def test_point_dist(self):
"""Test point distance calculation."""
data = (((0, 0), (8, 9), 12.0416), ((1, 3), (8, 9), 9.2195),
((-3, 34), (8, -99), 133.4541), ((3.5, 34.1), (8.0, 99.6),
65.6544))
for p1, p2, out in data:
self.assertEqual(round(ft.point_dist(p1, p2), 4), out)
def test_point_dist(self):
"""Test point distance calculation."""
data = (
((0,0), (8,9), 12.0416),
((1,3), (8,9), 9.2195),
((-3,34), (8,-99), 133.4541),
((3.5,34.1), (8.0,99.6), 65.6544)
)
for p1, p2, out in data:
self.assertEqual( round(ft.point_dist(p1, p2), 4), out )
def test_shape_360(self):
"""Test the shape:360 feature.
Extracts the shape from two slightly rotated versions of the same
image. Then the medium square error between the two extracted shapes is
calculated and checked.
"""
max_error = 0.05
shape = []
for i, path in enumerate(IMAGES_ERYCINA):
im_path = os.path.join(self.base_dir, path)
img = cv2.imread(im_path)
if img == None or img.size == 0:
raise SystemError("Failed to read %s" % im_path)
# Resize the image if it is larger then the threshold.
img = scale_max_perimeter(img, MAX_SIZE)
# Perform segmentation.
mask = grabcut(img, 5, None, 1)
# Create a binary mask. Foreground is made white, background black.
bin_mask = np.where((mask == cv2.GC_FGD) + (mask == cv2.GC_PR_FGD),
255, 0).astype('uint8')
# Obtain contours (all points) from the mask.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
# Fit an ellipse on the contour to get the rotation angle.
box = cv2.fitEllipse(contour)
rotation = int(box[2])
# Extract the shape.
intersects, center = ft.shape_360(contour, rotation)
# For each angle save the mean distance from center to contour and
# the standard deviation for the distances.
means = []
sds = []
for angle in range(0, 360):
distances = []
for p in intersects[angle]:
d = ft.point_dist(center, p)
distances.append(d)
if len(distances) == 0:
mean = 0
sd = 0
else:
mean = np.mean(distances, dtype=np.float32)
if len(distances) > 1:
sd = np.std(distances, ddof=1, dtype=np.float32)
else:
sd = 0
means.append(mean)
sds.append(sd)
# Normalize and save result.
means = cv2.normalize(np.array(means), None, -1, 1,
cv2.NORM_MINMAX)
sds = cv2.normalize(np.array(sds), None, -1, 1, cv2.NORM_MINMAX)
shape.append([means, sds])
# Check the medium square error for the means.
self.assertLess(error(shape[0][0].ravel(), shape[1][0].ravel(), mse),
max_error)
# Check the medium square error for the standard deviations.
self.assertLess(error(shape[0][1].ravel(), shape[1][1].ravel(), mse),
max_error)
def test_shape_360(self):
"""Test the shape:360 feature.
Extracts the shape from two slightly rotated versions of the same
image. Then the medium square error between the two extracted shapes is
calculated and checked.
"""
max_error = 0.05
shape = []
for i, path in enumerate(IMAGES_ERYCINA):
im_path = os.path.join(self.base_dir, path)
img = cv2.imread(im_path)
if img == None or img.size == 0:
raise SystemError("Failed to read %s" % im_path)
# Resize the image if it is larger then the threshold.
img = scale_max_perimeter(img, MAX_SIZE)
# Perform segmentation.
mask = grabcut(img, 5, None, 1)
# Create a binary mask. Foreground is made white, background black.
bin_mask = np.where((mask==cv2.GC_FGD) + (mask==cv2.GC_PR_FGD), 255, 0).astype('uint8')
# Obtain contours (all points) from the mask.
contour = ft.get_largest_contour(bin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Fit an ellipse on the contour to get the rotation angle.
box = cv2.fitEllipse(contour)
rotation = int(box[2])
# Extract the shape.
intersects, center = ft.shape_360(contour, rotation)
# For each angle save the mean distance from center to contour and
# the standard deviation for the distances.
means = []
sds = []
for angle in range(0, 360):
distances = []
for p in intersects[angle]:
d = ft.point_dist(center, p)
distances.append(d)
if len(distances) == 0:
mean = 0
sd = 0
else:
mean = np.mean(distances, dtype=np.float32)
if len(distances) > 1:
sd = np.std(distances, ddof=1, dtype=np.float32)
else:
sd = 0
means.append(mean)
sds.append(sd)
# Normalize and save result.
means = cv2.normalize(np.array(means), None, -1, 1, cv2.NORM_MINMAX)
sds = cv2.normalize(np.array(sds), None, -1, 1, cv2.NORM_MINMAX)
shape.append([means, sds])
# Check the medium square error for the means.
self.assertLess(error(shape[0][0].ravel(), shape[1][0].ravel(), mse), max_error)
# Check the medium square error for the standard deviations.
self.assertLess(error(shape[0][1].ravel(), shape[1][1].ravel(), mse), max_error)
