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 worker_padd(chunk):
(event_file, histogram_directory, lenth_codebook) = chunk
# print " ", event_file
e = utils.load_e(histogram_directory, event_file)
e.global_histogram = np.array([[0]*lenth_codebook])
ind = len(e.temp_histogram)
#Overlay the global histogram with the temp histogram
e.global_histogram[0][:ind] = e.temp_histogram
# utils.save_event(e)
return (e.uuid, e.global_histogram[0])
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 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 worker_padd(chunk):
(event_file, histogram_directory) = chunk
# print " ", event_file
e = utils.load_e(histogram_directory, event_file)
# e.global_histogram = np.array([[0]*lenth_codebook])
# ind = len(e.temp_histogram)
#Overlay the global histogram with the temp histogram
# e.global_histogram[0][:ind] = e.temp_histogram
# utils.save_event(e)
wordids = np.nonzero(e.temp_histogram)[0]
# print "wordids:", type(wordids), wordids
feature_counts = e.temp_histogram[wordids]
# print "counts:", type(feature_counts), feature_counts
if e.label != "NA":
e.uuid += "_" + e.label
rets = (wordids, feature_counts)
# return (e.uuid, e.global_histogram[0])
return (e.uuid, rets)
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")
