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feat: add fire detection training pipeline

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Kunpeng
2026-08-10 17:04:06 +08:00
parent c71e0be5cc
commit 67d5b40736
11 changed files with 759 additions and 14 deletions
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18
.gitignore vendored
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@@ -4,10 +4,22 @@ __pycache__/
build/
dist/
wheels/
*.egg-info
*.egg-info/
# Virtual environments
.venv
# Virtual environments and caches
.venv/
.ruff_cache/
# IDEs
.idea/
.vscode/
# Local datasets and training outputs
data/*
runs/
# Model artifacts
*.pt
*.onnx
*.engine
*.torchscript

9
configs/datasets/fire.yaml Normal file
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@@ -0,0 +1,9 @@
# YOLOv8 火灾检测数据集配置
path: data/fire-dataset
train: train/images
val: validation/images
nc: 1
names: ["fire"]

7
pyproject.toml
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@@ -1,7 +1,7 @@
[project]
name = "yolo"
version = "0.1.0"
description = "Add your description here"
description = "Fire detection training, evaluation, inference, and dataset utilities based on Ultralytics YOLO"
readme = "README.md"
authors = [
{ name = "Kunpeng", email = "mx18782708114@163.com" }
@@ -14,6 +14,7 @@ dependencies = [
"pandas>=3.0.5",
"torch>=2.13.0",
"torchvision>=0.28.0",
"ultralytics>=8.3.0",
]
[tool.uv.sources]
@@ -26,8 +27,8 @@ url = "https://download.pytorch.org/whl/cu132"
explicit = true
[project.scripts]
yolo = "yolo:main"
fire-yolo = "yolo.cli:main"
[build-system]
requires = ["uv_build>=0.12.0,<0.13.0"]
build-backend = "uv_build"
build-backend = "uv_build"

7
src/yolo/__init__.py
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@@ -1,6 +1,3 @@
def main() -> None:
import torch
__version__ = "0.1.0"
print(f"torch version: {torch.__version__}")
print(f"torch cuda available: {torch.cuda.is_available()}")
print("Hello from yolo!")
__all__ = ["__version__"]

138
src/yolo/cli.py Normal file
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@@ -0,0 +1,138 @@
import argparse
from collections.abc import Sequence
from pathlib import Path
from .data import convert_dataset
from .defaults import (
DEFAULT_DATA_CONFIG,
DEFAULT_DATASET_ROOT,
DEFAULT_DETECT_RUNS_DIR,
DEFAULT_PRETRAINED_MODEL,
)
from .engine import export_model, predict, train, validate
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
prog="fire-yolo",
description="Fire detection training, validation, prediction, and data utilities.",
)
subparsers = parser.add_subparsers(dest="command", required=True)
train_parser = subparsers.add_parser("train", help="Train a fire detection model")
train_parser.add_argument("--data", default=str(DEFAULT_DATA_CONFIG))
train_parser.add_argument("--model", default=str(DEFAULT_PRETRAINED_MODEL))
train_parser.add_argument("--epochs", type=int, default=150)
train_parser.add_argument("--imgsz", type=int, default=640)
train_parser.add_argument("--batch", type=int, default=32)
train_parser.add_argument("--workers", type=int, default=8)
train_parser.add_argument("--device")
train_parser.add_argument("--project", default=str(DEFAULT_DETECT_RUNS_DIR))
train_parser.add_argument("--name")
train_parser.add_argument("--cache", action="store_true")
train_parser.add_argument(
"--patience",
type=int,
default=20,
help="Stop after this many epochs without a fitness improvement (0 disables early stopping)",
)
val_parser = subparsers.add_parser("val", help="Evaluate model weights")
val_parser.add_argument("--weights", required=True)
val_parser.add_argument("--data", default=str(DEFAULT_DATA_CONFIG))
val_parser.add_argument("--imgsz", type=int, default=640)
val_parser.add_argument("--batch", type=int, default=32)
val_parser.add_argument("--device")
val_parser.add_argument("--project", default=str(DEFAULT_DETECT_RUNS_DIR))
val_parser.add_argument("--name", default="validation")
val_parser.add_argument("--no-plots", action="store_true")
predict_parser = subparsers.add_parser("predict", help="Run prediction")
predict_parser.add_argument("--weights", required=True)
predict_parser.add_argument("--source", required=True)
predict_parser.add_argument("--conf", type=float, default=0.25)
predict_parser.add_argument("--iou", type=float, default=0.45)
predict_parser.add_argument("--imgsz", type=int, default=640)
predict_parser.add_argument("--device")
predict_parser.add_argument("--project", default=str(DEFAULT_DETECT_RUNS_DIR))
predict_parser.add_argument("--name", default="predict")
predict_parser.add_argument("--no-save", action="store_true")
export_parser = subparsers.add_parser("export", help="Export model weights")
export_parser.add_argument("--weights", required=True)
export_parser.add_argument("--format", default="onnx")
export_parser.add_argument("--imgsz", type=int, default=640)
export_parser.add_argument("--device")
convert_parser = subparsers.add_parser("convert", help="Convert VOC XML to YOLO labels")
convert_parser.add_argument("--data-root", default=str(DEFAULT_DATASET_ROOT))
convert_parser.add_argument(
"--splits",
nargs="+",
default=["train", "validation"],
)
return parser
def main(argv: Sequence[str] | None = None) -> int:
args = build_parser().parse_args(argv)
if args.command == "train":
train(
data_yaml=args.data,
model_weights=args.model,
epochs=args.epochs,
imgsz=args.imgsz,
batch=args.batch,
workers=args.workers,
device=args.device,
project=args.project,
name=args.name,
cache=args.cache,
patience=args.patience,
)
elif args.command == "val":
validate(
weights=args.weights,
data_yaml=args.data,
imgsz=args.imgsz,
batch=args.batch,
device=args.device,
project=args.project,
name=args.name,
plots=not args.no_plots,
)
elif args.command == "predict":
predict(
weights=args.weights,
source=args.source,
conf=args.conf,
iou=args.iou,
imgsz=args.imgsz,
device=args.device,
project=args.project,
name=args.name,
save=not args.no_save,
)
elif args.command == "export":
export_model(
weights=args.weights,
fmt=args.format,
imgsz=args.imgsz,
device=args.device,
)
elif args.command == "convert":
data_root = Path(args.data_root).expanduser().resolve()
for split in args.splits:
converted = convert_dataset(
data_root / split / "annotations",
data_root / split / "labels",
)
print(f"{split}: converted {converted} XML files")
return 0
if __name__ == "__main__":
raise SystemExit(main())

