def frame_pipeline(frame, camera_properties, cache_length=20):
if frame_pipeline.cache is None:
left_lane = Lane(LEFT_LANE_BASE_PT * frame.shape[0],
frame.shape[:2],
cache_length=cache_length)
right_lane = Lane(RIGHT_LANE_BASE_PT * frame.shape[0],
frame.shape[:2],
cache_length=cache_length)
cache = dict(cam_mtx=camera_properties["mtx"],
cam_dist=camera_properties["dist"],
warp_m=None,
left=left_lane,
right=right_lane,
base_pts=None)
else:
cache = frame_pipeline.cache
left_lane, right_lane = cache['left'], cache['right']
mtx = camera_properties["mtx"]
dist = camera_properties["dist"]
undist = cv2.undistort(frame, mtx, dist, None, mtx)
if cache['warp_m'] is None:
src = find_perspective_points(undist)
warp_m, warp_minv = get_perspective_transform(frame, src_in=src)
if src is not None:
# Save only if customized perspective transform is found
cache['warp_m'] = warp_m
cache['warp_minv'] = warp_minv
else:
warp_m, warp_minv = cache['warp_m'], cache['warp_minv']
edges = find_edges(undist)
warp_edges = cv2.warpPerspective(edges,
warp_m, (frame.shape[1], frame.shape[0]),
flags=cv2.INTER_LINEAR)
base_pts = cache['base_pts']
if base_pts is None:
new_base_pts = histogram_base_points(warp_edges)
if new_base_pts is not None:
base_pts = new_base_pts
else:
# Could not find new base points
return undist
if any((left_lane.current_xfit is None
or left_lane.dropped_frames > 16, right_lane.current_xfit is None
or right_lane.dropped_frames > 16)):
left_lane.radius_of_curvature = None
right_lane.radius_of_curvature = None
sliding_window(warp_edges, left_lane, right_lane, base_pts)
else:
left_lane.detect_from_mask(warp_edges)
right_lane.detect_from_mask(warp_edges)
cache['base_pts'] = base_pts
frame_pipeline.cache = cache
# Create an image to draw the lines on
color_warp = np.zeros_like(frame).astype(np.uint8)
yvals = left_lane.yvals
left_fitx = left_lane.current_xfit
right_fitx = right_lane.current_xfit
# Create an image to draw the lines on
color_warp = np.zeros_like(frame).astype(np.uint8)
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, yvals]))])
pts_right = np.array(
[np.flipud(np.transpose(np.vstack([right_fitx, yvals])))])
pts = np.hstack((pts_left, pts_right))
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# Draw lane markers
pts = np.transpose(np.vstack([left_lane.current_xfit,
left_lane.yvals])).reshape(
(-1, 1, 2)).astype(np.int32)
cv2.drawContours(color_warp, pts, -1, (255, 0, 0), thickness=30)
pts = np.transpose(np.vstack([right_lane.current_xfit,
right_lane.yvals])).reshape(
(-1, 1, 2)).astype(np.int32)
cv2.drawContours(color_warp, pts, -1, (0, 0, 255), thickness=30)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, warp_minv,
(frame.shape[1], frame.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
left_r = left_lane.radius_of_curvature
right_r = right_lane.radius_of_curvature
middle = (left_fitx[-1] + right_fitx[-1]) // 2
veh_pos = frame.shape[1] // 2
dx = (veh_pos - middle
) * Lane.X_MTS_PER_PIX # Positive if on right, Negative on left
results_str = '{} radii of curvature = {:.2f}'
font = cv2.FONT_HERSHEY_TRIPLEX
cv2.putText(result, results_str.format("left m", left_r), (80, 50), font,
1, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(result, results_str.format("right m", right_r), (80, 80), font,
1, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(
result, "Vehicle postion: {:.2f} m {} of center".format(
abs(dx), 'left' if dx < 0 else 'right'), (80, 110), font, 1,
(255, 255, 255), 2, cv2.LINE_AA)
is_tracking = left_lane.detected or right_lane.detected
cv2.putText(result, 'Tracking Locked' if is_tracking else 'Tracking Lost',
(80, 140), font, 1, (0, 255, 0) if is_tracking else
(255, 0, 0), 3, cv2.LINE_AA)
cache['left'] = left_lane
cache['right'] = right_lane
return result
