forked from HQU-gxy/CVTH3PE
crosstyan
c3c93f6ca6
- Updated the play notebook to include new methods for unprojecting 2D points onto a 3D plane. - Introduced `unproject_points_onto_plane` and `unproject_points_to_z_plane` functions in the camera module for improved point handling. - Enhanced the `Camera` class with a method for unprojecting points to a specified z-plane. - Cleaned up execution counts in the notebook for better organization and clarity.
656 lines
22 KiB
Plaintext
656 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"from copy import deepcopy\n",
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"from datetime import datetime, timedelta\n",
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"from pathlib import Path\n",
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"from typing import (Any, Generator, Optional, Sequence, TypeAlias, TypedDict,\n",
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" cast, overload)\n",
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"\n",
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"import awkward as ak\n",
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"import jax\n",
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"import jax.numpy as jnp\n",
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"import numpy as np\n",
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"import orjson\n",
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"from beartype import beartype\n",
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"from cv2 import undistortPoints\n",
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"from jaxtyping import Array, Float, Num, jaxtyped\n",
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"from matplotlib import pyplot as plt\n",
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"from numpy.typing import ArrayLike\n",
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"from scipy.spatial.transform import Rotation as R\n",
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"\n",
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"from app.camera import Camera, CameraParams, Detection\n",
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"from app.visualize.whole_body import visualize_whole_body\n",
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"\n",
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"NDArray: TypeAlias = np.ndarray"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"DATASET_PATH = Path(\"samples\") / \"04_02\" \n",
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"AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH / \"camera_params.parquet\")\n",
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"display(AK_CAMERA_DATASET)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Resolution(TypedDict):\n",
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" width: int\n",
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" height: int\n",
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"\n",
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"class Intrinsic(TypedDict):\n",
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" camera_matrix: Num[Array, \"3 3\"]\n",
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" \"\"\"\n",
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" K\n",
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" \"\"\"\n",
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" distortion_coefficients: Num[Array, \"N\"]\n",
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" \"\"\"\n",
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" distortion coefficients; usually 5\n",
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" \"\"\"\n",
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"\n",
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"class Extrinsic(TypedDict):\n",
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" rvec: Num[NDArray, \"3\"]\n",
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" tvec: Num[NDArray, \"3\"]\n",
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"\n",
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"class ExternalCameraParams(TypedDict):\n",
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" name: str\n",
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" port: int\n",
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" intrinsic: Intrinsic\n",
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" extrinsic: Extrinsic\n",
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" resolution: Resolution\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"def read_dataset_by_port(port: int) -> ak.Array:\n",
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" P = DATASET_PATH / f\"{port}.parquet\"\n",
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" return ak.from_parquet(P)\n",
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"\n",
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"KEYPOINT_DATASET = {int(p): read_dataset_by_port(p) for p in ak.to_numpy(AK_CAMERA_DATASET[\"port\"])}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"KEYPOINT_DATASET[5601]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"class KeypointDataset(TypedDict):\n",
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" frame_index: int\n",
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" boxes: Num[NDArray, \"N 4\"]\n",
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" kps: Num[NDArray, \"N J 2\"]\n",
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" kps_scores: Num[NDArray, \"N J\"]\n",
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"\n",
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"\n",
