Kunpeng
25fa0c5825
Backend:
- Persist alert events and robot delivery counters in SQLite (data/app.db,
YOLO_DB_PATH override); EventStore keeps its interface, DeliveryStatsStore
uses read/write-through UPSERT; in-memory fallback remains for standalone
AlertManager use
- Serve /api/detect from a sync endpoint and serialize YOLO inference with a
lock, so concurrent requests no longer block the event loop
- Unify single-image and session detection: requests without session_id
share the fixed single-image session and run full frame confirmation
- Store UTC Z-suffixed timestamps for sortable string comparison; close
executor and database on shutdown via FastAPI lifespan
Frontend:
- Split app.js into ES modules (main.js + modules/{dom,api,ui,charts,store}
+ views/{dashboard,inspection,events,robots,robotCards}), no build step
- Escape all server data interpolated into innerHTML; guard missing
event.classes; replace lazy element ID list with memoized qs()
- Remove hardcoded fake stats (trend badge, device donut segment)
Docs:
- Rewrite README: accurate weight policy (only trained best.pt committed,
no Git LFS), SQLite persistence, single-worker note, data asset inventory
- Align models/pretrained/README.md with actual files; add YOLO_DB_PATH to
.env.example; add persistence unit tests; ruff clean
70 lines
3.3 KiB
Python
70 lines
3.3 KiB
Python
import csv
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from pathlib import Path
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import cv2
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from ultralytics import YOLO
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weights = Path(r"D:\work\yolo\runs\detect\smoke_fire_yolo11s_v1-4\weights\best.pt")
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candidates_path = Path(r"D:\work\yolo\runs\audit\suspected_missing_labels.csv")
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output_dir = Path(r"D:\work\yolo\runs\audit\missing_label_review_ge_0.50")
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threshold = 0.50
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class_names = ["smoke", "fire"]
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with candidates_path.open(encoding="utf-8", newline="") as csv_file:
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candidates = list(csv.DictReader(csv_file))
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image_paths = []
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for row in candidates:
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if float(row["confidence"]) < threshold:
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continue
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image_path = Path(row["image"])
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if image_path not in image_paths:
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image_paths.append(image_path)
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model = YOLO(str(weights))
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output_dir.mkdir(parents=True, exist_ok=True)
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manifest = []
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for index, image_path in enumerate(image_paths, start=1):
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image = cv2.imread(str(image_path))
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if image is None:
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continue
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height, width = image.shape[:2]
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label_path = image_path.parents[1] / "labels" / f"{image_path.stem}.txt"
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labeled = image.copy()
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label_lines = [line for line in label_path.read_text(encoding="utf-8").splitlines() if line.strip()] if label_path.is_file() else []
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for line in label_lines:
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values = line.split()
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class_id = int(float(values[0]))
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x_center, y_center, box_width, box_height = map(float, values[1:])
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x1 = int((x_center - box_width / 2) * width)
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y1 = int((y_center - box_height / 2) * height)
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x2 = int((x_center + box_width / 2) * width)
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y2 = int((y_center + box_height / 2) * height)
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cv2.rectangle(labeled, (x1, y1), (x2, y2), (0, 220, 0), 3)
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cv2.putText(labeled, class_names[class_id], (x1, max(28, y1 - 7)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 220, 0), 2)
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predicted = image.copy()
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result = model.predict(str(image_path), imgsz=640, conf=threshold, device=0, verbose=False)[0]
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predictions = []
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for box in result.boxes:
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class_id = int(box.cls.item())
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confidence = float(box.conf.item())
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x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
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predictions.append({"class": class_names[class_id], "confidence": round(confidence, 6), "xyxy": [x1, y1, x2, y2]})
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cv2.rectangle(predicted, (x1, y1), (x2, y2), (0, 0, 255), 3)
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cv2.putText(predicted, f"{class_names[class_id]} {confidence:.3f}", (x1, max(28, y1 - 7)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
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canvas = cv2.hconcat([labeled, predicted])
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cv2.rectangle(canvas, (0, 0), (canvas.shape[1], 58), (255, 255, 255), -1)
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cv2.putText(canvas, f"Original label: {image_path.name}", (20, 38), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 120, 0), 2)
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cv2.putText(canvas, "YOLO11s prediction", (width + 20, 38), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 180), 2)
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output_path = output_dir / f"{index:03d}_{image_path.stem}.jpg"
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if not cv2.imwrite(str(output_path), canvas):
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raise OSError(output_path)
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manifest.append({"source": str(image_path), "label": str(label_path), "review_image": str(output_path), "predictions": predictions})
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print(f"rendered {index}/{len(image_paths)} {image_path.name}", flush=True)
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manifest_path = output_dir / "manifest.json"
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import json
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manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
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print(f"completed={len(manifest)} manifest={manifest_path}") |