Exemple #1
0
markers = Markers.Markers(Markers.markers_clockwise, 4 * 107 / 100.)
Origin = int(2 * 1000 / 300. * 300 / 5)
Mult = 1000 / 300. * 50 / 5
#
#######################################

##############################
#
if 'N' not in locals():
    print("Loading trajectory data . . .")
    N = lo(trajectory_data_location)

if 'the_arenas' not in locals():
    print("Creating arenas . . .")
    arenas_tmp_lst = [
        Potential_Fields.Direct_Arena_Potential_Field(Origin, Mult, markers),
        Potential_Fields.Play_Arena_Potential_Field(Origin, Mult, markers),
        Potential_Fields.Follow_Arena_Potential_Field(Origin, Mult, markers),
        Potential_Fields.Furtive_Arena_Potential_Field(Origin, Mult, markers)
    ]
    the_arenas = {}
    for a in arenas_tmp_lst:
        the_arenas[a['type']] = a

cars = {}
for car_name in ['Mr_Black', 'Mr_Silver', 'Mr_Yellow', 'Mr_Orange', 'Mr_Blue']:
    cars[car_name] = Cars.Car(N, car_name, Origin, Mult, markers)
#
###############################

#######################################
Exemple #2
0

if __name__ == "__main__":


	DISPLAY_LEFT = True
	
	if 'N' not in locals():
		print("Loading trajectory data . . .")
		N = lo(opjD('N_pruned.pkl'))
	markers = Markers.Markers(Markers.markers_clockwise,4*107/100.)
	Origin = int(2*1000/300.*300 / 5)
	Mult = 1000/300.*50 / 5
	

	the_arena = Potential_Fields.Play_Arena_Potential_Field(Origin,Mult,markers)
	mode = the_arena['type']
	
	the_arena['Image']['img'] = z2o(the_arena['Image']['img'])
	if mode == 'Follow_Arena_Potential_Field':
		the_arena['Image']['img'] *= 0.5
		the_arena['Image']['img'] += 0.5
	
	if 'INITALIZED' not in locals():
		INITALIZED = True
		cars = {}
		for car_name in ['Mr_Black','Mr_Silver','Mr_Yellow','Mr_Orange','Mr_Blue']:
			cars[car_name] =  Cars.Car(N,car_name,Origin,Mult,markers)
		run_name = 'direct_rewrite_test_28Apr17_17h23m15s_Mr_Black'
		our_car = Cars.car_name_from_run_name(run_name)
		T0 = cars[our_car]['runs'][run_name]['trajectory']['ts'][0]
def Car(N,car_name,origin,mult,markers):
	D = {}
	D['Purpose'] = d2s(inspect.stack()[0][3],':','Car object.')
	D['car_name'] = car_name
	D['potential_field'] = Potential_Fields.Arena_Potential_Field(origin,mult,markers)
	D['runs'] = {}
	D['n_for_heading'] = 15
	for run_name in N[car_name].keys():
		D['runs'][run_name] = {}
		R = D['runs'][run_name]
		R['trajectory'] = N[car_name][run_name]['self_trajectory']
		R['list_of_other_car_trajectories'] = []
		"""
			for ot in N[car_name][run_name]['other_trajectories']:
				other_run_name = ot['run_name']
				other_car_name = car_name_from_run_name(other_run_name)
				R['list_of_other_car_trajectories'].append( [other_car_name,other_run_name] )
		"""		
		for other_run_name in N[car_name][run_name]['other_trajectories']:
			other_car_name = car_name_from_run_name(other_run_name)
			R['list_of_other_car_trajectories'].append( [other_car_name,other_run_name] )

	def _rewind():
		D['state_info'] = {}
		#D['state_info']['positions'] = {}
		D['state_info']['near_i'] = 0
		D['state_info']['near_t'] = 0
		D['state_info']['pts'] = []
		D['state_info']['heading'] = None
	D['rewind'] = _rewind


	def _check_trajectory_point(traj,side,i,t):
		assert(traj['ts'][i] <= t)
		if traj['ts'][i] == t:
			if traj[side]['t_vel'][i] > 2: # 1.788: # Above 4 mph
				return False
			if traj[side]['t_vel'][i]<0.2: #TEMP
				return False
			elif traj['camera_separation'][i] > 0.25: # almost larger than length of car
				return False
			elif traj[side]['timestamp_gap'][i] > 0.1: # missed data points
				return False
			elif length([traj[side]['x'][i],traj[side]['y'][i]]) > length(markers['xy'][0]):
				return False
			return True
		assert(False)


