def test_detect_intersection_rect_T(self):
image = np.array(
[
[False, False, False, False],
[True, True, True, True],
[True, True, True, True],
[True, True, True, True],
[False, True, True, False],
]
)
i_regions = [
DirectedLine.btwn(*np.array([(3, 1), (1, 1)])),
DirectedLine.btwn(*np.array([(3, 2), (1, 2)])),
DirectedLine.btwn(*np.array([(3, 1), (3, 2)])),
DirectedLine.btwn(*np.array([(2, 1), (2, 2)])),
DirectedLine.btwn(*np.array([(1, 1), (1, 2)])),
]
for line in i_regions:
(top_left, bottom_right) = detect_intersection_rect(image, line)
assert (
top_left == 1
).all(), f"{line.direction}: Top left of junction should be [1 1] but was {top_left}"
assert (
bottom_right == np.array([3, 2])
).all(), f"{line.direction}: Intersection BR should be [3 2] but was {bottom_right}"
line = line.reverse()
(top_left, bottom_right) = detect_intersection_rect(image, line)
assert (
top_left == 1
).all(), f"{line.direction}: Top left of junction should be [1 1] but was {top_left}"
assert (
bottom_right == np.array([3, 2])
).all(), f"{line.direction}: Intersection BR should be [3 2] but was {bottom_right}"
def test_detect_intersection_rect_L_270(self):
image = np.array(
[
[False, True, True, False],
[True, True, True, False],
[True, True, True, False],
[False, False, False, False],
]
)
i_regions = [
DirectedLine.btwn(*np.array([(1, 1), (1, 2)])),
DirectedLine.btwn(*np.array([(1, 2), (1, 1)])),
DirectedLine.btwn(*np.array([(1, 1), (2, 1)])),
DirectedLine.btwn(*np.array([(2, 1), (1, 1)])),
]
for line in i_regions:
(top_left, bottom_right) = detect_intersection_rect(image, line)
assert (top_left == 1).all()
assert (bottom_right == 2).all()
def test_detect_intersection_big(self):
image = np.zeros((20, 20), dtype=bool)
image[:, 12] = True
image[5, :] = True
line = DirectedLine.btwn(np.array([10, 12]), np.array([0, 12]))
intersection = line.detect_intersection(image)
assert intersection
(x, y, width, height) = intersection.bounding_box()
assert x == 12
assert y == 5
assert width == 1
assert height == 1
assert line.remove_overlap_into(intersection.bounding_box())
def test_detect_intersection_rect_L(self):
image = np.array(
[
[False, True, True, False],
[False, True, True, True],
[False, True, True, True],
[False, False, False, False],
]
)
i_regions = [
DirectedLine.btwn(*np.array([(1, 1), (1, 2)])),
DirectedLine.btwn(*np.array([(1, 2), (1, 1)])),
DirectedLine.btwn(*np.array([(1, 2), (2, 2)])),
DirectedLine.btwn(*np.array([(2, 2), (1, 2)])),
]
for line in i_regions:
(top_left, bottom_right) = detect_intersection_rect(image, line)
assert (
top_left == 1
).all(), f"Expected top left to be [1 1]. Found {top_left}"
assert (
bottom_right == 2
).all(), f"Expected bottom right to be [2 2]. Found {bottom_right}"
def test_detect_intersection(self):
image = np.array(
[
[False, False, True, False, False, False, False],
[False, False, True, True, True, True, True],
[False, False, True, False, False, False, False],
[False, False, True, False, True, False, False],
[False, False, True, False, False, False, False],
[False, False, True, False, False, False, False],
[False, False, True, False, False, False, False],
]
)
line = DirectedLine.btwn(np.array([6, 2]), np.array([0, 2]))
intersection = line.detect_intersection(image)
assert intersection
(x, y, width, height) = intersection.bounding_box()
assert width == 1
assert height == 1
assert line.remove_overlap_into(intersection.bounding_box())
def get_start_lines(image, annotation, min_length=5):
x, y, width, height = annotation.bounding_box(inclusive=True).astype(int)
directions = [
Direction.NORTH, Direction.SOUTH, Direction.EAST, Direction.WEST
]
box_size = np.array([height, width])
box_location = [y, x]
lines = []
for direction in directions:
# If we are vertical, "fixed dim" is y else it is x
# If we are positive, we need to add the width/height to the fixed dim
#
# First, we will define some masks that we will use to make the code generic
# including masks for vertical/horizontal values and the positivity mask
vh_mask = direction.value() != 0
pos_mask = direction.value() > 0
fixed_dim_offset = vh_mask * box_location + pos_mask * box_size
candidate_idx = [None, None]
idx = fixed_dim_offset.nonzero()[0].item()
candidate_idx[idx] = fixed_dim_offset[idx]
r_dir = direction.turn_right()
rvh_mask = r_dir.value() != 0
start = rvh_mask * box_location
end = rvh_mask * box_size + start
# Next, we will determine the "start (point) candidates". These are
# the locations on the bounding box that are black and might be the
# beginning of an outgoing line
o_idx = start.nonzero()[0].item()
candidate_idx[o_idx] = range(start[o_idx], end[o_idx] + 1)
start_candidates = (
np.where(image[candidate_idx[0], candidate_idx[1]])[0] +
start[o_idx])
# Now, we will determine the actual start points. These are the start
# candidates which extend at least the minimum distance.
region_idx = [None, None]
region_idx_start = fixed_dim_offset[idx]
region_idx_step = direction.value()[idx]
region_idx_end = (fixed_dim_offset +
min_length * direction.value())[idx]
min_len_is_off_image = region_idx_end > image.shape[
idx] or region_idx_end < 0
if min_len_is_off_image:
continue
region_idx[idx] = range(region_idx_start, region_idx_end,
region_idx_step)
region_idx[o_idx] = start_candidates
region_idx = np.ix_(*region_idx)
line_region = image[region_idx[0], region_idx[1]]
sum_axis = 0 if direction.is_vertical() else 1
start_values = start_candidates[np.where(
line_region.sum(axis=sum_axis) == min_length)[0]]
start_points = np.zeros((start_values.shape[0], 2))
start_points[:, o_idx] = start_values
start_points[:, idx] = fixed_dim_offset[idx]
# Finally, we will back each line up as far as we can into the bounding box
is_line = lambda pt: image[pt[0], pt[1]]
end_points = [
point + min_length * direction.value() for point in start_points
]
for pt in start_points:
possible_points = np.zeros(((vh_mask * box_size).sum() + 1, 2))
possible_points[:, o_idx] = pt[o_idx]
end_point = pt + (vh_mask *
(box_size + 1) * direction.reverse().value())
possible_points[:, idx] = range(int(pt[idx]), int(end_point[idx]),
-1 * direction.value()[idx])
assert len(
possible_points), f"No start points from {pt} for {annotation}"
last_valid_point = list(
takewhile(is_line, possible_points.astype(int)))[-1]
pt[idx] = last_valid_point[idx]
lines.extend([
DirectedLine(start, end, direction)
for (start, end) in zip(start_points, end_points)
])
return lines