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add openvino/yolov8 support for label aggregation

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This commit is contained in:
Indrek Mandre 2024-02-06 16:12:37 +02:00
parent c2baac608b
commit 35cc6a1749
1 changed files with 4 additions and 22 deletions
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26
frigate/detectors/plugins/openvino.py
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@ -51,6 +51,8 @@ class OvDetector(DetectionApi):
logger.info(f"YOLOX model has {self.num_classes} classes")
self.set_strides_grids()
self.class_aggregation = yolo_utils.generate_class_aggregation_from_config(detector_config)
def set_strides_grids(self):
grids = []
expanded_strides = []
@ -135,28 +137,8 @@ class OvDetector(DetectionApi):
)
return detections
elif self.ov_model_type == ModelTypeEnum.yolov8:
out_tensor = infer_request.get_output_tensor()
results = out_tensor.data[0]
output_data = np.transpose(results)
scores = np.max(output_data[:, 4:], axis=1)
if len(scores) == 0:
return np.zeros((20, 6), np.float32)
scores = np.expand_dims(scores, axis=1)
# add scores to the last column
dets = np.concatenate((output_data, scores), axis=1)
# filter out lines with scores below threshold
dets = dets[dets[:, -1] > 0.5, :]
# limit to top 20 scores, descending order
ordered = dets[dets[:, -1].argsort()[::-1]][:20]
detections = np.zeros((20, 6), np.float32)
for i, object_detected in enumerate(ordered):
detections[i] = self.process_yolo(
np.argmax(object_detected[4:-1]),
object_detected[-1],
object_detected[:4],
)
return detections
out_tensor = infer_request.get_output_tensor().data
return yolo_utils.yolov8_postprocess(self.interpreter.inputs[0].shape, out_tensor, class_aggregation = self.class_aggregation)
elif self.ov_model_type == ModelTypeEnum.yolov5:
out_tensor = infer_request.get_output_tensor()
output_data = out_tensor.data[0]