forked from HQU-gxy/CVTH3PE
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gxy/202506
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master
| Author | SHA1 | Date | |
|---|---|---|---|
| 6cd13064f3 | |||
| 5d816d92d5 | |||
| 4bc3fce0b1 | |||
| 1f8d70803f | |||
| 20b2cf59f2 | |||
| 4a5cfde245 | |||
| d2c1c8d624 | |||
| c31cc4e7bf | |||
| 46b8518a10 | |||
| 4e78165f12 | |||
| c78850855c |
2
.gitignore
vendored
2
.gitignore
vendored
@ -10,5 +10,3 @@ wheels/
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.venv
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.hypothesis
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samples
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*.jpg
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*.parquet
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@ -1,7 +1,6 @@
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from collections import OrderedDict, defaultdict
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from dataclasses import dataclass
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from datetime import datetime
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import string
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from typing import Any, TypeAlias, TypedDict, Optional, Sequence
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from beartype import beartype
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@ -523,14 +522,10 @@ def to_homogeneous(points: Num[Array, "N 2"] | Num[Array, "N 3"]) -> Num[Array,
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raise ValueError(f"Invalid shape for points: {points.shape}")
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import awkward as ak
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@jaxtyped(typechecker=beartype)
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def point_line_distance(
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points: Num[Array, "N 3"] | Num[Array, "N 2"],
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line: Num[Array, "N 3"],
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description: str,
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eps: float = 1e-9,
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):
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"""
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@ -549,12 +544,6 @@ def point_line_distance(
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"""
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numerator = abs(line[:, 0] * points[:, 0] + line[:, 1] * points[:, 1] + line[:, 2])
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denominator = jnp.sqrt(line[:, 0] * line[:, 0] + line[:, 1] * line[:, 1])
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# line_data = {"a": line[:, 0], "b": line[:, 1], "c": line[:, 2]}
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# line_x_y = {"x": points[:, 0], "y": points[:, 1]}
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# ak.to_parquet(
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# line_data, f"/home/admin/Code/CVTH3PE/line_a_b_c_{description}.parquet"
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# )
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# ak.to_parquet(line_x_y, f"/home/admin/Code/CVTH3PE/line_x_y_{description}.parquet")
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return numerator / (denominator + eps)
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@ -582,7 +571,7 @@ def left_to_right_epipolar_distance(
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"""
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F_t = fundamental_matrix.transpose()
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line1_in_2 = jnp.matmul(left, F_t)
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return point_line_distance(right, line1_in_2, "left_to_right")
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return point_line_distance(right, line1_in_2)
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@jaxtyped(typechecker=beartype)
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@ -608,7 +597,7 @@ def right_to_left_epipolar_distance(
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$$x^{\\prime T}Fx = 0$$
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"""
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line2_in_1 = jnp.matmul(right, fundamental_matrix)
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return point_line_distance(left, line2_in_1, "right_to_left")
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return point_line_distance(left, line2_in_1)
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def distance_between_epipolar_lines(
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@ -1,4 +1,3 @@
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import warnings
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import weakref
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from collections import deque
