class ExtractorPlayer(object):
def __init__(self, file_path, calibration):
self.calibration = calibration
self.clip = VideoFileClip(file_path)
self.n_frames = np.round(self.clip.fps * self.clip.duration).astype(np.int)
self.clip_time = 0
self.extractor = Extractor(thresh=self._load_thresholds(), th_change_callback=self.th_change_callback)
self._create_display_windows()
def _create_display_windows(self):
window_grid = WindowGrid()
pos_x, pos_y, width, height = window_grid.next()
self.win_input_image = 'Input image'
cv2.namedWindow(self.win_input_image, cv2.WINDOW_NORMAL)
cv2.moveWindow(self.win_input_image, pos_x, pos_y)
cv2.resizeWindow(self.win_input_image, width, height)
cv2.createTrackbar('frame', self.win_input_image, 0, self.n_frames - 1, self.frame_slider_callback)
self.extractor.init_show(window_grid)
def _process_frame(self):
rgb_image = self.clip.get_frame(self.clip_time)
rgb_image = self.calibration.undistort(rgb_image)
images = {
'rgb': rgb_image,
'gray': cv2.cvtColor(rgb_image, cv2.COLOR_RGB2GRAY),
'hls': cv2.cvtColor(rgb_image, cv2.COLOR_RGB2HLS),
'bgr': cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR),
}
self.extractor.apply(images)
cv2.imshow(self.win_input_image, images['bgr'])
self.extractor.show()
def process(self):
pause = False
while 1:
if not pause and (self.clip_time < self.clip.duration):
self._process_frame()
frame_idx = np.round(self.clip.fps * self.clip_time).astype(np.int)
cv2.setTrackbarPos('frame', self.win_input_image, frame_idx)
self.clip_time += 1.0 / self.clip.fps
k = cv2.waitKey(20) & 0xff
if k == ascii_dict['space']:
pause = not pause # toggle
elif k == ascii_dict['esc']:
break
self._save_thresholds()
cv2.destroyAllWindows()
def frame_slider_callback(self, value):
self.clip_time = value / self.clip.fps
self._process_frame()
def th_change_callback(self):
self._process_frame()
def _load_thresholds(self):
if not os.path.isfile(thresholds_path):
return dict()
with open(thresholds_path, 'rb') as fid:
thresholds = pickle.load(fid)
print("Loaded thresholds:")
print(thresholds)
return thresholds
def _save_thresholds(self):
thresholds = self.extractor.get_thresholds()
print("Saving thresholds:")
print(thresholds)
with open(thresholds_path, 'wb') as fid:
pickle.dump(thresholds, fid)
class LaneFindingPipeline(object):
def __init__(self):
if camera_calibration_available():
print("Loading camera calibration")
self.calibration = load_camera_calibration()
else:
print("Running camera calibration")
self.calibration = Calibrate()
store_camera_calibration(self.calibration)
self.extractor = Extractor(thresh=load_thresholds())
self.perspective = load_perspective()
self.detector = Detector(self.perspective)
def player(self, video_path='../input/project_video.mp4'):
for rgb_image in VideoFileClip(video_path).iter_frames():
bgr_image = self.process_frame(rgb_image)
cv2.imshow("Lane Detection", bgr_image)
k = cv2.waitKey(20) & 0xff
if k == 27:
break # Quit by pressing Esc
if k == 32:
cv2.waitKey() # Pause by pressing space
else:
cv2.waitKey()
cv2.destroyAllWindows()
def create_video(self,
video_path='../input/project_video.mp4',
output_video_path='../output/project_video.mp4'):
clip = VideoFileClip(video_path).fl_image(self.process_frame_rgb)
clip.write_videofile(output_video_path, audio=False)
def process_frame(self, rgb_image):
rgb_image = self.calibration.undistort(rgb_image)
# Calculate images
images = {
'rgb': rgb_image,
'gray': cv2.cvtColor(rgb_image, cv2.COLOR_RGB2GRAY),
'hls': cv2.cvtColor(rgb_image, cv2.COLOR_RGB2HLS),
'bgr': cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR),
}
# Extract possible lane lines pixels into a binary image.
extracted_image = self.extractor.apply(images).astype(np.uint8)
# Warp perspective to birds-eye-view
h, w = extracted_image.shape
perspective_image = cv2.warpPerspective(
extracted_image,
self.perspective['transformation_matrix'], (w, h),
flags=cv2.INTER_LINEAR)
# Fit lines to extracted lane line pixels and calculate curvature and vehicle lane position.
lines_curvature, lane_center_offset = self.detector.find(
perspective_image)
# Draw information directly into the visualized image.
images['bgr'] = self.draw_overlays(images['bgr'], lines_curvature,
lane_center_offset, extracted_image)
return images['bgr']
def process_frame_rgb(self, rgb_image):
"""Process frame and convert output to RGB format"""
bgr_image = self.process_frame(rgb_image)
return cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
def draw_overlays(self, bgr_image, lines_curvature, lane_center_offset,
extracted_image):
bgr_image = overlay_lane_detection(
bgr_image, self.detector,
self.perspective['inv_transformation_matrix'])
bgr_image = overlay_lane_curvature(bgr_image, lines_curvature)
bgr_image = overlay_lane_center_offset(bgr_image, lane_center_offset)
bgr_image = overlay_extracted_image(bgr_image, extracted_image)
bgr_image = overlay_detection_image(bgr_image, Line.demo_image)
bgr_image = overlay_perspective_image(
bgr_image, self.perspective['transformation_matrix'])
return bgr_image
