From 0d1692574101e0acf32a426bb1b717f21b4d8d12 Mon Sep 17 00:00:00 2001 From: crosstyan Date: Wed, 9 Jul 2025 10:31:22 +0800 Subject: [PATCH] add annotations --- single_people_detect_track.py | 51 ++++++++++++++++++++++------------- 1 file changed, 32 insertions(+), 19 deletions(-) diff --git a/single_people_detect_track.py b/single_people_detect_track.py index b7b1082..4f9cc59 100644 --- a/single_people_detect_track.py +++ b/single_people_detect_track.py @@ -976,44 +976,54 @@ tracking_initialized = False lost_frame_count = 0 lost_frame_threshold = 12 # 0.5秒，假设20fps + +# ===================== 主循环：逐帧处理检测与跟踪 ===================== while True: # 重新梳理跟踪逻辑，保证唯一目标、唯一初始化、鲁棒丢失判定 try: + # 获取下一个时间步的所有相机检测结果 detections = next(sync_gen) + # 过滤低置信度的检测，提升后续三角化和跟踪的准确性 detections = filter_keypoints_by_scores(detections, threshold=0.5) except StopIteration: + # 检测数据读取完毕，退出主循环 break # 1. 检查当前是否有已初始化的跟踪目标 + # 跟踪目标按id排序，便于后续一致性处理 trackings: list[Tracking] = sorted( global_tracking_state.trackings.values(), key=lambda x: x.id ) + # ========== 跟踪目标初始化流程 ========== if not tracking_initialized: - # 只初始化一次 - camera_port_this = filter_camera_port(detections) + # 只初始化一次跟踪目标，防止多次重复初始化 + camera_port_this = filter_camera_port(detections) # 获取当前帧检测到的相机端口 + # 如果检测到的相机数量小于总机位数-1，则认为初始化条件不满足，跳过本帧 if len(camera_port_this) < len(camera_port) - 1: print( "init tracking error, filter_detections len:", len(camera_port_this), ) continue + # 满足条件后，初始化全局跟踪状态，添加跟踪目标 global_tracking_state.add_tracking(detections) - tracking_initialized = True - lost_frame_count = 0 - # 保留第一帧的3d姿态数据 + tracking_initialized = True # 标记已初始化 + lost_frame_count = 0 # 丢失帧计数器归零 + # 保留第一帧的3d姿态数据，按id存储到all_3d_kps字典 for element_tracking in global_tracking_state.trackings.values(): if str(element_tracking.id) not in all_3d_kps.keys(): all_3d_kps[str(element_tracking.id)] = [ element_tracking.state.keypoints.tolist() ] print("init tracking:", global_tracking_state.trackings.values()) - continue + continue # 跳过本帧后续处理，进入下一帧 - # tracking_initialized = True + # ========== 丢失目标处理 ========== if len(detections) == 0: + # 当前帧没有检测到目标，进入丢失计数逻辑 print("no detections in this frame, continue") - lost_frame_count += 1 + lost_frame_count += 1 # 丢失帧数+1 # 进一步完善退出条件： # 1. 连续丢失阈值帧后才退出 # 2. 若丢失时，最后一次检测到的时间与当前帧时间间隔超过1秒，才彻底退出 @@ -1022,18 +1032,19 @@ while True: last_tracking = list(global_tracking_state.trackings.values())[0] if lost_frame_count >= lost_frame_threshold: should_remove = True - # 可选：可加时间间隔判定 + # 可选：可加时间间隔判定（可扩展） if should_remove: - global_tracking_state._trackings.clear() - tracking_initialized = False + global_tracking_state._trackings.clear() # 清空所有跟踪目标 + tracking_initialized = False # 允许后续重新初始化 print( f"tracking lost after {lost_frame_count} frames, reset tracking state" ) - lost_frame_count = 0 - continue + lost_frame_count = 0 # 丢失计数归零 + continue # 跳过本帧后续处理 - # 有检测，正常跟踪 - lost_frame_count = 0 + # ========== 正常跟踪流程 ========== + lost_frame_count = 0 # 检测到目标，丢失计数归零 + # 计算当前帧所有跟踪目标与检测目标的相似度矩阵（多机位） affinities: dict[str, AffinityResult] = calculate_affinity_matrix( trackings, detections, @@ -1044,13 +1055,13 @@ while True: lambda_a=LAMBDA_A, ) for element_tracking in trackings: - tracking_detection = [] - temp_matrix = [] + tracking_detection = [] # 存储每个跟踪目标在各相机下最优匹配的检测 + temp_matrix = [] # 打印用：每个相机的最大相似度 for camera_name in affinities.keys(): camera_matrix = jnp.array(affinities[camera_name].matrix).flatten() - detection_index = jnp.argmax(camera_matrix).item() + detection_index = jnp.argmax(camera_matrix).item() # 取最大相似度的检测索引 if isnan(camera_matrix[detection_index].item()): - breakpoint() + breakpoint() # 出现异常时调试 temp_matrix.append( f"{camera_name} : {camera_matrix[detection_index].item()}" ) @@ -1060,8 +1071,10 @@ while True: affinities[camera_name].detections[detection_index] ) print("affinities matrix:", temp_matrix) + # 只有匹配到足够多的检测目标时才更新跟踪（如多于2个相机） if len(tracking_detection) > 2: update_tracking(element_tracking, tracking_detection) + # 记录每一帧的3d关键点结果 all_3d_kps[str(element_tracking.id)].append( element_tracking.state.keypoints.tolist() )