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from dataclasses import dataclass
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@ -115,23 +114,13 @@ class LastDifferenceVelocityFilter(GenericVelocityFilter):
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def predict(self, timestamp: datetime) -> TrackingPrediction:
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delta_t_s = (timestamp - self._last_timestamp).total_seconds()
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if delta_t_s <= 0:
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warnings.warn(
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"delta_t={}; last={}; current={}".format(
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delta_t_s, self._last_timestamp, timestamp
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)
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)
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assert delta_t_s >= 0, f"delta_t_s is negative: {delta_t_s}"
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if self._last_velocity is None:
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return TrackingPrediction(
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velocity=None,
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keypoints=self._last_keypoints,
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)
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else:
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if delta_t_s <= 0:
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return TrackingPrediction(
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velocity=self._last_velocity,
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keypoints=self._last_keypoints,
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)
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return TrackingPrediction(
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velocity=self._last_velocity,
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keypoints=self._last_keypoints + self._last_velocity * delta_t_s,
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@ -139,12 +128,10 @@ class LastDifferenceVelocityFilter(GenericVelocityFilter):
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def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None:
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delta_t_s = (timestamp - self._last_timestamp).total_seconds()
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if delta_t_s <= 0:
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pass
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else:
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self._last_timestamp = timestamp
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assert delta_t_s >= 0, f"delta_t_s is negative: {delta_t_s}"
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self._last_velocity = (keypoints - self._last_keypoints) / delta_t_s
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self._last_keypoints = keypoints
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self._last_timestamp = timestamp
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def get(self) -> TrackingPrediction:
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if self._last_velocity is None:
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@ -175,8 +162,20 @@ class LeastMeanSquareVelocityFilter(GenericVelocityFilter):
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historical_timestamps: Sequence[datetime],
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max_samples: int = 10,
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):
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assert len(historical_3d_poses) == len(historical_timestamps)
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"""
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Args:
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historical_3d_poses: sequence of 3D poses, at least one element is required
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historical_timestamps: sequence of timestamps, whose length is the same as `historical_3d_poses`
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max_samples: maximum number of samples to keep
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"""
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assert (N := len(historical_3d_poses)) == len(
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historical_timestamps
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), f"the length of `historical_3d_poses` and `historical_timestamps` must be the same; got {N} and {len(historical_timestamps)}"
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if N < 1:
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raise ValueError("at least one historical 3D pose is required")
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temp = zip(historical_3d_poses, historical_timestamps)
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# sorted by timestamp