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"@jaxtyped(typechecker=beartype)\n",
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"def to_transformation_matrix(\n",
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" rvec: Num[NDArray, \"3\"], tvec: Num[NDArray, \"3\"]\n",
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") -> Num[NDArray, \"4 4\"]:\n",
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" res = np.eye(4)\n",
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" res[:3, :3] = R.from_rotvec(rvec).as_matrix()\n",
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" res[:3, 3] = tvec\n",
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" return res\n",
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"\n",
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"\n",
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"@jaxtyped(typechecker=beartype)\n",
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"def undistort_points(\n",
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" points: Num[NDArray, \"M 2\"],\n",
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" camera_matrix: Num[NDArray, \"3 3\"],\n",
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" dist_coeffs: Num[NDArray, \"N\"],\n",
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") -> Num[NDArray, \"M 2\"]:\n",
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" K = camera_matrix\n",
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" dist = dist_coeffs\n",
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" res = undistortPoints(points, K, dist, P=K) # type: ignore\n",
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" return res.reshape(-1, 2)\n",
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"\n",
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"\n",
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"def from_camera_params(camera: ExternalCameraParams) -> Camera:\n",
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" rt = jnp.array(\n",
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" to_transformation_matrix(\n",
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" ak.to_numpy(camera[\"extrinsic\"][\"rvec\"]),\n",
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" ak.to_numpy(camera[\"extrinsic\"][\"tvec\"]),\n",
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" )\n",
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" )\n",
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" K = jnp.array(camera[\"intrinsic\"][\"camera_matrix\"]).reshape(3, 3)\n",
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" dist_coeffs = jnp.array(camera[\"intrinsic\"][\"distortion_coefficients\"])\n",
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" image_size = jnp.array(\n",
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" (camera[\"resolution\"][\"width\"], camera[\"resolution\"][\"height\"])\n",
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" )\n",
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" return Camera(\n",
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" id=camera[\"name\"],\n",
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" params=CameraParams(\n",
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" K=K,\n",
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" Rt=rt,\n",
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" dist_coeffs=dist_coeffs,\n",
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" image_size=image_size,\n",
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" ),\n",
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" )\n",
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"\n",
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"\n",
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"def preprocess_keypoint_dataset(\n",
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" dataset: Sequence[KeypointDataset],\n",
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" camera: Camera,\n",
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" fps: float,\n",
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" start_timestamp: datetime,\n",
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") -> Generator[Detection, None, None]:\n",
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" frame_interval_s = 1 / fps\n",
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" for el in dataset:\n",
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" frame_index = el[\"frame_index\"]\n",
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" timestamp = start_timestamp + timedelta(seconds=frame_index * frame_interval_s)\n",
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" for kp, kp_score in zip(el[\"kps\"], el[\"kps_scores\"]):\n",
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" yield Detection(\n",
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" keypoints=jnp.array(kp),\n",
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" confidences=jnp.array(kp_score),\n",
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" camera=camera,\n",
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" timestamp=timestamp,\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"DetectionGenerator: TypeAlias = Generator[Detection, None, None]\n",
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"\n",
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"\n",
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"def sync_batch_gen(gens: list[DetectionGenerator], diff: timedelta):\n",
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" \"\"\"\n",
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" given a list of detection generators, return a generator that yields a batch of detections\n",
