def process_pipeline(frame, keep_state=True):
"""
Apply whole lane detection pipeline to an input color frame.
:param frame: input color frame
:param keep_state: if True, lane-line state is conserved (this permits to average results)
:return: output blend with detected lane overlaid
"""
global line_lt, line_rt, processed_frames
# undistort the image using coefficients found in calibration
img_undistorted = undistort(frame, mtx, dist, verbose=False)
# binarize the frame s.t. lane lines are highlighted as much as possible
img_binary = binarize(img_undistorted, verbose=False)
# compute perspective transform to obtain bird's eye view
img_birdeye, M, Minv = birdeye(img_binary, verbose=False)
# fit 2-degree polynomial curve onto lane lines found
if processed_frames > 0 and keep_state and line_lt.detected and line_rt.detected:
line_lt, line_rt, img_fit = get_fits_by_previous_fits(img_birdeye,
line_lt,
line_rt,
verbose=False)
else:
line_lt, line_rt, img_fit = get_fits_by_sliding_windows(img_birdeye,
line_lt,
line_rt,
n_windows=9,
verbose=False)
# compute offset in meter from center of the lane
offset_meter = compute_offset_from_center(line_lt,
line_rt,
frame_width=frame.shape[1])
# draw the surface enclosed by lane lines back onto the original frame
blend_on_road = draw_back_onto_the_road(img_undistorted, Minv, line_lt,
line_rt, keep_state)
# stitch on the top of final output images from different steps of the pipeline
blend_output = prepare_out_blend_frame(blend_on_road, img_binary,
img_birdeye, img_fit, line_lt,
line_rt, offset_meter)
processed_frames += 1
return blend_output
def process_pipeline(frame, keep_state=True):
"""
Apply whole lane detection pipeline to an input color frame.
:param frame: input color frame
:param keep_state: if True, lane-line state is conserved (this permits to average results)
:return: output blend with detected lane overlaid
"""
global line_lt, line_rt, processed_frames
# undistort the image using coefficients found in calibration
img_undistorted = undistort(frame, mtx, dist, verbose=False)
# binarize the frame s.t. lane lines are highlighted as much as possible
img_binary = binarize(img_undistorted, verbose=False)
# compute perspective transform to obtain bird's eye view
img_birdeye, M, Minv = birdeye(img_binary, verbose=False)
# fit 2-degree polynomial curve onto lane lines found
if processed_frames > 0 and keep_state and line_lt.detected and line_rt.detected:
line_lt, line_rt, img_fit = get_fits_by_previous_fits(img_birdeye, line_lt, line_rt, verbose=False)
else:
line_lt, line_rt, img_fit = get_fits_by_sliding_windows(img_birdeye, line_lt, line_rt, n_windows=9, verbose=False)
# compute offset in meter from center of the lane
offset_meter = compute_offset_from_center(line_lt, line_rt, frame_width=frame.shape[1])
# draw the surface enclosed by lane lines back onto the original frame
blend_on_road = draw_back_onto_the_road(img_undistorted, Minv, line_lt, line_rt, keep_state)
# stitch on the top of final output images from different steps of the pipeline
blend_output = prepare_out_blend_frame(blend_on_road, img_binary, img_birdeye, img_fit, line_lt, line_rt, offset_meter)
processed_frames += 1
return blend_output
def process_pipeline(frame, keep_state=True):
"""
Apply whole lane detection pipeline to an input color frame.
:param frame: input color frame
:param keep_state: if True, lane-line state is conserved (this permits to average results)
:return: output blend with detected lane overlaid
"""
global line_lt, line_rt, processed_frames
# step 2
# TODO 4: call undistort funcction and pass it to img_undistorted
# undistort the image using coefficients found in calibration
img_undistorted = undistort(frame, mtx, dist, verbose=False)
# step 3
# binarize the frame s.t. lane lines are highlighted as much as possible
# img_binary = binarize(img_undistorted, verbose=False)
# TODO 5: Binarize
img_binary = binarize_with_threshold(img_undistorted, verbose=False)
# step 4
# TODO 6: Perspetive transform
# perspective transform
binary_warped, m, m_inv = perspective_transform(img_binary, verbose=False)
# step 5
# TODO 7: Curve fitting
# fit 2-degree polynomial curve onto lane lines found
if processed_frames > 0 and keep_state and line_lt.detected and line_rt.detected:
# Fast line fit
line_lt, line_rt, img_fit = get_fits_by_previous_fits(binary_warped,
line_lt,
line_rt,
verbose=False)
else:
line_lt, line_rt, img_fit = get_fits_by_sliding_windows(binary_warped,
line_lt,
line_rt,
n_windows=9,
verbose=False)
# step 6
# TODO 8: Compute offset
# compute offset in meter from center of the lane
offset_meter = compute_offset_from_center(line_lt,
line_rt,
frame_width=frame.shape[1])
# step 7
# TODO 9: Final visualization
# Perform final visualization on top of original undistorted image
result = final_viz(img_undistorted, m_inv, line_lt, line_rt, keep_state)
# step 8
# TODO 11: Blending
# stitch on the top of final output images from different steps of the pipeline
blend_output = prepare_out_blend_frame(result, img_binary, binary_warped,
img_fit, line_lt, line_rt,
offset_meter)
processed_frames += 1
return blend_output
