def draw_props(properties):
global img, img_src
img = img_src.copy()
for props in properties:
if props['Ellipse'] != None:
cv2.circle(img, props['Centroid'], 5, BLUE, 1)
cv2.ellipse(img, props['Ellipse'], CYAN)
cv2.drawContours(img, [props['ConvexHull']], 0, RED, 1)
major_axis = ft.angled_line(props['Centroid'], props['Orientation'], props['MajorAxisLength']/2)
cv2.line(img, tuple(major_axis[0]), tuple(major_axis[1]), RED)
minor_axis = ft.angled_line(props['Centroid'], props['Orientation'] + 90, props['MinorAxisLength']/2)
cv2.line(img, tuple(minor_axis[0]), tuple(minor_axis[1]), BLUE)
box = cv2.cv.BoxPoints(props['BoundingBox'])
box = np.int0(box)
cv2.drawContours(img, [box], 0, CYAN, 1)
for p in props['Extrema']:
cv2.circle(img, p, 5, CYAN, 1)
cv2.imshow('image', img)
def draw_angle_shape(angle):
global rotation, img, center, intersects
# Draw the angle.
line = ft.extreme_points(intersects[angle] + intersects[angle + 180])
if line == None:
line = ft.angled_line(center, angle + rotation, 100)
cv2.line(img, tuple(line[0]), tuple(line[1]), GREEN)
# Draw main intersections.
for x, y in intersects[angle]:
cv2.circle(img, (x, y), 5, RED)
for x, y in intersects[angle + 180]:
cv2.circle(img, (x, y), 5, BLUE)
cv2.imshow('image', img)
def draw_angle_shape(angle):
global rotation, img, center, intersects
# Draw the angle.
line = ft.extreme_points(intersects[angle] + intersects[angle + 180])
if line == None:
line = ft.angled_line(center, angle + rotation, 100)
cv2.line(img, tuple(line[0]), tuple(line[1]), GREEN)
# Draw main intersections.
for x, y in intersects[angle]:
cv2.circle(img, (x, y), 5, RED)
for x, y in intersects[angle + 180]:
cv2.circle(img, (x, y), 5, BLUE)
cv2.imshow("image", img)
def shape_360_v1(contour, rotation=0, step=1, t=8):
"""Returns a shape feature from a contour.
The rotation in degrees of the contour can be set with `rotation`, which
must be a value between 0 and 179 inclusive. A rotation above 90 degrees
is interpreted as a rotation to the left (e.g. a rotation of 120 is
interpreted as 60 degrees to the left). The returned shape is shifted by
the rotation. The step size for the angle can be set with `step`. Argument
`t` is passed to :meth:`weighted_points_nearest`. The returned shape is
rotation invariant provided that the rotation argument is set properly and
the rotation is no more than 90 degrees left or right.
Shape is returned as a tuple ``(intersects, center)``, where ``intersects``
is a dict where the keys are 0 based angles and the values are lists
of intersecting points. If `step` is set to 1, then the dict will contain
the intersections for 360 degrees. ``center`` specifies the center of the
contour and can be used to calculate distances from center to contour.
"""
if len(contour) < 6:
raise ValueError("Contour must have at least 6 points, found %d" % len(contour))
if not 0 <= rotation <= 179:
raise ValueError("The rotation must be between 0 and 179 inclusive, found %d" % rotation)
# Get the center.
center, _ = cv2.minEnclosingCircle(contour)
center = np.int32(center)
# If the rotation is more than 90 degrees, assume the object is rotated to
# the left.
if rotation <= 90:
a = 0
b = 180
else:
a = 180
b = 0
# Get the intersecting points with the contour for every degree
# from the symmetry axis.
intersects = {}
for angle in range(0, 180, step):
# Get the slope for the linear function of this angle and account for
# the rotation of the object.
slope = ft.slope_from_angle(angle + rotation, inverse=True)
# Since pixel points can only be defined with natural numbers,
# resulting in gaps in between two points, means the maximum
# gap is equal to the slope of the linear function.
gap_max = math.ceil(abs(slope))
