dora-yolo 0.3.7

Dora Node for object detection with dora YOLOv8

Dora Node for detecting objects in images using YOLOv8

This node is used to detect objects in images using YOLOv8.

YAML

- id: object_detection
  build: pip install ../../node-hub/dora-yolo
  path: dora-yolo
  inputs:
    image: webcam/image

  outputs:
    - bbox
  env:
    MODEL: yolov5n.pt

Inputs

• image: Arrow array containing the base image

## Image data
image_data: UInt8Array # Example: pa.array(img.ravel())
metadata = {
  "width": 640,
  "height": 480,
  "encoding": str, # bgr8, rgb8
}

## Example
node.send_output(
  image_data, {"width": 640, "height": 480, "encoding": "bgr8"}
  )

## Decoding
storage = event["value"]

metadata = event["metadata"]
encoding = metadata["encoding"]
width = metadata["width"]
height = metadata["height"]

if encoding == "bgr8":
    channels = 3
    storage_type = np.uint8

frame = (
    storage.to_numpy()
    .astype(storage_type)
    .reshape((height, width, channels))
)

Outputs

• bbox: an arrow array containing the bounding boxes, confidence scores, and class names of the detected objects

bbox: {
    "bbox": np.array,  # flattened array of bounding boxes
    "conf": np.array,  # flat array of confidence scores
    "labels": np.array,  # flat array of class names
}

encoded_bbox = pa.array([bbox], {"format": "xyxy"})

decoded_bbox = {
    "bbox": encoded_bbox[0]["bbox"].values.to_numpy().reshape(-1, 4),
    "conf": encoded_bbox[0]["conf"].values.to_numpy(),
    "labels": encoded_bbox[0]["labels"].values.to_numpy(zero_copy_only=False),
}

Example

Check example at examples/python-dataflow

License

This project is licensed under Apache-2.0. Check out NOTICE.md for more information.