def worker_qsrs(chunk):
(file_, path, soma_objects, config) = chunk
e = utils.load_e(path, file_)
dynamic_args = {}
try:
dynamic_args['argd'] = config['argd_args']
dynamic_args['qtcbs'] = config['qtcbs_args']
dynamic_args["qstag"] = {"params" : config['qstag_args']}
dynamic_args['filters'] = {"median_filter": {"window": config['qsr_mean_window']}} # This has been updated since ECAI paper.
except KeyError:
print "check argd, qtcbs, qstag parameters in config file"
joint_types = {'head': 'head', 'torso': 'torso', 'left_hand': 'hand', 'right_hand': 'hand', 'left_knee': 'knee', 'right_knee': 'knee',
'left_shoulder': 'shoulder', 'right_shoulder': 'shoulder', 'head-torso': 'tpcc-plane'}
object_types = joint_types.copy()
add_objects = []
for region, objects in ce.get_soma_objects().items():
for o in objects:
add_objects.append(o)
try:
generic_object = "_".join(o.split("_")[:-1])
object_types[o] = generic_object
except:
print "didnt add:", object
dynamic_args["qstag"]["object_types"] = object_types
"""1. CREATE QSRs FOR joints & Object """
qsrlib = QSRlib()
qsrs_for=[]
for ob in objects:
qsrs_for.append((str(ob), 'left_hand'))
qsrs_for.append((str(ob), 'right_hand'))
qsrs_for.append((str(ob), 'torso'))
dynamic_args['argd']["qsrs_for"] = qsrs_for
dynamic_args['qtcbs']["qsrs_for"] = qsrs_for
req = QSRlib_Request_Message(config['which_qsr'], input_data=e.map_world, dynamic_args=dynamic_args)
e.qsr_object_frame = qsrlib.request_qsrs(req_msg=req)
# print ">", e.qsr_object_frame.qstag.graphlets.histogram
# for i in e.qsr_object_frame.qstag.episodes:
# print i
# sys.exit(1)
"""2. CREATE QSRs for joints - TPCC"""
# print "TPCC: ",
# # e.qsr_joint_frame = get_joint_frame_qsrs(file, e.camera_world, joint_types, dynamic_args)
# qsrs_for = [('head', 'torso', ob) if ob not in ['head', 'torso'] and ob != 'head-torso' else () for ob in joint_types.keys()]
# dynamic_args['tpcc'] = {"qsrs_for": qsrs_for}
# dynamic_args["qstag"]["params"] = {"min_rows": 1, "max_rows": 2, "max_eps": 4}
# qsrlib = QSRlib()
# req = QSRlib_Request_Message(which_qsr="tpcc", input_data=e.camera_world, dynamic_args=dynamic_args)
# e.qsr_joints_frame = qsrlib.request_qsrs(req_msg=req)
# # pretty_print_world_qsr_trace("tpcc", e.qsr_joints_frame)
# # print e.qsr_joints_frame.qstag.graphlets.histogram
utils.save_event(e, "Learning/QSR_Worlds")
def worker_qsrs(chunk):
(file_, path, soma_objects, qsr_mean_window) = chunk
e = utils.load_e(path, file_)
dynamic_args = {}
dynamic_args["qstag"] = {}
dynamic_args['filters'] = {"median_filter": {"window": qsr_mean_window}} # This has been updated since ECAI paper.
