extractor.py

import cv2
import numpy as np
from skimage.measure import ransac
from skimage.transform import FundamentalMatrixTransform

class Extractor(object):
    def __init__(self):
        self.orb = cv2.ORB_create()
        self.bf = cv2.BFMatcher.create(cv2.NORM_HAMMING)
        self.last = None

    def extract(self, img):
        # preprocessing
        # temporaly disabled because of np.float32() required for the followirng steps
        #gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # detection
        feats = cv2.goodFeaturesToTrack(np.mean(img, axis=2).astype(np.uint8), maxCorners=300, qualityLevel=0.01, minDistance=3)

        # extraction
        kps = [cv2.KeyPoint(x=f[0][0], y=f[0][1], _size=20) for f in feats]
        kps, des = self.orb.compute(img, kps)

        # matching
        ret = []
        if self.last is not None:
            matches = self.bf.knnMatch(des, self.last['des'], k=2)
            for m,n in matches:
                if m.distance < 0.75*n.distance:
                    kp1 = kps[m.queryIdx].pt
                    kp2 = self.last['kps'][m.trainIdx].pt
                    ret.append((kp1, kp2))
        
        # filter
        if len(ret) > 0:
            ret = np.array(ret)

            model, inliers = ransac((ret[:, 0], ret[:, 1]),
                                    FundamentalMatrixTransform,
                                    min_samples=8,
                                    residual_threshold=1, 
                                    max_trials=100)
            ret = ret[inliers]

        # return 
        self.last = {'kps': kps, 'des': des}
        return ret