def test_moments_normalized():
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
mu = moments_central(image, 14.5, 14.5)
nu = moments_normalized(mu)
# shift image by dx=-3, dy=-3 and scale by 0.5
image2 = np.zeros((20, 20), dtype=np.double)
image2[11:13, 11:13] = 1
mu2 = moments_central(image2, 11.5, 11.5)
nu2 = moments_normalized(mu2)
# central moments must be translation and scale invariant
assert_almost_equal(nu, nu2, decimal=1)
def test_moments_normalized():
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
mu = moments_central(image, (14.5, 14.5))
nu = moments_normalized(mu)
# shift image by dx=-3, dy=-3 and scale by 0.5
image2 = np.zeros((20, 20), dtype=np.double)
image2[11:13, 11:13] = 1
mu2 = moments_central(image2, (11.5, 11.5))
nu2 = moments_normalized(mu2)
# central moments must be translation and scale invariant
assert_almost_equal(nu, nu2, decimal=1)
def test_moments_normalized():
image = np.zeros((20, 20), dtype=np.float64)
image[13:17, 13:17] = 1
mu = moments_central(image, (14.5, 14.5))
nu = moments_normalized(mu)
# shift image by dx=-2, dy=-2 and scale non-zero extent by 0.5
image2 = np.zeros((20, 20), dtype=np.float64)
# scale amplitude by 0.7
image2[11:13, 11:13] = 0.7
mu2 = moments_central(image2, (11.5, 11.5))
nu2 = moments_normalized(mu2)
# central moments must be translation and scale invariant
assert_almost_equal(nu, nu2, decimal=1)
def test_moments_hu():
image = np.zeros((20, 20), dtype=np.double)
image[13:15, 13:17] = 1
mu = moments_central(image, 13.5, 14.5)
nu = moments_normalized(mu)
hu = moments_hu(nu)
# shift image by dx=2, dy=3, scale by 0.5 and rotate by 90deg
image2 = np.zeros((20, 20), dtype=np.double)
image2[11, 11:13] = 1
image2 = image2.T
mu2 = moments_central(image2, 11.5, 11)
nu2 = moments_normalized(mu2)
hu2 = moments_hu(nu2)
# central moments must be translation and scale invariant
assert_almost_equal(hu, hu2, decimal=1)
def test_moments_hu():
image = np.zeros((20, 20), dtype=np.double)
image[13:15, 13:17] = 1
mu = moments_central(image, (13.5, 14.5))
nu = moments_normalized(mu)
hu = moments_hu(nu)
# shift image by dx=2, dy=3, scale by 0.5 and rotate by 90deg
image2 = np.zeros((20, 20), dtype=np.double)
image2[11, 11:13] = 1
image2 = image2.T
mu2 = moments_central(image2, (11.5, 11))
nu2 = moments_normalized(mu2)
hu2 = moments_hu(nu2)
# central moments must be translation and scale invariant
assert_almost_equal(hu, hu2, decimal=1)
def featuresExtractor_Hu(image):
img = rgb2gray(image)
hu = moments_central(img)
hu = moments_normalized(hu)
hu = moments_hu(hu)
l = [norm(f) for f in hu]
return l
def collect(path, mean, std):
img = io.imread('./images/' + path + '.bmp')
hist = exposure.histogram(img)
th = get_threshold('./images/' + path + '.bmp')
img_binary = (img < th).astype(np.double)
img_label = label(img_binary, background=0)
regions = regionprops(img_label)
boxes = []
features = []
for props in regions:
box = []
minr, minc, maxr, maxc = props.bbox
if maxc - minc < 10 or maxr - minr < 10 or maxc - minc > 120 or maxr - minr > 120:
continue
box.append(minr)
box.append(maxr)
box.append(minc)
box.append(maxc)
boxes.append(box)
roi = img_binary[minr:maxr, minc:maxc]
m = moments(roi)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
features.append(hu)
feature_arr = normalize(features, mean, std)
return (boxes, feature_arr)
def extract_features(roi, props):
features = []
m = moments(roi)
# print(m)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, (cr, cc))
nu = moments_normalized(mu)
#finding Seven Features
hu = moments_hu(nu)
# seven features to be put into feature list
features.extend(hu)
# print(features)
features.append(roi.shape[1]/roi.shape[0])
features.append(props.eccentricity)
features.append(props.convex_area/props.area)
features.append(props.orientation)
features.append(props.euler_number)
return np.array([features])
def get_data(x, y, img):
# Create data and 5x5 image slice
data = []
new_image = img[x - 2:x + 2, y - 2:y + 2]
# Append location of pixel
data.append(x)
data.append(y)
# Append pixel values to data
for pixel_val in new_image.ravel():
data.append(pixel_val)
# Append central moments to data
central_moments = measure.moments_central(new_image)
for central_moment in central_moments.ravel():
data.append(central_moment)
# Append hu moments to data
hu_moments = measure.moments_hu(
measure.moments_normalized(central_moments))
for hu_moment in hu_moments:
data.append(hu_moment)
# Append variation of pixel values to data
variation = np.var(new_image)
data.append(variation)
return data
def get_hu_moments(samples):
print "getting hu moments..."
