def run_ie_on_dataset(model_xml, model_bin, cpu_extension_path, images_dir, prob_threshold=0.01):
plugin = IEPlugin(device='CPU')
plugin.add_cpu_extension(cpu_extension_path)
net = IENetwork.from_ir(model=model_xml, weights=model_bin)
assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
exec_net = plugin.load(network=net, num_requests=2)
num, chs, height, width = net.inputs[input_blob]
del net
cur_request_id = 0
detection_data = []
for image in os.listdir(images_dir):
im_path = os.path.join(images_dir, image)
frame = cv2.imread(im_path)
initial_h, initial_w, _ = frame.shape
in_frame = cv2.resize(frame, (width, height))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((num, chs, height, width))
objects_per_image = []
exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
res = exec_net.requests[cur_request_id].outputs[out_blob]
for obj in res[0][0]:
if obj[2] > prob_threshold:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
class_id = int(obj[1])
conf = obj[2]
objects_per_image.append({'bbox': [xmin, ymin, xmax, ymax], 'class_id': class_id, 'score': conf})
det_item = {'image': im_path, 'objects': objects_per_image}
detection_data.append(det_item)
del exec_net
del plugin
return detection_data
def main():
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
codec = data_utils.TextFeatureIO(char_dict_path='Config/char_dict.json',
ord_map_dict_path=r'Config/ord_map.json')
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
# if plugin.device == "CPU":
# supported_layers = plugin.get_supported_layers(net)
# not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
# if len(not_supported_layers) != 0:
# log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
# format(plugin.device, ', '.join(not_supported_layers)))
# log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
# "or --cpu_extension command line argument")
# sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
log.info("Preparing input blobs")
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
net.batch_size = len(args.input)
# Read and pre-process input images
n, c, h, w = net.inputs[input_blob].shape
images = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
if image.shape[:-1] != (h, w):
log.warning("Image {} is resized from {} to {}".format(
args.input[i], image.shape[:-1], (h, w)))
image = cv2.resize(image, (w, h))
image = image.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
images[i] = image
log.info("Batch size is {}".format(n))
# Loading model to the plugin
log.info("Loading model to the plugin")
exec_net = plugin.load(network=net)
del net
# Start sync inference
log.info("Starting inference ({} iterations)".format(args.number_iter))
infer_time = []
result_dir = os.path.join(args.output_dir, job_id)
if not os.path.isdir(result_dir):
print(result_dir)
os.makedirs(result_dir, exist_ok=True)
infer_file = os.path.join(result_dir, 'i_progress.txt')
t0 = time.time()
for i in range(args.number_iter):
res = exec_net.infer(inputs={input_blob: images})
if i % 10 == 0 or i == args.number_iter - 1:
progressUpdate(infer_file,
time.time() - t0, i + 1, args.number_iter)
t1 = (time.time() - t0)
log.info("Average running time of one iteration: {} ms".format(
np.average(np.asarray(infer_time))))
if args.perf_counts:
perf_counts = exec_net.requests[0].get_perf_counts()
log.info("Performance counters:")
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
# Processing output blob
log.info("Processing output blob")
res = res[out_blob]
preds = res.argmax(2)
preds = preds.transpose(1, 0)
preds = np.ascontiguousarray(preds, dtype=np.int8).view(dtype=np.int8)
values = codec.writer.ordtochar(preds[0].tolist())
values = [v for i, v in enumerate(values) if i == 0 or v != values[i - 1]]
values = [x for x in values if x != ' ']
res = ''.join(values)
print("The result is : " + res)
avg_time = round((t1 * 1000 / args.number_iter), 3)
with open(os.path.join(args.output_dir, job_id, 'result.txt'), 'w') as f:
f.write(res + "\n Inference performed in " + str(avg_time) + "ms")
stats = {}
stats['time'] = str(round(t1, 2))
stats['frames'] = str(args.number_iter * n)
stats['fps'] = str(round(args.number_iter * n / t1, 2))
stats_file = result_dir + "/stats.json"
with open(stats_file, 'w') as f:
json.dump(stats, f)
del exec_net
del plugin
def prepare_model(log, model, weights, cpu_extension, device_list, plugin_dir,
thread_num, stream_num):
model_xml = model
model_bin = weights
if len(device_list) == 1:
device = device_list[0]
elif len(device_list) == 2:
device = 'HETERO:{},{}'.format(device_list[0], device_list[1])
else:
log.error('Wrong count devices')
sys.exit(1)
log.info('Plugin initialization.');
plugin = IEPlugin(device = device, plugin_dirs = plugin_dir)
if cpu_extension and 'CPU' in device:
plugin.add_cpu_extension(cpu_extension)
log.info('Loading network files:\n\t {0}\n\t {1}'.format(
model_xml, model_bin))
net = IENetwork(model = model_xml, weights = model_bin)
if plugin.device == 'CPU':
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [ l for l in net.layers.keys() \
if l not in supported_layers ]
if len(not_supported_layers) != 0:
log.error('Following layers are not supported by the plugin \
for specified device {0}:\n {1}'.format(plugin.device,
', '.join(not_supported_layers)))
log.error('Please try to specify cpu extensions library path in \
sample\'s command line parameters using -l or --cpu_extension \
command line argument')
sys.exit(1)
if thread_num is not None:
if 'CPU' in device_list:
plugin.set_config({'CPU_THREADS_NUM': str(thread_num)})
else:
log.error('Parameter : Number of threads is used only for CPU')
sys.exit(1)
if stream_num is not None:
if 'CPU' in device_list:
plugin.set_config({'CPU_THROUGHPUT_STREAMS': str(stream_num)})
else:
log.error('Parameter : Number of streams is used only for CPU')
sys.exit(1)
if len(device_list) == 2:
plugin.set_config({'TARGET_FALLBACK': device})
plugin.set_initial_affinity(net)
return net, plugin
class Network:
"""
Load and configure inference plugins for the specified target devices
and performs synchronous and asynchronous modes for the specified infer requests.
"""
def __init__(self):
### TODO: Initialize any class variables desired ###
self.network = None
self.plugin = None
self.input_blob = None
self.out_blob = None
self.exec_network = None
self.infer_request_handle = None
def load_model(self, model_xml, device, input_size, output_size, num_requests, cpu_extension=None, plugin=None):
model_bin = os.path.splitext(model_xml)[0] + '.bin'
if not plugin:
log.info("Initializing plugin for {} device...".format(device))
self.plugin = IEPlugin(device = device)
else:
self.plugin = plugin
### TODO: Load the model ###
# Add Extensions
if cpu_extension and 'CPU' in device:
self.plugin.add_cpu_extension(cpu_extension)
# Read IR
log.info("Reading the Intermediate Representative(IR). . .")
self.network = IENetwork(model=model_xml, weights=model_bin)
log.info("Loading IR to the plugin. . .")
if self.plugin.device == "CPU":
supported_layers = self.plugin.get_supported_layers(self.network)
unSupported_layers = [l for l in self.network.layers.keys() if l not in supported_layers]
if len(unSupported_layers) != 0:
log.error("The Following layers are not supported by the plugin for specified device {}:\n {}".
format(self.plugin.device, ', '.join(unSupported_layers)))
log.error("Please specify cpu extensions library path in command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
# Loads network read from IR to the plugin
if num_requests == 0:
self.exec_network = self.plugin.load(network=self.network)
else:
self.exec_network = self.plugin.load(network=self.network, num_requests=num_requests)
return self.plugin, self.get_input_shape()
def get_input_shape(self):
### TODO: Return the shape of the input layer ###
self.input_blob = next(iter(self.network.inputs))
self.output_blob = next(iter(self.network.outputs))
return self.network.inputs[self.input_blob].shape
def exec_net(self,request_id,frame):
self.infer_request_handle = self.exec_network.start_async(request_id=request_id, inputs={self.input_blob: frame})
return self.exec_network
def wait(self,request_id):
status = self.exec_network.requests[request_id].wait(-1)
return status
def get_output(self,request_id,output=None):
if output == 0:
result = self.infer_request_handle.outputs[output]
else:
result = self.exec_network.requests[request_id].outputs[self.output_blob]
return result
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
log.info("Preparing input blobs")
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
net.batch_size = len(args.input)
# Read and pre-process input images
n, c, h, w = net.inputs[input_blob].shape
images = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
original_h = image.shape[0]
original_w = image.shape[1]
if image.shape[:-1] != (h, w):
log.warning("Image {} is resized from {} to {}".format(
args.input[i], image.shape[:-1], (h, w)))
image = cv2.resize(image, (w, h))
image = image.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
image = image / 255.
images[i] = image
log.info("Batch size is {}".format(n))
print("@@@@@@")
print(images[0].shape) # (1, 1, 224, 224)
print(images[0])
print("@@@@@@")
# Loading model to the plugin
log.info("Loading model to the plugin")
exec_net = plugin.load(network=net)
del net
# Start sync inference
log.info("Starting inference ({} iterations)".format(args.number_iter))
infer_time = []
for i in range(args.number_iter):
t0 = time()
res = exec_net.infer(inputs={input_blob: images})
infer_time.append((time() - t0) * 1000)
log.info("Average running time of one iteration: {} ms".format(
np.average(np.asarray(infer_time))))
if args.perf_counts:
perf_counts = exec_net.requests[0].get_perf_counts()
log.info("Performance counters:")
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
# Processing output blob
log.info("Processing output blob")
res = res[out_blob]
#np.set_printoptions(threshold=np.inf)
print("@@@@@@")
print(res.shape) # (1, 1, 224, 224)
print(res)
print("@@@@@@")
res = np.squeeze(res) # (1, 224, 224)
res = np.squeeze(res) # (224, 224)
result = sigmoid(res)
print("@@@@@@")
print(result.shape)
print(result)
print("@@@@@@")
result = cv2.resize(result, (original_w, original_h)) ## to 960x540
cv2.imwrite('out_openvino.png', (result * 255).astype(np.uint8))
del exec_net
del plugin
def infer_on_video(args):
args.ct = float(args.ct)
### TODO: Initialize the Inference Engine
# plugin = Network()
# ### TODO: Load the network model into the IE
# plugin.load_model(args.m, args.d, CPU_EXTENSION)
# net_input_shape = plugin.get_input_shape()
model_xml = args.m + '.xml'
model_bin = args.m + ".bin"
plugin = IEPlugin(device=args.d)
if "CPU" in args.d:
plugin.add_cpu_extension("lib/libcpu_extension.dylib")
net = IENetwork(model=model_xml, weights=model_bin)
input_blob = next(iter(net.inputs))
exec_net = plugin.load(network=net)
print("IR Sucessfully loaded into the Inference Engine")
camera_width = 640
camera_height = 480
fps = ""
framepos = 0
frame_count = 0
vidfps = 0
skip_frame = 0
elapsedTime = 0
new_w = int(camera_width * m_input_size/camera_width)
new_h = int(camera_height * m_input_size/camera_height)
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 30)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
time.sleep(1)
while cap.isOpened():
t1 = time.time()
## Uncomment only when playing video files
#cap.set(cv2.CAP_PROP_POS_FRAMES, framepos)
ret, image = cap.read()
if not ret:
break
resized_image = cv2.resize(image, (new_w, new_h), interpolation = cv2.INTER_CUBIC)
canvas = np.full((m_input_size, m_input_size, 3), 128)
canvas[(m_input_size-new_h)//2:(m_input_size-new_h)//2 + new_h,(m_input_size-new_w)//2:(m_input_size-new_w)//2 + new_w, :] = resized_image
prepimg = canvas
prepimg = prepimg[np.newaxis, :, :, :] # Batch size axis add
prepimg = prepimg.transpose((0, 3, 1, 2)) # NHWC to NCHW
outputs = exec_net.infer(inputs={input_blob: prepimg})
objects = []
for output in outputs.values():
objects = ParseYOLOV3Output(output, new_h, new_w, camera_height, camera_width, 0.4, objects)
# Filtering overlapping boxes
objlen = len(objects)
for i in range(objlen):
if (objects[i].confidence == 0.0):
continue
for j in range(i + 1, objlen):
if (IntersectionOverUnion(objects[i], objects[j]) >= 0.4):
if objects[i].confidence < objects[j].confidence:
objects[i], objects[j] = objects[j], objects[i]
objects[j].confidence = 0.0
# Drawing boxes
for obj in objects:
if obj.confidence < args.ct:
continue
label = obj.class_id
confidence = obj.confidence
#if confidence >= 0.2:
label_text = LABELS[label] + " (" + "{:.1f}".format(confidence * 100) + "%)"
if LABELS[label] in danger:
mixer.music.play()
cv2.rectangle(image, (obj.xmin, obj.ymin), (obj.xmax, obj.ymax), box_color, box_thickness)
cv2.putText(image, label_text, (obj.xmin, obj.ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, label_text_color, 1)
cv2.putText(image, fps, (camera_width - 170, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38, 0, 255), 1, cv2.LINE_AA)
cv2.imshow("Result", image)
if cv2.waitKey(1)&0xFF == ord('q'):
break
elapsedTime = time.time() - t1
fps = "(Playback) {:.1f} FPS".format(1/elapsedTime)
## frame skip, video file only
#skip_frame = int((vidfps - int(1/elapsedTime)) / int(1/elapsedTime))
#framepos += skip_frame
cv2.destroyAllWindows()
del net
del exec_net
del plugin
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
preprocess_times = collections.deque()
infer_times = collections.deque()
postprocess_times = collections.deque()
ROIfile = open("ROIs.txt", "w")
# output stored here, view with ROIviewer
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in demo's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
#Set Batch Size
batchSize = args.b
frameLimit = args.fr
assert len(
net.inputs.keys()) == 1, "Demo supports only single input topologies"
assert len(net.outputs) == 1, "Demo supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
output_dims = net.outputs[out_blob].shape
infer_width = w
infer_height = h
num_channels = c
channel_size = infer_width * infer_height
full_image_size = channel_size * num_channels
print("inputdims=", w, h, c, n)
print("outputdims=", output_dims[3], output_dims[2], output_dims[1],
output_dims[0])
if int(output_dims[3]) > 1:
print("SSD Mode")
output_mode = output_mode_type.SSD_MODE
else:
print("Single Classification Mode")
output_mode = CLASSIFICATION_MODE
output_data_size = int(output_dims[2]) * int(output_dims[1]) * int(
output_dims[0])
del net
if args.input == 'cam':
input_stream = 0
else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
if args.labels:
with open(args.labels, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
cap = cv2.VideoCapture(input_stream)
cur_request_id = 0
next_request_id = 1
log.info("Starting inference in async mode...")
