dreifus 0.1.17

dreifus lifts your 3D camera experience and facilitates computer vision applications

dreifus

OpenCV, OpenGL, Pytorch3D, ... you never know which camera coordinate convention you are currently using?
You always have to try invert() on your "pose" matrices because you never know whether they are cam2world or world2cam?
The dreifus library is what you need!
dreifus (German for tripod) assists you in dealing with 3D cameras in Python.

1. Installation

pip install dreifus

2. Usage

2.1. Extrinsic (Pose) matrices

Translating between coordinate conventions made easy:

from dreifus.matrix import Pose, CameraCoordinateConvention, PoseType

# wrap some 4x4 extrinsic matrix
# Default assumed coordinate convention: OPEN_CV
# Default assumed pose type: WORLD_2_CAM 
pose = Pose(some_extrinsic_matrix, pose_type=..., camera_coordinate_convention=...)

# Translate between coordinate conventions
pose.change_camera_coordinate_convention(CameraCoordinateConvention.OPEN_GL)

# Ensure your pose transforms into the direction you expect
pose.change_pose_type(PoseType.CAM_2_WORLD)

2.2. Intrinsics

from dreifus.matrix import Intrinsics

intrinsics = Intrinsics(fx, fy, cx, cy)

# Adapt your intrinsics to images downscaled by a factor of 2x
intrinsics.rescale(0.5)

# Adapt your intrinsics to an image cropped at (50, 50) left-top
intrinsics.crop(50, 50)

3. Visualization

The visualization tools will automatically interpret your camera poses correctly, as long as you specified camera_coordinate_convention and pose_type correctly.

import pyvista as pv
from dreifus.pyvista import add_coordinate_axes, add_camera_frustum

pose = Pose(...)  # Some extrinsics
intrinsics = Intrinsics(...)  # Some intrinsics
image = ...  # Some images taken from that view

p = pv.Plotter()

add_coordinate_axes(p)
add_camera_frustum(p, pose, intrinsics, image=image)

p.show()

Render a pyvista scene from a specific camera:

import pyvista as pv
from dreifus.pyvista import render_from_camera

p = pv.Plotter(window_size=[IMG_W, IMG_H], off_screen=True)
p.background_color = (0, 0, 0, 0)

image = render_from_camera(p, pose, intrinsics)