features = []
for sample in samples:
'''
sample = np.array(sample)
th = 200
img_binary = (sample < th).astype(np.double)
img_label = label(img_binary, background=255)
regions = regionprops(img_label)
if regions == []:
print "no regions"
for props in regions:
minr, minc, maxr, maxc = props.bbox
roi = img_binary[minr:maxr, minc:maxc]
'''
sample = np.array(sample)
sample = sample.astype(np.double)
m = moments(sample)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(sample, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
features.append(hu)
return features
def hist(image):
"""Create histogram"""
return moments_hu(
moments_normalized(
moments_central(image)
)
)
def __compute_moments(self, data, centroid, radius):
""" Compute moments"""
# - Compute central moments
mom_c= moments_central(data, center=centroid, order=3)
# - Compute normalized moments
mom_norm= moments_normalized(mom_c, 3)
# - Compute Hu moments
mom_hu= moments_hu(mom_norm)
# - Flatten moments
mom_c= mom_c.flatten()
# - Compute Zernike moments
# NB: mahotas takes only positive pixels and rescale image by sum(pix) internally
poldeg= 4
nmom_zernike= 9
mom_zernike= [-999]*nmom_zernike
try:
mom_zernike = mahotas.features.zernike_moments(data, radius, degree=poldeg, cm=centroid)
##mom_zernike = mahotas.features.zernike_moments(mask, radius, degree=poldeg, cm=centroid)
except Exception as e:
logger.warn("Failed to compute Zernike moments (err=%s)!" % (str(e)))
#print("--> mom_zernike")
#print(mom_zernike)
return (mom_c, mom_hu, mom_zernike)
def get_hu_moment_from_image(image):
"""
Compute the 7 Hu's moments from an image.
This set of moments is proofed to be translation, scale and rotation invariant.
Parameters
----------
image: array-like
a 2d array of double or uint8 corresponding to an image
Returns
-------
(7, 1) array of double
7 Hu's moments
References
----------
http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.moments
"""
order = 7
raw_moments = moments(image, order=order)
cr = raw_moments[0, 1] / raw_moments[0, 0]
cc = raw_moments[1, 0] / raw_moments[0, 0]
central_moments = moments_central(image, cr, cc, order=order)
normalized_moments = moments_normalized(central_moments, order)
hu_moments = moments_hu(normalized_moments)
return hu_moments
def test_moments_normalized_invalid():
with pytest.raises(TypeError):
moments_normalized(np.zeros((3, 3, 3)))
with pytest.raises(TypeError):
moments_normalized(np.zeros((3,)))
with pytest.raises(TypeError):
moments_normalized(np.zeros((3, 3)), 3)
with pytest.raises(TypeError):
moments_normalized(np.zeros((3, 3)), 4)
def extract_features(path, show, tag):
img = io.imread('./images/' + path + '.bmp')
hist = exposure.histogram(img)
th = get_threshold('./images/' + path + '.bmp')
img_binary = (img < th).astype(np.double)
img_label = label(img_binary, background=0)
# Show images
if show == 1:
io.imshow(img)
plt.title('Original Image')
io.show()
plt.bar(hist[1], hist[0])
plt.title('Histogram')
plt.show()
io.imshow(img_binary)
plt.title('Binary Image')
io.show()
io.imshow(img_label)
plt.title('Labeled Image')
io.show()
regions = regionprops(img_label)
if show == 1:
io.imshow(img_binary)
ax = plt.gca()
features = []
for props in regions:
minr, minc, maxr, maxc = props.bbox
if maxc - minc < 10 or maxr - minr < 10 or maxc - minc > 120 or maxr - minr > 120:
continue
if show == 1:
