def _update_racers_metrics(self):
""" Used to update the racers metric information
"""
if not rospy.is_shutdown():
video_metrics = self.mp4_video_metrics_srv(
VideoMetricsSrvRequest())
self._agents_metrics.put(video_metrics)
def _edit_major_cv_image(self, major_cv_image):
""" Apply all the editing for the Major 45degree camera image
Args:
major_cv_image (Image): Image straight from the camera
Returns:
Image: Edited main camera image
"""
# Applying gradient to whole major image and then writing text
major_cv_image = utils.apply_gradient(major_cv_image,
self.gradient_img,
self.gradient_alpha)
# Subscribing to the agent metrics
mp4_video_metrics_info = list()
for racecar_info, mp4_video_metrics_srv in zip(
self.racecars_info, self.mp4_video_metrics_srv_list):
mp4_video_metrics = mp4_video_metrics_srv(VideoMetricsSrvRequest())
mp4_video_metrics_info.append(mp4_video_metrics)
# Adding display name to the image
agents_speed = 0
agent_done = False
for i, racecar_info in enumerate(self.racecars_info):
loc_x, loc_y = XYPixelLoc.MULTI_AGENT_DISPLAY_NAME_LOC.value[i][
0], XYPixelLoc.MULTI_AGENT_DISPLAY_NAME_LOC.value[i][1]
# Display name (Racer name/Model name)
display_name = racecar_info['display_name']
display_name_txt = display_name if len(
display_name) < 15 else "{}...".format(display_name[:15])
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=display_name_txt,
loc=(loc_x, loc_y),
font=self.amazon_ember_regular_20px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Lap Counter
loc_y += 30
total_laps = rospy.get_param("NUMBER_OF_TRIALS", 0)
current_lap = int(mp4_video_metrics_info[i].lap_counter) + 1
lap_counter_text = "{}/{}".format(current_lap, total_laps)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=lap_counter_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_heavy_30px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Reset counter
loc_y += 45
reset_counter_text = "Reset | {}".format(
mp4_video_metrics_info[i].reset_counter)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=reset_counter_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_18px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
if self.racecar_name == racecar_info['name']:
agents_speed = mp4_video_metrics_info[i].throttle
# The race is complete when total lap is same as current lap and done flag is set
agent_done = agent_done or (mp4_video_metrics_info[i].done and
(current_lap == int(total_laps)))
# Speed
loc_x, loc_y = XYPixelLoc.SPEED_EVAL_LOC.value
if self.is_league_leaderboard:
loc_x, loc_y = XYPixelLoc.SPEED_LEADERBOARD_LOC.value
speed_text = "{} m/s".format(
utils.get_speed_formatted_str(agents_speed))
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=speed_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_20px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Leaderboard name
if self.is_league_leaderboard:
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=self.leaderboard_name,
loc=XYPixelLoc.LEADERBOARD_NAME_LOC.value,
font=self.amazon_ember_regular_16px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Evaluation type
loc_x, loc_y = XYPixelLoc.RACE_TYPE_EVAL_LOC.value
if self.is_league_leaderboard:
loc_x, loc_y = XYPixelLoc.RACE_TYPE_RACE_LOC.value
race_text = "race" if self.is_racing else "evaluation"
evaluation_type_txt = "{} {}".format(
RACE_TYPE_TO_VIDEO_TEXT_MAPPING[self.race_type], race_text)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=evaluation_type_txt,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_italic_20px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# total_evaluation_time (Race time)
loc_x, loc_y = XYPixelLoc.MULTI_AGENT_EVAL_TIME.value
total_eval_milli_seconds = mp4_video_metrics_info[
0].total_evaluation_time
time_delta = datetime.timedelta(milliseconds=total_eval_milli_seconds)
total_eval_time_text = "Race | {}".format(
utils.milliseconds_to_timeformat(time_delta))
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=total_eval_time_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_18px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# AWS Deepracer logo at the bottom for the community leaderboard
if self.is_league_leaderboard:
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=AWS_DEEPRACER_WATER_MARK,
loc=XYPixelLoc.AWS_DEEPRACER_WATER_MARK_LOC.value,
font=self.amazon_ember_regular_16px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Check if the done flag is set and set the banner appropriately
if agent_done:
# When the cv2 text is written, it automatically drops the alpha value of the image
rel_y_offset = XYPixelLoc.TRACK_IMG_WITH_OFFSET_LOC.value[
1] if self.is_league_leaderboard else 0
major_cv_image = cv2.cvtColor(major_cv_image, cv2.COLOR_RGB2RGBA)
racecomplete_image = utils.get_image(
TrackAssetsIconographicPngs.RACE_COMPLETE_OVERLAY_PNG.value,
IconographicImageSize.RACE_COMPLETE_IMAGE_SIZE.value)
x_offset = major_cv_image.shape[
1] - racecomplete_image.shape[1] // 2
y_offset = major_cv_image.shape[
0] - RACE_COMPLETE_Y_OFFSET - rel_y_offset - racecomplete_image.shape[
0] // 2
major_cv_image = utils.plot_rectangular_image_on_main_image(
major_cv_image, racecomplete_image, (x_offset, y_offset))
return major_cv_image
def _edit_major_cv_image(self, major_cv_image):
""" Apply all the editing for the Major 45degree camera image
Args:
major_cv_image (Image): Image straight from the camera
Returns:
Image: Edited main camera image
"""
# Applying gradient to whole major image and then writing text
major_cv_image = utils.apply_gradient(major_cv_image,
self.gradient_img,
self.gradient_alpha)
# Subscribing to the agent metrics
mp4_video_metrics_info = list()
for racecar_info, mp4_video_metrics_srv in zip(
self.racecars_info, self.mp4_video_metrics_srv_list):
mp4_video_metrics = mp4_video_metrics_srv(VideoMetricsSrvRequest())
mp4_video_metrics_info.append(mp4_video_metrics)
# Adding display name to the image
display_name_loc = [(10, 10), (450, 10)]
agents_speed = 0
agent_done = False
for i, racecar_info in enumerate(self.racecars_info):
loc_x, loc_y = display_name_loc[i][0], display_name_loc[i][1]
# Display name (Racer name/Model name)
display_name = racecar_info['display_name']
display_name_txt = display_name if len(
display_name) < 15 else "{}...".format(display_name[:15])
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=display_name_txt,
loc=(loc_x, loc_y),
font=self.amazon_ember_regular_20px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Lap Counter
loc_y += 30
total_laps = rospy.get_param("NUMBER_OF_TRIALS", 0)
lap_counter_text = "{}/{}".format(
int(mp4_video_metrics_info[i].lap_counter), total_laps)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=lap_counter_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_heavy_30px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Reset counter
loc_y += 45
reset_counter_text = "Reset | {}".format(
mp4_video_metrics_info[i].reset_counter)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=reset_counter_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_18px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
if self.racecar_name == racecar_info['name']:
agents_speed = mp4_video_metrics_info[i].throttle
agent_done = agent_done or mp4_video_metrics_info[i].done
# Speed
loc_x, loc_y = 10, 420
speed_text = "{} m/s".format(
utils.get_speed_formatted_str(agents_speed))
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=speed_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_20px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Evaluation type
loc_y += 25
# TODO - Show text based on whether its a race or customer run evaluation
race_text = "race"
evaluation_type_txt = "{} {}".format(
RACE_TYPE_TO_VIDEO_TEXT_MAPPING[self.race_type], race_text)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=evaluation_type_txt,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_italic_20px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# total_evaluation_time (Race time)
loc_x, loc_y = 240, 10
total_eval_milli_seconds = mp4_video_metrics_info[
0].total_evaluation_time
time_delta = datetime.timedelta(milliseconds=total_eval_milli_seconds)
total_eval_time_text = "Race | {}".format(
utils.milliseconds_to_timeformat(time_delta))
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=total_eval_time_text,
loc=(loc_x, loc_y),
font=self.amazon_ember_light_18px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Check if the done flag is set and set the banner appropriately
if agent_done:
# When the cv2 text is written, it automatically drops the alpha value of the image
major_cv_image = cv2.cvtColor(major_cv_image, cv2.COLOR_RGB2RGBA)
racecomplete_image = utils.get_image(
TrackAssetsIconographicPngs.RACE_COMPLETE_OVERLAY_PNG.value,
IconographicImageSize.RACE_COMPLETE_IMAGE_SIZE.value)
x_offset = major_cv_image.shape[
1] - racecomplete_image.shape[1] // 2
y_offset = major_cv_image.shape[
0] - 180 - racecomplete_image.shape[0] // 2
major_cv_image = utils.plot_rectangular_image_on_main_image(
major_cv_image, racecomplete_image, (x_offset, y_offset))
return major_cv_image