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feat: Enhance camera module with new data structures and utility functions

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- Introduced dataclass structures for CameraParams and Camera to improve type safety and clarity.
- Added Detection dataclass to encapsulate detection data, including keypoints and timestamps.
- Implemented classify_by_camera function to organize detections by camera.
- Added utility functions for converting points to homogeneous coordinates and calculating distances to lines.
- Updated dependencies in pyproject.toml to include new libraries for enhanced functionality.
This commit is contained in:
crosstyan
2025-04-15 10:20:49 +08:00
parent 3ce5b564bf
commit 92477b18d2
7 changed files with 815 additions and 6 deletions
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20
.cursor/.rules/posthug.mdc Normal file
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---
description: a default rule
globs:
alwaysApply: true
---
DO NOT GIVE ME HIGH LEVEL SHIT, UNLESS EXPLICITLY ASKED. IF I ASK FOR FIX OR EXPLANATION, I WANT ACTUAL CODE OR EXPLANATION! I DON'T WANT "Here's how you can blablabla"
- Be casual unless otherwise specified
- Be terse
- Suggest solutions that I didn't think about-anticipate my needs
- Treat me as an expert
- Be accurate and thorough
- Value good arguments over authorities, the source is irrelevant
- Consider new technologies and contrarian ideas, not just the conventional wisdom
- You may use high levels of speculation or prediction, just flag it for me
- No moral lectures
- Discuss safety only when it's crucial and non-obvious
- No need to mention your knowledge cutoff
- No need to disclose you're an AI
- Please respect my formatting preferences when you provide code.
- Please respect all code comments, they're usually there for a reason. Remove them ONLY if they're completely irrelevant after a code change. if unsure, do not remove the comment.

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.gitattributes vendored
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@ -1,2 +1,3 @@
*.pdf filter=lfs diff=lfs merge=lfs -text *.pdf filter=lfs diff=lfs merge=lfs -text
paper/*.pdf filter=lfs diff=lfs merge=lfs -text paper/*.pdf filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text

1
.gitignore vendored
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@ -8,3 +8,4 @@ wheels/
# Virtual environments # Virtual environments
.venv .venv
samples

