def edges_image(edge_sets):
blank = Image.blank(image_original.width, image_original.height, 3, 255)
for shape in edge_sets:
for edge in shape:
print(edge[1])
blank.draw_line(Point.from_array(edge[0]), Point.from_array(edge[1]), Color.Green(), 1)
return blank
def _center_minimiser(center, dist):
""" Used as the cost function in an optimisation routine.
for a trial center point, we calculate an error that is the sum of the squares of the deviation
of the distance of each slot from the mean distance of all the slots.
"""
errors = []
center = Point.from_array(center)
distances = [p.distance_to_sq(center) for p in dist]
mean = np.mean(distances)
layer_errors = [(d - mean)**2 for d in distances]
errors.extend(layer_errors)
return sum(errors)
def _center_minimiser(center, layers):
""" Used as the cost function in an optimisation routine. The puck consists of 2 layers of slots.
Within a given layer, each slot is the same distance from the center point of the puck. Therefore
for a trial center point, we calculate an error that is the sum of the squares of the deviation
of the distance of each slot from the mean distance of all the slots in the layer.
"""
errors = []
center = Point.from_array(center)
for layer in layers:
distances = [p.distance_to_sq(center) for p in layer]
mean = np.mean(distances)
layer_errors = [(d - mean)**2 for d in distances]
errors.extend(layer_errors)
return sum(errors)
def _center_minimiser(center, layers):
""" Used as the cost function in an optimisation routine. The puck consists of 2 layers of slots.
Within a given layer, each slot is the same distance from the center point of the puck. Therefore
for a trial center point, we calculate an error that is the sum of the squares of the deviation
of the distance of each slot from the mean distance of all the slots in the layer.
"""
errors = []
center = Point.from_array(center)
for layer in layers:
distances = [p.distance_to_sq(center) for p in layer]
mean = np.mean(distances)
layer_errors = [(d-mean)**2 for d in distances]
errors.extend(layer_errors)
return sum(errors)
def _locate_finder_in_square(image, transform, size):
""" For the located barcode in the image, identify which of the sides make
up the finder pattern.
"""
radius = int(round(size/2))
center = transform.trans
angle = transform.rot
rotated = image.rotate(angle, center)
sx1, sy1 = center.x-radius, center.y-radius
sx2, sy2 = center.x+radius, center.y+radius
thick = int(round(size / 14))
# Top
x1, y1 = sx1, sy1
x2, y2 = sx2, sy1 + thick
top = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Left
x1, y1 = sx1, sy1
x2, y2 = sx1 + thick, sy2
left = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Bottom
x1, y1 = sx1, sy2 - thick
x2, y2 = sx2, sy2
bottom = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Right
x1, y1 = sx2 - thick, sy1
x2, y2 = sx2, sy2
right = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Identify finder edges
if top < bottom and left < right:
c1 = [sx1, sy1]
c2 = [sx1, sy2]
c3 = [sx2, sy1]
elif top < bottom and right < left:
c1 = [sx2, sy1]
c2 = [sx1, sy1]
c3 = [sx2, sy2]
elif bottom < top and left < right:
c1 = [sx1, sy2]
c2 = [sx2, sy2]
c3 = [sx1, sy1]
elif bottom < top and right < left:
c1 = [sx2, sy2]
c2 = [sx2, sy1]
c3 = [sx1, sy2]
else:
return None
# rotate points around center of square
c1 = _rotate_around_point(Point.from_array(c1), angle, center)
c2 = _rotate_around_point(Point.from_array(c2), angle, center)
c3 = _rotate_around_point(Point.from_array(c3), angle, center)
# Create finder pattern
c1 = c1.intify()
side1 = (c2 - c1).intify()
side2 = (c3 - c1).intify()
fp = FinderPattern(c1, side1, side2)
return fp
def _locate_finder_in_square(image, transform, size):
""" For the located barcode in the image, identify which of the sides make
up the finder pattern.
"""
radius = int(round(size/2))
center = transform.trans
angle = transform.rot
rotated = image.rotate(angle, center)
sx1, sy1 = center.x-radius, center.y-radius
sx2, sy2 = center.x+radius, center.y+radius
thick = int(round(size / 14))
# Top
x1, y1 = sx1, sy1
x2, y2 = sx2, sy1 + thick
top = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Left
x1, y1 = sx1, sy1
x2, y2 = sx1 + thick, sy2
left = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Bottom
x1, y1 = sx1, sy2 - thick
x2, y2 = sx2, sy2
bottom = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Right
x1, y1 = sx2 - thick, sy1
x2, y2 = sx2, sy2
right = np.sum(rotated.img[y1:y2, x1:x2]) / (size * thick)
# Identify finder edges
if top < bottom and left < right:
c1 = [sx1, sy1]
c2 = [sx1, sy2]
c3 = [sx2, sy1]
elif top < bottom and right < left:
c1 = [sx2, sy1]
c2 = [sx1, sy1]
c3 = [sx2, sy2]
elif bottom < top and left < right:
c1 = [sx1, sy2]
c2 = [sx2, sy2]
c3 = [sx1, sy1]
elif bottom < top and right < left:
c1 = [sx2, sy2]
c2 = [sx2, sy1]
c3 = [sx1, sy2]
else:
return None
# rotate points around center of square
c1 = _rotate_around_point(Point.from_array(c1), angle, center)
c2 = _rotate_around_point(Point.from_array(c2), angle, center)
c3 = _rotate_around_point(Point.from_array(c3), angle, center)
# Create finder pattern
c1 = c1.intify()
side1 = (c2 - c1).intify()
side2 = (c3 - c1).intify()
fp = FinderPattern(c1, side1, side2)
return fp