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[Update] Update detector script coding format

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This commit is contained in:
GaryHuang-ASUS 2025-09-26 12:01:00 +08:00
parent 5bda917b31
commit f2df550a2a
1 changed files with 20 additions and 11 deletions
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23
frigate/detectors/plugins/synaptics.py
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@ -10,7 +10,11 @@ from synap.preprocessor import Preprocessor
from synap.postprocessor import Detector
from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum, InputTensorEnum
from frigate.detectors.detector_config import (
BaseDetectorConfig,
ModelTypeEnum,
InputTensorEnum,
)
logger = logging.getLogger(__name__)
@ -20,6 +24,7 @@ DETECTOR_KEY = "synaptics"
class SynapDetectorConfig(BaseDetectorConfig):
type: Literal[DETECTOR_KEY]
class SynapDetector(DetectionApi):
type_key = DETECTOR_KEY
@ -38,6 +43,7 @@ class SynapDetector(DetectionApi):
logger.error(f"Failed to init Synap NPU: {e}")
raise
self.width = detector_config.model.width
self.height = detector_config.model.height
self.model_type = detector_config.model.model_type
@ -52,28 +58,32 @@ class SynapDetector(DetectionApi):
def detect_raw(self, tensor_input: np.ndarray):
# It has only been testing for pre-converted mobilenet80 .tflite -> .synap model currently
layout = Layout.nhwc # default layout
detections = np.zeros((20, 6), np.float32)
if self.input_tensor_layout == InputTensorEnum.nhwc:
layout = Layout.nhwc
postprocess_data = self.preprocessor.assign(self.network.inputs, tensor_input, Shape(tensor_input.shape), layout)
postprocess_data = self.preprocessor.assign(
self.network.inputs, tensor_input, Shape(tensor_input.shape), layout
)
output_tensor_obj = self.network.predict()
output = self.detector.process(output_tensor_obj, postprocess_data)
if self.model_type == ModelTypeEnum.ssd:
detections = np.zeros((20, 6), np.float32)
for i, item in enumerate(output.items):
if i == 20:
break
bb = item.bounding_box
# Convert corner coordinates to normalized [0,1] range
x1 = bb.origin.x / self.width # Top-left X
y1 = bb.origin.y / self.height # Top-left Y
x2 = (bb.origin.x + bb.size.x) / self.width # Bottom-right X
y2 = (bb.origin.y + bb.size.y) / self.height # Bottom-right Y
detections[i] = [
item.class_index,
float(item.confidence),
@ -82,9 +92,8 @@ class SynapDetector(DetectionApi):
y2,
x2,
]
return detections
else:
print(f"Unsupported model type: {self.model_type}")
logger.error(f"Unsupported model type: {self.model_type}")
return np.zeros((20, 6), np.float32)