"""ADD OBJECT TYPES into DYNAMIC ARGS """
joint_types = {'head': 'head', 'torso': 'torso', 'left_hand': 'hand', 'right_hand': 'hand', 'left_knee': 'knee', 'right_knee': 'knee',
'left_shoulder': 'shoulder', 'right_shoulder': 'shoulder', 'head-torso': 'tpcc-plane'}
all_object_types = joint_types.copy()
add_objects = []
for region, objects in ce.get_soma_objects().items():
for o in objects:
add_objects.append(o)
try:
generic_object = "_".join(o.split("_")[:-1])
all_object_types[o] = generic_object
except:
print "didnt add:", object
dynamic_args["qstag"]["object_types"] = all_object_types
"""1. CREATE QSRs FOR Key joints & Object """
print " QTC,QDC: "
qsrlib = QSRlib()
# print ">>", e.map_world.get_sorted_timestamps()
# for t in e.map_world.trace:
# print "\nt", t
# for o, state in e.map_world.trace[t].objects.items():
# print o, state.x,state.y,state.z
# sys.exit(1)
req = QSRLib_param_mapping(e.map_world, dynamic_args, soma_objects)
e.qsr_object_frame = qsrlib.request_qsrs(req_msg=req)
# print ">", e.qsr_object_frame.qstag.graphlets.histogram
# for i in e.qsr_object_frame.qstag.episodes:
# print i
# sys.exit(1)
"""2. CREATE QSRs for joints - TPCC"""
# print "TPCC: ",
# # e.qsr_joint_frame = get_joint_frame_qsrs(file, e.camera_world, joint_types, dynamic_args)
# qsrs_for = [('head', 'torso', ob) if ob not in ['head', 'torso'] and ob != 'head-torso' else () for ob in joint_types.keys()]
# dynamic_args['tpcc'] = {"qsrs_for": qsrs_for}
# dynamic_args["qstag"]["params"] = {"min_rows": 1, "max_rows": 2, "max_eps": 4}
# qsrlib = QSRlib()
# req = QSRlib_Request_Message(which_qsr="tpcc", input_data=e.camera_world, dynamic_args=dynamic_args)
# e.qsr_joints_frame = qsrlib.request_qsrs(req_msg=req)
# # pretty_print_world_qsr_trace("tpcc", e.qsr_joints_frame)
# # print e.qsr_joints_frame.qstag.graphlets.histogram
utils.save_event(e, "Learning/QSR_Worlds")
def create_temp_histograms(qsr_path_, global_codebook, all_graphlets, batch):
"""sequentially create a temporary histogram whilst
generating the codebook from observations"""
wordids = []
wordcts = []
uuids = []
for e_cnt, event_file in enumerate(batch):
(date, time, uuid) = event_file.split("_")
qsr_path = os.path.join(qsr_path_, date)
#for e_cnt, event_file in sorted(enumerate(os.listdir(qsr_path))):
#if event_file.replace(".p","") not in batch: continue
e = utils.load_e(qsr_path, event_file)
if len(e.qsr_object_frame.qstag.graphlets.histogram) == 0:
print e_cnt, event_file, "len:0"
continue
e.temp_histogram = np.array([0] * (global_codebook.shape[0]))
if e.label != "NA": e.uuid += "_" + e.label
print " ", e_cnt, e.uuid, "len:", len(
e.qsr_object_frame.qstag.graphlets.histogram
) #, len(e.qsr_joints_frame.qstag.graphlets.histogram)
# feature_spaces = [e.qsr_object_frame.qstag.graphlets, e.qsr_joints_frame.qstag.graphlets]
feature_spaces = [e.qsr_object_frame.qstag.graphlets
] #, e.qsr_joints_frame.qstag.graphlets]
for cnt, f in enumerate(feature_spaces):
for freq, hash in zip(f.histogram, f.code_book):
try:
ind = np.where(global_codebook == hash)[0][0]
e.temp_histogram[ind] += freq
# If the hash doesn't exist in the global codebook yet - add it
except IndexError:
global_codebook = np.append(global_codebook, hash)
e.temp_histogram = np.append(e.temp_histogram, freq)
all_graphlets = np.append(all_graphlets, f.graphlets[hash])