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"\n",
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" Args:\n",
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" gens: list of detection generators\n",
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" diff: maximum timestamp difference between detections to consider them part of the same batch\n",
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" \"\"\"\n",
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" N = len(gens)\n",
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" last_batch_timestamp: Optional[datetime] = None\n",
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" next_batch_timestamp: Optional[datetime] = None\n",
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" current_batch: list[Detection] = []\n",
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" next_batch: list[Detection] = []\n",
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" paused: list[bool] = [False] * N\n",
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" finished: list[bool] = [False] * N\n",
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"\n",
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" def reset_paused():\n",
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" \"\"\"\n",
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" reset paused list based on finished list\n",
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" \"\"\"\n",
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" for i in range(N):\n",
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" if not finished[i]:\n",
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" paused[i] = False\n",
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" else:\n",
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" paused[i] = True\n",
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"\n",
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" EPS = 1e-6\n",
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" # a small epsilon to avoid floating point precision issues\n",
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" diff_esp = diff - timedelta(seconds=EPS)\n",
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" while True:\n",
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" for i, gen in enumerate(gens):\n",
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" try:\n",
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" if finished[i] or paused[i]:\n",
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" continue\n",
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" val = next(gen)\n",
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" if last_batch_timestamp is None:\n",
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" last_batch_timestamp = val.timestamp\n",
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" current_batch.append(val)\n",
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" else:\n",
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" if abs(val.timestamp - last_batch_timestamp) >= diff_esp:\n",
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" next_batch.append(val)\n",
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" if next_batch_timestamp is None:\n",
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" next_batch_timestamp = val.timestamp\n",
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" paused[i] = True\n",
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" if all(paused):\n",
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" yield current_batch\n",
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" current_batch = next_batch\n",
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" next_batch = []\n",
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" last_batch_timestamp = next_batch_timestamp\n",
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" next_batch_timestamp = None\n",
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" reset_paused()\n",
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" else:\n",
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" current_batch.append(val)\n",
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" except StopIteration:\n",
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" finished[i] = True\n",
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" paused[i] = True\n",
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" if all(finished):\n",
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" if len(current_batch) > 0:\n",
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" # All generators exhausted, flush remaining batch and exit\n",
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" yield current_batch\n",
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" break"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"@overload\n",
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"def to_projection_matrix(\n",
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" transformation_matrix: Num[NDArray, \"4 4\"], camera_matrix: Num[NDArray, \"3 3\"]\n",
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") -> Num[NDArray, \"3 4\"]: ...\n",
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"\n",
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"\n",
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"@overload\n",
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"def to_projection_matrix(\n",
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" transformation_matrix: Num[Array, \"4 4\"], camera_matrix: Num[Array, \"3 3\"]\n",
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") -> Num[Array, \"3 4\"]: ...\n",
