def run_simulation(self):
num_client = int(self.num_client.text())
num_severs = int(self.num_server.text())
iterations = int(self.num_iterations.text())
ml_algo_index = self.selectMLAlgorithm.currentIndex()
method = 'log_reg' # default
if ml_algo_index == 0:
method = 'log_reg'
elif ml_algo_index == 1:
method = 'perceptron'
else:
method = 'mlp'
random.seed(0)
np.random.seed(0)
initializer = Initializer(
num_clients=num_client,
iterations=iterations,
num_servers=num_severs,
method=method,
simulation_output_view=self.simulation_output)
# can use any amount of iterations less than config.ITERATIONS but the
# initializer has only given each client config.ITERATIONS datasets for training.
a = datetime.datetime.now()
initializer.run_simulation(iterations,
self.simulation_output,
server_agent_name='server_agent0')
b = datetime.datetime.now()
def instance(cls):
if cls._instance is None:
init = Initializer()
init.load_config()
cls._instance = Simulator(init.get("signal-time"),
init.get("script-name"),
init.get("error-detection"))
return cls._instance
def reset(self):
self.initializer = Initializer("braak1", self.numNodes, self.params)
self.concentration = self.initializer.get()
self.concentrationHistory = np.copy(self.concentration)
self.producer = Producer(self.types)
self.diffusor = Diffusor("euclidean",
self.params,
EuclideanAdjacency=self.euclideanAdjacency)
def __init__(self, camera, tracker, grountruth=None):
self.cam = camera
self.map = Map()
Frame.set_tracker(tracker) # set the static field of the class
# camera info
self.W, self.H = camera.width, camera.height
self.K = camera.K
self.Kinv = camera.Kinv
self.D = camera.D # distortion coefficients [k1, k2, p1, p2, k3]
self.stage = SLAMStage.NO_IMAGES_YET
self.intializer = Initializer()
self.cur_R = None # current rotation w.r.t. world frame
self.cur_t = None # current translation w.r.t. world frame
self.num_matched_kps = None # current number of matched keypoints
self.num_inliers = None # current number of matched inliers
self.num_vo_map_points = None # current number of valid VO map points (matched and found valid in current pose optimization)
self.trueX, self.trueY, self.trueZ = None, None, None
self.grountruth = grountruth
self.mask_match = None
self.velocity = None
self.init_history = True
self.poses = [] # history of poses
self.t0_est = None # history of estimated translations
self.t0_gt = None # history of ground truth translations (if available)
self.traj3d_est = [
] # history of estimated translations centered w.r.t. first one
self.traj3d_gt = [
] # history of estimated ground truth translations centered w.r.t. first one
self.timer_verbose = kTimerVerbose # set this to True if you want to print timings
self.timer_main_track = TimerFps('Track',
is_verbose=self.timer_verbose)
self.timer_pose_opt = TimerFps('Pose optimization',
is_verbose=self.timer_verbose)
self.timer_match = TimerFps('Match', is_verbose=self.timer_verbose)
self.timer_pose_est = TimerFps('Pose estimation',
is_verbose=self.timer_verbose)
self.timer_frame = TimerFps('Frame', is_verbose=self.timer_verbose)
self.timer_seach_map = TimerFps('Search Map',
is_verbose=self.timer_verbose)
self.timer_triangulation = TimerFps('Triangulation',
is_verbose=self.timer_verbose)
self.time_local_opt = TimerFps('Local optimization',
is_verbose=self.timer_verbose)
def initialize_repo(self):
# 初始化应用配置
support_languages = []
for k,v in self.support_languages.items():
if v.get():
for language in appConfig.languages[k]:
support_languages.append(language)
# 初始化项目仓库
init = Initializer()
init.initialize(self.android_resources_dir.get(), self.ios_resources_dir.get(), support_languages)
# 初始化完毕
result = askokcancel(title = '初始化完成', message='已完成项目仓库初始化，请重启程序')
if result:
self.root.quit()
def __init__(self, system):
if kDebugDrawMatches:
Frame.is_store_imgs = True
self.system = system
self.camera = system.camera
self.map = system.map
self.local_mapping = system.local_mapping
self.intializer = Initializer()
self.motion_model = MotionModel(
) # motion model for current frame pose prediction without damping
