def publ_multiple_signal_names():
print "Publish bsig signal names......."
ecal.initialize(sys.argv, "Python Signal name publisher")
BSIG_INFO_OBJ = Radar_pb2.BsigDataRequest()
publisher_roi_obj = ecal.publisher(topic_name="BsigSignalNames")
# sig_name = "SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Longitudinal.MotVar.Velocity"
sig_name_lst = ['SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Longitudinal.MotVar.Velocity',
'MTS.Package.TimeStamp',
'SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Longitudinal.MotVar.Accel',
'SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Lateral.YawRate.YawRate',
'SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Lateral.SlipAngle.SideSlipAngle',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Kinematic.fDistX',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Kinematic.fDistY',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Kinematic.fVrelX',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Legacy.uiLifeTime',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Attributes.eDynamicProperty',
'SIM VFB ALL.AlgoSenCycle.gSI_OOI_LIST.SI_OOI_LIST[%].object_id',
'SIM VFB ALL.AlgoSenCycle.gSI_OOI_LIST.SI_OOI_LIST[%].long_displacement',
'SIM VFB ALL.AlgoSenCycle.gSI_OOI_LIST.SI_OOI_LIST[%].lat_displacement_to_curvature']
BSIG_INFO_OBJ.bsigPath = bsig_path
BSIG_INFO_OBJ.objectIdCount = 100
# BSIG_INFO_OBJ.timestamp = 1465391170943194
BSIG_INFO_OBJ.timestamp = 8308331816
for sig_name in sig_name_lst:
BSIG_INFO_OBJ.signalNames.append(sig_name)
# while ecal.ok():
# print("BSIG_INFO_OBJ.SerializeToString() :: ", BSIG_INFO_OBJ.SerializeToString())
time.sleep(1)
publisher_roi_obj.send(BSIG_INFO_OBJ.SerializeToString())
def __init__(self):
# Initialize ecal service for H5 reader
# Initialize the proto message object
ecal.initialize(sys.argv, "H5 data publisher")
self.initialize_subscr_topics()
self.initialize_publsr_topics()
self.define_subscr_callbacks()
def __init__(self, H5FileName):
self.h5_file_name = H5FileName
self.tmstamp_lst = []
# Initialize eCAL
ecal.initialize(sys.argv, "Point cloud data publisher")
global ALGO_READINESS
ALGO_READINESS = False
self.initialize_subscr_topics()
self.initialize_publsr_topics()
self.define_subscr_callbacks()
def __init__(self):
# Subscribe to the eCAL message to get the bsig path and the
# signal names
# self.bsig_path = bsig_pth
#
ecal.initialize(sys.argv, "Python signal value publisher")
# Subscribe to RadarSignalRequest topic
self.bsig_subscr_obj = ecal.subscriber(topic_name="BsigSignalNames")
self.bsig_pub_obj = ecal.publisher(topic_name="BsigSignalValues")
self.bsig_tmstamp_lst = []
self.sig_name_lst = []
self.subscribe_ecal_msgs()
def publ_signal_names():
print("Publish tracking data......")
ecal.initialize(sys.argv, "Tracker data")
publisher_roi_obj = ecal.publisher(topic_name="SR_Request")
label_req_obj = AlgoInterface_pb2.LabelRequest()
# image_path = r'D:\Work\2018\code\Tensorflow_code\Tracker\DLBasedSmartAnnotation\data\BlurCar2\img\0001.jpg'
image_path = r'D:\Work\2018\code\Tensorflow_code\Tracker\DLBasedSmartAnnotation\data\LT52\img\0001.jpeg'
# read the image
# img = cv2.imread(image_path, cv2.IMREAD_COLOR)
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
