forked from HQU-gxy/CVTH3PE
Single-person tracking, modified the tracking initialization and tracking exit conditions, and also modified the weighted calculation process of DLT
This commit is contained in:
@ -133,15 +133,9 @@ def get_camera_detect(
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def get_segment(
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def get_segment(
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camera_port: list[int], frame_index: list[int], keypoint_data: dict[int, ak.Array]
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camera_port: list[int], frame_index: list[int], keypoint_data: dict[int, ak.Array]
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) -> dict[int, ak.Array]:
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) -> dict[int, ak.Array]:
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# for port in camera_port:
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# keypoint_data[port] = [
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# element_frame
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# for element_frame in KEYPOINT_DATASET[port]
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# if element_frame["frame_index"] in frame_index
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# ]
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for port in camera_port:
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for port in camera_port:
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segement_data = []
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segement_data = []
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camera_data = KEYPOINT_DATASET[port]
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camera_data = keypoint_data[port]
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for index, element_frame in enumerate(ak.to_numpy(camera_data["frame_index"])):
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for index, element_frame in enumerate(ak.to_numpy(camera_data["frame_index"])):
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if element_frame in frame_index:
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if element_frame in frame_index:
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segement_data.append(camera_data[index])
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segement_data.append(camera_data[index])
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@ -431,43 +425,32 @@ def triangulate_one_point_from_multiple_views_linear_time_weighted(
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else:
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else:
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confi = jnp.sqrt(jnp.clip(confidences, 0, 1))
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confi = jnp.sqrt(jnp.clip(confidences, 0, 1))
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A = jnp.zeros((N * 2, 4), dtype=np.float32)
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time_weights = jnp.exp(-lambda_t * delta_t)
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weights = time_weights * confi
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sum_weights = jnp.sum(weights)
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weights = jnp.where(sum_weights > 0, weights / sum_weights, weights)
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# First build the coefficient matrix without weights
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A = jnp.zeros((N * 2, 4), dtype=np.float32)
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for i in range(N):
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for i in range(N):
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x, y = points[i]
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x, y = points[i]
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A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0])
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row1 = proj_matrices[i, 2] * x - proj_matrices[i, 0]
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A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1])
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row2 = proj_matrices[i, 2] * y - proj_matrices[i, 1]
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A = A.at[2 * i].set(row1 * weights[i])
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A = A.at[2 * i + 1].set(row2 * weights[i])
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# Then apply the time-based and confidence weights
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for i in range(N):
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# Calculate time-decay weight: e^(-λ_t * Δt)
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time_weight = jnp.exp(-lambda_t * delta_t[i])
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# Calculate normalization factor: ||c^i^T||_2
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row_norm_1 = jnp.linalg.norm(A[2 * i])
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row_norm_2 = jnp.linalg.norm(A[2 * i + 1])
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# Apply combined weight: time_weight / row_norm * confidence
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w1 = (time_weight / row_norm_1) * confi[i]
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w2 = (time_weight / row_norm_2) * confi[i]
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A = A.at[2 * i].mul(w1)
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A = A.at[2 * i + 1].mul(w2)
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# Solve using SVD
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_, _, vh = jnp.linalg.svd(A, full_matrices=False)
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_, _, vh = jnp.linalg.svd(A, full_matrices=False)
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point_3d_homo = vh[-1] # shape (4,)
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point_3d_homo = vh[-1]
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point_3d_homo = jnp.where(point_3d_homo[3] < 0, -point_3d_homo, point_3d_homo)
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# Ensure homogeneous coordinate is positive
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is_zero_weight = jnp.sum(weights) == 0
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point_3d_homo = jnp.where(
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point_3d = jnp.where(
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point_3d_homo[3] < 0,
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is_zero_weight,
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-point_3d_homo,
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jnp.full((3,), jnp.nan, dtype=jnp.float32),
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point_3d_homo,
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jnp.where(
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jnp.abs(point_3d_homo[3]) > 1e-8,
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point_3d_homo[:3] / point_3d_homo[3],
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jnp.full((3,), jnp.nan, dtype=jnp.float32),
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),
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)
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)
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# Convert from homogeneous to Euclidean coordinates
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point_3d = point_3d_homo[:3] / point_3d_homo[3]
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return point_3d
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return point_3d
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@ -489,36 +472,37 @@ def triangulate_one_point_from_multiple_views_linear(
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"""
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"""
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assert len(proj_matrices) == len(points)
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assert len(proj_matrices) == len(points)
