diff --git a/app.yaml b/app.yaml index d244d48..30bbb91 100644 --- a/app.yaml +++ b/app.yaml @@ -12,7 +12,6 @@ files: - at_client.py - camera_manager.py - cameraParameters.xml - - charging_exit.sh - config.py - hardware.py - laser_detector.py diff --git a/config.py b/config.py index 971b6f4..ff8b2e4 100644 --- a/config.py +++ b/config.py @@ -262,6 +262,16 @@ TRIANGLE_SAMPLE_PATCH_HALF_PX = 2 # 开机阶段预加载 YOLO detector；detect 使用 dual_buff=False，避免返回上一帧结果。 TRIANGLE_YOLO_PRELOAD_ON_BOOT = False +# YOLO target size classification: class 0=20cm, class 1=40cm. +TARGET_CLASS_YOLO_ENABLE = True +TARGET_CLASS_YOLO_MODEL_PATH = APP_DIR + "/model_285484.mud" +TARGET_CLASS_YOLO_LABELS = (20, 40) +TARGET_CLASS_YOLO_CONF_TH = 0.50 +TARGET_CLASS_YOLO_IOU_TH = 0.45 +TARGET_CLASS_YOLO_RETRY_ON_EMPTY = False +TARGET_CLASS_YOLO_RETRY_CONF_TH = 0.25 +TARGET_CLASS_YOLO_PRELOAD_ON_BOOT = True + # ── 第二段 YOLO：仅在 Stage1 裁切出的靶环图上推理（与合成 stage2 训练数据一致）→ 子框内传统算法取直角点 ── # Stage1 靶环裁切内如何找黑三角标记（对比耗时时可切换）： # "yolo" — 调 Stage2 黑三角模型得子框，再子框内传统提取（需 TRIANGLE_BLACK_YOLO_ENABLE=True）。 diff --git a/main.py b/main.py index 4797e69..a0862a3 100644 --- a/main.py +++ b/main.py @@ -162,7 +162,11 @@ def cmd_str(): and _loc_black == "yolo" and bool(getattr(config, "TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT", True)) ) - _preload_yolo = _preload_yolo or _need_black_preload + _need_target_preload = ( + bool(getattr(config, "TARGET_CLASS_YOLO_ENABLE", False)) + and bool(getattr(config, "TARGET_CLASS_YOLO_PRELOAD_ON_BOOT", True)) + ) + _preload_yolo = _preload_yolo or _need_black_preload or _need_target_preload if _preload_yolo: preload_yolo_detector(logger) except Exception as e: diff --git a/shoot_manager.py b/shoot_manager.py index d788fa0..078cc9e 100644 --- a/shoot_manager.py +++ b/shoot_manager.py @@ -325,6 +325,18 @@ def process_shot(adc_val): # 网络事件移到拍照之后，避免阻塞拍照 network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True) + # Classify only the current shot frame; never reuse a previous result. + target_class_result = None + try: + from target_roi_yolo import try_get_target_class_from_yolo + + target_class_result = try_get_target_class_from_yolo(frame, logger=logger) + if logger: + logger.info(f"[YOLO-TARGET] 当前箭业务结果: {target_class_result}") + except Exception as exc: + if logger: + logger.warning(f"[YOLO-TARGET] 当前箭分类失败，按未知处理: {exc}") + # 调用算法分析 analysis_result = analyze_shot(frame) @@ -384,11 +396,25 @@ def process_shot(adc_val): srv_y = round(float(dy), 4) if dy is not None else 200.0 # 构造上报数据 + target_label = ( + target_class_result.get("label") + if isinstance(target_class_result, dict) + else None + ) + target_confidence = ( + target_class_result.get("confidence") + if isinstance(target_class_result, dict) + else None + ) inner_data = { "shot_id": shot_id, "x": srv_x, "y": srv_y, "r": 20.0, # 保留字段（服务端当前忽略，物理外环半径 cm） + "target_class": target_label, + "target_class_confidence": ( + float(target_confidence) if target_confidence is not None else None + ), "d": round((distance_m or 0.0) * 100), "d_laser": round((laser_distance_m or 0.0) * 100), "d_laser_quality": laser_signal_quality, @@ -416,6 +442,11 @@ def process_shot(adc_val): inner_data["ellipse_center_y"] = None report_data = {"cmd": 1, "data": inner_data} + if logger: + logger.info( + f"[REPORT-TARGET] enqueue shot_id={shot_id}, " + f"target_class={target_label}, confidence={target_confidence}" + ) network_manager.safe_enqueue(report_data, msg_type=2, high=True) # 数据上报后再画标注，不干扰检测阶段的原始画面 diff --git a/target_roi_yolo.py b/target_roi_yolo.py index 21fbcdd..fec5342 100644 --- a/target_roi_yolo.py +++ b/target_roi_yolo.py @@ -89,6 +89,29 @@ def _stage2_roi_crop_save_worker( _detector_by_path = {} +def _resolve_model_path(model_path: str): + """Resolve a model in either the installed app or MaixVision run directory.""" + model_path = (model_path or "").strip() + if model_path and os.path.isfile(model_path): + return model_path + if not model_path: + return "" + name = os.path.basename(model_path) + module_dir = os.path.dirname(os.path.abspath(__file__)) + candidates = ( + os.path.join(module_dir, name), + os.path.join(module_dir, "test", name), + os.path.join("/tmp/maixpy_run", name), + os.path.join("/tmp/maixpy_run", "test", name), + os.path.join(os.getcwd(), name), + os.path.join(os.getcwd(), "test", name), + ) + for