	def _valid_time_and_index(run_name,t):
		traj = D['runs'][run_name]['trajectory']
		if t>traj['ts'][0] and t<traj['ts'][-1]:
			near_t = -1
			for i in range(D['state_info']['near_i'],len(traj['ts'])):
				if traj['ts'][i-1]<t and traj['ts'][i]>t:
					near_t = traj['ts'][i]
					near_i = i
					break
			if near_t > 0:
				D['state_info']['near_i'] = near_i
				D['state_info']['near_t'] = near_t
				for side in ['left','right']:
					if not _check_trajectory_point(traj,side,near_i,near_t):
						return False,False
				return near_t,near_i
		return False,False


	def _report_camera_positions(run_name,t):
		near_t,near_i = _valid_time_and_index(run_name,t)
		if not near_t:
			return []
		traj = D['runs'][run_name]['trajectory']
		positions = []
		for side in ['left','right']:
			positions.append([traj[side]['x'][near_i],traj[side]['y'][near_i]])
		D['state_info']['pts'].append(array(positions).mean(axis=0))

		if len(D['state_info']['pts']) >= D['n_for_heading']:
			n = D['n_for_heading']
			D['state_info']['heading'] = normalized_vector_from_pts(D['state_info']['pts'][-n:])
			if D['state_info']['pts'][-n][0] > D['state_info']['pts'][-1][0]:
				D['state_info']['heading'] *= -1
		else:
			D['state_info']['heading'] = None
		return D['state_info']['pts'][-1] #positions
	D['report_camera_positions'] = _report_camera_positions

	"""
	def _report_camera_positions(run_name,t):
		near_t,near_i = _valid_time_and_index(run_name,t)
		if not near_t:
			return False
		traj = D['runs'][run_name]['trajectory']
		positions = []
		for side in ['left','right']:
			positions.append([traj[side]['x'][near_i],traj[side]['y'][near_i]])
			D['state_info']['pts'].append(positions[0])

			if len(D['state_info']['pts']) >= D['n_for_heading']:
				n = D['n_for_heading']
				D['state_info']['heading'] = normalized_vector_from_pts(D['state_info']['pts'][-n:])
				if D['state_info']['pts'][-n][0] > D['state_info']['pts'][-1][0]:
					D['state_info']['heading'] *= -1
			else:
				D['state_info']['heading'] = None
		return positions
	D['report_camera_positions'] = _report_camera_positions
	"""

	def _get_left_image(run_name):
		traj = D['runs'][run_name]['trajectory']
		index = traj['data']['t_to_indx'][D['state_info']['near_t']]
		img = traj['data']['left'][index]
		return img
	D['get_left_image'] = _get_left_image


	def _load_image_and_meta_data(run_name,bair_car_data_location):
		import data.utils.general
		import data.utils.multi_preprocess_pkl_files_1
		bag_folders_dst_rgb1to4_path = opj(bair_car_data_location,'rgb_1to4')
		bag_folders_dst_meta_path = opj(bair_car_data_location,'meta')
		D['runs'][run_name]['trajectory']['data'] = data.utils.general.get_new_Data_dic()
		data.utils.multi_preprocess_pkl_files_1.multi_preprocess_pkl_files(
			D['runs'][run_name]['trajectory']['data'],
				opj(bag_folders_dst_meta_path,run_name),
				opj(bag_folders_dst_rgb1to4_path,run_name),
				print_b=True)
	D['load_image_and_meta_data'] = _load_image_and_meta_data


	return D
markers = Markers.Markers(Markers.markers_clockwise,4*107/100.)
Origin = int(2*1000/300.*300 / 5)
Mult = 1000/300.*50 / 5
#
#######################################

##############################
#
if 'N' not in locals():
	print("Loading trajectory data . . .")
	N = lo(trajectory_data_location)

if 'the_arenas' not in locals():
	print("Creating arenas . . .")
	warp_image = True
	arenas_tmp_lst = [Potential_Fields.Direct_Arena_Potential_Field(Origin,Mult,markers,warp_image=warp_image),
		Potential_Fields.Play_Arena_Potential_Field(Origin,Mult,markers,warp_image),
		Potential_Fields.Follow_Arena_Potential_Field(Origin,Mult,markers,warp_image),
		Potential_Fields.Furtive_Arena_Potential_Field(Origin,Mult,markers,warp_image)]
	the_arenas = {}
	for a in arenas_tmp_lst:
		the_arenas[a['type']] = a

cars = {}
for car_name in ['Mr_Black','Mr_Silver','Mr_Yellow','Mr_Orange','Mr_Blue']:
	cars[car_name] =  Cars.Car(N,car_name,Origin,Mult,markers)
#
###############################

#######################################
#