is_async_mode = True
render_time = 0
framenum = 0
process_more_frames = True
frames_in_output = batchSize
while process_more_frames:
time1 = time.time()
for mb in range(0, batchSize):
ret, frame = cap.read()
if not ret or (framenum >= frameLimit):
process_more_frames = False
frames_in_output = mb
break
# convert image to blob
# Fill input tensor with planes. First b channel, then g and r channels
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
time2 = time.time()
diffPreProcess = time2 - time1
if process_more_frames:
preprocess_times.append(diffPreProcess * 1000)
# Main sync point:
# in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately wait for it's completion
inf_start = time.time()
if is_async_mode:
exec_net.start_async(request_id=next_request_id,
inputs={input_blob: in_frame})
else:
exec_net.start_async(request_id=cur_request_id,
inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
inf_end = time.time()
det_time = inf_end - inf_start
infer_times.append(det_time * 1000)
time1 = time.time()
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
for obj in res[0][0]:
# Write into ROIs.txt only objects when probability more than specified threshold
if obj[2] > args.prob_threshold:
confidence = obj[2]
locallabel = obj[1] - 1
print(str(0),
str(framenum),
str(locallabel),
str(confidence),
str(obj[3]),
str(obj[4]),
str(obj[5]),
str(obj[6]),
file=ROIfile)
sys.stdout.write("\rframenum:" + str(framenum))
sys.stdout.flush()
render_start = time.time()
framenum = framenum + 1
time2 = time.time()
diffPostProcess = time2 - time1
postprocess_times.append(diffPostProcess * 1000)
if is_async_mode:
cur_request_id, next_request_id = next_request_id, cur_request_id
print("\n")
preprocesstime = 0
inferencetime = 0
postprocesstime = 0
for obj in preprocess_times:
preprocesstime += obj
for obj in infer_times:
inferencetime += obj
for obj in postprocess_times:
postprocesstime += obj
print("Preprocess: ", preprocesstime / (len(preprocess_times) * batchSize),
"\tms/frame")
print("Inference: ", inferencetime / (len(infer_times) * batchSize),
"\tms/frame")
print("Postprocess:",
postprocesstime / (len(postprocess_times) * batchSize), "\tms/frame")
del exec_net
del plugin
class DetectorOpenVINO(detector_base.DetectorBase):
def __init__(self, dyda_config_path='', debug=False):
""" __init__ of DetectorOpenVINO
Trainer Variables:
input_data: a list of image array
results: defined by lab_tools.output_pred_detection()
Arguments:
dyda_config_path -- Trainer config filepath
"""
if debug:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
# Setup dyda config
super(DetectorOpenVINO,
self).__init__(dyda_config_path=dyda_config_path)
self.set_param(self.class_name)
self.check_param_keys()
if "threshold" in self.param.keys():
self.threshold = self.param["threshold"]
else:
self.threshold = 0.3
# Setup DL model
model_xml = self.param['model_description']
model_bin = self.param['model_file']
with open(self.param['label_file'], 'r') as f:
self.labels_map = [x.strip() for x in f]
# Setup OpenVINO
#
# Plugin initialization for specified device and
# load extensions library if specified
#
# Note: MKLDNN CPU-targeted custom layer support is not included
# because we do not use it yet.
self.plugin = IEPlugin(device=self.param['device'],
plugin_dirs=self.param['plugin_dirs'])
if self.param['device'] == 'CPU':
for ext in self.param['cpu_extensions']:
logger.info('Add cpu extension: {}'.format(ext))
self.plugin.add_cpu_extension(ext)
logger.debug("Computation device: {}".format(self.param['device']))
# Read IR
logger.debug("Loading network files:\n\t{}\n\t{}".format(
model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
if self.plugin.device == "CPU":
supported_layers = self.plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
logger.error(
('Following layers are not supported '
'by the plugin for specified device {}:\n {}').format(
self.plugin.device, ', '.join(not_supported_layers)))
logger.error("Please try to specify cpu "
"extensions library path in demo's "
"command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net.inputs.keys()) == 1, (
'Demo supports only single input topologies')
assert len(
net.outputs) == 1, ('Demo supports only single output topologies')
# input_blob and and out_blob are the layer names in string format.
logger.debug("Preparing input blobs")
self.input_blob = next(iter(net.inputs))
self.out_blob = next(iter(net.outputs))
self.n, self.c, self.h, self.w = net.inputs[self.input_blob].shape
# Loading model to the plugin
self.exec_net = self.plugin.load(network=net, num_requests=2)
del net
# Initialize engine mode: sync or async
#
# FIXME: async mode does not work currently.
# process_input needs to provide two input tensors for async.
self.is_async_mode = False
self.cur_request_id = 0
self.next_request_id = 1
def __delete__(self, instance):
del self.exec_net
del self.plugin
# def create_labinfo(self, results):
# """Create DT42 labinfo based on def in spec"""
# orders = results.argsort()[::-1]
# labinfo = {'classifier': {}}
# for i in range(0, len(orders)):
# index = orders[i]
# labinfo['classifier'][self.labels[index]] = results[index]
# return orders, labinfo
def check_param_keys(self):
"""
Check if any default key is missing in the self.param.
"""
default_keys = [
"model_file", "model_description", "label_file", "device"
]
for _key in default_keys:
if _key not in self.param.keys():
logger.error("%s missing in self.param" % _key)
self.terminate_flag = True
else:
continue
logger.debug("keys of self.param are checked")
def process_input(self, tensor, next_tensor=None):
frame = tensor
next_frame = next_tensor
# original input shape will be used in process_output
self.img_w = tensor.shape[1]
self.img_h = tensor.shape[0]
# Main sync point:
# in the truly Async mode we start the NEXT infer request,
# while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately
# wait for it's completion
if self.is_async_mode:
in_frame = cv2.resize(next_frame, (self.w, self.h))
# Change data layout from HWC to CHW
in_frame = in_frame.transpose((2, 0, 1))
in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))
else:
in_frame = cv2.resize(frame, (self.w, self.h))
# Change data layout from HWC to CHW
in_frame = in_frame.transpose((2, 0, 1))
in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))
return in_frame
def inference(self, tensor):
inf_start = time()
if self.is_async_mode:
self.exec_net.start_async(request_id=self.next_request_id,
inputs={self.input_blob: tensor})
else:
self.exec_net.start_async(request_id=self.cur_request_id,
inputs={self.input_blob: tensor})
if self.exec_net.requests[self.cur_request_id].wait(-1) == 0:
inf_end = time()
det_time = inf_end - inf_start
if self.is_async_mode:
logger.debug(r'Inference time: N\A for async mode')
else:
logger.debug("Inference time: {:.3f} ms".format(det_time *
1000))
# Parse detection results of the current request
res = self.exec_net.requests[self.cur_request_id].outputs[
self.out_blob]
else:
res = None
return res
# FIXME: async mode does not work currently.
# process_input needs to provide two input tensors for async.
if self.is_async_mode:
self.cur_request_id, self.next_request_id = \
self.next_request_id, self.cur_request_id
frame = next_frame
def process_output(self, output):
logger.debug("Processing output blob")
logger.debug("Threshold: {} ".format(self.threshold))
annotations = []
for obj in output[0][0]:
# Collect objects when probability more than specified threshold
if obj[2] > self.threshold:
xmin = int(obj[3] * self.img_w)
ymin = int(obj[4] * self.img_h)
xmax = int(obj[5] * self.img_w)
ymax = int(obj[6] * self.img_h)
class_id = int(obj[1])
if self.labels_map:
det_label = self.labels_map[class_id]
else:
str(class_id)
annotations.append({
'label': det_label,
'confidence': float(obj[2]),
'left': xmin,
'top': ymin,
'right': xmax,
'bottom': ymax
})
logger.debug('process_output return: {}'.format(annotations))
return annotations
def main_process(self):
if len(self.input_data) == 0:
logger.error('no input_data found')
self.terminate_flag = True
logger.debug('self.input_data len: {}'.format(len(self.input_data)))
for img_array in self.input_data:
orig_h, orig_w = img_array.shape[:-1]
img_array = self.process_input(img_array)
inf_results = self.inference(img_array)
det_results = self.process_output(inf_results)
# why this code looks so redundant here is beacause that
# this script is modified from berrynet openvino_engine.py,
# and I follow the principle that make least change of original
# code. the annotations structure in process_output is original
# defined in berrynet, and in order to use the lab_tools, we
# must re-define the structure
annotations = [[
det_result['label'], det_result['confidence'],
[
det_result['top'], det_result['bottom'],
det_result['left'], det_result['right']
]
] for det_result in det_results]
# orders, labinfo = self.create_labinfo(inf_results)
res = lab_tools.output_pred_detection(
# input_path=self.orig_input_path,
input_path='',
annotations=annotations,
img_size=(orig_h, orig_w),
# labinfo=labinfo
labinfo={})
self.results.append(res)
logger.debug('self.results: {}'.format(self.results))
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
# Plugin initialization for specified device and
# load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and "CPU" in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
# If using MYRIAD then we need to load FP16 model version
model_xml, model_bin = load_model(args.device == "MYRIAD")
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
# net = IENetwork.from_ir(model=model_xml, weights=model_bin) # Old API
"""
This code checks to see if all of the graphs in the IR are
compatible with OpenVINO. If not, then you'll need to probably
try to load in an extension library from ${INTEL_CVSDK_DIR}/inference_engine/lib
"""
if "CPU" in plugin.device:
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error("Following layers are not supported by the plugin "
" for specified device {}:\n {}".format(
plugin.device, ", ".join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path "
"in sample's command line parameters using -l "
"or --cpu_extension command line argument")
log.error(
"On CPU this is usually -l ${INTEL_CVSDK_DIR}/inference_engine/lib/centos_7.4/intel64/libcpu_extension_avx2.so"
)
log.error(
"You may need to build the OpenVINO samples directory for this library to be created on your system."
