Source code for psychopy.tools.mathtools

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Various math functions for working with vectors, matrices, and quaternions.
#

# Part of the PsychoPy library
# Copyright (C) 2002-2018 Jonathan Peirce (C) 2019-2021 Open Science Tools Ltd.
# Distributed under the terms of the GNU General Public License (GPL).

__all__ = ['normalize',
           'lerp',
           'slerp',
           'multQuat',
           'quatFromAxisAngle',
           'quatToMatrix',
           'scaleMatrix',
           'rotationMatrix',
           'transform',
           'translationMatrix',
           'concatenate',
           'applyMatrix',
           'invertQuat',
           'quatToAxisAngle',
           'posOriToMatrix',
           'applyQuat',
           'orthogonalize',
           'reflect',
           'cross',
           'distance',
           'dot',
           'quatMagnitude',
           'length',
           'project',
           'bisector',
           'surfaceNormal',
           'invertMatrix',
           'angleTo',
           'surfaceBitangent',
           'surfaceTangent',
           'vertexNormal',
           'isOrthogonal',
           'isAffine',
           'perp',
           'ortho3Dto2D',
           'intersectRayPlane',
           'matrixToQuat',
           'lensCorrection',
           'matrixFromEulerAngles',
           'alignTo',
           'quatYawPitchRoll',
           'intersectRaySphere',
           'intersectRayAABB',
           'intersectRayOBB',
           'intersectRayTriangle',
           'scale',
           'multMatrix',
           'normalMatrix',
           'fitBBox',
           'computeBBoxCorners',
           'zeroFix',
           'accumQuat',
           'fixTangentHandedness',
           'articulate',
           'forwardProject',
           'reverseProject',
           'lensCorrectionSpherical']


import numpy as np
import functools
import itertools


VEC_AXES = {'+x': (1, 0, 0), '-x': (-1, 0, 0),
            '+y': (0, 1, 0), '-y': (0, -1, 0),
            '+z': (0, 0, 1), '-z': (0, 0, -1)}


# ------------------------------------------------------------------------------
# Vector Operations
#

[docs]def length(v, squared=False, out=None, dtype=None): """Get the length of a vector. Parameters ---------- v : array_like Vector to normalize, can be Nx2, Nx3, or Nx4. If a 2D array is specified, rows are treated as separate vectors. squared : bool, optional If ``True`` the squared length is returned. The default is ``False``. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- float or ndarray Length of vector `v`. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v = np.asarray(v, dtype=dtype) if v.ndim == 2: assert v.shape[1] <= 4 toReturn = np.zeros((v.shape[0],), dtype=dtype) if out is None else out v2d, vr = np.atleast_2d(v, toReturn) # 2d view of array if squared: vr[:, :] = np.sum(np.square(v2d), axis=1) else: vr[:, :] = np.sqrt(np.sum(np.square(v2d), axis=1)) elif v.ndim == 1: assert v.shape[0] <= 4 if squared: toReturn = np.sum(np.square(v)) else: toReturn = np.sqrt(np.sum(np.square(v))) else: raise ValueError("Input arguments have invalid dimensions.") return toReturn
[docs]def normalize(v, out=None, dtype=None): """Normalize a vector or quaternion. v : array_like Vector to normalize, can be Nx2, Nx3, or Nx4. If a 2D array is specified, rows are treated as separate vectors. All vectors should have nonzero length. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Normalized vector `v`. Notes ----- * If the vector has length is zero, a vector of all zeros is returned after normalization. Examples -------- Normalize a vector:: v = [1., 2., 3., 4.] vn = normalize(v) The `normalize` function is vectorized. It's considerably faster to normalize large arrays of vectors than to call `normalize` separately for each one:: v = np.random.uniform(-1.0, 1.0, (1000, 4,)) # 1000 length 4 vectors vn = np.zeros((1000, 4)) # place to write values normalize(v, out=vn) # very fast! # don't do this! for i in range(1000): vn[i, :] = normalize(v[i, :]) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type toReturn = np.array(v, dtype=dtype) else: toReturn = out v2d = np.atleast_2d(toReturn) # 2d view of array norm = np.linalg.norm(v2d, axis=1) norm[norm == 0.0] = np.NaN # make sure if length==0 division succeeds v2d /= norm[:, np.newaxis] np.nan_to_num(v2d, copy=False) # fix NaNs return toReturn
[docs]def orthogonalize(v, n, out=None, dtype=None): """Orthogonalize a vector relative to a normal vector. This function ensures that `v` is perpendicular (or orthogonal) to `n`. Parameters ---------- v : array_like Vector to orthogonalize, can be Nx2, Nx3, or Nx4. If a 2D array is specified, rows are treated as separate vectors. n : array_like Normal vector, must have same shape as `v`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Orthogonalized vector `v` relative to normal vector `n`. Warnings -------- If `v` and `n` are the same, the direction of the perpendicular vector is indeterminate. The resulting vector is degenerate (all zeros). """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v = np.asarray(v, dtype=dtype) n = np.asarray(n, dtype=dtype) if out is None: toReturn = np.zeros_like(v, dtype=dtype) else: toReturn = out toReturn.fill(0.0) v, n, vr = np.atleast_2d(v, n, toReturn) vr[:, :] = v vr[:, :] -= n * np.sum(n * v, axis=1)[:, np.newaxis] # dot product normalize(vr, out=vr) return toReturn
[docs]def reflect(v, n, out=None, dtype=None): """Reflection of a vector. Get the reflection of `v` relative to normal `n`. Parameters ---------- v : array_like Vector to reflect, can be Nx2, Nx3, or Nx4. If a 2D array is specified, rows are treated as separate vectors. n : array_like Normal vector, must have same shape as `v`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Reflected vector `v` off normal `n`. """ # based off https://github.com/glfw/glfw/blob/master/deps/linmath.h if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v = np.asarray(v, dtype=dtype) n = np.asarray(n, dtype=dtype) if out is None: toReturn = np.zeros_like(v, dtype=dtype) else: toReturn = out toReturn.fill(0.0) v, n, vr = np.atleast_2d(v, n, toReturn) vr[:, :] = v vr[:, :] -= (dtype(2.0) * np.sum(n * v, axis=1))[:, np.newaxis] * n return toReturn
[docs]def dot(v0, v1, out=None, dtype=None): """Dot product of two vectors. The behaviour of this function depends on the format of the input arguments: * If `v0` and `v1` are 1D, the dot product is returned as a scalar and `out` is ignored. * If `v0` and `v1` are 2D, a 1D array of dot products between corresponding row vectors are returned. * If either `v0` and `v1` are 1D and 2D, an array of dot products between each row of the 2D vector and the 1D vector are returned. Parameters ---------- v0, v1 : array_like Vector(s) to compute dot products of (e.g. [x, y, z]). `v0` must have equal or fewer dimensions than `v1`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Dot product(s) of `v0` and `v1`. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v0 = np.asarray(v0, dtype=dtype) v1 = np.asarray(v1, dtype=dtype) if v0.ndim == v1.ndim == 2 or v0.ndim == 2 and v1.ndim == 1: toReturn = np.zeros((v0.shape[0],), dtype=dtype) if out is None else out vr = np.atleast_2d(toReturn) # make sure we have a 2d view vr[:] = np.sum(v1 * v0, axis=1) elif v0.ndim == v1.ndim == 1: toReturn = np.sum(v1 * v0) elif v0.ndim == 1 and v1.ndim == 2: toReturn = np.zeros((v1.shape[0],), dtype=dtype) if out is None else out vr = np.atleast_2d(toReturn) # make sure we have a 2d view vr[:] = np.sum(v1 * v0, axis=1) else: raise ValueError("Input arguments have invalid dimensions.") return toReturn
[docs]def cross(v0, v1, out=None, dtype=None): """Cross product of 3D vectors. The behavior of this function depends on the dimensions of the inputs: * If `v0` and `v1` are 1D, the cross product is returned as 1D vector. * If `v0` and `v1` are 2D, a 2D array of cross products between corresponding row vectors are returned. * If either `v0` and `v1` are 1D and 2D, an array of cross products between each row of the 2D vector and the 1D vector are returned. Parameters ---------- v0, v1 : array_like Vector(s) in form [x, y, z] or [x, y, z, 1]. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Cross product of `v0` and `v1`. Notes ----- * If input vectors are 4D, the last value of cross product vectors is always set to one. * If input vectors `v0` and `v1` are Nx3 and `out` is Nx4, the cross product is computed and the last column of `out` is filled with ones. Examples -------- Find the cross product of two vectors:: a = normalize([1, 2, 3]) b = normalize([3, 2, 1]) c = cross(a, b) If input arguments are 2D, the function returns the cross products of corresponding rows:: # create two 6x3 arrays with random numbers shape = (6, 3,) a = normalize(np.random.uniform(-1.0, 1.0, shape)) b = normalize(np.random.uniform(-1.0, 1.0, shape)) cprod = np.zeros(shape) # output has the same shape as inputs cross(a, b, out=cprod) If a 1D and 2D vector are specified, the cross product of each row of the 2D array and the 1D array is returned as a 2D array:: a = normalize([1, 2, 3]) b = normalize(np.random.uniform(-1.0, 1.0, (6, 3,))) cprod = np.zeros(a.shape) cross(a, b, out=cprod) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v0 = np.asarray(v0, dtype=dtype) v1 = np.asarray(v1, dtype=dtype) if v0.ndim == v1.ndim == 2: # 2D x 2D assert v0.shape == v1.shape toReturn = np.zeros(v0.shape, dtype=dtype) if out is None else out vr = np.atleast_2d(toReturn) vr[:, 0] = v0[:, 1] * v1[:, 2] - v0[:, 2] * v1[:, 1] vr[:, 1] = v0[:, 2] * v1[:, 0] - v0[:, 0] * v1[:, 2] vr[:, 2] = v0[:, 0] * v1[:, 1] - v0[:, 1] * v1[:, 0] if vr.shape[1] == 4: vr[:, 3] = dtype(1.0) elif v0.ndim == v1.ndim == 1: # 1D x 1D assert v0.shape == v1.shape toReturn = np.zeros(v0.shape, dtype=dtype) if out is None else out toReturn[0] = v0[1] * v1[2] - v0[2] * v1[1] toReturn[1] = v0[2] * v1[0] - v0[0] * v1[2] toReturn[2] = v0[0] * v1[1] - v0[1] * v1[0] if toReturn.shape[0] == 4: toReturn[3] = dtype(1.0) elif v0.ndim == 2 and v1.ndim == 1: # 2D x 1D toReturn = np.zeros(v0.shape, dtype=dtype) if out is None else out vr = np.atleast_2d(toReturn) vr[:, 0] = v0[:, 1] * v1[2] - v0[:, 2] * v1[1] vr[:, 1] = v0[:, 2] * v1[0] - v0[:, 0] * v1[2] vr[:, 2] = v0[:, 0] * v1[1] - v0[:, 1] * v1[0] if vr.shape[1] == 4: vr[:, 3] = dtype(1.0) elif v0.ndim == 1 and v1.ndim == 2: # 1D x 2D toReturn = np.zeros(v1.shape, dtype=dtype) if out is None else out vr = np.atleast_2d(toReturn) vr[:, 0] = v1[:, 2] * v0[1] - v1[:, 1] * v0[2] vr[:, 1] = v1[:, 0] * v0[2] - v1[:, 2] * v0[0] vr[:, 2] = v1[:, 1] * v0[0] - v1[:, 0] * v0[1] if vr.shape[1] == 4: vr[:, 3] = dtype(1.0) else: raise ValueError("Input arguments have incorrect dimensions.") return toReturn
[docs]def project(v0, v1, out=None, dtype=None): """Project a vector onto another. Parameters ---------- v0 : array_like Vector can be Nx2, Nx3, or Nx4. If a 2D array is specified, rows are treated as separate vectors. v1 : array_like Vector to project onto `v0`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray or float Projection of vector `v0` on `v1`. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v0 = np.asarray(v0, dtype=dtype) v1 = np.asarray(v1, dtype=dtype) if v0.ndim == v1.ndim == 2 or v0.ndim == 1 and v1.ndim == 2: toReturn = np.zeros_like(v1, dtype=dtype) if out is None else out toReturn[:, :] = v1[:, :] toReturn *= (dot(v0, v1, dtype=dtype) / length(v1))[:, np.newaxis] elif v0.ndim == v1.ndim == 1: toReturn = v1 * (dot(v0, v1, dtype=dtype) / np.sum(np.square(v1))) elif v0.ndim == 2 and v1.ndim == 1: toReturn = np.zeros_like(v0, dtype=dtype) if out is None else out toReturn[:, :] = v1[:] toReturn *= (dot(v0, v1, dtype=dtype) / length(v1))[:, np.newaxis] else: raise ValueError("Input arguments have invalid dimensions.") toReturn += 0.0 # remove negative zeros return toReturn
[docs]def lerp(v0, v1, t, out=None, dtype=None): """Linear interpolation (LERP) between two vectors/coordinates. Parameters ---------- v0 : array_like Initial vector/coordinate. Can be 2D where each row is a point. v1 : array_like Final vector/coordinate. Must be the same shape as `v0`. t : float Interpolation weight factor [0, 1]. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Vector at `t` with same shape as `v0` and `v1`. Examples -------- Find the coordinate of the midpoint between two vectors:: u = [0., 0., 0.] v = [0., 0., 1.] midpoint = lerp(u, v, 0.5) # 0.5 to interpolate half-way between points """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type t = dtype(t) t0 = dtype(1.0) - t v0 = np.asarray(v0, dtype=dtype) v1 = np.asarray(v1, dtype=dtype) toReturn = np.zeros_like(v0, dtype=dtype) if out is None else out v0, v1, vr = np.atleast_2d(v0, v1, toReturn) vr[:, :] = v0 * t0 vr[:, :] += v1 * t return toReturn