# print "\n>", hash, f.graphlets[hash]
# print "> ",len(e.temp_histogram)
# print global_codebook, e.temp_histogram, all_graphlets
# print ">", np.nonzero(e.temp_histogram)[0]
uuids.append(e.uuid)
ids = np.nonzero(e.temp_histogram)[0]
wordids.append(ids)
wordcts.append(e.temp_histogram[ids])
# print "<", e.temp_histogram[ids]
utils.save_event(e, "Learning/Histograms")
ret = (uuids, wordids, wordcts)
return global_codebook, all_graphlets, ret
def on_message(message):
if message.embeds and (message.author.name in os.environ['DISCORD_AUTHOR']):
for e in message.embeds:
t = e['title'] + e['description']
evt = {'pds':e}
for p, c in pats:
m = p.search(t)
if m:
evt.update(m.groupdict())
elif c:
break
else:
utils.save_event(evt)
def create_temp_histograms(qsr_path, accu_path):
"""sequentially create a temporary histogram whilst
generating the codebook from observations"""
global_codebook = np.array([])
all_graphlets = np.array([])
for d_cnt, dir_ in sorted(enumerate(os.listdir(qsr_path))):
directory = os.path.join(qsr_path, dir_)
print "dir: ", directory, d_cnt, dir_
for e_cnt, event_file in sorted(enumerate(os.listdir(directory))):
e = utils.load_e(directory, event_file)
if len(e.qsr_object_frame.qstag.graphlets.histogram) == 0:
print "removed:", e_cnt, event_file
continue
e.temp_histogram = np.array([0] * (global_codebook.shape[0]))
print " ", d_cnt, e_cnt, e.uuid, "len:", len(e.qsr_object_frame.qstag.graphlets.histogram) #, len(e.qsr_joints_frame.qstag.graphlets.histogram)
# feature_spaces = [e.qsr_object_frame.qstag.graphlets, e.qsr_joints_frame.qstag.graphlets]
feature_spaces = [e.qsr_object_frame.qstag.graphlets]#, e.qsr_joints_frame.qstag.graphlets]
for cnt, f in enumerate(feature_spaces):
for freq, hash in zip(f.histogram, f.code_book):
try:
ind = np.where(global_codebook == hash)[0][0]
e.temp_histogram[ind] += freq
# If the hash doesn't exist in the global codebook yet - add it
except IndexError:
global_codebook = np.append(global_codebook, hash)
e.temp_histogram = np.append(e.temp_histogram, freq)
all_graphlets = np.append(all_graphlets, f.graphlets[hash])
# print "\n>", hash, f.graphlets[hash]
# print global_codebook, e.temp_histogram, all_graphlets
utils.save_event(e, "Learning/Histograms")
print "Code book shape:", global_codebook.shape
f = open(os.path.join(accu_path, "code_book_all.p"), "w")
pickle.dump(global_codebook, f)
f.close()
f = open(os.path.join(accu_path, "graphlets_all.p"), "w")
pickle.dump(all_graphlets, f)
f.close()
return global_codebook.shape[0]
def get_event(recording, path, soma_objects, reduce_frame_rate=2, mean_window=5):
"""create event class from a recording"""
"""directories containing the data"""
d1 = os.path.join(path, recording)
d_sk = os.path.join(d1, "skeleton/")
d_robot = os.path.join(d1, "robot/")
"""information stored in the filename"""
try:
uuid = recording.split("_")[-1]
date = recording.split("_")[0]
time = recording.split("_")[1]
except:
print "recording not found"
return
print uuid, date, time
print "uid: %s. date: %s. time: %s." % (uuid, date, time)
"""initialise event"""
e = event(uuid, d1)
"""get the skeleton data from each timepoint file"""
sk_files = [f for f in os.listdir(d_sk) if os.path.isfile(os.path.join(d_sk, f))]
"""reduce the number of frames by a rate. Re-number from 1."""