img_encoded = cv2.imencode('.png', img)[1].tostring()
# print("img_encoded ::", img)
img_str = img_encoded
# Read its respective coordinates
gt_boxes_path = r'D:\Work\2018\code\Tensorflow_code\Tracker\DLBasedSmartAnnotation\data\BlurCar2\groundtruth_rect.txt'
image_nmber_idx = 1 - 1
with open(gt_boxes_path, 'r') as f:
lines = f.readlines()
line = lines[image_nmber_idx]
img_trackid_lst = [2]
x, y, w, h = [int(x) for x in line.split()]
# 227 207 122 99
# print("ordinate:", x, y, w, h)
# (xmin, ymin, xmax, ymax) = (227, 207, 349, 306)
# (xmin, ymin, xmax, ymax) = (227, 204, 349, 306)
(xmin, ymin, xmax, ymax) = (622, 180, 660, 335)
# xmin = 227
# ymin = 207
# xmax = 349
# ymax = 306
print("ordinate::", xmin, ymin, xmax, ymax)
label_req_obj.NextImg.imageData = img_str
for evy_img_trkid in img_trackid_lst:
# Assign the trackid here
attrib_typ_obj = label_req_obj.CurrentAttr.add()
attrib_typ_obj.trackID = evy_img_trkid
attrib_typ_obj.trackername = "ADNet"
# Loop in to send across the coordinates
roi_min_obj = attrib_typ_obj.ROI.add()
roi_min_obj.X = xmin
roi_min_obj.Y = ymin
roi_max_obj = attrib_typ_obj.ROI.add()
roi_max_obj.X = xmax
roi_max_obj.Y = ymax
time.sleep(1)
publisher_roi_obj.send(label_req_obj.SerializeToString())
def subscribe_signl_names():
ecal.initialize(sys.argv, "Detector mask subscriber")
subscribe_sig_names_obj = ecal.subscriber(topic_name="Pixel_Response")
rdr_sig_response = AlgoInterface_pb2.LabelResponse()
while ecal.ok():
ret, msg, time = subscribe_sig_names_obj.receive(500)
print("---:: ", ret, msg, time, type(msg))
if msg is not None:
rdr_sig_response.ParseFromString(msg)
object_attibute_lst = rdr_sig_response.NextAttr
# print("object_attibute :: ", object_attibute_lst)
for evry_obj_attr in object_attibute_lst:
track_id = evry_obj_attr.trackID
# print("track_id ::> ", track_id)
class_obj = evry_obj_attr.type.object_class
# print("class_obj :: ", class_obj)
x1 = evry_obj_attr.ROI[0].X
y1 = evry_obj_attr.ROI[0].Y
x2 = evry_obj_attr.ROI[1].X
y2 = evry_obj_attr.ROI[1].Y
x3 = evry_obj_attr.ROI[2].X
y3 = evry_obj_attr.ROI[2].Y
x4 = evry_obj_attr.ROI[3].X
y4 = evry_obj_attr.ROI[3].Y
print("ordinates :: ", x1, x2, x3, x4, y1, y2, y3, y4)
mask = evry_obj_attr.mask
with open('img.txt', 'w+') as fhandle:
fhandle.write(str(mask))
nparr = np.fromstring(mask, np.uint8)
print("nparr :: ", nparr)
re_img_np_ary = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
img_shape = re_img_np_ary.shape
print("shape::", img_shape)
cv2.imwrite('color_img.jpg', re_img_np_ary)
cv2.imshow('Color image', re_img_np_ary)
cv2.waitKey(0)
cv2.destroyAllWindows()
def publ_signal_names():
print "Publish bsig signal names......."
ecal.initialize(sys.argv, "Python Signal name publisher")
BSIG_INFO_OBJ = Radar_pb2.BsigDataRequest()
publisher_roi_obj = ecal.publisher(topic_name="BsigSignalNames")
sig_name_lst = ['MTS.Package.TimeStamp']
BSIG_INFO_OBJ.bsigPath = bsig_path
BSIG_INFO_OBJ.objectIdCount = 40
BSIG_INFO_OBJ.timestamp = -1
for sig_name in sig_name_lst:
BSIG_INFO_OBJ.signalNames.append(sig_name)
# while ecal.ok():
# print("BSIG_INFO_OBJ.SerializeToString() :: ", BSIG_INFO_OBJ.SerializeToString())
time.sleep(1)
publisher_roi_obj.send(BSIG_INFO_OBJ.SerializeToString())
def publ_signal_names():
print "Publish bsig signal names......."
