Instructions to use mayanktak15/yolo8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use mayanktak15/yolo8 with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("mayanktak15/yolo8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| """Movement heatmap generation from track histories.""" | |
| from __future__ import annotations | |
| from pathlib import Path | |
| import cv2 | |
| import numpy as np | |
| from src.tracking.tracker import TrackHistory | |
| def generate_heatmap( | |
| history: TrackHistory, | |
| frame_shape: tuple[int, int, int], | |
| output_path: str | Path, | |
| radius: int = 18, | |
| ) -> np.ndarray: | |
| """Create and save a heatmap image showing tracked centroid density.""" | |
| height, width = frame_shape[:2] | |
| heat = np.zeros((height, width), dtype=np.float32) | |
| for points in history.points.values(): | |
| for _, x, y in points: | |
| cx = int(np.clip(x, 0, width - 1)) | |
| cy = int(np.clip(y, 0, height - 1)) | |
| cv2.circle(heat, (cx, cy), radius, 1.0, -1) | |
| heat = cv2.GaussianBlur(heat, (0, 0), sigmaX=radius / 2) | |
| normalized = cv2.normalize(heat, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8) | |
| colored = cv2.applyColorMap(normalized, cv2.COLORMAP_JET) | |
| output_path = Path(output_path) | |
| output_path.parent.mkdir(parents=True, exist_ok=True) | |
| cv2.imwrite(str(output_path), colored) | |
| return colored | |