def __init__(self, movidius_id=0, longrange=False):
dir_path = os.path.dirname(os.path.realpath(__file__)) + "/../model"
# Define constants
self.NETWORK_INPUT_SIZE = 300
self.NETWORK_OUTPUT_SIZE = 707
# Get Movidius Devices
devices = mvnc.enumerate_devices()
if len(devices) < movidius_id+1:
print('Not enough devices found')
quit()
# Load SSD Graph
if longrange:
graphfile = dir_path + "/ssd_longrange.graph"
else:
graphfile = dir_path + "/ssd.graph"
with open(graphfile, mode='rb') as rf:
graphfile = rf.read()
self.SSDGraphDevice = mvnc.Device(devices[movidius_id])
self.SSDGraphDevice.open()
self.SSDGraph = mvnc.Graph("detection_graph")
self.graph_fifo_in, self.graph_fifo_out = self.SSDGraph.allocate_with_fifos(self.SSDGraphDevice, graphfile)
def __init__(self,
network_graph_filename: str,
ncs_device: mvnc.Device,
name=None):
self._device = ncs_device
self._network_graph_filename = network_graph_filename
try:
with open(self._network_graph_filename, mode='rb') as graph_file:
graph_in_memory = graph_file.read()
self._graph = mvnc.Graph("AgeNet Graph")
self._fifo_in, self._fifo_out = self._graph.allocate_with_fifos(
self._device, graph_in_memory)
self._input_fifo_capacity = self._fifo_in.get_option(
mvnc.FifoOption.RO_CAPACITY)
self._output_fifo_capacity = self._fifo_out.get_option(
mvnc.FifoOption.RO_CAPACITY)
except:
print('\n\n')
print('Error - could not load neural network graph file: ' +
network_graph_filename)
print('\n\n')
raise
self._end_flag = True
self._name = name
if (self._name is None):
self._name = "no name"
self._async_count_lock = threading.Lock()
self._async_inference_count = 0
def __init__(self):
#parameter
self.publish_image = rospy.get_param("detecter/publish_image", False)
self.dest_rate = rospy.get_param("detecter/dest_rate", 30)
self.input_rate = rospy.get_param("detecter/input_rate", 30)
self.threshold = rospy.get_param("detecter/threshold", 0.2)
self.sim = rospy.get_param("detecter/sim", False)
self.PREPROCESS_DIMS = (300, 300)
# grab a list of all NCS devices plugged in to USB
rospy.loginfo("finding NCS devices...")
self.devices = mvnc.enumerate_devices()
# if no devices found, exit the script
if len(self.devices) == 0:
rospy.loginfo("No devices found. Please plug in a NCS")
# use the first device since this is a simple test script
# (you'll want to modify this is using multiple NCS devices)
rospy.loginfo("found {} devices. "
"opening device0...".format(len(self.devices)))
self.device = mvnc.Device(self.devices[0])
self.device.open()
# open the CNN graph file
rospy.loginfo("loading the graph file into memory...")
my_dir = os.path.abspath(os.path.dirname(__file__))
path = os.path.join(my_dir, "../graphs/mobilenetgraph")
with open(path, mode="rb") as f:
graph_in_memory = f.read()
# load the graph into the NCS
rospy.loginfo("allocating the graph on the NCS...")
