Using Python, image a polygonal model in a right camera and left camera position

Question

Using Python, image a polygonal model in a right camera and left camera positioned for a non-obstructed view of your model. Do not use parallel cameras. Use point correspondences from the images of your model to estimate the fundamental matrix. Estimate the fundamental matrix using the 8-point algorithm. Estimate the fundamental matrix using the function findFundamentalMat(). Provide a written description of the 8-point algorithm and why the function findFundamental- Mat() may obtain a different fundamental

Explanation / Answer

import cv2 import numpy as np from matplotlib import pyplot as plt img1 = cv2.imread('myleft.jpg',0) #queryimage # left image img2 = cv2.imread('myright.jpg',0) #trainimage # right image sift = cv2.SIFT() # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = sift.detectAndCompute(img2,None) # FLANN parameters FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params,search_params) matches = flann.knnMatch(des1,des2,k=2) good = [] pts1 = [] pts2 = [] # ratio test as per Lowe's paper for i,(m,n) in enumerate(matches): if m.distance < 0.8*n.distance: good.append(m) pts2.append(kp2[m.trainIdx].pt) pts1.append(kp1[m.queryIdx].pt)

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