def split_on_lines(regions, frame):
rois = []
for region in regions:
roi = Rectangle.from_bbox(region.bbox)
fragment = Rectangle.from_bbox(region.bbox).sample_from_image(frame)
lines = lines_y(fragment)
if not len(lines):
rois.append(roi)
continue
distances = []
line_midpoints = list(map(lambda line: Point((line[0] + line[2]) // 2, (line[1] + line[3]) // 2), lines))
center = Point(roi.w // 2, roi.h // 2)
dist_from_center = lambda point: Point.point_distance(center, point)
distances = list(map(dist_from_center, line_midpoints))
_, central_line_midpoint = min(zip(distances, line_midpoints), key= lambda pair: pair[0])
mid_x = central_line_midpoint.x
left_w = mid_x
right_w = roi.w - mid_x
if (left_w / right_w > 0.5 and left_w / right_w < 2):
left = Rectangle(roi.x, roi.y, left_w, roi.h)
right = Rectangle(roi.x + left_w, roi.y, right_w, roi.h)
rois.extend([left, right])
else:
rois.append(roi)
return rois
def detect(frame):
img_hsv = bgr_to_equalized_hsv(frame)
mask = cv2.inRange(img_hsv, (10, 0, 0), (80, 360, 360))
cleared = remove_large_border_segments(mask)
regions = select_regions(cleared)
center = Point(cleared.shape[0] // 2, cleared.shape[1] // 2)
dist_from_center = lambda region: Point.point_distance(center, Point(*region.centroid))
sorted_regions = sorted(regionprops(label(regions)), key=dist_from_center)
vertical = filter(lambda region: abs(region.orientation - sorted_regions[0].orientation) < 1.5, sorted_regions)
close_to_center = filter(lambda region: dist_from_center(region) < frame.shape[1]/3.2, vertical)
split = split_on_lines(close_to_center, frame)
if len(split) < 2:
return split
upper, lower = split[:2]
if upper.x > lower.x:
lower, upper = upper, lower
return [upper, lower]