ax.add_patch(
Rectangle((minc, minr),
maxc - minc,
maxr - minr,
fill=False,
edgecolor='red',
linewidth=1))
roi = img_binary[minr:maxr, minc:maxc]
m = moments(roi)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
features.append(hu)
if (len(path) == 1):
tag.append(ord(path))
if show == 1:
plt.title('Bounding Boxes')
io.show()
return features
def compute_hu_moments(i):
b = cells_aligned_padded[i].astype(np.uint8)
m = moments(b, order=1)
hu = moments_hu(
moments_normalized(
moments_central(b, cc=m[0, 1] / m[0, 0],
cr=m[1, 0] / m[0, 0])))
return hu
def test_moments_hu_dtype(dtype):
image = np.zeros((20, 20), dtype=np.double)
image[13:15, 13:17] = 1
mu = moments_central(image, (13.5, 14.5))
nu = moments_normalized(mu)
hu = moments_hu(nu.astype(dtype))
assert hu.dtype == dtype
def describe(self, image):
#calculate daisy feature descriptors
mc = measure.moments_central(image)
mn = measure.moments_normalized(mc)
mh = measure.moments_hu(mn)
# return Hu moments
return mh
def testKNN():
trainFeatures, trainLebels = extractFeatures()
knn = neighbors.KNeighborsClassifier()
knn.fit(trainFeatures, trainLebels)
#score = knn.score(trainFeatures, trainLebels)
testNames = ['test1', 'test2']
#testNames = ['test2']
testFeatures = []
testLabels = []
testTruth = []
correct = 0
#textPosition = []
for i in range(len(testNames)):
classes, locations = readPkl(testNames[i])
img = io.imread(testNames[i] + '.bmp')
#testTruth = ['a']*7+['d']*7+['m']*7+['n']*7+['o']*7+['p']*7+['q']*7+['r']*7+['u']*7+['w']*7
ret, binary = cv.threshold(img, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
#ret, binary = cv.threshold(img, 0, 255, cv.THRESH_BINARY | cv.THRESH_TRIANGLE)
th = ret
img_binary = (img < th).astype(np.double)
img_dilation = morphology.binary_dilation(img_binary, selem=None)
img_erosion = morphology.binary_erosion(img_binary, selem=None)
img_label = label(img_binary, background=0)
regions = regionprops(img_label)
io.imshow(img_binary)
ax = plt.gca()
thresholdR = 15
thresholdC = 15
for props in regions:
minr, minc, maxr, maxc = props.bbox
# Computing Hu Moments and Removing Small Components
if (maxr - minr) >= thresholdR and (maxc - minc) >= thresholdC:
#textPosition.append((maxc, minr))
roi = img_binary[minr:maxr, minc:maxc]
m = moments(roi)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
testFeatures.append(hu)
testLabels.append(knn.predict([testFeatures[-1]]))
indexFix = locationFix(locations, minr, minc, maxr, maxc)
if indexFix is not None:
if testLabels[-1] == classes[indexFix]:
correct += 1
plt.text(maxc, minr, testLabels[-1][0], bbox=dict(facecolor='white', alpha=0.5))
ax.add_patch(Rectangle((minc, minr), maxc - minc, maxr - minr, fill=False, edgecolor='red', linewidth=1))
plt.title('Bounding Boxes')
io.show()
print correct, len(testLabels)
correctRate = correct / len(testLabels)
print correctRate
def extractFeature(name, showall, showbb, flag):
(img, regions, ax, rthre, cthre) = extractImage(name, showall, showbb,
flag)
Features = []
boxes = []
for props in regions:
tmp = []
minr, minc, maxr, maxc = props.bbox
if maxc - minc < cthre or maxr - minr < rthre or maxc - minc > cthre * 9 or maxr - minr > rthre * 9:
continue
tmp.append(minr)
tmp.append(minc)
tmp.append(maxr)
tmp.append(maxc)
boxes.append(tmp)
if showbb == 1:
ax.add_patch(
Rectangle((minc, minr),
maxc - minc,
maxr - minr,
fill=False,