)
log.error(
"e.g. bash ${INTEL_CVSDK_DIR}/inference_engine/samples/build_samples.sh will trigger the library to be built."
)
log.error(
"Replace 'centos_7.4' with the pathname on your computer e.g. ('ubuntu_16.04')"
)
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
"""
Ask OpenVINO for input and output tensor names and sizes
"""
input_blob = next(iter(net.inputs)) # Name of the input layer
out_blob = next(iter(net.outputs)) # Name of the output layer
batch_size, n_channels, height, width = net.inputs[input_blob].shape
batch_size, n_out_channels, height_out, width_out = net.outputs[
out_blob].shape
net.batch_size = batch_size
# Load data
input_data, label_data, img_indicies = load_data()
# Loading model to the plugin
exec_net = plugin.load(network=net)
del net
if args.stats:
# Print the latency and throughput for inference
print_stats(exec_net, input_data, n_channels, batch_size, input_blob,
out_blob, args)
"""
OpenVINO inference code
input_blob is the name (string) of the input tensor in the graph
out_blob is the name (string) of the output tensor in the graph
Essentially, this looks exactly like a feed_dict for TensorFlow inference
"""
# Go through the sample validation dataset to plot predictions
predictions = np.zeros(
(img_indicies.shape[0], n_out_channels, height_out, width_out))
for idx in range(0, img_indicies.shape[0], batch_size):
res = exec_net.infer(
inputs={
input_blob: input_data[idx:(idx + batch_size), :n_channels]
})
# Save the predictions to array
predictions[idx:(idx + batch_size), ] = res[out_blob]
if idx != (len(img_indicies) - 1): # Partial batch left in data
log.info("Partial batch left over in dataset.")
"""
Evaluate model with Dice metric
"""
for idx in range(img_indicies.shape[0]):
dice = dice_score(predictions[idx, 0, :, :], label_data[idx, 0, :, :])
log.info("Image #{}: Dice score = {:.4f}".format(
img_indicies[idx], dice))
if args.plot:
plot_predictions(predictions, input_data, label_data, img_indicies,
args)
del exec_net
del plugin
#!/usr/bin/env python3
from openvino.inference_engine import IENetwork, IEPlugin
import cv2
import os
import time
image_path = "/opt/intel/openvino/deployment_tools/demo/car.png"
model_dir = "/opt/intel/openvino/inference_engine/samples/python_samples/object_detection_demo_ssd_async/"
model_xml = model_dir + "VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.xml"
model_bin = model_dir + "VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.bin"
plugin_dir = "/opt/intel/openvino/inference_engine/lib/intel64"
cpu_extension = "/opt/intel/openvino/inference_engine/lib/intel64/libcpu_extension_sse4.so"
plugin = IEPlugin(device="CPU", plugin_dirs=plugin_dir)
plugin.add_cpu_extension(cpu_extension)
net = IENetwork(model=model_xml, weights=model_bin)
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
n, c, h, w = net.inputs[input_blob].shape
exec_net = plugin.load(network=net, num_requests=1)
start_time = time.time()
image_number = 200
for i in range(1, 1 + image_number):
image = cv2.imread(image_path)
image = cv2.resize(image, (w, h))
image = image.transpose((2, 0, 1))
image = image.reshape((n, c, h, w))
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
assert args.device.split(':')[0] == "HETERO", "This demo supports only Hetero Plugin. " \
"Please specify correct device, e.g. HETERO:FPGA,CPU"
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
format(plugin.device, ', '.join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path in demo's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
net_ops = set([l.type for l in net.layers.values()])
if not any([op == "Convolution" for op in net_ops]):
log.warning("Specified IR doesn't contain any Convolution operations for which affinity going to be set.\n"
"Try to use another topology to make the affinity setting result more visible.")
# Configure the plugin to initialize default affinity for network in set_initial_affinity() function.
plugin.set_config({"TARGET_FALLBACK": args.device.split(':')[1]})
# Enable graph visualization
plugin.set_config({"HETERO_DUMP_GRAPH_DOT": "YES"})
plugin.set_initial_affinity(net)
for l in net.layers.values():
if l.type == "Convolution":
l.affinity = "GPU"
assert len(net.inputs.keys()) == 1, "Demo supports only single input topologies"
assert len(net.outputs) == 1, "Demo supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
image = cv2.imread(args.input)
image = cv2.resize(image, (w, h))
image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW
image = image.reshape((n, c, h, w))
# Load network to the plugin
exec_net = plugin.load(network=net)
del net
# Start sync inference
res = exec_net.infer(inputs={input_blob: image})
top_ind = np.argsort(res[out_blob], axis=1)[0, -args.number_top:][::-1]
for i in top_ind:
log.info("%f #%d" % (res[out_blob][0, i], i))
del exec_net
del plugin
cwd = os.getcwd()
log.info(
"Graphs representing default and resulting affinities dumped to {} and {} files respectively"
.format(os.path.join(cwd, 'hetero_affinity.dot'), os.path.join(cwd, 'hetero_subgraphs.dot'))
)
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
log.info("Loading plugins for {} device...".format(args.device))
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
format(plugin.device, ', '.join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
# Read and pre-process input image
if isinstance(net.inputs[input_blob], list):
n, c, h, w = net.inputs[input_blob]
else:
n, c, h, w = net.inputs[input_blob].shape
del net
out_file_name = os.path.splitext(os.path.basename(args.input))[0]
if args.output_dir:
out_path = os.path.join(args.output_dir, out_file_name+'.mp4')
if args.labels:
with open(args.labels, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
cur_request_id = 0
next_request_id = 1
job_id = os.environ['PBS_JOBID']
inf_progress_file_path = os.path.join(args.output_dir,'i_progress_'+str(job_id)+'.txt')
ren_progress_file_path = os.path.join(args.output_dir,'v_progress_'+str(job_id)+'.txt')
is_async_mode = True
fps_sum = 0
frame_count = 0
read_time = 0
result_list = []
try:
# Set up the capture stream
input_stream = args.input
cap = cv2.VideoCapture(input_stream)
if cap.isOpened():
video_len = args.count
log.info("Live stream running at {} fps".format(cv2.CAP_PROP_FPS))
initial_w = cap.get(3)
initial_h = cap.get(4)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)
log.info("Starting inference in async mode...")
infer_time_start = time.time()
while frame_count < video_len:
ret, frame = cap.read()
if not ret:
break
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
# Main sync point:
# in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately wait for it's completion
inf_start = time.time()
if is_async_mode:
exec_net.start_async(request_id=next_request_id, inputs={input_blob: in_frame})
else:
exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})
read_time_0 = time.time()
if exec_net.requests[cur_request_id].wait(-1) == 0:
inf_end = time.time()
det_time = inf_end - inf_start
#Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
processBoxes(frame_count, res, labels_map, args.prob_threshold, frame, result_list, det_time)
read_time += time.time() - read_time_0
frame_count+=1
#Write data to progress tracker
if frame_count%10 == 0:
progressUpdate(inf_progress_file_path, time.time()-infer_time_start, frame_count, video_len)
key = cv2.waitKey(1)
if key == 27:
break
if (9 == key):
is_async_mode = not is_async_mode
log.info("Switched to {} mode".format("async" if is_async_mode else "sync"))
if is_async_mode:
cur_request_id, next_request_id = next_request_id, cur_request_id
##End while loop /
cap.release()
log.info("{} seconds were spent reading".format(read_time))
if args.output_dir is None:
cv2.destroyAllWindows()
else:
total_time = time.time() - infer_time_start
with open(os.path.join(args.output_dir, 'stats_'+str(job_id)+'.txt'), 'w') as f:
f.write(str(round(total_time, 1))+'\n')
f.write(str(frame_count)+'\n')
o_video = os.path.join(args.output_dir, 'output_'+str(job_id)+'.mp4')
post_process_t = time.time()
postProcess(result_list, int(initial_w), int(initial_h), labels_map, o_video, is_async_mode, ren_progress_file_path)
log.info("Post processing time: {0} sec" .format(time.time()-post_process_t))
finally:
del exec_net
del plugin
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
log.info("Preparing input blobs")
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
net.batch_size = len(args.input)
# Read and pre-process input images
n, c, h, w = net.inputs[input_blob].shape
images = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
if image.shape[:-1] != (h, w):
log.warning("Image {} is resized from {} to {}".format(
args.input[i], image.shape[:-1], (h, w)))
image = cv2.resize(image, (w, h))
image = image.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
images[i] = image
log.info("Batch size is {}".format(n))
# Loading model to the plugin
log.info("Loading model to the plugin")
exec_net = plugin.load(network=net)
# Start sync inference
log.info("Starting inference ({} iterations)".format(args.number_iter))
infer_time = []
for i in range(args.number_iter):
t0 = time()
res = exec_net.infer(inputs={input_blob: images})
infer_time.append((time() - t0) * 1000)
log.info("Average running time of one iteration: {} ms".format(
np.average(np.asarray(infer_time))))
if args.perf_counts:
perf_counts = exec_net.requests[0].get_perf_counts()
log.info("Performance counters:")
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
# Processing output blob
log.info("Processing output blob")
res = res[out_blob]
_, _, out_h, out_w = res.shape
for batch, data in enumerate(res):
classes_map = np.zeros(shape=(out_h, out_w, 3), dtype=np.int)
for i in range(out_h):
for j in range(out_w):
if len(data[:, i, j]) == 1:
pixel_class = int(data[:, i, j])
else:
pixel_class = np.argmax(data[:, i, j])
classes_map[i, j, :] = classes_color_map[min(pixel_class, 20)]
out_img = os.path.join(os.path.dirname(__file__),
"out_{}.bmp".format(batch))
cv2.imwrite(out_img, classes_map)
log.info("Result image was saved to {}".format(out_img))
import sys
import os
#print(os.path.exists("/opt/intel/openvino/bin/setupvars.sh"))
import subprocess
exec(open("/opt/intel/openvino/bin/setupvars.sh").read())
from openvino.inference_engine import IENetwork, IEPlugin
import numpy as np
from skimage.transform import resize
from skimage.io import imread, imsave
import sys
# load the intel optimized model for inference
model_xml = sys.argv[1]
model_bin = sys.argv[2]
plugin = IEPlugin("CPU", plugin_dirs=None)
# Build Inference Engine network using xml and bin files
net = IENetwork(model=model_xml, weights=model_bin)
# Establish input and output blobs (images)
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
exec_net = plugin.load(network=net)
del net
#preprocess image
# resize to 512,512
# normalize it to 0-1
# transpose and reshape image channel format as openvino IE engine requires it as n,c,h,w
fileName = '/workspace/Usecases_Code/Image_Segmentation/training_512/0cdf5b5d0ce1_01.jpg'
img = imread(fileName)
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork.from_ir(model=model_xml, weights=model_bin)
if "CPU" in plugin.device:
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
format(plugin.device, ', '.join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob]
del net
predictions = []
data = Input(args.input_type, args.input)
cur_request_id = 0
fps = 25
out_width = 640
out_height = 480
if args.dump_output_video:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(args.path_to_output_video, fourcc, fps, (int(out_width), int(out_height)))
while not data.is_finished():
frame, img_id = data.get_next_item()
initial_h, initial_w, channels = frame.shape
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
coco_detections = []
for obj in res[0][0]:
# Draw only objects when probability more than specified threshold
if obj[2] > args.prob_threshold:
x1 = float(obj[3] * initial_w)
y1 = float(obj[4] * initial_h)
x2 = float(obj[5] * initial_w)
y2 = float(obj[6] * initial_h)
x_, y_ = round(x1, 1), round(y1, 1)
w_ = round(x2 - x1, 1)
h_ = round(y2 - y1, 1)
class_id = int(obj[1])
coco_det = {}
coco_det['image_id'] = img_id
coco_det['category_id'] = class_id
coco_det['bbox'] = [x_, y_, w_, h_]
coco_det['score'] = round(float(obj[2]), 1)
coco_detections.append(coco_det)
# Draw box and label\class_id
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)
cv2.putText(frame, str(class_id) + ' ' + str(round(obj[2] * 100, 1)) + ' %', (int(x1), int(y1) - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
predictions.extend(coco_detections)
if args.dump_output_video:
img_resized = cv2.resize(frame, (out_width, out_height))
out.write(img_resized)
if args.show:
cv2.imshow("Detection Results", frame)
key = cv2.waitKey(10)
if key == 27:
break
if args.dump_predictions_to_json:
with open(args.output_json_path, 'w') as output_file:
json.dump(predictions, output_file, sort_keys=True, indent=4)
cv2.destroyAllWindows()
del exec_net
del plugin
def load_model(self,
model,
device,
input_size,
output_size,
num_requests,
cpu_extension=None,
plugin=None):
"""
Loads a network and an image to the Inference Engine plugin.
