grad_color_stack, img = color_gradient_threshold_pipeline(image, img_processor)
birds_eye, M, Minv = calibration.perspective_transform(img, src, dst)
undist = calibration.undistort(image)
search, left_fitx, right_fitx, ploty, image_final = find_lane_lines.find(birds_eye, undist, Minv, left_line, right_line)
return image_final
#__main__
# Camera Calibration
img = mpimg.imread('camera_cal/calibration1.jpg')
img_size = (img.shape[1], img.shape[0])
calibration = CameraCalibration(chessboard_images_dir='camera_cal', pattern_size=(9,6))
calibration.calibrate_camera(img_size)
# Image Processor
img_processor = ImageProcessor()
# src and dst for perspective transform
test_img = mpimg.imread('test_images/straight_lines2.jpg')
test_img_size = (test_img.shape[1], test_img.shape[0])
src = np.float32([[580,460], [710,460], [1150,720], [150,720]])
offset = 200
dst = np.float32([ [offset, 0],
[test_img_size[0]-offset, 0],
[test_img_size[0]-offset, test_img_size[1]-0],
[offset, test_img_size[1]-0]
])
def scaled_sobel(abs_sobel):
return np.uint8(255 * abs_sobel / np.max(abs_sobel))
@staticmethod
def create_binary(image):
return np.zeros_like(image)
@staticmethod
def region_of_interest(img, vertices):
mask = np.zeros_like(img)
if len(img.shape) > 2:
channel_count = img.shape[2]
ignore_mask_color = (255, ) * channel_count
else:
ignore_mask_color = 255
cv2.fillPoly(mask, vertices, ignore_mask_color)
masked_image = cv2.bitwise_and(img, mask)
return masked_image
run = False
if run:
camcal = CameraCalibration()
camcal.calibrate_camera()
det = LaneLineDetector(camcal)
output = 'harder_challenge_video_submit.mp4'
clip1 = VideoFileClip('harder_challenge_video.mp4')
clip = clip1.fl_image(det.pipeline)
clip.write_videofile(output, audio=False)