ecal.initialize(sys.argv, "Python Signal name publisher")
BSIG_INFO_OBJ = Radar_pb2.BsigDataRequest()
publisher_roi_obj = ecal.publisher(topic_name="BsigSignalNames")
bsig_path = r"D:\Work\2018\code\LT5G\OLT\Radar_labelling_bsig\bsig_readers\Continuous_2014.05.15_at_08.48.40.bsig"
# bsig_path = r"D:\Work\2018\code\LT5G\OLT\Radar_labelling_bsig\bsig_readers\20160608_1306_{4556C7A6-7F27-4A91-923E-4392C9785675}.bsig"
# sig_name = "SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Longitudinal.MotVar.Velocity"
sig_name_lst = [
'SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Longitudinal.MotVar.Velocity',
'MTS.Package.TimeStamp',
'SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Longitudinal.MotVar.Accel',
'SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Lateral.YawRate.YawRate',
'SIM VFB ALL.DataProcCycle.ObjSyncEgoDynamic.Lateral.SlipAngle.SideSlipAngle',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Kinematic.fDistX',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Kinematic.fDistY',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Kinematic.fVrelX',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Legacy.uiLifeTime',
'SIM VFB ALL.DataProcCycle.EMPublicObjData.Objects[%].Attributes.eDynamicProperty',
'SIM VFB ALL.AlgoSenCycle.gSI_OOI_LIST.SI_OOI_LIST[%].object_id',
'SIM VFB ALL.AlgoSenCycle.gSI_OOI_LIST.SI_OOI_LIST[%].long_displacement',
'SIM VFB ALL.AlgoSenCycle.gSI_OOI_LIST.SI_OOI_LIST[%].lat_displacement_to_curvature'
]
# sig_name_lst = ['MTS.Package.TimeStamp'
# ]
BSIG_INFO_OBJ.bsigPath = bsig_path
BSIG_INFO_OBJ.objectIdCount = 40
for sig_name in sig_name_lst:
BSIG_INFO_OBJ.signalNames.append(sig_name)
# while ecal.ok():
# print("BSIG_INFO_OBJ.SerializeToString() :: ", BSIG_INFO_OBJ.SerializeToString())
time.sleep(1)
publisher_roi_obj.send(BSIG_INFO_OBJ.SerializeToString())
def publ_coord_multiple_obj():
print("Publish tracking data......")
ecal.initialize(sys.argv, "Tracker data")
publisher_roi_obj = ecal.publisher(topic_name="SR_Request")
label_req_obj = algointerface_pb2.LabelRequest()
image_path = r'D:\Work\2018\code\LT5G\ticket_folders\Images\MFC4xxLongImageRight_7674957039.jpeg'
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
img_encoded = cv2.imencode('.png', img)[1].tostring()
# print("img_encoded ::", img)
img_str = img_encoded
img_trackid_lst = [2, 3]
coordinate_tpl_lst = [(622, 180, 660, 335), (227, 204, 349, 306)]
# (xmin, ymin, xmax, ymax) = (622, 180, 660, 335)
# print("ordinate::", xmin, ymin, xmax, ymax)
chnl_obj = label_req_obj.channelobject.add()
chnl_obj.channelName = 'LongImage'
tmstamp_obj = chnl_obj.timestampobject.add()
tmstamp_obj.timestamp = 54376457
tmstamp_obj.NextImg.imageData = img_str
for idx, evy_img_trkid in enumerate(img_trackid_lst):
attrib_typ_obj = tmstamp_obj.CurrentAttr.add()
attrib_typ_obj.trackID = evy_img_trkid
attrib_typ_obj.trackername = "Re3"
attrib_typ_obj.hasUserCorrected = 1
# Loop in to send across the coordinates
roi_min_obj = attrib_typ_obj.ROI.add()
roi_min_obj.X = coordinate_tpl_lst[idx][0]
roi_min_obj.Y = coordinate_tpl_lst[idx][1]
roi_max_obj = attrib_typ_obj.ROI.add()
roi_max_obj.X = coordinate_tpl_lst[idx][2]
roi_max_obj.Y = coordinate_tpl_lst[idx][3]
time.sleep(1)
publisher_roi_obj.send(label_req_obj.SerializeToString())
def __init__(self):
self.tracker = re3_tracker.Re3Tracker()
self.trkid_lst = []
# Initialize eCAL
ecal.initialize(sys.argv, "object tracking")
# Read the JSON files
with open(topics_json_path) as data_file:
self.json_data = json.load(data_file)
# Topic names
self.tracker_request = self.json_data['image_request']
self.tracker_response = self.json_data['image_response']
print("Tracker initialization done!!")