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"\n",
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"\n",
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"@jaxtyped(typechecker=beartype)\n",
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"def to_projection_matrix(\n",
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" transformation_matrix: Num[Any, \"4 4\"],\n",
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" camera_matrix: Num[Any, \"3 3\"],\n",
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") -> Num[Any, \"3 4\"]:\n",
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" return camera_matrix @ transformation_matrix[:3, :]\n",
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"\n",
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"to_projection_matrix_jit = jax.jit(to_projection_matrix)\n",
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"\n",
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"\n",
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"@jaxtyped(typechecker=beartype)\n",
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"def dlt(\n",
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" H1: Num[NDArray, \"3 4\"],\n",
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" H2: Num[NDArray, \"3 4\"],\n",
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" p1: Num[NDArray, \"2\"],\n",
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" p2: Num[NDArray, \"2\"],\n",
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") -> Num[NDArray, \"3\"]:\n",
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" \"\"\"\n",
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" Direct Linear Transformation\n",
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" \"\"\"\n",
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" A = [\n",
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" p1[1] * H1[2, :] - H1[1, :],\n",
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" H1[0, :] - p1[0] * H1[2, :],\n",
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" p2[1] * H2[2, :] - H2[1, :],\n",
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" H2[0, :] - p2[0] * H2[2, :],\n",
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" ]\n",
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" A = np.array(A).reshape((4, 4))\n",
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"\n",
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" B = A.transpose() @ A\n",
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" from scipy import linalg\n",
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"\n",
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" U, s, Vh = linalg.svd(B, full_matrices=False)\n",
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" return Vh[3, 0:3] / Vh[3, 3]\n",
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"\n",
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"\n",
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"@overload\n",
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"def homogeneous_to_euclidean(points: Num[NDArray, \"N 4\"]) -> Num[NDArray, \"N 3\"]: ...\n",
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"\n",
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"\n",
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"@overload\n",
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"def homogeneous_to_euclidean(points: Num[Array, \"N 4\"]) -> Num[Array, \"N 3\"]: ...\n",
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"\n",
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"\n",
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"@jaxtyped(typechecker=beartype)\n",
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"def homogeneous_to_euclidean(\n",
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" points: Num[Any, \"N 4\"],\n",
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") -> Num[Any, \"N 3\"]:\n",
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" \"\"\"\n",
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" 将齐次坐标转换为欧几里得坐标\n",
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"\n",
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" Args:\n",
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" points: homogeneous coordinates (x, y, z, w) in numpy array or jax array\n",
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"\n",
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" Returns:\n",
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" euclidean coordinates (x, y, z) in numpy array or jax array\n",
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" \"\"\"\n",
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" return points[..., :-1] / points[..., -1:]\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"FPS = 24\n",
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"image_gen_5600 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5600], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET[\"port\"] == 5600][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore\n",
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"image_gen_5601 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5601], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET[\"port\"] == 5601][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore\n",
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"image_gen_5602 = preprocess_keypoint_dataset(KEYPOINT_DATASET[5602], from_camera_params(AK_CAMERA_DATASET[AK_CAMERA_DATASET[\"port\"] == 5602][0]), FPS, datetime(2024, 4, 2, 12, 0, 0)) # type: ignore\n",
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"\n",
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"display(1/FPS)\n",
