def fade_in(self, background_image):
""" The function takes the new frame from the camera and multiplies with the pre-computed gradients.
This will allow it fade the image. (Brighter to darker image). The pointer keeps track of the which
gradient to apply at a specific period of time.
Args:
background_image (Image): Frame from the camera
Returns:
(Image): The edited image where the gradient is applied.
"""
image = utils.apply_gradient(
background_image, self._gradient_alpha_rgb_mul_list[self._pointer],
self._one_minus_gradient_alpha_list[self._pointer])
self._pointer = min(self._pointer + 1,
len(self._gradient_alpha_rgb_mul_list) - 1)
return image
def _default_edit(self, major_cv_image):
""" This is used as a default edit.
Arguments:
major_cv_image (Image): Main camera image for the racecar
Returns:
major_cv_image (Image): Edited Main camera image
"""
# Applying gradient to whole major image and then writing text
# F1 logo at the top left
# loc_x, loc_y = XYPixelLoc.F1_LOGO_LOC.value
# f1_logo_image = utils.get_image(TrackAssetsIconographicPngs.F1_LOGO_PNG.value,
# IconographicImageSize.F1_LOGO_IMAGE_SIZE.value)
# major_cv_image = utils.plot_rectangular_image_on_main_image(major_cv_image, f1_logo_image, (loc_x, loc_y))
major_cv_image = utils.apply_gradient(
major_cv_image, self.gradient_default_alpha_rgb_mul,
self.one_minus_gradient_default_alpha)
return major_cv_image
def fade_out(self, background_image):
""" The function takes the new frame from the camera and multiplies with the pre-computed gradients.
This will allow it fade out. (Darker to Brighter image). The pointer keeps track of the which
gradient to apply at a specific period of time. This iterates in the reverse order to that of fade_in
Args:
background_image (Image): Frame from the camera
Returns:
(Image): The edited image where the gradient is applied.
"""
if self._pointer > 0:
image = utils.apply_gradient(
background_image,
self._gradient_alpha_rgb_mul_list[self._pointer],
self._one_minus_gradient_alpha_list[self._pointer])
self._pointer = max(0, self._pointer - 1)
return image
return background_image
def _edit_major_cv_image(self, major_cv_image, cur_training_phase):
""" 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_alpha_rgb_mul,
self.one_minus_gradient_alpha)
# Add the label that lets the user know the training phase
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=cur_training_phase,
loc=XYPixelLoc.TRAINING_PHASE_LOC.value,
font=self.training_phase_font,
font_color=None,
font_shadow_color=RaceCarColorToRGB.Black.value)
major_cv_image = cv2.cvtColor(major_cv_image, cv2.COLOR_RGB2BGRA)
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
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_top_camera_image_util(self, top_camera_image, rank_name_gap_time,
mp4_video_metrics_info):
""" Showing stats on the top view camera image
Arguments:
major_cv_image (Image): Main camera image for the racecar
rank_name_gap_time (list): Sorted list based on the ranking along with rank, name, gap time, racer_number
mp4_video_metrics_info (list): All the racers metric information
Returns:
major_cv_image (Image): Edited Main camera image
"""
# Applying gradient to whole major image and then writing text
top_camera_image = utils.apply_gradient(
top_camera_image, self.gradient_top_camera_alpha_rgb_mul,
self.one_minus_gradient_top_camera_alpha)
top_camera_image = cv2.cvtColor(top_camera_image, cv2.COLOR_BGR2RGBA)
# LAP Title
loc_x, loc_y = XYPixelLoc.F1_TOP_CAMERA_LAP_TEXT_LOC.value
lap_txt = "LAP"
top_camera_image = utils.write_text_on_image(
image=top_camera_image,
text=lap_txt,
loc=(loc_x, loc_y),
font=self.formula1_display_wide_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# LAP counter