self.graph = mvnc.Graph("MobileNet-SSD")
self.ssd_fifo_in, self.ssd_fifo_out = self.graph.allocate_with_fifos(
self.device, graph_in_memory)
self.node_name = rospy.get_name()
if self.sim:
self.subscriber = rospy.Subscriber(
"camera/rgb/image_rect_color/compressed",
CompressedImage,
self.cbimage,
queue_size=1,
buff_size=2**24)
else:
self.subscriber = rospy.Subscriber("camera_node/image/compressed",
CompressedImage,
self.cbimage,
queue_size=1,
buff_size=2**24)
self.pubImg = rospy.Publisher("detecter/image/compressed",
CompressedImage,
queue_size=1)
self.pubBoxlist = rospy.Publisher("detecter/predictions",
Boxlist,
queue_size=1)
rospy.loginfo("[%s] Initializing " % (self.node_name))
self.bridge = CvBridge()
self.frame_counter = 0
def __init__(self, graphfile):
select = 1
self.detector = YoloDetector(select)
for i in range(ObjectWrapper.devNum):
ObjectWrapper.devHandle.append(
mvnc.Device(ObjectWrapper.devices[i])) ##------get devices
ObjectWrapper.devHandle[i].open() ##------ open device_i
# load blob
with open(graphfile, mode='rb') as f:
blob = f.read()
# create graph instance
ObjectWrapper.graphHandle.append(mvnc.Graph('inst' + str(i)))
# allocate resources
fifoIn, fifoOut = ObjectWrapper.graphHandle[i].allocate_with_fifos(
ObjectWrapper.devHandle[i], blob)
ObjectWrapper.fifoInHandle.append(fifoIn)
ObjectWrapper.fifoOutHandle.append(fifoOut)
if (graphfile.endswith('416')):
self.dim = (416, 416)
elif (graphfile.endswith('288')):
self.dim = (288, 288)
elif (graphfile.endswith('352')):
self.dim = (352, 352)
else:
self.dim = (416, 416)
self.blockwd = int(self.dim[0] / 32)
self.wh = self.blockwd * self.blockwd
self.targetBlockwd = int(self.dim[0] / 32)
self.classes = 6
self.threshold = 0.35
self.nms = 0.45
def execute_graph(graph_file, img):
try:
with open(graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph(graph_file)
input_fifo, output_fifo = graph.allocate_with_fifos(
device, graph_from_disk)
except:
print('Error - could not load graph file')
device.close()
device.destroy()
return 1
graph.queue_inference_with_fifo_elem(input_fifo, output_fifo, img,
'user object')
output, userobj = output_fifo.read_elem()
inference_time = graph.get_option(mvnc.GraphOption.RO_TIME_TAKEN)
input_fifo.destroy()
output_fifo.destroy()
graph.destroy()
device.close()
device.destroy()
return output, userobj, inference_time
def __init__(self, graphfile):
select = 1
self.detector = YoloDetector(select)
for i in range(ObjectWrapper.devNum):
ObjectWrapper.devHandle.append(
mvnc.Device(ObjectWrapper.devices[i]))
ObjectWrapper.devHandle[i].open()
# load blob
with open(graphfile, mode='rb') as f:
blob = f.read()
# create graph instance
ObjectWrapper.graphHandle.append(mvnc.Graph('inst' + str(i)))
# allocate resources
fifoIn, fifoOut = ObjectWrapper.graphHandle[i].allocate_with_fifos(
ObjectWrapper.devHandle[i], blob)
ObjectWrapper.fifoInHandle.append(fifoIn)
ObjectWrapper.fifoOutHandle.append(fifoOut)
self.dim = (416, 416)
self.blockwd = 12
self.wh = self.blockwd * self.blockwd
self.targetBlockwd = 13
self.classes = 20
self.threshold = 0.2
self.nms = 0.4
def __init__(self, graphfile):
select = 1
self.detector = YoloDetector(select)
for i in range(
ObjectWrapper.devNum): # will loop for each device detected
ObjectWrapper.devHandle.append(
mvnc.Device(ObjectWrapper.devices[i])
) # pass in list of devices, append that device to device list.
ObjectWrapper.devHandle[i].open() # open that device.
#opt = ObjectWrapper.devHandle[i].GetDeviceOption(mvnc.DeviceOption.OPTIMISATION_LIST)
# load blob
with open(graphfile, mode='rb') as f:
blob = f.read()
graph = mvnc.Graph('graph1') # creates a graph instance
# Allocate the graph and store to array
#ObjectWrapper.graphHandle.append(graph.allocate(ObjectWrapper.devHandle[i], blob))
input_fifo, output_fifo = graph.allocate_with_fifos(
ObjectWrapper.devHandle[i], blob)
ObjectWrapper.graphHandle.append(graph)
ObjectWrapper.inputHandle.append(input_fifo)
ObjectWrapper.outputHandle.append(output_fifo)
self.dim = (416, 416)
self.blockwd = 12
self.wh = self.blockwd * self.blockwd
self.targetBlockwd = 13
self.classes = 1
self.threshold = 0.2
self.nms = 0.4
def __init__(self, movidius_id=0):
dir_path = os.path.dirname(os.path.realpath(__file__))
# Get a list of ALL the sticks that are plugged in
# we need at least one
devices = mvnc.enumerate_devices()
if len(devices) < movidius_id + 1:
print('Not enough devices found')
quit()
# Pick the first stick to run the network
self.device = mvnc.Device(devices[movidius_id])
# Open the NCS
self.device.open()
# Read in the graph file to memory buffer
graphfile = dir_path + "/../model/20170512-110547_mvds/facenet_movidius_ncs.graph"
with open(graphfile, mode='rb') as f:
graph_in_memory = f.read()
