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feat: Enhance play notebook and camera module with new projection and distortion functionalities

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- Updated play notebook to include new tracking and clustering functionalities.
- Introduced `distortion` and `project` functions for applying distortion to 2D points and projecting 3D points to 2D, respectively.
- Enhanced `CameraParams` and `Camera` classes with methods for distortion and projection, improving usability.
- Cleaned up execution counts in the notebook for better organization.
This commit is contained in:
crosstyan
2025-04-21 18:38:08 +08:00
parent 40f3150417
commit 032eb684ec
2 changed files with 504 additions and 36 deletions
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385
play.ipynb
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"<Array [{frame_index: 0, boxes: ..., ...}, ...] type='529 * {frame_index: i...'>"
]
},
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"output_type": "execute_result"
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@ -703,28 +703,345 @@
},
{
"cell_type": "code",
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"execution_count": 18,
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"source": [
"def triangle_from_cluster(cluster: list[Detection]) -> Float[Array, \"3\"]:\n",
" proj_matrices = jnp.array(\n",
" [\n",
" el.camera.params.projection_matrix\n",
" for el in cluster\n",
" ]\n",
" )\n",
"from dataclasses import dataclass\n",
"from copy import copy as shallow_copy, deepcopy as deep_copy\n",
"\n",
"\n",
"@jaxtyped(typechecker=beartype)\n",
"@dataclass\n",
"class Tracking:\n",
" id: int\n",
" keypoints: Float[Array, \"J 3\"]\n",
"\n",
"\n",
"@jaxtyped(typechecker=beartype)\n",
"def triangle_from_cluster(cluster: list[Detection]) -> Float[Array, \"N 3\"]:\n",
" proj_matrices = jnp.array([el.camera.params.projection_matrix for el in cluster])\n",
" points = jnp.array([el.keypoints for el in cluster])\n",
" confidences = jnp.array([el.confidences for el in cluster])\n",
" return triangulate_points_from_multiple_views_linear(proj_matrices, points, confidences=confidences)\n",
" return triangulate_points_from_multiple_views_linear(\n",
" proj_matrices, points, confidences=confidences\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",
"class GlobalTrackingState:\n",
" _last_id: int\n",
" _trackings: list[Tracking]\n",
"\n",
" def __init__(self):\n",
" self._last_id = 0\n",
" self._trackings = []\n",
"\n",
" @property\n",
" def trackings(self) -> list[Tracking]:\n",
" return shallow_copy(self._trackings)\n",
"\n",
" def add_tracking(self, cluster: list[Detection]) -> Tracking:\n",
" tracking = Tracking(id=self._last_id, keypoints=triangle_from_cluster(cluster))\n",
" self._last_id += 1\n",
" self._trackings.append(tracking)\n",
" return tracking\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))"
"global_tracking_state = GlobalTrackingState()\n",
"for cluster in clusters_detections:\n",
" global_tracking_state.add_tracking(cluster)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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" [-1.2295499 , 0.666676 , -0.14252058],\n",
" [-1.2398047 , 0.6640044 , -0.15045756],\n",
" [-1.2491337 , 0.6642992 , -0.15746638],\n",
" [-1.2494593 , 0.67095923, -0.16590758],\n",
" [-1.2318163 , 0.6709184 , -0.15842697],\n",
" [-1.2360069 , 0.6675048 , -0.16684474],\n",
" [-1.2418597 , 0.6643395 , -0.17036118],\n",
" [-1.2491734 , 0.658755 , -0.17511088],\n",
" [-1.227513 , 0.6711193 , -0.16040966],\n",
" [-1.2349824 , 0.6654219 , -0.17266446],\n",
" [-1.2394276 , 0.65952814, -0.17668773],\n",
" [-1.2448883 , 0.65159357, -0.18011504],\n",
" [-1.2285581 , 0.6685526 , -0.16612191],\n",
" [-1.2304138 , 0.6656384 , -0.17683747],\n",
" [-1.2352761 , 0.6589148 , -0.18067938],\n",
" [-1.2397696 , 0.6542397 , -0.18179603],\n",
" [-1.2297099 , 0.66609055, -0.17332217],\n",
" [-1.230786 , 0.664324 , -0.1790045 ],\n",
" [-1.2307583 , 0.6601091 , -0.18404593],\n",
" [-1.234234 , 0.6567872 , -0.18384361],\n",
" [-1.5696746 , 0.63732606, -0.10729273],\n",
" [-1.55086 , 0.64279026, -0.10464413],\n",
" [-1.5384696 , 0.64851606, -0.10692033],\n",
" [-1.5344497 , 0.64882326, -0.11430191],\n",
" [-1.5279433 , 0.650463 , -0.12476842],\n",
" [-1.5458598 , 0.6470458 , -0.11982083],\n",
" [-1.5357145 , 0.6531256 , -0.1302915 ],\n",
" [-1.5323898 , 0.65010923, -0.131393 ],\n",
" [-1.5330654 , 0.64818895, -0.13211855],\n",
" [-1.551035 , 0.6446348 , -0.12478309],\n",
" [-1.5443487 , 0.6461636 , -0.1344983 ],\n",
" [-1.5416367 , 0.6473249 , -0.13531932],\n",
" [-1.5427909 , 0.6441275 , -0.13736817],\n",
" [-1.5514573 , 0.6438553 , -0.13022557],\n",
" [-1.5493276 , 0.6440739 , -0.13788472],\n",
" [-1.5467429 , 0.64533913, -0.13838342],\n",
" [-1.5477741 , 0.64245623, -0.13843 ],\n",
" [-1.5572879 , 0.6400873 , -0.13644902],\n",
" [-1.5551068 , 0.64055866, -0.14215696],\n",
" [-1.5568593 , 0.6414328 , -0.14450715],\n",
" [-1.5548064 , 0.64144945, -0.1446854 ]], dtype=float32))]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next_group = next(sync_gen)\n",
"display(next_group)"
]
}
],