class Capsule(BaseCapsule):
name = "classifier_mask_openvino"
description = "OpenVINO face mask classifier."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(
size=NodeDescription.Size.SINGLE,
detections=["face"])
output_type = NodeDescription(
size=NodeDescription.Size.SINGLE,
detections=["face"],
attributes={"mask": Backend.LABELS},
extra_data=["mask_confidence"])
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files["face_mask.xml"],
weights_bin=capsule_files["face_mask.bin"],
device_name=device
)
options = {
"threshold": FloatOption(
description="Scores under this value are deemed to not be not "
"wearing a mask.",
default=0.3,
min_val=None,
max_val=None
)
}
class Capsule(BaseCapsule):
name = "detector_text_openvino"
description = "✨ OpenVINO text detector and reader."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(size=NodeDescription.Size.NONE)
output_type = NodeDescription(size=NodeDescription.Size.ALL,
detections=["text"],
extra_data=["detection_confidence", "text"])
backend_loader = lambda capsule_files, device: Backend(
detector=OpenVINOModel(
model_xml=capsule_files["models/text-spotting-0001-detector.xml"],
weights_bin=capsule_files["models/text-spotting-0001-detector.bin"
],
device_name=device),
recognizer_encoder=OpenVINOModel(
model_xml=capsule_files[
"models/text-spotting-0001-recognizer-encoder.xml"],
weights_bin=capsule_files[
"models/text-spotting-0001-recognizer-encoder.bin"],
device_name=device),
recognizer_decoder=OpenVINOModel(
model_xml=capsule_files[
"models/text-spotting-0001-recognizer-decoder.xml"],
weights_bin=capsule_files[
"models/text-spotting-0001-recognizer-decoder.bin"],
device_name=device))
options = common_detector_options
class Capsule(BaseCapsule):
name = "classifier_gait_example"
version = 1
device_mapper = DeviceMapper.map_to_all_gpus()
input_type = NodeDescription(size=NodeDescription.Size.SINGLE,
detections=["person"])
output_type = NodeDescription(size=NodeDescription.Size.SINGLE,
detections=["person"],
attributes={config.category: config.values},
extra_data=[config.extra_data])
options = {}
@staticmethod
def backend_loader(capsule_files: Dict[str, bytes], device: str) \
-> BaseBackend:
# Real capsules do not need to do this check. This is only to provide
# a warning for this example because the model is not included in the
# repo.
model_filename = "classification_gait_model.pb"
try:
model_file = capsule_files[model_filename]
except KeyError as exc:
message = f"Model [{model_filename}] not found. Did you make " \
f"sure to run tests? Example models files are not " \
f"stored directly in the repo, but are downloaded " \
f"when tests are run."
raise FileNotFoundError(message) from exc
return Backend(model_bytes=model_file,
metadata_bytes=capsule_files["dataset_metadata.json"],
model_name="inception_resnet_v2",
device=device)
class Capsule(BaseCapsule):
name = "detector_person_example"
version = 1
device_mapper = DeviceMapper.map_to_all_gpus()
# This capsule takes no input from other capsules
input_type = NodeDescription(size=NodeDescription.Size.NONE)
# This capsule produces DetectionNodes of people
output_type = NodeDescription(size=NodeDescription.Size.ALL,
detections=["person"])
options = {
"detection_threshold":
FloatOption(
description="The confidence threshold for the model. A higher "
"value means fewer detections",
default=0.5,
min_val=0.1,
max_val=1.0),
"scale_frame":
BoolOption(
description="If true, the frame width and height will be clamped "
"to the value of scale_frame_max_side_length, "
"preserving aspect ratio",
default=False),
"scale_frame_max_side_length":
IntOption(description="The width or height to scale frames down to "
"if scale_frames is True",
default=2000,
min_val=200,
max_val=4000)
}
@staticmethod
def backend_loader(capsule_files: Dict[str, bytes], device: str) \
-> BaseBackend:
# Real capsules do not need to do this check. This is only to provide
# a warning for this example because the model is not included in the
# repo.
model_filename = "ssd_mobilenet_v1_coco.pb"
try:
model_file = capsule_files[model_filename]
except KeyError as exc:
message = f"Model [{model_filename}] not found. Did you make " \
f"sure to run tests? Example models files are not " \
f"stored directly in the repo, but are downloaded " \
f"when tests are run."