# create the NCAPI graph instance from the memory buffer containing the graph file.
self.graph = mvnc.Graph("embedding_graph")
self.graph_fifo_in, self.graph_fifo_out = self.graph.allocate_with_fifos(
self.device, graph_in_memory)
def main():
devices = mvnc.enumerate_devices() #list all avilabile ncs.
if (len(devices) == 0):
print("please insert at least one NCS!")
print(str(len(devices)) + " ncs detected") # print the number of ncs.
device_0 = mvnc.Device(devices[0])
#device_1 = mvnc.Device(devices[1])
device_0.open()
#device_1.open()
graph_file_name_0 = 'test_0.graph' #read the graph file respectively
#graph_file_name_1 = 'test_0.graph'
with open(graph_file_name_0, mode='rb') as f:
graph_in_memory_0 = f.read()
#with open(graph_file_name_1, mode='rb') as f:
#graph_in_memory_1 = f.read()
graph_0 = mvnc.Graph(graph_file_name_0)
#graph_1 = mvnc.Graph(graph_file_name_1)
fifoIn_0, fifoOut_0 = graph_0.allocate_with_fifos(device_0,
graph_in_memory_0)
#fifoIn_1, fifoOut_1 = graph_1.allocate_with_fifos(device_1, graph_in_memory_1)
infer_image_0 = cv2.imread(IMAGE_FULL_PATH_0)
#infer_image_1 = cv2.imread(IMAGE_FULL_PATH_1)
run_inference(infer_image_0, graph_0, fifoIn_0, fifoOut_0)
# run_inference(infer_image_1, graph_1,fifoIn_1, fifoOut_1)
graph_0.destroy() #close the all ncs
fifoIn_0.destroy()
fifoOut_0.destroy()
device_0.close()
def __init__(self,
network_graph_filename: str,
ncs_device: mvnc.Device,
inital_box_prob_thresh: float,
classification_mask: list = None,
name=None):
"""Initializes an instance of the class
:param network_graph_filename: is the path and filename to the graph
file that was created by the ncsdk compiler
:param ncs_device: is an open ncs device object to use for inferences for this graph file
:param inital_box_prob_thresh: the initial box probablity threshold. between 0.0 and 1.0
:param classification_mask: a list of 0 or 1 values, one for each classification label in the
_classification_mask list. if the value is 0 then the corresponding classification won't be reported.
:param name: A name to use for the processor. Nice to have when debugging multiple instances
on multiple threads
:return : None
"""
self._device = ncs_device
self._network_graph_filename = network_graph_filename
# Load graph from disk and allocate graph.
try:
with open(self._network_graph_filename, mode='rb') as graph_file:
graph_in_memory = graph_file.read()
self._graph = mvnc.Graph("SSD MobileNet Graph")
self._fifo_in, self._fifo_out = self._graph.allocate_with_fifos(
self._device, graph_in_memory)
self._input_fifo_capacity = self._fifo_in.get_option(
mvnc.FifoOption.RO_CAPACITY)
self._output_fifo_capacity = self._fifo_out.get_option(
mvnc.FifoOption.RO_CAPACITY)
except:
print('\n\n')
print('Error - could not load neural network graph file: ' +
network_graph_filename)
print('\n\n')
raise
self._classification_labels = SsdMobileNetProcessor.get_classification_labels(
)
self._box_probability_threshold = inital_box_prob_thresh
self._classification_mask = classification_mask
if (self._classification_mask is None):
# if no mask passed then create one to accept all classifications
self._classification_mask = [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
]
self._end_flag = True
self._name = name
if (self._name is None):
self._name = "no name"
# lock to let us count calls to asynchronus inferences and results
self._async_count_lock = threading.Lock()