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N = len(proj_matrices)
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N = len(proj_matrices)
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# 置信度加权DLT
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# 置信度加权DLT
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if confidences is None:
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if confidences is None:
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weights = jnp.ones(N, dtype=jnp.float32)
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weights = jnp.ones(N, dtype=jnp.float32)
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else:
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else:
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# 置信度低于阈值的点权重为0，其余为sqrt(conf)
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valid_mask = confidences >= conf_threshold
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valid_mask = confidences >= conf_threshold
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weights = jnp.where(valid_mask, jnp.sqrt(jnp.clip(confidences, 0, 1)), 0.0)
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weights = jnp.where(valid_mask, confidences, 0.0)
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# 归一化权重，避免某一帧权重过大
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sum_weights = jnp.sum(weights)
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sum_weights = jnp.sum(weights)
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weights = jnp.where(sum_weights > 0, weights / sum_weights, weights)
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weights = jnp.where(sum_weights > 0, weights / sum_weights, weights)
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A = jnp.zeros((N * 2, 4), dtype=jnp.float32)
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A = jnp.zeros((N * 2, 4), dtype=jnp.float32)
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for i in range(N):
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for i in range(N):
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x, y = points[i]
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x, y = points[i]
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A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0])
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row1 = proj_matrices[i, 2] * x - proj_matrices[i, 0]
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A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1])
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row2 = proj_matrices[i, 2] * y - proj_matrices[i, 1]
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A = A.at[2 * i].mul(weights[i])
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A = A.at[2 * i].set(row1 * weights[i])
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A = A.at[2 * i + 1].mul(weights[i])
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A = A.at[2 * i + 1].set(row2 * weights[i])
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# https://docs.jax.dev/en/latest/_autosummary/jax.numpy.linalg.svd.html
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_, _, vh = jnp.linalg.svd(A, full_matrices=False)
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_, _, vh = jnp.linalg.svd(A, full_matrices=False)
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point_3d_homo = vh[-1] # shape (4,)
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point_3d_homo = vh[-1]
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point_3d_homo = jnp.where(point_3d_homo[3] < 0, -point_3d_homo, point_3d_homo)
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# replace the Python `if` with a jnp.where
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is_zero_weight = jnp.sum(weights) == 0
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point_3d_homo = jnp.where(
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point_3d = jnp.where(
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point_3d_homo[3] < 0, # predicate (scalar bool tracer)
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is_zero_weight,
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-point_3d_homo, # if True
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jnp.full((3,), jnp.nan, dtype=jnp.float32),
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point_3d_homo, # if False
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jnp.where(
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jnp.abs(point_3d_homo[3]) > 1e-8,
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point_3d_homo[:3] / point_3d_homo[3],
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jnp.full((3,), jnp.nan, dtype=jnp.float32),
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),
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)
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)
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point_3d = point_3d_homo[:3] / point_3d_homo[3]
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return point_3d
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return point_3d
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@ -542,16 +526,16 @@ def triangulate_points_from_multiple_views_linear(
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N, P, _ = points.shape
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N, P, _ = points.shape
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assert proj_matrices.shape[0] == N
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assert proj_matrices.shape[0] == N
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if confidences is None:
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conf = jnp.ones((N, P), dtype=jnp.float32)
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else:
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conf = jnp.array(confidences)
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conf = jnp.array(confidences)
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# vectorize your one‐point routine over P
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vmap_triangulate = jax.vmap(
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vmap_triangulate = jax.vmap(
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triangulate_one_point_from_multiple_views_linear,
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triangulate_one_point_from_multiple_views_linear,
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in_axes=(None, 1, 1), # proj_matrices static, map over points[:,p,:], conf[:,p]
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in_axes=(None, 1, 1),
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out_axes=0,
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out_axes=0,
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)
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)
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# returns (P, 3)
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return vmap_triangulate(proj_matrices, points, conf)
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return vmap_triangulate(proj_matrices, points, conf)
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@ -924,17 +908,20 @@ def smooth_3d_keypoints(
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# 通过平均置信度筛选2d检测数据
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# 通过平均置信度筛选2d检测数据
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def filter_keypoints_by_scores(detections: Iterable[Detection], threshold: float = 0.5):
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def filter_keypoints_by_scores(
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detections: Iterable[Detection], threshold: float = 0.5
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) -> list[Detection]:
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"""
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"""
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Filter detections based on the average confidence score of their keypoints.