:param model: .xml file of pre trained model
:param cpu_extension: extension for the CPU device
:param device: Target device
:param input_size: Number of input layers
:param output_size: Number of output layers
:param num_requests: Index of Infer request value. Limited to device capabilities.
:param plugin: Plugin for specified device
:return: Shape of input layer
"""
model_xml = model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device
# and load extensions library if specified
if not plugin:
log.info("Initializing plugin for {} device...".format(device))
self.plugin = IEPlugin(device=device)
else:
self.plugin = plugin
if cpu_extension and 'CPU' in device:
self.plugin.add_cpu_extension(cpu_extension)
# Read IR
log.info("Reading IR...")
self.core = IECore()
self.net = IENetwork(model=model_xml, weights=model_bin)
#supported_layers = self.core.query_network(self.net, device)
log.info("Loading IR to the plugin...")
#self.net_plugin2 = self.core.load_network(self.net, device)
# if self.plugin.device == "CPU":
# supported_layers = self.plugin.get_supported_layers(self.net)
# not_supported_layers = \
# [l for l in self.net.layers.keys() if l not in supported_layers]
# if len(not_supported_layers) != 0:
# log.error("Following layers are not supported by "
# "the plugin for specified device {}:\n {}".
# format(self.plugin.device,
# ', '.join(not_supported_layers)))
# log.error("Please try to specify cpu extensions library path"
# " in command line parameters using -l "
# "or --cpu_extension command line argument")
# sys.exit(1)
if num_requests == 0:
# Loads network read from IR to the plugin
self.net_plugin = self.plugin.load(network=self.net)
else:
self.net_plugin = self.plugin.load(network=self.net,
num_requests=num_requests)
self.input_blob = next(iter(self.net.inputs))
self.out_blob = next(iter(self.net.outputs))
assert len(self.net.inputs.keys()) == input_size, \
"Supports only {} input topologies".format(len(self.net.inputs))
assert len(self.net.outputs) == output_size, \
"Supports only {} output topologies".format(len(self.net.outputs))
return self.plugin, self.get_input_shape()
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml_xml = args.model
model_xml_bin = os.path.splitext(model_xml_xml)[0] + ".bin"
model_va_xml = args.model_va
model_va_bin = os.path.splitext(model_va_xml)[0] + ".bin"
model_lpr_xml = args.model_lpr
model_lpr_bin = os.path.splitext(model_lpr_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
#If you are running in CPU, Please uncomment the below lines.
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
log.info("Reading IR...")
net_recognition = IENetwork(model=model_xml_xml, weights=model_xml_bin)
#If you are running in CPU, Please uncomment the below lines.
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net_recognition)
not_supported_layers = [
l for l in net_recognition.layers.keys()
if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in demo's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net_recognition.inputs.keys()
) == 1, "Demo supports only single input topologies"
assert len(net_recognition.outputs
) == 1, "Demo supports only single output topologies"
input_blob = next(iter(net_recognition.inputs))
out_blob = next(iter(net_recognition.outputs))
log.info("Loading IR to the plugin...")
exec_net_recognition = plugin.load(network=net_recognition, num_requests=2)
# Read and pre-process input image
n, c, h, w = net_recognition.inputs[input_blob].shape
print(" n, c, h, w ", n, c, h, w)
del net_recognition
if args.input == 'cam':
input_stream = 0
else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
if args.labels:
with open(args.labels, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
cap = cv2.VideoCapture(input_stream)
cur_request_id = 0
next_request_id = 1
log.info("Starting inference in async mode...")
log.info("To switch between sync and async modes press Tab button")
log.info("To stop the demo execution press Esc button")
is_async_mode = False
render_time = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
next_frame = frame
initial_w = frame.shape[0]
initial_h = frame.shape[1]
# Main sync point:
# in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately wait for it's completion
inf_start = time.time()
if is_async_mode:
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
exec_net_recognition.start_async(request_id=next_request_id,
inputs={input_blob: in_frame})
else:
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
exec_net_recognition.start_async(request_id=cur_request_id,
inputs={input_blob: in_frame})
if exec_net_recognition.requests[cur_request_id].wait(-1) == 0:
inf_end = time.time()
det_time = inf_end - inf_start
# Parse detection results of the current request
res = exec_net_recognition.requests[cur_request_id].outputs[
out_blob]
for obj in res[0][0]:
# Draw only objects when probability more than specified threshold
if obj[2] > args.prob_threshold:
if obj[2] == 1:
xmin = int(obj[3] * initial_h)
ymin = int(obj[4] * initial_w)
xmax = int(obj[5] * initial_h)
ymax = int(obj[6] * initial_w)
class_id_vehicle = int(obj[1])
# Draw box and label\class_id
color = (min(class_id_vehicle * 12.5,
10), min(class_id_vehicle * 7,
10), min(class_id_vehicle * 5,
10))
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax),
(0, 255, 255), 2)
crop_vehicle = frame[ymin:ymax, xmin:xmax]
crop_img = crop_vehicle
net_va_recognition = IENetwork(model=model_va_xml,
weights=model_va_bin)
input_blob_va = next(iter(net_va_recognition.inputs))
out_blob_va = next(iter(net_va_recognition.outputs))
log.info("Loading IR to the plugin...")
exec_net_va_recog = plugin.load(
network=net_va_recognition, num_requests=2)
n_va, c_va, h_va, w_va = net_va_recognition.inputs[
input_blob_va].shape
if is_async_mode == False:
if crop_img.any():
in_frame_va = cv2.resize(
crop_img, (w_va, h_va))
else:
in_frame_va = cv2.resize(frame, (w_va, h_va))
in_frame_va = in_frame_va.transpose(
(2, 0,
1)) # Change data layout from HWC to CHW
in_frame_va = in_frame_va.reshape(
(n_va, c_va, h_va, w_va))
res_va = exec_net_va_recog.infer(
inputs={input_blob_va: in_frame_va})
# Processing output blob
if exec_net_recognition.requests[cur_request_id].wait(
-1) == 0:
lis_res_color = []
log.info("Processing output blob")
res_colord = res_va["color"]
for l_i in np.nditer(res_colord):
val1 = int(l_i * 100)
lis_res_color.append(val1)
labels_map_list_color = [
"white", "gray", "yellow", "red", "green",
"blue", "black"
]
max_color_value, max_color_index = maximum(
lis_res_color, len(lis_res_color))
class_id_va = max_color_index
det_label_va = labels_map_list_color[class_id_va]
lis_res_type = []
res_type = res_va["type"]
for type_i in np.nditer(res_type):
val1_type_i = int(type_i * 100)
lis_res_type.append(val1_type_i)
labels_map_list_type = [
"car", "van", "truck", "bus"
]
max_type_value, max_type_index = maximum(
lis_res_type, len(lis_res_type))
class_id_type = max_type_index
det_label_type = labels_map_list_type[
class_id_type]
cv2.putText(frame,
det_label_va + ' ' + det_label_type + ' ',
(xmin, ymin - 7), cv2.FONT_HERSHEY_COMPLEX,
0.6, (10, 10, 200), 2)
del exec_net_va_recog
if obj[2] != 1:
xmin = int(obj[3] * initial_h)
ymin = int(obj[4] * initial_w)
xmax = int(obj[5] * initial_h)
ymax = int(obj[6] * initial_w)
class_id = int(obj[1])
# Draw box and label\class_id
color = (min(class_id * 12.5,
10), min(class_id * 7,
10), min(class_id * 5, 10))
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax),
(0, 255, 255), 2)
crop_plate = frame[ymin:ymax, xmin:xmax]
crop_img_lpr = crop_plate
net_lpr_recognition = IENetwork(model=model_lpr_xml,
weights=model_lpr_bin)
input_blob_lpr = list(
net_lpr_recognition.inputs.keys())[0]
seq_id = list(net_lpr_recognition.inputs.keys())[1]
out_blob_lpr = next(iter(net_lpr_recognition.outputs))
log.info("Loading IR to the plugin...")
exec_net_lpr_recog = plugin.load(
network=net_lpr_recognition, num_requests=2)
n_lpr, c_lpr, h_lpr, w_lpr = net_lpr_recognition.inputs[
input_blob_lpr].shape
in_frame_seq_id = np.ones(88, dtype=np.int32)
in_frame_seq_id = in_frame_seq_id.reshape((88, 1))
in_frame_seq_id[0] = 0
if is_async_mode == False:
if crop_img_lpr.any():
in_frame_lpr = cv2.resize(
crop_img_lpr, (w_lpr, h_lpr))
else:
in_frame_lpr = cv2.resize(
frame, (w_lpr, h_lpr))
in_frame_lpr = in_frame_lpr.transpose(
(2, 0,
1)) # Change data layout from HWC to CHW
in_frame_lpr = in_frame_lpr.reshape(
(n_lpr, c_lpr, h_lpr, w_lpr))
res_lpr = exec_net_lpr_recog.infer(
inputs={
input_blob_lpr: in_frame_lpr,
seq_id: in_frame_seq_id
})
lis_res_lpr = []
res_lpr_decode = res_lpr['decode']
for type_i in np.nditer(res_lpr_decode):
val1_lpr_i = int(type_i)
lis_res_lpr.append(val1_lpr_i)
class_id_lpr = lis_res_lpr[0]
det_label_lpr = list_lpr[class_id_lpr]
list_plate = []
for item_list in lis_res_lpr:
if item_list == -1:
break
else:
list_plate.append(list_lpr[item_list])
print("Number_plate", list_plate)
if len(list_plate) == 6:
list_plate.remove(list_plate[0])
str_lpr = ''.join(list_plate)
cv2.putText(frame, det_label_lpr + ' ' + str_lpr,
(xmin, ymin - 7),
cv2.FONT_HERSHEY_COMPLEX, 0.8,
(10, 10, 200), 2)
del exec_net_lpr_recog
# Draw performance stats
inf_time_message = "Inference time: N\A for async mode" if is_async_mode else \
"Inference time: {:.3f} ms".format(det_time * 1000)
render_time_message = "OpenCV rendering time: {:.3f} ms".format(
render_time * 1000)
async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
"Async mode is off. Processing request {}".format(cur_request_id)
cv2.putText(frame, inf_time_message, (15, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
cv2.putText(frame, render_time_message, (15, 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
render_start = time.time()
cv2.imshow("Detection Results", frame)
render_end = time.time()
render_time = render_end - render_start
if is_async_mode:
cur_request_id, next_request_id = next_request_id, cur_request_id
frame = next_frame
key = cv2.waitKey(500)
if key == 27:
break
if (9 == key):
is_async_mode = not is_async_mode
log.info("Switched to {} mode".format(
"async" if is_async_mode else "sync"))
cv2.destroyAllWindows()
del exec_net_recognition
del plugin
def __init__(self, dyda_config_path='', debug=False):
""" __init__ of DetectorOpenVINO
Trainer Variables:
input_data: a list of image array
results: defined by lab_tools.output_pred_detection()
Arguments:
dyda_config_path -- Trainer config filepath
"""
if debug:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
# Setup dyda config
super(DetectorOpenVINO,
self).__init__(dyda_config_path=dyda_config_path)
self.set_param(self.class_name)
self.check_param_keys()
if "threshold" in self.param.keys():
self.threshold = self.param["threshold"]
else:
self.threshold = 0.3
# Setup DL model
model_xml = self.param['model_description']
model_bin = self.param['model_file']
with open(self.param['label_file'], 'r') as f:
self.labels_map = [x.strip() for x in f]