[docs]def distance(v0, v1, out=None, dtype=None): """Get the distance between vectors/coordinates. The behaviour of this function depends on the format of the input arguments: * If `v0` and `v1` are 1D, the distance is returned as a scalar and `out` is ignored. * If `v0` and `v1` are 2D, an array of distances between corresponding row vectors are returned. * If either `v0` and `v1` are 1D and 2D, an array of distances between each row of the 2D vector and the 1D vector are returned. Parameters ---------- v0, v1 : array_like Vectors to compute the distance between. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Distance between vectors `v0` and `v1`. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v0 = np.asarray(v0, dtype=dtype) v1 = np.asarray(v1, dtype=dtype) if v0.ndim == v1.ndim == 2 or (v0.ndim == 2 and v1.ndim == 1): dist = np.zeros((v1.shape[0],), dtype=dtype) if out is None else out dist[:] = np.sqrt(np.sum(np.square(v1 - v0), axis=1)) elif v0.ndim == v1.ndim == 1: dist = np.sqrt(np.sum(np.square(v1 - v0))) elif v0.ndim == 1 and v1.ndim == 2: dist = np.zeros((v1.shape[0],), dtype=dtype) if out is None else out dist[:] = np.sqrt(np.sum(np.square(v0 - v1), axis=1)) else: raise ValueError("Input arguments have invalid dimensions.") return dist
[docs]def perp(v, n, norm=True, out=None, dtype=None): """Project `v` to be a perpendicular axis of `n`. Parameters ---------- v : array_like Vector to project [x, y, z], may be Nx3. n : array_like Normal vector [x, y, z], may be Nx3. norm : bool Normalize the resulting axis. Default is `True`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Perpendicular axis of `n` from `v`. Examples -------- Determine the local `up` (y-axis) of a surface or plane given `normal`:: normal = [0., 0.70710678, 0.70710678] up = [1., 0., 0.] yaxis = perp(up, normal) Do a cross product to get the x-axis perpendicular to both:: xaxis = cross(yaxis, normal) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v = np.asarray(v, dtype=dtype) n = np.asarray(n, dtype=dtype) toReturn = np.zeros_like(v, dtype=dtype) if out is None else out v2d, n2d, r2d = np.atleast_2d(v, n, toReturn) # from GLM `glm/gtx/perpendicular.inl` r2d[:, :] = v2d - project(v2d, n2d, dtype=dtype) if norm: normalize(toReturn, out=toReturn) toReturn += 0.0 # clear negative zeros return toReturn
[docs]def bisector(v0, v1, norm=False, out=None, dtype=None): """Get the angle bisector. Computes a vector which bisects the angle between `v0` and `v1`. Input vectors `v0` and `v1` must be non-zero. Parameters ---------- v0, v1 : array_like Vectors to bisect [x, y, z]. Must be non-zero in length and have the same shape. Inputs can be Nx3 where the bisector for corresponding rows will be returned. norm : bool, optional Normalize the resulting bisector. Default is `False`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Bisecting vector [x, y, z]. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v0 = np.asarray(v0, dtype=dtype) v1 = np.asarray(v1, dtype=dtype) assert v0.shape == v1.shape toReturn = np.zeros_like(v0, dtype=dtype) if out is None else out v02d, v12d, r2d = np.atleast_2d(v0, v1, toReturn) r2d[:, :] = v02d * length(v12d, dtype=dtype)[:, np.newaxis] + \ v12d * length(v02d, dtype=dtype)[:, np.newaxis] if norm: normalize(r2d, out=r2d) return toReturn
[docs]def angleTo(v, point, degrees=True, out=None, dtype=None): """Get the relative angle to a point from a vector. The behaviour of this function depends on the format of the input arguments: * If `v0` and `v1` are 1D, the angle is returned as a scalar and `out` is ignored. * If `v0` and `v1` are 2D, an array of angles between corresponding row vectors are returned. * If either `v0` and `v1` are 1D and 2D, an array of angles between each row of the 2D vector and the 1D vector are returned. Parameters ---------- v : array_like Direction vector [x, y, z]. point : array_like Point(s) to compute angle to from vector `v`. degrees : bool, optional Return the resulting angles in degrees. If `False`, angles will be returned in radians. Default is `True`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Distance between vectors `v0` and `v1`. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v = np.asarray(v, dtype=dtype) point = np.asarray(point, dtype=dtype) if v.ndim == point.ndim == 2 or (v.ndim == 2 and point.ndim == 1): angle = np.zeros((v.shape[0],), dtype=dtype) if out is None else out u = np.sqrt(length(v, squared=True, dtype=dtype) * length(point, squared=True, dtype=dtype)) angle[:] = np.arccos(dot(v, point, dtype=dtype) / u) elif v.ndim == 1 and point.ndim == 2: angle = np.zeros((point.shape[0],), dtype=dtype) if out is None else out u = np.sqrt(length(v, squared=True, dtype=dtype) * length(point, squared=True, dtype=dtype)) angle[:] = np.arccos(dot(v, point, dtype=dtype) / u) elif v.ndim == point.ndim == 1: u = np.sqrt(length(v, squared=True, dtype=dtype) * length(point, squared=True, dtype=dtype)) angle = np.arccos(dot(v, point, dtype=dtype) / u) else: raise ValueError("Input arguments have invalid dimensions.") return np.degrees(angle) if degrees else angle
[docs]def surfaceNormal(tri, norm=True, out=None, dtype=None): """Compute the surface normal of a given triangle. Parameters ---------- tri : array_like Triangle vertices as 2D (3x3) array [p0, p1, p2] where each vertex is a length 3 array [vx, xy, vz]. The input array can be 3D (Nx3x3) to specify multiple triangles. norm : bool, optional Normalize computed surface normals if ``True``, default is ``True``. out : ndarray, optional Optional output array. Must have one fewer dimensions than `tri`. The shape of the last dimension must be 3. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Surface normal of triangle `tri`. Examples -------- Compute the surface normal of a triangle:: vertices = [[-1., 0., 0.], [0., 1., 0.], [1, 0, 0]] norm = surfaceNormal(vertices) Find the normals for multiple triangles, and put results in a pre-allocated array:: vertices = [[[-1., 0., 0.], [0., 1., 0.], [1, 0, 0]], # 2x3x3 [[1., 0., 0.], [0., 1., 0.], [-1, 0, 0]]] normals = np.zeros((2, 3)) # normals from two triangles triangles surfaceNormal(vertices, out=normals) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type tris = np.asarray(tri, dtype=dtype) if tris.ndim == 2: tris = np.expand_dims(tri, axis=0) if tris.shape[0] == 1: toReturn = np.zeros((3,), dtype=dtype) if out is None else out else: if out is None: toReturn = np.zeros((tris.shape[0], 3), dtype=dtype) else: toReturn = out # from https://www.khronos.org/opengl/wiki/Calculating_a_Surface_Normal nr = np.atleast_2d(toReturn) u = tris[:, 1, :] - tris[:, 0, :] v = tris[:, 2, :] - tris[:, 1, :] nr[:, 0] = u[:, 1] * v[:, 2] - u[:, 2] * v[:, 1] nr[:, 1] = u[:, 2] * v[:, 0] - u[:, 0] * v[:, 2] nr[:, 2] = u[:, 0] * v[:, 1] - u[:, 1] * v[:, 0] if norm: normalize(nr, out=nr) return toReturn
[docs]def surfaceBitangent(tri, uv, norm=True, out=None, dtype=None): """Compute the bitangent vector of a given triangle. This function can be used to generate bitangent vertex attributes for normal mapping. After computing bitangents, one may orthogonalize them with vertex normals using the :func:`orthogonalize` function, or within the fragment shader. Uses texture coordinates at each triangle vertex to determine the direction of the vector. Parameters ---------- tri : array_like Triangle vertices as 2D (3x3) array [p0, p1, p2] where each vertex is a length 3 array [vx, xy, vz]. The input array can be 3D (Nx3x3) to specify multiple triangles. uv : array_like Texture coordinates associated with each face vertex as a 2D array (3x2) where each texture coordinate is length 2 array [u, v]. The input array can be 3D (Nx3x2) to specify multiple texture coordinates if multiple triangles are specified. norm : bool, optional Normalize computed bitangents if ``True``, default is ``True``. out : ndarray, optional Optional output array. Must have one fewer dimensions than `tri`. The shape of the last dimension must be 3. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Surface bitangent of triangle `tri`. Examples -------- Computing the bitangents for two triangles from vertex and texture coordinates (UVs):: # array of triangle vertices (2x3x3) tri = np.asarray([ [(-1.0, 1.0, 0.0), (-1.0, -1.0, 0.0), (1.0, -1.0, 0.0)], # 1 [(-1.0, 1.0, 0.0), (-1.0, -1.0, 0.0), (1.0, -1.0, 0.0)]]) # 2 # array of triangle texture coordinates (2x3x2) uv = np.asarray([ [(0.0, 1.0), (0.0, 0.0), (1.0, 0.0)], # 1 [(0.0, 1.0), (0.0, 0.0), (1.0, 0.0)]]) # 2 bitangents = surfaceBitangent(tri, uv, norm=True) # bitangets (2x3) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type tris = np.asarray(tri, dtype=dtype) if tris.ndim == 2: tris = np.expand_dims(tri, axis=0) if tris.shape[0] == 1: toReturn = np.zeros((3,), dtype=dtype) if out is None else out else: if out is None: toReturn = np.zeros((tris.shape[0], 3), dtype=dtype) else: toReturn = out uvs = np.asarray(uv, dtype=dtype) if uvs.ndim == 2: uvs = np.expand_dims(uvs, axis=0) # based off the implementation from # https://learnopengl.com/Advanced-Lighting/Normal-Mapping e1 = tris[:, 1, :] - tris[:, 0, :] e2 = tris[:, 2, :] - tris[:, 0, :] d1 = uvs[:, 1, :] - uvs[:, 0, :] d2 = uvs[:, 2, :] - uvs[:, 0, :] # compute the bitangent nr = np.atleast_2d(toReturn) nr[:, 0] = -d2[:, 0] * e1[:, 0] + d1[:, 0] * e2[:, 0] nr[:, 1] = -d2[:, 0] * e1[:, 1] + d1[:, 0] * e2[:, 1] nr[:, 2] = -d2[:, 0] * e1[:, 2] + d1[:, 0] * e2[:, 2] f = dtype(1.0) / (d1[:, 0] * d2[:, 1] - d2[:, 0] * d1[:, 1]) nr *= f[:, np.newaxis] if norm: normalize(toReturn, out=toReturn, dtype=dtype) return toReturn
[docs]def surfaceTangent(tri, uv, norm=True, out=None, dtype=None): """Compute the tangent vector of a given triangle. This function can be used to generate tangent vertex attributes for normal mapping. After computing tangents, one may orthogonalize them with vertex normals using the :func:`orthogonalize` function, or within the fragment shader. Uses texture coordinates at each triangle vertex to determine the direction of the vector. Parameters ---------- tri : array_like Triangle vertices as 2D (3x3) array [p0, p1, p2] where each vertex is a length 3 array [vx, xy, vz]. The input array can be 3D (Nx3x3) to specify multiple triangles. uv : array_like Texture coordinates associated with each face vertex as a 2D array (3x2) where each texture coordinate is length 2 array [u, v]. The input array can be 3D (Nx3x2) to specify multiple texture coordinates if multiple triangles are specified. If so `N` must be the same size as the first dimension of `tri`. norm : bool, optional Normalize computed tangents if ``True``, default is ``True``. out : ndarray, optional Optional output array. Must have one fewer dimensions than `tri`. The shape of the last dimension must be 3. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Surface normal of triangle `tri`. Examples -------- Compute surface normals, tangents, and bitangents for a list of triangles:: # triangle vertices (2x3x3) vertices = [[[-1., 0., 0.], [0., 1., 0.], [1, 0, 0]], [[1., 0., 0.], [0., 1., 0.], [-1, 0, 0]]] # array of triangle texture coordinates (2x3x2) uv = np.asarray([ [(0.0, 1.0), (0.0, 0.0), (1.0, 0.0)], # 1 [(0.0, 1.0), (0.0, 0.0), (1.0, 0.0)]]) # 2 normals = surfaceNormal(vertices) tangents = surfaceTangent(vertices, uv) bitangents = cross(normals, tangents) # or use `surfaceBitangent` Orthogonalize a surface tangent with a vertex normal vector to get the vertex tangent and bitangent vectors:: vertexTangent = orthogonalize(faceTangent, vertexNormal) vertexBitangent = cross(vertexTangent, vertexNormal) Ensure computed vectors have the same handedness, if not, flip the tangent vector (important for applications like normal mapping):: # tangent, bitangent, and normal are 2D tangent[dot(cross(normal, tangent), bitangent) < 0.0, :] *= -1.0 """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type tris = np.asarray(tri, dtype=dtype) if tris.ndim == 2: tris = np.expand_dims(tri, axis=0) if tris.shape[0] == 1: toReturn = np.zeros((3,), dtype=dtype) if out is None else out else: if out is None: toReturn = np.zeros((tris.shape[0], 3), dtype=dtype) else: toReturn = out uvs = np.asarray(uv, dtype=dtype) if uvs.ndim == 2: uvs = np.expand_dims(uvs, axis=0) # based off the implementation from # https://learnopengl.com/Advanced-Lighting/Normal-Mapping e1 = tris[:, 1, :] - tris[:, 0, :] e2 = tris[:, 2, :] - tris[:, 0, :] d1 = uvs[:, 1, :] - uvs[:, 0, :] d2 = uvs[:, 2, :] - uvs[:, 0, :] # compute the bitangent nr = np.atleast_2d(toReturn) nr[:, 0] = d2[:, 1] * e1[:, 0] - d1[:, 1] * e2[:, 0] nr[:, 1] = d2[:, 1] * e1[:, 1] - d1[:, 1] * e2[:, 1] nr[:, 2] = d2[:, 1] * e1[:, 2] - d1[:, 1] * e2[:, 2] f = dtype(1.0) / (d1[:, 0] * d2[:, 1] - d2[:, 0] * d1[:, 1]) nr *= f[:, np.newaxis] if norm: normalize(toReturn, out=toReturn, dtype=dtype) return toReturn
