Yolov8

Making the video stream intelligent

Let's add a yolov8 object detection operator, that you can find as an example. This will help us detect object as bounding boxes within the webcam stream.

Install required dependencies

conda create -n example_env python=3.11
conda activate test_env
pip install -r https://raw.githubusercontent.com/dora-rs/dora/v0.3.6/examples/python-operator-dataflow/requirements.txt

Create a new object_detection.py python file with the following content

wget https://raw.githubusercontent.com/dora-rs/dora/v0.3.6/examples/python-operator-dataflow/object_detection.py

object_detection.py:

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np
import pyarrow as pa

from dora import DoraStatus
from ultralytics import YOLO

pa.array([])

CAMERA_WIDTH = 640
CAMERA_HEIGHT = 480

class Operator:
    def __init__(self):
        self.model = YOLO("yolov8n.pt")

    def on_event(
        self,
        dora_event,
        send_output,
    ) -> DoraStatus:
        if dora_event["type"] == "INPUT":
            return self.on_input(dora_event, send_output)
        return DoraStatus.CONTINUE

    def on_input(
        self,
        dora_input,
        send_output,
    ) -> DoraStatus:

        frame = dora_input["value"].to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
        frame = frame[:, :, ::-1]  # OpenCV image (BGR to RGB)
        results = self.model(frame)  # includes NMS
        # Process results
        boxes = np.array(results[0].boxes.xyxy.cpu())
        conf = np.array(results[0].boxes.conf.cpu())
        label = np.array(results[0].boxes.cls.cpu())
        # concatenate them together
        arrays = np.concatenate((boxes, conf[:, None], label[:, None]), axis=1)

        send_output("bbox", pa.array(arrays.ravel()), dora_input["metadata"])
        return DoraStatus.CONTINUE

Operators are composed of:

on_event methods is called when an event is received. There is currently 4 event types:

STOP: meaning that the operator was signalled to stop.
INPUT: meannig that an input was received.
- You can use dora_event['id'], to get the id.
- You can use dora_event['data'], to get the data as bytes.
- You can use dora_event['value'], to get the data as arrow array.
- You can use dora_event['metadata'], to get the metadata.
INPUT_CLOSED: meannig that an input source was closed. This could be useful if the input is critical for the well behaviour of the operator.
ERROR: meaning that error message was received.
UNKNOWN: meaning that an unknown message was received.

Add an operator within the dataflow

wget https://raw.githubusercontent.com/dora-rs/dora/v0.3.6/examples/python-operator-dataflow/dataflow.yaml

dataflow.yaml:

nodes:
  - id: webcam
    operator:
      python: webcam.py
      inputs:
        tick: dora/timer/millis/100
      outputs:
        - image

  - id: object_detection
    operator:
      python: object_detection.py
      inputs:
        image: webcam/image
      outputs:
        - bbox

  - id: plot
    operator:
      python: plot.py
      inputs:
        image: webcam/image
        bbox: object_detection/bbox

In this case, we have connected the webcam/image output to the image input of yolov8. object_detection/bbox is then connected to the plot/obstacles_bbox.

Inputs are prefixed by the node name to be able to separate name conflicts.

run
```
dora up
dora start dataflow.yml --attach
```
The plot will show object detected in the image as bounding box with a label and a confidence score.

Yolov8

Making the video stream intelligent​

Making the video stream intelligent