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frigate/detectors: renamed yolov8_preprocess->preprocess, pass input tensor element type

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This commit is contained in:
Indrek Mandre 2024-02-04 22:56:57 +02:00
parent 4bcb071823
commit a982698933
4 changed files with 19 additions and 7 deletions
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2
frigate/detectors/plugins/onnx.py
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@ -51,7 +51,7 @@ class ONNXDetector(DetectionApi):
model_input_name = self.model.get_inputs()[0].name model_input_name = self.model.get_inputs()[0].name
model_input_shape = self.model.get_inputs()[0].shape model_input_shape = self.model.get_inputs()[0].shape
tensor_input = yolo_utils.yolov8_preprocess(tensor_input, model_input_shape) tensor_input = yolo_utils.preprocess(tensor_input, model_input_shape, np.float32)
tensor_output = self.model.run(None, {model_input_name: tensor_input})[0] tensor_output = self.model.run(None, {model_input_name: tensor_input})[0]

5
frigate/detectors/plugins/openvino.py
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@ -8,6 +8,8 @@ from typing_extensions import Literal
from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
import frigate.detectors.yolo_utils as yolo_utils
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
DETECTOR_KEY = "openvino" DETECTOR_KEY = "openvino"
@ -33,7 +35,7 @@ class OvDetector(DetectionApi):
model=self.ov_model, device_name=detector_config.device model=self.ov_model, device_name=detector_config.device
) )
logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}") logger.info(f"Model Input Shape: {self.interpreter.input(0).shape} {self.interpreter.input(0).element_type.to_dtype()}")
self.output_indexes = 0 self.output_indexes = 0
while True: while True:
@ -80,6 +82,7 @@ class OvDetector(DetectionApi):
] ]
def detect_raw(self, tensor_input): def detect_raw(self, tensor_input):
tensor_input = yolo_utils.preprocess(tensor_input, self.interpreter.inputs[0].shape, self.interpreter.inputs[0].element_type.to_dtype())
infer_request = self.interpreter.create_infer_request() infer_request = self.interpreter.create_infer_request()
infer_request.infer([tensor_input]) infer_request.infer([tensor_input])

2
frigate/detectors/plugins/rocm.py
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@ -102,7 +102,7 @@ class ROCmDetector(DetectionApi):
model_input_name = self.model.get_parameter_names()[0]; model_input_name = self.model.get_parameter_names()[0];
model_input_shape = tuple(self.model.get_parameter_shapes()[model_input_name].lens()); model_input_shape = tuple(self.model.get_parameter_shapes()[model_input_name].lens());
tensor_input = yolo_utils.yolov8_preprocess(tensor_input, model_input_shape) tensor_input = yolo_utils.preprocess(tensor_input, model_input_shape, np.float32)
detector_result = self.model.run({model_input_name: tensor_input})[0] detector_result = self.model.run({model_input_name: tensor_input})[0]

17
frigate/detectors/yolo_utils.py
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@ -5,11 +5,20 @@ import cv2
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
def yolov8_preprocess(tensor_input, model_input_shape): def preprocess(tensor_input, model_input_shape, model_input_element_type):
model_input_shape = tuple(model_input_shape)
assert tensor_input.dtype == np.uint8, f'tensor_input.dtype: {tensor_input.dtype}'
if len(tensor_input.shape) == 3:
tensor_input = tensor_input[np.newaxis, :]
if model_input_element_type == np.uint8:
# nothing to do for uint8 model input
assert model_input_shape == tensor_input.shape, f'model_input_shape: {model_input_shape}, tensor_input.shape: {tensor_input.shape}'
return tensor_input
assert model_input_element_type == np.float32, f'model_input_element_type: {model_input_element_type}'
# tensor_input must be nhwc # tensor_input must be nhwc
assert tensor_input.shape[3] == 3 assert tensor_input.shape[3] == 3, f'tensor_input.shape: {tensor_input.shape}'
if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]): if tensor_input.shape[1:3] != model_input_shape[2:4]:
logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!") logger.warn(f"preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
# cv2.dnn.blobFromImage is faster than numpying it # cv2.dnn.blobFromImage is faster than numpying it
return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False) return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)