3
src/yolo/data/__init__.py Normal file
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@@ -0,0 +1,3 @@
from .convert import convert_dataset, voc_to_yolo
__all__ = ["convert_dataset", "voc_to_yolo"]

83
src/yolo/data/convert.py Normal file
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@@ -0,0 +1,83 @@
import xml.etree.ElementTree as ET
from collections.abc import Mapping
from pathlib import Path
YoloBox = tuple[int, float, float, float, float]
def voc_to_yolo(
xml_path: str | Path,
class_map: Mapping[str, int] | None = None,
) -> list[YoloBox]:
classes = class_map or {"fire": 0}
source = Path(xml_path)
root = ET.parse(source).getroot()
size = root.find("size")
if size is None:
raise ValueError(f"Missing <size> in {source}")
image_width = float(size.findtext("width") or 0)
image_height = float(size.findtext("height") or 0)
if image_width <= 0 or image_height <= 0:
raise ValueError(
f"Invalid image size in {source}: {image_width}x{image_height}"
)
labels: list[YoloBox] = []
for obj in root.findall("object"):
name = obj.findtext("name")
if name not in classes:
continue
box = obj.find("bndbox")
if box is None:
raise ValueError(f"Missing <bndbox> in {source}")
xmin = float(box.findtext("xmin") or 0)
ymin = float(box.findtext("ymin") or 0)
xmax = float(box.findtext("xmax") or 0)
ymax = float(box.findtext("ymax") or 0)
if xmin < 0 or ymin < 0 or xmax <= xmin or ymax <= ymin:
raise ValueError(
f"Invalid bounding box in {source}: {xmin}, {ymin}, {xmax}, {ymax}"
)
if xmax > image_width or ymax > image_height:
raise ValueError(f"Bounding box exceeds image bounds in {source}")
labels.append(
(
classes[name],
((xmin + xmax) / 2) / image_width,
((ymin + ymax) / 2) / image_height,
(xmax - xmin) / image_width,
(ymax - ymin) / image_height,
)
)
return labels
def convert_dataset(
xml_dir: str | Path,
output_dir: str | Path,
class_map: Mapping[str, int] | None = None,
) -> int:
source_dir = Path(xml_dir)
destination_dir = Path(output_dir)
if not source_dir.is_dir():
raise FileNotFoundError(f"Annotation directory does not exist: {source_dir}")
destination_dir.mkdir(parents=True, exist_ok=True)
xml_files = sorted(source_dir.glob("*.xml"))
for xml_file in xml_files:
labels = voc_to_yolo(xml_file, class_map)
output_file = destination_dir / f"{xml_file.stem}.txt"
output_file.write_text(
"".join(
f"{class_id} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n"
for class_id, x_center, y_center, width, height in labels
),
encoding="utf-8",
)
return len(xml_files)

