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STM32N6 NPU Deployment — Politecnico di Milano
1.0
Documentation for Neural Network Deployment on STM32N6 NPU - Politecnico di Milano 2024-2025
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Postprocessing utilities for pose estimation model outputs. More...
Go to the source code of this file.
Namespaces | |
| postprocess | |
Functions | |
| def | postprocess.heatmaps_spe_postprocess (tf.Tensor tensor) |
| def | postprocess.spe_postprocess (tf.Tensor tensor) |
| def | postprocess._padded_nms (tf.Tensor tensor, int max_output_size, float iou_threshold, float score_threshold) |
| def | postprocess.yolo_mpe_postprocess (tf.Tensor tensor, int max_output_size=20, float iou_threshold=0.7, float score_threshold=0.25) |
| def | postprocess.hand_landmarks_postprocess ([tf.Tensor] tensor) |
| def | postprocess.head_landmarks_postprocess ([tf.Tensor] tensor) |
Postprocessing utilities for pose estimation model outputs.
For MoveNet (heatmaps_spe): decodes (H/4 x W/4 x K) heatmaps to normalized (x, y, confidence) keypoint coordinates. For YOLOv8 (yolo_mpe): applies Non-Maximum Suppression (NMS) to multi-scale bounding box predictions and decodes keypoints.
Definition in file postprocess.py.