| # Quick Inference with Ultralytics YOLO |
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| 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. |
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| ## Environment Setup |
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| ```bash |
| python3 -m venv .venv |
| source .venv/bin/activate |
| pip install ultralytics |
| ``` |
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| ## Inference Example |
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| ```python |
| # 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 |
| ``` |
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| ## 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. |
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| ## References |
| - [Ultralytics YOLO Docs](https://docs.ultralytics.com/) |
| - [YOLOv8 GitHub](https://github.com/ultralytics/ultralytics) |
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