[docs]def vertexNormal(faceNorms, norm=True, out=None, dtype=None): """Compute a vertex normal from shared triangles. This function computes a vertex normal by averaging the surface normals of the triangles it belongs to. If model has no vertex normals, first use :func:`surfaceNormal` to compute them, then run :func:`vertexNormal` to compute vertex normal attributes. While this function is mainly used to compute vertex normals, it can also be supplied triangle tangents and bitangents. Parameters ---------- faceNorms : array_like An array (Nx3) of surface normals. norm : bool, optional Normalize computed normals if ``True``, default is ``True``. out : ndarray, optional Optional output array. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Vertex normal. Examples -------- Compute a vertex normal from the face normals of the triangles it belongs to:: normals = [[1., 0., 0.], [0., 1., 0.]] # adjacent face normals vertexNorm = vertexNormal(normals) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type triNorms2d = np.atleast_2d(np.asarray(faceNorms, dtype=dtype)) nFaces = triNorms2d.shape[0] if out is None: toReturn = np.zeros((3,), dtype=dtype) else: toReturn = out toReturn[0] = np.sum(triNorms2d[:, 0]) toReturn[1] = np.sum(triNorms2d[:, 1]) toReturn[2] = np.sum(triNorms2d[:, 2]) toReturn /= nFaces if norm: normalize(toReturn, out=toReturn, dtype=dtype) return toReturn
[docs]def fixTangentHandedness(tangents, normals, bitangents, out=None, dtype=None): """Ensure the handedness of tangent vectors are all the same. Often 3D computed tangents may not have the same handedness due to how texture coordinates are specified. This function takes input surface vectors are ensures that tangents have the same handedness. Use this function if you notice that normal mapping shading appears reversed with respect to the incident light direction. The output array of corrected tangents can be used inplace of the original. Parameters ---------- tangents, normals, bitangents : array_like Input Nx3 arrays of triangle tangents, normals and bitangents. All arrays must have the same size. out : ndarray, optional Optional output array for tangents. If not specified, a new array of tangents will be allocated. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Array of tangents with handedness corrected. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type tangents = np.asarray(tangents, dtype=dtype) normals = np.asarray(normals, dtype=dtype) bitangents = np.asarray(bitangents, dtype=dtype) toReturn = np.zeros_like(tangents, dtype=dtype) if out is None else out toReturn[:, :] = tangents toReturn[dot(cross(normals, tangents, dtype=dtype), bitangents, dtype=dtype) < 0.0, :] *= -1.0 return toReturn
# ------------------------------------------------------------------------------ # Collision Detection, Interaction and Kinematics #
[docs]def fitBBox(points, dtype=None): """Fit an axis-aligned bounding box around points. This computes the minimum and maximum extents for a bounding box to completely enclose `points`. Keep in mind the the output in bounds are axis-aligned and may not optimally fits the points (i.e. fits the points with the minimum required volume). However, this should work well enough for applications such as visibility testing (see `~psychopy.tools.viewtools.volumeVisible` for more information.. Parameters ---------- points : array_like Nx3 or Nx4 array of points to fit the bounding box to. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Extents (mins, maxs) as a 2x3 array. See Also -------- computeBBoxCorners : Convert bounding box extents to corners. """ dtype = np.float64 if dtype is None else np.dtype(dtype).type points = np.asarray(points, dtype=dtype) extents = np.zeros((2, 3), dtype=dtype) extents[0, :] = (np.min(points[:, 0]), np.min(points[:, 1]), np.min(points[:, 2])) extents[1, :] = (np.max(points[:, 0]), np.max(points[:, 1]), np.max(points[:, 2])) return extents
[docs]def computeBBoxCorners(extents, dtype=None): """Get the corners of an axis-aligned bounding box. Parameters ---------- extents : array_like 2x3 array indicating the minimum and maximum extents of the bounding box. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray 8x4 array of points defining the corners of the bounding box. Examples -------- Compute the corner points of a bounding box:: minExtent = [-1, -1, -1] maxExtent = [1, 1, 1] corners = computeBBoxCorners([minExtent, maxExtent]) # [[ 1. 1. 1. 1.] # [-1. 1. 1. 1.] # [ 1. -1. 1. 1.] # [-1. -1. 1. 1.] # [ 1. 1. -1. 1.] # [-1. 1. -1. 1.] # [ 1. -1. -1. 1.] # [-1. -1. -1. 1.]] """ extents = np.asarray(extents, dtype=dtype) assert extents.shape == (2, 3,) corners = np.zeros((8, 4), dtype=dtype) idx = np.arange(0, 8) corners[:, 0] = np.where(idx[:] & 1, extents[0, 0], extents[1, 0]) corners[:, 1] = np.where(idx[:] & 2, extents[0, 1], extents[1, 1]) corners[:, 2] = np.where(idx[:] & 4, extents[0, 2], extents[1, 2]) corners[:, 3] = 1.0 return corners
[docs]def intersectRayPlane(rayOrig, rayDir, planeOrig, planeNormal, dtype=None): """Get the point which a ray intersects a plane. Parameters ---------- rayOrig : array_like Origin of the line in space [x, y, z]. rayDir : array_like Direction vector of the line [x, y, z]. planeOrig : array_like Origin of the plane to test [x, y, z]. planeNormal : array_like Normal vector of the plane [x, y, z]. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- tuple or None Position (`ndarray`) in space which the line intersects the plane and the distance the intersect occurs from the origin (`float`). `None` is returned if the line does not intersect the plane at a single point or at all. Examples -------- Find the point in the scene a ray intersects the plane:: # plane information planeOrigin = [0, 0, 0] planeNormal = [0, 0, 1] planeUpAxis = perp([0, 1, 0], planeNormal) # ray rayDir = [0, 0, -1] rayOrigin = [0, 0, 5] # get the intersect and distance in 3D world space pnt, dist = intersectRayPlane(rayOrigin, rayDir, planeOrigin, planeNormal) """ dtype = np.float64 if dtype is None else np.dtype(dtype).type # based off the method from GLM rayOrig = np.asarray(rayOrig, dtype=dtype) rayDir = np.asarray(rayDir, dtype=dtype) planeOrig = np.asarray(planeOrig, dtype=dtype) planeNormal = np.asarray(planeNormal, dtype=dtype) denom = dot(rayDir, planeNormal, dtype=dtype) if denom == 0.0: return None # distance to collision dist = dot((planeOrig - rayOrig), planeNormal, dtype=dtype) / denom intersect = dist * rayDir + rayOrig return intersect, dist
[docs]def intersectRaySphere(rayOrig, rayDir, sphereOrig=(0., 0., 0.), sphereRadius=1.0, dtype=None): """Calculate the points which a ray/line intersects a sphere (if any). Get the 3D coordinate of the point which the ray intersects the sphere and the distance to the point from `orig`. The nearest point is returned if the line intersects the sphere at multiple locations. All coordinates should be in world/scene units. Parameters ---------- rayOrig : array_like Origin of the ray in space [x, y, z]. rayDir : array_like Direction vector of the ray [x, y, z], should be normalized. sphereOrig : array_like Origin of the sphere to test [x, y, z]. sphereRadius : float Sphere radius to test in scene units. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- tuple Coordinate in world space of the intersection and distance in scene units from `orig`. Returns `None` if there is no intersection. """ # based off example from http://antongerdelan.net/opengl/raycasting.html dtype = np.float64 if dtype is None else np.dtype(dtype).type rayOrig = np.asarray(rayOrig, dtype=dtype) rayDir = np.asarray(rayDir, dtype=dtype) sphereOrig = np.asarray(sphereOrig, dtype=dtype) sphereRadius = np.asarray(sphereRadius, dtype=dtype) d = rayOrig - sphereOrig b = np.dot(rayDir, d) c = np.dot(d, d) - np.square(sphereRadius) b2mc = np.square(b) - c # determinant if b2mc < 0.0: # no roots, ray does not intersect sphere return None u = np.sqrt(b2mc) nearestDist = np.minimum(-b + u, -b - u) pos = (rayDir * nearestDist) + rayOrig return pos, nearestDist
[docs]def intersectRayAABB(rayOrig, rayDir, boundsOffset, boundsExtents, dtype=None): """Find the point a ray intersects an axis-aligned bounding box (AABB). Parameters ---------- rayOrig : array_like Origin of the ray in space [x, y, z]. rayDir : array_like Direction vector of the ray [x, y, z], should be normalized. boundsOffset : array_like Offset of the bounding box in the scene [x, y, z]. boundsExtents : array_like Minimum and maximum extents of the bounding box. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- tuple Coordinate in world space of the intersection and distance in scene units from `rayOrig`. Returns `None` if there is no intersection. Examples -------- Get the point on an axis-aligned bounding box that the cursor is over and place a 3D stimulus there. The eye location is defined by `RigidBodyPose` object `camera`:: # get the mouse position on-screen mx, my = mouse.getPos() # find the point which the ray intersects on the box result = intersectRayAABB( camera.pos, camera.transformNormal(win.coordToRay((mx, my))), myStim.pos, myStim.thePose.bounds.extents) # if the ray intersects, set the position of the cursor object to it if result is not None: cursorModel.thePose.pos = result[0] cursorModel.draw() # don't draw anything if there is no intersect Note that if the model is rotated, the bounding box may not be aligned anymore with the axes. Use `intersectRayOBB` if your model rotates. """ # based of the example provided here: # https://www.scratchapixel.com/lessons/3d-basic-rendering/ # minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection dtype = np.float64 if dtype is None else np.dtype(dtype).type rayOrig = np.asarray(rayOrig, dtype=dtype) rayDir = np.asarray(rayDir, dtype=dtype) boundsOffset = np.asarray(boundsOffset, dtype=dtype) extents = np.asarray(boundsExtents, dtype=dtype) + boundsOffset invDir = 1.0 / rayDir sign = np.zeros((3,), dtype=np.int) sign[invDir < 0.0] = 1 tmin = (extents[sign[0], 0] - rayOrig[0]) * invDir[0] tmax = (extents[1 - sign[0], 0] - rayOrig[0]) * invDir[0] tymin = (extents[sign[1], 1] - rayOrig[1]) * invDir[1] tymax = (extents[1 - sign[1], 1] - rayOrig[1]) * invDir[1] if tmin > tymax or tymin > tmax: return None if tymin > tmin: tmin = tymin if tymax < tmax: tmax = tymax tzmin = (extents[sign[2], 2] - rayOrig[2]) * invDir[2] tzmax = (extents[1 - sign[2], 2] - rayOrig[2]) * invDir[2] if tmin > tzmax or tzmin > tmax: return None if tzmin > tmin: tmin = tzmin if tzmax < tmax: tmax = tzmax if tmin < 0: if tmax < 0: return None return (rayDir * tmin) + rayOrig, tmin
