def pipeline(img, isVideo=False):
# Image Preprocessing
undst, binary, binary_warped = PreProcessing.preprocess_image(img)
# Lane Detection Code Start
lanes, leftx, lefty, rightx, righty, ploty = LaneFinding.get_lane_lines(
binary_warped, isVideo)
lcurve, rcurve = Support.get_real_lanes_curvature(ploty, leftx, lefty,
rightx, righty)
output = draw_lane_area(undst, binary_warped, ploty, leftx, lefty, rightx,
righty, isVideo)
left_fit, right_fit, dummy = Support.fit_polylines(binary_warped.shape[0],
leftx,
lefty,
rightx,
righty,
x_scale_factor=1,
y_scale_factor=1)
left_fitx, right_fitx = Support.get_polylines_points(
ploty, left_fit, right_fit)
if (isVideo is True):
lcurve, rcurve = getSmoothedCurveData(lcurve, rcurve)
left_fitx, right_fitx = getSmoothedLanesData(left_fitx, right_fitx)
shiftFromLaneCenter_m, side = calculate_shift_from_lane_center(
binary_warped, left_fitx, right_fitx)
Font = cv2.FONT_HERSHEY_SIMPLEX
color = (255, 255, 255)
cv2.putText(output, 'curve = ' + str((lcurve + rcurve) / 2) + ' m',
(10, 100), Font, 1, color, 2, cv2.LINE_AA)
cv2.putText(
output, 'Vehicle is ' + str(shiftFromLaneCenter_m) + ' (m) ' + side +
' of lane center', (10, 150), Font, 1, color, 2, cv2.LINE_AA)
# Lane Detection Code End
# Vehicle Detection Code Start
cars_boxs = get_classified_cars_boxs(undst)
classified_boxs = Visualisation.draw_boxes(undst,
cars_boxs,
color=(0, 0, 255),
thick=6)
filtered_boxs, heat_map = get_heat_map_boxs(cars_boxs, undst, isVideo)
output = Visualisation.draw_boxes(output,
filtered_boxs,
color=(0, 0, 255),
thick=6)
# Vehicle Detection Code End
return undst, classified_boxs, heat_map, output