12
src/yolo/defaults.py Normal file
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@@ -0,0 +1,12 @@
from pathlib import Path
PROJECT_ROOT = Path(__file__).resolve().parents[2]
CONFIG_DIR = PROJECT_ROOT / "configs"
DATA_DIR = PROJECT_ROOT / "data"
MODELS_DIR = PROJECT_ROOT / "models"
RUNS_DIR = PROJECT_ROOT / "runs"
DEFAULT_DATA_CONFIG = CONFIG_DIR / "datasets" / "fire.yaml"
DEFAULT_DATASET_ROOT = DATA_DIR / "fire-dataset"
DEFAULT_PRETRAINED_MODEL = MODELS_DIR / "pretrained" / "yolov8n.pt"
DEFAULT_DETECT_RUNS_DIR = RUNS_DIR / "detect"

180
src/yolo/engine.py Normal file
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@@ -0,0 +1,180 @@
from datetime import datetime
import csv
import json
from pathlib import Path
from typing import Any
import torch
from ultralytics import YOLO, settings
from .defaults import (
DEFAULT_DATA_CONFIG,
DEFAULT_DETECT_RUNS_DIR,
DEFAULT_PRETRAINED_MODEL,
PROJECT_ROOT,
)
PathLike = str | Path
def _write_best_point(run_dir: Path) -> None:
results_path = run_dir / "results.csv"
if not results_path.is_file():
return
with results_path.open(encoding="utf-8-sig", newline="") as results_file:
rows = list(csv.DictReader(results_file))
if not rows:
return
metric_keys = ("metrics/mAP50(B)", "metrics/mAP50-95(B)")
if any(metric_key not in rows[0] for metric_key in metric_keys):
return
best_points = {}
for metric_key in metric_keys:
best_row = max(rows, key=lambda row: float(row[metric_key]))
best_points[metric_key] = {
"epoch": int(float(best_row["epoch"])),
"value": float(best_row[metric_key]),
}
summary = {
"selection_metric": "metrics/mAP50-95(B)",
"best_point": best_points["metrics/mAP50-95(B)"],
"best_map50": best_points["metrics/mAP50(B)"],
"best_weights": str(run_dir / "weights" / "best.pt"),
"last_weights": str(run_dir / "weights" / "last.pt"),
"epochs_completed": len(rows),
}
(run_dir / "best_point.json").write_text(
json.dumps(summary, ensure_ascii=False, indent=2),
encoding="utf-8",
)
def resolve_device(device: str | None = None) -> str:
if device:
return device
return "cuda" if torch.cuda.is_available() else "cpu"
def _existing_file(path: PathLike, description: str) -> Path:
resolved = Path(path).expanduser().resolve()
if not resolved.is_file():
raise FileNotFoundError(f"{description} does not exist: {resolved}")
return resolved
def _configure_ultralytics() -> None:
settings.update({"datasets_dir": str(PROJECT_ROOT)})
def train(
data_yaml: PathLike = DEFAULT_DATA_CONFIG,
model_weights: PathLike = DEFAULT_PRETRAINED_MODEL,
epochs: int = 150,
imgsz: int = 640,
batch: int = 32,
workers: int = 8,
device: str | None = None,
project: PathLike = DEFAULT_DETECT_RUNS_DIR,
name: str | None = None,
patience: int = 20,
**kwargs: Any,
) -> Any:
data_path = _existing_file(data_yaml, "Dataset config")
weights_path = _existing_file(model_weights, "Model weights")
_configure_ultralytics()
run_name = name or f"fire_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
run_dir = Path(project) / run_name
model = YOLO(str(weights_path))
results = model.train(
data=str(data_path),
epochs=epochs,
imgsz=imgsz,
batch=batch,
workers=workers,
device=resolve_device(device),
project=str(Path(project)),
name=run_name,
patience=patience,
**kwargs,
)
_write_best_point(run_dir)
return results
def validate(
weights: PathLike,
data_yaml: PathLike = DEFAULT_DATA_CONFIG,
imgsz: int = 640,
batch: int = 32,
device: str | None = None,
project: PathLike = DEFAULT_DETECT_RUNS_DIR,
name: str = "validation",
plots: bool = True,
**kwargs: Any,
) -> Any:
weights_path = _existing_file(weights, "Model weights")
data_path = _existing_file(data_yaml, "Dataset config")
_configure_ultralytics()
model = YOLO(str(weights_path))
return model.val(
data=str(data_path),
imgsz=imgsz,
batch=batch,
device=resolve_device(device),
project=str(Path(project)),
name=name,
plots=plots,
**kwargs,
)
def predict(
weights: PathLike,
source: str,
conf: float = 0.25,
iou: float = 0.45,
imgsz: int = 640,
device: str | None = None,
project: PathLike = DEFAULT_DETECT_RUNS_DIR,
name: str = "predict",
save: bool = True,
**kwargs: Any,
) -> Any:
weights_path = _existing_file(weights, "Model weights")
model = YOLO(str(weights_path))
return model.predict(
source=source,
conf=conf,
iou=iou,
imgsz=imgsz,
device=resolve_device(device),
project=str(Path(project)),
name=name,
save=save,
**kwargs,
)
def export_model(
weights: PathLike,
fmt: str = "onnx",
imgsz: int = 640,
device: str | None = None,
**kwargs: Any,
) -> str:
weights_path = _existing_file(weights, "Model weights")
model = YOLO(str(weights_path))
return str(
model.export(
format=fmt,
imgsz=imgsz,
device=resolve_device(device),
**kwargs,
)
)