self._async_inference_count = 0
def __init__(self, name, model_path, labels):
self._device = None
self._get_device()
self._graph = mvnc.Graph(name)
self._input = None
self._output = None
self._labels = labels
self.allocate_model(model_path)
def AllocateGraph(self, graphfile):
api2_graph = mvncapi2.Graph("mvnc_simple_api graph")
api2_fifo_in, api2_fifo_out = api2_graph.allocate_with_fifos(
self._api2_device,
graphfile,
input_fifo_data_type=mvncapi2.FifoDataType.FP16,
output_fifo_data_type=mvncapi2.FifoDataType.FP16)
return Graph(api2_graph, api2_fifo_in, api2_fifo_out)
def setup(args):
global facenet_graph, facenet_input_fifo, facenet_output_fifo, ssd_graph, ssd_input_fifo, ssd_output_fifo
devices = mvncapi.enumerate_devices()
device = mvncapi.Device(devices[0])
device.open()
with open(args.facenetGraph, mode="rb") as f:
facenet_graph_data = f.read()
facenet_graph = mvncapi.Graph('facenet_graph')
facenet_input_fifo, facenet_output_fifo = facenet_graph.allocate_with_fifos(
device, facenet_graph_data)
with open(args.ssdGraph, mode="rb") as f:
ssd_graph_data = f.read()
ssd_graph = mvncapi.Graph('ssd_graph')
ssd_input_fifo, ssd_output_fifo = ssd_graph.allocate_with_fifos(
device, ssd_graph_data)
def __init__(self, device, h, w):
with open('movidius/pnet-{}x{}.graph'.format(h, w), mode='rb') as f:
graphFileBuff = f.read()
self.pnetGraph = mvnc.Graph('PNet Graph {}x{}'.format(h, w))
self.pnetIn, self.pnetOut = self.pnetGraph.allocate_with_fifos(
device, graphFileBuff)
self.h = h
self.w = w
def load_graph(device, graph_file):
global input_fifo, output_fifo
with open(graph_file, mode='rb') as f:
graph_buffer = f.read()
graph = mvnc.Graph(graph_file)
input_fifo, output_fifo = graph.allocate_with_fifos(device, graph_buffer)
return graph,
def main():
vs=VideoStream(usePiCamera=True).start()
time.sleep(1)
print('Running NCS Caffe TinyYolo example')
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = #TODO mvnc.enumerate_devices()
if len(devices) == 0:
print('No devices found')
return 1
device = #TODO:mvnc.Device(devices[0]) use the mvnc API to assign the first device in devices to the device variable.
device.open()
#Load graph from disk and allocate graph via API
with open(tiny_yolo_graph_file, mode='rb') as f:
graph_from_disk = f.read()
graph = mvnc.Graph("Tiny Yolo Graph")
fifo_in, fifo_out = #TODO: graph.allocate_with_fifos(device, graph_file_buffer)
#may need another line here
#Instantiate fifo_in and fifo_out using the graph file above.
# Read image from file, resize it to network width and height
# save a copy in display_image for display, then convert to float32, normalize (divide by 255),
# and finally convert to convert to float16 to pass to LoadTensor as input for an inference
while True:
frame=vs.read()# Get a frame from a video stream.
input_image=frame.copy()# copy frame to an input image.
display_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = cv2.resize(input_image, (NETWORK_IMAGE_WIDTH, NETWORK_IMAGE_HEIGHT), cv2.INTER_LINEAR)
input_image = input_image.astype(np.float32)
input_image = # TODO: Scale values between 0 and 255 *= (255.0/image.max())
input_image = input_image[:, :, ::-1] # convert to RGB
#TODO: graph.queue_inference_with_fifo_elem(input_fifo, output_fifo, input_image, 'user object')
#Use the queue_inference_with_fifo_elem to load the image and get the result from the NCS. This should be one line of code.
