def draw_lane_polygon(img):
offset_from_lane_edge = 20
color = Lane.COLORS['region_stable']
if not Lane.lines_exist(): return img
# Polygon points
p1 = [Lane.left_line.x1, Lane.left_line.y1]
p2 = [
Lane.left_line.get_x_coord(Lane.left_line.y2 + offset_from_lane_edge),
Lane.left_line.y2 + offset_from_lane_edge
]
p3 = [
Lane.right_line.get_x_coord(Lane.left_line.y2 + offset_from_lane_edge),
Lane.right_line.y2 + offset_from_lane_edge
]
p4 = [Lane.right_line.x1, Lane.right_line.y1]
polygon_points = np.array([p1, p2, p3, p4], np.int32).reshape((-1, 1, 2))
if not Lane.left_line.stable or not Lane.right_line.stable:
color = Lane.COLORS['region_unstable']
poly_img = np.zeros_like(img)
cv2.fillPoly(poly_img, [polygon_points], color)
return weighted_img(img, poly_img)
def draw_dashboard(img, snapshot1, snapshot2):
# TODO: refactor this
if not Lane.lines_exist():
return img
cv2.CV_FILLED = -1
image_copy = deepcopy(img)
cv2.rectangle(image_copy, (0, 0), (540, 175), (0, 0, 0), cv2.CV_FILLED)
img = weighted_img(image_copy, img, α=0.3, β=0.7)
img[20:155, 20:260, :] = snapshot1
img[20:155, 280:520, :] = snapshot2
return img
def update_lane(segments, image):
if segments is not None:
left = [
segment for segment in segments if segment.lane_line == 'left_line'
]
right = [
segment for segment in segments
if segment.lane_line == 'right_line'
]
if not Lane.lines_exist():
Lane.left_line = Lane(left)
Lane.right_line = Lane(right)
Lane.update_vanishing_point(Lane.left_line, Lane.right_line)
Lane.left_line.update_lane_line([l for l in left if l.candidate],
image)
Lane.right_line.update_lane_line([r for r in right if r.candidate],
image)
def image_pipeline(image):
"""
Main image pipeline with 3 phases:
* Raw image preprocessing and noise filtering;
* Lane lines state update with the information gathered in preprocessing phase;
* Drawing updated lane lines and other objects on image.
"""
### Phase 1: Image Preprocessing
hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
binary_mask = get_lane_lines_mask(hsv_image, [WHITE_LINES, YELLOW_LINES])
masked_image = draw_binary_mask(binary_mask, hsv_image)
masked_image = gaussian_blur(masked_image, 7)
blank_image = np.zeros_like(image)
edges_mask = canny(masked_image, 280, 360)
if not Lane.lines_exist():
edges_mask = region_of_interest(edges_mask, ROI_VERTICES)
edges_image = draw_canny_edges(edges_mask, blank_image)
segments = hough_line_transform(edges_mask, 1, math.pi / 180, 5, 5, 8)
### Stage 2: Lane lines state update
# was (segments, image)
update_lane(segments, edges_image)
### Stage 3: Drawing
# Snapshot 1
out_snap1 = np.zeros_like(image)
out_snap1 = draw_binary_mask(binary_mask, out_snap1)
# plt.imshow(out_snap1)
# plt.show()
out_snap1 = draw_filtered_lines(segments, out_snap1)
snapshot1 = cv2.resize(deepcopy(out_snap1), (240, 135))
# plt.imshow(out_snap1)
# plt.imshow(snapshot1)
# plt.show()
# Snapshot 2
out_snap2 = np.zeros_like(image)
out_snap2 = draw_canny_edges(edges_mask, out_snap2)
# plt.imshow(out_snap2)
# plt.show()
out_snap2 = draw_points(Lane.left_line.points, out_snap2,
Lane.COLORS['left_line'])
out_snap2 = draw_points(Lane.right_line.points, out_snap2,
Lane.COLORS['right_line'])
out_snap2 = draw_lane_polygon(out_snap2)
snapshot2 = cv2.resize(deepcopy(out_snap2), (240, 135))
# plt.imshow(snapshot2)
# plt.show()
# Augmented image
output = deepcopy(image)
output = draw_lane_lines([Lane.left_line, Lane.right_line],
output,
shade_background=True)
output = draw_dashboard(output, snapshot1, snapshot2)
return output