# Define the callbacks for publisher subscriber
self.initialize_subscr_topics()
self.initialize_publsr_topics()
# The callbacks will redirect to the tracker function and publish predicted ROI
self.define_subscr_callbacks()
import ecal
import AlgoInterface_pb2
import imageservice_pb2
import sys
ecal.initialize(sys.argv, "HFL data requestor")
hfl_publ_obj = ecal.publisher("Request_Image")
import time
time.sleep(2)
hfl_req_proto_obj = imageservice_pb2.ImageRequest()
def request_hfl_data():
# while ecal.ok():
# hfl_req_proto_obj.required_timestamp = 1504816617728550
hfl_req_proto_obj.image_index = 8 #1504816617788522
# hfl_req_proto_obj.hfl_file_name = "D:\\Work\\2018\\code\\LT5G\\HDF5_reader\\2017.09.07_at_20.37.57_camera-mi_1449.h5"
hfl_publ_obj.send(hfl_req_proto_obj.SerializeToString())
ecal.finalize()
request_hfl_data()
import imageservice_pb2
import json
import cv2
if getattr(sys, 'frozen', False):
os.chdir(sys._MEIPASS)
with open('topics.json') as data_file:
json_data = json.load(data_file)
# print(json_data)
request = str(json_data['request'])
response = str(json_data['response'])
h5_filename = str(json_data['h5_filename'])
ecal.initialize(sys.argv, "HFL data publisher")
hfl_subscr_obj = ecal.subscriber(request)
hfl_publs_obj = ecal.publisher(topic_name=response)
hfl_req_proto_obj = imageservice_pb2.ImageRequest()
hfl_resp_proto_obj = imageservice_pb2.HFLResponse()
class H5ReaderSequence(object):
def __init__(self, fname):
self.reset(fname)
self.lastLoadFilename = ""
def reset(self, fname):
if not os.path.isfile(fname):
print("Error - H5 file not available: %s!" % fname)
import ecal
import common_pb2
import sys
import time
ecal.initialize(sys.argv, "Point Cloud data requestor")
tmstamp_publ_obj = ecal.publisher("PointCloud_Device_Request")
pcl_publ_obj = ecal.publisher("PointCloud_Data_Request")
pcl_finalize_obj = ecal.publisher("Finalize")
time.sleep(2)
def req_timestamp():
dvc_data_proto_obj = common_pb2.DevicesDataRequest()
dvc_data_proto_obj.requiredDevicesData = True
payload = dvc_data_proto_obj.SerializeToString()
# print "payload :: ", payload
tmstamp_publ_obj.send(payload)
ecal.finalize()
def req_pcl_points():
pcl_data_proto_obj = common_pb2.DataRequest()
pcl_data_proto_obj.requiredTimestamp = 1521225338975243 #1521225351544589
pcl_data_proto_obj.requestDeviceName = "HFL"
pcl_data_proto_obj.requestChannelName = "Valodyne"
pcl_data_proto_obj.uniqueId = 1
payload = pcl_data_proto_obj.SerializeToString()
# print "payload :: ", payload
pcl_publ_obj.send(payload)
def __init__(self, tracker_request, tracker_response):
# Topic names
self.tracker_request = tracker_request
self.tracker_response = tracker_response
# Tracker initializers './conf/repo.yaml'
ADNetConf.get(conf_yaml_path)
self.tensor_input = tf.placeholder(tf.float32,
shape=(None, 112, 112, 3),
name='patch')
self.tensor_action_history = tf.placeholder(tf.float32,
shape=(None, 1, 1, 110),
name='action_history')
self.tensor_lb_action = tf.placeholder(tf.int32,
shape=(None, ),
name='lb_action')
self.tensor_lb_class = tf.placeholder(tf.int32,
shape=(None, ),
name='lb_class')
self.tensor_is_training = tf.placeholder(tf.bool, name='is_training')
self.learning_rate_placeholder = tf.placeholder(tf.float32, [],
name='learning_rate')
self.persistent_sess = tf.Session(config=tf.ConfigProto(
inter_op_parallelism_threads=1, intra_op_parallelism_threads=1))
self.adnet = ADNetwork(self.learning_rate_placeholder)
self.adnet.create_network(self.tensor_input, self.tensor_lb_action,
self.tensor_lb_class,
self.tensor_action_history,
self.tensor_is_training)
if 'ADNET_MODEL_PATH' in os.environ.keys():
self.adnet.read_original_weights(self.persistent_sess,
os.environ['ADNET_MODEL_PATH'])
else:
self.adnet.read_original_weights(self.persistent_sess)
# print("self.action_histories >>", ADNetConf.get())
self.action_histories = np.array([0] *
ADNetConf.get()['action_history'],
dtype=np.int8)
self.action_histories_old = np.array([0] *
ADNetConf.get()['action_history'],
dtype=np.int8)
self.histories = []
self.iteration = 0
self.imgwh = None
self.callback_redetection = self.redetection_by_sampling
print("Tracker initialization Done!!")