[docs]def intersectRayOBB(rayOrig, rayDir, modelMatrix, boundsExtents, dtype=None): """Find the point a ray intersects an oriented bounding box (OBB). Parameters ---------- rayOrig : array_like Origin of the ray in space [x, y, z]. rayDir : array_like Direction vector of the ray [x, y, z], should be normalized. modelMatrix : array_like 4x4 model matrix of the object and bounding box. boundsExtents : array_like Minimum and maximum extents of the bounding box. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- tuple Coordinate in world space of the intersection and distance in scene units from `rayOrig`. Returns `None` if there is no intersection. Examples -------- Get the point on an oriented bounding box that the cursor is over and place a 3D stimulus there. The eye location is defined by `RigidBodyPose` object `camera`:: # get the mouse position on-screen mx, my = mouse.getPos() # find the point which the ray intersects on the box result = intersectRayOBB( camera.pos, camera.transformNormal(win.coordToRay((mx, my))), myStim.thePose.getModelMatrix(), myStim.thePose.bounds.extents) # if the ray intersects, set the position of the cursor object to it if result is not None: cursorModel.thePose.pos = result[0] cursorModel.draw() # don't draw anything if there is no intersect """ # based off algorithm: # http://www.opengl-tutorial.org/miscellaneous/clicking-on-objects/ # picking-with-custom-ray-obb-function/ dtype = np.float64 if dtype is None else np.dtype(dtype).type rayOrig = np.asarray(rayOrig, dtype=dtype) rayDir = np.asarray(rayDir, dtype=dtype) modelMatrix = np.asarray(modelMatrix, dtype=dtype) boundsOffset = np.asarray(modelMatrix[:3, 3], dtype=dtype) extents = np.asarray(boundsExtents, dtype=dtype) tmin = 0.0 tmax = np.finfo(dtype).max d = boundsOffset - rayOrig # solve intersects for each pair of planes along each axis for i in range(3): axis = modelMatrix[:3, i] e = np.dot(axis, d) f = np.dot(rayDir, axis) if np.fabs(f) > 1e-5: t1 = (e + extents[0, i]) / f t2 = (e + extents[1, i]) / f if t1 > t2: temp = t1 t1 = t2 t2 = temp if t2 < tmax: tmax = t2 if t1 > tmin: tmin = t1 if tmin > tmax: return None else: # very close to parallel with the face if -e + extents[0, i] > 0.0 or -e + extents[1, i] < 0.0: return None return (rayDir * tmin) + rayOrig, tmin
[docs]def intersectRayTriangle(rayOrig, rayDir, tri, dtype=None): """Get the intersection of a ray and triangle(s). This function can be used to achieve 'pixel-perfect' ray picking/casting on meshes defined with triangles. However, high-poly meshes may lead to performance issues. Parameters ---------- rayOrig : array_like Origin of the ray in space [x, y, z]. rayDir : array_like Direction vector of the ray [x, y, z], should be normalized. tri : array_like Triangle vertices as 2D (3x3) array [p0, p1, p2] where each vertex is a length 3 array [vx, xy, vz]. The input array can be 3D (Nx3x3) to specify multiple triangles. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- tuple Coordinate in world space of the intersection, distance in scene units from `rayOrig`, and the barycentric coordinates on the triangle [x, y]. Returns `None` if there is no intersection. """ # based off `intersectRayTriangle` from GLM (https://glm.g-truc.net) dtype = np.float64 if dtype is None else np.dtype(dtype).type rayOrig = np.asarray(rayOrig, dtype=dtype) rayDir = np.asarray(rayDir, dtype=dtype) triVerts = np.asarray(tri, dtype=dtype) edge1 = triVerts[1, :] - triVerts[0, :] edge2 = triVerts[2, :] - triVerts[0, :] baryPos = np.zeros((2,), dtype=dtype) p = np.cross(rayDir, edge2) det = np.dot(edge1, p) if det > np.finfo(dtype).eps: dist = rayOrig - triVerts[0, :] baryPos[0] = np.dot(dist, p) if baryPos[0] < 0.0 or baryPos[0] > det: return None ortho = np.cross(dist, edge1) baryPos[1] = np.dot(rayDir, ortho) if baryPos[1] < 0.0 or baryPos[0] + baryPos[1] > det: return None elif det < -np.finfo(dtype).eps: dist = rayOrig - triVerts[0, :] baryPos[0] = np.dot(dist, p) if baryPos[0] > 0.0 or baryPos[0] < det: return None ortho = np.cross(dist, edge1) baryPos[1] = np.dot(rayDir, ortho) if baryPos[1] > 0.0 or baryPos[0] + baryPos[1] < det: return None else: return None invDet = 1.0 / det dist = np.dot(edge2, ortho) * invDet baryPos *= invDet return (rayDir * dist) + rayOrig, dist, baryPos
[docs]def ortho3Dto2D(p, orig, normal, up, right=None, dtype=None): """Get the planar coordinates of an orthogonal projection of a 3D point onto a 2D plane. This function gets the nearest point on the plane which a 3D point falls on the plane. Parameters ---------- p : array_like Point to be projected on the plane. orig : array_like Origin of the plane to test [x, y, z]. normal : array_like Normal vector of the plane [x, y, z], must be normalized. up : array_like Normalized up (+Y) direction of the plane's coordinate system. Must be perpendicular to `normal`. right : array_like, optional Perpendicular right (+X) axis. If not provided, the axis will be computed via the cross product between `normal` and `up`. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Coordinates on the plane [X, Y] where the 3D point projects towards perpendicularly. Examples -------- This function can be used with :func:`intersectRayPlane` to find the location on the plane the ray intersects:: # plane information planeOrigin = [0, 0, 0] planeNormal = [0, 0, 1] # must be normalized planeUpAxis = perp([0, 1, 0], planeNormal) # must also be normalized # ray rayDir = [0, 0, -1] rayOrigin = [0, 0, 5] # get the intersect in 3D world space pnt = intersectRayPlane(rayOrigin, rayDir, planeOrigin, planeNormal) # get the 2D coordinates on the plane the intersect occurred planeX, planeY = ortho3Dto2D(pnt, planeOrigin, planeNormal, planeUpAxis) """ dtype = np.float64 if dtype is None else np.dtype(dtype).type p = np.asarray(p, dtype=dtype) orig = np.asarray(orig, dtype=dtype) normal = np.asarray(normal, dtype=dtype) up = np.asarray(up, dtype=dtype) toReturn = np.zeros((2,)) offset = p - orig if right is None: # derive X axis with cross product toReturn[0] = dot(offset, cross(normal, up, dtype=dtype), dtype=dtype) else: toReturn[0] = dot(offset, np.asarray(right, dtype=dtype), dtype=dtype) toReturn[1] = dot(offset, up) return toReturn
[docs]def articulate(boneVecs, boneOris, dtype=None): """Articulate an armature. This function is used for forward kinematics and posing by specifying a list of 'bones'. A bone has a length and orientation, where sequential bones are linked end-to-end. Returns the transformed origins of the bones in scene coordinates and their orientations. There are many applications for forward kinematics such as posing armatures and stimuli for display (eg. mocap data). Another application is for getting the location of the end effector of coordinate measuring hardware, where encoders measure the joint angles and the length of linking members are known. This can be used for computing pose from "Sword of Damocles"[1]_ like hardware or some other haptic input devices which the participant wears (eg. a glove that measures joint angles in the hand). The computed pose of the joints can be used to interact with virtual stimuli. Parameters ---------- boneVecs : array_like Bone lengths [x, y, z] as an Nx3 array. boneOris : array_like Orientation of the bones as quaternions in form [x, y, z, w], relative to the previous bone. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- tuple Array of bone origins and orientations. The first origin is root position which is always at [0, 0, 0]. Use :func:`transform` to reposition the armature, or create a transformation matrix and use `applyMatrix` to translate and rotate the whole armature into position. References ---------- .. [1] Sutherland, I. E. (1968). "A head-mounted three dimensional display". Proceedings of AFIPS 68, pp. 757-764 Examples -------- Compute the orientations and origins of segments of an arm:: # bone lengths boneLengths = [[0., 1., 0.], [0., 1., 0.], [0., 1., 0.]] # create quaternions for joints shoulder = mt.quatFromAxisAngle('-y', 45.0) elbow = mt.quatFromAxisAngle('+z', 45.0) wrist = mt.quatFromAxisAngle('+z', 45.0) # articulate the parts of the arm boxPos, boxOri = mt.articulate(pos, [shoulder, elbow, wrist]) # assign positions and orientations to 3D objects shoulderModel.thePose.posOri = (boxPos[0, :], boxOri[0, :]) elbowModel.thePose.posOri = (boxPos[1, :], boxOri[1, :]) wristModel.thePose.posOri = (boxPos[2, :], boxOri[2, :]) """ dtype = np.float64 if dtype is None else np.dtype(dtype).type boneVecs = np.asarray(boneVecs, dtype=dtype) boneOris = np.asarray(boneOris, dtype=dtype) jointOri = accumQuat(boneOris, dtype=dtype) # get joint orientations bonesRotated = applyQuat(jointOri, boneVecs, dtype=dtype) # rotate bones # accumulate bonesTranslated = np.asarray( tuple(itertools.accumulate(bonesRotated[:], lambda a, b: a + b)), dtype=dtype) bonesTranslated -= bonesTranslated[0, :] # offset root length return bonesTranslated, jointOri
# ------------------------------------------------------------------------------ # Quaternion Operations #
[docs]def slerp(q0, q1, t, shortest=True, out=None, dtype=None): """Spherical linear interpolation (SLERP) between two quaternions. The behaviour of this function depends on the types of arguments: * If `q0` and `q1` are both 1-D and `t` is scalar, the interpolation at `t` is returned. * If `q0` and `q1` are both 2-D Nx4 arrays and `t` is scalar, an Nx4 array is returned with each row containing the interpolation at `t` for each quaternion pair at matching row indices in `q0` and `q1`. Parameters ---------- q0 : array_like Initial quaternion in form [x, y, z, w] where w is real and x, y, z are imaginary components. q1 : array_like Final quaternion in form [x, y, z, w] where w is real and x, y, z are imaginary components. t : float Interpolation weight factor within interval 0.0 and 1.0. shortest : bool, optional Ensure interpolation occurs along the shortest arc along the 4-D hypersphere (default is `True`). out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Quaternion [x, y, z, w] at `t`. Examples -------- Interpolate between two orientations:: q0 = quatFromAxisAngle(90.0, degrees=True) q1 = quatFromAxisAngle(-90.0, degrees=True) # halfway between 90 and -90 is 0.0 or quaternion [0. 0. 0. 1.] qr = slerp(q0, q1, 0.5) Example of smooth rotation of an object with fixed angular velocity:: degPerSec = 10.0 # rotate a stimulus at 10 degrees per second # initial orientation, axis rotates in the Z direction qr = quatFromAxisAngle([0., 0., -1.], 0.0, degrees=True) # amount to rotate every second qv = quatFromAxisAngle([0., 0., -1.], degPerSec, degrees=True) # ---- within main experiment loop ---- # `frameTime` is the time elapsed in seconds from last `slerp`. qr = multQuat(qr, slerp((0., 0., 0., 1.), qv, degPerSec * frameTime)) _, angle = quatToAxisAngle(qr) # discard axis, only need angle # myStim is a GratingStim or anything with an 'ori' argument which # accepts angle in degrees myStim.ori = angle myStim.draw() """ # Implementation based on code found here: # https://en.wikipedia.org/wiki/Slerp # if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type q0 = normalize(q0, dtype=dtype) q1 = normalize(q1, dtype=dtype) assert q0.shape == q1.shape toReturn = np.zeros(q0.shape, dtype=dtype) if out is None else out toReturn.fill(0.0) t = dtype(t) q0, q1, qr = np.atleast_2d(q0, q1, toReturn) d = np.clip(np.sum(q0 * q1, axis=1), -1.0, 1.0) if shortest: d[d < 0.0] *= -1.0 q1[d < 0.0] *= -1.0 theta0 = np.arccos(d) theta = theta0 * t sinTheta = np.sin(theta) s1 = sinTheta / np.sin(theta0) s0 = np.cos(theta[:, np.newaxis]) - d[:, np.newaxis] * s1[:, np.newaxis] qr[:, :] = q0 * s0 qr[:, :] += q1 * s1[:, np.newaxis] qr[:, :] += 0.0 return toReturn
[docs]def quatToAxisAngle(q, degrees=True, dtype=None): """Convert a quaternion to `axis` and `angle` representation. This allows you to use quaternions to set the orientation of stimuli that have an `ori` property. Parameters ---------- q : tuple, list or ndarray of float Quaternion in form [x, y, z, w] where w is real and x, y, z are imaginary components. degrees : bool, optional Indicate `angle` is to be returned in degrees, otherwise `angle` will be returned in radians. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- tuple Axis and angle of quaternion in form ([ax, ay, az], angle). If `degrees` is `True`, the angle returned is in degrees, radians if `False`. Examples -------- Using a quaternion to rotate a stimulus a fixed angle each frame:: # initial orientation, axis rotates in the Z direction qr = quatFromAxisAngle([0., 0., -1.], 0.0, degrees=True) # rotation per-frame, here it's 0.1 degrees per frame qf = quatFromAxisAngle([0., 0., -1.], 0.1, degrees=True) # ---- within main experiment loop ---- # myStim is a GratingStim or anything with an 'ori' argument which # accepts angle in degrees qr = multQuat(qr, qf) # cumulative rotation _, angle = quatToAxisAngle(qr) # discard axis, only need angle myStim.ori = angle myStim.draw() """ dtype = np.float64 if dtype is None else np.dtype(dtype).type q = normalize(q, dtype=dtype) # returns ndarray v = np.sqrt(np.sum(np.square(q[:3]))) if np.count_nonzero(q[:3]): axis = q[:3] / v angle = dtype(2.0) * np.arctan2(v, q[3]) else: axis = np.zeros((3,), dtype=dtype) axis[0] = 1. angle = 0.0 axis += 0.0 return axis, np.degrees(angle) if degrees else angle