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temp_sorted = sorted(temp, key=lambda x: x[1])
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self._historical_3d_poses = deque(
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map(lambda x: x[0], temp_sorted), maxlen=max_samples
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@ -202,6 +201,7 @@ class LeastMeanSquareVelocityFilter(GenericVelocityFilter):
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latest_timestamp = self._historical_timestamps[-1]
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delta_t_s = (timestamp - latest_timestamp).total_seconds()
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assert delta_t_s >= 0, f"delta_t_s is negative: {delta_t_s}"
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if self._velocity is None:
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return TrackingPrediction(
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@ -243,7 +243,6 @@ class LeastMeanSquareVelocityFilter(GenericVelocityFilter):
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keypoints_reshaped = keypoints.reshape(n_samples, -1)
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# Use JAX's lstsq to solve the least squares problem
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# This is more numerically stable than manually computing pseudoinverse
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coefficients, _, _, _ = jnp.linalg.lstsq(X, keypoints_reshaped, rcond=None)
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# Coefficients shape is [2, J*3]
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@ -560,8 +559,8 @@ class AffinityResult:
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matrix: Float[Array, "T D"]
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trackings: Sequence[Tracking]
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detections: Sequence[Detection]
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indices_T: Int[Array, "A"] # pylint: disable=invalid-name
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indices_D: Int[Array, "A"] # pylint: disable=invalid-name
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indices_T: Int[Array, "T"] # pylint: disable=invalid-name
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indices_D: Int[Array, "D"] # pylint: disable=invalid-name
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def tracking_association(
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self,
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File diff suppressed because one or more lines are too long
@ -1,208 +0,0 @@
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from narwhals import Boolean
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import numpy as np
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import cv2
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from typing import (
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TypeAlias,
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TypedDict,
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)
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from jaxtyping import Array, Num
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from shapely.geometry import Polygon
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from sympy import false, true
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NDArray: TypeAlias = np.ndarray
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# 盒子各个面的三维三角形集合
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box_triangles_list = [
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["4", "6", "7"],
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["4", "5", "6"],
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["2", "5", "6"],
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["1", "2", "5"],
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["1", "2", "3"],
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["0", "1", "3"],
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["0", "3", "7"],
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["0", "4", "7"],
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["2", "6", "7"],
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["2", "3", "7"],
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["0", "4", "5"],
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["0", "1", "5"],
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]
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class Camera_Params(TypedDict):
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rvec: Num[NDArray, "3"]
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tvec: Num[NDArray, "3"]
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camera_matrix: Num[Array, "3 3"]
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dist: Num[Array, "N"]
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width: int
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height: int
