diff --git a/scripts/libraries/mtx.py b/scripts/libraries/mtx.py
deleted file mode 100644
index e53fdda63..000000000
--- a/scripts/libraries/mtx.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# The MIT License (MIT)
-#
-# Copyright (c) 2019 Nick Lee
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of
-# this software and associated documentation files (the "Software"), to deal in
-# the Software without restriction, including without limitation the rights to
-# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
-# the Software, and to permit persons to whom the Software is furnished to do so,
-# subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
-# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
-# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
-def _dot(a, b):
- s = 0
- for i in range(0, len(b)):
- s += a[i] * b[i]
- return s
-
-def mul(A, b):
- return [_dot(row, b) for row in A]
-
-def _height(A):
- return len(A)
-
-def _width(A):
- return len(A[0])
-
-def _max_magnitude_row(A, column):
- first_row = column
-
- max_row = first_row
- max_value = A[first_row][column]
-
- for i in range(first_row+1, _height(A)):
- if abs(A[i][column]) > abs(max_value):
- max_row = i
- max_value = A[i][column]
-
- return max_row
-
-def _subtract_rows_below(A, row, column, pivot_value):
- # Fill in factors where entries will become zero.
- for i in range(row+1, _height(A)):
- A[i][column] /= pivot_value
-
- for i in range(row+1, _height(A)):
- for j in range(column+1, _width(A)):
- A[i][j] -= (A[i][column] * A[row][j])
-
-def lu(A):
- P = None
- for pivot_column in range(0, _width(A)-1):
- pivot_row = pivot_column
- max_row = _max_magnitude_row(A, pivot_column)
- pivot_value = A[max_row][pivot_column]
-
- if max_row != pivot_row:
- A[pivot_row], A[max_row] = A[max_row], A[pivot_row]
-
- if P is None:
- P = list(range(0, _height(A)))
-
- P[pivot_row], P[max_row] = P[max_row], P[pivot_row]
-
- _subtract_rows_below(A, pivot_row, pivot_column, pivot_value)
- return P,A
-
-def _forward_substitute(A, b):
- for i in range(1, len(b)):
- for j in range(0, i):
- b[i] = b[i] - (b[j] * A[i][j])
-
-def _backward_substitute(A, b):
- size = len(b)
- b[-1] = b[-1] / A[-1][-1]
- for i in range(size-2, -1, -1):
- for j in range(i+1, size):
- b[i] = b[i] - (b[j] * A[i][j])
- b[i] = b[i] / A[i][i]
-
-def solve(PLU, b):
- P, LU = PLU
-
- x = b[:] if P is None else [b[i] for i in P] # permutation
- _forward_substitute(LU, x)
- _backward_substitute(LU, x)
- return x
diff --git a/scripts/libraries/rv/LICENSE.txt b/scripts/libraries/rv/LICENSE.txt
deleted file mode 100644
index f288702d2..000000000
--- a/scripts/libraries/rv/LICENSE.txt
+++ /dev/null
@@ -1,674 +0,0 @@
- GNU GENERAL PUBLIC LICENSE
- Version 3, 29 June 2007
-
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-parties who would receive the covered work from you, a discriminatory
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-or that patent license was granted, prior to 28 March 2007.
-
- Nothing in this License shall be construed as excluding or limiting
-any implied license or other defenses to infringement that may
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-
- 12. No Surrender of Others' Freedom.
-
- If conditions are imposed on you (whether by court order, agreement or
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-to collect a royalty for further conveying from those to whom you convey
-the Program, the only way you could satisfy both those terms and this
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- 13. Use with the GNU Affero General Public License.
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- 14. Revised Versions of this License.
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- The Free Software Foundation may publish revised and/or new versions of
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-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
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- Later license versions may give you additional or different
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-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
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- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
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-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
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-to attach them to the start of each source file to most effectively
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- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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- You should have received a copy of the GNU General Public License
- along with this program. If not, see .
-
-Also add information on how to contact you by electronic and paper mail.
-
- If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
- Copyright (C)
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
- This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License. Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
- You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-.
-
- The GNU General Public License does not permit incorporating your program
-into proprietary programs. If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library. If this is what you want to do, use the GNU Lesser General
-Public License instead of this License. But first, please read
-.
diff --git a/scripts/libraries/rv/README.md b/scripts/libraries/rv/README.md
deleted file mode 100644
index e96cbd386..000000000
--- a/scripts/libraries/rv/README.md
+++ /dev/null
@@ -1,173 +0,0 @@
-# OpenRV - Robot Vision routines for OpenMV
-
-[OpenMV](https://openmv.io) is a small camera board for machine vision. I use it
-to direct my robot. [The software
-library](http://docs.openmv.io/library/index.html) is adequate, but lacks some
-algorithms I need. Out comes this project, in which I implement a few missing
-pieces myself. I hope it could save time for someone who tries to do the same.
-
-**Everything is tested on OpenMV Cam H7**
-
-## Prerequisites
-
-Vector and matrix are mainstay in machine vision. Finding no satisfactory
-libraries, I have again implemented my own.
