6.2 KiB
With Supervision, you can easily annotate predictions obtained from a variety of object detection and segmentation models. This document outlines how to run inference with the YOLOv8 model using the Inference package or the Ultralytics package, load these predictions into Supervision, and annotate the image.
Run Inference
First, you'll need to obtain predictions from your object detection or segmentation model.
=== "Ultralytics"
```python
import cv2
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
image = cv2.imread("image.jpg")
results = model(image)[0]
```
=== "Inference"
To run inference, you will need a [free Roboflow API key]().
```python
import cv2
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(model_id="yolov8n-640", api_key="YOUR_ROBOFLOW_API_KEY")
image = cv2.imread("image.jpg")
results = model.infer(image)[0]
```
Load Predictions into Supervision
Now that we have predictions from a model, we can load them into Supervision.
=== "Ultralytics"
We can do so using the [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
```python
import cv2
from ultralytics import YOLO
import supervision as sv
model = YOLO("yolov8n.pt")
image = cv2.imread("image.jpg")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
```
=== "Inference"
We can do so using the [`sv.Detections.from_inference`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
```python
import cv2
from inference.models.utils import get_roboflow_model
import supervision as sv
model = get_roboflow_model(model_id="yolov8n-640", api_key="YOUR_ROBOFLOW_API_KEY")
image = cv2.imread("image.jpg")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
```
You can conveniently load predictions from other computer vision frameworks and libraries using:
from_deepsparse(Deepsparse)from_detectron2(Detectron2)from_mmdetection(MMDetection)from_inference(Roboflow Inference)from_sam(Segment Anything Model)from_transformers(HuggingFace Transformers)from_yolo_nas(YOLO-NAS)
Annotate Image
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the sv.BoundingBoxAnnotator and sv.LabelAnnotator classes. If you are running the segmentation model sv.MaskAnnotator is a drop-in replacement for sv.BoundingBoxAnnotator that will allow you to draw masks instead of boxes.
=== "Ultralytics"
```python
import cv2
from ultralytics import YOLO
import supervision as sv
model = YOLO("yolov8n.pt")
image = cv2.imread("image.jpg")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()
labels = [
results.names[class_id]
for class_id
in detections.class_id
]
annotated_image = bounding_box_annotator.annotate(
scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections, labels=labels)
```
=== "Inference"
```python
import cv2
from inference.models.utils import get_roboflow_model
import supervision as sv
model = get_roboflow_model(model_id="yolov8n-640", api_key="YOUR_ROBOFLOW_API_KEY")
image = cv2.imread("image.jpg")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()
labels = [
results.names[class_id]
for class_id
in detections.class_id
]
annotated_image = bounding_box_annotator.annotate(
scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections, labels=labels)
```
Display Annotated Image
To display the annotated image in Jupyter Notebook or Google Colab, use the sv.plot_image function.
sv.plot_image(annotated_image)