298
app/camera/__init__.py
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@ -4,12 +4,16 @@ from typing_extensions import NotRequired
from jaxtyping import Num, jaxtyped from jaxtyping import Num, jaxtyped
from beartype import beartype from beartype import beartype
from jax import numpy as jnp, Array from jax import numpy as jnp, Array
from dataclasses import dataclass
from collections import defaultdict, OrderedDict
from datetime import datetime
CameraID: TypeAlias = str CameraID: TypeAlias = str
@jaxtyped(typechecker=beartype) @jaxtyped(typechecker=beartype)
class CameraParams(TypedDict): @dataclass
class CameraParams:
""" """
Camera parameters: intrinsic matrix, extrinsic matrix, and distortion coefficients Camera parameters: intrinsic matrix, extrinsic matrix, and distortion coefficients
""" """
@ -32,10 +36,15 @@ class CameraParams(TypedDict):
radial distortion coefficient and pi is the ith radial distortion coefficient and pi is the ith
tangential distortion coeff. tangential distortion coeff.
""" """
image_size: Num[Array, "2"]
"""
The size of image plane (width, height)
"""
@jaxtyped(typechecker=beartype) @jaxtyped(typechecker=beartype)
class Camera(TypedDict): @dataclass
class Camera:
""" """
a description of a camera a description of a camera
""" """
@ -48,7 +57,288 @@ class Camera(TypedDict):
""" """
Camera parameters Camera parameters
""" """
size: tuple[int, int]
@jaxtyped(typechecker=beartype)
@dataclass
class Detection:
""" """
Image size One detection from a camera
""" """
keypoints: Num[Array, "N 2"]
"""
Keypoints
"""
confidences: Num[Array, "N"]
"""
Confidences
"""
camera: Camera
"""
Camera
"""
timestamp: datetime
"""
Timestamp of the detection
"""
def classify_by_camera(
detections: list[Detection],
) -> OrderedDict[CameraID, list[Detection]]:
"""
Classify detections by camera
"""
# or use setdefault
camera_wise_split: dict[CameraID, list[Detection]] = defaultdict(list)
for entry in detections:
camera_id = entry.camera.id
camera_wise_split[camera_id].append(entry)
return OrderedDict(camera_wise_split)
@jaxtyped(typechecker=beartype)
def to_homogeneous(points: Num[Array, "N 2"] | Num[Array, "N 3"]) -> Num[Array, "N 3"]:
"""
Convert points to homogeneous coordinates
"""
if points.shape[-1] == 2:
return jnp.hstack((points, jnp.ones((points.shape[0], 1))))
elif points.shape[-1] == 3:
return points
else:
raise ValueError(f"Invalid shape for points: {points.shape}")
@jaxtyped(typechecker=beartype)
def point_line_distance(
point: Num[Array, "N 3"] | Num[Array, "N 2"],
line: Num[Array, "N 3"],
eps: float = 1e-9,
):
"""
Calculate the distance from a point to a line
Args:
point: (possibly homogeneous) points :math:`(N, 2 or 3)`.
line: lines coefficients :math:`(a, b, c)` with shape :math:`(N, 3)`, where :math:`ax + by + c = 0`.
eps: Small constant for safe sqrt.
Returns:
the computed distance with shape :math:`(N)`.
See also:
https://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line
"""
numerator = abs(line[:, 0] * point[:, 0] + line[:, 1] * point[:, 1] + line[:, 2])
denominator = jnp.sqrt(line[:, 0] * line[:, 0] + line[:, 1] * line[:, 1])
return numerator / (denominator + eps)
@jaxtyped(typechecker=beartype)
def left_to_right_epipolar_distance(
left: Num[Array, "N 3"],
right: Num[Array, "N 3"],
fundamental_matrix: Num[Array, "3 3"],
):
"""
Return one-sided epipolar distance for correspondences given the fundamental matrix.
Args:
left: points in the left image (homogeneous) :math:`(N, 3)`
right: points in the right image (homogeneous) :math:`(N, 3)`
fundamental_matrix: fundamental matrix :math:`(3, 3)`
Returns:
the computed distance with shape :math:`(N)`.
See also:
https://en.wikipedia.org/wiki/Fundamental_matrix_%28computer_vision%29
$$x^{\\prime T}Fx = 0$$
"""
F_t = fundamental_matrix.transpose()
line1_in_2 = jnp.matmul(left, F_t)
return point_line_distance(right, line1_in_2)
@jaxtyped(typechecker=beartype)
def right_to_left_epipolar_distance(
left: Num[Array, "N 3"],
right: Num[Array, "N 3"],
fundamental_matrix: Num[Array, "3 3"],
):
"""
Return one-sided epipolar distance for correspondences given the fundamental matrix.
Args:
left: points in the left image (homogeneous) :math:`(N, 3)`
right: points in the right image (homogeneous) :math:`(N, 3)`
fundamental_matrix: fundamental matrix :math:`(3, 3)`
Returns:
the computed distance with shape :math:`(N)`.
See also:
