How to count objects in YOLOv9 predictions

Once you know what objects are in an image, you can count them, either in aggregate or by class. This is useful for a range of use cases. For example, you can check if all five of a list of classes are present (which you could use in a quality assurance checklist), count the number of scratches in a product, and more.

In this guide, we are going to walk through how to count objects in

YOLOv9

detections.

We will:

1. Install supervision
2. Run inference with a model
3. Count objects with Python
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Without further ado, let's get started!

Step #1: Install supervision

First, install the supervision pip package:

pip install supervision

Once you have installed supervision, you are ready to load your data and start writing logic to filter detections.

Step #2: Load Data

First, we are going to load our dataset into a supervision.DetectionDataset() object. This object will contain information about all the images in a dataset. You can load datasets from many different model types, from YOLO to MMDetection. For this guide, we will use the

YOLOv9

data loader.

You can load data using the following code:
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import cv2
import inference
import supervision as sv

annotator = sv.BoxAnnotator()

def render(predictions, image):
    classes = {item["class_id"]: item["class"] for item in predictions["predictions"]}

    detections = sv.Detections.from_roboflow(predictions)

    print(predictions)

    image = annotator.annotate(
        scene=image, detections=detections, labels=[classes[i] for i in detections.class_id]
    )

    cv2.imshow("Prediction", image)
    cv2.waitKey(1)


inference.Stream(
    source="webcam",
    model="microsoft-coco/9",
    output_channel_order="BGR",
    use_main_thread=True,
    on_prediction=render,
    api_key="api_key"
)

Above, replace "microsoft-coco/9" with the model ID of a YOLOv9 model hosted on Roboflow.

To upload a model to Roboflow, first install the Roboflow Python package:

pip install roboflow

Then, create a new Python file and paste in the following code:


from roboflow import Roboflow

rf = Roboflow(api_key="API_KEY")
project = rf.workspace().project("PROJECT_ID")
project.version(DATASET_VERSION).deploy(model_type="yolov8", model_path=f"{HOME}/runs/detect/train/")

In the code above, add your API key and the path to the model weights you want to upload. Learn how to retrieve your API key. Your weights will be uploaded to Roboflow. Your model will shortly be accessible over an API, and available for use in Inference. To learn more about uploading model weights to Roboflow, check out our full guide to uploading weights to Roboflow.

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Replace the ... with the response object from your model.

Step #3: Count Objects

Count Objects in a Single Class

To count all of the objects that match a single class, we can use the following code:


detections = detections[detections.class_id == classes.index("class name")]
print(len(detections))

Replace "class name" with the name of the class with which you are working.

Count Objects in Multiple Classes

To count all of the objects that match one of multiple classes, we can use the following code:


selected_classes = [class_names.index(i) for i in detections.class_id)
detections = detections[np.isin(detections.class_id, selected_classes)]
print(len(detections))

Replace the selected_classes list with the IDs of the classes with which you are working.

Next steps

supervision provides an extensive range of functionalities for working with computer vision models. With supervision, you can:

1. Process and filter detections and segmentation masks from a range of popular models (YOLOv5, Ultralytics YOLOv8, MMDetection, and more).
2. Process and filter classifications.
3. Compute confusion matrices.

And more! To learn about the full range of functionality in supervision, check out the supervision documentation.