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"sync_gen = sync_batch_gen([image_gen_5600, image_gen_5601, image_gen_5602], timedelta(seconds=1/FPS))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"detections = next(sync_gen)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"from app.camera import calculate_affinity_matrix_by_epipolar_constraint\n",
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"\n",
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"sorted_detections, affinity_matrix = calculate_affinity_matrix_by_epipolar_constraint(detections, \n",
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" alpha_2d=2000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"display(list(map(lambda x: {\"timestamp\": str(x.timestamp), \"camera\": x.camera.id}, sorted_detections)))\n",
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"with jnp.printoptions(precision=3, suppress=True):\n",
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" display(affinity_matrix)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from app.solver._old import GLPKSolver\n",
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"\n",
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"def clusters_to_detections(clusters: list[list[int]], sorted_detections: list[Detection]) -> list[list[Detection]]:\n",
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" \"\"\"\n",
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" given a list of clusters (which is the indices of the detections in the sorted_detections list),\n",
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" extract the detections from the sorted_detections list\n",
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"\n",
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" Args:\n",
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" clusters: list of clusters, each cluster is a list of indices of the detections in the `sorted_detections` list\n",
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" sorted_detections: list of SORTED detections\n",
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"\n",
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" Returns:\n",
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" list of clusters, each cluster is a list of detections\n",
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" \"\"\"\n",
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" return [[sorted_detections[i] for i in cluster] for cluster in clusters]\n",
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"\n",
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"solver = GLPKSolver()\n",
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"aff_np = np.asarray(affinity_matrix).astype(np.float64)\n",
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"clusters, sol_matrix = solver.solve(aff_np)\n",
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"display(clusters)\n",
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"display(sol_matrix)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"WIDTH = 2560\n",
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"HEIGHT = 1440\n",
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"\n",
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"clusters_detections = clusters_to_detections(clusters, sorted_detections)\n",
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"im = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)\n",
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"for el in clusters_detections[0]:\n",
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" im = visualize_whole_body(np.asarray(el.keypoints), im)\n",
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"\n",
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"p = plt.imshow(im)\n",
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"display(p)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"im_prime = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)\n",
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"for el in clusters_detections[1]:\n",
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" im_prime = visualize_whole_body(np.asarray(el.keypoints), im_prime)\n",
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"\n",
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"p_prime = plt.imshow(im_prime)\n",
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"display(p_prime)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
|
||
"@jaxtyped(typechecker=beartype)\n",
|
||
"def triangulate_one_point_from_multiple_views_linear(\n",
|
||
" proj_matrices: Float[Array, \"N 3 4\"],\n",
|
||
" points: Num[Array, \"N 2\"],\n",
|
||
" confidences: Optional[Float[Array, \"N\"]] = None,\n",
|
||
") -> Float[Array, \"3\"]:\n",
|
||
" \"\"\"\n",
|
||
" Args:\n",
|
||
" proj_matrices: 形状为(N, 3, 4)的投影矩阵序列\n",
|
||
" points: 形状为(N, 2)的点坐标序列\n",
|
||
" confidences: 形状为(N,)的置信度序列，范围[0.0, 1.0]\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" point_3d: 形状为(3,)的三角测量得到的3D点\n",
|
||
" \"\"\"\n",
|
||
" assert len(proj_matrices) == len(points)\n",
|
||
"\n",
|
||
" N = len(proj_matrices)\n",
|
||
" confi: Float[Array, \"N\"]\n",
|
||
" if confidences is None:\n",
|
||
" confi = jnp.ones(N, dtype=np.float32)\n",
|
||
" else:\n",
|
||
" # Use square root of confidences for weighting - more balanced approach\n",
|
||
" confi = jnp.sqrt(jnp.clip(confidences, 0, 1))\n",
|
||
"\n",
|
||
" A = jnp.zeros((N * 2, 4), dtype=np.float32)\n",
|
||
" for i in range(N):\n",
|
||
" x, y = points[i]\n",
|
||
" A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0])\n",
|
||
" A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1])\n",
|
||
" A = A.at[2 * i].mul(confi[i])\n",
|
||
" A = A.at[2 * i + 1].mul(confi[i])\n",
