Matching Features with ORB and Brute Force using OpenCV

# -*- coding: utf-8 -*-

"""

@author: Javier Perez

@email: javier_e_perez21@hotmail.com

"""

import numpy as np

import cv2

   

ESC=27  

camera = cv2.VideoCapture(0)

orb = cv2.ORB_create()

bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

imgTrainColor=cv2.imread('ms.jpg')

imgTrainGray = cv2.cvtColor(imgTrainColor, cv2.COLOR_BGR2GRAY)

kpTrain = orb.detect(imgTrainGray,None)

kpTrain, desTrain = orb.compute(imgTrainGray, kpTrain)

firsttime=True

while True:

    

    ret, imgCamColor = camera.read()

    imgCamGray = cv2.cvtColor(imgCamColor, cv2.COLOR_BGR2GRAY)

    kpCam = orb.detect(imgCamGray,None)

    kpCam, desCam = orb.compute(imgCamGray, kpCam)

    matches = bf.match(desCam,desTrain)

    dist = [m.distance for m in matches]

    thres_dist = (sum(dist) / len(dist)) * 0.5

    matches = [m for m in matches if m.distance < thres_dist]  

    if firsttime==True:

        h1, w1 = imgCamColor.shape[:2]

        h2, w2 = imgTrainColor.shape[:2]

        nWidth = w1+w2

        nHeight = max(h1, h2)

        hdif = (h1-h2)/2

        firsttime=False

        

    result = np.zeros((nHeight, nWidth, 3), np.uint8)

    result[hdif:hdif+h2, :w2] = imgTrainColor

    result[:h1, w2:w1+w2] = imgCamColor

    for i in range(len(matches)):

        pt_a=(int(kpTrain[matches[i].trainIdx].pt[0]), int(kpTrain[matches[i].trainIdx].pt[1]+hdif))

        pt_b=(int(kpCam[matches[i].queryIdx].pt[0]+w2), int(kpCam[matches[i].queryIdx].pt[1]))

        cv2.line(result, pt_a, pt_b, (255, 0, 0))

    cv2.imshow('Camara', result)

   

    ch = 0xFF & cv2.waitKey(1)

    if ch == 27:

       break

cv2.destroyAllWindows()

camera.release()

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AndrewRoot

2:27 PM

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