106
src/yolo/visualization.py Normal file
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@@ -0,0 +1,106 @@
from collections.abc import Sequence
from pathlib import Path
import cv2
import numpy as np
PixelBox = tuple[int, int, int, int]
def draw_boxes(
image: np.ndarray,
boxes: Sequence[PixelBox],
labels: Sequence[str] | None = None,
confidences: Sequence[float] | None = None,
color: tuple[int, int, int] = (0, 0, 255),
thickness: int = 2,
) -> np.ndarray:
output = image.copy()
for index, (x1, y1, x2, y2) in enumerate(boxes):
cv2.rectangle(output, (x1, y1), (x2, y2), color, thickness)
text_parts: list[str] = []
if labels and index < len(labels):
text_parts.append(labels[index])
if confidences and index < len(confidences):
text_parts.append(f"{confidences[index]:.2f}")
text = " ".join(text_parts)
if not text:
continue
(text_width, text_height), _ = cv2.getTextSize(
text,
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
1,
)
cv2.rectangle(
output,
(x1, y1 - text_height - 4),
(x1 + text_width + 4, y1),
color,
-1,
)
cv2.putText(
output,
text,
(x1 + 2, y1 - 3),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
return output
def compare(
image_path: str | Path,
boxes: Sequence[PixelBox],
labels: Sequence[str] | None = None,
confidences: Sequence[float] | None = None,
save_path: str | Path | None = None,
) -> np.ndarray:
image = cv2.imread(str(image_path))
if image is None:
raise FileNotFoundError(f"Cannot read image: {image_path}")
annotated = draw_boxes(image, boxes, labels, confidences)
height, width = image.shape[:2]
padding = 20
comparison = np.full(
(height + padding, width * 2 + padding * 3, 3),
255,
dtype=np.uint8,
)
cv2.putText(
comparison,
"Original",
(padding + width // 2 - 30, 15),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 0, 0),
1,
)
cv2.putText(
comparison,
"Detected",
(padding * 2 + width + width // 2 - 30, 15),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 0, 0),
1,
)
comparison[padding : padding + height, padding : padding + width] = image
comparison[
padding : padding + height,
padding * 2 + width : padding * 2 + width * 2,
] = annotated
if save_path is not None:
destination = Path(save_path)
destination.parent.mkdir(parents=True, exist_ok=True)
if not cv2.imwrite(str(destination), comparison):
raise OSError(f"Failed to write image: {destination}")
return comparison

210
uv.lock generated
View File

@@ -2,9 +2,56 @@ version = 1
revision = 3
requires-python = ">=3.14"
resolution-markers = [
"sys_platform == 'win32'",
"sys_platform == 'emscripten'",
"sys_platform != 'emscripten' and sys_platform != 'win32'",
"python_full_version >= '3.15' and sys_platform == 'win32'",
"python_full_version < '3.15' and sys_platform == 'win32'",
"python_full_version >= '3.15' and sys_platform == 'emscripten'",
"python_full_version < '3.15' and sys_platform == 'emscripten'",
"python_full_version >= '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'",
"python_full_version < '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'",
]
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