edgecolor='red',
linewidth=1))
# computing hu moments and removing small components
roi = img[minr:maxr, minc:maxc]
m = moments(roi)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
area = (maxr - minr) * (maxc - minc)
# add convexity
p = perimeter(img[minr:maxr, minc:maxc])
con = (area / (p * p)) * 4 * math.pi
convex = np.array([con])
hu = np.concatenate((hu, convex))
# add density
den = area / float(props.convex_area)
dense = np.array([den])
hu = np.concatenate((hu, dense))
Features.append(hu)
# print boxes
plt.title('Bounding Boxes')
if showbb == 1:
io.show()
return Features, boxes,
def test_moments_normalized_spacing(anisotropic):
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
if not anisotropic:
spacing1 = (1, 1)
spacing2 = (3, 3)
else:
spacing1 = (1, 2)
spacing2 = (2, 4)
mu = moments_central(image, spacing=spacing1)
nu = moments_normalized(mu, spacing=spacing1)
mu2 = moments_central(image, spacing=spacing2)
nu2 = moments_normalized(mu2, spacing=spacing2)
# result should be invariant to absolute scale of spacing
assert_almost_equal(nu, nu2)
def test_moments_normalized_3d():
image = draw.ellipsoid(1, 1, 10)
mu_image = moments_central(image)
nu = moments_normalized(mu_image)
assert nu[0, 0, 2] > nu[0, 2, 0]
assert_almost_equal(nu[0, 2, 0], nu[2, 0, 0])
coords = np.where(image)
mu_coords = moments_coords_central(coords)
assert_almost_equal(mu_coords, mu_image)
def test_moments_normalized_3d():
image = draw.ellipsoid(1, 1, 10)
mu_image = moments_central(image)
nu = moments_normalized(mu_image)
assert nu[0, 0, 2] > nu[0, 2, 0]
assert_almost_equal(nu[0, 2, 0], nu[2, 0, 0])
coords = np.where(image)
mu_coords = moments_coords_central(coords)
assert_almost_equal(mu_coords, mu_image)
def _describe(self, binary, steps=None):
clipped = binary.clip(max=1)
m = measure.moments(clipped)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
central = measure.moments_central(clipped, cr, cc)
normalized = measure.moments_normalized(central)
moments = measure.moments_hu(normalized)
# nan determines, that moment could not be described,
# but is hard to handle in prediction, set to zero instead
moments[np.isnan(moments)] = 0
return moments
def match_shapes(img_a: ndarray, img_b: ndarray):
'''
This function takes in input two images and returns
the distances between the images using the HU moments.
'''
# calculating the hu moments
hu_a = moments_hu(moments_normalized(moments_central(img_a)))
hu_b = moments_hu(moments_normalized(moments_central(img_b)))
# changing to log scale
hu_a = -1 * sign(hu_a) * log10(abs(hu_a))
hu_b = -1 * sign(hu_b) * log10(abs(hu_b))
# calculating 3 distaces
d1 = sum(abs((1 / hu_b) - (1 / hu_a)))
d2 = sum(abs(hu_b - hu_a))
d3 = sum(divide(abs(hu_a - hu_b), abs(hu_a)))
# returning the distances
return d1, d2, d3
def test_moments_dtype(dtype):
image = np.zeros((20, 20), dtype=dtype)
image[13:15, 13:17] = 1
expected_dtype = _supported_float_type(image)
mu = moments_central(image, (13.5, 14.5))
assert mu.dtype == expected_dtype
nu = moments_normalized(mu)
assert nu.dtype == expected_dtype
hu = moments_hu(nu)
assert hu.dtype == expected_dtype
def extract_features(img):
# This function extract our features out of an image. It basically
# computes the 8 (and not 7) Hu geometrical moments. To do this we
# first compute the moments, centralize and normalize them before
# computing Hu moments
m = moments(img)