output, userobj = fifo_out.read_elem()
# filter out all the objects/boxes that don't meet thresholds
filtered_objs = filter_objects(output.astype(np.float32), input_image.shape[1], input_image.shape[0])
print('Displaying image with objects detected in GUI')
print('Click in the GUI window and hit any key to exit')
#display the filtered objects/boxes in a GUI window
display_objects_in_gui(display_image, filtered_objs)
fifo_in.destroy()
fifo_out.destroy()
graph.destroy()
device.close()
device.destroy()
print('Finished')
def __init__(self, movidius_id_pnet=0, movidius_id_onet=1):
dir_path = os.path.dirname(os.path.realpath(__file__)) + "/../model"
#self.minsize = 20
self.threshold = [0.6, 0.7, 0.7]
self.factor = 0.709
# Get Movidius Devices
devices = mvnc.enumerate_devices()
if movidius_id_pnet != movidius_id_onet and len(devices) < max(
movidius_id_pnet, movidius_id_onet) + 1:
print('Not enough devices found')
quit()
# Load PNet Graph
with open(dir_path + "/pnet.graph", mode='rb') as rf:
pgraphfile = rf.read()
self.PNetDevice = mvnc.Device(devices[movidius_id_pnet])
self.PNetDevice.open()
self.PNet = mvnc.Graph("pnet_graph")
self.PNet_fifo_in, self.PNet_fifo_out = self.PNet.allocate_with_fifos(
self.PNetDevice, pgraphfile)
# RNet se mantiene en Caffe
#caffe.set_mode_cpu()
#self.RNet = caffe.Net(caffe_model_path + "/rnet.prototxt", caffe_model_path + "/rnet.caffemodel", caffe.TEST)
# Load ONet Graph
with open(dir_path + "/onet.graph", mode='rb') as rf:
ographfile = rf.read()
if movidius_id_onet == movidius_id_pnet:
self.ONetDevice = self.PNetDevice
else:
self.ONetDevice = mvnc.Device(devices[movidius_id_onet])
self.ONetDevice.open()
self.ONet = mvnc.Graph("onet_graph")
self.ONet_fifo_in, self.ONet_fifo_out = self.ONet.allocate_with_fifos(
self.ONetDevice, ographfile)
def load_graph(device):
# Read the graph file into a buffer
with open(ARGS.graph, mode='rb') as f:
blob = f.read()
# Load the grpah buffer into the Intel® Movidius™ NCS
graph = mvnc.Graph(ARGS.graph)
# Create and allocate a network graph to the device in FIFOs
fifo_in, fifo_out = graph.allocate_with_fifos(device, blob)
return graph, fifo_in, fifo_out
def load_graph(self, graph_filename: str):
logging.info("Load graph from `{}`.".format(graph_filename))
with open(graph_filename, mode='rb') as graph_f:
in_memory_graph = graph_f.read()
api2_graph = mvncapi2.Graph(os.path.basename(graph_filename))
api2_fifo_in, api2_fifo_out = api2_graph.allocate_with_fifos(self.device, in_memory_graph,
input_fifo_data_type=mvncapi2.FifoDataType.FP16,
output_fifo_data_type=mvncapi2.FifoDataType.FP16)
self.graph = NCSGraph(api2_graph, api2_fifo_in, api2_fifo_out)
if self.graph is None:
raise SystemError("Cannot open NCS device {} with graph {}.".format(self.index, graph_filename))
def load_graph(self, device):
# Load graph file data
with open(self.graph_path, mode='rb') as f:
blob = f.read()
# Initialize a Graph object
graph = mvnc.Graph('graph')
# Allocate the graph to the device
fifo_in, fifo_out = graph.allocate_with_fifos(device, blob)
return graph, fifo_in, fifo_out
def load_graph( device ):
# Read the graph file into a buffer
with open( ARGS.graph, mode='rb' ) as f:
blob = f.read()
# Load the graph buffer into the NCS
graph = mvnc.Graph( ARGS.graph )
# Set up fifos
fifo_in, fifo_out = graph.allocate_with_fifos(device, blob)
return graph, fifo_in, fifo_out
def load_graph(device):
# Read the graph file into a buffer
with open(ARGS.graph, mode='rb') as f:
graph_buffer = f.read()
# Initialize Graph object
graph = mvnc.Graph('graph')
# Allocate the graph to the device and create input/output Fifos with default options in one call
input_fifo, output_fifo = graph.allocate_with_fifos(device, graph_buffer)
return graph, input_fifo, output_fifo
def load_graph(device):
# Read the graph file into a buffer
with open(ARGS.graph, mode='rb') as f:
graph_buffer = f.read()
# Load the graph buffer into the NCS
# graph = device.AllocateGraph( blob )
graph = mvncapi.Graph(ARGS.graph)
# graph.allocate(device, graph_buffer) # New way of allocating graph
input_fifo, output_fifo = graph.allocate_with_fifos(device, graph_buffer)
return graph, input_fifo, output_fifo
def _setup(self, device):
graph = mvncapi.Graph(self.graph_name)
graph_buffer = read_graph_buffer(self.graph_path)
fifo_in, fifo_out = graph.allocate_with_fifos(
device,
graph_buffer,
input_fifo_data_type=mvncapi.FifoDataType.FP16,
output_fifo_data_type=mvncapi.FifoDataType.FP16)
self.graph = graph
self.fifo_in = fifo_in
self.fifo_out = fifo_out
def embed(faces_gen, conf):
"""
The same old embedding, but using NCS device for network forward pass
:return: embeddings for a given list of faces
"""
logging.debug('using conf: ' + str(conf))
# The graph file that was created with the ncsdk compiler
graph_file_name = conf['graph file']
# read in the graph file to memory buffer
with open(graph_file_name, mode='rb') as f:
graph_in_memory = f.read()