loc_x, loc_y = XYPixelLoc.F1_TOP_CAMERA_LAP_COUNTER_LOC.value
# For Top view, lap counter should be based on leader's lap count.
leader_index = rank_name_gap_time[0][3]
current_lap = min(
int(mp4_video_metrics_info[leader_index].lap_counter) + 1,
int(self.total_laps))
lap_counter_text = "{} / {}".format(current_lap, int(self.total_laps))
top_camera_image = utils.write_text_on_image(
image=top_camera_image,
text=lap_counter_text,
loc=(loc_x, loc_y),
font=self.formula1_display_bold_16px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Each racers ranking, name and gap time
rank_loc_x, rank_loc_y = XYPixelLoc.F1_TOP_CAMERA_LEADER_RANK_LOC.value
color_code_loc_x, color_code_loc_y = XYPixelLoc.F1_TOP_CAMERA_LEADER_COLOR_CODE_LOC.value
display_name_loc_x, display_name_loc_y = XYPixelLoc.F1_TOP_CAMERA_LEADER_DISPLAY_NAME_LOC.value
gap_loc_x, gap_loc_y = XYPixelLoc.F1_TOP_CAMERA_LEADER_GAP_LOC.value
for row in range(4):
for col in range(3):
if (row + 4 * col) >= len(rank_name_gap_time):
continue
rank, display_name, gap_time, racer_number = rank_name_gap_time[
row + 4 * col]
# Rank
loc_x, loc_y = rank_loc_x + (col * 185), rank_loc_y + (row *
20)
racer_rank = "{}".format(rank)
top_camera_image = utils.write_text_on_image(
image=top_camera_image,
text=racer_rank,
loc=(loc_x, loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Draw racer color code icon
loc_x, loc_y = color_code_loc_x + (
col * 185), color_code_loc_y + (row * 20)
top_camera_image = utils.plot_rectangular_image_on_main_image(
top_camera_image,
self._racer_color_code_rect_img[racer_number],
(loc_x, loc_y))
# Adding display name to the table
loc_x, loc_y = display_name_loc_x + (
col * 185), display_name_loc_y + (row * 20)
display_name_txt = display_name if len(
display_name) <= 6 else "{}".format(display_name[:6])
top_camera_image = utils.write_text_on_image(
image=top_camera_image,
text=display_name_txt,
loc=(loc_x, loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Adding gap to the table
if row != 0 or col != 0:
loc_x, loc_y = gap_loc_x + (col * 180), gap_loc_y + (row *
20)
# The gradient box images are not equally placed for gaps
if col == 2:
loc_x, loc_y = gap_loc_x + (col * 182), gap_loc_y + (
row * 20)
gap_time_text = "+{:.3f}".format(gap_time)
top_camera_image = utils.write_text_on_image(
image=top_camera_image,
text=gap_time_text,
loc=(loc_x, loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
return top_camera_image
def _race_finish_display(self, major_cv_image, rank_name_gap_time):
""" This displays the stats of all the racers who have finished the lap. When the car
finishes the lap the finisher racers get added into this list.
Arguments:
major_cv_image (Image): Main camera image for the racecar
rank_name_gap_time (list): Sorted list based on the ranking along with rank, name, gap time, racer_number
Returns:
major_cv_image (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_finisher_alpha_rgb_mul,
self.one_minus_gradient_finisher_alpha)
major_cv_image = cv2.cvtColor(major_cv_image, cv2.COLOR_BGR2RGBA)
# F1 logo
# loc_x, loc_y = XYPixelLoc.F1_LOGO_LOC.value
# f1_logo_image = utils.get_image(TrackAssetsIconographicPngs.F1_LOGO_PNG.value,
# IconographicImageSize.F1_LOGO_IMAGE_SIZE.value,
# is_rgb=True)
# major_cv_image = utils.plot_rectangular_image_on_main_image(major_cv_image, f1_logo_image, (loc_x, loc_y))
# Finish Title
loc_x, loc_y = XYPixelLoc.F1_FINISHED_TITLE_LOC.value
finisher_txt = "Finishers"
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=finisher_txt,
loc=(loc_x, loc_y),
font=self.formula1_display_bold_16px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
rank_loc_x, rank_loc_y = XYPixelLoc.F1_FINISHED_RANK_LOC.value
color_code_loc_x, color_code_loc_y = XYPixelLoc.F1_FINISHED_COLOR_CODE_LOC.value
display_name_loc_x, display_name_loc_y = XYPixelLoc.F1_FINISHED_DISPLAY_NAME_LOC.value
for rank, display_name, _, racer_number in rank_name_gap_time:
if racer_number not in self._finished_lap_time:
continue
# All racers name and gap
racer_rank = "{}".format(rank)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=racer_rank,
loc=(rank_loc_x, rank_loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Draw racer color code icon
major_cv_image = utils.plot_rectangular_image_on_main_image(
major_cv_image, self._racer_color_code_rect_img[racer_number],
(color_code_loc_x, color_code_loc_y))
# Adding display name to the table
display_name_txt = display_name if len(
display_name) <= 6 else "{}".format(display_name[:6])
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=display_name_txt,
loc=(display_name_loc_x, display_name_loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
rank_loc_y += 20
color_code_loc_y += 20
display_name_loc_y += 20
return cv2.cvtColor(major_cv_image, cv2.COLOR_RGB2BGRA)