[docs]def quatFromAxisAngle(axis, angle, degrees=True, dtype=None): """Create a quaternion to represent a rotation about `axis` vector by `angle`. Parameters ---------- axis : tuple, list, ndarray or str Axis vector components or axis name. If a vector, input must be length 3 [x, y, z]. A string can be specified for rotations about world axes (eg. `'+x'`, `'-z'`, `'+y'`, etc.) angle : float Rotation angle in radians (or degrees if `degrees` is `True`. Rotations are right-handed about the specified `axis`. degrees : bool, optional Indicate `angle` is in degrees, otherwise `angle` will be treated as radians. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Quaternion [x, y, z, w]. Examples -------- Create a quaternion from specified `axis` and `angle`:: axis = [0., 0., -1.] # rotate about -Z axis angle = 90.0 # angle in degrees ori = quatFromAxisAngle(axis, angle, degrees=True) # using degrees! """ dtype = np.float64 if dtype is None else np.dtype(dtype).type toReturn = np.zeros((4,), dtype=dtype) if degrees: halfRad = np.radians(angle, dtype=dtype) / dtype(2.0) else: halfRad = np.dtype(dtype).type(angle) / dtype(2.0) try: axis = VEC_AXES[axis] if isinstance(axis, str) else axis except KeyError: raise ValueError( "Value of `axis` must be either '+X', '-X', '+Y', '-Y', '+Z' or " "'-Z' or length 3 vector.") axis = normalize(axis, dtype=dtype) if np.count_nonzero(axis) == 0: raise ValueError("Value for `axis` is zero-length.") np.multiply(axis, np.sin(halfRad), out=toReturn[:3]) toReturn[3] = np.cos(halfRad) toReturn += 0.0 # remove negative zeros return toReturn
[docs]def quatYawPitchRoll(q, degrees=True, out=None, dtype=None): """Get the yaw, pitch, and roll of a quaternion's orientation relative to the world -Z axis. You can multiply the quaternion by the inverse of some other one to make the returned values referenced to a local coordinate system. Parameters ---------- q : tuple, list or ndarray of float Quaternion in form [x, y, z, w] where w is real and x, y, z are imaginary components. degrees : bool, optional Indicate angles are to be returned in degrees, otherwise they will be returned in radians. out : ndarray Optional output array. Must have same `shape` and `dtype` as what is expected to be returned by this function of `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Yaw, pitch and roll [yaw, pitch, roll] of quaternion `q`. """ # based off code found here: # https://en.wikipedia.org/wiki/Conversion_between_quaternions_and_Euler_angles # Yields the same results as PsychXR's LibOVRPose.getYawPitchRoll method. dtype = np.float64 if dtype is None else np.dtype(dtype).type q = np.asarray(q, dtype=dtype) toReturn = np.zeros((3,), dtype=dtype) if out is None else out sinRcosP = 2.0 * (q[3] * q[0] + q[1] * q[2]) cosRcosP = 1.0 - 2.0 * (q[0] * q[0] + q[1] * q[1]) toReturn[0] = np.arctan2(sinRcosP, cosRcosP) sinp = 2.0 * (q[3] * q[1] - q[2] * q[0]) if np.fabs(sinp) >= 1.: toReturn[1] = np.copysign(np.pi / 2., sinp) else: toReturn[1] = np.arcsin(sinp) sinYcosP = 2.0 * (q[3] * q[2] + q[0] * q[1]) cosYcosP = 1.0 - 2.0 * (q[1] * q[1] + q[2] * q[2]) toReturn[2] = np.arctan2(sinYcosP, cosYcosP) if degrees: toReturn[:] = np.degrees(toReturn[:]) return toReturn
[docs]def quatMagnitude(q, squared=False, out=None, dtype=None): """Get the magnitude of a quaternion. A quaternion is normalized if its magnitude is 1. Parameters ---------- q : array_like Quaternion(s) in form [x, y, z, w] where w is real and x, y, z are imaginary components. squared : bool, optional If ``True`` return the squared magnitude. If you are just checking if a quaternion is normalized, the squared magnitude will suffice to avoid the square root operation. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- float or ndarray Magnitude of quaternion `q`. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type q = np.asarray(q, dtype=dtype) if q.ndim == 1: assert q.shape[0] == 4 if squared: toReturn = np.sum(np.square(q)) else: toReturn = np.sqrt(np.sum(np.square(q))) elif q.ndim == 2: assert q.shape[1] == 4 toReturn = np.zeros((q.shape[0],), dtype=dtype) if out is None else out if squared: toReturn[:] = np.sum(np.square(q), axis=1) else: toReturn[:] = np.sqrt(np.sum(np.square(q), axis=1)) else: raise ValueError("Input argument 'q' has incorrect dimensions.") return toReturn
[docs]def multQuat(q0, q1, out=None, dtype=None): """Multiply quaternion `q0` and `q1`. The orientation of the returned quaternion is the combination of the input quaternions. Parameters ---------- q0, q1 : array_like Quaternions to multiply in form [x, y, z, w] where w is real and x, y, z are imaginary components. If 2D (Nx4) arrays are specified, quaternions are multiplied row-wise between each array. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Combined orientations of `q0` amd `q1`. Notes ----- * Quaternions are normalized prior to multiplication. Examples -------- Combine the orientations of two quaternions:: a = quatFromAxisAngle([0, 0, -1], 45.0, degrees=True) b = quatFromAxisAngle([0, 0, -1], 90.0, degrees=True) c = multQuat(a, b) # rotates 135 degrees about -Z axis """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type q0 = normalize(q0, dtype=dtype) q1 = normalize(q1, dtype=dtype) assert q0.shape == q1.shape toReturn = np.zeros(q0.shape, dtype=dtype) if out is None else out toReturn.fill(0.0) # clear array q0, q1, qr = np.atleast_2d(q0, q1, toReturn) # multiply quaternions for each row of the operand arrays qr[:, :3] = np.cross(q0[:, :3], q1[:, :3], axis=1) qr[:, :3] += q0[:, :3] * np.expand_dims(q1[:, 3], axis=1) qr[:, :3] += q1[:, :3] * np.expand_dims(q0[:, 3], axis=1) qr[:, 3] = q0[:, 3] qr[:, 3] *= q1[:, 3] qr[:, 3] -= np.sum(np.multiply(q0[:, :3], q1[:, :3]), axis=1) # dot product qr += 0.0 return toReturn
[docs]def invertQuat(q, out=None, dtype=None): """Get tht multiplicative inverse of a quaternion. This gives a quaternion which rotates in the opposite direction with equal magnitude. Multiplying a quaternion by its inverse returns an identity quaternion as both orientations cancel out. Parameters ---------- q : ndarray, list, or tuple of float Quaternion to invert in form [x, y, z, w] where w is real and x, y, z are imaginary components. If `q` is 2D (Nx4), each row is treated as a separate quaternion and inverted. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Inverse of quaternion `q`. Examples -------- Show that multiplying a quaternion by its inverse returns an identity quaternion where [x=0, y=0, z=0, w=1]:: angle = 90.0 axis = [0., 0., -1.] q = quatFromAxisAngle(axis, angle, degrees=True) qinv = invertQuat(q) qr = multQuat(q, qinv) qi = np.array([0., 0., 0., 1.]) # identity quaternion print(np.allclose(qi, qr)) # True Notes ----- * Quaternions are normalized prior to inverting. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type q = normalize(q, dtype=dtype) toReturn = np.zeros(q.shape, dtype=dtype) if out is None else out qn, qinv = np.atleast_2d(q, toReturn) # 2d views # conjugate the quaternion qinv[:, :3] = -qn[:, :3] qinv[:, 3] = qn[:, 3] qinv /= np.sum(np.square(qn), axis=1)[:, np.newaxis] qinv += 0.0 # remove negative zeros return toReturn
[docs]def applyQuat(q, points, out=None, dtype=None): """Rotate points/coordinates using a quaternion. This is similar to using `applyMatrix` with a rotation matrix. However, it is computationally less intensive to use `applyQuat` if one only wishes to rotate points. Parameters ---------- q : array_like Quaternion to invert in form [x, y, z, w] where w is real and x, y, z are imaginary components. points : array_like 2D array of vectors or points to transform, where each row is a single point. Only the x, y, and z components (the first three columns) are rotated. Additional columns are copied. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Transformed points. Examples -------- Rotate points using a quaternion:: points = [[1., 0., 0.], [0., -1., 0.]] quat = quatFromAxisAngle(-90.0, [0., 0., -1.], degrees=True) pointsRotated = applyQuat(quat, points) # [[0. 1. 0.] # [1. 0. 0.]] Show that you get the same result as a rotation matrix:: axis = [0., 0., -1.] angle = -90.0 rotMat = rotationMatrix(axis, angle)[:3, :3] # rotation sub-matrix only rotQuat = quatFromAxisAngle(angle, axis, degrees=True) points = [[1., 0., 0.], [0., -1., 0.]] isClose = np.allclose(applyMatrix(rotMat, points), # True applyQuat(rotQuat, points)) Specifying an array to `q` where each row is a quaternion transforms points in corresponding rows of `points`:: points = [[1., 0., 0.], [0., -1., 0.]] quats = [quatFromAxisAngle(-90.0, [0., 0., -1.], degrees=True), quatFromAxisAngle(45.0, [0., 0., -1.], degrees=True)] applyQuat(quats, points) """ # based on 'quat_mul_vec3' implementation from linmath.h if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type qin = np.asarray(q, dtype=dtype) points = np.asarray(points, dtype=dtype) if out is not None: assert points.shape == out.shape toReturn = np.zeros(points.shape, dtype=dtype) if out is None else out pin, pout = np.atleast_2d(points, toReturn) pout[:, :] = pin[:, :] # copy values into output array if qin.ndim == 1: assert qin.shape[0] == 4 t = cross(qin[:3], pin[:, :3]) * dtype(2.0) u = cross(qin[:3], t) t *= qin[3] pout[:, :3] += t pout[:, :3] += u elif qin.ndim == 2: assert qin.shape[1] == 4 and qin.shape[0] == pin.shape[0] t = cross(qin[:, :3], pin[:, :3]) t *= dtype(2.0) u = cross(qin[:, :3], t) t *= np.expand_dims(qin[:, 3], axis=1) pout[:, :3] += t pout[:, :3] += u else: raise ValueError("Input arguments have invalid dimensions.") return toReturn
[docs]def accumQuat(qlist, out=None, dtype=None): """Accumulate quaternion rotations. Chain multiplies an Nx4 array of quaternions, accumulating their rotations. This function can be used for computing the orientation of joints in an armature for forward kinematics. The first quaternion is treated as the 'root' and the last is the orientation of the end effector. Parameters ---------- q : array_like Nx4 array of quaternions to accumulate, where each row is a quaternion. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. In this case, the same shape as `qlist`. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Nx4 array of quaternions. Examples -------- Get the orientation of joints in an armature if we know their relative angles:: shoulder = quatFromAxisAngle('-x', 45.0) # rotate shoulder down 45 deg elbow = quatFromAxisAngle('+x', 45.0) # rotate elbow up 45 deg wrist = quatFromAxisAngle('-x', 45.0) # rotate wrist down 45 deg finger = quatFromAxisAngle('+x', 0.0) # keep finger in-line with wrist armRotations = accumQuat([shoulder, elbow, wrist, finger]) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type qlist = np.asarray(qlist, dtype=dtype) qlist = np.atleast_2d(qlist) qr = np.zeros_like(qlist, dtype=dtype) if out is None else out qr[:, :] = tuple(itertools.accumulate( qlist[:], lambda a, b: multQuat(a, b, dtype=dtype))) return qr
[docs]def alignTo(v, t, out=None, dtype=None): """Compute a quaternion which rotates one vector to align with another. Parameters ---------- v : array_like Vector [x, y, z] to rotate. Can be Nx3, but must have the same shape as `t`. t : array_like Target [x, y, z] vector to align to. Can be Nx3, but must have the same shape as `v`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Quaternion which rotates `v` to `t`. Examples -------- Rotate some vectors to align with other vectors, inputs should be normalized:: vec = [[1, 0, 0], [0, 1, 0], [1, 0, 0]] targets = [[0, 1, 0], [0, -1, 0], [-1, 0, 0]] qr = alignTo(vec, targets) vecRotated = applyQuat(qr, vec) numpy.allclose(vecRotated, targets) # True Get matrix which orients vertices towards a point:: point = [5, 6, 7] vec = [0, 0, -1] # initial facing is -Z (forward in GL) targetVec = normalize(point - vec) qr = alignTo(vec, targetVec) # get rotation to align M = quatToMatrix(qr) # 4x4 transformation matrix """ # based off Quaternion::align from Quaternion.hpp from OpenMP if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type v = normalize(v, dtype=dtype) t = normalize(t, dtype=dtype) if out is None: if v.ndim == 1: toReturn = np.zeros((4,), dtype=dtype) else: toReturn = np.zeros((v.shape[0], 4), dtype=dtype) else: toReturn = out qr, v2d, t2d = np.atleast_2d(toReturn, v, t) b = bisector(v2d, t2d, norm=True, dtype=dtype) cosHalfAngle = dot(v2d, b, dtype=dtype) nonparallel = cosHalfAngle > 0.0 # rotation is not 180 degrees qr[nonparallel, :3] = cross(v2d[nonparallel], b[nonparallel], dtype=dtype) qr[nonparallel, 3] = cosHalfAngle[nonparallel] if np.alltrue(nonparallel): # don't bother handling special cases return toReturn + 0.0 # deal with cases where the vectors are facing exact opposite directions ry = np.logical_and(np.abs(v2d[:, 0]) >= np.abs(v2d[:, 1]), ~nonparallel) rx = np.logical_and(~ry, ~nonparallel) getLength = lambda x, y: np.sqrt(x * x + y * y) if not np.alltrue(rx): invLength = getLength(v2d[ry, 0], v2d[ry, 2]) invLength = np.where(invLength > 0.0, 1.0 / invLength, invLength) # avoid x / 0 qr[ry, 0] = -v2d[ry, 2] * invLength qr[ry, 2] = v2d[ry, 0] * invLength if not np.alltrue(ry): # skip if all the same edge case invLength = getLength(v2d[rx, 1], v2d[rx, 2]) invLength = np.where(invLength > 0.0, 1.0 / invLength, invLength) qr[rx, 1] = v2d[rx, 2] * invLength qr[rx, 2] = -v2d[rx, 1] * invLength return toReturn + 0.0