frame = 1
for file in sorted(sk_files):
original_frame = int(file.split(".")[0].split("_")[1])
if original_frame % reduce_frame_rate != 0:
continue
e.skeleton_data[frame] = {}
e.sorted_timestamps.append(frame)
f1 = open(d_sk + file, "r")
for count, line in enumerate(f1):
if count == 0:
t = line.split(":")[1].split("\n")[0]
e.sorted_ros_timestamps.append(np.float64(t))
# read the joint name
elif (count - 1) % 11 == 0:
j = line.split("\n")[0]
e.skeleton_data[frame][j] = []
# read the x value
elif (count - 1) % 11 == 2:
a = float(line.split("\n")[0].split(":")[1])
e.skeleton_data[frame][j].append(a)
# read the y value
elif (count - 1) % 11 == 3:
a = float(line.split("\n")[0].split(":")[1])
e.skeleton_data[frame][j].append(a)
# read the z value
elif (count - 1) % 11 == 4:
a = float(line.split("\n")[0].split(":")[1])
e.skeleton_data[frame][j].append(a)
frame += 1
# for frame, data in e.skeleton_data.items():
# print frame, data['head']
# sys.exit(1)
""" apply a skeleton data filter and create a QSRLib.World_Trace object"""
# e.apply_mean_filter(window_length=mean_window)
e.apply_median_filter(mean_window)
""" read robot odom data"""
r_files = [f for f in os.listdir(d_robot) if os.path.isfile(os.path.join(d_robot, f))]
for file in sorted(r_files):
frame = int(file.split(".")[0].split("_")[1])
e.robot_data[frame] = [[], []]
f1 = open(d_robot + file, "r")
for count, line in enumerate(f1):
if count == 1: # read the x value
a = float(line.split("\n")[0].split(":")[1])
e.robot_data[frame][0].append(a)
elif count == 2: # read the y value
a = float(line.split("\n")[0].split(":")[1])
e.robot_data[frame][0].append(a)
elif count == 3: # read the z value
a = float(line.split("\n")[0].split(":")[1])
e.robot_data[frame][0].append(a)
elif count == 5: # read roll pitch yaw
ax = float(line.split("\n")[0].split(":")[1])
elif count == 6:
ay = float(line.split("\n")[0].split(":")[1])
elif count == 7:
az = float(line.split("\n")[0].split(":")[1])
elif count == 8:
aw = float(line.split("\n")[0].split(":")[1])
elif count == 10:
pan = float(line.split("\n")[0].split(":")[1])
elif count == 11:
tilt = float(line.split("\n")[0].split(":")[1])
# ax,ay,az,aw
roll, pitch, yaw = euler_from_quaternion([ax, ay, az, aw]) # odom
# print ">", roll, pitch, yaw
yaw += (
pan
) # *math.pi / 180. # this adds the pan of the ptu state when recording took place.
pitch += (
tilt
) # *math.pi / 180. # this adds the tilt of the ptu state when recording took place.
e.robot_data[frame][1] = [roll, pitch, yaw]
# add the map frame data for the skeleton detection
for frame in e.sorted_timestamps:
"""Note frame does not start from 0. It is the actual file frame number"""
e.map_frame_data[frame] = {}
xr, yr, zr = e.robot_data[frame][0]
yawr = e.robot_data[frame][1][2]
pr = e.robot_data[frame][1][1]
# because the Nite tracker has z as depth, height as y and left/right as x
# we translate this to the map frame with x, y and z as height.
for joint, (y, z, x, x2d, y2d) in e.filtered_skeleton_data[frame].items():
# transformation from camera to map
rot_y = np.matrix([[np.cos(pr), 0, np.sin(pr)], [0, 1, 0], [-np.sin(pr), 0, np.cos(pr)]])
rot_z = np.matrix([[np.cos(yawr), -np.sin(yawr), 0], [np.sin(yawr), np.cos(yawr), 0], [0, 0, 1]])
rot = rot_z * rot_y
pos_r = np.matrix([[xr], [yr], [zr + 1.66]]) # robot's position in map frame
pos_p = np.matrix([[x], [-y], [-z]]) # person's position in camera frame
map_pos = rot * pos_p + pos_r # person's position in map frame
x_mf = map_pos[0, 0]
y_mf = map_pos[1, 0]
z_mf = map_pos[2, 0]
j = (x_mf, y_mf, z_mf)
e.map_frame_data[frame][joint] = j
# for i in e.sorted_timestamps:
# print i, e.map_frame_data[i]['head'], e.map_frame_data[i]['left_hand']#, e.map_frame_data[i]['right_hand'] #e.skeleton_data[i]['right_hand'], e.map_frame_data[i]['right_hand'] , yaw, pitch
# sys.exit(1)
e.get_world_frame_trace(soma_objects)