https://en.wikipedia.org/wiki/Fundamental_matrix_%28computer_vision%29
$$x^{\\prime T}Fx = 0$$
"""
line2_in_1 = jnp.matmul(right, fundamental_matrix)
return point_line_distance(left, line2_in_1)
def distance_between_epipolar_lines(
x1: Num[Array, "N 2"] | Num[Array, "N 3"],
x2: Num[Array, "N 2"] | Num[Array, "N 3"],
fundamental_matrix: Num[Array, "3 3"],
):
"""
Calculate the total epipolar line distance between x1 and x2.
"""
if x1.shape[0] != x2.shape[0]:
raise ValueError(
f"x1 and x2 must have the same number of points: {x1.shape[0]} != {x2.shape[0]}"
)
if x1.shape[-1] == 2:
point1 = to_homogeneous(x1)
elif x1.shape[-1] == 3:
point1 = x1
else:
raise ValueError(f"Invalid shape for correspondence1: {x1.shape}")
if x2.shape[-1] == 2:
point2 = to_homogeneous(x2)
elif x2.shape[-1] == 3:
point2 = x2
else:
raise ValueError(f"Invalid shape for correspondence2: {x2.shape}")
if fundamental_matrix.shape != (3, 3):
raise ValueError(
f"Invalid shape for fundamental_matrix: {fundamental_matrix.shape}"
)
# points 1 and 2 are unnormalized points
dist_1 = jnp.mean(
right_to_left_epipolar_distance(point1, point2, fundamental_matrix)
)
dist_2 = jnp.mean(
left_to_right_epipolar_distance(point1, point2, fundamental_matrix)
)
distance = dist_1 + dist_2
return distance
@jaxtyped(typechecker=beartype)
def calculate_fundamental_matrix(
camera_left: Camera, camera_right: Camera
) -> Num[Array, "3 3"]:
"""
Calculate the fundamental matrix for the given cameras.
"""
# Intrinsics
K1 = camera_left.params.K
K2 = camera_right.params.K
# Extrinsics (World to Camera transforms)
Rt1 = camera_left.params.Rt
Rt2 = camera_right.params.Rt
# Convert to Camera to World (Inverse)
T2: Array = jnp.linalg.inv(Rt2)
# Relative transform from Left to Right
T_rel = T2 @ Rt1
R = T_rel[:3, :3]
t = T_rel[:3, 3:]
# Skew-symmetric matrix for cross product
def skew(v: Num[Array, "3"]) -> Num[Array, "3 3"]:
return jnp.array(
[
[0, -v[2], v[1]],
[v[2], 0, -v[0]],
[-v[1], v[0], 0],
]
)
t_skew = skew(t.reshape(-1))
# Essential Matrix
E = t_skew @ R
# Fundamental Matrix
F = jnp.linalg.inv(K2).T @ E @ jnp.linalg.inv(K1)
return F
def compute_affinity_epipolar_constraint_with_pairs(
left: Detection, right: Detection, alpha_2D: float
):
fundamental_matrix = calculate_fundamental_matrix(left.camera, right.camera)
d = distance_between_epipolar_lines(
left.keypoints, right.keypoints, fundamental_matrix
)
return 1 - (d / alpha_2D)
def get_affinity_matrix_epipolar_constraint(
detections: list[Detection],
alpha_2D: float,
) -> Num[Array, "N N"]:
camera_wise_split = classify_by_camera(detections)
num_entries = sum(len(entries) for entries in camera_wise_split.values())
affinity_matrix = jnp.zeros((num_entries, num_entries), dtype=jnp.float32)
affinity_matrix_mask = jnp.zeros((num_entries, num_entries), dtype=jnp.bool_)
acc = 0
total_indices = set(range(num_entries))
camera_id_index_map: dict[CameraID, set[int]] = defaultdict(set)
camera_id_index_map_inverse: dict[CameraID, set[int]] = defaultdict(set)
# sorted by [D0_C0, D1_C0, D2_C0, D0_C1, D1_C1, D0_C2, D1_C2...]
sorted_detections: list[Detection] = []
for camera_id, entries in camera_wise_split.items():
for i, _ in enumerate(entries):
camera_id_index_map[camera_id].add(acc + i)
sorted_detections.append(entries[i])
acc += 1
camera_id_index_map_inverse[camera_id] = (
total_indices - camera_id_index_map[camera_id]
)
# assuming we have 3 cameras
# C0 has 3 detections: D0_C0, D1_C0, D2_C0
# C1 has 2 detections: D0_C1, D1_C1
# C2 has 2 detections: D0_C2, D1_C2
#
# D0_C0(0), D1_C0(1), D2_C0(2), D0_C1(3), D1_C1(4), D0_C2(5), D1_C2(6)
# D0_C0(0) 0 0 0 a_03 a_04 a_05 a_06
# D1_C0(1) 0 0 0 a_13 a_14 a_15 a_16
# ...
# D0_C1(3) a_30 a_31 a_32 0 0 a_35 a_36
# ...
# D1_C2(6) a_60 a_61 a_62 a_63 a_64 0 0
# ignore self-affinity
# ignore same-camera affinity
# assuming commutative property of epipolar constraint
for i, det in enumerate(sorted_detections):
other_indices = camera_id_index_map_inverse[det.camera.id]
for j in other_indices:
if affinity_matrix_mask[i, j] or affinity_matrix_mask[j, i]:
continue
a = compute_affinity_epipolar_constraint_with_pairs(
det, sorted_detections[j], alpha_2D
)
affinity_matrix = affinity_matrix.at[i, j].set(a)
affinity_matrix = affinity_matrix.at[j, i].set(a)
affinity_matrix_mask = affinity_matrix_mask.at[i, j].set(True)
affinity_matrix_mask = affinity_matrix_mask.at[j, i].set(True)
return affinity_matrix