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Filter detections based on the average confidence score of their keypoints.
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Only keep detections with an average score above the threshold.
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Only keep detections with an average score above the threshold.
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"""
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"""
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def filter_detection(detection: Detection) -> bool:
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def filter_detection(detection: Detection) -> bool:
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avg_score = np.mean(detection.confidences)
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median_score = np.mean(detection.confidences[:17])
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return float(avg_score) >= threshold
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# print(f"Mean score: {median_score}")
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return float(median_score) >= threshold
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return filter(filter_detection, detections)
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return [d for d in detections if filter_detection(d)]
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def filter_camera_port(detections: list[Detection]):
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def filter_camera_port(detections: list[Detection]):
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@ -956,7 +943,7 @@ camera_port = [5602, 5603, 5604, 5605]
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KEYPOINT_DATASET = get_camera_detect(DATASET_PATH, camera_port, AK_CAMERA_DATASET)
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KEYPOINT_DATASET = get_camera_detect(DATASET_PATH, camera_port, AK_CAMERA_DATASET)
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# 获取一段完整的跳跃片段
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# 获取一段完整的跳跃片段
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FRAME_INDEX = [i for i in range(552, 1488)] # 552， 1488
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FRAME_INDEX = [i for i in range(700, 1600)] # 552， 1488
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KEYPOINT_DATASET = get_segment(camera_port, FRAME_INDEX, KEYPOINT_DATASET)
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KEYPOINT_DATASET = get_segment(camera_port, FRAME_INDEX, KEYPOINT_DATASET)
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@ -965,7 +952,7 @@ sync_gen: Generator[list[Detection], Any, None] = get_batch_detect(
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KEYPOINT_DATASET,
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KEYPOINT_DATASET,
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AK_CAMERA_DATASET,
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AK_CAMERA_DATASET,
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camera_port,
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camera_port,
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batch_fps=20,
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batch_fps=24,
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)
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)
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# 建立追踪目标
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# 建立追踪目标
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@ -985,68 +972,68 @@ trackings: list[Tracking] = []
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# 3d数据，键为追踪目标id，值为该目标的所有3d数据
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# 3d数据，键为追踪目标id，值为该目标的所有3d数据
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all_3d_kps: dict[str, list] = {}
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all_3d_kps: dict[str, list] = {}
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# 遍历2d数据，测试追踪状态
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tracking_initialized = False
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while True:
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lost_frame_count = 0
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# 获得当前追踪目标
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lost_frame_threshold = 12 # 0.5秒，假设20fps
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trackings: list[Tracking] = sorted(
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global_tracking_state.trackings.values(), key=lambda x: x.id
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)
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while True:
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# 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
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try:
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try:
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detections = next(sync_gen)
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detections = next(sync_gen)
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# 通过平均置信度筛选2d检测数据
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detections = filter_keypoints_by_scores(detections, threshold=0.5)
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# detections = list(filter_keypoints_by_scores(detections, threshold=0.5))
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except StopIteration:
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except StopIteration:
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break
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break
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if len(detections) == 0:
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# 1. 检查当前是否有已初始化的跟踪目标
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print("no detections in this frame, continue")