utils.save_event(e, "Learning/Events")
# -*- coding: utf-8 -*-
import code, datetime, time, rocket, utils, os
from random import shuffle
data = []
with open('/scan/.scan') as f:
for r in f.read().split('\n'):
try:
u, *c = r.split(' ')
if u:
c = [float(v) for v in c]
data.append((u, c))
except:
pass
while True:
shuffle(data)
for u, c in data:
R = rocket.Rocket(u)
i = j = 0
for g in R.gyms(c)['gyms']:
if g.get('raid_end', False):
if utils.save_event(g):
i += 1
else:
j += 1
for p in R.pokemons(c)['pokemons']:
if utils.save_event(p):
i += 1
else:
j += 1
time.sleep(10)
def get_event(recording, path, soma_objects, config):
"""create event class from a recording"""
"""directories containing the data"""
d1 = os.path.join(path, recording)
if config["use_cpm"]:
d_sk = os.path.join(d1, 'cpm_skeleton/')
else:
d_sk = os.path.join(d1, 'skeleton/')
d_robot = os.path.join(d1, 'robot/')
# If recording has been restricted with ROIs:
region = ""
if os.path.isfile(os.path.join(d1, 'meta.txt')):
f1 = open(os.path.join(d1, 'meta.txt'), 'r')
for count, line in enumerate(f1):
if count == 0:
region = line.split('\n')[0].split(':')[1].replace(" ", "")
objects = {}
try:
objects = soma_objects[region]
except KeyError:
for r, objs in soma_objects.items():
for ob, position in objs.items():
objects[ob] = position
try:
"""information stored in the filename - differently for ecai dataset"""
date = recording.split('_')[0]
time = recording.split('_')[1]
uuid = recording.split('_')[2]
except IndexError:
date = ""
time = recording.split('_')[0]
uuid = recording.split('_')[1]
print "date: %s. time: %s. uid: %s. " % (date, time, uuid),
# print "objs: %s" % use_objects
labels = get_labels(d1, d_sk)
for iter, (label, st, end) in labels.items():
# print iter
"""initialise event"""
e = event(uuid, d1, st, end, label)
e.objects = objects
"""get the skeleton data from each timepoint file"""
sk_files = [
f for f in os.listdir(d_sk)
if os.path.isfile(os.path.join(d_sk, f))
]
"""reduce the number of frames by a rate. Re-number from 1."""
# frame = 1
for file in sorted(sk_files):
# original_frame = int(file.split('.')[0].split('_')[1])
# if original_frame % config['reduce_frame_rate'] != 0: continue
# new file format: "cpm_skl_00540.txt"
if config["use_cpm"]:
frame = int(file.split('.')[0].split('_')[-1])
else:
frame = int(file.split('.')[0].split('_')[1])
if frame < int(st) or frame > int(end): continue
e.sorted_timestamps.append(frame)
f1 = open(d_sk + file, 'r')
# e.skeleton_data[frame] = {}
e.skeleton_data[frame] = get_openni_values(f1)
# frame+=1
# if config['rec_inc_confidence_vals']:
# for count,line in enumerate(f1):
# if count == 0:
# t = line.split(':')[1].split('\n')[0]
# e.sorted_ros_timestamps.append(np.float64(t))
#
# # read the joint name
# elif (count-1)%11 == 0:
# j = line.split('\n')[0]
# e.skeleton_data[frame][j] = []
# # read the x value
# elif (count-1)%11 == 2:
# a = float(line.split('\n')[0].split(':')[1])
# e.skeleton_data[frame][j].append(a)
# # read the y value
# elif (count-1)%11 == 3:
# a = float(line.split('\n')[0].split(':')[1])
# e.skeleton_data[frame][j].append(a)
# # read the z value
# elif (count-1)%11 == 4:
# a = float(line.split('\n')[0].split(':')[1])
# e.skeleton_data[frame][j].append(a)
# else:
# for count,line in enumerate(f1):
# if count == 0:
# t = line.split(':')[1].split('\n')[0]
# e.sorted_ros_timestamps.append(np.float64(t))
#
# # read the joint name
# elif (count-1)%10 == 0:
# j = line.split('\n')[0]
# e.skeleton_data[frame][j] = []
# # read the x value
# elif (count-1)%10 == 2:
# a = float(line.split('\n')[0].split(':')[1])
# e.skeleton_data[frame][j].append(a)
# # read the y value
# elif (count-1)%10 == 3:
# a = float(line.split('\n')[0].split(':')[1])
# e.skeleton_data[frame][j].append(a)
# # read the z value
# elif (count-1)%10 == 4:
# a = float(line.split('\n')[0].split(':')[1])
# e.skeleton_data[frame][j].append(a)
# for frame, data in e.skeleton_data.items():
# print frame, data['head']
# sys.exit(1)
""" apply a skeleton data filter and create a QSRLib.World_Trace object"""