# Find al contour points that closely fit the angle's linear function.
# Only save points for which the distance to the expected point is
# no more than the maximum gap. Save each point with a weight value,
# which is used for clustering.
weighted_points = []
for p in contour:
p = p[0]
x = p[0] - center[0]
y = p[1] - center[1]
if math.isinf(slope):
if x == 0:
# Save points that are on the vertical axis.
weighted_points.append( (1, tuple(p)) )
else:
y_exp = slope * x
d = abs(y - y_exp)
if d <= gap_max:
w = 1 / (d+1)
weighted_points.append( (w, tuple(p)) )
assert len(weighted_points) != 0, "No intersections found"
# Cluster the points.
weighted_points = ft.weighted_points_nearest(weighted_points, t)
_, points = zip(*weighted_points)
# Figure out on which side of the symmetry the points lie.
# Create a line that separates points on the left from points on
# the right.
if (angle + rotation) != 0:
division_line = ft.angled_line(center, angle + rotation + 90, 100)
else:
# Points cannot be separated when the division line is horizontal.
# So make a division line that is rotated 45 degrees to the left
# instead, so that the points are properly separated.
division_line = ft.angled_line(center, angle + rotation - 45, 100)
intersects[angle] = []
intersects[angle+180] = []
for p in points:
side = ft.side_of_line(division_line, p)
if side < 0:
intersects[angle+a].append(p)
elif side > 0:
intersects[angle+b].append(p)
else:
assert side != 0, "A point cannot be on the division line"
return (intersects, center)
def shape_360_v1(contour, rotation=0, step=1, t=8):
"""Returns a shape feature from a contour.
The rotation in degrees of the contour can be set with `rotation`, which
must be a value between 0 and 179 inclusive. A rotation above 90 degrees
is interpreted as a rotation to the left (e.g. a rotation of 120 is
interpreted as 60 degrees to the left). The returned shape is shifted by
the rotation. The step size for the angle can be set with `step`. Argument
`t` is passed to :meth:`weighted_points_nearest`. The returned shape is
rotation invariant provided that the rotation argument is set properly and
the rotation is no more than 90 degrees left or right.
Shape is returned as a tuple ``(intersects, center)``, where ``intersects``
is a dict where the keys are 0 based angles and the values are lists
of intersecting points. If `step` is set to 1, then the dict will contain
the intersections for 360 degrees. ``center`` specifies the center of the
contour and can be used to calculate distances from center to contour.
"""
if len(contour) < 6:
raise ValueError("Contour must have at least 6 points, found %d" %
len(contour))
if not 0 <= rotation <= 179:
raise ValueError(
"The rotation must be between 0 and 179 inclusive, found %d" %
rotation)
# Get the center.
center, _ = cv2.minEnclosingCircle(contour)
center = np.int32(center)
# If the rotation is more than 90 degrees, assume the object is rotated to
# the left.
if rotation <= 90:
a = 0
b = 180
else:
a = 180
b = 0
# Get the intersecting points with the contour for every degree
# from the symmetry axis.
intersects = {}
for angle in range(0, 180, step):
# Get the slope for the linear function of this angle and account for
# the rotation of the object.
slope = ft.slope_from_angle(angle + rotation, inverse=True)
# Since pixel points can only be defined with natural numbers,
# resulting in gaps in between two points, means the maximum
# gap is equal to the slope of the linear function.
gap_max = math.ceil(abs(slope))
# Find al contour points that closely fit the angle's linear function.
# Only save points for which the distance to the expected point is
# no more than the maximum gap. Save each point with a weight value,
# which is used for clustering.
weighted_points = []
for p in contour:
p = p[0]
x = p[0] - center[0]
y = p[1] - center[1]
if math.isinf(slope):
if x == 0:
# Save points that are on the vertical axis.
weighted_points.append((1, tuple(p)))
else:
y_exp = slope * x
d = abs(y - y_exp)
if d <= gap_max:
w = 1 / (d + 1)
weighted_points.append((w, tuple(p)))
assert len(weighted_points) != 0, "No intersections found"
# Cluster the points.
weighted_points = ft.weighted_points_nearest(weighted_points, t)
_, points = zip(*weighted_points)
# Figure out on which side of the symmetry the points lie.
# Create a line that separates points on the left from points on
# the right.
if (angle + rotation) != 0:
division_line = ft.angled_line(center, angle + rotation + 90, 100)
else:
# Points cannot be separated when the division line is horizontal.
# So make a division line that is rotated 45 degrees to the left
# instead, so that the points are properly separated.
division_line = ft.angled_line(center, angle + rotation - 45, 100)
intersects[angle] = []
intersects[angle + 180] = []
for p in points:
side = ft.side_of_line(division_line, p)
if side < 0:
intersects[angle + a].append(p)
elif side > 0:
intersects[angle + b].append(p)
else:
assert side != 0, "A point cannot be on the division line"
return (intersects, center)