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continue
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# 获得最新一帧的数据2d数据
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# 判断追踪状态是否建立成功，若不成功则跳过这一帧数据，直到追踪建立
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if not trackings:
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"""离机时使用，用于初始化第一帧"""
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"""
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# 使用盒子筛选后的2d检测数据
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filter_detections = get_filter_detections(detections)
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# 当3个机位均有目标时才建立追踪状态
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# if len(filter_detections) == 0:
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# continue
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if len(filter_detections) < len(camera_port):
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print(
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"init traincking error, filter filter_detections len:",
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len(filter_detections),
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)
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continue
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"""
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# 通过平均置信度筛选2d检测数据
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# detections = list(filter_keypoints_by_scores(detections, threshold=0.7))
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# 当4个机位都识别到目标时才建立追踪状态
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camera_port = filter_camera_port(detections)
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if len(detections) < len(camera_port):
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print(
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"init traincking error, filter_detections len:",
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len(detections),
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)
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else:
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# 添加第一帧数据构建追踪目标
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global_tracking_state.add_tracking(detections) # 离机时：filter_detections
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# 获得当前追踪目标
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trackings: list[Tracking] = sorted(
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trackings: list[Tracking] = sorted(
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global_tracking_state.trackings.values(), key=lambda x: x.id
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global_tracking_state.trackings.values(), key=lambda x: x.id
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)
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)
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if not tracking_initialized:
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# 只初始化一次
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camera_port_this = filter_camera_port(detections)
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if len(camera_port_this) < len(camera_port) - 1:
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print(
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"init tracking error, filter_detections len:",
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len(camera_port_this),
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)
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continue
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global_tracking_state.add_tracking(detections)
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tracking_initialized = True
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lost_frame_count = 0
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# 保留第一帧的3d姿态数据
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# 保留第一帧的3d姿态数据
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for element_tracking in trackings:
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for element_tracking in global_tracking_state.trackings.values():
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if str(element_tracking.id) not in all_3d_kps.keys():
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if str(element_tracking.id) not in all_3d_kps.keys():
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all_3d_kps[str(element_tracking.id)] = [
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all_3d_kps[str(element_tracking.id)] = [
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element_tracking.state.keypoints.tolist()
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element_tracking.state.keypoints.tolist()
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]
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]
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print("initer tracking:", trackings)
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print("init tracking:", global_tracking_state.trackings.values())
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else:
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continue
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# 计算相似度矩阵匹配结果
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# tracking_initialized = True
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if len(detections) == 0:
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print("no detections in this frame, continue")
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lost_frame_count += 1
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# 进一步完善退出条件：
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# 1. 连续丢失阈值帧后才退出
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# 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
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last_tracking = None