def _midway_racers_progress_display(self, major_cv_image,
rank_name_gap_time,
mp4_video_metrics_info):
""" Half way of the track we show the stats of the racers with there ranks and gap time
Arguments:
major_cv_image (Image): Main camera image for the racecar
rank_name_gap_time (list): Sorted list based on the ranking along with rank, name, gap time, racer_number
mp4_video_metrics_info (list): All the racers metric information
Returns:
major_cv_image (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_midway_alpha_rgb_mul,
self.one_minus_gradient_midway_alpha)
major_cv_image = cv2.cvtColor(major_cv_image, cv2.COLOR_BGR2RGBA)
# LAP Title
loc_x, loc_y = XYPixelLoc.F1_MIDWAY_LAP_TEXT_LOC.value
lap_txt = "LAP"
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=lap_txt,
loc=(loc_x, loc_y),
font=self.formula1_display_wide_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# LAP counter
loc_x, loc_y = XYPixelLoc.F1_MIDWAY_LAP_COUNTER_LOC.value
current_lap = min(
int(mp4_video_metrics_info[self.racecar_index].lap_counter) + 1,
int(self.total_laps))
lap_counter_text = "{} / {}".format(current_lap, int(self.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.formula1_display_bold_16px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Each racers ranking, name and gap time
rank_loc_x, rank_loc_y = XYPixelLoc.F1_MIDWAY_LEADER_RANK_LOC.value
color_code_loc_x, color_code_loc_y = XYPixelLoc.F1_MIDWAY_LEADER_COLOR_CODE_LOC.value
display_name_loc_x, display_name_loc_y = XYPixelLoc.F1_MIDWAY_LEADER_DISPLAY_NAME_LOC.value
gap_loc_x, gap_loc_y = XYPixelLoc.F1_MIDWAY_LEADER_GAP_LOC.value
# All the other racers name and gap
for i, val in enumerate(rank_name_gap_time):
rank, display_name, gap_time, racer_number = val
# Rank
racer_rank = "{}".format(rank)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=racer_rank,
loc=(rank_loc_x, rank_loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Draw racer color code icon
major_cv_image = utils.plot_rectangular_image_on_main_image(
major_cv_image, self._racer_color_code_rect_img[racer_number],
(color_code_loc_x, color_code_loc_y))
# Adding display name to the table
display_name_txt = display_name if len(
display_name) <= 6 else "{}".format(display_name[:6])
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=display_name_txt,
loc=(display_name_loc_x, display_name_loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Adding gap to the table (Do not write gap for leader)
if i:
gap_time_text = "+{:.3f}".format(gap_time)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=gap_time_text,
loc=(gap_loc_x, gap_loc_y),
font=self.formula1_display_regular_12px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
rank_loc_y += 20
color_code_loc_y += 20
display_name_loc_y += 20
gap_loc_y += 20
return cv2.cvtColor(major_cv_image, cv2.COLOR_RGB2BGRA)
def _edit_major_cv_image(self, major_cv_image, mp4_video_metrics_info):
""" 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_alpha_rgb_mul,
self.one_minus_gradient_alpha)
# Top left location of the picture
loc_x, loc_y = XYPixelLoc.SINGLE_AGENT_DISPLAY_NAME_LOC.value
# Display name (Racer name/Model name)
display_name = self.racecar_info[self.racecar_index]['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 = min(
int(mp4_video_metrics_info[self.racecar_index].lap_counter) + 1,
total_laps)
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)
# total_evaluation_time (Race time)
loc_y += 45
total_eval_milli_seconds = mp4_video_metrics_info[
self.racecar_index].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)
# Reset counter
loc_y += 25
reset_counter_text = "Reset | {}".format(
mp4_video_metrics_info[self.racecar_index].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)
# 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(
mp4_video_metrics_info[self.racecar_index].throttle))
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:
loc_x, loc_y = XYPixelLoc.LEADERBOARD_NAME_LOC.value
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=self.leaderboard_name,
loc=(loc_x, loc_y),
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)
# 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 mp4_video_metrics_info[self.racecar_index].done and (
int(total_laps) >= current_lap):
# 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
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))
major_cv_image = cv2.cvtColor(major_cv_image, cv2.COLOR_RGB2BGRA)
return major_cv_image
def _edit_major_cv_image(self, major_cv_image, mp4_video_metrics_info):
""" Apply all the editing for the Major 45degree camera image
Args:
major_cv_image (Image): Image straight from the camera
mp4_video_metrics_info (dict): rest image editting info
Returns:
Image: Edited main camera image
"""
major_cv_image = utils.apply_gradient(major_cv_image,
self.gradient_alpha_rgb_mul,