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class KeypointDataset(TypedDict):
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frame_index: int
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boxes: Num[NDArray, "N 4"]
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kps: Num[NDArray, "N J 2"]
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kps_scores: Num[NDArray, "N J"]
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# 三维坐标系根据相机内外参计算该镜头下的二维重投影坐标
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def reprojet_3d_to_2d(point_3d, camera_param):
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point_2d, _ = cv2.projectPoints(
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objectPoints=point_3d,
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rvec=np.array(camera_param.params.Rt[:3, :3]),
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tvec=np.array(camera_param.params.Rt[:3, 3]),
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cameraMatrix=np.array(camera_param.params.K),
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distCoeffs=np.array(camera_param.params.dist_coeffs),
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)
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point_2d = point_2d.reshape(-1).astype(int)
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return point_2d
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# 计算盒子三维坐标系
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def calculaterCubeVersices(position, dimensions):
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[cx, cy, cz] = position
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[width, height, depth] = dimensions
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halfWidth = width / 2
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halfHeight = height / 2
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halfDepth = depth / 2
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return [
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[cx - halfWidth, cy - halfHeight, cz - halfDepth],
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[cx + halfWidth, cy - halfHeight, cz - halfDepth],
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[cx + halfWidth, cy + halfHeight, cz - halfDepth],
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[cx - halfWidth, cy + halfHeight, cz - halfDepth],
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[cx - halfWidth, cy - halfHeight, cz + halfDepth],
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[cx + halfWidth, cy - halfHeight, cz + halfDepth],
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[cx + halfWidth, cy + halfHeight, cz + halfDepth],
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[cx - halfWidth, cy + halfHeight, cz + halfDepth],
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]
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# 获得盒子三维坐标系
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def calculater_box_3d_points():
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# 盒子原点位置，相对于六面体中心偏移
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box_ori_potision = [0.205 + 0.2, 0.205 + 0.50, -0.205 - 0.45]
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# 盒子边长，宽：1.5米，高：1.5米，深度：1.8米
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box_geometry = [0.65, 1.8, 1]
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filter_box_points_3d = calculaterCubeVersices(box_ori_potision, box_geometry)
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filter_box_points_3d = {
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str(index): element for index, element in enumerate(filter_box_points_3d)
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}
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return filter_box_points_3d
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# 计算盒子坐标系的二维重投影数据
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def calculater_box_2d_points(filter_box_points_3d, camera_param):
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box_points_2d = dict()
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for element_index, elment_point_3d in enumerate(filter_box_points_3d.values()):
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box_points_2d[str(element_index)] = reprojet_3d_to_2d(
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np.array(elment_point_3d), camera_param
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).tolist()
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return box_points_2d
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# 盒子总的二维平面各三角形坐标点
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def calculater_box_common_scope(box_points_2d):
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box_triangles_all_points = []
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# 遍历三角形个数
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for i in range(len(box_triangles_list)):
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# 获取单个三角形二维平面坐标点