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if global_tracking_state.trackings:
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last_tracking = list(global_tracking_state.trackings.values())[0]
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if lost_frame_count >= lost_frame_threshold:
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should_remove = True
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# 可选：可加时间间隔判定
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if should_remove:
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global_tracking_state._trackings.clear()
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tracking_initialized = False
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print(
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f"tracking lost after {lost_frame_count} frames, reset tracking state"
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)
|
||||||
|
lost_frame_count = 0
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 有检测，正常跟踪
|
||||||
|
lost_frame_count = 0
|
||||||
affinities: dict[str, AffinityResult] = calculate_affinity_matrix(
|
affinities: dict[str, AffinityResult] = calculate_affinity_matrix(
|
||||||
trackings,
|
trackings,
|
||||||
detections,
|
detections,
|
||||||
@ -1056,39 +1043,25 @@ while True:
|
|||||||
alpha_3d=ALPHA_3D,
|
alpha_3d=ALPHA_3D,
|
||||||
lambda_a=LAMBDA_A,
|
lambda_a=LAMBDA_A,
|
||||||
)
|
)
|
||||||
|
|
||||||
# 遍历追踪目标，获得该目标的匹配2d数据
|
|
||||||
for element_tracking in trackings:
|
for element_tracking in trackings:
|
||||||
tracking_detection = []
|
tracking_detection = []
|
||||||
# 匹配检测目标的索引值
|
|
||||||
detection_index = None
|
|
||||||
|
|
||||||
temp_matrix = []
|
temp_matrix = []
|
||||||
|
|
||||||
# 遍历相机的追踪相似度匹配结果
|
|
||||||
for camera_name in affinities.keys():
|
for camera_name in affinities.keys():
|
||||||
# 获得与每个跟踪目标匹配的相似度矩阵
|
|
||||||
camera_matrix = jnp.array(affinities[camera_name].matrix).flatten()
|
camera_matrix = jnp.array(affinities[camera_name].matrix).flatten()
|
||||||
detection_index = jnp.argmax(camera_matrix).item()
|
detection_index = jnp.argmax(camera_matrix).item()
|
||||||
|
|
||||||
if isnan(camera_matrix[detection_index].item()):
|
if isnan(camera_matrix[detection_index].item()):
|
||||||
breakpoint()
|
breakpoint()
|
||||||
temp_matrix.append(
|
temp_matrix.append(
|
||||||
f"{camera_name} : {camera_matrix[detection_index].item()}"
|
f"{camera_name} : {camera_matrix[detection_index].item()}"
|
||||||
)
|
)
|
||||||
|
# 选取相似度大于阈值的检测目标更新跟踪状态
|
||||||
# 判断相似度矩阵极大值是否大于阈值
|
# if camera_matrix[detection_index].item() > affinities_threshold:
|
||||||
# 目前只有一个跟踪目标，还未实现多跟踪目标的匹配-------------------------
|
|
||||||
if camera_matrix[detection_index].item() > affinities_threshold:
|
|
||||||
# 保留对应的2d检测数据
|
|
||||||
tracking_detection.append(
|
tracking_detection.append(
|
||||||
affinities[camera_name].detections[detection_index]
|
affinities[camera_name].detections[detection_index]
|
||||||
)
|
)
|
||||||
print("affinities matrix:", temp_matrix)
|
print("affinities matrix:", temp_matrix)
|
||||||
# 当2个及以上数量的机位同时检测到追踪目标时，更新追踪状态
|
if len(tracking_detection) > 2:
|
||||||
if len(tracking_detection) >= 2:
|
|
||||||
update_tracking(element_tracking, tracking_detection)
|
update_tracking(element_tracking, tracking_detection)
|
||||||
# 保留对应的3d姿态数据
|
|
||||||
all_3d_kps[str(element_tracking.id)].append(
|
all_3d_kps[str(element_tracking.id)].append(
|
||||||
element_tracking.state.keypoints.tolist()
|
element_tracking.state.keypoints.tolist()
|
||||||
)
|
)
|
||||||
@ -1096,23 +1069,7 @@ while True:
|
|||||||
"update tracking:",
|
"update tracking:",
|
||||||
global_tracking_state.trackings.values(),
|
global_tracking_state.trackings.values(),
|
||||||
)
|
)
|
||||||
else:
|
# 不再在else分支里删除tracking，只用lost_frame_count判定
|
||||||
# if len(detections) == 0:
|
|
||||||
# continue
|
|
||||||
# 追踪目标丢失的时间间隔
|
|
||||||
time_gap = (
|
|
||||||
detections[0].timestamp
|
|
||||||
- element_tracking.state.last_active_timestamp
|
|
||||||
)
|
|
||||||
# 当时间间隔超过1s，删除保留的追踪状态
|
|
||||||
if time_gap.seconds > 0.5:
|
|
||||||
global_tracking_state._trackings.pop(element_tracking.id)
|
|
||||||
print(
|
|
||||||
"remove trackings:",
|
|
||||||
global_tracking_state.trackings.values(),
|
|
||||||
"time:",
|
|
||||||
detections[0].timestamp,
|
|
||||||
)
|
|
||||||
|
|
||||||
# 对每一个3d目标进行滑动窗口平滑处理
|
# 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)
|
smoothed_points = smooth_3d_keypoints(all_3d_kps, window_size=5)
|
||||||
|
|||||||
828
temp.ipynb
Normal file
828
temp.ipynb
Normal file
@ -0,0 +1,828 @@
|
|||||||
|
{
|
||||||
|
"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",
|
||||||
|
"Frame Index: 10, Average Score: 0.58\n",
|
||||||
|
"Frame Index: 11, Average Score: 0.58\n",
|
||||||
|
"Frame Index: 12, Average Score: 0.57\n",
|
||||||
|
"Frame Index: 13, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 14, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 15, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 16, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 17, Average Score: 0.58\n",
|
||||||
|
"Frame Index: 18, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 19, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 20, Average Score: 0.60\n",
|
||||||
|
"Frame Index: 21, Average Score: 0.57\n",
|
||||||
|
"Frame Index: 22, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 23, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 24, Average Score: 0.57\n",
|
||||||
|
"Frame Index: 25, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 26, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 27, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 28, Average Score: 0.54\n",
|
||||||
|
"Frame Index: 29, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 30, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 31, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 32, Average Score: 0.54\n",
|
||||||