-
-#### Vector Operations on MicroPython
-
-- [Project](https://gitlab.com/nickoala/micropython-vec)
-- [Package](https://pypi.org/project/micropython-vec/)
-
-#### Fast Matrix Multiplication and Linear Solver on MicroPython
-
-- [Project](https://gitlab.com/nickoala/micropython-mtx)
-- [Package](https://pypi.org/project/micropython-mtx/)
-
-Along with this project's `rv` package, the OpenMV camera's SD card should
-contain:
-
-```
-/
-├── mtx.py
-├── rv
-│ ├── __init__.py
-│ ├── moments.py
-│ ├── planar.py
-│ └── quickshiftpp.py
-└── vec
- ├── distance.py
- └── __init__.py
-```
-
-**In addition, you need some theoretical backgrounds to use vision algorithms
-effectively. This page does not give you those backgrounds. Study them
-yourself.**
-
-## Hu moments
-
-[Hu moments](https://docs.opencv.org/2.4/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#humoments)
-[is a shape descriptor](https://www.pyimagesearch.com/2014/10/27/opencv-shape-descriptor-hu-moments-example/)
-[invariant to translation, scale, and rotation](https://www.learnopencv.com/shape-matching-using-hu-moments-c-python/).
-That means it can recognize the same shape no matter its location, size, and orientation in the picture.
-
-#### Usage
-
-The last element of Hu moments is somewhat of an oddball. It indicates
-reflection rather than the general shape. For matching, the last element should
-be dropped.
-
-Use the function `vec.distance.euclidean()` to see how close two vectors are.
-
-```python
-import rv.moments
-import vec.distance
-import image
-
-a = image.Image('/images/a.pgm')
-b = image.Image('/images/b.pgm')
-
-ha = rv.moments.hu(a)
-hb = rv.moments.hu(b)
-
-print(vec.distance.euclidean(ha[:-1], hb[:-1]))
-```
-
-**Remark:** Although accepting gray-level images, this implementation treats
-pixels as either 0 or 1. Pixels having a non-zero brightness are treated as 1.
-This speeds up calculation.
-
-**More:** [test_moments.py](test/test_moments.py)
-
-## Planar homography
-
-Map points from one coordinate system to another. For example, a red ball sits
-at (90, 50) on the image and you know it is on the floor (not floating in air),
-planar homography can map the image point (90, 50) to a position on the floor,
-telling you how far the red ball is in front of the robot and how much left or
-right. In this case, points are essentially mapped from the *image coordinate
-system* to the *floor coordinate system*.
-
-Once you can map points, finding out the size of objects is straight-forward.
-
-#### Usage
-
-It works only when two coordinate systems (i.e. the two planes) are fixed
-relative to each other. In other words, the camera's height and orientation
-relative to the floor cannot change.
-
-**[First, you have to calibrate for a homography matrix, which is a very long
-story. I have devoted an entire directory to discuss the process. Take a look
-there.](homography)**
-
-Once you have the matrix, the rest is easy.
-
-```python
-import rv.planar
-
-H = [[ 3.14916496e+01, -9.79038178e+02, 1.03951636e+05],
- [ 7.57939015e+02, -3.31912533e+01, -5.86807545e+04],
- [ 2.06572544e-01, 2.03579263e+00, 1.00000000e+00]]
-
-p = rv.planar.Planar(H)
-
-image_points = [[83, 109],
- [70, 100],
- [51, 92]]
-
-print(p.project(image_points))
-```
-
-**More:** [test_planar.py](test/test_planar.py)
-
-## Quickshift++
-
-[The latest member](https://github.com/google/quickshift) [of the
-Meanshift](http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/)
-[family of clustering
-algorithms](https://github.com/Nick-Ol/MedoidShift-and-QuickShift), Quickshift++
-accepts a bunch of points and group them. I use it to "discover" the colors of
-disks on the floor, before using colors to pick out the disks. This saves me
-from hard-coding the colors beforehand, and makes the robot adaptive.
-
-It is not optimized to handle a large number of points. OpenMV's limited memory
-precludes handling a lot of points anyway. Don't expect to use it to segment an
-entire image.
-
-#### Usage
-
-```python
-import rv.quickshiftpp
-
-points = [
- # cluster 1
- [1, 1],
- [1.0, 1.2],
- [0.9, 1.1],
- [0.95, 0.99],
-
- # cluster 2
- [3.3, 3.0],
-
- # cluster 3
- [5.0, 8.2],
- [5.5, 7.9],
- [4.8, 8.1],
- [5.1, 7.7],
-]
-
-print(rv.quickshiftpp.cluster(points,
- k=2,
- beta=0.2))
-```
-
-The parameter `k` determines how density is estimated. It uses *distance to the
-k-th nearest neighbor* to estimate density around each point.
-
-The parameter `beta` determines how much density is allowed to vary within
-cluster cores. Here is not the place to explain what "cluster core" means. Some
-theoretical understanding cannot be avoided.
-
-In short, use `k` and `beta` to tune the clustering.