self.one_minus_gradient_alpha)
#########################
# update display params #
#########################
episode_status = mp4_video_metrics_info[
self.racecar_index].episode_status
# Display name (Racer name/Model name)
display_name = self.racecar_info[self.racecar_index]['display_name']
# total_evaluation_time (Race time)
total_eval_milli_seconds = mp4_video_metrics_info[
self.racecar_index].total_evaluation_time
# Reset counter
reset_counter = mp4_video_metrics_info[
self.racecar_index].reset_counter
# Speed
speed = mp4_video_metrics_info[self.racecar_index].throttle
# Current progress
current_progress = mp4_video_metrics_info[
self.racecar_index].completion_percentage
# Prepare a dict for finite state machine on event call
info_dict = {
VirtualEventMP4Params.COUNTDOWN_TIMER.value:
mp4_video_metrics_info[self.racecar_index].pause_duration,
VirtualEventMP4Params.MAJOR_CV_IMAGE.value:
major_cv_image,
VirtualEventMP4Params.CURRENT_LAP.value:
self._current_lap,
VirtualEventMP4Params.TOTAL_EVAL_SECONDS.value:
total_eval_milli_seconds,
VirtualEventMP4Params.RESET_COUNTER.value:
reset_counter,
VirtualEventMP4Params.SPEED.value:
speed,
VirtualEventMP4Params.CURR_PROGRESS.value:
current_progress,
VirtualEventMP4Params.LAST_EVAL_SECONDS.value:
self._last_eval_time,
VirtualEventMP4Params.X_MIN.value:
self._track_x_min,
VirtualEventMP4Params.X_MAX.value:
self._track_x_max,
VirtualEventMP4Params.Y_MIN.value:
self._track_y_min,
VirtualEventMP4Params.Y_MAX.value:
self._track_y_max,
VirtualEventMP4Params.SECTOR_TIMES.value:
self._sector_times,
VirtualEventMP4Params.CURR_LAP_TIME.value:
self._curr_lap_time,
VirtualEventMP4Params.SECTOR_IMAGES.value:
self._sectors_img_dict,
VirtualEventMP4Params.FADER_OBJ.value:
self._fader_obj
}
#####################
# run state machine #
#####################
# virtual event image edit finite state machine on event
info_dict = self._image_edit_fsm.execute(input_val={
'event': episode_status,
'info_dict': info_dict
})
# update display param from the finite state machine return value
major_cv_image = info_dict[VirtualEventMP4Params.MAJOR_CV_IMAGE.value]
total_eval_milli_seconds = info_dict[
VirtualEventMP4Params.TOTAL_EVAL_SECONDS.value]
reset_counter = info_dict[VirtualEventMP4Params.RESET_COUNTER.value]
speed = info_dict[VirtualEventMP4Params.SPEED.value]
self._current_lap = info_dict[VirtualEventMP4Params.CURRENT_LAP.value]
self._last_eval_time = info_dict[
VirtualEventMP4Params.LAST_EVAL_SECONDS.value]
self._sector_times = info_dict[
VirtualEventMP4Params.SECTOR_TIMES.value]
self._curr_lap_time = info_dict[
VirtualEventMP4Params.CURR_LAP_TIME.value]
# Time remaining digit
loc_x, loc_y = VirtualEventXYPixelLoc.TIME_REMAINING_DIGIT.value
time_remaining = self.race_duration - total_eval_milli_seconds
time_remaining = time_remaining if time_remaining > 0.0 else 0.0
time_remaining = datetime.timedelta(milliseconds=time_remaining)
time_remaining = utils.milliseconds_to_timeformat(time_remaining)
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=time_remaining,
loc=(loc_x, loc_y),
font=self.amazon_ember_regular_28px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Speed digit
loc_x, loc_y = VirtualEventXYPixelLoc.SPEED_DIGIT.value
speed_text = utils.get_speed_formatted_str(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_regular_28px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# Reset digit
loc_x, loc_y = VirtualEventXYPixelLoc.RESET_DIGIT.value
reset_counter_text = "{}".format(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_regular_28px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# curent lap time digit
loc_x, loc_y = VirtualEventXYPixelLoc.CURRENT_LAP_TIME_DIGIT.value
curr_lap_time = utils.milliseconds_to_timeformat(
datetime.timedelta(milliseconds=self._curr_lap_time))
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=curr_lap_time,
loc=(loc_x, loc_y),
font=self.amazon_ember_regular_28px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
# best lap time digit
loc_x, loc_y = VirtualEventXYPixelLoc.BEST_LAP_TIME_DIGIT.value
best_lap_time = mp4_video_metrics_info[
self.racecar_index].best_lap_time
# The initial default best_lap_time from s3_metrics.py is inf
# If the ros service in s3_metrics.py has not come up yet, best_lap_time is 0
best_lap_time = utils.milliseconds_to_timeformat(
datetime.timedelta(milliseconds=best_lap_time)) \
if best_lap_time != float("inf") and best_lap_time != 0 else "--:--.---"
major_cv_image = utils.write_text_on_image(
image=major_cv_image,
text=best_lap_time,
loc=(loc_x, loc_y),
font=self.amazon_ember_regular_28px,
font_color=RaceCarColorToRGB.White.value,
font_shadow_color=RaceCarColorToRGB.Black.value)
major_cv_image = cv2.cvtColor(major_cv_image, cv2.COLOR_RGB2BGRA)
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
