/
test_features.py
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/
test_features.py
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from __future__ import division
import numpy as np
from numpy.testing import assert_allclose
import random
from menpo.image import Image, MaskedImage
from menpo.feature import hog, lbp, es, igo, daisy
import menpo.io as mio
def test_imagewindowiterator_hog_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
for i in range(n_cases):
image = MaskedImage(np.random.randn(1, image_height[i, 0],
image_width[i, 0]))
hog_im = hog(image,
mode='dense', window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels', padding=True)
n_windows_horizontal = len(range(0, image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(range(0, image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(hog_im.shape, (n_windows_vertical,
n_windows_horizontal))
def test_windowiterator_hog_no_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
window_width = np.random.randint(3, 20, [n_cases, 1])
window_height = np.random.randint(3, 20, [n_cases, 1])
for i in range(n_cases):
image = MaskedImage(np.random.randn(1, image_height[i, 0],
image_width[i, 0]))
hog_img = hog(image,
mode='dense', cell_size=3, block_size=1,
window_height=window_height[i, 0], window_width=window_width[i, 0],
window_unit='pixels',
window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels', padding=False)
n_windows_horizontal = len(range(window_width[i, 0] - 1,
image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(range(window_height[i, 0] - 1,
image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(hog_img.shape, (n_windows_vertical,
n_windows_horizontal))
def test_windowiterator_lbp_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
for i in range(n_cases):
image = MaskedImage(np.random.randn(1, image_height[i, 0],
image_width[i, 0]))
lbp_img = lbp(image,
window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels', padding=True)
n_windows_horizontal = len(range(0, image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(range(0, image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(lbp_img.shape, (n_windows_vertical,
n_windows_horizontal))
def test_windowiterator_lbp_no_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
radius = np.random.randint(3, 5, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(1, image_height[i, 0],
image_width[i, 0]))
lbp_img = lbp(image,
radius=radius[i, 0], samples=8,
window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels', padding=False)
window_size = 2 * radius[i, 0] + 1
n_windows_horizontal = len(range(window_size - 1, image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(range(window_size - 1, image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(lbp_img.shape, (n_windows_vertical,
n_windows_horizontal))
def test_hog_channels_dalaltriggs():
n_cases = 3
cell_size = np.random.randint(1, 10, [n_cases, 1])
block_size = np.random.randint(1, 3, [n_cases, 1])
num_bins = np.random.randint(7, 9, [n_cases, 1])
channels = np.random.randint(1, 4, [n_cases, 1])
for i in range(n_cases):
image = MaskedImage(np.random.randn(channels[i, 0], 40, 40))
block_size_pixels = cell_size[i, 0] * block_size[i, 0]
window_width = np.random.randint(block_size_pixels, 40, 1)
window_height = np.random.randint(block_size_pixels, 40, 1)
hog_img = hog(image, mode='dense', algorithm='dalaltriggs',
cell_size=cell_size[i, 0], block_size=block_size[i, 0],
num_bins=num_bins[i, 0], window_height=window_height[0],
window_width=window_width[0], window_unit='pixels',
window_step_vertical=3, window_step_horizontal=3,
window_step_unit='pixels', padding=True)
length_per_block = block_size[i, 0] * block_size[i, 0] * num_bins[i, 0]
n_blocks_horizontal = len(range(block_size_pixels - 1, window_width[0],
cell_size[i, 0]))
n_blocks_vertical = len(range(block_size_pixels - 1, window_height[0],
cell_size[i, 0]))
n_channels = n_blocks_horizontal * n_blocks_vertical * length_per_block
assert_allclose(hog_img.n_channels, n_channels)
def test_hog_channels_zhuramanan():
n_cases = 3
cell_size = np.random.randint(2, 10, [n_cases])
channels = np.random.randint(1, 4, [n_cases])
for i in range(n_cases):
image = MaskedImage(np.random.randn(channels[i], 40, 40))
win_width = np.random.randint(3 * cell_size[i], 40, 1)
win_height = np.random.randint(3 * cell_size[i], 40, 1)
hog_img = hog(image, mode='dense', algorithm='zhuramanan',
cell_size=cell_size[i],
window_height=win_height[0],
window_width=win_width[0],
window_unit='pixels', window_step_vertical=3,
window_step_horizontal=3,