-
-**More:** [test_quickshiftpp.py](test/test_quickshiftpp.py) and
- [test_quickshiftpp_colors.py](test/test_quickshiftpp_colors.py)
diff --git a/scripts/libraries/rv/__init__.py b/scripts/libraries/rv/__init__.py
deleted file mode 100644
index 8b1378917..000000000
--- a/scripts/libraries/rv/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/scripts/libraries/rv/moments.py b/scripts/libraries/rv/moments.py
deleted file mode 100644
index 8c8dd6db8..000000000
--- a/scripts/libraries/rv/moments.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import math
-
-def _sum(img, roi, f):
- x, y, w, h = roi or (0, 0, img.width(), img.height())
- s = 0
- for i in range(x, x + w):
- for j in range(y, y + h):
- if img.get_pixel(i,j) != 0:
- s += f(i, j)
- return s
-
-def centroid(img, roi=None, return_intermediate=False):
- m10 = _sum(img, roi, lambda x,y: x)
- m01 = _sum(img, roi, lambda x,y: y)
- m00 = _sum(img, roi, lambda x,y: 1)
-
- c = [0, 0] if m00 == 0 else [float(m10 / m00), float(m01 / m00)]
-
- return [m00, c] if return_intermediate else c
-
-def central(img, roi=None, return_intermediate=False):
- results = centroid(img, roi, return_intermediate=True)
- cx, cy = results[-1]
-
- u20 = _sum(img, roi, lambda x,y: (x-cx)**2)
- u11 = _sum(img, roi, lambda x,y: (x-cx) * (y-cy))
- u02 = _sum(img, roi, lambda x,y: (y-cy)**2)
-
- u30 = _sum(img, roi, lambda x,y: (x-cx)**3)
- u21 = _sum(img, roi, lambda x,y: (x-cx)**2 * (y-cy))
- u12 = _sum(img, roi, lambda x,y: (x-cx) * (y-cy)**2)
- u03 = _sum(img, roi, lambda x,y: (y-cy)**3)
-
- us = [u20, u11, u02, u30, u21, u12, u03]
-
- return (results + [us]) if return_intermediate else us
-
-def normalized_central(img, roi=None, return_intermediate=False):
- results = central(img, roi, return_intermediate=True)
- m00, _, [u20, u11, u02, u30, u21, u12, u03] = results
-
- if m00 == 0:
- ns = [0, 0, 0, 0, 0, 0, 0]
- else:
- m2 = m00**2
- m3 = m00**2.5
- n20, n11, n02 = u20/m2, u11/m2, u02/m2
- n30, n21, n12, n03 = u30/m3, u21/m3, u12/m3, u03/m3
-
- ns = [n20, n11, n02, n30, n21, n12, n03]
-
- return (results + [ns]) if return_intermediate else ns
-
-def hu(img, roi=None, return_intermediate=False):
- results = normalized_central(img, roi, return_intermediate=True)
- n20, n11, n02, n30, n21, n12, n03 = results[-1]
-
- h0 = n20 + n02
-
- h1 = (n20 - n02)**2 + 4 * n11 * n11
-
- _n30_3n12 = n30 - 3 * n12
- _3n21_n03 = 3 * n21 - n03
-
- h2 = _n30_3n12**2 + _3n21_n03**2
-
- n3012 = n30 + n12
- n3012_2 = n3012 * n3012
-
- n2103 = n21 + n03
- n2103_2 = n2103 * n2103
-
- h3 = n3012_2 + n2103_2
-
- h4 = (_n30_3n12 * n3012 * ( n3012_2 - 3 * n2103_2 )
- + _3n21_n03 * n2103 * ( 3 * n3012_2 - n2103_2 ))
-
- h5 = ( n20 - n02 ) * ( n3012_2 - n2103_2 ) + 4 * n11 * n3012 * n2103
-
- h6 = (_3n21_n03 * n2103 * ( 3 * n3012_2 - n2103_2 )
- - _n30_3n12 * n2103 * ( 3 * n3012_2 - n2103_2 ))
-
- hs = [0 if h == 0 else math.copysign(math.log10(math.fabs(h)), h)
- for h in [h0, h1, h2, h3, h4, h5, h6]]
-
- return (results + [hs]) if return_intermediate else hs
diff --git a/scripts/libraries/rv/planar.py b/scripts/libraries/rv/planar.py
deleted file mode 100644
index f5bcc639c..000000000
--- a/scripts/libraries/rv/planar.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import mtx, vec
-
-class Planar(object):
- def __init__(self, H, offset=[0,0]):
- self.H = H
- self.H_ = None # LU factorization of H
- self.offset = offset
-
- def project(self, points, reverse=False):
- if reverse:
- if self.H_ is None:
- self.H_ = mtx.lu([r[:] for r in self.H])
-
- qs = [mtx.solve(self.H_,
- vec.sub(p, self.offset)+[1]) for p in points]
- return [vec.div(q[:-1], q[-1]) for q in qs]
- else:
- qs = [mtx.mul(self.H, p+[1]) for p in points]
- return [vec.add(self.offset,
- vec.div(q[:-1], q[-1])) for q in qs]
diff --git a/scripts/libraries/rv/quickshiftpp.py b/scripts/libraries/rv/quickshiftpp.py
deleted file mode 100644
index b373f5d64..000000000
--- a/scripts/libraries/rv/quickshiftpp.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import vec.distance
-
-def distance_to_kth_nearest_neighbor(points, i, k):
- fix = points[i]
- d = [vec.distance.euclidean(fix, p) for p in points]
- d.sort()
- return d[k]
- # Smallest distance is always to the point itself.
- # First element can be ignored.
