matches))
    good = []
    for m,n in matches:
        if m.distance < 0.7*n.distance:
            good.append(m)
    
    #if enough points were found it takes the corners of the picture and with math magic calculates the points in the picture.
    #It gives a transform matrix that can be used for the calculation
    #finally it draws a rectangle to those points
    if len(good)> MIN_MATCH_COUNT:
        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)
        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
        matchesMask = mask.ravel().tolist()
        h,w,d = img.shape
        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
        dst = cv2.perspectiveTransform(pts,M)
        frame = cv2.polylines(frame,[np.int32(dst)],True,255,3, cv2.LINE_AA)
    else:
        print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
        matchesMask = None
    #it draws the keypoints that it matched
    draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)
    show = cv2.drawMatches(img,kp1,frame,kp2,good,None,**draw_params)
    #plt.imshow(show, 'gray'),plt.show()
    cv2.imshow('image',show)
    if cv2.waitKey(1) == ord('q'):
        break
camera.release()
cv2.destroyAllWindows()