# create the NCAPI graph instance from the memory buffer containing the graph file.
graph = mvncapi.Graph('graph1')
devices = mvncapi.enumerate_devices()
device_to_use = conf['device to use']
if len(devices) < (device_to_use + 1):
sys.exit("NCS device " + str(device_to_use) + " can't be found!")
device = mvncapi.Device(devices[device_to_use])
device.open()
input_fifo, output_fifo = graph.allocate_with_fifos(
device, graph_in_memory)
for frame, aligned_list in faces_gen:
faces = emb_helper.get_face_in_frame(frame, aligned_list,
conf['image size'])
emb_array = np.empty(shape=(len(aligned_list), 512))
for i, face in enumerate(faces):
# Write the image to the input queue and queue the inference in one call
graph.queue_inference_with_fifo_elem(input_fifo, output_fifo,
face.astype(np.float32), None)
# Get the results from the output queue
output, user_obj = output_fifo.read_elem()
emb_array[i] = output
yield emb_array
logging.debug('exiting NCS facenet embedding')
logging.debug('destroying graph')
graph.destroy()
logging.debug('closing NSC queues')
input_fifo.destroy()
output_fifo.destroy()
logging.debug('destroying device')
device.close()
device.destroy()
def __init__(self, graph_fpath, device_idx=0, fp16=True):
# fx.global_set_option(fx.GlobalOption.RW_LOG_LEVEL, 0)
self.dev = None
self.graph = None
self.fifoIn = None
self.fifoOut = None
devices = fx.enumerate_devices()
if len(devices) < 1:
print("Error - no NCS devices detected, verify an NCS device is connected.")
return
if device_idx > len(devices):
print("Error - device NCS idx is out of range.")
return
self.fp16 = fp16
self.dev = fx.Device(devices[device_idx])
try:
self.dev.open()
except:
print("Error - Could not open NCS device.")
quit()
print("Hello NCS! Device opened normally.")
print("Opening graph: {}".format(graph_fpath))
with open(graph_fpath, mode='rb') as f:
graph_file_buff = f.read()
self.graph = fx.Graph('graph')
print("FIFO Allocation / FP16: {}".format(fp16))
if self.fp16:
self.fifoIn, self.fifoOut = self.graph.allocate_with_fifos(self.dev, graph_file_buff,
input_fifo_data_type=fx.FifoDataType.FP16,
output_fifo_data_type=fx.FifoDataType.FP16)
else:
self.fifoIn, self.fifoOut = self.graph.allocate_with_fifos(self.dev, graph_file_buff)
output_tensor_list = self.graph.get_option(fx.GraphOption.RO_OUTPUT_TENSOR_DESCRIPTORS)
self.output_shape = (output_tensor_list[0].h, output_tensor_list[0].w, output_tensor_list[0].c)
input_tensor_list = self.graph.get_option(fx.GraphOption.RO_INPUT_TENSOR_DESCRIPTORS)
self.input_shape = (input_tensor_list[0].h, input_tensor_list[0].w, input_tensor_list[0].c)
self.input_cv_sz = (input_tensor_list[0].w, input_tensor_list[0].h)
print('Input shape: {}'.format(self.input_shape))
print('Output shape: {}'.format(self.output_shape))
def __init__(self):
self.node_name = rospy.get_name()
rospy.loginfo("[%s] Initializing " % (self.node_name))
# Param
self.sim = rospy.get_param('~sim', True)
self.model_path = rospy.get_param("~model")
# Variables
self.brdige = CvBridge()
self.motor_cmd = None
self.frame_counter = 0
# NCS
rospy.loginfo("finding NCS devices...")