[docs]def matrixToQuat(m, out=None, dtype=None): """Convert a rotation matrix to a quaternion. Parameters ---------- m : array_like 3x3 rotation matrix (row-major). A 4x4 affine transformation matrix may be provided, assuming the top-left 3x3 sub-matrix is orthonormal and is a rotation group. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Rotation quaternion. Notes ----- * Depending on the input, returned quaternions may not be exactly the same as the one used to construct the rotation matrix (i.e. by calling `quatToMatrix`), typically when a large rotation angle is used. However, the returned quaternion should result in the same rotation when applied to points. Examples -------- Converting a rotation matrix from the OpenGL matrix stack to a quaternion:: glRotatef(45., -1, 0, 0) m = np.zeros((4, 4), dtype='float32') # store the matrix GL.glGetFloatv( GL.GL_MODELVIEW_MATRIX, m.ctypes.data_as(ctypes.POINTER(ctypes.c_float))) qr = matrixToQuat(m.T) # must be transposed Interpolation between two 4x4 transformation matrices:: interpWeight = 0.5 posStart = mStart[:3, 3] oriStart = matrixToQuat(mStart) posEnd = mEnd[:3, 3] oriEnd = matrixToQuat(mEnd) oriInterp = slerp(qStart, qEnd, interpWeight) posInterp = lerp(posStart, posEnd, interpWeight) mInterp = posOriToMatrix(posInterp, oriInterp) """ # based off example `Maths - Conversion Matrix to Quaternion` from # https://www.euclideanspace.com/ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type m = np.asarray(m, dtype=dtype) if m.shape == (4, 4,) or m.shape == (3, 4,): m = m[:3, :3] # keep only rotation group sub-matrix elif m.shape == (3, 3,): pass # fine, nop else: raise ValueError("Input matrix `m` must be 3x3 or 4x4.") toReturn = np.zeros((4,), dtype=dtype) if out is None else out tr = m[0, 0] + m[1, 1] + m[2, 2] if tr > 0.0: s = np.sqrt(tr + 1.0) * 2.0 toReturn[3] = dtype(0.25) * s toReturn[0] = (m[2, 1] - m[1, 2]) / s toReturn[1] = (m[0, 2] - m[2, 0]) / s toReturn[2] = (m[1, 0] - m[0, 1]) / s elif m[0, 0] > m[1, 1] and m[0, 0] > m[2, 2]: s = np.sqrt(dtype(1.0) + m[0, 0] - m[1, 1] - m[2, 2]) * dtype(2.0) toReturn[3] = (m[2, 1] - m[1, 2]) / s toReturn[0] = dtype(0.25) * s toReturn[1] = (m[0, 1] + m[1, 0]) / s toReturn[2] = (m[0, 2] + m[2, 0]) / s elif m[1, 1] > m[2, 2]: s = np.sqrt(dtype(1.0) + m[1, 1] - m[0, 0] - m[2, 2]) * dtype(2.0) toReturn[3] = (m[0, 2] - m[2, 0]) / s toReturn[0] = (m[0, 1] + m[1, 0]) / s toReturn[1] = dtype(0.25) * s toReturn[2] = (m[1, 2] + m[2, 1]) / s else: s = np.sqrt(dtype(1.0) + m[2, 2] - m[0, 0] - m[1, 1]) * dtype(2.0) toReturn[3] = (m[1, 0] - m[0, 1]) / s toReturn[0] = (m[0, 2] + m[2, 0]) / s toReturn[1] = (m[1, 2] + m[2, 1]) / s toReturn[2] = dtype(0.25) * s return toReturn
# ------------------------------------------------------------------------------ # Matrix Operations #
[docs]def quatToMatrix(q, out=None, dtype=None): """Create a 4x4 rotation matrix from a quaternion. Parameters ---------- q : tuple, list or ndarray of float Quaternion to convert in form [x, y, z, w] where w is real and x, y, z are imaginary components. out : ndarray or None Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray or None 4x4 rotation matrix in row-major order. Examples -------- Convert a quaternion to a rotation matrix:: point = [0., 1., 0., 1.] # 4-vector form [x, y, z, 1.0] ori = [0., 0., 0., 1.] rotMat = quatToMatrix(ori) # rotate 'point' using matrix multiplication newPoint = np.matmul(rotMat.T, point) # returns [-1., 0., 0., 1.] Rotate all points in an array (each row is a coordinate):: points = np.asarray([[0., 0., 0., 1.], [0., 1., 0., 1.], [1., 1., 0., 1.]]) newPoints = points.dot(rotMat) Notes ----- * Quaternions are normalized prior to conversion. """ # based off implementations from # https://github.com/glfw/glfw/blob/master/deps/linmath.h if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type R = np.zeros((4, 4,), dtype=dtype) else: dtype = np.dtype(out.dtype).type R = out R.fill(0.0) q = normalize(q, dtype=dtype) b, c, d, a = q[:] vsqr = np.square(q) u = dtype(2.0) R[0, 0] = vsqr[3] + vsqr[0] - vsqr[1] - vsqr[2] R[1, 0] = u * (b * c + a * d) R[2, 0] = u * (b * d - a * c) R[0, 1] = u * (b * c - a * d) R[1, 1] = vsqr[3] - vsqr[0] + vsqr[1] - vsqr[2] R[2, 1] = u * (c * d + a * b) R[0, 2] = u * (b * d + a * c) R[1, 2] = u * (c * d - a * b) R[2, 2] = vsqr[3] - vsqr[0] - vsqr[1] + vsqr[2] R[3, 3] = dtype(1.0) R[:, :] += 0.0 # remove negative zeros return R
[docs]def scaleMatrix(s, out=None, dtype=None): """Create a scaling matrix. The resulting matrix is the same as a generated by a `glScale` call. Parameters ---------- s : array_like, float or int Scaling factor(s). If `s` is scalar (float), scaling will be uniform. Providing a vector of scaling values [sx, sy, sz] will result in an anisotropic scaling matrix if any of the values differ. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray 4x4 scaling matrix in row-major order. """ # from glScale if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type S = np.zeros((4, 4,), dtype=dtype) else: dtype = np.dtype(out.dtype).type S = out S.fill(0.0) if isinstance(s, (float, int,)): S[0, 0] = S[1, 1] = S[2, 2] = dtype(s) else: S[0, 0] = dtype(s[0]) S[1, 1] = dtype(s[1]) S[2, 2] = dtype(s[2]) S[3, 3] = 1.0 return S
[docs]def rotationMatrix(angle, axis=(0., 0., -1.), out=None, dtype=None): """Create a rotation matrix. The resulting matrix will rotate points about `axis` by `angle`. The resulting matrix is similar to that produced by a `glRotate` call. Parameters ---------- angle : float Rotation angle in degrees. axis : array_like or str Axis vector components or axis name. If a vector, input must be length 3. A string can be specified for rotations about world axes (eg. `'+x'`, `'-z'`, `'+y'`, etc.) out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray 4x4 scaling matrix in row-major order. Will be the same array as `out` if specified, if not, a new array will be allocated. Notes ----- * Vector `axis` is normalized before creating the matrix. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type R = np.zeros((4, 4,), dtype=dtype) else: dtype = np.dtype(out.dtype).type R = out R.fill(0.0) try: axis = VEC_AXES[axis] if isinstance(axis, str) else axis except KeyError: raise ValueError( "Value of `axis` must be either '+x', '-x', '+y', '-x', '+z' or " "'-z' or length 3 vector.") axis = normalize(axis, dtype=dtype) if np.count_nonzero(axis) == 0: raise ValueError("Value for `axis` is zero-length.") angle = np.radians(angle, dtype=dtype) c = np.cos(angle, dtype=dtype) s = np.sin(angle, dtype=dtype) xs, ys, zs = axis * s x2, y2, z2 = np.square(axis) # type inferred by input x, y, z = axis cd = dtype(1.0) - c R[0, 0] = x2 * cd + c R[0, 1] = x * y * cd - zs R[0, 2] = x * z * cd + ys R[1, 0] = y * x * cd + zs R[1, 1] = y2 * cd + c R[1, 2] = y * z * cd - xs R[2, 0] = x * z * cd - ys R[2, 1] = y * z * cd + xs R[2, 2] = z2 * cd + c R[3, 3] = dtype(1.0) R[:, :] += 0.0 # remove negative zeros return R
[docs]def translationMatrix(t, out=None, dtype=None): """Create a translation matrix. The resulting matrix is the same as generated by a `glTranslate` call. Parameters ---------- t : ndarray, tuple, or list of float Translation vector [tx, ty, tz]. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray 4x4 translation matrix in row-major order. Will be the same array as `out` if specified, if not, a new array will be allocated. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type T = np.identity(4, dtype=dtype) else: dtype = np.dtype(out.dtype).type T = out T.fill(0.0) np.fill_diagonal(T, 1.0) T[:3, 3] = np.asarray(t, dtype=dtype) return T
[docs]def invertMatrix(m, out=None, dtype=None): """Invert a square matrix. Parameters ---------- m : array_like Square matrix to invert. Inputs can be 4x4, 3x3 or 2x2. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Matrix which is the inverse of `m` """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = out.dtype m = np.asarray(m, dtype=dtype) # input as array toReturn = np.empty_like(m, dtype=dtype) if out is None else out toReturn.fill(0.0) if m.shape == (4, 4,): # Special handling of 4x4 matrices, if affine and orthogonal # (homogeneous), simply transpose the matrix rather than doing a full # invert. if isOrthogonal(m[:3, :3]) and isAffine(m): rg = m[:3, :3] toReturn[:3, :3] = rg.T toReturn[:3, 3] = -m[:3, 3].dot(rg) #toReturn[0, 3] = \ # -(m[0, 0] * m[0, 3] + m[1, 0] * m[1, 3] + m[2, 0] * m[2, 3]) #toReturn[1, 3] = \ # -(m[0, 1] * m[0, 3] + m[1, 1] * m[1, 3] + m[2, 1] * m[2, 3]) #toReturn[2, 3] = \ # -(m[0, 2] * m[0, 3] + m[1, 2] * m[1, 3] + m[2, 2] * m[2, 3]) toReturn[3, 3] = 1.0 else: toReturn[:, :] = np.linalg.inv(m) elif m.shape[0] == m.shape[1]: # square, other than 4x4 toReturn[:, :] = np.linalg.inv(m) if not isOrthogonal(m) else m.T else: toReturn[:, :] = np.linalg.inv(m) return toReturn
[docs]def multMatrix(matrices, reverse=False, out=None, dtype=None): """Chain multiplication of two or more matrices. Multiply a sequence of matrices together, reducing to a single product matrix. For instance, specifying `matrices` the sequence of matrices (A, B, C, D) will return the product (((AB)C)D). If `reverse=True`, the product will be (A(B(CD))). Alternatively, a 3D array can be specified to `matrices` as a stack, where an index along axis 0 references a 2D slice storing matrix values. The product of the matrices along the axis will be returned. This is a bit more efficient than specifying separate matrices in a sequence, but the difference is negligible when only a few matrices are being multiplied. Parameters ---------- matrices : list, tuple or ndarray Sequence or stack of matrices to multiply. All matrices must have the same dimensions. reverse : bool, optional Multiply matrices right-to-left. This is useful when dealing with transformation matrices, where the order of operations for transforms will appear the same as the order the matrices are specified. Default is 'False'. When `True`, this function behaves similarly to :func:`concatenate`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Matrix product. Notes ----- * You may use `numpy.matmul` when dealing with only two matrices instead of `multMatrix`. * If a single matrix is specified, the returned product will have the same values. Examples -------- Chain multiplication of SRT matrices:: translate = translationMatrix((0.035, 0, -0.5)) rotate = rotationMatrix(90.0, (0, 1, 0)) scale = scaleMatrix(2.0) SRT = multMatrix((translate, rotate, scale)) Same as above, but matrices are in a 3x4x4 array:: matStack = np.array((translate, rotate, scale)) # or ... # matStack = np.zeros((3, 4, 4)) # matStack[0, :, :] = translate # matStack[1, :, :] = rotate # matStack[2, :, :] = scale SRT = multMatrix(matStack) Using `reverse=True` allows you to specify transformation matrices in the order which they will be applied:: SRT = multMatrix(np.array((scale, rotate, translate)), reverse=True) """ # convert matrix types dtype = np.float64 if dtype is None else np.dtype(dtype).type matrices = np.asarray(matrices, dtype=dtype) # convert to array matrices = np.atleast_3d(matrices) prod = functools.reduce( np.matmul, matrices[:] if not reverse else matrices[::-1]) if out is not None: toReturn = out toReturn[:, :] = prod else: toReturn = prod return toReturn