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@ -2,10 +2,116 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": [
"from pathlib import Path\n",
"import awkward as ak\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import jax\n",
"import jax.numpy as jnp"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre>{AE_01: {extrinsic: {rvec: [...], ...}, intrinsic: {...}, keypoints: ..., ...},\n",
" AE_1A: {extrinsic: {rvec: [...], ...}, intrinsic: {...}, keypoints: ..., ...},\n",
" AE_08: {extrinsic: {rvec: [...], ...}, intrinsic: {...}, keypoints: ..., ...}}\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"backend: cpu\n",
"nbytes: 5.9 MB\n",
"type: {\n",
" AE_01: {\n",
" extrinsic: {\n",
" rvec: var * float64,\n",
" tvec: var * float64\n",
" },\n",
" intrinsic: {\n",
" camera_matrix: var * var * float64,\n",
" distortion_coefficients: var * float64\n",
" },\n",
" keypoints: var * var * var * float64,\n",
" confidences: var * var * float64,\n",
" matrix: var * var * float64,\n",
" projection_matrix: var * var * float64\n",
" },\n",
" AE_1A: {\n",
" extrinsic: {\n",
" rvec: var * float64,\n",
" tvec: var * float64\n",
" },\n",
" intrinsic: {\n",
" camera_matrix: var * var * float64,\n",
" distortion_coefficients: var * float64\n",
" },\n",
" keypoints: var * var * var * float64,\n",
" confidences: var * var * float64,\n",
" matrix: var * var * float64,\n",
" projection_matrix: var * var * float64\n",
" },\n",
" AE_08: {\n",
" extrinsic: {\n",
" rvec: var * float64,\n",
" tvec: var * float64\n",
" },\n",
" intrinsic: {\n",
" camera_matrix: var * var * float64,\n",
" distortion_coefficients: var * float64\n",
" },\n",
" keypoints: var * var * var * float64,\n",
" confidences: var * var * float64,\n",
" matrix: var * var * float64,\n",
" projection_matrix: var * var * float64\n",
" }\n",
"}</pre>"
],
"text/plain": [
"<Record {AE_01: {...}, AE_1A: {...}, ...} type='{AE_01: {extrinsic: {rvec: ...'>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"DATASET_PATH = Path(\"samples/camera_dataset.parquet\")\n",
"AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH)[0]\n",
"display(AK_CAMERA_DATASET)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from app.camera import Camera, CameraParams\n",
"\n",
"\n",
"def preprocess(key:str, record: ak.Record) -> Camera:\n",
" K = jnp.array(ak.to_numpy(record[\"intrinsic\"][\"camera_matrix\"])) # type: ignore\n",
" Rt = jnp.array(ak.to_numpy(record[\"matrix\"]))\n",
" dist_coeffs = jnp.array(ak.to_numpy(record[\"intrinsic\"][\"distortion_coefficients\"])) # type: ignore\n",
" size = (2560, 1440)\n",
" return Camera(id=key, params=CameraParams(K=K, Rt=Rt, dist_coeffs=dist_coeffs), size=size)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"CAMERA_DATASET = {key: preprocess(key, AK_CAMERA_DATASET[key]) for key in AK_CAMERA_DATASET.fields} # type: ignore"
]
} }
], ],
"metadata": { "metadata": {
@ -15,7 +121,15 @@
"name": "python3" "name": "python3"
}, },
"language_info": { "language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python", "name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9" "version": "3.12.9"
} }
}, },

3
pyproject.toml
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@ -11,8 +11,11 @@ dependencies = [
"cvxopt>=1.3.2", "cvxopt>=1.3.2",
"jax[cuda12]>=0.5.1", "jax[cuda12]>=0.5.1",
"jaxtyping>=0.2.38", "jaxtyping>=0.2.38",
"matplotlib>=3.10.1",
"opencv-contrib-python-headless>=4.11.0.86", "opencv-contrib-python-headless>=4.11.0.86",
"orjson>=3.10.15", "orjson>=3.10.15",
"pandas>=2.2.3",
"pyarrow>=19.0.1",
"torch>=2.6.0", "torch>=2.6.0",
"torchvision>=0.21.0", "torchvision>=0.21.0",
"typeguard>=4.4.2", "typeguard>=4.4.2",

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