def start(self, data):
config = get_config()
self.max_ball_distance = config['field']['length'] + 2000
config = config["vision"]
self.export_frame_skip = config["export_every_n_th_frame"]
self.ball_pos_is_ball_footpoint = config["ball_pos_is_ball_footprint"]
color_config = self.load_color_config(config["VISION_COLOR_CONFIG"])
tr_config = config["vision-thresholds"]
width, heigth = data[DATA_KEY_CAMERA_RESOLUTION]
self.use_kinematic_horizon = config["use_kinematic_horizon"]
self.vision = RobotVision(tr_config["green_y"],
tr_config["green_u"], tr_config["green_v"],
tr_config["green_dynamic"], width, heigth, config["vision-ignore_goals_out_of_field"])
self.vision.set_color_config(color_config)
self.vision.set_ball_enabled(config["vision-ball_enabled"])
self.vision.set_goals_enabled(config["vision-goals_enabled"])
self.vision.set_lines_enabled(config["vision-lines_enabled"])
self.vision.set_pylons_enabled(config["vision-pylons_enabled"])
self.vision.set_shape_vectors_enabled(
config["vision-shape_vectors_enabled"])
self.vision.set_team_marker_enabled(config["vision-team_marker_enabled"])
self.vision.set_is_picture_inverted(config["invertetPicture"])
self.vision.set_ball_pos_is_ball_footpoint(self.ball_pos_is_ball_footpoint)
self.vision.set_rgb_step_factor(config["RGB_STEP_FACTOR"])
self.vision.set_b_w_debug_image(config["SEND_BW_DEBUG"])
self.vision.set_min_intensity(config["vision-intensity-min"])
self.vision.set_max_intensity(config["vision-intensity-max"])
# setzen des erwarteten bildformates
self.vision.set_image_format(data[DATA_KEY_IMAGE_FORMAT])
self.transformer = Transformer()
data[DATA_KEY_TRANSFORMER] = self.transformer
self.transformer.set_camera_angle(config["CameraAngle"])
self.vision.set_camera_exposure_callback(data["CameraExposureCalback"])
data[DATA_KEY_CAMERA_EXPOSURE_CALLBACK] = None
data[DATA_KEY_SUPPORT_LEG] = 0
data[DATA_KEY_OBSTACLE_FOUND] = False
# obstacle infos --- veraltet
# self.extract_obstacle_infos(data, self.vision.obstacles)
data[DATA_KEY_GOAL_FOUND] = 0
data[DATA_KEY_GOAL_INFO] = None
data[DATA_KEY_OBSTACLE_FOUND] = 0
data[DATA_KEY_OBSTACLE_INFO] = []
#if do_not_override:
if os.path.exists("/home/darwin"):
files=os.listdir("/home/darwin")
while "frame-%04d.yuv" % self.idx in files:
self.idx = self.idx + 1
class VisionModule(AbstractModule):
"""Verhalten zur suche vom Ball"""
def __init__(self):
self.last_frame_version = 0
self.last_ball_info = None
self.idx=0
def start(self, data):
config = get_config()
self.max_ball_distance = config['field']['length'] + 2000
config = config["vision"]
self.export_frame_skip = config["export_every_n_th_frame"]
self.ball_pos_is_ball_footpoint = config["ball_pos_is_ball_footprint"]
color_config = self.load_color_config(config["VISION_COLOR_CONFIG"])
tr_config = config["vision-thresholds"]
width, heigth = data[DATA_KEY_CAMERA_RESOLUTION]
self.use_kinematic_horizon = config["use_kinematic_horizon"]
self.vision = RobotVision(tr_config["green_y"],
tr_config["green_u"], tr_config["green_v"],
tr_config["green_dynamic"], width, heigth, config["vision-ignore_goals_out_of_field"])
self.vision.set_color_config(color_config)
self.vision.set_ball_enabled(config["vision-ball_enabled"])
self.vision.set_goals_enabled(config["vision-goals_enabled"])
self.vision.set_lines_enabled(config["vision-lines_enabled"])
self.vision.set_pylons_enabled(config["vision-pylons_enabled"])
self.vision.set_shape_vectors_enabled(