- # Distance to k-th nearest neighbor is at index == k.
-
-def calculate_threshold(r, d, beta):
- return r / (1 - beta)**(1 / d)
-
-def find_threshold_position(threshold, ascending):
- lower = 0
- upper = len(ascending) - 1
-
- if ascending[upper][0] <= threshold:
- return upper
-
- # binary search
- while upper - lower > 1:
- i = (upper + lower) // 2
- r = ascending[i][0]
-
- if r == threshold:
- # push `i` to last element equal to threshold
- while i+1 < len(ascending) and ascending[i+1][0] == threshold:
- i += 1
- return i
- elif r < threshold:
- lower = i
- else:
- upper = i
-
- if ascending[upper][0] <= threshold:
- return upper
- else:
- return lower
-
-def in_which(x, sets):
- for s in sets:
- if x in s:
- return s
- return None
-
-def form_cluster_core(core_set,
- existing_cores,
- points,
- sorted_radii,
- seed_position,
- threshold_position):
-
- def include(current_position):
- current_radius, current_index = sorted_radii[current_position]
-
- core_set.add(current_index)
-
- for other_position in range(0, threshold_position + 1):
- other_radius, other_index = sorted_radii[other_position]
-
- if other_index == current_index or other_index in core_set:
- continue
-
- distance = vec.distance.euclidean(
- points[current_index], points[other_index])
-
- if distance <= min(current_radius, other_radius):
- # Connect current point to the other point
-
- if in_which(other_index, existing_cores) is not None:
- # The other point is already in another core.
- # Current set will not be disjoint from existing cores.
- # Current set cannot form a new core.
- return False
- else:
- # Continue making connections, starting from new point.
- clean = include(other_position)
- if not clean:
- return False
- return True
-
- return include(seed_position)
-
-def assign_cluster(clusters,
- points,
- sorted_radii,
- position):
-
- def find_cluster_to_belong(current_position):
- _, current_index = sorted_radii[current_position]
-
- cluster_set = in_which(current_index, clusters)
- if cluster_set is not None:
- # already belong to a cluster
- return cluster_set
-
- nearest_neighbor_distance = None
- nearest_neighbor_position = None
-
- # find nearest neighbor
- for i in range(0, current_position):
- _, other_index = sorted_radii[i]
-
- distance = vec.distance.euclidean(
- points[current_index], points[other_index])
-
- if nearest_neighbor_distance is None \
- or distance < nearest_neighbor_distance:
- nearest_neighbor_distance = distance
- nearest_neighbor_position = i
-
- # join nearest neighbor's cluster
- cluster_set = find_cluster_to_belong(nearest_neighbor_position)
- cluster_set.add(current_index)
- return cluster_set
-
- find_cluster_to_belong(position)
-
-def cluster(points,
- k,
- beta,
- return_modes=False):
- dimension = len(points[0])
-
- sorted_radii = [(distance_to_kth_nearest_neighbor(points, i, k=k), i)
- for i in range(0, len(points))]
- sorted_radii.sort()
- # smallest radius first, i.e. highest density first.
-
- modes = []
- clusters = []
- proposed_core = set()
-
- for position in range(0, len(sorted_radii)):
- radius, index = sorted_radii[position]
-
- threshold = calculate_threshold(radius, dimension, beta)
- threshold_position = find_threshold_position(threshold, sorted_radii)
-
- if form_cluster_core(proposed_core,
- clusters,
- points,
- sorted_radii,
- seed_position=position,
- threshold_position=threshold_position):
- clusters.append(proposed_core)
- proposed_core = set()
-
- if return_modes:
- modes.append(index)
- else:
- proposed_core.clear()
-
- for position in range(0, len(sorted_radii)):
- assign_cluster(clusters,
- points,
- sorted_radii,
- position=position)
-
- return (clusters, modes) if return_modes else clusters
diff --git a/scripts/libraries/ulinalg.py b/scripts/libraries/ulinalg.py
deleted file mode 100644
index 7ad79c911..000000000
--- a/scripts/libraries/ulinalg.py
+++ /dev/null
@@ -1,173 +0,0 @@
-'''
-
-Part of the micro-linalg project to provide a small
-matrix / linear algebra package for Micropython (Python3)
-
-The MIT License (MIT)
-
-Copyright (c) 2015 Jamie Lawson
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-'''
-import math
-import umatrix
-
-
-def zeros(m, n, dtype=umatrix.ddtype):
- return umatrix.matrix([[0 for i in range(n)] for j in range(m)], dtype=dtype)
-
-
-def ones(m, n, dtype=umatrix.ddtype):
- return zeros(m, n, dtype) + 1
-
-
-def eye(m, dtype=umatrix.ddtype):