# e.apply_mean_filter(window_length=config['joints_mean_window'])
if not e.apply_median_filter(config['joints_mean_window']): return
""" read robot odom data"""
r_files = [
f for f in os.listdir(d_robot)
if os.path.isfile(os.path.join(d_robot, f))
]
# frame = 1
for file in sorted(r_files):
frame = int(file.split('.')[0].split('_')[1])
if frame not in e.skeleton_data.keys(): continue
# if original_frame % config['reduce_frame_rate'] != 0: continue
e.robot_data[frame] = [[], []]
f1 = open(d_robot + file, 'r')
for count, line in enumerate(f1):
# print count, line, config['ptu_vals_stored']
if count == 1: # read the x value
a = float(line.split('\n')[0].split(':')[1])
e.robot_data[frame][0].append(a)
elif count == 2: # read the y value
a = float(line.split('\n')[0].split(':')[1])
e.robot_data[frame][0].append(a)
elif count == 3: # read the z value
a = float(line.split('\n')[0].split(':')[1])
e.robot_data[frame][0].append(a)
elif count == 5: # read roll pitch yaw
ax = float(line.split('\n')[0].split(':')[1])
elif count == 6:
ay = float(line.split('\n')[0].split(':')[1])
elif count == 7:
az = float(line.split('\n')[0].split(':')[1])
elif count == 8:
aw = float(line.split('\n')[0].split(':')[1])
if not config['ptu_vals_stored']:
roll, pitch, yaw = euler_from_quaternion(
[ax, ay, az, aw]) #odom
pitch = 10 * math.pi / 180. #we pointed the pan tilt 10 degrees
e.robot_data[frame][1] = [roll, pitch, yaw]
elif count > 8 and config['ptu_vals_stored']:
if count == 10:
pan = float(line.split('\n')[0].split(':')[1])
elif count == 11:
tilt = float(line.split('\n')[0].split(':')[1])
roll, pitch, yaw = euler_from_quaternion(
[ax, ay, az, aw]) #odom
yaw += pan #*math.pi / 180. # this adds the pan of the ptu state when recording took place.
pitch += tilt #*math.pi / 180. # this adds the tilt of the ptu state when recording took place.
e.robot_data[frame][1] = [roll, pitch, yaw]
# frame+=1
# add the map frame data for the skeleton detection
for frame in e.sorted_timestamps:
"""Note frame does not start from 0. It is the actual file frame number"""
e.map_frame_data[frame] = {}
xr, yr, zr = e.robot_data[frame][0]
yawr = e.robot_data[frame][1][2]
pr = e.robot_data[frame][1][1]
# because the Nite tracker has z as depth, height as y and left/right as x
# we translate this to the map frame with x, y and z as height.
for joint, (y, z, x, x2d,
y2d) in e.filtered_skeleton_data[frame].items():
# transformation from camera to map
rot_y = np.matrix([[np.cos(pr), 0, np.sin(pr)], [0, 1, 0],
[-np.sin(pr), 0, np.cos(pr)]])
rot_z = np.matrix([[np.cos(yawr), -np.sin(yawr), 0],
[np.sin(yawr),
np.cos(yawr), 0], [0, 0, 1]])
rot = rot_z * rot_y
pos_r = np.matrix([[xr], [yr], [zr + 1.66]
]) # robot's position in map frame
pos_p = np.matrix([[x], [-y],
[-z]]) # person's position in camera frame
map_pos = rot * pos_p + pos_r # person's position in map frame
x_mf = map_pos[0, 0]
y_mf = map_pos[1, 0]
z_mf = map_pos[2, 0]
j = (x_mf, y_mf, z_mf)
e.map_frame_data[frame][joint] = j
# print ">",e.sorted_timestamps
# for i in e.sorted_timestamps:
# print i, e.map_frame_data[i]['head'], e.map_frame_data[i]['left_hand']#, e.map_frame_data[i]['right_hand'] #e.skeleton_data[i]['right_hand'], e.map_frame_data[i]['right_hand'] , yaw, pitch
# sys.exit(1)
if len(e.sorted_timestamps) >= 5:
e.get_world_frame_trace(e.objects)
utils.save_event(e, "Learning/Events")
return True
else:
print " >dont save me - recording too short."
