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 = frame
# 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, intensity_lt, intensity_rt, intensity_road = draw_back_onto_the_road(
img_undistorted, Minv, line_lt, line_rt, keep_state)
csv.write("{}, {:.3f}, {:.3f}, {:.3f}, {}\n".format(
processed_frames, intensity_lt, intensity_rt, intensity_road,
str(line_rt.detected)))
csv.flush()
# 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
# binarize the frame s.t. lane lines are highlighted as much as possible
img_binary = binarize(frame, verbose=False)
# compute perspective transform to obtain bird's eye view, forward and backward transformation matrices
img_birdeye, _, Minv = birdeye(img_binary, verbose=False)
# fit 2-degree polynomial curve onto lane lines found
line_lt, line_rt = get_fits_by_sliding_windows(img_birdeye,
line_lt,
line_rt,
n_windows=9)
# 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(frame, 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, 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
line_warp = line_rt.draw(line_warp, color=(0, 0, 255), average=keep_state)
line_dewarped = cv2.warpPerspective(line_warp, Minv, (width, height))
lines_mask = blend_onto_road.copy()
idx = np.any([line_dewarped != 0][0], axis=2)
lines_mask[idx] = line_dewarped[idx]
blend_onto_road = cv2.addWeighted(src1=lines_mask, alpha=0.8, src2=blend_onto_road, beta=0.5, gamma=0.)
return blend_onto_road, intensity_lt, intensity_rt, intensity_road
if __name__ == '__main__':
line_lt, line_rt = Line(buffer_len=10), Line(buffer_len=10)
ret, mtx, dist, rvecs, tvecs = calibrate_camera(calib_images_dir='camera_cal')
# show result on test images
for test_img in glob.glob('test_images/*.jpg'):
img = cv2.imread(test_img)
img_undistorted = undistort(img, mtx, dist, verbose=False)
img_binary = binarize(img_undistorted, verbose=False)
img_birdeye, M, Minv = birdeye(img_binary, verbose=False)
line_lt, line_rt, img_out = get_fits_by_sliding_windows(img_birdeye, line_lt, line_rt, n_windows=7,
verbose=True)
return blend_onto_road
if __name__ == '__main__':
line_lt, line_rt = Line(buffer_len=10), Line(buffer_len=10)
ret, mtx, dist, rvecs, tvecs = calibrate_camera(calib_images_dir='camera_cal')
# show result on test images
for test_img in glob.glob('test_images/*.jpg'):
img = cv2.imread(test_img)
img_undistorted = undistort(img, mtx, dist, verbose=False)
img_binary = binarize(img_undistorted, verbose=False)
img_birdeye, M, Minv = birdeye(img_binary, verbose=False)
line_lt, line_rt, img_out = get_fits_by_sliding_windows(img_birdeye, line_lt, line_rt, n_windows=7, verbose=True)