|
||
"\n",
|
||
" # https://docs.jax.dev/en/latest/_autosummary/jax.numpy.linalg.svd.html\n",
|
||
" _, _, vh = jnp.linalg.svd(A, full_matrices=False)\n",
|
||
" point_3d_homo = vh[-1] # shape (4,)\n",
|
||
"\n",
|
||
" # replace the Python `if` with a jnp.where\n",
|
||
" point_3d_homo = jnp.where(\n",
|
||
" point_3d_homo[3] < 0, # predicate (scalar bool tracer)\n",
|
||
" -point_3d_homo, # if True\n",
|
||
" point_3d_homo, # if False\n",
|
||
" )\n",
|
||
"\n",
|
||
" point_3d = point_3d_homo[:3] / point_3d_homo[3]\n",
|
||
" return point_3d\n",
|
||
"\n",
|
||
"\n",
|
||
"@jaxtyped(typechecker=beartype)\n",
|
||
"def triangulate_points_from_multiple_views_linear(\n",
|
||
" proj_matrices: Float[Array, \"N 3 4\"],\n",
|
||
" points: Num[Array, \"N P 2\"],\n",
|
||
" confidences: Optional[Float[Array, \"N P\"]] = None,\n",
|
||
") -> Float[Array, \"P 3\"]:\n",
|
||
" \"\"\"\n",
|
||
" Batch-triangulate P points observed by N cameras, linearly via SVD.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" proj_matrices: (N, 3, 4) projection matrices\n",
|
||
" points: (N, P, 2) image-coordinates per view\n",
|
||
" confidences: (N, P, 1) optional per-view confidences in [0,1]\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" (P, 3) 3D point for each of the P tracks\n",
|
||
" \"\"\"\n",
|
||
" N, P, _ = points.shape\n",
|
||
" assert proj_matrices.shape[0] == N\n",
|
||
" if confidences is None:\n",
|
||
" conf = jnp.ones((N, P), dtype=jnp.float32)\n",
|
||
" else:\n",
|
||
" conf = jnp.sqrt(jnp.clip(confidences, 0.0, 1.0))\n",
|
||
"\n",
|
||
" # vectorize your one‐point routine over P\n",
|
||
" vmap_triangulate = jax.vmap(\n",
|
||
" triangulate_one_point_from_multiple_views_linear,\n",
|
||
" in_axes=(None, 1, 1), # proj_matrices static, map over points[:,p,:], conf[:,p]\n",
|
||
" out_axes=0,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # returns (P, 3)\n",
|
||
" return vmap_triangulate(proj_matrices, points, conf)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from dataclasses import dataclass\n",
|
||
"from copy import copy as shallow_copy, deepcopy as deep_copy\n",
|
||
"\n",
|
||
"\n",
|
||
"@jaxtyped(typechecker=beartype)\n",
|
||
"@dataclass(frozen=True)\n",
|
||
"class Tracking:\n",
|
||
" id: int\n",
|
||
" keypoints: Float[Array, \"J 3\"]\n",
|
||
" last_active_timestamp: datetime\n",
|
||
"\n",
|
||
" def __repr__(self) -> str:\n",
|
||
" return f\"Tracking({self.id}, {self.last_active_timestamp})\"\n",
|
||
"\n",
|
||
"\n",
|
||
"@jaxtyped(typechecker=beartype)\n",
|
||
"def triangle_from_cluster(\n",
|
||
" cluster: list[Detection],\n",
|
||
") -> tuple[Float[Array, \"N 3\"], datetime]:\n",
|
||
" proj_matrices = jnp.array([el.camera.params.projection_matrix for el in cluster])\n",
|
||
" points = jnp.array([el.keypoints_undistorted for el in cluster])\n",
|
||
" confidences = jnp.array([el.confidences for el in cluster])\n",
|
||
" latest_timestamp = max(el.timestamp for el in cluster)\n",
|
||
" return (\n",
|
||
" triangulate_points_from_multiple_views_linear(\n",
|
||
" proj_matrices, points, confidences=confidences\n",
|
||
" ),\n",
|
||
" latest_timestamp,\n",
|
||
" )\n",
|
||
"\n",
|
||
"\n",
|
||
"# res = {\n",
|
||
"# \"a\": triangle_from_cluster(clusters_detections[0]).tolist(),\n",
|
||
"# \"b\": triangle_from_cluster(clusters_detections[1]).tolist(),\n",
|
||
"# }\n",
|
||
"# with open(\"samples/res.json\", \"wb\") as f:\n",
|
||
"# f.write(orjson.dumps(res))\n",
|
||
"\n",
|
||
"\n",
|
||
"class GlobalTrackingState:\n",
|
||
" _last_id: int\n",
|
||
" _trackings: dict[int, Tracking]\n",
|
||
"\n",
|
||
" def __init__(self):\n",
|
||
" self._last_id = 0\n",
|
||
" self._trackings = {}\n",
|
||
"\n",
|
||
" def __repr__(self) -> str:\n",
|
||
" return (\n",
|
||
" f\"GlobalTrackingState(last_id={self._last_id}, trackings={self._trackings})\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" @property\n",
|
||
" def trackings(self) -> dict[int, Tracking]:\n",
|
||
" return shallow_copy(self._trackings)\n",
|
||
"\n",
|
||
" def add_tracking(self, cluster: list[Detection]) -> Tracking:\n",
|
||
" kps_3d, latest_timestamp = triangle_from_cluster(cluster)\n",
|
||
" next_id = self._last_id + 1\n",
|
||
" tracking = Tracking(\n",
|
||
" id=next_id, keypoints=kps_3d, last_active_timestamp=latest_timestamp\n",
|
||
" )\n",
|
||
" self._trackings[next_id] = tracking\n",
|
||
" self._last_id = next_id\n",
|
||
" return tracking\n",
|
||
"\n",
|
||
"\n",
|
||
"global_tracking_state = GlobalTrackingState()\n",
|
||
"for cluster in clusters_detections:\n",
|
||
" global_tracking_state.add_tracking(cluster)\n",
|
||
"display(global_tracking_state)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"next_group = next(sync_gen)\n",
|
||
"display(next_group)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from app.camera import classify_by_camera\n",
|
||
"\n",
|
||
"# let's do cross-view association\n",
|
||
"trackings = sorted(global_tracking_state.trackings.values(), key=lambda x: x.id)\n",
|
||
"detections = shallow_copy(next_group)\n",
|
||
"# cross-view association matrix with shape (T, D), where T is the number of trackings, D is the number of detections\n",
|
||
"affinity = np.zeros((len(trackings), len(detections)))\n",
|
||
"detection_by_camera = classify_by_camera(detections)\n",
|
||
"for i, tracking in enumerate(trackings):\n",
|
||
" for c, detections in detection_by_camera.items():\n",
|
||
" camera = next(iter(detections)).camera\n",
|
||
" # pixel space, unnormalized\n",
|
||
" tracking_2d_projection = camera.project(tracking.keypoints)\n",
|
||
" \n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"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": 2
|
||
}
|