config["vision-shape_vectors_enabled"])
self.vision.set_team_marker_enabled(config["vision-team_marker_enabled"])
self.vision.set_is_picture_inverted(config["invertetPicture"])
self.vision.set_ball_pos_is_ball_footpoint(self.ball_pos_is_ball_footpoint)
self.vision.set_rgb_step_factor(config["RGB_STEP_FACTOR"])
self.vision.set_b_w_debug_image(config["SEND_BW_DEBUG"])
self.vision.set_min_intensity(config["vision-intensity-min"])
self.vision.set_max_intensity(config["vision-intensity-max"])
# setzen des erwarteten bildformates
self.vision.set_image_format(data[DATA_KEY_IMAGE_FORMAT])
self.transformer = Transformer()
data[DATA_KEY_TRANSFORMER] = self.transformer
self.transformer.set_camera_angle(config["CameraAngle"])
self.vision.set_camera_exposure_callback(data["CameraExposureCalback"])
data[DATA_KEY_CAMERA_EXPOSURE_CALLBACK] = None
data[DATA_KEY_SUPPORT_LEG] = 0
data[DATA_KEY_OBSTACLE_FOUND] = False
# obstacle infos --- veraltet
# self.extract_obstacle_infos(data, self.vision.obstacles)
data[DATA_KEY_GOAL_FOUND] = 0
data[DATA_KEY_GOAL_INFO] = None
data[DATA_KEY_OBSTACLE_FOUND] = 0
data[DATA_KEY_OBSTACLE_INFO] = []
#if do_not_override:
if os.path.exists("/home/darwin"):
files=os.listdir("/home/darwin")
while "frame-%04d.yuv" % self.idx in files:
self.idx = self.idx + 1
def load_color_config(self, which):
# Farbraum-Konfiguration laden
conf = numpy.zeros((256, 768), dtype=numpy.uint8)
for idx, name in enumerate(("ball", "cyan", "yellow", "carpet", "white", "magenta")):
if name != "carpet": # ignoring carpet, because of autoconfig
debug_m(DebugLevel.LEVEL_4, "searching vision-color-config/%s/%s.png" % (which, name))
name = find_resource(
"vision-color-config/%s/%s.png" % (which, name))
debug_m(DebugLevel.LEVEL_4, "Loading %s" % name)
im = PngImage(name)
mask = im.get_png_as_numpy_array()
mask[mask != 0] = (1 << idx)
conf += mask
debug_m(DebugLevel.LEVEL_4, "vision-color-config/%s/%s.png geladen" % (which, name))
else:
debug_m(DebugLevel.LEVEL_4, "vision-color-config/%s/%s.png ignoriert (autocolorconfig)" % (which, name))
return conf
def save_color_config(self, name="ball"):
config = self.vision.get_color_config(name)
path = find_resource("vision-color-config/auto-color-config")
save_png_image("%s/%s.png" % (path, name), config)
def extract_obstacle_infos(self, data, obstacles_from_vision):
""" This method is extracting a lot of information from the results the vision has given """
debug_m(4, "ObstacleNumber", len(obstacles_from_vision))
if not obstacles_from_vision:
data[DATA_KEY_OBSTACLE_FOUND] = False
debug_m(4, "ObstacleFound", False)
return
else:
data[DATA_KEY_OBSTACLE_FOUND] = True
debug_m(4, "ObstacleFound", True)
obstacles = []
i = 0
for ob_data in obstacles_from_vision:
x, y, h, w, result = ob_data
u, v = self.transformer.transform_point_to_location(x, y, 0)
u = int(u)
v = int(v)
ob_info = ObstacleInfo(*[u, v, x, y, h, w, result])
obstacles.append(ob_info)
debug_m(4, "Obstacle.%d.u" % i, u)
debug_m(4, "Obstacle.%d.v" % i, v)
debug_m(4, "Obstacle.%d.colour" % i, result)
i += 1
data[DATA_KEY_OBSTACLE_INFO] = obstacles
def extract_horizon_obstacles(self, data, obstacles):
obstacles = [self.transformer.transform_point_to_location(obs[0], obs[1], 0.0) for obs in obstacles]
data[DATA_KEY_HORIZON_OBSTACLES] = obstacles
i = 0
for obstacle in obstacles:
debug_m(4, "Obstacle.%d.u" % i, obstacle[0])
debug_m(4, "Obstacle.%d.v" % i, obstacle[1])
i += 1
debug_m(4, "AnzObstacles", len(obstacles))
