feat: 2.18.10 靶规格复检与置信度门槛
- 检出靶框裁剪放大后二次 detect 复检(conf 0.25)，以复检结果为准 - 全图结果上报门槛 MIN_REPORT_CONF=0.5，复检结果 >0.25 即采纳 - 0 结果时按 RETRY_CONF_TH 自动重检；class_id=1 解析阶段过滤 - 换用 model_326162，去除调试 print 改走 logger
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+141
-33
@@ -312,7 +312,8 @@ def _det_obj_score(o):
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def try_get_target_class_from_yolo(maix_frame, logger=None):
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"""调用 YOLO 检测：class_id∈IGNORE_CLASS_IDS(默认 1) 的框在解析时直接过滤；
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detections 输出其余全部框（不按置信度过滤）；
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上报字段 label/class_id/confidence 仅在有效靶规格(20/40)中取置信度最高的一条。"""
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上报以「检出框裁剪放大后的复检结果」为主，复检不可用时用全图结果；
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有效靶规格(20/40)中取置信度最高的一条，且低于 MIN_REPORT_CONF 不上报类别。"""
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try:
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import config as cfg
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except Exception:
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@@ -365,7 +366,6 @@ def try_get_target_class_from_yolo(maix_frame, logger=None):
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try:
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return detector.detect(maix_frame, conf_th=threshold, iou_th=iou_th)
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except Exception as exc:
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print(f"[YOLO-TARGET] detect 异常: {exc}")
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if logger:
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logger.warning(f"[YOLO-TARGET] detect 异常: {exc}")
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return None
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@@ -408,57 +408,165 @@ def try_get_target_class_from_yolo(maix_frame, logger=None):
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# 第 1 次检测
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raw = _run(conf_th)
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detections = _parse(raw)
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print(f"[YOLO-TARGET] 第1次 conf_th={conf_th} iou_th={iou_th} raw={raw!r}")
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print(
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f"[YOLO-TARGET] 检测结果={detections}"
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f"（已忽略 class_id∈{tuple(sorted(ignore_class_ids))} 共{n_ignored[0]} 条）"
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)
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# 0 结果时重新检测一次（阈值取更小值，仍为 0 则同参数重跑）
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if not detections:
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retry_th = float(getattr(cfg, "TARGET_CLASS_YOLO_RETRY_CONF_TH", 0.25) or 0)
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if not (0 < retry_th < conf_th):
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retry_th = conf_th
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print(f"[YOLO-TARGET] 0 结果，重新检测 第2次 conf_th={retry_th}")
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if logger:
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logger.info(f"[YOLO-TARGET] 0 结果，重新检测 conf_th={retry_th}")
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raw_retry = _run(retry_th)
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detections_retry = _parse(raw_retry)
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print(f"[YOLO-TARGET] 第2次 raw={raw_retry!r}")
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print(
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f"[YOLO-TARGET] 检测结果={detections_retry}"
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f"（已忽略 class_id∈{tuple(sorted(ignore_class_ids))} 共{n_ignored[0]} 条）"
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)
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if detections_retry:
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raw, detections = raw_retry, detections_retry
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detections = detections_retry
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if logger:
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logger.info(f"[YOLO-TARGET] 检测结果={detections}")
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logger.info(
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f"[YOLO-TARGET] 检测结果={detections}"
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f"（已忽略 class_id∈{tuple(sorted(ignore_class_ids))} 共{n_ignored[0]} 条）"
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)
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if not detections:
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if logger:
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logger.warning("[YOLO-TARGET] 未检测到目标（已重检 1 次）")
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return None
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# 上报口径：只在有效靶规格(20/40)里选置信度最高的一条，忽略 label=0 等无效类
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valid = [d for d in detections if d["label"] in valid_labels]
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if valid:
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picked = max(valid, key=lambda d: d["confidence"])
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else:
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picked = {
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"class_id": None,
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"label": None,
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"confidence": None,
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"box_xywh": None,
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}
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def _best_valid(dets):
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valid = [d for d in dets if d["label"] in valid_labels]
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if not valid:
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return None
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return max(valid, key=lambda d: d["confidence"])
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print(
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f"[YOLO-TARGET] 上报={picked['label']}, class_id={picked['class_id']}, "
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f"conf={picked['confidence']}, 有效候选={[(d['label'], d['confidence']) for d in valid]}"
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)
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def _crop_target(box, margin_frac):
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"""按检出框(带 margin)裁出放大 ROI；返回 Maix image，失败返回 None。"""
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if not box or len(box) != 4:
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return None
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x, y, w, h = [float(v) for v in box]
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try:
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fw, fh = int(maix_frame.width()), int(maix_frame.height())
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except Exception:
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fw = fh = 0
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mx, my = w * margin_frac, h * margin_frac
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x0, y0 = int(max(0, x - mx)), int(max(0, y - my))
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x1 = int(min(fw, x + w + mx)) if fw else int(x + w + mx)
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y1 = int(min(fh, y + h + my)) if fh else int(y + h + my)
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if (x1 - x0) < 32 or (y1 - y0) < 32:
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if logger:
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logger.warning(
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f"[YOLO-TARGET] 复检裁剪过小 [{x0},{y0},{x1},{y1}]"
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)
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return None
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try:
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cropped = maix_frame.crop(x0, y0, x1 - x0, y1 - y0)
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if cropped is not None:
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return cropped
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except Exception:
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pass
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try:
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from maix import image as maix_image
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arr = maix_frame.to_numpy()
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slab = np.ascontiguousarray(arr[y0:y1, x0:x1], dtype=np.uint8)
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if slab.size == 0:
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return None
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return maix_image.cv2image(slab, False, True)
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except Exception as exc:
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if logger:
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logger.warning(f"[YOLO-TARGET] 复检裁剪失败: {exc}")
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return None
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# 上报口径：只在有效靶规格(20/40)里取置信度最高的一条，class_id=1 已在解析阶段过滤
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full_best = _best_valid(detections)
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# 二次裁剪复检：把全图检出的靶框裁剪放大后再 detect 一次，与全图结果互相印证
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# 复检用更低阈值 RECHECK_CONF_TH，置信度大于该值即接受
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recheck_th = float(getattr(cfg, "TARGET_CLASS_YOLO_RECHECK_CONF_TH", 0.25))
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crop_best = None
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recheck_status = "off"
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if full_best is not None and bool(
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getattr(cfg, "TARGET_CLASS_YOLO_RECHECK_CROP", True)
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):
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recheck_status = "fail"
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margin_frac = float(
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getattr(cfg, "TARGET_CLASS_YOLO_RECHECK_MARGIN_FRAC", 0.1)
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)
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crop_img = _crop_target(full_best["box_xywh"], margin_frac)
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if crop_img is not None:
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try:
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raw_crop = detector.detect(
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crop_img, conf_th=recheck_th, iou_th=iou_th
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)
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except Exception as exc:
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raw_crop = None
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if logger:
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logger.warning(f"[YOLO-TARGET] 复检 detect 异常: {exc}")
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if raw_crop is not None:
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crop_dets = _parse(raw_crop)
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recheck_status = "ok"
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crop_best = _best_valid(crop_dets)
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if crop_best is not None and crop_best["confidence"] <= recheck_th:
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crop_best = None
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if logger:
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logger.info(
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f"[YOLO-TARGET] 复检(conf_th={recheck_th}, "
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f"裁剪框={full_best['box_xywh']}) 结果={crop_dets}"
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)
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# 合并：以复检（裁剪放大）结果为主；复检无结果才用全图
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picked = full_best
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picked_from_crop = False
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note = ""
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if recheck_status == "ok":
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if crop_best is None:
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note = f"复检无 conf>{recheck_th} 的有效框，沿用全图"
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else:
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picked = crop_best
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picked_from_crop = True
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if crop_best["label"] == full_best["label"]:
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note = f"复检一致({crop_best['label']})，以复检为准"
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else:
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note = (
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f"复检与全图不一致(全图={full_best['label']}@{full_best['confidence']}, "
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f"裁剪={crop_best['label']}@{crop_best['confidence']})，以复检为准"
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)
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# 置信度门槛：低于阈值不上报类别，避免硬猜（复检结果只需 > RECHECK_CONF_TH）
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if picked is not None:
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if picked_from_crop:
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min_report_conf = recheck_th
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below = picked["confidence"] <= min_report_conf
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else:
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min_report_conf = float(
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getattr(cfg, "TARGET_CLASS_YOLO_MIN_REPORT_CONF", 0.5)
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)
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below = picked["confidence"] < min_report_conf
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if below:
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note = (note + "; " if note else "") + (
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f"conf={picked['confidence']} 未达门槛{min_report_conf}，置信度不足"
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)
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picked = None
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if picked is None:
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label, class_id, confidence = None, None, None
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else:
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label = picked["label"]
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class_id = picked["class_id"]
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confidence = picked["confidence"]
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if logger:
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logger.info(
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f"[YOLO-TARGET] 上报={label}, class_id={class_id}, conf={confidence}, "
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f"全图候选={[(d['label'], d['confidence']) for d in detections if d['label'] in valid_labels]}, "
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f"复检={recheck_status} {note}"
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)
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if note:
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logger.warning(f"[YOLO-TARGET] {note}")
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return {
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"label": picked["label"],
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"class_id": picked["class_id"],
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"confidence": picked["confidence"],
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"label": label,
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"class_id": class_id,
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"confidence": confidence,
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"detections": detections,
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}
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