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||||
single_triangles = []
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||||
for element_key in box_triangles_list[i]:
|
||||
single_triangles.append(box_points_2d[element_key])
|
||||
box_triangles_all_points.append(single_triangles)
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return box_triangles_all_points
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||||
|
||||
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def calculate_triangle_union(triangles):
|
||||
"""
|
||||
计算多个三角形的并集区域
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||||
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||||
参数:
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||||
triangles: 包含多个三角形的列表，每个三角形由三个点的坐标组成
|
||||
|
||||
返回:
|
||||
union_area: 并集区域的面积
|
||||
union_polygon: 表示并集区域的多边形对象
|
||||
"""
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||||
# 创建多边形对象列表
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||||
polygons = [Polygon(tri) for tri in triangles]
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||||
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||||
# 计算并集
|
||||
union_polygon = polygons[0]
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||||
for polygon in polygons[1:]:
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||||
union_polygon = union_polygon.union(polygon)
|
||||
|
||||
# 计算并集面积
|
||||
union_area = union_polygon.area
|
||||
|
||||
return union_area, union_polygon
|
||||
|
||||
|
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# 射线法，判断坐标点是否在box二维重投影的区域内
|
||||
def point_in_polygon(p, polygon):
|
||||
x, y = p
|
||||
n = len(polygon)
|
||||
intersections = 0
|
||||
on_boundary = False
|
||||
|
||||
for i in range(n):
|
||||
xi, yi = polygon[i]
|
||||
xj, yj = polygon[(i + 1) % n] # 闭合多边形
|
||||
|
||||
# 检查点是否在顶点上
|
||||
if (x == xi and y == yi) or (x == xj and y == yj):
|
||||
on_boundary = True
|
||||
break
|
||||
|
||||
# 检查点是否在线段上（非顶点情况）
|
||||
if (min(xi, xj) <= x <= max(xi, xj)) and (min(yi, yj) <= y <= max(yi, yj)):
|
||||
cross = (x - xi) * (yj - yi) - (y - yi) * (xj - xi)
|
||||
if cross == 0:
|
||||
on_boundary = True
|
||||
break
|
||||
|
||||
# 计算射线与边的交点（非水平边）
|
||||
if (yi > y) != (yj > y):
|
||||
slope = (xj - xi) / (yj - yi) if (yj - yi) != 0 else float("inf")
|
||||
x_intersect = xi + (y - yi) * slope
|
||||
if x <= x_intersect:
|
||||
intersections += 1
|
||||
|
||||
if on_boundary:
|
||||
return false
|
||||
return intersections % 2 == 1 # 奇数为内部，返回True
|
||||
|
||||
|
||||
# 获取并集区域坐标点
|
||||
def get_contours(union_polygon):
|
||||
if union_polygon.geom_type == "Polygon":
|
||||
# 单一多边形
|
||||
x, y = union_polygon.exterior.xy
|
||||
|
||||
contours = [(list(x)[i], list(y)[i]) for i in range(len(x))]
|
||||
contours = np.array(contours, np.int32)
|
||||
return contours
|
||||
|
||||
|
||||
# 筛选落在盒子二维重投影区域内的关键点信息
|
||||
def filter_kps_in_contours(kps, contours) -> Boolean:
|
||||
|
||||
# 4 5 16 17
|
||||
keypoint_index: list[list[int]] = [[4, 5], [16, 17]]
|
||||
centers = []
|
||||
for element_keypoint in keypoint_index:
|
||||
x1, y1 = kps[element_keypoint[0]]
|
||||
x2, y2 = kps[element_keypoint[1]]
|
||||
centers.append([(x1 + x2) / 2, (y1 + y2) / 2])
|
||||
|
||||
if point_in_polygon(centers[0], contours) and point_in_polygon(
|
||||
centers[1], contours
|
||||
):
|
||||
return true
|
||||
else:
|
||||
return false
|
||||
@ -1,282 +0,0 @@
|
||||
[
|
||||
{
|
||||
"kps": [
|
||||
419.0,
|
||||
154.0
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 0
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
419.0521240234375,
|
||||
154.07498168945312
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 1
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
418.5992736816406,
|
||||
154.3507080078125
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 2
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
417.0777893066406,
|
||||
154.17327880859375
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 3
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
416.8981628417969,
|
||||
154.15330505371094
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 4
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
415.1317443847656,
|
||||
153.68324279785156
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 5
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
413.2596130371094,
|
||||
153.39761352539062
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 6
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
412.7089538574219,
|
||||
153.3645782470703
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 7
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
409.3253173828125,
|
||||
152.9347686767578
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 8
|
||||
},
|
||||
{
|
||||
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||||
},
|
||||
{
|
||||
"kps": [
|
||||
437.3090515136719,
|
||||
162.88577270507812
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 13