|
"Frame Index: 33, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 34, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 35, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 36, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 37, Average Score: 0.54\n",
|
||||||
|
"Frame Index: 38, Average Score: 0.51\n",
|
||||||
|
"Frame Index: 39, Average Score: 0.51\n",
|
||||||
|
"Frame Index: 40, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 41, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 42, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 43, Average Score: 0.51\n",
|
||||||
|
"Frame Index: 44, Average Score: 0.50\n",
|
||||||
|
"Frame Index: 45, Average Score: 0.50\n",
|
||||||
|
"Frame Index: 46, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 47, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 48, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 49, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 50, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 51, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 52, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 53, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 54, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 55, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 56, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 57, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 58, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 59, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 60, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 61, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 62, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 63, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 64, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 65, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 66, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 67, Average Score: 0.51\n",
|
||||||
|
"Frame Index: 68, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 69, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 70, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 71, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 72, Average Score: 0.51\n",
|
||||||
|
"Frame Index: 73, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 74, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 75, Average Score: 0.40\n",
|
||||||
|
"Frame Index: 76, Average Score: 0.46\n",
|
||||||
|
"Frame Index: 77, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 78, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 79, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 80, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 81, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 82, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 83, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 84, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 85, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 86, Average Score: 0.57\n",
|
||||||
|
"Frame Index: 87, Average Score: 0.57\n",
|
||||||
|
"Frame Index: 88, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 89, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 90, Average Score: 0.63\n",
|
||||||
|
"Frame Index: 91, Average Score: 0.62\n",
|
||||||
|
"Frame Index: 92, Average Score: 0.63\n",
|
||||||
|
"Frame Index: 93, Average Score: 0.64\n",
|
||||||
|
"Frame Index: 94, Average Score: 0.63\n",
|
||||||
|
"Frame Index: 95, Average Score: 0.61\n",
|
||||||
|
"Frame Index: 96, Average Score: 0.63\n",
|
||||||
|
"Frame Index: 97, Average Score: 0.63\n",
|
||||||
|
"Frame Index: 98, Average Score: 0.64\n",
|
||||||
|
"Frame Index: 99, Average Score: 0.65\n",
|
||||||
|
"Frame Index: 100, Average Score: 0.60\n",
|
||||||
|
"Frame Index: 101, Average Score: 0.59\n",
|
||||||
|
"Frame Index: 102, Average Score: 0.58\n",
|
||||||
|
"Frame Index: 103, Average Score: 0.57\n",
|
||||||
|
"Frame Index: 104, Average Score: 0.58\n",
|
||||||
|
"Frame Index: 105, Average Score: nan\n",
|
||||||
|
"Frame Index: 106, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 107, Average Score: 0.56\n",
|
||||||
|
"Frame Index: 108, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 109, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 110, Average Score: 0.58\n",
|
||||||
|
"Frame Index: 111, Average Score: 0.55\n",
|
||||||
|
"Frame Index: 112, Average Score: 0.58\n",
|
||||||
|
"Frame Index: 113, Average Score: 0.54\n",
|
||||||
|
"Frame Index: 114, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 115, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 116, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 117, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 118, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 119, Average Score: 0.53\n",
|
||||||
|
"Frame Index: 120, Average Score: 0.50\n",
|
||||||
|
"Frame Index: 121, Average Score: 0.50\n",
|
||||||
|
"Frame Index: 122, Average Score: 0.52\n",
|
||||||
|
"Frame Index: 123, Average Score: 0.51\n",
|
||||||
|
"Frame Index: 124, Average Score: 0.51\n",
|
||||||
|
"Frame Index: 125, Average Score: 0.50\n",
|
||||||
|
"Frame Index: 126, Average Score: 0.47\n",
|
||||||
|
"Frame Index: 127, Average Score: 0.50\n",
|
||||||
|
"Frame Index: 128, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 129, Average Score: 0.49\n",
|
||||||
|
"Frame Index: 130, Average Score: 0.48\n",
|
||||||
|
"Frame Index: 131, Average Score: 0.46\n",
|
||||||
|
"Frame Index: 132, Average Score: 0.44\n",
|
||||||
|
"Frame Index: 133, Average Score: 0.44\n",
|
||||||
|
"Frame Index: 134, Average Score: 0.44\n",
|
||||||
|
"Frame Index: 135, Average Score: 0.47\n",
|
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|
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"Frame Index: 699, Average Score: 0.81\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
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user