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:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("mayanktak15/yolo8", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,115 Bytes
c94751c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | """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
|