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)
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)
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_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_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_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_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 blue_peter():
import menpo.io as mio
import h5it
from menpo.visualize.image import glyph
from menpo.feature import hog
import matplotlib.pyplot as plt
# Loading the pre-built HOG AAM
import cPickle as pickle
with open('/Users/pts08/hog_lfpw_aam.pkl', 'rb') as f:
hog_aam = pickle.load(f)
#hog_aam = h5it.load('/Users/pts08/sparse_hog.hdf5')
print('Here is one I made earlier!')
bp = mio.import_image('blue_peter.jpg')
hog_blue_peter = hog(bp)
plt.figure()
plt.subplot(121)
bp.view()
plt.axis('off')
plt.gcf().set_size_inches(11, 11)
plt.title('RGB')
plt.subplot(122)
glyph(hog_blue_peter).view()
plt.axis('off')
plt.gcf().set_size_inches(11, 11)
plt.title('HOG')
return hog_aam
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_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_hog_channels_zhuramanan():
n_cases = 3
cell_size = np.random.randint(2, 10, [n_cases, 1])
channels = np.random.randint(1, 4, [n_cases, 1])
for i in range(n_cases):
image = MaskedImage(np.random.randn(40, 40, channels[i, 0]))
window_width = np.random.randint(3 * cell_size[i, 0], 40, 1)
window_height = np.random.randint(3 * cell_size[i, 0], 40, 1)
hog_img = hog(image, mode='dense', algorithm='zhuramanan',
cell_size=cell_size[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 = 31
n_blocks_horizontal = round(np.float(window_width[0])
/ np.float(cell_size[i, 0])) - 2
n_blocks_vertical = round(np.float(window_height[0])
/ np.float(cell_size[i, 0])) - 2
n_channels = n_blocks_horizontal * n_blocks_vertical * length_per_block
assert_allclose(hog_img.n_channels, n_channels)
def hog_svs_shape(pc, xr, yr, groups=None):
store_image = hog(svs_shape(pc, xr, yr, groups))
return store_image
def float32_hog(x):
return hog(x).astype(np.float32)