# Initialize eCAL
ecal.initialize(sys.argv, "object tracking")
# Read the JSON files
with open(topics_json_path) as data_file:
self.json_data = json.load(data_file)
# Define the callbacks for publisher subscriber
self.initialize_subscr_topics()
self.initialize_publsr_topics()
# The callbacks will redirect to the tracker function and publish predicted ROI
self.define_subscr_callbacks()
def publ_coord_single_obj():
print("Publish tracking data......")
ecal.initialize(sys.argv, "Tracker data")
publisher_roi_obj = ecal.publisher(topic_name="SR_Request")
label_req_obj = algointerface_pb2.LabelRequest()
image_path = r'D:\Work\2018\code\LT5G\ticket_folders\Images\MFC4xxLongImageRight_7674957039.jpeg'
# image_path = r'0001.jpeg'
# read the image
# img = cv2.imread(image_path, cv2.IMREAD_COLOR)
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
img_encoded = cv2.imencode('.png', img)[1].tostring()
# print("img_encoded ::", img)
img_str = img_encoded
# Read its respective coordinates
# gt_boxes_path = r'D:\Work\2018\code\Tensorflow_code\Tracker\DLBasedSmartAnnotation\data\BlurCar2\groundtruth_rect.txt'
image_nmber_idx = 1 - 1
# with open(gt_boxes_path, 'r') as f:
# lines = f.readlines()
# line = lines[image_nmber_idx]
img_trackid_lst = [2]
# x, y, w, h = [int(x) for x in line.split()]
# 227 207 122 99
# print("ordinate:", x, y, w, h)
# (xmin, ymin, xmax, ymax) = (227, 207, 349, 306)
# (xmin, ymin, xmax, ymax) = (227, 204, 349, 306)
(xmin, ymin, xmax, ymax) = (622, 180, 660, 335)
# xmin = 227
# ymin = 207
# xmax = 349
# ymax = 306
print("ordinate::", xmin, ymin, xmax, ymax)
# label_req_obj.NextImg.imageData = img_str
# for evy_img_trkid in img_trackid_lst:
# # Assign the trackid here
# attrib_typ_obj = label_req_obj.CurrentAttr.add()
# attrib_typ_obj.trackID = evy_img_trkid
# attrib_typ_obj.trackername = "ADNet"
# # Loop in to send across the coordinates
# roi_min_obj = attrib_typ_obj.ROI.add()
# roi_min_obj.X = xmin
# roi_min_obj.Y = ymin
# roi_max_obj = attrib_typ_obj.ROI.add()
# roi_max_obj.X = xmax
# roi_max_obj.Y = ymax
chnl_obj = label_req_obj.channelobject.add()
chnl_obj.channelName = 'LongImage'
tmstamp_obj = chnl_obj.timestampobject.add()
tmstamp_obj.timestamp = 54376457
tmstamp_obj.NextImg.imageData = img_str
for evy_img_trkid in img_trackid_lst:
attrib_typ_obj = tmstamp_obj.CurrentAttr.add()
attrib_typ_obj.trackID = evy_img_trkid
attrib_typ_obj.trackername = "Re3"
attrib_typ_obj.hasUserCorrected = 1
# Loop in to send across the coordinates
roi_min_obj = attrib_typ_obj.ROI.add()
roi_min_obj.X = xmin
roi_min_obj.Y = ymin
roi_max_obj = attrib_typ_obj.ROI.add()
roi_max_obj.X = xmax
roi_max_obj.Y = ymax
time.sleep(1)
publisher_roi_obj.send(label_req_obj.SerializeToString())
# if getattr(sys, 'frozen', False):
# os.chdir(sys.executable)
FROZEN_MODEL = "mask_rcnn_coco.h5"
TOPICS_JSON = 'topics.json'
with open(TOPICS_JSON) as data_file:
json_data = json.load(data_file)
# print(json_data)
request = str(json_data['image_request'])
response = str(json_data['image_response'])
vis_flag = True if str(json_data['visualization']) == "True" else False
full_eff_flag = True if str(json_data['full_efficiency']) == "True" else False
ecal.initialize(sys.argv, "Mask RCNN MS COCO detector")
ld_req_obj = imageservice_pb2.ImageResponse()
subscriber_obj = ecal.subscriber(topic_name=request)
lbl_response_obj = AlgoInterface_pb2.LabelResponse()
publisher_roi_obj = ecal.publisher(topic_name=response)
class InferenceConfig(coco.CocoConfig):
# Set batch size to 1 since we'll be running inference on
# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
GPU_COUNT = 1
IMAGES_PER_GPU = 1
if not full_eff_flag:
IMAGE_MIN_DIM = 512
IMAGE_MAX_DIM = 512
