def test_windowed_histogram():
# check the number of valid pixels in the neighborhood
image8 = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0],
[0, 1, 1, 1, 0], [0, 0, 0, 0, 0]],
dtype=np.uint8)
elem = np.ones((3, 3), dtype=np.uint8)
outf = np.empty(image8.shape + (2, ), dtype=float)
mask = np.ones(image8.shape, dtype=np.uint8)
# Population so we can normalize the expected output while maintaining
# code readability
pop = np.array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6],
[6, 9, 9, 9, 6], [4, 6, 6, 6, 4]],
dtype=float)
r0 = np.array([[3, 4, 3, 4, 3], [4, 5, 3, 5, 4], [3, 3, 0, 3, 3],
[4, 5, 3, 5, 4], [3, 4, 3, 4, 3]],
dtype=float) / pop
r1 = np.array([[1, 2, 3, 2, 1], [2, 4, 6, 4, 2], [3, 6, 9, 6, 3],
[2, 4, 6, 4, 2], [1, 2, 3, 2, 1]],
dtype=float) / pop
rank.windowed_histogram(image=image8, selem=elem, out=outf, mask=mask)
assert_array_equal(r0, outf[:, :, 0])
assert_array_equal(r1, outf[:, :, 1])
# Test n_bins parameter
larger_output = rank.windowed_histogram(image=image8,
selem=elem,
mask=mask,
n_bins=5)
assert larger_output.shape[2] == 5
def test_windowed_histogram():
# check the number of valid pixels in the neighborhood
image8 = np.array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]], dtype=np.uint8)
elem = np.ones((3, 3), dtype=np.uint8)
outf = np.empty(image8.shape+(2,), dtype=float)
mask = np.ones(image8.shape, dtype=np.uint8)
# Population so we can normalize the expected output while maintaining
# code readability
pop = np.array([[4, 6, 6, 6, 4],
[6, 9, 9, 9, 6],
[6, 9, 9, 9, 6],
[6, 9, 9, 9, 6],
[4, 6, 6, 6, 4]], dtype=float)
r0 = np.array([[3, 4, 3, 4, 3],
[4, 5, 3, 5, 4],
[3, 3, 0, 3, 3],
[4, 5, 3, 5, 4],
[3, 4, 3, 4, 3]], dtype=float) / pop
r1 = np.array([[1, 2, 3, 2, 1],
[2, 4, 6, 4, 2],
[3, 6, 9, 6, 3],
[2, 4, 6, 4, 2],
[1, 2, 3, 2, 1]], dtype=float) / pop
rank.windowed_histogram(image=image8, selem=elem, out=outf, mask=mask)
assert_array_equal(r0, outf[:,:,0])
assert_array_equal(r1, outf[:,:,1])
# Test n_bins parameter
larger_output = rank.windowed_histogram(image=image8, selem=elem,
mask=mask, n_bins=5)
assert larger_output.shape[2] == 5
def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
# Compute normalized windowed histogram feature vector for each pixel
px_histograms = rank.windowed_histogram(image, selem, n_bins=n_bins)
# Reshape coin histogram to (1,1,N) for broadcast when we want to use it in
# arithmetic operations with the windowed histograms from the image
reference_hist = reference_hist.reshape((1, 1) + reference_hist.shape)
# Compute Chi squared distance metric: sum((X-Y)^2 / (X+Y));
# a measure of distance between histograms
X = px_histograms
Y = reference_hist
num = (X - Y)**2
denom = X + Y
frac = num / denom
frac[denom == 0] = 0
chi_sqr = 0.5 * np.sum(frac, axis=2)
# Generate a similarity measure. It needs to be low when distance is high
# and high when distance is low; taking the reciprocal will do this.
# Chi squared will always be >= 0, add small value to prevent divide by 0.
similarity = 1 / (chi_sqr + 1.0e-4)
return similarity
def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
# Compute normalized windowed histogram feature vector for each pixel
px_histograms = rank.windowed_histogram(image, selem, n_bins=n_bins)
# Reshape coin histogram to (1,1,N) for broadcast when we want to use it in
# arithmetic operations with the windowed histograms from the image
reference_hist = reference_hist.reshape((1, 1) + reference_hist.shape)
# Compute Chi squared distance metric: sum((X-Y)^2 / (X+Y));
# a measure of distance between histograms
X = px_histograms
Y = reference_hist
num = (X - Y) ** 2
denom = X + Y
frac = num / denom
frac[denom == 0] = 0
chi_sqr = 0.5 * np.sum(frac, axis=2)
# Generate a similarity measure. It needs to be low when distance is high
# and high when distance is low; taking the reciprocal will do this.
# Chi squared will always be >= 0, add small value to prevent divide by 0.
similarity = 1 / (chi_sqr + 1.0e-4)
return similarity