self.devices = mvnc.enumerate_devices()
if len(self.devices) == 0:
rospy.loginfo("No devices found. Please plug in a NCS")
rospy.loginfo("found {} devices. "
"opening device0...".format(len(self.devices)))
self.device = mvnc.Device(self.devices[0])
self.device.open()
with open(self.model_path, mode="rb") as f:
self.graph_in_memory = f.read()
rospy.loginfo("allocating the graph on the NCS...")
self.graph = mvnc.Graph("Imitation-Learning")
self.fifo_in, self.fifo_out = self.graph.allocate_with_fifos(
self.device, self.graph_in_memory)
# Timer
rospy.Timer(rospy.Duration(0.2), self.send_motor_cmd)
# Publisher
if self.sim:
from duckiepond_vehicle.msg import UsvDrive
self.pub_motion = rospy.Publisher("~cmd_drive",
UsvDrive,
queue_size=1)
else:
self.pub_motion = rospy.Publisher("~cmd_drive",
MotorCmd,
queue_size=1)
# Subscriber
self.sub_image = rospy.Subscriber("~compressed_image",
CompressedImage,
self.cb_image,
queue_size=1,
buff_size=2**24)
def inferencer(results, frameBuffer):
graph = None
graphHandle0 = None
graphHandle1 = None
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 4)
devices = mvnc.enumerate_devices()
if len(devices) == 0:
print("No NCS devices found")
sys.exit(1)
print(len(devices))
with open(join(graph_folder, "graph"), mode="rb") as f:
graph_buffer = f.read()
graph = mvnc.Graph('MobileNet-SSD')
devopen = False
for devnum in range(len(devices)):
try:
device = mvnc.Device(devices[devnum])
device.open()
graphHandle0, graphHandle1 = graph.allocate_with_fifos(
device, graph_buffer)
devopen = True
break
except:
continue
if devopen == False:
print("NCS Devices open Error!!!")
sys.exit(1)
print("Loaded Graphs!!! " + str(devnum))
while True:
try:
if frameBuffer.empty():
continue
color_image = frameBuffer.get()
prepimg = preprocess_image(color_image)
graph.queue_inference_with_fifo_elem(graphHandle0, graphHandle1,
prepimg.astype(np.float32),
color_image)
out, _ = graphHandle1.read_elem()
results.put(out)
except:
import traceback
traceback.print_exc()
def load_multidevice_graph(device, graph_file):
"""
Function to work with 2 NCS devices.
:param device: NCS device to use
:param graph_file: graph file for the particular device
:return: opened graph object, input_fifo, and output_fifo for the device
"""
with open(graph_file, mode='rb') as f:
graph_buffer = f.read()
graph = mvnc.Graph(graph_file)
external_input_fifo, external_output_fifo = graph.allocate_with_fifos(device, graph_buffer)
return graph, external_input_fifo, external_output_fifo
def __init__(self, resolution=(480, 640), framerate=20):
from picamera.array import PiRGBArray
from picamera import PiCamera
resolution = (resolution[1], resolution[0])
# initialize the camera and stream
self.camera = PiCamera() # PiCamera gets resolution (height, width)
self.camera.resolution = resolution
self.height, self.width = (resolution[1], resolution[0])
self.camera.framerate = framerate
self.rawCapture = PiRGBArray(self.camera, size=resolution)
self.stream = self.camera.capture_continuous(self.rawCapture,
format="rgb",
use_video_port=True)
#Initialize and import MobileNet SSD graph and activate NCS device
self.GRAPH = 'graph/graph'
self.CLASSES = ('background',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
self.input_size = (300, 300)
np.random.seed(3)
self.colors = 255 * np.random.rand(len(self.CLASSES), 3)
# discover our device
devices = mvnc.enumerate_devices()
device = mvnc.Device(devices[0])
device.open()
# load graph onto device
with open(self.GRAPH, 'rb') as f:
graph_file = f.read()
self.graph = mvnc.Graph('graph1')
# graph.allocate(device, graph_file)
self.input_fifo, self.output_fifo = self.graph.allocate_with_fifos(device, graph_file)
# initialize the frame and the variable used to indicate
# if the thread should be stopped
self.frame = None
self.on = True
print('PiCamera loaded.. .warming camera')
time.sleep(2)
