240 lines
8.4 KiB
Diff
240 lines
8.4 KiB
Diff
diff --git a/opensfm/exif.py b/opensfm/exif.py
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--- a/opensfm/exif.py
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+++ b/opensfm/exif.py
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@@ -509,7 +509,7 @@ class EXIF:
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)
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)
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- if np.all(ypr) is not None:
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+ if np.all(ypr != None):
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ypr = np.radians(ypr)
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# Convert YPR --> OPK
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diff --git a/opensfm/transformations.py b/opensfm/transformations.py
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--- a/opensfm/transformations.py
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+++ b/opensfm/transformations.py
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@@ -232,7 +232,7 @@ def translation_from_matrix(matrix: numpy.ndarray) -> numpy.ndarray:
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True
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"""
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- return numpy.array(matrix, copy=False)[:3, 3].copy()
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+ return numpy.asarray(matrix)[:3, 3].copy()
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def reflection_matrix(point: numpy.ndarray, normal: numpy.ndarray) -> numpy.ndarray:
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@@ -275,7 +275,7 @@ def reflection_from_matrix(
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True
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"""
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- M = numpy.array(matrix, dtype=numpy.float64, copy=False)
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+ M = numpy.asarray(matrix, dtype=numpy.float64)
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# normal: unit eigenvector corresponding to eigenvalue -1
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w, V = numpy.linalg.eig(M[:3, :3])
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i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0]
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@@ -339,7 +339,7 @@ def rotation_matrix(
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M[:3, :3] = R
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if point is not None:
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# rotation not around origin
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- point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
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+ point = numpy.asarray(point[:3], dtype=numpy.float64)
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M[:3, 3] = point - numpy.dot(R, point)
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return M
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@@ -359,7 +359,7 @@ def rotation_from_matrix(
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True
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"""
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- R = numpy.array(matrix, dtype=numpy.float64, copy=False)
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+ R = numpy.asarray(matrix, dtype=numpy.float64)
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R33 = R[:3, :3]
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# direction: unit eigenvector of R33 corresponding to eigenvalue of 1
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w, W = numpy.linalg.eig(R33.T)
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@@ -444,7 +444,7 @@ def scale_from_matrix(
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True
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"""
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- M = numpy.array(matrix, dtype=numpy.float64, copy=False)
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+ M = numpy.asarray(matrix, dtype=numpy.float64)
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M33 = M[:3, :3]
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factor = numpy.trace(M33) - 2.0
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try:
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@@ -505,11 +505,11 @@ def projection_matrix(
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"""
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M = numpy.identity(4)
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- point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
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+ point = numpy.asarray(point[:3], dtype=numpy.float64)
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normal = unit_vector(normal[:3])
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if perspective is not None:
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# perspective projection
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- perspective = numpy.array(perspective[:3], dtype=numpy.float64, copy=False)
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+ perspective = numpy.asarray(perspective[:3], dtype=numpy.float64)
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M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective - point, normal)
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M[:3, :3] -= numpy.outer(perspective, normal)
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if pseudo:
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@@ -522,7 +522,7 @@ def projection_matrix(
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M[3, 3] = numpy.dot(perspective, normal)
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elif direction is not None:
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# parallel projection
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- direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False)
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+ direction = numpy.asarray(direction[:3], dtype=numpy.float64)
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scale = numpy.dot(direction, normal)
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M[:3, :3] -= numpy.outer(direction, normal) / scale
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M[:3, 3] = direction * (numpy.dot(point, normal) / scale)
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@@ -569,7 +569,7 @@ def projection_from_matrix(
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True
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"""
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- M = numpy.array(matrix, dtype=numpy.float64, copy=False)
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+ M = numpy.asarray(matrix, dtype=numpy.float64)
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M33 = M[:3, :3]
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w, V = numpy.linalg.eig(M)
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i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
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@@ -726,7 +726,7 @@ def shear_from_matrix(
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True
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"""
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- M = numpy.array(matrix, dtype=numpy.float64, copy=False)
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+ M = numpy.asarray(matrix, dtype=numpy.float64)
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M33 = M[:3, :3]
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# normal: cross independent eigenvectors corresponding to the eigenvalue 1
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w, V = numpy.linalg.eig(M33)
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@@ -790,7 +790,7 @@ def decompose_matrix(
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True
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"""
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- M = numpy.array(matrix, dtype=numpy.float64, copy=True).T
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+ M = numpy.asarray(matrix, dtype=numpy.float64, copy=True).T
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if abs(M[3, 3]) < _EPS:
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raise ValueError("M[3, 3] is zero")
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M /= M[3, 3]
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@@ -982,8 +982,8 @@ def affine_matrix_from_points(
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More examples in superimposition_matrix()
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"""
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- v0 = numpy.array(v0, dtype=numpy.float64, copy=True)
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- v1 = numpy.array(v1, dtype=numpy.float64, copy=True)
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+ v0 = numpy.asarray(v0, dtype=numpy.float64, copy=True)