cr = m[0,1] / m[0,0]
cc = m[1,0] / m[0,0]
mc = moments_central(img, cr, cc)
mn = moments_normalized(mc)
hu = moments_hu(mn)
i8 = mn[1, 1] * ( (mn[3, 0] + mn[1, 2])**2 - (mn[0,3]+mn[2,1])**2 ) - (mn[2,0] - mn[0,2]) * (mn[3,0] + mn[1,2]) * (mn[0,3] + mn[2,1])
return append(hu, [i8])
def extract_features(roi, props):
features = []
m = moments(roi)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
features.extend(hu)
features.append(roi.shape[1] / roi.shape[0])
features.append(props.eccentricity)
features.append(props.convex_area / props.area)
features.append(props.orientation)
features.append(props.euler_number)
return np.array([features])
def apply_threshold(img, fname, classes, locations, Features):
th = 230
img_binary = (img < th).astype(np.double)
io.imshow(img_binary)
plt.title('Binary Image')
io.show()
img_label = label(img_binary, background=0)
io.imshow(img_label)
plt.title('Labeled Image')
io.show()
print np.amax(img_label)
regions = regionprops(img_label)
io.imshow(img_binary)
ax = plt.gca()
ypred = []
for props in regions:
minr, minc, maxr, maxc = props.bbox
ax.add_patch(
Rectangle((minc, minr),
maxc - minc,
maxr - minr,
fill=False,
edgecolor='red',
linewidth=1))
roi = img_binary[minr:maxr, minc:maxc]
m = moments(roi)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
pred_coord = np.array(list(props.centroid))
ypred.append(np.flipud(pred_coord))
ax.set_title('Bounding Boxes')
plt.savefig('bounding_boxes_image_' + fname + '.png')
io.show()
t = np.array(ypred)
x = t.astype(int)
D = cdist(x, locations)
print_matrix(D)
def _hu_moments(roi: coo_matrix) -> np.ndarray:
"""Returns the 7 Hu moments for an ROI image. See
https://scikit-image.org/docs/0.17.x/api/skimage.measure.html#moments-hu # noqa
for more information.
Returns
-------
7-element, 1d np.array of Hu's image moments
References
----------
M. K. Hu, “Visual Pattern Recognition by Moment Invariants”,
IRE Trans. Info. Theory, vol. IT-8, pp. 179-187, 1962
"""
roi_image = roi.toarray()
mu = moments_central(roi_image)
nu = moments_normalized(mu)
return moments_hu(nu)
def apply_threshold(img, fname, show, th_value):
global Features, label_img
th = th_value
img_binary = (img < th).astype(np.double)
if show[2]:
io.imshow(img_binary)
plt.title('Binary Image')
io.show()
img_label = label(img_binary, background=0)
if show[3]:
io.imshow(img_label)
plt.title('Labeled Image')
io.show()
print fname + str(th)
print np.amax(img_label)
regions = regionprops(img_label)
io.imshow(img_binary)
ax = plt.gca()
for props in regions:
minr, minc, maxr, maxc = props.bbox
ax.add_patch(
Rectangle((minc, minr),
maxc - minc,
maxr - minr,
fill=False,
edgecolor='red',
linewidth=1))
roi = img_binary[minr:maxr, minc:maxc]
m = moments(roi)
cr = m[0, 1] / m[0, 0]
cc = m[1, 0] / m[0, 0]
mu = moments_central(roi, cr, cc)
nu = moments_normalized(mu)
hu = moments_hu(nu)
Features.append(hu)
label_img.append(fname)
if show[4]:
ax.set_title('Bounding Boxes')
plt.savefig('bounding_boxes_image_' + fname + '.png')
io.show()
else:
ax.cla()
def calculate_features(self, feature_mode, debug=False):
fruit_image = io.imread(self.path, as_gray=True)
sigma = 0.005 * fruit_image.shape[0]
filtered_fruit = filters.gaussian(fruit_image, sigma=sigma)
# Apply triangle threshold to gaussian filtered image
self.threshold = filters.threshold_triangle(filtered_fruit)