[docs]def concatenate(matrices, out=None, dtype=None): """Concatenate matrix transformations. Chain multiply matrices describing transform operations into a single matrix product, that when applied, transforms points and vectors with each operation in the order they're specified. Parameters ---------- matrices : list or tuple List of matrices to concatenate. All matrices must all have the same size, usually 4x4 or 3x3. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Matrix product. See Also -------- * multMatrix : Chain multiplication of matrices. Notes ----- * This function should only be used for combining transformation matrices. Use `multMatrix` for general matrix chain multiplication. Examples -------- Create an SRT (scale, rotate, and translate) matrix to convert model-space coordinates to world-space:: S = scaleMatrix([2.0, 2.0, 2.0]) # scale model 2x R = rotationMatrix(-90., [0., 0., -1]) # rotate -90 about -Z axis T = translationMatrix([0., 0., -5.]) # translate point 5 units away # product matrix when applied to points will scale, rotate and transform # in that order. SRT = concatenate([S, R, T]) # transform a point in model-space coordinates to world-space pointModel = np.array([0., 1., 0., 1.]) pointWorld = np.matmul(SRT, pointModel.T) # point in WCS # ... or ... pointWorld = matrixApply(SRT, pointModel) Create a model-view matrix from a world-space pose represented by an orientation (quaternion) and position (vector). The resulting matrix will transform model-space coordinates to eye-space:: # eye pose as quaternion and vector stimOri = quatFromAxisAngle([0., 0., -1.], -45.0) stimPos = [0., 1.5, -5.] # create model matrix R = quatToMatrix(stimOri) T = translationMatrix(stimPos) M = concatenate(R, T) # model matrix # create a view matrix, can also be represented as 'pos' and 'ori' eyePos = [0., 1.5, 0.] eyeFwd = [0., 0., -1.] eyeUp = [0., 1., 0.] V = lookAt(eyePos, eyeFwd, eyeUp) # from viewtools # modelview matrix MV = concatenate([M, V]) You can put the created matrix in the OpenGL matrix stack as shown below. Note that the matrix must have a 32-bit floating-point data type and needs to be loaded transposed since OpenGL takes matrices in column-major order:: GL.glMatrixMode(GL.GL_MODELVIEW) # pyglet MV = np.asarray(MV, dtype='float32') # must be 32-bit float! ptrMV = MV.ctypes.data_as(ctypes.POINTER(ctypes.c_float)) GL.glLoadTransposeMatrixf(ptrMV) # PyOpenGL MV = np.asarray(MV, dtype='float32') GL.glLoadTransposeMatrixf(MV) Furthermore, you can convert a point from model-space to homogeneous clip-space by concatenating the projection, view, and model matrices:: # compute projection matrix, functions here are from 'viewtools' screenWidth = 0.52 screenAspect = w / h scrDistance = 0.55 frustum = computeFrustum(screenWidth, screenAspect, scrDistance) P = perspectiveProjectionMatrix(*frustum) # multiply model-space points by MVP to convert them to clip-space MVP = concatenate([M, V, P]) pointModel = np.array([0., 1., 0., 1.]) pointClipSpace = np.matmul(MVP, pointModel.T) """ return multMatrix(matrices, reverse=True, out=out, dtype=dtype)
[docs]def matrixFromEulerAngles(rx, ry, rz, degrees=True, out=None, dtype=None): """Construct a 4x4 rotation matrix from Euler angles. Rotations are combined by first rotating about the X axis, then Y, and finally Z. Parameters ---------- rx, ry, rz : float Rotation angles (pitch, yaw, and roll). degrees : bool, optional Rotation angles are specified in degrees. If `False`, they will be assumed as radians. Default is `True`. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray 4x4 rotation matrix. Examples -------- Demonstration of how a combination of axis-angle rotations is equivalent to a single call of `matrixFromEulerAngles`:: m1 = matrixFromEulerAngles(90., 45., 135.)) # construct rotation matrix from 3 orthogonal rotations rx = rotationMatrix(90., (1, 0, 0)) # x-axis ry = rotationMatrix(45., (0, 1, 0)) # y-axis rz = rotationMatrix(135., (0, 0, 1)) # z-axis m2 = concatenate([rz, ry, rx]) # note the order print(numpy.allclose(m1, m2)) # True Not only does `matrixFromEulerAngles` require less code, it also is considerably more efficient than constructing and multiplying multiple matrices. """ # from http://www.j3d.org/matrix_faq/matrfaq_latest.html if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type toReturn = np.zeros((4, 4,), dtype=dtype) else: dtype = np.dtype(dtype).type toReturn = out toReturn.fill(0.0) angles = np.asarray([rx, ry, rz], dtype=dtype) if degrees: angles = np.radians(angles) a, c, e = np.cos(angles) b, d, f = np.sin(angles) ad = a * d bd = b * d toReturn[0, 0] = c * e toReturn[0, 1] = -c * f toReturn[0, 2] = d toReturn[1, 0] = bd * e + a * f toReturn[1, 1] = -bd * f + a * e toReturn[1, 2] = -b * c toReturn[2, 0] = -ad * e + b * f toReturn[2, 1] = ad * f + b * e toReturn[2, 2] = a * c toReturn[3, 3] = 1.0 return toReturn
[docs]def isOrthogonal(m): """Check if a square matrix is orthogonal. If a matrix is orthogonal, its columns form an orthonormal basis and is non-singular. An orthogonal matrix is invertible by simply taking the transpose of the matrix. Parameters ---------- m : array_like Square matrix, either 2x2, 3x3 or 4x4. Returns ------- bool `True` if the matrix is orthogonal. """ if not isinstance(m, (np.ndarray,)): m = np.asarray(m) assert 2 <= m.shape[0] <= 4 # 2x2 to 4x4 assert m.shape[0] == m.shape[1] # must be square dtype = np.dtype(m.dtype).type return np.allclose(np.matmul(m.T, m, dtype=dtype), np.identity(m.shape[0], dtype))
[docs]def isAffine(m): """Check if a 4x4 square matrix describes an affine transformation. Parameters ---------- m : array_like 4x4 transformation matrix. Returns ------- bool `True` if the matrix is affine. """ assert m.shape[0] == m.shape[1] == 4 if not isinstance(m, (np.ndarray,)): m = np.asarray(m) dtype = np.dtype(m.dtype).type eps = np.finfo(dtype).eps return np.all(m[3, :3] < eps) and (dtype(1.0) - m[3, 3]) < eps
[docs]def applyMatrix(m, points, out=None, dtype=None): """Apply a matrix over a 2D array of points. This function behaves similarly to the following `Numpy` statement:: points[:, :] = points.dot(m.T) Transformation matrices specified to `m` must have dimensions 4x4, 3x4, 3x3 or 2x2. With the exception of 4x4 matrices, input `points` must have the same number of columns as the matrix has rows. 4x4 matrices can be used to transform both Nx4 and Nx3 arrays. Parameters ---------- m : array_like Matrix with dimensions 2x2, 3x3, 3x4 or 4x4. points : array_like 2D array of points/coordinates to transform. Each row should have length appropriate for the matrix being used. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Transformed coordinates. Notes ----- * Input (`points`) and output (`out`) arrays cannot be the same instance for this function. * In the case of 4x4 input matrices, this function performs optimizations based on whether the input matrix is affine, greatly improving performance when working with Nx3 arrays. Examples -------- Construct a matrix and transform a point:: # identity 3x3 matrix for this example M = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]] pnt = [1.0, 0.0, 0.0] pntNew = applyMatrix(M, pnt) Construct an SRT matrix (scale, rotate, transform) and transform an array of points:: S = scaleMatrix([5.0, 5.0, 5.0]) # scale 5x R = rotationMatrix(180., [0., 0., -1]) # rotate 180 degrees T = translationMatrix([0., 1.5, -3.]) # translate point up and away M = concatenate([S, R, T]) # create transform matrix # points to transform points = np.array([[0., 1., 0., 1.], [-1., 0., 0., 1.]]) # [x, y, z, w] newPoints = applyMatrix(M, points) # apply the transformation Convert CIE-XYZ colors to sRGB:: sRGBMatrix = [[3.2404542, -1.5371385, -0.4985314], [-0.969266, 1.8760108, 0.041556 ], [0.0556434, -0.2040259, 1.0572252]] colorsRGB = applyMatrix(sRGBMatrix, colorsXYZ) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(out.dtype).type m = np.asarray(m, dtype=dtype) points = np.asarray(points, dtype=dtype) if out is None: toReturn = np.zeros_like(points, dtype=dtype) else: if id(out) == id(points): raise ValueError('Output array cannot be same as input.') toReturn = out pout, p = np.atleast_2d(toReturn, points) if m.shape[0] == m.shape[1] == 4: # 4x4 matrix if pout.shape[1] == 3: # Nx3 pout[:, :] = p.dot(m[:3, :3].T) pout += m[:3, 3] # find `rcpW` as suggested in OpenXR's xr_linear.h header # reciprocal of `w` if the matrix is not orthonormal if not isAffine(m): rcpW = 1.0 / (m[3, 0] * p[:, 0] + m[3, 1] * p[:, 1] + m[3, 2] * p[:, 2] + m[3, 3]) pout *= rcpW[:, np.newaxis] elif pout.shape[1] == 4: # Nx4 pout[:, :] = p.dot(m.T) else: raise ValueError( 'Input array dimensions invalid. Should be Nx3 or Nx4 when ' 'input matrix is 4x4.') elif m.shape[0] == 3 and m.shape[1] == 4: # 3x4 matrix if pout.shape[1] == 3: # Nx3 pout[:, :] = p.dot(m[:3, :3].T) pout += m[:3, 3] else: raise ValueError( 'Input array dimensions invalid. Should be Nx3 when input ' 'matrix is 3x4.') elif m.shape[0] == m.shape[1] == 3: # 3x3 matrix, e.g colors if pout.shape[1] == 3: # Nx3 pout[:, :] = p.dot(m.T) else: raise ValueError( 'Input array dimensions invalid. Should be Nx3 when ' 'input matrix is 3x3.') elif m.shape[0] == m.shape[1] == pout.shape[1] == 2: # 2x2 matrix if pout.shape[1] == 2: # Nx2 pout[:, :] = p.dot(m.T) else: raise ValueError( 'Input array dimensions invalid. Should be Nx2 when ' 'input matrix is 2x2.') else: raise ValueError( 'Only a square matrix with dimensions 2, 3 or 4 can be used.') return toReturn
[docs]def posOriToMatrix(pos, ori, out=None, dtype=None): """Convert a rigid body pose to a 4x4 transformation matrix. A pose is represented by a position coordinate `pos` and orientation quaternion `ori`. Parameters ---------- pos : ndarray, tuple, or list of float Position vector [x, y, z]. ori : tuple, list or ndarray of float Orientation quaternion in form [x, y, z, w] where w is real and x, y, z are imaginary components. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray 4x4 transformation matrix. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type toReturn = np.zeros((4, 4,), dtype=dtype) else: dtype = np.dtype(dtype).type toReturn = out transMat = translationMatrix(pos, dtype=dtype) rotMat = quatToMatrix(ori, dtype=dtype) return np.matmul(transMat, rotMat, out=toReturn)
[docs]def transform(pos, ori, points, out=None, dtype=None): """Transform points using a position and orientation. Points are rotated then translated. Parameters ---------- pos : array_like Position vector in form [x, y, z] or [x, y, z, 1]. ori : array_like Orientation quaternion in form [x, y, z, w] where w is real and x, y, z are imaginary components. points : array_like Point(s) [x, y, z] to transform. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Transformed points. Examples -------- Transform points by a position coordinate and orientation quaternion:: # rigid body pose ori = quatFromAxisAngle([0., 0., -1.], 90.0, degrees=True) pos = [0., 1.5, -3.] # points to transform points = np.array([[0., 1., 0., 1.], [-1., 0., 0., 1.]]) # [x, y, z, 1] outPoints = np.zeros_like(points) # output array transform(pos, ori, points, out=outPoints) # do the transformation You can get the same results as the previous example using a matrix by doing the following:: R = rotationMatrix(90., [0., 0., -1]) T = translationMatrix([0., 1.5, -3.]) M = concatenate([R, T]) applyMatrix(M, points, out=outPoints) If you are defining transformations with quaternions and coordinates, you can skip the costly matrix creation process by using `transform`. Notes ----- * In performance tests, `applyMatrix` is noticeably faster than `transform` for very large arrays, however this is only true if you are applying the same transformation to all points. * If the input arrays for `points` or `pos` is Nx4, the last column is ignored. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(dtype).type pos = np.asarray(pos, dtype=dtype) ori = np.asarray(ori, dtype=dtype) points = np.asarray(points, dtype=dtype) if out is None: toReturn = np.zeros_like(points, dtype=dtype) else: if out.shape != points.shape: raise ValueError( "Array 'out' and 'points' do not have matching shapes.") toReturn = out pout, points, pos2d = np.atleast_2d(toReturn, points, pos) # create 2d views # apply rotation applyQuat(ori, points, out=pout) # apply translation pout[:, 0] += pos2d[:, 0] pout[:, 1] += pos2d[:, 1] pout[:, 2] += pos2d[:, 2] return toReturn