- Z = zeros(m, m, dtype=dtype)
- for i in range(m):
- Z[i, i] = 1
- return Z
-
-def det_inv(x):
- ''' Return (det(x) and inv(x))
-
- Operates on a copy of x
- Using elementary row operations convert X to an upper matrix
- the product of the diagonal = det(X)
- Continue to convert X to the identity matrix
- All the operation carried out on the original identity matrix
- makes it the inverse of X
- '''
- if not x.is_square:
- raise ValueError('Matrix must be square')
- else:
- # divide each row element by [0] to give a one in the first position
- # (may have to find a row to switch with if first element is 0)
- x = x.copy()
- inverse = eye(len(x), dtype=float)
- sign = 1
- factors = []
- p = 0
- while p < len(x):
- d = x[p, p]
- if abs(d) < umatrix.flt_eps:
- # pivot == 0 need to swap a row
- # check if swap row also has a zero at the same position
- np = 1
- while (p + np) < len(x) and abs(x[p + np, p]) < umatrix.flt_eps:
- np += 1
- if (p + np) == len(x):
- # singular
- return [0, []]
- # swap rows
- z = x[p + np]
- x[p + np, :] = x[p]
- x[p, :] = z
- # do identity
- z = inverse[p + np]
- inverse[p + np, :] = inverse[p]
- inverse[p, :] = z
- # change sign of det
- sign = -sign
- continue
- factors.append(d)
- # change target row
- for n in range(p, len(x)):
- x[p, n] = x[p, n] / d
- # need to do the entire row for the inverse
- for n in range(len(x)):
- inverse[p, n] = inverse[p, n] / d
- # eliminate position in the following rows
- for i in range(p + 1, len(x)):
- # multiplier is that column entry
- t = x[i, p]
- for j in range(p, len(x)):
- x[i, j] = x[i, j] - (t * x[p, j])
- for j in range(len(x)):
- inverse[i, j] = inverse[i, j] - (t * inverse[p, j])
- p = p + 1
- s = sign
- for i in factors:
- s = s * i # determinant
- # travel through the rows eliminating upper diagonal non-zero values
- for i in range(len(x) - 1):
- # final row should already be all zeros
- # except for the final position
- for p in range(i + 1, len(x)):
- # multiplier is that column entry
- t = x[i, p]
- for j in range(i + 1, len(x)):
- x[i, j] = x[i, j] - (t * x[p, j])
- for j in range(len(x)):
- inverse[i, j] = inverse[i, j] - (t * inverse[p, j])
- return (s, inverse)
-
-
-def pinv(X):
- ''' Calculates the pseudo inverse Adagger = (A'A)^-1.A' '''
- Xt = X.transpose()
- d, Z = det_inv(dot(Xt, X))
- return dot(Z, Xt)
-
-
-def dot(X, Y):
- ''' Dot product '''
- if X.size(2) == Y.size(1):
- Z = []
- for k in range(X.size(1)):
- for j in range(Y.size(2)):
- Z.append(sum([X[k, i] * Y[i, j] for i in range(Y.size(1))]))
- return umatrix.matrix(Z, cstride=1, rstride=Y.size(2))
- else:
- raise ValueError('shapes not aligned')
-
-
-def cross(X, Y, axis=1):
- ''' Cross product
- axis=1 Numpy default
- axis=0 MATLAB, Octave, SciLab default
- '''
- if axis == 0:
- X = X.T
- Y = Y.T
- if (X.n in (2, 3)) and (Y.n in (2, 3)):
- if X.m == Y.m:
- Z = []
- for k in range(min(X.m, Y.m)):
- z = X[k, 0] * Y[k, 1] - X[k, 1] * Y[k, 0]
- if (X.n == 3) and (Y.n == 3):
- Z.append([X[k, 1] * Y[k, 2] - X[k, 2] * Y[k, 1],
- X[k, 2] * Y[k, 0] - X[k, 0] * Y[k, 2], z])
- else:
- Z.append([z])
- if axis == 0:
- return umatrix.matrix(Z).T
- else:
- return umatrix.matrix(Z)
- else:
- raise ValueError('shape mismatch')
- else:
- raise ValueError('incompatible dimensions for cross product'
- ' (must be 2 or 3)')
-
-def eps(x = 0):
- # ref. numpy.spacing(), Octave/MATLAB eps() function
- if x:
- return 2**(math.floor(math.log(abs(x))/math.log(2)))*umatrix.flt_eps
- else:
- return umatrix.flt_eps
diff --git a/scripts/libraries/umatrix.py b/scripts/libraries/umatrix.py
deleted file mode 100644
index e7597864e..000000000
--- a/scripts/libraries/umatrix.py
+++ /dev/null
@@ -1,473 +0,0 @@
-'''
-
-Part of the micro-linalg project to provide a small
-matrix / linear algebra package for MicroPython (Python3)
-
-The MIT License (MIT)
-
-Copyright (c) 2015 Jamie Lawson
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-'''
-
-import sys
-
-stypes = [bool, int]
-ddtype = int
-estypes = []
-flt_eps = 1
-
-
-class matrix(object):
-
- def __init__(self, data, cstride=0, rstride=0, dtype=None):
- ''' Builds a matrix representation of 'data'.
- 'data' can be a list (columns) of lists (rows)
- [[1,2,3],[4,5,6]] or
- a simple list organized as determined by rstride and cstride:
- [1,2,3,4,5,6] cstride=1, rstride=3.
- Elements will be of highest type included in 'data' or
- 'dtype' can be used to force the type.