# Setup OpenVINO
#
# Plugin initialization for specified device and
# load extensions library if specified
#
# Note: MKLDNN CPU-targeted custom layer support is not included
# because we do not use it yet.
self.plugin = IEPlugin(device=self.param['device'],
plugin_dirs=self.param['plugin_dirs'])
if self.param['device'] == 'CPU':
for ext in self.param['cpu_extensions']:
logger.info('Add cpu extension: {}'.format(ext))
self.plugin.add_cpu_extension(ext)
logger.debug("Computation device: {}".format(self.param['device']))
# Read IR
logger.debug("Loading network files:\n\t{}\n\t{}".format(
model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
if self.plugin.device == "CPU":
supported_layers = self.plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
logger.error(
('Following layers are not supported '
'by the plugin for specified device {}:\n {}').format(
self.plugin.device, ', '.join(not_supported_layers)))
logger.error("Please try to specify cpu "
"extensions library path in demo's "
"command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net.inputs.keys()) == 1, (
'Demo supports only single input topologies')
assert len(
net.outputs) == 1, ('Demo supports only single output topologies')
# input_blob and and out_blob are the layer names in string format.
logger.debug("Preparing input blobs")
self.input_blob = next(iter(net.inputs))
self.out_blob = next(iter(net.outputs))
self.n, self.c, self.h, self.w = net.inputs[self.input_blob].shape
# Loading model to the plugin
self.exec_net = self.plugin.load(network=net, num_requests=2)
del net
# Initialize engine mode: sync or async
#
# FIXME: async mode does not work currently.
# process_input needs to provide two input tensors for async.
self.is_async_mode = False
self.cur_request_id = 0
self.next_request_id = 1
class emotionInfer():
def __init__(self):
self.model_xml_path = "1.xml"
self.model_bin_path = "1.bin"
self.model_landmark_path = "./shape_predictor_68_face_landmarks.dat"
self.model_facedet_path = "./haarcascade_frontalface_alt2.xml"
self.capPic = "capture.jpg"
self.result = {}
self.maxLen = 5
self.averData = [0] * 7
self.averDataQue = deque()
self.plugin = IEPlugin(device='MYRIAD')
self.net = IENetwork.from_ir(model=self.model_xml_path,
weights=self.model_bin_path)
self.exec_net = self.plugin.load(network=self.net)
self.dataQueue = deque(maxlen=self.maxLen)
assert len(self.net.inputs.keys()) == 1
assert len(self.net.outputs) == 1
self.input_blob = next(iter(self.net.inputs))
# input_blob = 'input'
self.out_blob = next(iter(self.net.outputs))
# out_blob = 'output/BiasAdd'
def capturePic(self):
success, frame = cameraCapture.read()
img_path = self.capPic
cv2.imwrite(img_path, frame)
return img_path
def infer_init(self):
plugin = IEPlugin(device='MYRIAD')
net = IENetwork.from_ir(model=self.model_xml_path,
weights=self.model_bin_path)
exec_net = plugin.load(network=net)
assert len(self.net.inputs.keys()) == 1
assert len(self.net.outputs) == 1
input_blob = next(iter(net.inputs))
# input_blob = 'input'
out_blob = next(iter(net.outputs))
# out_blob = 'output/BiasAdd'
def pre_process_image(self, img_path):
# Model input format
n, c, h, w = [1, 3, 224, 224]
image = Image.open(img_path)
processedImg = image.resize((h, w), resample=Image.BILINEAR)
# Normalize to keep data between 0 - 1
processedImg = (np.array(processedImg) - 0) / 255.0
# Change data layout from HWC to CHW
processedImg = processedImg.transpose((2, 0, 1))
processingImg = processedImg.reshape((n, c, h, w))
return image, processingImg, img_path
def emotion_infer(self, cap):
#print(1)
#self.averData = [0] * 7
image, processedImg, imagePath = self.pre_process_image(cap)
infer_result = self.exec_net.infer(
inputs={self.input_blob: processedImg})
emotion_array = infer_result["dense_2/Softmax"][0]
#request_data_json = {"vehicle_id": random.randint(0,100), "latitude": "12.34",
# "longitude": "12.34", "tired": "12.34", "生气": emotion_array[0],
# "厌恶": emotion_array[1], "恐惧": emotion_array[2],
# "开心": emotion_array[3], "伤心": emotion_array[4], "惊讶": emotion_array[5], "正常": emotion_array[6]}
#self.result = request_data_json
self.dataQueue.append(emotion_array)
#print(len(self.dataQueue))
self.averData = [0] * 7
while len(self.dataQueue) == 5:
for data in self.dataQueue:
#print(data)
for num in range(7):
self.averData[num] += data[num]
self.averData = [i / 5 for i in self.averData]
#print(self.averData)
#self.averDataQue.appendleft(self.averData)
break
return self.averData
def main():
# Setup log config
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
# Parse args
args = parse_args()
log.info("Start Fall Detection")
# Plugin initialization for specified device
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Load model
model_xml = os.path.join(os.getcwd(), "models",
"human-pose-estimation-0001",
args.model_precision,
"human-pose-estimation-0001.xml")
model_bin = os.path.splitext(model_xml)[0] + ".bin"
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error("""Following layers are not supported by the plugin
for specified device {}:\n {}""".format(
plugin.device, ', '.join(not_supported_layers)))
log.error("""Please try to specify cpu extensions library
path in demo's command line parameters using -l
or --cpu_extension command line argument
""")
sys.exit(1)
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
del net
if args.input == 'cam':
input_stream = 0
else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
cap = cv2.VideoCapture(input_stream)
# Grab the shape of the input
width = int(cap.get(3))
height = int(cap.get(4))
fps = cap.get(cv2.CAP_PROP_FPS)
font_scale = round(height / 360)
font_thickness = round(3 * font_scale)
cur_request_id = 0
next_request_id = 1
# Fall Detection variables
previous_head_avg_position = 0
previous_head_detection_frame = 0
last_fall_detected_frame = 0
# Fall Detection threshold speed is depedent of the frame height
fall_threshold = 0.04 * height
framerate_threshold = round(fps / 5.0)
fall_detected_text_position = (20, round(0.15 * height))
render_time = 0
ret, frame = cap.read()
frame_number = 0
out_file = None
if args.input != 'cam':
out_filename = os.path.splitext(input_stream)[0] + '_output.mp4'
out_file = cv2.VideoWriter(out_filename, 0x00000021, fps,
(width, height))
log.info("Evaluating video file stream...")
else:
log.info("Evaluating webcam stream...")
while cap.isOpened():
ret, next_frame = cap.read()
if not ret:
break
# Pre-process inputs
in_frame = cv2.resize(next_frame, (w, h))
in_frame = in_frame.transpose((2, 0, 1))
in_frame = in_frame.reshape((n, c, h, w))
# Inference
exec_net.start_async(request_id=next_request_id,
inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs
kp_heatmaps = res['Mconv7_stage2_L2']
threshold = 0.5
points = []
head_elements_y_pos = []
for i in range(POSE_POINTS_NUMBER):
# confidence map of corresponding body's part.
probMap = kp_heatmaps[0, i, :, :]
# Find global maxima of the probMap.
minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
# Scale the point to fit on the original image
x = frame.shape[1] / probMap.shape[1] * point[0]
y = frame.shape[0] / probMap.shape[0] * point[1]
# Add point if the probability is greater than the threshold
if prob > threshold:
point = (int(x), int(y))
# If point is a component of the head (including neck and
# sholders) append to the header elemnts
if (i == 0 or i == 1 or i == 2 or i == 5 or i == 14
or i == 15 or i == 16 or i == 17):
head_elements_y_pos.append(point[1])
points.append(point)
else:
points.append(None)
# Draw Skeleton
for num, pair in enumerate(POSE_PAIRS):
partA = pair[0]
partB = pair[1]
if points[partA] and points[partB]:
cv2.line(frame, points[partA], points[partB],
JOINT_COLORS[num], 3)
# Calculate head average position from its components
if (len(head_elements_y_pos) > 0):
head_avg_position = sum(head_elements_y_pos)
head_avg_position /= len(head_elements_y_pos)
# log.info(head_avg_position)
# Compare previous head position
# to detect if falling
if (previous_head_detection_frame
and (head_avg_position - previous_head_avg_position) >
fall_threshold
and (frame_number - previous_head_detection_frame) <
framerate_threshold):
# print("Fall detected.")
last_fall_detected_frame = frame_number
previous_head_avg_position = head_avg_position
previous_head_detection_frame = frame_number
# Draw Fall Detection Text if last fall event
# ocurred max 2 seconds ago
if (last_fall_detected_frame
and (frame_number - last_fall_detected_frame) <= 2 * fps):
cv2.putText(frame, "FALL DETECTED!",
fall_detected_text_position,
cv2.FONT_HERSHEY_COMPLEX, font_scale, (0, 0, 255),
font_thickness, cv2.LINE_AA)
render_start = time.time()
# If webcam mode
if out_file:
out_file.write(frame)
else:
cv2.imshow("Detection Results", frame)
cur_request_id, next_request_id = next_request_id, cur_request_id
frame = next_frame
# Increment frame number
frame_number += 1
key = cv2.waitKey(1)
if key == 27:
break
if out_file:
# Release the out writer, capture, and destroy any OpenCV windows
out_file.release()
out_filename = os.path.splitext(input_stream)[0] + '_output.mp4'
log.info("Finished. %s saved." % (out_filename))
else:
log.info("Finished.")
