| # Arm YOLO26n Python APIs |
|
|
| This package provides two intentionally separate interfaces to the fixed |
| YOLO26n runtime: |
|
|
| - `Yolo26nTensorSession` is the low-level interface for callers that already |
| own preprocessing and postprocessing. It accepts and returns FP32 NumPy |
| arrays. |
| - `Yolo26Detector` is the high-level image interface. It accepts uint8 NumPy |
| images and returns class-labelled detections in source-image coordinates. |
|
|
| Both interfaces accept the runtime library and converted model directory as |
| separate, explicit inputs. The package validates the model and tensor contract |
| before creating the native model handle. It reads `model.bin` once, verifies |
| those bytes against the manifest, and passes them to the native runtime. |
|
|
| ## Raw tensor execution |
|
|
| Use `Yolo26nTensorSession` when integrating with an existing tensor pipeline: |
|
|
| ```python |
| import numpy as np |
| |
| from arm_yolo26n import Yolo26nTensorSession |
| |
| with Yolo26nTensorSession( |
| runtime="/path/to/libarm-yolo26n.so.0", |
| model="/path/to/converted-model", |
| ) as session: |
| model_input = np.random.default_rng().random(session.input_shape, dtype=np.float32) |
| raw_prediction = session.execute(model_input) |
| ``` |
|
|
| Inputs must be contiguous or convertible to contiguous FP32 NCHW arrays with |
| the exact shape recorded by the model. Outputs are FP32 NumPy arrays with the |
| model's validated YOLO26n output shape. |
|
|
| ## Image detection |
|
|
| Use `Yolo26Detector` when starting with an image: |
|
|
| ```python |
| from arm_yolo26n import DetectorConfig, Yolo26Detector |
| |
| config = DetectorConfig(confidence=0.25, iou=0.45) |
| with Yolo26Detector( |
| runtime="/path/to/libarm-yolo26n.so.0", |
| model="/path/to/converted-model", |
| config=config, |
| ) as detector: |
| result = detector.detect(frame_bgr, pixel_format="BGR") |
| for detection in result.detections: |
| print(detection.class_name, detection.confidence, detection.box) |
| ``` |
|
|
| The detector handles letterboxing, channel conversion, normalization, native |
| execution, non-maximum suppression, and scaling boxes back to source-image |
| coordinates. It accepts interleaved `BGR`, `RGB`, and four-channel `BGRX` |
| arrays. The `BGRX` path lets Picamera2 `XRGB8888` buffers be consumed without |
| repacking them into a second three-channel image. |
|
|
| Sessions and detectors own native resources and are intentionally |
| non-reentrant. Use them as context managers, and use independent processes |
| when concurrent isolation is required. |
|
|
| See the [prerecorded-video example](../../../../../examples/video/README.md) for repeatable |
| workload benchmarking and the [Picamera2 example](../../../../../examples/picamera2/README.md) |
| for live camera integration. |
|
|