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
437.2088623046875,
|
||||
162.84994506835938
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 14
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
429.199951171875,
|
||||
164.5860595703125
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 15
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
429.32745361328125,
|
||||
164.66001892089844
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 16
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
424.8293762207031,
|
||||
166.40106201171875
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 17
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
419.6496887207031,
|
||||
168.80294799804688
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 18
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
419.6795349121094,
|
||||
168.93418884277344
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 19
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
414.8919677734375,
|
||||
172.65428161621094
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 20
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
410.0992431640625,
|
||||
175.77218627929688
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 21
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
410.0442810058594,
|
||||
175.911376953125
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 22
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
400.20159912109375,
|
||||
184.33380126953125
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 23
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
396.4606628417969,
|
||||
186.7172088623047
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 24
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
396.3185119628906,
|
||||
186.76808166503906
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 25
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
382.623291015625,
|
||||
192.941650390625
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 26
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
376.8236999511719,
|
||||
195.2269744873047
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 27
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
376.66937255859375,
|
||||
195.1109161376953
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 28
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
362.7231750488281,
|
||||
209.30923461914062
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 29
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
355.9901123046875,
|
||||
216.26303100585938
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 30
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
356.3956298828125,
|
||||
216.3310546875
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 31
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
343.6780090332031,
|
||||
235.2663116455078
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 32
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
332.50238037109375,
|
||||
261.8990783691406
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 33
|
||||
},
|
||||
{
|
||||
"kps": [
|
||||
332.8721923828125,
|
||||
261.7060546875
|
||||
],
|
||||
"kps_scores": 1.0,
|
||||
"index": 34
|
||||
}
|
||||
]
|
||||
File diff suppressed because one or more lines are too long
3268
play.ipynb
3268
play.ipynb
File diff suppressed because it is too large
Load Diff
@ -64,6 +64,7 @@ from app.tracking import (
|
||||
TrackingID,
|
||||
AffinityResult,
|
||||
LastDifferenceVelocityFilter,
|
||||
LeastMeanSquareVelocityFilter,
|
||||
Tracking,
|
||||
TrackingState,
|
||||
)
|
||||
@ -438,12 +439,12 @@ def triangulate_one_point_from_multiple_views_linear(
|
||||
) -> Float[Array, "3"]:
|
||||
"""
|
||||
Args:
|
||||
proj_matrices: 形状为(N, 3, 4)的投影矩阵序列
|
||||
points: 形状为(N, 2)的点坐标序列
|
||||
confidences: 形状为(N,)的置信度序列，范围[0.0, 1.0]
|
||||
proj_matrices: (N, 3, 4) projection matrices
|
||||
points: (N, 2) image-coordinates per view
|
||||
confidences: (N,) optional per-view confidences in [0,1]
|
||||
|
||||
Returns:
|
||||
point_3d: 形状为(3,)的三角测量得到的3D点
|
||||
(3,) 3D point
|
||||
"""
|
||||
assert len(proj_matrices) == len(points)
|
||||
|
||||
@ -469,7 +470,7 @@ def triangulate_one_point_from_multiple_views_linear(
|
||||
|
||||
# replace the Python `if` with a jnp.where
|
||||
point_3d_homo = jnp.where(
|
||||
point_3d_homo[3] < 0, # predicate (scalar bool tracer)
|
||||
point_3d_homo[3] <= 0, # predicate (scalar bool tracer)
|
||||
-point_3d_homo, # if True
|
||||
point_3d_homo, # if False
|
||||
)
|
||||
@ -493,14 +494,14 @@ def triangulate_points_from_multiple_views_linear(
|
||||
confidences: (N, P, 1) optional per-view confidences in [0,1]
|
||||
|
||||
Returns:
|
||||
(P, 3) 3D point for each of the P tracks
|
||||
(P, 3) 3D point for each of the P
|
||||
"""
|
||||
N, P, _ = points.shape
|
||||
assert proj_matrices.shape[0] == N
|
||||
if confidences is None:
|
||||
conf = jnp.ones((N, P), dtype=jnp.float32)
|
||||
else:
|
||||
conf = jnp.sqrt(jnp.clip(confidences, 0.0, 1.0))