cv2.destroyAllWindows()
del exec_net
import argparse
from openvino.inference_engine import IENetwork, IEPlugin
from test import preprocessing, postprocessing
args = argparse.ArgumentParser()
args.add_argument("images", nargs='*', type=str)
args.add_argument("-d", "--device" , type=str, default="MYRIAD", help="Default MYRIAD or CPU")
args = args.parse_args()
data_type="FP16"
if args.device == "CPU": data_type="FP32"
model_xml='./'+data_type+'/y.xml'
model_bin='./'+data_type+'/y.bin'
plugin = IEPlugin(device=args.device, plugin_dirs=None)
extension = "/inference_engine_samples/intel64/Release/lib/libcpu_extension.so"
extension = os.environ['HOME']+extension
if args.device == "CPU":plugin.add_cpu_extension(extension)
net = IENetwork(model=model_xml, weights=model_bin) # R5
print(model_bin, "on", args.device)
exec_net = plugin.load(network=net, num_requests=1)
input_blob = next(iter(net.inputs)) #input_blob = 'data'
out_blob = next(iter(net.outputs)) #out_blob = 'detection_out'
print(net.inputs[input_blob].shape)
model_n, model_c, model_h, model_w = net.inputs[input_blob].shape #Tool kit R4
print("input_blob shape(from xml)", net.inputs[input_blob].shape)
print("name (input_blob : out_blob) =",input_blob,":",out_blob)
from openvino.inference_engine import IENetwork, IEPlugin
import os
import numpy as np
plugin = IEPlugin(dev=args.device, plugin_dirs=args.plugin_dirs)
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + '.bin'
net = IENetwork(model=model_xml, weights=model_bin)
Exec_net = plugin.load(network=net)
# Read and preprocess the images
n, c, h, w = net.inputs[input_blob].shape
images = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
if image.shape[:-1] != (h, w):
image = cv2.resize(image, (w, h))
image = image.transpose((2, 0, 1))
images[i] = image
res = Exec_net.infer(inputs={input_blob: images})
res = res[out_blob]
for i, probs in enumerate(res):
probs = np.squeeze(probs)
top_ind = np.argsort(probs)[-args.number_top:][::-1]
print(f'Input {args.input[i]}\n')
for id in top_ind:
def Inference():
try:
#Get the Model XML file from the XML entry of the GUI
model_xml = xml_entry.get()
if (not model_xml):
msg = messagebox.showinfo(
"Open Vino Inference",
"Please browse and select the model XML file")
return
#Set the Model file as per the naming convention of the XML. Assumption here is that the XML and BIN files are the same naming format
model_bin = os.path.splitext(model_xml)[0] + ".bin"
#Get and set the label file path
labels_filepath = label_entry.get()
if (not labels_filepath):
msg = messagebox.showinfo(
"Inference", "Please browse and select the labels file")
return
#Get the device option from one of the three options(CPU, GPU or Movidius)
device_option = strDevice.get()
if (device_option == "Movidius"):
device_option = "MYRIAD"
#Get the feed option here as the input. This is from one of the three options(Image, Video or Movidius)
feed_option = strCapture.get()
fileext = os.path.splitext(image_entry.get())
#Valiating ths user input for the selected image. If it is empty promt to select the Image again
if ((feed_option == "Image") and (not image_entry.get())):
msg = messagebox.showinfo(
"Inference", "Please browse and select the Image files")
return
#Valiating ths user input for the selected Video. If it is empty promt to select the Video file again
if ((feed_option == "Video") and (not image_entry.get())):
msg = messagebox.showinfo(
"Inference", "Please browse and select the Video files")
return
#Valiating ths selected image if it is a JPG image
if (feed_option == "Image" and
(fileext[1].lower() != ".jpg" and fileext[1].lower() != ".jpeg")):
msg = messagebox.showinfo(
"Inference",
"Please select valid Image files. Jpeg files are supported")
return
#Valiating ths selected Video if it is a .MP4,.MPEG or .AVI format
if (feed_option == "Video" and
(fileext[1].lower() != ".mp4" and fileext[1].lower() != ".avi"
and fileext[1].lower() != ".mpeg")):
msg = messagebox.showinfo(
"Inference",
"Please enter valid Video files. MP4,AVI or MPEG files are supported"
)
return
image_list = []
#if the Option to Scan the full folder is selected
if (scanFolder.get()):
#For video option only one video that is selected is appended. Does not support multiple videos if exist in that folder
if (feed_option == "Video"):
image_list.append(image_entry.get())
#For images when Scan folder is selected, it browses through all the JPG files in the folder and appends to the list to do Inference
elif (feed_option == "Image"):
full_folder = os.path.dirname(image_entry.get())
for file_name in os.listdir(full_folder):
if file_name.endswith(".jpg") or file_name.endswith(
".jpeg"):
image_list.append(os.path.join(full_folder, file_name))
else:
continue
else:
pass
else:
image_list.append(image_entry.get())
# Plugin initialization for specified device
plugin = IEPlugin(device=device_option)
# Read IR
net = IENetwork(model=model_xml, weights=model_bin)
input_blob, out_blob = next(iter(net.inputs)), next(iter(net.outputs))
net.batch_size = 1
################################################################################
#Loading the labels file
with open(labels_filepath, 'r') as f:
labels_map = [x.split(sep=' ', maxsplit=1)[-1].strip() for x in f]
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
exec_net = plugin.load(network=net)
#if option selected is image
if (feed_option == "Image"):
#Scan thorugh the Image folder list and starting Inference on the Images
for i in range(len(image_list)):
tm = time.time()
cap = cv2.imread(image_list[i])
frame_ = cap
frame_ = preprocess_input(frame_)
res = exec_net.infer(inputs={
input_blob:
[cv2.resize(frame_, (w, h)).transpose((2, 0, 1))]
})
res = res[out_blob]
clslbl = []
#Calculating the threshold probabablity
for i, probs in enumerate(res):
top_ind = np.argsort(np.squeeze(probs))[-10:][::-1]
for id in top_ind:
clslbl.append("{} ({:.2f}%)".format(
labels_map[id], 100 * probs[id]))
frame_ = cv2.resize(frame_, (800, 460))
#Showing the Image, threshold and the Inferences per second details on the frame
txt = ('[%02d INF/S] Prediction: ' %
(1 / (time.time() - tm))) + clslbl[0]
txtlabel.config(text=txt)
#If scanning thorugh the complete folder, showing the Image slide show,shows the Inference results along with the Threshold details
#The image changes every 2 seconds in the slide show and one can quit by pressing the q button
if (scanFolder.get()):
final_txt = "Press \\'q\\' to Quit"
cv2.putText(frame_, txt, (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.80, (0, 255, 0), 2)
cv2.putText(frame_, final_txt, (10, 65),
cv2.FONT_HERSHEY_SIMPLEX, .80, (0, 255, 0), 2)
cv2.imshow('Inference', frame_)
if cv2.waitKey(2000) & 0xFF == ord('q'): break
#If Camera option is selected, starts the live camera feed
elif (feed_option == "Camera"):
cap = cv2.VideoCapture(0)
while TRUE:
tm = time.time()
ret, frame_ = cap.read()
frame_ = preprocess_input(frame_)
res = exec_net.infer(inputs={
input_blob:
[cv2.resize(frame_, (w, h)).transpose((2, 0, 1))]
})
res = res[out_blob]
clslbl = []
#Calculating the threshold probabablity
for i, probs in enumerate(res):
top_ind = np.argsort(np.squeeze(probs))[-10:][::-1]
for id in top_ind:
clslbl.append("{} ({:.2f}%)".format(
labels_map[id], 100 * probs[id]))
frame_ = cv2.resize(frame_, (820, 460))
txt = ('[%02d FPS] Prediction: ' %
(1 / (time.time() - tm))) + clslbl[0]
final_txt = "Press \\'q\\' to Quit"
cv2.putText(frame_, txt, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.80, (0, 255, 0), 2)
cv2.putText(frame_, final_txt, (10, 65),
cv2.FONT_HERSHEY_SIMPLEX, .80, (0, 255, 0), 2)
#showing the Camera live feed and shows the Inference results in a separate frame along with the Threshold details and the FPS
# one can quit by pressing the q button
cv2.imshow('Inference', frame_)
if cv2.waitKey(1) & 0xFF == ord('q'): break
else:
#If Video is selected as the capture option, loads the selected video and starts inference process
cap = cv2.VideoCapture(image_list[0])
while (cap.isOpened()):
tm = time.time()
ret, frame_ = cap.read()
frame_ = preprocess_input(frame_)
res = exec_net.infer(inputs={
input_blob:
[cv2.resize(frame_, (w, h)).transpose((2, 0, 1))]
})
res = res[out_blob]
clslbl = []
for i, probs in enumerate(res):
top_ind = np.argsort(np.squeeze(probs))[-10:][::-1]
for id in top_ind:
clslbl.append("{} ({:.2f}%)".format(
labels_map[id], 100 * probs[id]))
frame_ = cv2.resize(frame_, (820, 460))
txt = ('[%02d FPS] Prediction: ' %
(1 / (time.time() - tm))) + clslbl[0]
final_txt = "Press \\'q\\' to Quit"
cv2.putText(frame_, txt, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.80, (0, 255, 0), 2)
cv2.putText(frame_, final_txt, (10, 65),
cv2.FONT_HERSHEY_SIMPLEX, .80, (0, 255, 0), 2)
#Loads the video file in a separate frame and shows the inference results like Threshold details and the FPS
# one can quit by pressing the q button
cv2.imshow('Inference', frame_)
if cv2.waitKey(1) & 0xFF == ord('q'): break
#cap.release()
cv2.destroyAllWindows()
del exec_net, plugin, net
except BaseException as e:
msg = messagebox.showinfo("Inference",
"An exception is encountered. " + str(e))
def main() :
words = ['a', 'b', 'c', 'd', 'e', 'f',
'g', 'h', 'i', 'j', 'k', 'l',
'm', 'n', 'o', 'p', 'q', 'r',
's', 't', 'u', 'v', 'w', 'x',
'y', 'z', '0', '1', '2', '3',
'4', '5', '6', '7', '8', '9',
'-']
args = parsing().parse_args()
model_graph = args.model
model_weight = args.model[:-3] + 'bin'
net = IENetwork(model = model_graph,
weights = model_weight)
# net.batch_size = 2
iter_inputs = iter(net.inputs)
iter_outputs = iter(net.outputs)
# inputs_num = len(net.inputs)
# print (inputs_num)
'''
input_blob = []
for _inputs in iter_inputs:
input_blob.append(_inputs)
output_blob = []
for _outputs in iter_outputs:
output_blob.append(_outputs)
'''
'''
input_l = []
for i in input_blob:
print (net.inputs[i].shape)
input_l.append(np.ones(shape=net.inputs[i].shape, dtype=np.float32))
inputs = dict()
for i in range (inputs_num):
inputs[input_blob[i]] = input_l[i]
'''
input_blob = next(iter_inputs)
output_blob = next(iter_outputs)
if args.input == '':
input = np.ones(shape=net.inputs[input_blob].shape, dtype = np.float32)
else :
b, c, h, w = net.inputs[input_blob].shape
print (b, c, h, w)
import cv2
input = cv2.imread(args.input)
print (input.shape)
input = cv2.resize(input, (w, h))
input = input.transpose((2, 0, 1)).reshape(1, c, h, w)
plugin = IEPlugin(device = 'CPU')
exec_net = plugin.load(network = net)
# if args.cpu_extension :
# plugin.add_cpu_extension(args.cpu_extension)
# res = plugin.impl.CTCGreedyDecoder();
a = np.concatenate((input, input), 0)
print (a.shape)
inputs = {input_blob: input}
out = exec_net.infer(inputs)
# print (out[output_blob])
print (decoder.CTCGreedyDecoder(out[output_blob], words, words[-1]))
print (decoder.CTCBeamSearchDecoder(out[output_blob], words, words[-1], 50))
'''
def main():
# Plugin initialization for specified device and load extensions library
global rolling_log
#defaultTarget = TARGET_DEVICE
env_parser()
args_parser()
check_args()
parse_conf_file()
# if TARGET_DEVICE not in acceptedDevices:
# print ("Unsupporterd device " + TARGET_DEVICE + ". Defaulting to CPU")
# TARGET_DEVICE = 'CPU'
print("Initializing plugin for {} device...".format(TARGET_DEVICE))
plugin = IEPlugin(device=TARGET_DEVICE)
if CPU_EXTENSION and 'CPU' == TARGET_DEVICE:
plugin.add_cpu_extension(CPU_EXTENSION)
# Read IR
print("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
# Load the IR
print("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
del net
minFPS = min([i.cap.get(cv2.CAP_PROP_FPS) for i in videoCaps])
minlength = min([i.cap.get(cv2.CAP_PROP_FRAME_COUNT) for i in videoCaps])
for vc in videoCaps:
vc.rate = int(math.ceil(vc.length / minlength))
print(minFPS)
waitTime = int(
round(1000 / minFPS /
len(videoCaps))) # wait time in ms between showing frames
frames_sum = 0
for vc in videoCaps:
vc.init_vw(h, w, minFPS)
frames_sum += vc.length
statsWidth = w if w > 345 else 345
statsHeight = h if h > (len(videoCaps) * 20 + 15) else (
len(videoCaps) * 20 + 15)
statsVideo = cv2.VideoWriter(os.path.join(output_dir,
'Statistics.mp4'), 0x00000021,