candidate in candidates: + if os.path.isfile(candidate): + return candidate + return model_path + + def reset_yolo_detector_cache(): """切换模型路径时可调用（通常不必）。""" global _detector_by_path @@ -175,6 +198,23 @@ def preload_yolo_detector(logger=None): % (_loc_black,) ) + if bool(getattr(cfg, "TARGET_CLASS_YOLO_ENABLE", False)) and bool( + getattr(cfg, "TARGET_CLASS_YOLO_PRELOAD_ON_BOOT", True) + ): + class_model_path = _resolve_model_path( + getattr(cfg, "TARGET_CLASS_YOLO_MODEL_PATH", "") or "" + ) + class_detector = _get_detector(class_model_path) + if class_detector is None: + if logger: + logger.warning( + f"[YOLO-TARGET] 预加载失败：无法加载模型 {class_model_path}" + ) + else: + ok = True + if logger: + logger.info(f"[YOLO-TARGET] 靶规格模型已预加载: {class_model_path}") + return ok @@ -206,8 +246,10 @@ def _det_obj_class_id(o): if v is None: continue try: + if callable(v): + v = v() return int(float(v)) - except (TypeError, ValueError): + except (TypeError, ValueError, AttributeError): continue return None @@ -242,6 +284,106 @@ def _normalize_objs(objs): return out +def _det_obj_score(o): + """Return confidence across supported Maix YOLO result formats.""" + for key in ("score", "confidence", "conf", "prob"): + if hasattr(o, key): + try: + value = getattr(o, key) + if callable(value): + value = value() + value = float(value) + if value == value: + return value + except (TypeError, ValueError, AttributeError): + pass + return 0.0 + + +def try_get_target_class_from_yolo(maix_frame, logger=None): + """Classify the current target as 20cm or 40cm; return None if unknown.""" + try: + import config as cfg + except Exception: + return None + + if not bool(getattr(cfg, "TARGET_CLASS_YOLO_ENABLE", False)): + return None + model_path = _resolve_model_path( + getattr(cfg, "TARGET_CLASS_YOLO_MODEL_PATH", "") or "" + ) + if not os.path.isfile(model_path): + if logger: + logger.warning(f"[YOLO-TARGET] 模型文件不存在: {model_path}") + return None + detector = _get_detector(model_path) + if detector is None: + if logger: + logger.warning("[YOLO-TARGET] 无法加载 nn.YOLOv5") + return None + + conf_th = float(getattr(cfg, "TARGET_CLASS_YOLO_CONF_TH", 0.5)) + iou_th = float(getattr(cfg, "TARGET_CLASS_YOLO_IOU_TH", 0.45)) + labels = getattr(cfg, "TARGET_CLASS_YOLO_LABELS", (20, 40)) + if isinstance(labels, str): + labels = tuple(x.strip() for x in labels.split(",") if x.strip()) + labels = tuple(labels) + + def _detect(threshold): + try: + raw = detector.detect(maix_frame, conf_th=threshold, iou_th=iou_th) + except Exception as exc: + if logger: + logger.warning(f"[YOLO-TARGET] detect 异常: {exc}") + return [] + return _normalize_objs(raw if raw is not None else []) + + def _candidates(objs): + found = [] + for obj in objs: + class_id = _det_obj_class_id(obj) + if class_id is None or class_id < 0 or class_id >= len(labels): + continue + try: + label = int(float(labels[class_id])) + except (TypeError, ValueError): + continue + if label in (20, 40): + found.append((label, class_id, _det_obj_score(obj))) + return found + + objects = _detect(conf_th) + candidates = _candidates(objects) + if logger and objects: + logger.info( + "[YOLO-TARGET] 原始框=%d, 解析类别=%s" + % ( + len(objects), + [(_det_obj_class_id(o), _det_obj_score(o)) for o in objects[:8]], + ) + ) + if not candidates and bool( + getattr(cfg, "TARGET_CLASS_YOLO_RETRY_ON_EMPTY", False) + ): + retry_th = float(getattr(cfg, "TARGET_CLASS_YOLO_RETRY_CONF_TH", conf_th)) + if 0 < retry_th < conf_th: + candidates = _candidates(_detect(retry_th)) + + if not candidates: + if logger: + logger.warning("[YOLO-TARGET] 当前帧未识别到 20/40，按未知处理") + return None + + label, class_id, confidence = max(candidates, key=lambda item: item[2]) + result = {"label": label, "class_id": class_id, "confidence": confidence} + if logger: + logger.info( + f"[YOLO-TARGET] 当前帧分类={label}, class_id={class_id}, " + f"conf={confidence:.3f}" + ) + return result + + def _det_to_src_xyxy(o, coord_mode: str, src_w: int, src_h: int, net_w: int, net_h: int): """把单个检测框转为全图坐标系下的 xyxy（半开区间语义与后续 clip 一致）。""" x, y, w, h = float(o.x), float(o.y), float(o.w), float(o.h)