def run(model, config_file):
global nn, pre_process, post_process
filename, file_extension = os.path.splitext(model)
supported_files = ['.so', '.pb']
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer) or .so (shared object) file is supported.
""" % (filename, file_extension))
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
if file_extension == '.so': # Shared library
nn = NNLib()
nn.load(model)
elif file_extension == '.pb': # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
run_impl(config)
def _run(model, image_data, config):
filename, file_extension = os.path.splitext(model)
supported_files = ['.so', '.pb']
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer), .so (shared object) file is supported.
""" % (filename, file_extension))
if file_extension == '.so': # Shared library
# load and initialize the generated shared model
nn = NNLib()
nn.load(model)
nn.init()
elif file_extension == '.pb': # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
nn.init()
# run the graph
output = nn.run(image_data)
return output
def run(model, config_file):
global nn, pre_process, post_process
filename, file_extension = os.path.splitext(model)
supported_files = ['.so', '.pb']
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer) or .so (shared object) file is supported.
""" % (filename, file_extension))
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
if file_extension == '.so': # Shared library
nn = NNLib()
nn.load(model)
elif file_extension == '.pb': # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
if config.TASK == "IMAGE.CLASSIFICATION":
run_classification(config)
if config.TASK == "IMAGE.OBJECT_DETECTION":
run_object_detection(config)
if config.TASK == "IMAGE.SEMANTIC_SEGMENTATION":
run_sementic_segmentation(config)
def run(model, config_file, port=80):
global nn, pre_process, post_process, config, stream, pool
filename, file_extension = os.path.splitext(model)
supported_files = [".so", ".pb"]
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer) or .so (shared object) file is supported.
""" % (filename, file_extension))
if file_extension == ".so": # Shared library
nn = NNLib()
nn.load(model)
elif file_extension == ".pb": # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
nn = NNLib()
nn.load(model)
stream = VideoStream(CAMERA_SOURCE, CAMERA_WIDTH, CAMERA_HEIGHT,
CAMERA_FPS)
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
pool = Pool(processes=1, initializer=_init_worker)
try:
server = ThreadedHTTPServer(("", port), MotionJpegHandler)
print("server starting")
server.serve_forever()
except KeyboardInterrupt:
print("KeyboardInterrpt in server - ending server")
stream.release()
pool.terminate()
pool.join()
server.socket.close()
server.shutdown()
return
def _run(model, input_image, config):
filename, file_extension = os.path.splitext(model)
supported_files = ['.so', '.pb']
if file_extension not in supported_files:
raise Exception("""
Unknown file type. Got %s%s.
Please check the model file (-m).
Only .pb (protocol buffer) or .so (shared object) file is supported.
""" % (filename, file_extension))
# load the image
img = Image.open(input_image).convert("RGB")
# convert into numpy array
data = np.asarray(img)
raw_image = data
# pre process for image
data = _pre_process(data, config.PRE_PROCESSOR, config.DATA_FORMAT)
# add the batch dimension
data = np.expand_dims(data, axis=0)
if file_extension == '.so': # Shared library
# load and initialize the generated shared model
nn = NNLib()
nn.load(model)
nn.init()
elif file_extension == '.pb': # Protocol Buffer file
# only load tensorflow if user wants to use GPU
from lmnet.tensorflow_graph_runner import TensorflowGraphRunner
nn = TensorflowGraphRunner(model)
nn.init()
# run the graph
output = nn.run(data)
return output, raw_image
def run(library, config_file):
global nn, pre_process, post_process
nn = NNLib()
nn.load(library)
nn.init()
config = load_yaml(config_file)
pre_process = build_pre_process(config.PRE_PROCESSOR)
post_process = build_post_process(config.POST_PROCESSOR)
if config.TASK == "IMAGE.CLASSIFICATION":
run_classification(config)
if config.TASK == "IMAGE.OBJECT_DETECTION":
run_object_detection(config)