#!/usr/bin/env python ''' Affine invariant feature-based image matching sample. This sample is similar to find_obj.py, but uses the affine transformation space sampling technique, called ASIFT [1]. While the original implementation is based on SIFT, you can try to use SURF or ORB detectors instead. Homography RANSAC is used to reject outliers. Threading is used for faster affine sampling. [1] http://www.ipol.im/pub/algo/my_affine_sift/ USAGE asift.py [--feature=<sift|surf|orb|brisk>[-flann]] [ <image1> <image2> ] --feature - Feature to use. Can be sift, surf, orb or brisk. Append '-flann' to feature name to use Flann-based matcher instead bruteforce. Press left mouse button on a feature point to see its matching point. ''' # Python 2/3 compatibility from __future__ import print_function import numpy as np import cv2 as cv # built-in modules import itertools as it from multiprocessing.pool import ThreadPool # local modules from common import Timer from find_obj import init_feature, filter_matches, explore_match def affine_skew(tilt, phi, img, mask=None): ''' affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask, Ai Ai - is an affine transform matrix from skew_img to img ''' h, w = img.shape[:2] if mask is None: mask = np.zeros((h, w), np.uint8) mask[:] = 255 A = np.float32([[1, 0, 0], [0, 1, 0]]) if phi != 0.0: phi = np.deg2rad(phi) s, c = np.sin(phi), np.cos(phi) A = np.float32([[c,-s], [ s, c]]) corners = [[0, 0], [w, 0], [w, h], [0, h]] tcorners = np.int32( np.dot(corners, A.T) ) x, y, w, h = cv.boundingRect(tcorners.reshape(1,-1,2)) A = np.hstack([A, [[-x], [-y]]]) img = cv.warpAffine(img, A, (w, h), flags=cv.INTER_LINEAR, borderMode=cv.BORDER_REPLICATE) if tilt != 1.0: s = 0.8*np.sqrt(tilt*tilt-1) img = cv.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01) img = cv.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv.INTER_NEAREST) A[0] /= tilt if phi != 0.0 or tilt != 1.0: h, w = img.shape[:2] mask = cv.warpAffine(mask, A, (w, h), flags=cv.INTER_NEAREST) Ai = cv.invertAffineTransform(A) return img, mask, Ai def affine_detect(detector, img, mask=None, pool=None): ''' affine_detect(detector, img, mask=None, pool=None) -> keypoints, descrs Apply a set of affine transformations to the image, detect keypoints and reproject them into initial image coordinates. See http://www.ipol.im/pub/algo/my_affine_sift/ for the details. ThreadPool object may be passed to speedup the computation. ''' params = [(1.0, 0.0)] for t in 2**(0.5*np.arange(1,6)): for phi in np.arange(0, 180, 72.0 / t): params.append((t, phi)) def f(p): t, phi = p timg, tmask, Ai = affine_skew(t, phi, img) keypoints, descrs = detector.detectAndCompute(timg, tmask) for kp in keypoints: x, y = kp.pt kp.pt = tuple( np.dot(Ai, (x, y, 1)) ) if descrs is None: descrs = [] return keypoints, descrs keypoints, descrs = [], [] if pool is None: ires = it.imap(f, params) else: ires = pool.imap(f, params) for i, (k, d) in enumerate(ires): print('affine sampling: %d / %d\r' % (i+1, len(params)), end='') keypoints.extend(k) descrs.extend(d) print() return keypoints, np.array(descrs) def main(): import sys, getopt opts, args = getopt.getopt(sys.argv[1:], '', ['feature=']) opts = dict(opts) feature_name = opts.get('--feature', 'brisk-flann') try: fn1, fn2 = args except: fn1 = 'aero1.jpg' fn2 = 'aero3.jpg' img1 = cv.imread(cv.samples.findFile(fn1), cv.IMREAD_GRAYSCALE) img2 = cv.imread(cv.samples.findFile(fn2), cv.IMREAD_GRAYSCALE) detector, matcher = init_feature(feature_name) if img1 is None: print('Failed to load fn1:', fn1) sys.exit(1) if img2 is None: print('Failed to load fn2:', fn2) sys.exit(1) if detector is None: print('unknown feature:', feature_name) sys.exit(1) print('using', feature_name) pool=ThreadPool(processes = cv.getNumberOfCPUs()) kp1, desc1 = affine_detect(detector, img1, pool=pool) kp2, desc2 = affine_detect(detector, img2, pool=pool) print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))) def match_and_draw(win): with Timer('matching'): raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2 p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches) if len(p1) >= 4: H, status = cv.findHomography(p1, p2, cv.RANSAC, 5.0) print('%d / %d inliers/matched' % (np.sum(status), len(status))) # do not draw outliers (there will be a lot of them) kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag] else: H, status = None, None print('%d matches found, not enough for homography estimation' % len(p1)) explore_match(win, img1, img2, kp_pairs, None, H) match_and_draw('affine find_obj') cv.waitKey() print('Done') if __name__ == '__main__': print(__doc__) main() cv.destroyAllWindows()