data[DATA_KEY_OBSTACLE_FOUND] = len(obstacles) > 0
def check_is_new_frame(self, data):
""" This method checks if there is a new frame available """
if self.last_frame_version >= data[DATA_KEY_CAMERA_FRAME_VERSION]:
return False
else:
self.last_frame_version = data[DATA_KEY_CAMERA_FRAME_VERSION]
supportLeg = self.transformer.update_pose(data[DATA_KEY_IMAGE_POSE])
data[DATA_KEY_TRANSFORMER_UPDATED] = True
data[DATA_KEY_SUPPORT_LEG] = supportLeg
return True
def extract_horizon_line_properties(self, data):
# Get the current horizon
c_horizon_x, c_horizon_y = self.vision.get_current_relativ_horizon()
c_horizon_u, c_horizon_v = self.transformer.transform_point_to_location(0, c_horizon_y, 0)
data[DATA_KEY_CURRENT_HORIZON_UV] = [c_horizon_u, c_horizon_v]
data[DATA_KEY_CURRENT_HORIZON_ORIENTATION] = c_horizon_x
""" @Debug(update_time=0.5, list_of_data_keys= {
DebugLevel.DURING_GAME_DEBUG: [
DATA_KEY_GOAL_FOUND,
DATA_KEY_OBSTACLE_FOUND,
],
DebugLevel.GAME_PREPARE_DEBUG: [
(DATA_KEY_GOAL_INFO, ComplexObjectConverter.convert_goal_info),
# TODO define a converter
(DATA_KEY_OBSTACLE_INFO, ComplexObjectConverter.convert_nothing)
]
})
"""
def update(self, data):
""" This Method checks if there is new data to process and if so let the
robot vision process the image and extract data for further behaviour """
if not self.check_is_new_frame(data):
data[DATA_KEY_IS_NEW_FRAME] = False
return
else:
data[DATA_KEY_IS_NEW_FRAME] = True
need_to_recalibrate_ball_config = data[DATA_KEY_RECALIBRATE_BALL]
# Process the Image
if self.use_kinematic_horizon:
robo_horizon_k = get_robot_horizon_p(self.transformer.robot, data[DATA_KEY_CAMERA_FOOT_PHASE])
robo_horizon_imu = get_robot_horizon_r(self.transformer.robot , data[DATA_KEY_IPC].get_robot_angle()) \
if data.get(DATA_KEY_IPC, False) else robo_horizon_k
#print robo_horizon_k - robo_horizon_imu
robo_horizon = (robo_horizon_k + robo_horizon_imu) / 2
#robo_horizon = robo_horizon_k
robo_horizon[1] = robo_horizon[1] / self.transformer.get_camera_angle()
self.vision.set_robot_horizon(robo_horizon / self.transformer.get_camera_angle())
self.vision.process(data[DATA_KEY_RAW_IMAGE], need_to_recalibrate_ball_config)
if need_to_recalibrate_ball_config is True:
self.save_color_config("ball")
# Get the Informations from the Cython Class
data[DATA_KEY_RAW_BALL] = self.vision.ball_info
goal_info = self.vision.goal_info
data[DATA_KEY_RAW_GOAL_DATA] = goal_info
obstacles = self.vision.obstacle
# Pass the Information to the extractor
#self.extract_goal_infos(data, goal_info)
self.extract_horizon_obstacles(data, obstacles)
self.extract_horizon_line_properties(data)
data[DATA_KEY_IGNORE_MASQ_HITS] = self.vision.ignore_masq_hits
#debug_m(5, "IgnoreMasq", self.vision.ignore_masq_hits)
data[DATA_KEY_LINE_POINTS] = self.vision.line_points
#debug_m(5, "NumberLinePoints", data["LinePoints"].size)
if self.export_frame_skip > 0 and data[DATA_KEY_CAMERA_FRAME_VERSION] % self.export_frame_skip is 0:
# Dateiname für den nächsten Frame
name = "frame-%04d.yuv" % self.idx
self.idx += 1
with Timer("Speichere " + name, self.debug):
yuyv = data[DATA_KEY_RAW_IMAGE]
with open("/home/darwin/" + name + ".json", "wb") as fp:
angle = data[DATA_KEY_IPC].get_robot_angle()
json.dump({"positions": data[DATA_KEY_IMAGE_POSE].positions, "robot_angle": [angle.x, angle.y, angle.z]}, fp)
with gzip.GzipFile("/home/darwin/" + name + ".gz", "wb", compresslevel=1) as fp:
fp.write(yuyv.tostring())