minFPS, (statsWidth, statsHeight), True)
if not statsVideo.isOpened():
print("Couldn't open stats video for writing")
sys.exit(4)
# Read the labels file
if labels_file:
with open(labels_file, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
# Init a rolling log to store events
rolling_log_size = int((h - 15) / 20)
rolling_log = collections.deque(maxlen=rolling_log_size)
# Init inference request IDs
cur_request_id = 0
next_request_id = 1
# Start with async mode enabled
is_async_mode = True
if not UI_OUTPUT:
# Arrange windows so they are not overlapping
#arrange_windows(w, h)
print("To stop the execution press Esc button")
no_more_data = False
for vc in videoCaps:
vc.start_time = datetime.datetime.now()
frame_count = 0
job_id = os.environ['PBS_JOBID']
progress_file_path = os.path.join(output_dir,
'i_progress_' + job_id + '.txt')
infer_start_time = time.time()
#Start while loop
while True:
# If all video captures are closed stop the loop
if False not in [videoCap.closed for videoCap in videoCaps]:
print("I broke here line 387")
break
no_more_data = False
# loop over all video captures
for idx, videoCapInfer in enumerate(videoCaps):
# read the next frame
#print("Video {0} has length {1} and fps {2}".format(idx, videoCapInfer.length, videoCapInfer.fps))
if not videoCapInfer.closed:
#print("ID {0}".format(idx))
vfps = int(round(videoCapInfer.cap.get(cv2.CAP_PROP_FPS)))
#for i in range(0, int(round(vfps / minFPS))):
for i in range(videoCapInfer.rate):
frame_count += 1
#print("i = {0}".format(i))
ret, frame = videoCapInfer.cap.read()
videoCapInfer.cur_frame_count += 1
# If the read failed close the program
if not ret:
videoCapInfer.closed = True
no_more_data = True
break
if videoCapInfer.closed:
print("Video {0} is done".format(idx))
print("Video has {0} frames ".format(
videoCapInfer.length))
break
# Copy the current frame for later use
videoCapInfer.cur_frame = frame.copy()
videoCapInfer.initial_w = videoCapInfer.cap.get(3)
videoCapInfer.initial_h = videoCapInfer.cap.get(4)
# Resize and change the data layout so it is compatible
in_frame = cv2.resize(videoCapInfer.cur_frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
infer_start = datetime.datetime.now()
if is_async_mode:
exec_net.start_async(request_id=next_request_id,
inputs={input_blob: in_frame})
# Async enabled and only one video capture
if (len(videoCaps) == 1):
videoCapResult = videoCapInfer
# Async enabled and more than one video capture
else:
# Get previous index
videoCapResult = videoCaps[idx - 1 if idx -
1 >= 0 else len(videoCaps) -
1]
else:
# Async disabled
exec_net.start_async(request_id=cur_request_id,
inputs={input_blob: in_frame})
videoCapResult = videoCapInfer
if exec_net.requests[cur_request_id].wait(-1) == 0:
infer_end = datetime.datetime.now()
infer_duration = infer_end - infer_start
current_count = 0
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
for obj in res[0][0]:
class_id = int(obj[1])
# Draw only objects when probability more than specified threshold
if (obj[2] > PROB_THRESHOLD
and videoCapResult.req_label in labels_map
and labels_map.index(
videoCapResult.req_label) == class_id - 1):
current_count += 1
xmin = int(obj[3] * videoCapResult.initial_w)
ymin = int(obj[4] * videoCapResult.initial_h)
xmax = int(obj[5] * videoCapResult.initial_w)
ymax = int(obj[6] * videoCapResult.initial_h)
# Draw box
cv2.rectangle(videoCapResult.cur_frame,
(xmin, ymin), (xmax, ymax),
(0, 255, 0), 4, 16)
if videoCapResult.candidate_count is current_count:
videoCapResult.candidate_confidence += 1
else:
videoCapResult.candidate_confidence = 0
videoCapResult.candidate_count = current_count
if videoCapResult.candidate_confidence is FRAME_THRESHOLD:
videoCapResult.candidate_confidence = 0
if current_count > videoCapResult.last_correct_count:
videoCapResult.total_count += current_count - videoCapResult.last_correct_count
if current_count is not videoCapResult.last_correct_count:
if UI_OUTPUT:
currtime = datetime.datetime.now().strftime(
"%H:%M:%S")
fr = FrameInfo(videoCapResult.frames,
current_count, currtime)
videoCapResult.countAtFrame.append(fr)
new_objects = current_count - videoCapResult.last_correct_count
for _ in range(new_objects):
str = "{} - {} detected on {}".format(
time.strftime("%H:%M:%S"),
videoCapResult.req_label,
videoCapResult.cap_name)
rolling_log.append(str)
videoCapResult.frames += 1
videoCapResult.last_correct_count = current_count
else:
videoCapResult.frames += 1
videoCapResult.cur_frame = cv2.resize(
videoCapResult.cur_frame, (w, h))
if not UI_OUTPUT:
# Add log text to each frame
log_message = "Async mode is on." if is_async_mode else \
"Async mode is off."
cv2.putText(videoCapResult.cur_frame, log_message,
(15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 1)
log_message = "Total {} count: {}".format(
videoCapResult.req_label,
videoCapResult.total_count)
cv2.putText(videoCapResult.cur_frame, log_message,
(10, h - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 255, 255), 1)
log_message = "Current {} count: {}".format(
videoCapResult.req_label,
videoCapResult.last_correct_count)
cv2.putText(videoCapResult.cur_frame, log_message,
(10, h - 30), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 255, 255), 1)
cv2.putText(
videoCapResult.cur_frame, 'Infer wait: %0.3fs' %
(infer_duration.total_seconds()), (10, h - 70),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# Display inferred frame and stats
stats = numpy.zeros((statsHeight, statsWidth, 1),
dtype='uint8')
for i, log in enumerate(rolling_log):
cv2.putText(stats, log, (10, i * 20 + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 1)
#cv2.imshow(STATS_WINDOW_NAME, stats)
if idx == 0:
stats = cv2.cvtColor(stats, cv2.COLOR_GRAY2BGR)
#Write
statsVideo.write(stats)
end_time = datetime.datetime.now()
cv2.putText(
videoCapResult.cur_frame, 'FPS: %0.2fs' %
(1 / (end_time -
videoCapResult.start_time).total_seconds()),
(10, h - 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 1)
#cv2.imshow(videoCapResult.cap_name, videoCapResult.cur_frame)
videoCapResult.start_time = datetime.datetime.now()
#Write
videoCapResult.video.write(videoCapResult.cur_frame)
if frame_count % 10 == 0:
progressUpdate(progress_file_path,
time.time() - infer_start_time, frame_count,
frames_sum)
# Wait if necessary for the required time
#key = cv2.waitKey(waitTime)
key = cv2.waitKey(1)
# Esc key pressed
if key == 27:
cv2.destroyAllWindows()
del exec_net
del plugin
print("Finished")
return
# Tab key pressed
if key == 9:
is_async_mode = not is_async_mode
print("Switched to {} mode".format(
"async" if is_async_mode else "sync"))
if is_async_mode:
# Swap infer request IDs
cur_request_id, next_request_id = next_request_id, cur_request_id
# Loop video if LOOP_VIDEO = True and input isn't live from USB camera
if LOOP_VIDEO and not videoCapInfer.is_cam:
vfps = int(round(videoCapInfer.cap.get(cv2.CAP_PROP_FPS)))
# If a video capture has ended restart it
if (videoCapInfer.cur_frame_count >
videoCapInfer.cap.get(cv2.CAP_PROP_FRAME_COUNT) -
int(round(vfps / minFPS))):
videoCapInfer.cur_frame_count = 0
videoCapInfer.cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
if no_more_data:
progressUpdate(progress_file_path,
time.time() - infer_start_time, frames_sum,
frames_sum)
break
#End of while loop--------------------
no_more_data = True
t2 = time.time() - infer_start_time
for videos in videoCaps:
print(videos.length)
print(videos.closed)
print("End loop")
print("Total time {0}".format(t2))
print("Total frame count {0}".format(frame_count))
print("fps {0}".format(frame_count / t2))
with open(os.path.join(output_dir, 'stats.txt'), 'w') as f:
f.write('{} \n'.format(round(t2)))
f.write('{} \n'.format(frame_count))
for vc in videoCaps:
print("Frames processed {}".format(vc.cur_frame_count))
print("Frames count {}".format(vc.length))
for vc in videoCaps:
vc.video.release()
vc.cap.release()
if no_more_data:
break
default="CPU",
type=str)
return parser
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=None)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
video_capture = cv2.VideoCapture(0)
# Prepare labels map
with open(args.labels) as f:
labels_map = json.load(f)
# switch keys and values
labels_map = dict((y, x) for (x, y) in labels_map.items())
# setup logger
logging.basicConfig(format='[ %(levelname)s ] %(message)s',
level=logging.INFO,
stream=sys.stdout)
# Load model into inference engine
plugin = IEPlugin(args.device)
net = IENetwork(model=args.model, weights=args.weights)
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
exec_net = plugin.load(network=net, num_requests=2)
# Input shape: [n_samples, n_channels, height, width]
input_shape = net.inputs[input_blob].shape
input_height = input_shape[2]
input_width = input_shape[3]
assert input_shape[0] == 1
def preprocess(frame):
frame = cv2.resize(frame, (input_width, input_height))
frame = frame.astype(np.float32)
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for %s device...", args.device)
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if "CPU" in plugin.device:
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if not_supported_layers:
log.error(
"Following layers are not supported by the plugin for specified device %s:\n %s",
plugin.device, ', '.join(not_supported_layers))
log.error(
"Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
# Read and pre-process input image
batch, channels, height, width = net.inputs[input_blob].shape
del net
predictions = []
data = Input(args.input_type, args.input)
cur_request_id = 0
fps = 25
out_width = 640
out_height = 480
if args.dump_output_video:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(args.path_to_output_video, fourcc, fps,
(int(out_width), int(out_height)))
while not data.is_finished():
frame, img_id = data.get_next_item()
initial_h, initial_w, _ = frame.shape
in_frame = cv2.resize(frame, (width, height))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((batch, channels, height, width))
exec_net.start_async(request_id=cur_request_id,
inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
coco_detections = []
for obj in res[0][0]:
# Draw only objects when probability more than specified threshold
if obj[2] > args.prob_threshold:
top_left_x = float(obj[3] * initial_w)
top_left_y = float(obj[4] * initial_h)
bottom_right_x = float(obj[5] * initial_w)
bottom_right_y = float(obj[6] * initial_h)
obj_width = round(bottom_right_x - top_left_x, 1)
obj_height = round(bottom_right_y - top_left_y, 1)
class_id = int(obj[1])
coco_det = {}
coco_det['image_id'] = img_id
coco_det['category_id'] = class_id
coco_det['bbox'] = [
round(top_left_x, 1),
round(top_left_y, 1), obj_width, obj_height
]
coco_det['score'] = round(float(obj[2]), 1)
coco_detections.append(coco_det)
# Draw box and label\class_id
cv2.rectangle(frame, (int(top_left_x), int(top_left_y)),
(int(bottom_right_x), int(bottom_right_y)),
(255, 0, 0), 2)
cv2.putText(
frame,
str(class_id) + ' ' + str(round(obj[2] * 100, 1)) +
' %', (int(top_left_x), int(top_left_y) - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
predictions.extend(coco_detections)
if args.dump_output_video:
img_resized = cv2.resize(frame, (out_width, out_height))
out.write(img_resized)
if args.show:
cv2.imshow("Detection Results", frame)
key = cv2.waitKey(args.delay)
if key == 27:
break
if args.dump_predictions_to_json:
with open(args.output_json_path, 'w') as output_file:
json.dump(predictions, output_file, sort_keys=True, indent=4)
cv2.destroyAllWindows()
del exec_net
del plugin
help="Path to the input configuration file")
ap.add_argument("-i", "--input", help="path to the input video file")
args = vars(ap.parse_args())
# load the configuration file
conf = Conf(args["conf"])
# load the COCO class labels our YOLO model was trained on and
# initialize a list of colors to represent each possible class
# label
LABELS = open(conf["labels_path"]).read().strip().split("\n")
np.random.seed(42)
COLORS = np.random.uniform(0, 255, size=(len(LABELS), 3))
# initialize the plugin in for specified device
plugin = IEPlugin(device="MYRIAD")
# read the IR generated by the Model Optimizer (.xml and .bin files)
print("[INFO] loading models...")