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+ v1 = numpy.asarray(v1, dtype=numpy.float64, copy=True)
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ndims = v0.shape[0]
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if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape:
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@@ -1099,8 +1099,8 @@ def superimposition_matrix(
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True
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"""
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- v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3]
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- v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3]
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+ v0 = numpy.asarray(v0, dtype=numpy.float64)[:3]
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+ v1 = numpy.asarray(v1, dtype=numpy.float64)[:3]
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return affine_matrix_from_points(v0, v1, shear=False, scale=scale, usesvd=usesvd)
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@@ -1198,7 +1198,7 @@ def euler_from_matrix(
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j = _NEXT_AXIS[i + parity]
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k = _NEXT_AXIS[i - parity + 1]
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- M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3]
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+ M = numpy.asarray(matrix, dtype=numpy.float64)[:3, :3]
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if repetition:
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sy = math.sqrt(M[i, j] * M[i, j] + M[i, k] * M[i, k])
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if sy > _EPS:
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@@ -1329,7 +1329,7 @@ def quaternion_matrix(quaternion: numpy.ndarray) -> numpy.ndarray:
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True
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"""
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- q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
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+ q = numpy.asarray(quaternion, dtype=numpy.float64, copy=True)
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n = numpy.dot(q, q)
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if n < _EPS:
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return numpy.identity(4)
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@@ -1379,7 +1379,7 @@ def quaternion_from_matrix(
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True
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"""
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- M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4]
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+ M = numpy.asarray(matrix, dtype=numpy.float64)[:4, :4]
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if isprecise:
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q = numpy.empty((4,))
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t = numpy.trace(M)
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@@ -1460,7 +1460,7 @@ def quaternion_conjugate(quaternion: numpy.ndarray) -> numpy.ndarray:
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True
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"""
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- q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
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+ q = numpy.asarray(quaternion, dtype=numpy.float64, copy=True)
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numpy.negative(q[1:], q[1:])
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return q
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@@ -1474,7 +1474,7 @@ def quaternion_inverse(quaternion: numpy.ndarray) -> numpy.ndarray:
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True
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"""
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- q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
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+ q = numpy.asarray(quaternion, dtype=numpy.float64, copy=True)
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numpy.negative(q[1:], q[1:])
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return q / numpy.dot(q, q)
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@@ -1496,7 +1496,7 @@ def quaternion_imag(quaternion: numpy.ndarray) -> numpy.ndarray:
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array([ 0., 1., 2.])
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"""
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- return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True)
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+ return numpy.asarray(quaternion[1:4], dtype=numpy.float64, copy=True)
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def quaternion_slerp(
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@@ -1654,7 +1654,7 @@ def vector_norm(
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1.0
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"""
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- data = numpy.array(data, dtype=numpy.float64, copy=True)
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+ data = numpy.asarray(data, dtype=numpy.float64, copy=True)
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if out is None:
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if data.ndim == 1:
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return math.sqrt(numpy.dot(data, data))
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@@ -1697,13 +1697,13 @@ def unit_vector(
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"""
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if out is None:
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- data = numpy.array(data, dtype=numpy.float64, copy=True)
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+ data = numpy.asarray(data, dtype=numpy.float64, copy=True)
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if data.ndim == 1:
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data /= math.sqrt(numpy.dot(data, data))
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return data
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else:
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if out is not data:
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- out[:] = numpy.array(data, copy=False)
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+ out[:] = numpy.asarray(data)
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data = out
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length = numpy.atleast_1d(numpy.sum(data * data, axis))
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numpy.sqrt(length, length)
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@@ -1777,8 +1777,8 @@ def angle_between_vectors(
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True
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"""
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- v0 = numpy.array(v0, dtype=numpy.float64, copy=False)
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- v1 = numpy.array(v1, dtype=numpy.float64, copy=False)
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+ v0 = numpy.asarray(v0, dtype=numpy.float64)
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+ v1 = numpy.asarray(v1, dtype=numpy.float64)
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dot = numpy.sum(v0 * v1, axis=axis)
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dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis)
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dot = numpy.clip(dot, -1.0, 1.0)
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@@ -1826,9 +1826,9 @@ def is_same_transform(matrix0: numpy.ndarray, matrix1: numpy.ndarray) -> numpy.n
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False
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"""
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- matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True)
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+ matrix0 = numpy.asarray(matrix0, dtype=numpy.float64, copy=True)
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matrix0 /= matrix0[3, 3]
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- matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True)
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+ matrix1 = numpy.asarray(matrix1, dtype=numpy.float64, copy=True)
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matrix1 /= matrix1[3, 3]
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return numpy.allclose(matrix0, matrix1)
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@@ -1874,3 +1874,4 @@ if __name__ == "__main__":
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numpy.set_printoptions(suppress=True, precision=5)
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doctest.testmod()
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+
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