def main():
# calibrate the camera using the given chessboard images
ret, mtx, dist, rvecs, tvecs = calibrate(
path='../camera_cal/calibration*.jpg', xy=(9, 6), draw_corners=False)
# inst. Lane object
lane = Lane()
# read video
predicted_frames = []
input_video = 'project_video.mp4'
cap = cv2.VideoCapture(os.path.join('../input_videos/', input_video))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
print('Cant receive frame. Exiting..')
break
# undistort an image
rgb_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # BGR => RGB
undist = undistort(rgb_img, mtx, dist)
# convert to gray
gray = cv2.cvtColor(undist, cv2.COLOR_RGB2GRAY) # RGB => GRAY
# apply gradient and color thresholding
gradx = th.abs_sobel_thresh(gray)
direction = th.dir_thresh(gray)
gradient_binary = np.zeros_like(direction)
gradient_binary[(gradx == 1) & (direction == 1)] = 1
color_binary = th.saturation_thresh(frame)
# combine gradient and color thresholding
thresholded_img = th.threshold(gradient_binary, color_binary)
# perspective transform: easier to measure curvature of lane from bird's eye view
# also makes it easier to match car's location with a road map
src, dst, M, M_inv = pt.get_transform_matrix()
# transform image
size = (thresholded_img.shape[1], thresholded_img.shape[0])
transformed_img = cv2.warpPerspective(thresholded_img, M, size)
# draw lines on transformed
gray_transformed_img = np.uint8(transformed_img * 255)
bgr_transformed_img = cv2.cvtColor(gray_transformed_img,
cv2.COLOR_GRAY2BGR)
#pt.draw_plot_save(bgr_transformed_img, dst, 'Test Transformation', '../output_images/test_transform.png')
# fit lines
left_fit, right_fit, y, offset_meters = lane.fit_polynomials(
transformed_img)
# create blank for drawing lane lines
zeros = np.zeros_like(transformed_img).astype(np.uint8)
draw_img = np.dstack((zeros, zeros, zeros))
# format points for fill poly
pts_left = np.array([np.transpose(np.vstack([left_fit,
y]))]) # [left_fit ... y]
pts_right = np.array(
[np.flipud(np.transpose(np.vstack([right_fit, y])))])
pts = np.hstack((pts_left, pts_right)) # [pts_left, pts_right]
cv2.fillPoly(draw_img, np.int_([pts]), (0, 255, 0))
# unwarp transformed image
unwarped = cv2.warpPerspective(draw_img, M_inv,
(gray.shape[1], gray.shape[0]))
# combine lane drawing w/ original image
final_image = cv2.addWeighted(undist, 1, unwarped, 0.25, 0)
# add measurement data to frame
offset_side = 'left' if offset_meters < 0 else 'right'
final_image = cv2.putText(
final_image,
f'Offset: {abs(offset_meters):0.2f}m {offset_side} of center',
(50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2,
cv2.LINE_AA)
# show predict
cv2.imshow('frame', cv2.cvtColor(final_image, cv2.COLOR_RGB2BGR))
# store predicted frames
predicted_frames.append(final_image)
if cv2.waitKey(1) == ord('q'):
break
# release cap object
cap.release()
cv2.destroyAllWindows()