[docs]def scale(sf, points, out=None, dtype=None): """Scale points by a factor. This is useful for converting points between units, and to stretch or compress points along a given axis. Scaling can be uniform which the same factor is applied along all axes, or anisotropic along specific axes. Parameters ---------- sf : array_like or float Scaling factor. If scalar, all points will be scaled uniformly by that factor. If a vector, scaling will be anisotropic along an axis. points : array_like Point(s) [x, y, z] to scale. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Scaled points. Examples -------- Apply uniform scaling to points, here we scale to convert points in centimeters to meters:: CM_TO_METERS = 1.0 / 100.0 pointsCM = [[1, 2, 3], [4, 5, 6], [-1, 1, 0]] pointsM = scale(CM_TO_METERS, pointsCM) Anisotropic scaling along the X and Y axis:: pointsM = scale((SCALE_FACTOR_X, SCALE_FACTOR_Y), pointsCM) Scale only on the X axis:: pointsM = scale((SCALE_FACTOR_X,), pointsCM) Apply scaling on the Z axis only:: pointsM = scale((1.0, 1.0, SCALE_FACTOR_Z), pointsCM) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(dtype).type points = np.asarray(points, dtype=dtype) toReturn = np.zeros_like(points, dtype=dtype) if out is None else out toReturn, points = np.atleast_2d(toReturn, points) # create 2d views # uniform scaling if isinstance(sf, (float, int)): toReturn[:, :] = points * sf elif isinstance(sf, (list, tuple, np.ndarray)): # anisotropic sf = np.asarray(sf, dtype=dtype) sfLen = len(sf) if sfLen <= 3: toReturn[:, :] = points toReturn[:, :len(sf)] *= sf else: raise ValueError("Scale factor array must have length <= 3.") return toReturn
[docs]def normalMatrix(modelMatrix, out=None, dtype=None): """Get the normal matrix from a model matrix. Parameters ---------- modelMatrix : array_like 4x4 homogeneous model matrix. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Normal matrix. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(dtype).type modelMatrix = np.asarray(modelMatrix, dtype=dtype) toReturn = np.zeros((4, 4), dtype=dtype) if out is None else out toReturn[:, :] = np.linalg.inv(modelMatrix).T return toReturn
[docs]def forwardProject(objPos, modelView, proj, viewport=None, out=None, dtype=None): """Project a point in a scene to a window coordinate. This function is similar to `gluProject` and can be used to find the window coordinate which a point projects to. Parameters ---------- objPos : array_like Object coordinates (x, y, z). If an Nx3 array of coordinates is specified, where each row contains a window coordinate this function will return an array of projected coordinates with the same size. modelView : array_like 4x4 combined model and view matrix for returned value to be object coordinates. Specify only the view matrix for a coordinate in the scene. proj : array_like 4x4 projection matrix used for rendering. viewport : array_like Viewport rectangle for the window [x, y, w, h]. If not specified, the returned values will be in normalized device coordinates. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Normalized device or viewport coordinates [x, y, z] of the point. The `z` component is similar to the depth buffer value for the object point. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(dtype).type toReturn = np.zeros_like(objPos, dtype=dtype) if out is None else out winCoord, objPos = np.atleast_2d(toReturn, objPos) # transformation matrix mvp = np.matmul(proj, modelView) # must have `w` for this one if objPos.shape[1] == 3: temp = np.zeros((objPos.shape[1], 4), dtype=dtype) temp[:, :3] = objPos objPos = temp # transform the points objNorm = applyMatrix(mvp, objPos, dtype=dtype) if viewport is not None: # if we have a viewport, transform it objNorm[:, :] += 1.0 winCoord[:, 0] = viewport[0] + viewport[2] * objNorm[:, 0] winCoord[:, 1] = viewport[1] + viewport[3] * objNorm[:, 1] winCoord[:, 2] = objNorm[:, 2] winCoord[:, :] /= 2.0 else: # already in NDC winCoord[:, :] = objNorm return toReturn # ref to winCoord
[docs]def reverseProject(winPos, modelView, proj, viewport=None, out=None, dtype=None): """Unproject window coordinates into object or scene coordinates. This function works like `gluUnProject` and can be used to find to an object or scene coordinate at the point on-screen (mouse coordinate or pixel). The coordinate can then be used to create a direction vector from the viewer's eye location. Another use of this function is to convert depth buffer samples to object or scene coordinates. This is the inverse operation of :func:`forwardProject`. Parameters ---------- winPos : array_like Window coordinates (x, y, z). If `viewport` is not specified, these should be normalized device coordinates. If an Nx3 array of coordinates is specified, where each row contains a window coordinate this function will return an array of unprojected coordinates with the same size. Usually, you only need to specify the `x` and `y` coordinate, leaving `z` as zero. However, you can specify `z` if sampling from a depth map or buffer to convert a depth sample to an actual location. modelView : array_like 4x4 combined model and view matrix for returned value to be object coordinates. Specify only the view matrix for a coordinate in the scene. proj : array_like 4x4 projection matrix used for rendering. viewport : array_like Viewport rectangle for the window [x, y, w, h]. Do not specify one if `winPos` is in already in normalized device coordinates. out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Object or scene coordinates. """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(dtype).type toReturn = np.zeros_like(winPos, dtype=dtype) if out is None else out objCoord, winPos = np.atleast_2d(toReturn, winPos) # get inverse of model and projection matrix invMVP = np.linalg.inv(np.matmul(proj, modelView)) if viewport is not None: # if we have a viewport, we need to transform to NDC first objCoord[:, 0] = ((2 * winPos[:, 0] - viewport[0]) / viewport[2]) objCoord[:, 1] = ((2 * winPos[:, 1] - viewport[1]) / viewport[3]) objCoord[:, 2] = 2 * winPos[:, 2] objCoord -= 1 objCoord[:, :] = applyMatrix(invMVP, objCoord, dtype=dtype) else: # already in NDC, just apply objCoord[:, :] = applyMatrix(invMVP, winPos, dtype=dtype) return toReturn # ref to objCoord
# ------------------------------------------------------------------------------ # Misc. Math Functions #
[docs]def zeroFix(a, inplace=False, threshold=None): """Fix zeros in an array. This function truncates very small numbers in an array to zero and removes any negative zeros. Parameters ---------- a : ndarray Input array, must be a Numpy array. inplace : bool Fix an array inplace. If `True`, the input array will be modified, otherwise a new array will be returned with same `dtype` and shape with the fixed values. threshold : float or None Threshold for truncation. If `None`, the machine epsilon value for the input array `dtype` will be used. You can specify a custom threshold as a float. Returns ------- ndarray Output array with zeros fixed. """ toReturn = np.copy(a) if not inplace else a toReturn += 0.0 # remove negative zeros threshold = np.finfo(a.dtype).eps if threshold is None else float(threshold) toReturn[np.abs(toReturn) < threshold] = 0.0 # make zero return toReturn
[docs]def lensCorrection(xys, coefK=(1.0,), distCenter=(0., 0.), out=None, dtype=None): """Lens correction (or distortion) using the division model with even polynomial terms. Calculate new vertex positions or texture coordinates to apply radial warping, such as 'pincushion' and 'barrel' distortion. This is to compensate for optical distortion introduced by lenses placed in the optical path of the viewer and the display (such as in an HMD). See references[1]_ for implementation details. Parameters ---------- xys : array_like Nx2 list of vertex positions or texture coordinates to distort. Works correctly only if input values range between -1.0 and 1.0. coefK : array_like or float Distortion coefficients K_n. Specifying multiple values will add more polynomial terms to the distortion formula. Positive values will produce 'barrel' distortion, whereas negative will produce 'pincushion' distortion. In most cases, two or three coefficients are adequate, depending on the degree of distortion. distCenter : array_like, optional X and Y coordinate of the distortion center (eg. (0.2, -0.4)). out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Array of distorted vertices. Notes ----- * At this time tangential distortion (i.e. due to a slant in the display) cannot be corrected for. References ---------- .. [1] Fitzgibbon, W. (2001). Simultaneous linear estimation of multiple view geometry and lens distortion. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. Examples -------- Creating a lens correction mesh with barrel distortion (eg. for HMDs):: vertices, textureCoords, normals, faces = gltools.createMeshGrid( subdiv=11, tessMode='center') # recompute vertex positions vertices[:, :2] = mt.lensCorrection(vertices[:, :2], coefK=(5., 5.)) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(dtype).type if isinstance(coefK, (float, int,)): coefK = (coefK,) xys = np.asarray(xys, dtype=dtype) coefK = np.asarray(coefK, dtype=dtype) d_minus_c = xys - np.asarray(distCenter, dtype=dtype) r = np.power(length(d_minus_c, dtype=dtype)[:, np.newaxis], np.arange(len(coefK), dtype=dtype) * 2. + 2.) toReturn = np.zeros_like(xys, dtype=dtype) if out is None else out denom = dtype(1.0) + dot(coefK, r, dtype=dtype) toReturn[:, :] = xys + (d_minus_c / denom[:, np.newaxis]) return toReturn
[docs]def lensCorrectionSpherical(xys, coefK=1.0, aspect=1.0, out=None, dtype=None): """Simple lens correction. Lens correction for a spherical lenses with distortion centered at the middle of the display. See references[1]_ for implementation details. Parameters ---------- xys : array_like Nx2 list of vertex positions or texture coordinates to distort. Assumes the output will be rendered to normalized device coordinates where points range from -1.0 to 1.0. coefK : float Distortion coefficent. Use positive numbers for pincushion distortion and negative for barrel distortion. aspect : float Aspect ratio of the target window or buffer (width / height). out : ndarray, optional Optional output array. Must be same `shape` and `dtype` as the expected output if `out` was not specified. dtype : dtype or str, optional Data type for computations can either be 'float32' or 'float64'. If `out` is specified, the data type of `out` is used and this argument is ignored. If `out` is not provided, 'float64' is used by default. Returns ------- ndarray Array of distorted vertices. References ---------- .. [1] Lens Distortion White Paper, Andersson Technologies LLC, www.ssontech.com/content/lensalg.html (obtained 07/28/2020) Examples -------- Creating a lens correction mesh with barrel distortion (eg. for HMDs):: vertices, textureCoords, normals, faces = gltools.createMeshGrid( subdiv=11, tessMode='center') # recompute vertex positions vertices[:, :2] = mt.lensCorrection2(vertices[:, :2], coefK=2.0) """ if out is None: dtype = np.float64 if dtype is None else np.dtype(dtype).type else: dtype = np.dtype(dtype).type toReturn = np.empty_like(xys, dtype=dtype) if out is None else out xys = np.asarray(xys, dtype=dtype) toReturn[:, 0] = u = xys[:, 0] toReturn[:, 1] = v = xys[:, 1] coefKCubed = np.power(coefK, 3, dtype=dtype) r2 = aspect * aspect * u * u + v * v r2sqr = np.sqrt(r2, dtype=dtype) f = 1. + r2 * (coefK + coefKCubed * r2sqr) toReturn[:, 0] *= f toReturn[:, 1] *= f return toReturn
class infrange(): """ Similar to base Python `range`, but allowing the step to be a float or even 0, useful for specifying ranges for logical comparisons. """ def __init__(self, min, max, step=0): self.min = min self.max = max self.step = step @property def range(self): return abs(self.max-self.min) def __lt__(self, other): return other > self.max def __le__(self, other): return other > self.min def __gt__(self, other): return self.min > other def __ge__(self, other): return self.max > other def __contains__(self, item): if self.step == 0: return self.min < item < self.max else: return item in np.linspace(self.min, self.max, int(self.range/self.step)+1) def __eq__(self, item): if isinstance(item, self.__class__): return all(( self.min == item.min, self.max == item.max, self.step == item.step )) return item in self def __add__(self, other): return self.__class__(self.min+other, self.max+other, self.step) def __sub__(self, other): return self.__class__(self.min - other, self.max - other, self.step) def __mul__(self, other): return self.__class__(self.min * other, self.max * other, self.step * other) def __truedic__(self, other): return self.__class__(self.min / other, self.max / other, self.step / other) if __name__ == "__main__": pass

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