- '''
- if cstride != 0:
- if cstride == 1:
- self.n = rstride
- self.m = int(len(data) / self.n)
- else:
- self.m = cstride
- self.n = int(len(data) / self.m)
- self.cstride = cstride
- self.rstride = rstride
- self.data = data
- else:
- # else determine shape from list passed in
- self.n = 1
- if type(data) == int:
- self.m = 1
- else: # it is a list
- self.m = len(data)
- # is data[0] a list
- if (type(data[0]) == list):
- self.n = len(data[0])
- self.data = [data[i][j]
- for i in range(self.m) for j in range(self.n)]
- self.cstride = 1
- self.rstride = self.n
- # ensure all elements are of the same type
- if dtype is None:
- self.dtype = stypes[max([stypes.index(type(i)) for i in self.data])]
- else:
- if dtype in stypes:
- self.dtype = dtype
- else:
- raise TypeError('unsupported type', dtype)
- self.data = [self.dtype(i) for i in self.data]
-
- def __len__(self):
- return self.m
-
- def __eq__(self, other):
- if self.shape == other.shape:
- res = all([self.data[i] == other.data[i] for i in range(self.size())])
- return res and (self.shape == other.shape)
- else:
- raise ValueError('shapes not equal')
-
- def __ne__(self, other):
- return not __eq__(other)
-
- def __iter__(self):
- self.cur = 0
- # determine proper axis
- if self.m == 1:
- self.cnt_lim = self.n
- else:
- self.cnt_lim = self.m
- return self
-
- def __next__(self):
- '''
- Returns a matrix if m > 1
- else the next numeric element of the vector.
- (Numpy returns vectors if selected via slice)
- '''
- if self.cur >= self.cnt_lim:
- raise StopIteration
- self.cur = self.cur + 1
- if self.m == 1:
- return self.data[self.cur - 1]
- else:
- return self[self.cur - 1]
-
- def slice_to_offset(self, r0, r1, c0, c1):
- # check values and limit them
- nd = [self.data[i * self.rstride + j * self.cstride]
- for i in range(r0, r1) for j in range(c0, c1)]
- return matrix(nd, cstride=1, rstride=(c1 - c0))
-
- def slice_indices(self, index, axis=0):
- # handles the unsupported slice.indices() method in uPy.
- # If implemented:
- # midx = index.indices(self.m)
- # should work.
- if isinstance(index.start, type(None)):
- s0 = 0
- else:
- s0 = min(int(index.start), self.shape[axis])
- if isinstance(index.stop, type(None)):
- p0 = self.shape[axis]
- else:
- p0 = min(int(index.stop), self.shape[axis])
- return (s0, p0)
-
- def __getitem__(self, index):
- if type(index) == tuple:
- # int and int
- # int and slice
- # slice and int
- # slice and slice
- if isinstance(index[0], int):
- s0 = index[0]
- p0 = s0 + 1
- else: # row slice
- s0, p0 = self.slice_indices(index[0], 0)
- if isinstance(index[1], int):
- s1 = index[1]
- p1 = s1 + 1
- else: # column slice
- s1, p1 = self.slice_indices(index[1], 1)
- elif type(index) == list:
- # list of indices etc
- raise NotImplementedError('Fancy indexing')
- else:
- # type is int? This will default to returning a row
- s0 = index
- p0 = s0 + 1
- s1 = 0
- p1 = self.n
- # resultant matrix
- z = self.slice_to_offset(s0, p0, s1, p1)
- # if it's a single entry then return that entry as int, float etc.
- if (p0 == s0 + 1) and (p1 == s1 + 1):
- return z.data[0]
- else:
- return z
-
- def __setitem__(self, index, val):
- if type(index) != tuple:
- # need to make it a slice without the slice function
- raise NotImplementedError('Need to use the slice [1,:] format.')
- # int and int => single entry gets changed
- # combinations of int and slice => row and columns take on elements from val
- if isinstance(index[0], int):
- s0 = index[0]
- p0 = s0 + 1
- else: # slice
- s0, p0 = self.slice_indices(index[0], 0)
- if isinstance(index[1], int):
- s1 = index[1]
- p1 = s1 + 1
- else: # slice
- s1, p1 = self.slice_indices(index[1], 1)
- if type(val) == matrix:
- val = val.data
- elif type(val) not in [list, tuple]:
- val = [val]
- if not all([type(i) in stypes for i in val]):
- raise ValueError('Non numeric entry')
- else:
- # assign list values wrapping as necessary to fill destination
- k = 0
- for i in range(s0, p0):
- for j in range(s1, p1):
- self.data[i * self.rstride + j * self.cstride] = (self.dtype(val[k]))
- k = (k + 1) % len(val)
-
- # there is also __delitem__
-
- # def __str__(self):
- def __repr__(self):
- # things that use __str__ will fallback to __repr__
- # find max string field size for formatting
- l = 0
- for i in self.data:
- l = max(l, len(repr(i)))
- s = 'mat(['
- r = 0
- for i in range(self.m):
- c = 0
- s = s + '['
- for j in range(self.n):
- s1 = repr(self.data[r + c])
- s = s + s1 + ' ' * (l - len(s1))
- if (j < (self.n - 1)):
- s = s + ', '
- c = c + self.cstride
- if (i < (self.m - 1)):
- s = s + '],\n '
- else:
- s = s + ']'
- r = r + self.rstride
- s = s + '])'
- return s
-
- # Reflected operations are not yet implemented in MicroPython
- # __rmul__ for example will not be invoked
-
- def __neg__(self):
- ndat =[self.data[i] * (-1) for i in range(len(self.data))]
- return matrix(ndat, cstride=self.cstride, rstride=self.rstride)
