quocanh34/bottle_detection_data

Quick Inference with Ultralytics YOLO

This guide shows how to load a trained or pretrained YOLO model and run inference, returning the center coordinates of detected objects for class 0 and 1.

Environment Setup

python3 -m venv .venv
source .venv/bin/activate
pip install ultralytics

Inference Example

# 1. Load your model
from ultralytics import YOLO
model = YOLO('/absolute/path/to/weights/best.pt')
centers = get_centers_from_image(model, '/path/to/image.jpg')
print(centers)

def get_centers_from_image(model, image_path):
    results = model.predict(source=image_path, conf=0.15, classes=[0, 1])
    centers = {0: [], 1: []}
    try:
        for r in results:
            for box in r.boxes:
                cls = int(box.cls)
                if cls in [0, 1]:
                    x1, y1, x2, y2 = box.xyxy[0].tolist()
                    cx = (x1 + x2) / 2
                    cy = (y1 + y2) / 2
                    centers[cls].append((cx, cy))
        if not centers[0] and not centers[1]:
            return False
        return centers
    except Exception:
        return False

Notes

• Replace /absolute/path/to/weights/best.pt with your trained or pretrained model path.
• Replace /path/to/image/or/folder with your image or folder path.
• The function get_centers returns a dictionary with lists of center coordinates for class 0 and 1.

References

• Ultralytics YOLO Docs
• YOLOv8 GitHub