def _image_to_tensor(image):
if isinstance(image, (bytes, bytearray)):
# Only JPEG is supported on the bonnet side.
return pb2.ByteTensor(shape=pb2.TensorShape(batch=1,
height=0,
width=0,
depth=0),
data=image)
width, height = image.size
if image.mode == 'RGB':
r, g, b = image.split()
return pb2.ByteTensor(shape=pb2.TensorShape(batch=1,
height=height,
width=width,
depth=3),
data=r.tobytes() + g.tobytes() + b.tobytes())
if image.mode == 'L':
return pb2.ByteTensor(shape=pb2.TensorShape(batch=1,
height=height,
width=width,
depth=1),
data=image.tobytes())
raise InferenceException(
'Unsupported image format: %s. Must be L or RGB.' % image.mode)
def load_model(self, descriptor):
"""Loads model on VisionBonnet.
Args:
descriptor: ModelDescriptor, meta info that defines model name,
where to get the model and etc.
Returns:
Model identifier.
"""
_check_firmware_info(self.get_firmware_info())
mean, stddev = descriptor.input_normalizer
batch, height, width, depth = descriptor.input_shape
if batch != 1:
raise ValueError('Unsupported batch value: %d. Must be 1.')
if depth != 3:
raise ValueError('Unsupported depth value: %d. Must be 3.')
try:
logger.info('Load model "%s".', descriptor.name)
self._communicate(
pb2.Request(load_model=pb2.Request.LoadModel(
model_name=descriptor.name,
input_shape=pb2.TensorShape(
batch=batch, height=height, width=width, depth=depth),
input_normalizer=pb2.TensorNormalizer(mean=mean,
stddev=stddev),
compute_graph=descriptor.compute_graph)))
except InferenceException as e:
logger.warning(str(e))
return descriptor.name
def _image_to_tensor(image):
width, height = image.size
if image.mode == 'RGB':
r, g, b = image.split()
return pb2.ByteTensor(shape=pb2.TensorShape(batch=1,
height=height,
width=width,
depth=3),
data=r.tobytes() + g.tobytes() + b.tobytes())
elif image.mode == 'L':
return pb2.ByteTensor(shape=pb2.TensorShape(batch=1,
height=height,
width=width,
depth=1),
data=image.tobytes())
else:
raise InferenceException(
'Unsupported image format: %s. Must be L or RGB.' % image.mode)