-
- def __do_op__(self, a, b, op):
- if op == '+':
- return (a + b)
- elif op == '-':
- return (a - b)
- elif op == '*':
- return (a * b)
- elif op == '**':
- return (a ** b)
- elif op == '/':
- try:
- return (a / b)
- except ZeroDivisionError:
- raise ZeroDivisionError('division by zero')
- elif op == '//':
- try:
- return (a // b)
- except ZeroDivisionError:
- raise ZeroDivisionError('division by zero')
- else:
- raise NotImplementedError('Unknown operator ', op)
-
- def __OP__(self, a, op):
- if type(a) in stypes:
- # matrix - scaler elementwise operation
- ndat = [self.__do_op__(self.data[i], a, op) for i in range(len(self.data))]
- return matrix(ndat, cstride=self.cstride, rstride=self.rstride)
- elif (type(a) == list):
- # matrix - list elementwise operation
- # hack - convert list to matrix and resubmit then it gets handled below
- # if self.n = 1 try transpose otherwise broadcast error to match numpy
- if (self.n == 1) and (len(a) == self.m):
- return self.__OP__(matrix([a]).T, op)
- elif len(a) == self.n:
- return self.__OP__(matrix([a]), op)
- else:
- raise ValueError('could not be broadcast')
- elif (type(a) == matrix):
- if (self.m == a.m) and (self.n == a.n):
- # matrix - matrix elementwise operation
- # use matrix indices to handle views
- ndat = [self.__do_op__(self[i, j], a[i, j], op) for i in range(self.m) for j in range(self.n)]
- return matrix(ndat, cstride=1, rstride=self.n)
- # generalize the following two elif for > 2 dimensions?
- elif (self.m == a.m):
- # m==m n!=n => column-wise row operation
- Y = self.copy()
- for i in range(self.n):
- # this call _OP_ once for each row and __do_op__ for each element
- for j in range(self.m):
- Y[j, i] = self.__do_op__(Y[j, i], a[j, 0], op)
- return Y
- elif (self.n == a.n):
- # m!=m n==n => row-wise col operation
- Y = self.copy()
- for i in range(self.m):
- # this call _OP_ once for each col and __do_op__ for each element
- for j in range(self.n):
- Y[i, j] = self.__do_op__(Y[i, j], a[0, j], op)
- return Y
- else:
- raise ValueError('could not be broadcast')
- raise NotImplementedError('__OP__ matrix + ', type(a))
-
- def __add__(self, a):
- ''' matrix - scaler elementwise addition'''
- return self.__OP__(a, '+')
-
- def __radd__(self, a):
- ''' scaler - matrix elementwise addition'''
- ''' commutative '''
- return self.__add__(a)
-
- def __sub__(self, a):
- ''' matrix - scaler elementwise subtraction '''
- if type(a) in estypes:
- return self.__add__(-a)
- raise NotImplementedError('__sub__ matrix -', type(a))
-
- def __rsub__(self, a):
- ''' scaler - matrix elementwise subtraction '''
- self = -self
- return self.__add__(a)
-
- def __mul__(self, a):
- ''' matrix scaler elementwise multiplication '''
- return self.__OP__(a, '*')
-
- def __rmul__(self, a):
- ''' scaler * matrix elementwise multiplication
- commutative
- '''
- return self.__mul__(a)
-
- def __truediv__(self, a):
- ''' matrix / scaler elementwise division '''
- return self.__OP__(a, '/')
-
- def __rtruediv__(self, a):
- ''' scaler / matrix elementwise division '''
- return self.__OP__(a, '/')
-
- def __floordiv__(self, a):
- ''' matrix // scaler elementwise integer division '''
- return self.__OP__(a, '//')
-
- def __rfloordiv__(self, a):
- ''' scaler // matrix elementwise integer division '''
- return self.__OP__(a, '//')
-
- def __pow__(self, a):
- ''' matrix ** scaler elementwise power '''
- return self.__OP__(a, '**')
-
- def __rpow__(self, a):
- ''' scaler ** matrix elementwise power '''
- return self.__OP__(a, '**')
-
- def copy(self):
- """ Return a copy of matrix, not just a view """
- return matrix([i for i in self.data],
- cstride=self.cstride, rstride=self.rstride)
-
- def size(self, axis=0):
- """ 0 entries
- 1 rows
- 2 columns
- """
- return [self.m * self.n, self.m, self.n][axis]
-
- @property
- def shape(self):
- return (self.m, self.n)
-
- @shape.setter
- def shape(self, nshape):
- """ check for proper length """
- if (nshape[0] * nshape[1]) == self.size():
- self.m, self.n = nshape
- self.cstride = 1
- self.rstride = self.n
- else:
- raise ValueError('total size of new matrix must be unchanged')
- return self
-
- @property
- def is_square(self):
- return self.m == self.n
-
- def reshape(self, nshape):
- """ check for proper length """
- X = self.copy()
- X.shape = nshape
- return X
-
- @property
- def T(self):
- return self.transpose()
-
- def transpose(self):
- """ Return a view """
- X = matrix(self.data, cstride=self.rstride, rstride=self.cstride)
- if self.cstride == self.rstride:
- # handle column vector
- X.shape = (self.n, self.m)
- return X
-
- def reciprocal(self, n=1):
- return matrix([n / i for i in self.data], cstride=self.cstride, rstride=self.rstride)
-
- def apply(self, func, *args, **kwargs):
- """ call a scalar function on each element, returns a new matrix
- passes *args and **kwargs to func unmodified
- note: this is not useful for matrix-matrix operations
- e.g.