thresholded_fruit = filtered_fruit < self.threshold
fruit_central_moments = measure.moments_central(thresholded_fruit)
hu_moments = measure.moments_hu(
measure.moments_normalized(fruit_central_moments))
# We only keep relevant hu moments, that is, components 1 and 3
self.hu_moments = hu_moments[[1, 3]]
# And we apply a log transform to them
self.hu_moments = np.array([
-1 * np.sign(j) * np.log10(np.abs(j)) for j in self.hu_moments[:]
])
fruit_eigvalues = measure.inertia_tensor_eigvals(
thresholded_fruit, mu=fruit_central_moments)
self.moment_ratio = max(fruit_eigvalues) / min(fruit_eigvalues)
if feature_mode == 'hu_plus_ratio':
self.features = np.append(self.hu_moments, self.moment_ratio)
self.feature_size = 3
elif feature_mode == 'hu_only':
self.features = np.array(self.hu_moments)
self.feature_size = 2
if debug:
print("threshold: \n", self.threshold)
print("Central moments: \n", fruit_central_moments)
print("Hu moments:\n", hu_moments)
print(
"Normalized Hu moments:\n",
[-1 * np.sign(j) * np.log10(np.abs(j)) for j in hu_moments[:]])
print("Inertia tensor eigenvalues:\n", fruit_eigvalues)
print("Moment ratio:\n", self.moment_ratio)
print("Features: \n", self.features)
def momentos(self):
"""
Calcula os 7 momentos de Hu e os momentos raw e centralizados de ordem 1 e 2
"""
m = measure.moments(self.imagemTonsDeCinza)
valores_m = [m[p, q] for (p, q) in momentsOrder]
nomes_m = [
M + str(p) + str(q)
for M, (p, q) in zip(['M_'] * len(momentsOrder), momentsOrder)
]
row = m[0, 1] / m[0, 0]
col = m[1, 0] / m[0, 0]
mu = measure.moments_central(self.imagemTonsDeCinza, row, col)
valores_mu = [mu[p, q] for (p, q) in momentsOrder]
nomes_mu = [
M + str(p) + str(q)
for M, (p, q) in zip(['Mu_'] * len(momentsOrder), momentsOrder)
]
nu = measure.moments_normalized(mu)
hu = measure.moments_hu(nu)
valores_hu = list(hu)
nomes_hu = [
m + n for m, n in zip(['Hu_'] * len(valores_hu),
map(str, range(0, len(valores_hu))))
]
valores = valores_m + valores_mu + valores_hu
nomes = nomes_m + nomes_mu + nomes_hu
tipos = [numerico] * len(nomes)
return nomes, tipos, valores
def momentos_hu(self):
"""
Calcula os 7 momentos de Hu
"""
m = measure.moments(self.imagemTonsDeCinza)
row = m[0, 1] / m[0, 0]
col = m[1, 0] / m[0, 0]
mu = measure.moments_central(self.imagemTonsDeCinza,row,col)
nu = measure.moments_normalized(mu)
hu = measure.moments_hu(nu)
valores = list(hu)
nomes = [m+n for m,n in zip(['hu_'] * len(valores),map(str,range(0,len(valores))))]
tipos = [numerico] * len(nomes)
return nomes, tipos, valores
def momentos_hu(self):
"""
Calcula os 7 momentos de Hu
"""
m = measure.moments(self.imagemTonsDeCinza)
row = m[0, 1] / m[0, 0]
col = m[1, 0] / m[0, 0]
mu = measure.moments_central(self.imagemTonsDeCinza,row,col)
nu = measure.moments_normalized(mu)
hu = measure.moments_hu(nu)
valores = list(hu)
nomes = [m+n for m,n in zip(['hu_'] * len(valores),map(str,range(0,len(valores))))]
tipos = [numerico] * len(nomes)
return nomes, tipos, valores
def test_moments_normalized_invalid():
with testing.raises(ValueError):
moments_normalized(np.zeros((3, 3)), 3)
with testing.raises(ValueError):
moments_normalized(np.zeros((3, 3)), 4)
def compute_hu_moments(i):
b = cells_aligned_padded[i].astype(np.uint8)
m = moments(b, order=1)
hu = moments_hu(moments_normalized(moments_central(b, cc=m[0,1]/m[0,0], cr=m[1,0]/m[0,0])))
return hu