diff --git a/CVTH3PE b/CVTH3PE deleted file mode 160000 index e79e899..0000000 --- a/CVTH3PE +++ /dev/null @@ -1 +0,0 @@ -Subproject commit e79e899b874806b134ea7cc50f2f02bbaefd9507 diff --git a/app/tracking/__init__.py b/app/tracking/__init__.py index 860ab0b..f03c4cc 100644 --- a/app/tracking/__init__.py +++ b/app/tracking/__init__.py @@ -560,8 +560,8 @@ class AffinityResult: matrix: Float[Array, "T D"] trackings: Sequence[Tracking] detections: Sequence[Detection] - indices_T: Int[Array, "T"] # pylint: disable=invalid-name - indices_D: Int[Array, "T"] # pylint: disable=invalid-name + indices_T: Int[Array, "A"] # pylint: disable=invalid-name + indices_D: Int[Array, "A"] # pylint: disable=invalid-name def tracking_association( self, diff --git a/test_filter_object_by_box.ipynb b/filter_object_by_box.ipynb similarity index 99% rename from test_filter_object_by_box.ipynb rename to filter_object_by_box.ipynb index 6d70b2c..ce075b2 100644 --- a/test_filter_object_by_box.ipynb +++ b/filter_object_by_box.ipynb @@ -1430,7 +1430,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "cvth3pe", "language": "python", "name": "python3" }, diff --git a/single_people_detect_track.py b/single_people_detect_track.py index 4f9cc59..d99408c 100644 --- a/single_people_detect_track.py +++ b/single_people_detect_track.py @@ -459,7 +459,7 @@ def triangulate_one_point_from_multiple_views_linear( proj_matrices: Float[Array, "N 3 4"], points: Num[Array, "N 2"], confidences: Optional[Float[Array, "N"]] = None, - conf_threshold: float = 0.2, + conf_threshold: float = 0.4, # 0.2 ) -> Float[Array, "3"]: """ Args: @@ -473,7 +473,6 @@ def triangulate_one_point_from_multiple_views_linear( assert len(proj_matrices) == len(points) N = len(proj_matrices) # 置信度加权DLT - # 置信度加权DLT if confidences is None: weights = jnp.ones(N, dtype=jnp.float32) else: @@ -932,18 +931,18 @@ def filter_camera_port(detections: list[Detection]): # 相机内外参路径 -CAMERA_PATH = Path("/home/admin/Documents/ActualTest_WeiHua/camera_params") +CAMERA_PATH = Path("/home/admin/Documents/ActualTest_WeiHua/camera_params/") # 所有机位的相机内外参 AK_CAMERA_DATASET: ak.Array = get_camera_params(CAMERA_PATH) # 2d检测数据路径 -DATASET_PATH = Path("/home/admin/Documents/ActualTest_WeiHua/Test_Video") +DATASET_PATH = Path("/home/admin/Documents/ActualTest_WeiHua/Segment_1/") # 指定机位的2d检测数据 camera_port = [5602, 5603, 5604, 5605] KEYPOINT_DATASET = get_camera_detect(DATASET_PATH, camera_port, AK_CAMERA_DATASET) # 获取一段完整的跳跃片段 -FRAME_INDEX = [i for i in range(700, 1600)] # 552， 1488 +FRAME_INDEX = [i for i in range(700, 1600)] # Segement_1:(700, 1600) KEYPOINT_DATASET = get_segment(camera_port, FRAME_INDEX, KEYPOINT_DATASET) @@ -974,8 +973,10 @@ all_3d_kps: dict[str, list] = {} tracking_initialized = False lost_frame_count = 0 -lost_frame_threshold = 12 # 0.5秒，假设20fps +lost_frame_threshold = 12 # 0.5秒 +# 丢失目标帧数计数器 +loss_track_count = 0 # ===================== 主循环：逐帧处理检测与跟踪 ===================== while True: @@ -984,7 +985,8 @@ while True: # 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) + detections = filter_keypoints_by_scores(detections, threshold=0.2) + # detections = get_filter_detections(detections) # 伞降跳台时使用 except StopIteration: # 检测数据读取完毕，退出主循环 break @@ -1026,7 +1028,7 @@ while True: 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 + # 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0.5秒，才彻底退出 last_tracking = None if global_tracking_state.trackings: last_tracking = list(global_tracking_state.trackings.values())[0] @@ -1058,6 +1060,10 @@ while True: 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(): + # indices_T:表示匹配到检测的tracking的索引(在tracking列表中的下标) + # indices_D:表示匹配到tracking的detection的索引(在detections列表中的下标) + indices_T = affinities[camera_name].indices_T.item() + indices_D = affinities[camera_name].indices_D.item() camera_matrix = jnp.array(affinities[camera_name].matrix).flatten() 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()): @@ -1065,11 +1071,11 @@ while True: temp_matrix.append( f"{camera_name} : {camera_matrix[detection_index].item()}" ) + match_tracking = affinities[camera_name].trackings[indices_T] # 选取相似度大于阈值的检测目标更新跟踪状态 # if camera_matrix[detection_index].item() > affinities_threshold: - tracking_detection.append( - affinities[camera_name].detections[detection_index] - ) + # if match_tracking == element_tracking: + tracking_detection.append(affinities[camera_name].detections[indices_D]) 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: @@ -1082,13 +1088,17 @@ while True: "update tracking:", global_tracking_state.trackings.values(), ) - # 不再在else分支里删除tracking，只用lost_frame_count判定 + else: + loss_track_count += 1 + # ======如果单帧数据量不够，考虑如何更新跟踪===== # 对每一个3d目标进行滑动窗口平滑处理 smoothed_points = smooth_3d_keypoints(all_3d_kps, window_size=5) +print("Tracking completed, total loss frames processed:", count) + # 将结果保存到json文件中 -with open("samples/Test_WeiHua.json", "wb") as f: +with open("samples/Test_WeiHua_Segment_1.json", "wb") as f: f.write(orjson.dumps(smoothed_points)) # 输出每个3d目标的维度 for element_3d_kps_id in smoothed_points.keys(): diff --git a/temp.ipynb b/temp.ipynb deleted file mode 100644 index 3b7292b..0000000 --- a/temp.ipynb +++ /dev/null @@ -1,828 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "727f95c9", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import awkward as ak\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fc96704c", - "metadata": {}, - "outputs": [], - "source": [ - "def get_camera_params(camera_path: Path) -> ak.Array:\n", - " camera_dataset: ak.Array = ak.from_parquet(camera_path / \"camera_params.parquet\")\n", - " return camera_dataset\n", - "\n", - "\n", - "def get_camera_detect(\n", - " detect_path: Path, camera_port: list[int], camera_dataset: ak.Array\n", - ") -> dict[int, ak.Array]:\n", - " keypoint_data: dict[int, ak.Array] = {}\n", - " for element_port in ak.to_numpy(camera_dataset[\"port\"]):\n", - " if element_port in camera_port:\n", - " keypoint_data[int(element_port)] = ak.from_parquet(\n", - " detect_path / f\"{element_port}_detected.parquet\"\n", - " )\n", - " return keypoint_data\n", - "\n", - "\n", - "def get_segment(\n", - " camera_port: list[int], frame_index: list[int], keypoint_data: dict[int, ak.Array]\n", - ") -> dict[int, ak.Array]:\n", - " # for port in camera_port:\n", - " # keypoint_data[port] = [\n", - " # element_frame\n", - " # for element_frame in KEYPOINT_DATASET[port]\n", - " # if element_frame[\"frame_index\"] in frame_index\n", - " # ]\n", - " for port in camera_port:\n", - " segement_data = []\n", - " camera_data = keypoint_data[port]\n", - " for index, element_frame in enumerate(ak.to_numpy(camera_data[\"frame_index\"])):\n", - " if element_frame in frame_index:\n", - " segement_data.append(camera_data[index])\n", - " keypoint_data[port] = ak.Array(segement_data)\n", - "\n", - " return keypoint_data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "01455135", - "metadata": {}, - "outputs": [], - "source": [ - "# 相机内外参路径\n", - "CAMERA_PATH = Path(\"/home/admin/Documents/ActualTest_WeiHua/camera_params\")\n", - "# 所有机位的相机内外参\n", - "AK_CAMERA_DATASET: ak.Array = get_camera_params(CAMERA_PATH)\n", - "\n", - "# 2d检测数据路径\n", - "DATASET_PATH = Path(\"/home/admin/Documents/ActualTest_WeiHua/Test_Video\")\n", - "# 指定机位的2d检测数据\n", - "camera_port = [5602, 5603, 5604, 5605]\n", - "KEYPOINT_DATASET = get_camera_detect(DATASET_PATH, camera_port, AK_CAMERA_DATASET)\n", - "\n", - "# 获取一段完整的跳跃片段\n", - "FRAME_INDEX = [i for i in range(700)] # 552， 1488\n", - "KEYPOINT_DATASET = get_segment(camera_port, FRAME_INDEX, KEYPOINT_DATASET)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6a4aac82", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Frame Index: 0, Average Score: 0.54\n", - "Frame Index: 1, Average Score: 0.53\n", - "Frame Index: 2, Average Score: 0.51\n", - "Frame Index: 3, Average Score: 0.54\n", - "Frame Index: 4, Average Score: 0.53\n", - "Frame Index: 5, Average Score: 0.55\n", - "Frame Index: 6, Average Score: 0.54\n", - "Frame Index: 7, Average Score: 0.56\n", - "Frame Index: 8, Average Score: 0.56\n", - "Frame Index: 9, Average Score: 0.56\n", - 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] - } - ], - "source": [ - "INDEX=5604\n", - "# display(KEYPOINT_DATASET[INDEX])\n", - "data = KEYPOINT_DATASET[INDEX]\n", - "for frame_data in data:\n", - " avg_score = np.mean(ak.to_numpy(frame_data[\"kps_scores\"][:17]))\n", - " print(\n", - " f\"Frame Index: {frame_data['frame_index']}, Average Score: {avg_score:.2f}\"\n", - " )\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cvth3pe", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}