- y = x.apply(math.sin)
- y = x.apply(lambda a,b: a>b, 5) # equivalent to y = x > 5
- y = x.apply(operators.gt, 5) # equivalent to y = x > 5 (not in micropython)
- """
- return matrix([func(i, *args, **kwargs) for i in self.data],
- cstride=self.cstride, rstride=self.rstride)
-
-def matrix_isclose(x, y, rtol=1E-05, atol=flt_eps):
- ''' Returns a matrix indicating equal elements within tol'''
- for i in range(x.size()):
- try:
- data = [abs(x.data[i] - y.data[i]) <= atol+rtol*abs(y.data[i]) for i in range(len(x.data))]
- except (AttributeError, IndexError):
- data = [False for i in range(len(x.data))]
- return matrix(data, cstride=x.cstride, rstride=x.rstride, dtype=bool)
-
-
-def matrix_equal(x, y, tol=0):
- ''' Matrix equality test with tolerance same shape'''
- res = False
- if type(y) == matrix:
- if x.shape == y.shape:
- res = all([abs(x.data[i] - y.data[i]) <= tol for i in range(x.size())])
- return res
-
-
-def matrix_equiv(x, y):
- ''' Returns a boolean indicating if X and Y share the same data and are broadcastable'''
- res = False
- if type(y) == matrix:
- if x.size() == y.size():
- res = all([x.data[i] == y.data[i] for i in range(len(x.data))])
- return res
-
-def fp_eps():
- ''' Determine floating point resolution '''
- e = 1
- while 1 + e > 1:
- e = e / 2
- return 2 * e
-
-flt_eps = fp_eps()
-try:
- if sys.implementation.name == 'micropython' and sys.platform == 'linux':
- # force this as there seems to be some interaction with
- # some operations done using the C library with a smaller epsilon (doubles)
- flt_eps = 1.19E-7 # single precision IEEE 2**-23 double 2.22E-16 == 2**-52
-except:
- pass
-# Determine supported types
-try:
- stypes.append(float)
- ddtype = float
-except:
- pass
-try:
- stypes.append(complex)
-except:
- pass
-# extended types
-estypes = [matrix]
-estypes.extend(stypes)
diff --git a/scripts/libraries/vec/LICENSE.md b/scripts/libraries/vec/LICENSE.md
deleted file mode 100644
index 88e3dffbd..000000000
--- a/scripts/libraries/vec/LICENSE.md
+++ /dev/null
@@ -1,20 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2019 Nick Lee
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software is furnished to do so,
-subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
-FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
-COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/scripts/libraries/vec/README.md b/scripts/libraries/vec/README.md
deleted file mode 100644
index 3a1136913..000000000
--- a/scripts/libraries/vec/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# Vector Operations on MicroPython
-
-Nothing fancy. Treating list as vector, this library performs common
-operations. I personally use it on [OpenMV](https://openmv.io) for robot vision.
-
-```python
-import vec
-
-a = [1, 2, 3]
-b = [4, 5, 6]
-
-vec.add(a, b) # [5, 7, 9]
-vec.sub(a, b) # [-3, -3, -3]
-vec.dot(a, b) # 32
-
-vec.mul(a, 2) # [2, 4, 6]
-vec.div(a, 2) # [0.5, 1.0, 1.5]
-```
-
-```python
-import vec.distance
-
-a = [2, 3]
-b = [5, 7]
-
-vec.distance.manhattan(a, b) # 7
-vec.distance.euclidean(a, b) # 5
-```
diff --git a/scripts/libraries/vec/__init__.py b/scripts/libraries/vec/__init__.py
deleted file mode 100644
index 45ca5b380..000000000
--- a/scripts/libraries/vec/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-def add(a, b):
- return [a[i] + b[i] for i in range(0, min(len(a), len(b)))]
-
-def sub(a, b):
- return [a[i] - b[i] for i in range(0, min(len(a), len(b)))]
-
-def mul(a, n):
- return [a[i] * n for i in range(0, len(a))]
-
-def div(a, n):
- return [a[i] / n for i in range(0, len(a))]
-
-def dot(a, b):
- s = 0
- for i in range(0, min(len(a), len(b))):
- s += a[i] * b[i]
- return s
diff --git a/scripts/libraries/vec/distance.py b/scripts/libraries/vec/distance.py
deleted file mode 100644
index 7042a369e..000000000
--- a/scripts/libraries/vec/distance.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import math
-
-def manhattan(a, b):
- s = 0
- for i in range(0, min(len(a), len(b))):
- s += abs(a[i] - b[i])
- return s
-
-def euclidean(a, b):
- s = 0
- for i in range(0, min(len(a), len(b))):
- s += (a[i] - b[i])**2
- return math.sqrt(s)