net = IENetwork(model=conf["xml_path"], weights=conf["bin_path"])
# prepare inputs
print("[INFO] preparing inputs...")
inputBlob = next(iter(net.inputs))
# set the default batch size as 1 and get the number of input blobs,
# number of channels, the height, and width of the input blob
net.batch_size = 1
(n, c, h, w) = net.inputs[inputBlob].shape
# if a video path was not supplied, grab a reference to the webcam
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in demo's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Demo supports only single input topologies"
assert len(net.outputs) == 1, "Demo supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
print(n, c, h, w)
del net
if args.input == 'cam':
input_stream = 0
else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
if args.labels:
with open(args.labels, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
cap = cv2.VideoCapture(input_stream)
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
videWriter = cv2.VideoWriter("label.avi",
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
fps, (width, height))
cur_request_id = 0
next_request_id = 1
log.info("Starting inference in async mode...")
log.info("To switch between sync and async modes press Tab button")
log.info("To stop the demo execution press Esc button")
is_async_mode = True
render_time = 0
ret, frame = cap.read()
max_cost_time = 0
total_cost_time = 0
cnt = 0
while cap.isOpened():
if is_async_mode:
ret, next_frame = cap.read()
else:
ret, frame = cap.read()
if not ret:
break
initial_w = cap.get(3)
initial_h = cap.get(4)
# Main sync point:
# in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately wait for it's completion
inf_start = time.time()
if is_async_mode:
in_frame = cv2.resize(next_frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
exec_net.start_async(request_id=next_request_id,
inputs={input_blob: in_frame})
else:
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
exec_net.start_async(request_id=cur_request_id,
inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
inf_end = time.time()
det_time = inf_end - inf_start
max_cost_time = det_time if det_time > max_cost_time else max_cost_time
total_cost_time += det_time
cnt += 1
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
for obj in res[0][0]:
# Draw only objects when probability more than specified threshold
if obj[2] > args.prob_threshold:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
class_id = int(obj[1])
# Draw box and label\class_id
color = (min(class_id * 12.5,
255), min(class_id * 7,
255), min(class_id * 5, 255))
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2)
det_label = labels_map[class_id] if labels_map else str(
class_id)
cv2.putText(
frame,
det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %',
(xmin, ymin - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, color,
1)
# Draw performance stats
inf_time_message = "Inference time: N\A for async mode" if is_async_mode else \
"Inference time: {:.3f} ms".format(det_time * 1000)
render_time_message = "OpenCV rendering time: {:.3f} ms".format(
render_time * 1000)
async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
"Async mode is off. Processing request {}".format(cur_request_id)
cv2.putText(frame, inf_time_message, (15, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
cv2.putText(frame, render_time_message, (15, 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
#
render_start = time.time()
cv2.imshow("Detection Results", frame)
videWriter.write(frame)
render_end = time.time()
render_time = render_end - render_start
if is_async_mode:
cur_request_id, next_request_id = next_request_id, cur_request_id
frame = next_frame
key = cv2.waitKey(1)
if key == 27:
break
if (9 == key):
is_async_mode = not is_async_mode
log.info("Switched to {} mode".format(
"async" if is_async_mode else "sync"))
print("Inference Max Cost {:.3f}ms".format(max_cost_time * 1000))
print("Total Cost {:.3f}ms, mean cost {:.3f}ms, frame cnt {}".format(
total_cost_time * 1000, total_cost_time / cnt * 1000, cnt))
cv2.destroyAllWindows()
del exec_net
del plugin
def greengrass_object_detection_sample_ssd_run():
client.publish(topic=PARAM_TOPIC_NAME, payload="OpenVINO: Initializing...")
model_bin = os.path.splitext(PARAM_MODEL_XML)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=PARAM_DEVICE, plugin_dirs="")
if "CPU" in PARAM_DEVICE:
plugin.add_cpu_extension(PARAM_CPU_EXTENSION_PATH)
# Read IR
net = IENetwork.from_ir(model=PARAM_MODEL_XML, weights=model_bin)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
cap = cv2.VideoCapture(PARAM_INPUT_SOURCE)
exec_net = plugin.load(network=net)
del net
client.publish(topic=PARAM_TOPIC_NAME,
payload="Starting inference on %s" % PARAM_INPUT_SOURCE)
start_time = timeit.default_timer()
inf_seconds = 0.0
frame_count = 0
labeldata = None
if PARAM_LABELMAP_FILE is not None:
with open(PARAM_LABELMAP_FILE) as labelmap_file:
labeldata = json.load(labelmap_file)
while (cap.isOpened()):
ret, frame = cap.read()
if not ret:
break
frameid = cap.get(cv2.CAP_PROP_POS_FRAMES)
initial_w = cap.get(3)
initial_h = cap.get(4)
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
# Start synchronous inference
inf_start_time = timeit.default_timer()
res = exec_net.infer(inputs={input_blob: in_frame})
inf_seconds += timeit.default_timer() - inf_start_time
# Parse detection results of the current request
res_json = OrderedDict()
frame_timestamp = datetime.datetime.now()
object_id = 0
for obj in res[out_blob][0][0]:
if obj[2] > 0.5:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax),
(255, 165, 20), 4)
obj_id = "Object" + str(object_id)
classlabel = labeldata[str(int(obj[1]))] if labeldata else ""
res_json[obj_id] = {
"label": int(obj[1]),
"class": classlabel,
"confidence": round(obj[2], 2),
"xmin": round(obj[3], 2),
"ymin": round(obj[4], 2),
"xmax": round(obj[5], 2),
"ymax": round(obj[6], 2)
}
object_id += 1
frame_count += 1
# Measure elapsed seconds since the last report
seconds_elapsed = timeit.default_timer() - start_time
if seconds_elapsed >= reporting_interval:
res_json["timestamp"] = frame_timestamp.isoformat()
res_json["frame_id"] = int(frameid)
res_json["inference_fps"] = frame_count / inf_seconds
start_time = timeit.default_timer()
report(res_json, frame)
frame_count = 0
inf_seconds = 0.0
client.publish(topic=PARAM_TOPIC_NAME,
payload="End of the input, exiting...")
del exec_net
del plugin
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = IENetwork.from_ir(model=model_xml, weights=model_bin)
if "CPU" in plugin.device:
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
log.info("Preparing input blobs")
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
net.batch_size = 1 # One frame per inference
# Read and pre-process input images
n, c, h, w = net.inputs[input_blob].shape
images = np.ndarray(shape=(n, c, h, w))
log.info("Batch size is {}".format(n))
# Loading model to the plugin
log.info("Loading model to the plugin")
exec_net = plugin.load(network=net)
del net
cap = cv2.VideoCapture(0)
cap.set(3, 320)
cap.set(4, 240)
ret, image = cap.read()
# Start sync inference
log.info("Starting inference ({} iterations)".format(args.number_iter))
step = 0
while ret:
step += 1
ret, image = cap.read()
cv2.imshow('Raw', cv2.resize(image, (455, 256)))
if image.shape[:-1] != (h, w):
image = cv2.resize(image, (w, h))
image = image.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
images[0] = image
infer_time = []
for i in range(args.number_iter):
t0 = time()
res = exec_net.infer(inputs={input_blob: images})
infer_time.append((time() - t0) * 1000)
log.info("Average running time of one iteration: {} ms".format(
np.average(np.asarray(infer_time))))
if args.perf_counts:
perf_counts = exec_net.requests[0].get_perf_counts()
log.info("Performance counters:")
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
# Processing output blob
log.info("Processing output blob")
res = res[out_blob]
# Post process output
for batch, data in enumerate(res):
# Clip values to [0, 255] range
data = np.swapaxes(data, 0, 2)
data = np.swapaxes(data, 0, 1)
data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
data[data < 0] = 0
data[data > 255] = 255
data = data[::] - (args.mean_val_r, args.mean_val_g,
args.mean_val_b)
data /= 255
cv2.imshow('Video', cv2.resize(data, (455, 256)))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
del exec_net
del plugin
def greengrass_object_detection_sample_ssd_run():
client.publish(topic=PARAM_TOPIC_NAME, payload="OpenVINO: Initializing...")
model_bin = os.path.splitext(PARAM_MODEL_XML)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=PARAM_DEVICE, plugin_dirs="")
if "CPU" in PARAM_DEVICE:
plugin.add_cpu_extension(PARAM_CPU_EXTENSION_PATH)
# Read IR
net = IENetwork(model=PARAM_MODEL_XML, weights=model_bin)
client.publish(topic=PARAM_TOPIC_NAME, payload="OpenVINO: Read IR...")
assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
cap = cv2.VideoCapture(PARAM_INPUT_SOURCE)
exec_net = plugin.load(network=net)
del net
client.publish(topic=PARAM_TOPIC_NAME, payload="Starting inference on %s" % PARAM_INPUT_SOURCE)
start_time = timeit.default_timer()
inf_seconds = 0.0
frame_count = 0
labeldata = None
if PARAM_LABELMAP_FILE is not None:
with open(PARAM_LABELMAP_FILE) as labelmap_file:
labeldata = json.load(labelmap_file)
while (cap.isOpened()):
ret, frame = cap.read()
if not ret:
break
frameid = cap.get(cv2.CAP_PROP_POS_FRAMES)
initial_w = cap.get(3)
initial_h = cap.get(4)
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
# Start synchronous inference
inf_start_time = timeit.default_timer()
res = exec_net.infer(inputs={input_blob: in_frame})
inf_seconds += timeit.default_timer() - inf_start_time
# Parse detection results of the current request
res_json = OrderedDict()
frame_timestamp = datetime.datetime.now()
object_id = 0
for obj in res[out_blob][0][0]:
if obj[2] > 0.5:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (255, 165, 20), 4)
obj_id = "Object" + str(object_id)
classlabel = labeldata[str(int(obj[1]))] if labeldata else ""
res_json[obj_id] = {"label": int(obj[1]), "class": classlabel, "confidence": round(obj[2], 2), "xmin": round(
obj[3], 2), "ymin": round(obj[4], 2), "xmax": round(obj[5], 2), "ymax": round(obj[6], 2)}
object_id += 1
frame_count += 1
# Measure elapsed seconds since the last report
seconds_elapsed = timeit.default_timer() - start_time
if seconds_elapsed >= reporting_interval:
res_json["timestamp"] = frame_timestamp.isoformat()
res_json["frame_id"] = int(frameid)
res_json["inference_fps"] = frame_count / inf_seconds
start_time = timeit.default_timer()
report(res_json, frame)
frame_count = 0
inf_seconds = 0.0
client.publish(topic=PARAM_TOPIC_NAME, payload="End of the input, exiting...")
del exec_net
del plugin