return False
import utils, re, time, code
pats = [
(re.compile('ate (?P<hour>\d\d):(?P<minute>\d\d):(?P<second>\d\d)'),True),
(re.compile('http://maps.google.com/maps.q=(?P<latitude>[-.0-9]+),(?P<longitude>[-.0-9]+)'),True),
(re.compile('^Um (?P<pokemon_name>.+) selvagem'),False),
(re.compile('^Detectada raid de (?P<raid_pokemon_name>[^!]+)!'),False)]
while True:
for e in utils.read_raw_events():
print(e)
if 'text' in e['evt']:
t = e['evt']['text']
evt = {'btg':e['evt']}
for p, c in pats:
m = p.search(t)
if m:
evt.update(m.groupdict())
elif c:
break
else:
if ('pokemon_name' in evt) or ('raid_pokemon_name' in evt):
utils.save_event(evt)
else:
print('failed msg'+t)
utils.close_raw_event(e)
utils.clean()
time.sleep(2)
def worker_qsrs(chunk):
(file_, path, config) = chunk
#print "\n", path, file_
e = utils.load_e(path, file_)
dynamic_args = {}
try:
dynamic_args['argd'] = config['argd_args']
dynamic_args['qtcbs'] = config['qtcbs_args']
dynamic_args["qstag"] = {"params": config['qstag_args']}
dynamic_args['filters'] = {
"median_filter": {
"window": config['qsr_mean_window']
}
} # This has been updated since ECAI paper.
except KeyError:
print "check argd, qtcbs, qstag parameters in config file"
joint_types = {
'head': 'head',
'torso': 'torso',
'left_hand': 'hand',
'right_hand': 'hand',
'left_knee': 'knee',
'right_knee': 'knee',
'left_shoulder': 'shoulder',
'right_shoulder': 'shoulder',
'head-torso': 'tpcc-plane'
}
object_types = joint_types.copy()
for ob, pos in e.objects.items():
try:
generic_object = "_".join(ob.split("_")[:-1])
object_types[ob] = generic_object
# print "object generic", generic_object
except:
print "didnt add:", object
dynamic_args["qstag"]["object_types"] = object_types
# for i,j in object_types.items():
# print ">", i,j
"""1. CREATE QSRs FOR joints & Object """
qsrlib = QSRlib()
qsrs_for = []
for ob, pos in e.objects.items():
qsrs_for.append((str(ob), 'left_hand'))
qsrs_for.append((str(ob), 'right_hand'))
qsrs_for.append((str(ob), 'torso'))
dynamic_args['argd']["qsrs_for"] = qsrs_for
dynamic_args['qtcbs']["qsrs_for"] = qsrs_for
# for (i,j) in qsrs_for:
# print ">", (i,j)
req = QSRlib_Request_Message(config['which_qsr'],
input_data=e.map_world,
dynamic_args=dynamic_args)
e.qsr_object_frame = qsrlib.request_qsrs(req_msg=req)
# print ">", e.qsr_object_frame.qstag.graphlets.histogram
# for i in e.qsr_object_frame.qstag.episodes:
# print i
# sys.exit(1)
"""2. CREATE QSRs for joints - TPCC"""
# print "TPCC: ",
# # e.qsr_joint_frame = get_joint_frame_qsrs(file, e.camera_world, joint_types, dynamic_args)
# qsrs_for = [('head', 'torso', ob) if ob not in ['head', 'torso'] and ob != 'head-torso' else () for ob in joint_types.keys()]
# dynamic_args['tpcc'] = {"qsrs_for": qsrs_for}
# dynamic_args["qstag"]["params"] = {"min_rows": 1, "max_rows": 2, "max_eps": 4}
# qsrlib = QSRlib()
# req = QSRlib_Request_Message(which_qsr="tpcc", input_data=e.camera_world, dynamic_args=dynamic_args)
# e.qsr_joints_frame = qsrlib.request_qsrs(req_msg=req)
# # pretty_print_world_qsr_trace("tpcc", e.qsr_joints_frame)
# # print e.qsr_joints_frame.qstag.graphlets.histogram
utils.save_event(e, "Learning/QSR_Worlds")