|
||||
conf = confidences
|
||||
|
||||
# vectorize your one-point routine over P
|
||||
vmap_triangulate = jax.vmap(
|
||||
@ -578,7 +579,7 @@ def triangulate_one_point_from_multiple_views_linear_time_weighted(
|
||||
|
||||
# Ensure homogeneous coordinate is positive
|
||||
point_3d_homo = jnp.where(
|
||||
point_3d_homo[3] < 0,
|
||||
point_3d_homo[3] <= 0,
|
||||
-point_3d_homo,
|
||||
point_3d_homo,
|
||||
)
|
||||
@ -679,6 +680,8 @@ def group_by_cluster_by_camera(
|
||||
eld = r[el.camera.id]
|
||||
preserved = max([eld, el], key=lambda x: x.timestamp)
|
||||
r[el.camera.id] = preserved
|
||||
else:
|
||||
r[el.camera.id] = el
|
||||
return pmap(r)
|
||||
|
||||
|
||||
@ -714,7 +717,11 @@ class GlobalTrackingState:
|
||||
tracking = Tracking(
|
||||
id=next_id,
|
||||
state=tracking_state,
|
||||
velocity_filter=LastDifferenceVelocityFilter(kps_3d, latest_timestamp),
|
||||
# velocity_filter=LastDifferenceVelocityFilter(kps_3d, latest_timestamp),
|
||||
velocity_filter=LeastMeanSquareVelocityFilter(
|
||||
historical_3d_poses=[kps_3d],
|
||||
historical_timestamps=[latest_timestamp],
|
||||
),
|
||||
)
|
||||
self._trackings[next_id] = tracking
|
||||
self._last_id = next_id
|
||||
|
||||
File diff suppressed because one or more lines are too long
@ -14,7 +14,6 @@ dependencies = [
|
||||
"jaxtyping>=0.2.38",
|
||||
"jupytext>=1.17.0",
|
||||
"matplotlib>=3.10.1",
|
||||
"more-itertools>=10.7.0",
|
||||
"opencv-python-headless>=4.11.0.86",
|
||||
"optax>=0.2.4",
|
||||
"orjson>=3.10.15",
|
||||
@ -24,7 +23,6 @@ dependencies = [
|
||||
"pyrsistent>=0.20.0",
|
||||
"pytest>=8.3.5",
|
||||
"scipy>=1.15.2",
|
||||
"shapely>=2.1.1",
|
||||
"torch>=2.6.0",
|
||||
"torchvision>=0.21.0",
|
||||
"typeguard>=4.4.2",
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,122 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "0d48b7eb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"from pathlib import Path\n",
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "dfd27584",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"KPS_PATH = Path(\"samples/WeiHua_03.json\")\n",
|
||||
"with open(KPS_PATH, \"r\") as file:\n",
|
||||
" data = json.load(file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "360f9c50",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'index:1, shape: (33, 133, 3)'"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'index:2, shape: (662, 133, 3)'"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for item_object_index in data.keys():\n",
|
||||
" item_object = np.array(data[item_object_index])\n",
|
||||
" display(f'index:{item_object_index}, shape: {item_object.shape}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 对data['2']的662帧3d关键点数据进行滑动窗口平滑处理\n",
|
||||
"object_points = np.array(data['2']) # shape: (662, 133, 3)\n",
|
||||
"window_size = 5\n",
|
||||
"kernel = np.ones(window_size) / window_size\n",
|
||||
"# 对每个关键点的每个坐标轴分别做滑动平均\n",
|
||||
"smoothed_points = np.zeros_like(object_points)\n",
|
||||
"# 遍历133个关节\n",
|
||||
"for kp_idx in range(object_points.shape[1]):\n",
|
||||
" # 遍历每个关节的空间三维坐标点\n",
|
||||
" for axis in range(3):\n",
|
||||
" # 对第i帧的滑动平滑方式 smoothed[i] = (point[i-2] + point[i-1] + point[i] + point[i+1] + point[i+2]) / 5\n",
|
||||
" smoothed_points[:, kp_idx, axis] = np.convolve(object_points[:, kp_idx, axis], kernel, mode='same')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"id": "24c6c0c9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'smoothed_points shape: (662, 133, 3)'"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"display(f'smoothed_points shape: {smoothed_points.shape}')\n",
|
||||
"with open(\"samples/smoothed_3d_kps.json\", \"w\") as file:\n",
|
||||
" json.dump({'1':smoothed_points.tolist()}, file)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
@ -1,193 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "11cc2345",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import awkward as ak\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "84348d97",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"CAMERA_INDEX ={\n",
|
||||
" 2:\"5602\",\n",
|
||||
" 4:\"5604\",\n",
|
||||
"}\n",
|
||||
"index = 4\n",
|
||||
"CAMERA_PATH = Path(\"/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params\")\n",
|
||||
"camera_data = ak.from_parquet(CAMERA_PATH / CAMERA_INDEX[index]/ \"extrinsic.parquet\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "1d771740",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<pre>[{rvec: [[-2.26], [0.0669], [-2.15]], tvec: [[0.166], ...]},\n",
|
||||
" {rvec: [[2.07], [0.144], [2.21]], tvec: [[0.143], ...]},\n",
|
||||
" {rvec: [[2.09], [0.0872], [2.25]], tvec: [[0.141], ...]},\n",
|
||||
" {rvec: [[2.16], [0.172], [2.09]], tvec: [[0.162], ...]},\n",
|
||||
" {rvec: [[2.15], [0.18], [2.09]], tvec: [[0.162], ...]},\n",
|
||||
" {rvec: [[-2.22], [0.117], [-2.14]], tvec: [[0.162], ...]},\n",
|
||||
" {rvec: [[2.18], [0.176], [2.08]], tvec: [[0.166], ...]},\n",
|
||||
" {rvec: [[2.18], [0.176], [2.08]], tvec: [[0.166], ...]},\n",