window_step_unit='pixels', padding=True, verbose=True)
length_per_block = 31
n_blocks_horizontal = np.floor((win_width[0] / cell_size[i]) + 0.5) - 2
n_blocks_vertical = np.floor((win_height[0] / cell_size[i]) + 0.5) - 2
n_channels = n_blocks_horizontal * n_blocks_vertical * length_per_block
assert_allclose(hog_img.n_channels, n_channels)
def test_lbp_channels():
n_cases = 3
n_combs = np.random.randint(1, 6, [n_cases, 1])
channels = np.random.randint(1, 4, [n_cases, 1])
for i in range(n_cases):
radius = random.sample(range(1, 10), n_combs[i, 0])
samples = random.sample(range(4, 12), n_combs[i, 0])
image = MaskedImage(np.random.randn(channels[i, 0], 40, 40))
lbp_img = lbp(image, radius=radius, samples=samples,
window_step_vertical=3, window_step_horizontal=3,
window_step_unit='pixels', padding=True)
assert_allclose(lbp_img.n_channels, n_combs[i, 0] * channels[i, 0])
def test_igo_channels():
n_cases = 3
channels = np.random.randint(1, 10, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(channels[i, 0], 40, 40))
igo_img = igo(image)
igo2_img = igo(image, double_angles=True)
assert_allclose(igo_img.shape, image.shape)
assert_allclose(igo2_img.shape, image.shape)
assert_allclose(igo_img.n_channels, 2 * channels[i, 0])
assert_allclose(igo2_img.n_channels, 4 * channels[i, 0])
def test_es_channels():
n_cases = 3
channels = np.random.randint(1, 10, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(channels[i, 0], 40, 40))
es_img = es(image)
assert_allclose(es_img.shape, image.shape)
assert_allclose(es_img.n_channels, 2 * channels[i, 0])
def test_daisy_channels():
n_cases = 3
rings = np.random.randint(1, 3, [n_cases, 1])
orientations = np.random.randint(1, 7, [n_cases, 1])
histograms = np.random.randint(1, 6, [n_cases, 1])
channels = np.random.randint(1, 5, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(channels[i, 0], 40, 40))
daisy_img = daisy(image, step=4, rings=rings[i, 0],
orientations=orientations[i, 0],
histograms=histograms[i, 0])
assert_allclose(daisy_img.shape, (3, 3))
assert_allclose(daisy_img.n_channels,
((rings[i, 0]*histograms[i, 0]+1)*orientations[i, 0]))
def test_igo_values():
image = Image([[1., 2.], [2., 1.]])
igo_img = igo(image)
res = np.array(
[[[0.70710678, 0.70710678],
[-0.70710678, -0.70710678]],
[[0.70710678, -0.70710678],
[0.70710678, -0.70710678]]])
assert_allclose(igo_img.pixels, res)
image = Image([[0., 0.], [0., 0.]])
igo_img = igo(image)
res = np.array([[[0., 0.], [0., 0.]], [[1., 1.], [1., 1.]]])
assert_allclose(igo_img.pixels, res)
def test_es_values():
image = Image([[1., 2.], [2., 1.]])
es_img = es(image)
k = 1.0 / (2 * (2 ** 0.5))
res = np.array([[[k, -k], [k, -k]], [[k, k], [-k, -k]]])
assert_allclose(es_img.pixels, res)
def test_daisy_values():
image = Image([[1., 2., 3., 4.], [2., 1., 3., 4.], [1., 2., 3., 4.],
[2., 1., 3., 4.]])
daisy_img = daisy(image, step=1, rings=2, radius=1, orientations=8,
histograms=8)
assert_allclose(np.around(daisy_img.pixels[10, 0, 0], 6), 0.001355)
assert_allclose(np.around(daisy_img.pixels[20, 0, 1], 6), 0.032237)
assert_allclose(np.around(daisy_img.pixels[30, 1, 0], 6), 0.002032)
assert_allclose(np.around(daisy_img.pixels[40, 1, 1], 6), 0.000163)
def test_lbp_values():
image = Image([[0., 6., 0.], [5., 18., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='none',
padding=False)
assert_allclose(lbp_img.pixels, 8.)
image = Image([[0., 6., 0.], [5., 25., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='riu2',
padding=False)
assert_allclose(lbp_img.pixels, 0.)
image = Image([[0., 6., 0.], [5., 13., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='u2', padding=False)
assert_allclose(lbp_img.pixels, 8.)
image = Image([[0., 6., 0.], [5., 6., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='ri', padding=False)
assert_allclose(lbp_img.pixels, 4.)
def test_constrain_landmarks():
breaking_bad = mio.import_builtin_asset('breakingbad.jpg').as_masked()
breaking_bad = breaking_bad.crop_to_landmarks(boundary=20)
breaking_bad = breaking_bad.resize([50, 50])
breaking_bad.constrain_mask_to_landmarks()
hog_b = hog(breaking_bad, mode='sparse')
x = np.where(hog_b.landmarks['PTS'].lms.points[:, 0] > hog_b.shape[1] - 1)
y = np.where(hog_b.landmarks['PTS'].lms.points[:, 0] > hog_b.shape[0] - 1)
assert_allclose(len(x[0]) + len(y[0]), 12)
hog_b = hog(breaking_bad, mode='sparse')
hog_b.constrain_landmarks_to_bounds()
x = np.where(hog_b.landmarks['PTS'].lms.points[:, 0] > hog_b.shape[1] - 1)
y = np.where(hog_b.landmarks['PTS'].lms.points[:, 0] > hog_b.shape[0] - 1)
assert_allclose(len(x[0]) + len(y[0]), 0)