pil_image1 = np.array(pil_image1)
print(np.shape(pil_image1))
# pil_image1=((pil_image1[:,:,0]+pil_image1[:,:,1]+pil_image1[:,:,2])/3)
pil_image2 = Image.open(im2_str).convert('L')
pil_image2.thumbnail(resolution, resolution_method)
pil_image2 = np.array(pil_image2)
# pil_image2=((pil_image2[:,:,0]+pil_image2[:,:,1]+pil_image2[:,:,2])/3)
print('pil image type: ' + str(type(pil_image2)) + ' pil image shape: ' +
str(np.shape(pil_image2)))
time_overall_st = time.time()
U, V = pyoptflow.HornSchunck(pil_image1,
pil_image2,
alpha=5,
Niter=10,
verbose=False)
time_overall_end = time.time()
# for graph
im_gr_1 = mpimg.imread(im1_str)
im_gr_2 = mpimg.imread(im2_str)
pil_image1 = Image.open(im1_str)
pil_image1.thumbnail(resolution, resolution_method)
pil_image1 = np.array(pil_image1)
pil_image2 = Image.open(im2_str)
pil_image2.thumbnail(resolution, resolution_method)
pil_image2 = np.array(pil_image2)
def test_hornschunck():
U, V = pof.HornSchunck(IM1, IM2, 1., 100)
np.testing.assert_allclose(U[1, 1], -0.07192193)
def test_hornschunck():
U, V = pof.HornSchunck(IM1, IM2, 1., 100)
assert U[7, 7] == approx(-0.0594501756)
def test_hornschunck():
U, V = pof.HornSchunck(IM1, IM2, alpha=1.0, Niter=100)
assert U[7, 7] == approx(-0.0594501756)
def test_hornschunck():
U, V = pof.HornSchunck(IM1, IM2, 1., 100)
np.testing.assert_allclose(U[7, 7], -0.0594501756)
def test_hornschunck():
U, V = pof.HornSchunck(IM1, IM2, alpha=1.0, Niter=100)
assert U[7, 7] == approx(0.05951344974325876)