raise FileNotFoundError(message) from exc
return Backend(model_bytes=model_file,
metadata_bytes=capsule_files["dataset_metadata.json"],
device=device)
class Capsule(BaseCapsule):
name = "detector_face_openvino"
description = "✨ OpenVINO fast face detector."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(size=NodeDescription.Size.NONE)
output_type = NodeDescription(size=NodeDescription.Size.ALL,
detections=["face"])
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files["face-detection-adas-0001.xml"],
weights_bin=capsule_files["face-detection-adas-0001.bin"],
device_name=device)
options = common_detector_options
class Capsule(BaseCapsule):
name = "detector_person_overhead_openvino"
description = "OpenVINO fast person detector. Works best in " \
"surveillance perspectives from a downwards facing point " \
"of view."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(size=NodeDescription.Size.NONE)
output_type = NodeDescription(
size=NodeDescription.Size.ALL,
detections=["person"])
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files["person-detection-retail-0013.xml"],
weights_bin=capsule_files["person-detection-retail-0013.bin"],
device_name=device
)
options = common_detector_options
class Capsule(BaseCapsule):
name = "classifier_face_age_gender_openvino"
description = "OpenVINO face age/gender classifier."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(size=NodeDescription.Size.SINGLE,
detections=["face"])
output_type = NodeDescription(size=NodeDescription.Size.SINGLE,
detections=["face"],
attributes={
"gender": config.genders,
"age": list(config.age_bins.values())
},
extra_data=["age", "gender_confidence"])
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files["age-gender-recognition-retail-0013.xml"],
weights_bin=capsule_files["age-gender-recognition-retail-0013.bin"],
device_name=device)
class Capsule(BaseCapsule):
name = "classifier_vehicle_color_openvino"
description = "OpenVINO vehicle color classifier."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(
size=NodeDescription.Size.SINGLE,
detections=config.vehicle_types)
output_type = NodeDescription(
size=NodeDescription.Size.SINGLE,
detections=config.vehicle_types,
attributes={"color": config.colors})
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files[
"vehicle-attributes-recognition-barrier-0039.xml"],
weights_bin=capsule_files[
"vehicle-attributes-recognition-barrier-0039.bin"],
device_name=device
)
class Capsule(BaseCapsule):
name = "detector_person_vehicle_bike_openvino"
description = ("OpenVINO person, vehicle, and bike detector. Optimized "
"for surveillance camera scenarios.")
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(size=NodeDescription.Size.NONE)
output_type = NodeDescription(size=NodeDescription.Size.ALL,
detections=["vehicle", "person", "bike"])
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files[
"person-vehicle-bike-detection-crossroad-1016-fp32.xml"],
weights_bin=capsule_files[
"person-vehicle-bike-detection-crossroad-1016-fp32.bin"],
device_name=device)
options = {
**common_detector_options, "only_person_detections":
BoolOption(
default=False,
description="Filter out anything that's not a person detection")
}
class Capsule(BaseCapsule):
name = "classifier_face_emotion_openvino"
description = "OpenVINO face emotion classifier."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(
size=NodeDescription.Size.SINGLE,
detections=["face"])
output_type = NodeDescription(
size=NodeDescription.Size.SINGLE,
detections=["face"],
attributes={"emotion": EMOTION_TYPES},
extra_data=["emotion_confidence"]
)
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files[
"emotions-recognition-retail-0003.xml"],
weights_bin=capsule_files[
"emotions-recognition-retail-0003.bin"],
device_name=device
)
class Capsule(BaseCapsule):
name = "detector_safety_gear_openvino"
description = "OpenVino's safety gear detector (safety vest and safety " \
"hat)"
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(size=NodeDescription.Size.NONE)
output_type = NodeDescription(
size=NodeDescription.Size.ALL,
detections=["safety vest", "safety hat"]
)
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files["worker_safety_mobilenet_FP16.xml"],
weights_bin=capsule_files["worker_safety_mobilenet_FP16.bin"],
device_name=device
)
options = {
"threshold": FloatOption(
default=0.5,
min_val=0.0,
max_val=None
)
}
class Capsule(BaseCapsule):
name = "classifier_safety_gear_openvino"
description = "Roughly identify if person is wearing safety hat " \
"and safety vest."
version = 1
device_mapper = DeviceMapper.map_to_openvino_devices()
input_type = NodeDescription(
size=NodeDescription.Size.ALL,
detections=["person"])
output_type = NodeDescription(
size=NodeDescription.Size.ALL,
detections=["person"],
attributes={safety_hat: with_safety_hat,
safety_vest: with_safety_vest},
extra_data=["safety_hat_iou", "safety_hat_confidence",
"safety_vest_iou", "safety_vest_confidence"],
)
backend_loader = lambda capsule_files, device: BackendRpcProcess(
Backend,
model_xml=capsule_files["worker_safety_mobilenet_FP16.xml"],
weights_bin=capsule_files["worker_safety_mobilenet_FP16.bin"],
device_name=device
)
options = capsule_options