|
||||
" {rvec: [[-2.26], [0.116], [-2.1]], tvec: [[0.17], ...]},\n",
|
||||
" {rvec: [[-2.26], [0.124], [-2.09]], tvec: [[0.171], ...]},\n",
|
||||
" ...,\n",
|
||||
" {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
|
||||
" {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
|
||||
" {rvec: [[2.12], [0.151], [2.16]], tvec: [[0.152], ...]},\n",
|
||||
" {rvec: [[-2.3], [0.0733], [-2.1]], tvec: [[0.175], ...]},\n",
|
||||
" {rvec: [[2.1], [0.16], [2.17]], tvec: [[0.149], ...]},\n",
|
||||
" {rvec: [[2.1], [0.191], [2.13]], tvec: [[0.153], ...]},\n",
|
||||
" {rvec: [[2.11], [0.196], [2.12]], tvec: [[0.154], ...]},\n",
|
||||
" {rvec: [[2.19], [0.171], [2.08]], tvec: [[0.166], ...]},\n",
|
||||
" {rvec: [[2.24], [0.0604], [2.12]], tvec: [[0.166], ...]}]\n",
|
||||
"---------------------------------------------------------------------------\n",
|
||||
"backend: cpu\n",
|
||||
"nbytes: 10.1 kB\n",
|
||||
"type: 90 * {\n",
|
||||
" rvec: var * var * float64,\n",
|
||||
" tvec: var * var * float64\n",
|
||||
"}</pre>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<Array [{rvec: [...], tvec: [...]}, ..., {...}] type='90 * {rvec: var * var...'>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"display(camera_data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "59fde11b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = []\n",
|
||||
"for element in camera_data:\n",
|
||||
" rvec = element[\"rvec\"]\n",
|
||||
" if rvec[0]<0:\n",
|
||||
" data.append({\"rvec\": rvec, \"tvec\": element[\"tvec\"]})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "4792cbc4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<pyarrow._parquet.FileMetaData object at 0x7799cbf62d40>\n",
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||||
" created_by: parquet-cpp-arrow version 19.0.1\n",
|
||||
" num_columns: 2\n",
|
||||
" num_rows: 30\n",
|
||||
" num_row_groups: 1\n",
|
||||
" format_version: 2.6\n",
|
||||
" serialized_size: 0"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ak.to_parquet(ak.from_iter(data),\"/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params/5604/re_extrinsic.parquet\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "8225ee33",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
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"text/html": [
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" {rvec: [[-2.22], [0.117], [-2.14]], tvec: [[0.162], ...]},\n",
|
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" {rvec: [[-2.26], [0.116], [-2.1]], tvec: [[0.17], ...]},\n",
|
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" {rvec: [[-2.26], [0.124], [-2.09]], tvec: [[0.171], ...]},\n",
|
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" {rvec: [[-2.24], [0.133], [-2.11]], tvec: [[0.167], ...]},\n",
|
||||
" {rvec: [[-2.22], [0.0556], [-2.2]], tvec: [[0.158], ...]},\n",
|
||||
" {rvec: [[-2.27], [0.119], [-2.09]], tvec: [[0.172], ...]},\n",
|
||||
" {rvec: [[-2.34], [0.0663], [-2.06]], tvec: [[0.181], ...]},\n",
|
||||
" {rvec: [[-2.21], [0.117], [-2.15]], tvec: [[0.161], ...]},\n",
|
||||
" {rvec: [[-2.33], [0.0731], [-2.08]], tvec: [[0.179], ...]},\n",
|
||||
" ...,\n",
|
||||
" {rvec: [[-2.23], [0.106], [-2.13]], tvec: [[0.166], ...]},\n",
|
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" {rvec: [[-2.21], [0.054], [-2.2]], tvec: [[0.157], ...]},\n",
|
||||
" {rvec: [[-2.19], [0.0169], [-2.25]], tvec: [[0.151], ...]},\n",
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" {rvec: [[-2.2], [0.0719], [-2.19]], tvec: [[0.157], ...]},\n",
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||||
" {rvec: [[-2.22], [0.0726], [-2.18]], tvec: [[0.161], ...]},\n",
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" {rvec: [[-2.2], [0.0742], [-2.19]], tvec: [[0.158], ...]},\n",
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" {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
|
||||
" {rvec: [[-2.2], [0.0998], [-2.17]], tvec: [[0.158], ...]},\n",
|
||||
" {rvec: [[-2.3], [0.0733], [-2.1]], tvec: [[0.175], ...]}]\n",
|
||||
"---------------------------------------------------------------------------\n",
|
||||
"backend: cpu\n",
|
||||
"nbytes: 3.4 kB\n",
|
||||
"type: 30 * {\n",
|
||||
" rvec: var * var * float64,\n",
|
||||
" tvec: var * var * float64\n",
|
||||
"}</pre>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<Array [{rvec: [...], tvec: [...]}, ..., {...}] type='30 * {rvec: var * var...'>"
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]
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||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"temp_data = ak.from_parquet(\"/home/admin/Documents/ActualTest_QuanCheng/camera_ex_params_1_2025_4_20/camera_params/5604/re_extrinsic.parquet\")\n",
|
||||
"display(temp_data)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
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"version": 3
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},
|
||||
"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