#self.motion_model = MotionModelDamping() # motion model for current frame pose prediction with damping
self.dyn_config = SLAMDynamicConfig()
self.descriptor_distance_sigma = Parameters.kMaxDescriptorDistance
self.reproj_err_frame_map_sigma = Parameters.kMaxReprojectionDistanceMap
self.max_frames_between_kfs = int(system.camera.fps)
self.min_frames_between_kfs = 0
self.state = SlamState.NO_IMAGES_YET
self.num_matched_kps = None # current number of matched keypoints
self.num_inliers = None # current number of matched points
self.num_matched_map_points = None # current number of matched map points (matched and found valid in current pose optimization)
self.num_kf_ref_tracked_points = None # number of tracked points in k_ref (considering a minimum number of observations)
self.mask_match = None
self.pose_is_ok = False
self.predicted_pose = None
self.velocity = None
self.f_cur = None
self.idxs_cur = None
self.f_ref = None
self.idxs_ref = None
self.kf_ref = None # reference keyframe (in general, different from last keyframe depending on the used approach)
self.kf_last = None # last keyframe
self.kid_last_BA = -1 # last keyframe id when performed BA
self.local_keyframes = [] # local keyframes
self.local_points = [] # local points
self.tracking_history = TrackingHistory()
self.timer_verbose = kTimerVerbose # set this to True if you want to print timings
self.timer_main_track = TimerFps('Track',
is_verbose=self.timer_verbose)
self.timer_pose_opt = TimerFps('Pose optimization',
is_verbose=self.timer_verbose)
self.timer_seach_frame_proj = TimerFps('Search frame by proj',
is_verbose=self.timer_verbose)
self.timer_match = TimerFps('Match', is_verbose=self.timer_verbose)
self.timer_pose_est = TimerFps('Ess mat pose estimation',
is_verbose=self.timer_verbose)
self.timer_frame = TimerFps('Frame', is_verbose=self.timer_verbose)
self.timer_seach_map = TimerFps('Search map',
is_verbose=self.timer_verbose)
self.init_history = True
self.poses = [] # history of poses
self.t0_est = None # history of estimated translations
self.t0_gt = None # history of ground truth translations (if available)
self.traj3d_est = [
] # history of estimated translations centered w.r.t. first one
self.traj3d_gt = [
] # history of estimated ground truth translations centered w.r.t. first one
self.cur_R = None # current rotation w.r.t. world frame
self.cur_t = None # current translation w.r.t. world frame
self.trueX, self.trueY, self.trueZ = None, None, None
self.groundtruth = system.groundtruth # not actually used here; could be used for evaluating performances
if kLogKFinfoToFile:
self.kf_info_logger = Logging.setup_file_logger(
'kf_info_logger',
'kf_info.log',
formatter=Logging.simple_log_formatter)
def __config_logging():
'''配置日志'''
LOG_FORMAT = "%(asctime)s - %(levelname)s - %(message)s"
DATE_FORMAT = "%m/%d/%Y %H:%M:%S %p"
logging.basicConfig(filename='app.log',
filemode='a',
level=logging.DEBUG,
format=LOG_FORMAT,
datefmt=DATE_FORMAT)
logging.FileHandler(filename='app.log', encoding='utf-8')
if __name__ == "__main__":
__config_logging()
# 加载多语言资源
factory = LanguageFactory()
factory.load_languages()
# Tk
root = Tk()
root.title(factory.get_entry("app_title"))
# 进行项目初始化
initializer = Initializer()
if not initializer.is_repo_initialized():
# 进入项目初始化页面
RepoInitDialog(root).pack()
else:
# 进入正常编辑页面
MainDialog(root).pack()
root.mainloop()
def init():
"""交互式初始化Graia项目"""
initializer = Initializer()
initializer.run()
import random
import warnings
import datetime
import config
import numpy as np
from initializer import Initializer
if __name__ == '__main__':
random.seed(0)
np.random.seed(0)
initializer = Initializer(num_clients=config.NUM_CLIENTS,
iterations=config.ITERATIONS,
num_servers=config.NUM_SERVERS)
# can use any amount of iterations less than config.ITERATIONS but the
# initializer has only given each client config.ITERATIONS datasets for training.
a = datetime.datetime.now()
initializer.run_simulation(config.ITERATIONS,
server_agent_name='server_agent0')
b = datetime.datetime.now()
