def test_basic(self): img1 = np.zeros((128, 128), dtype=np.uint8) img2 = np.zeros((128, 128), dtype=np.uint8) rand = lambda x: (np.random.random(x) * x).astype(np.uint8) img1[rand(128), rand(128)] = 255 img2[rand(128), rand(128)] = 255 tc = klt.TrackingContext() fl = klt.FeatureList(100) klt.select_good_features(tc, img1, fl) klt.track_features(tc, img1, img2, fl) fl.to_image(img2) assert_equal(len(fl), 100) fa = fl.as_array() values = fa['val'] assert 0 <= len(values[values != -1]) <= 100
import numpy as N from os.path import dirname from supreme.lib import klt import scipy as S imread = S.misc.pilutil.imread imsave = S.misc.pilutil.imsave img1 = imread(dirname(__file__) + '/img0.pgm') img2 = imread(dirname(__file__) + '/img1.pgm') tc = klt.TrackingContext() print tc fl = klt.FeatureList(100) klt.select_good_features(tc, img1, fl) print '\nIn first image:' print fl imsave('feat1.ppm', fl.to_image(img1)) klt.track_features(tc, img1, img2, fl) print '\nIn second image:\n' print fl imsave('feat2.ppm', fl.to_image(img2))
import numpy as N from os.path import dirname from supreme.lib import klt import scipy as S imread = S.misc.pilutil.imread imsave = S.misc.pilutil.imsave img1 = imread(dirname(__file__) + '/img0.pgm') img2 = imread(dirname(__file__) + '/img1.pgm') tc = klt.TrackingContext() print tc fl = klt.FeatureList(100) klt.select_good_features(tc, img1, fl) print '\nIn first image:' print fl imsave('feat1.ppm',fl.to_image(img1)) klt.track_features(tc, img1, img2, fl) print '\nIn second image:\n' print fl imsave('feat2.ppm',fl.to_image(img2))