From db99d966633c5d7a5b33d03d653f1839d9feaaea Mon Sep 17 00:00:00 2001
From: Paul Elder <paul.elder@ideasonboard.com>
Date: Fri, 11 Nov 2022 02:05:53 +0900
Subject: utils: tuning: libtuning: Implement math helpers

Implement math helpers for libtuning. This includes:
- Average, a wrapper class for numpy averaging functions
- Gradient, a class that represents gradients, for distributing and
  mapping
- Smoothing, a wrapper class for cv2 smoothing functions

Signed-off-by: Paul Elder <paul.elder@ideasonboard.com>
Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
---
 utils/tuning/libtuning/average.py   | 21 +++++++++++
 utils/tuning/libtuning/gradient.py  | 75 +++++++++++++++++++++++++++++++++++++
 utils/tuning/libtuning/smoothing.py | 24 ++++++++++++
 3 files changed, 120 insertions(+)
 create mode 100644 utils/tuning/libtuning/average.py
 create mode 100644 utils/tuning/libtuning/gradient.py
 create mode 100644 utils/tuning/libtuning/smoothing.py

(limited to 'utils')

diff --git a/utils/tuning/libtuning/average.py b/utils/tuning/libtuning/average.py
new file mode 100644
index 00000000..e28770d7
--- /dev/null
+++ b/utils/tuning/libtuning/average.py
@@ -0,0 +1,21 @@
+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+#
+# average.py - Wrapper for numpy averaging functions to enable duck-typing
+
+import numpy as np
+
+
+# @brief Wrapper for np averaging functions so that they can be duck-typed
+class Average(object):
+    def __init__(self):
+        pass
+
+    def average(self, np_array):
+        raise NotImplementedError
+
+
+class Mean(Average):
+    def average(self, np_array):
+        return np.mean(np_array)
diff --git a/utils/tuning/libtuning/gradient.py b/utils/tuning/libtuning/gradient.py
new file mode 100644
index 00000000..5106f821
--- /dev/null
+++ b/utils/tuning/libtuning/gradient.py
@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+#
+# gradient.py - Gradients that can be used to distribute or map numbers
+
+import libtuning as lt
+
+import math
+from numbers import Number
+
+
+# @brief Gradient for how to allocate pixels to sectors
+# @description There are no parameters for the gradients as the domain is the
+#              number of pixels and the range is the number of sectors, and
+#              there is only one curve that has a startpoint and endpoint at
+#              (0, 0) and at (#pixels, #sectors). The exception is for curves
+#              that *do* have multiple solutions for only two points, such as
+#              gaussian, and curves of higher polynomial orders if we had them.
+#
+# \todo There will probably be a helper in the Gradient class, as I have a
+# feeling that all the other curves (besides Linear and Gaussian) can be
+# implemented in the same way.
+class Gradient(object):
+    def __init__(self):
+        pass
+
+    # @brief Distribute pixels into sectors (only in one dimension)
+    # @param domain Number of pixels
+    # @param sectors Number of sectors
+    # @return A list of number of pixels in each sector
+    def distribute(self, domain: list, sectors: list) -> list:
+        raise NotImplementedError
+
+    # @brief Map a number on a curve
+    # @param domain Domain of the curve
+    # @param rang Range of the curve
+    # @param x Input on the domain of the curve
+    # @return y from the range of the curve
+    def map(self, domain: tuple, rang: tuple, x: Number) -> Number:
+        raise NotImplementedError
+
+
+class Linear(Gradient):
+    # @param remainder Mode of handling remainder
+    def __init__(self, remainder: lt.Remainder = lt.Remainder.Float):
+        self.remainder = remainder
+
+    def distribute(self, domain: list, sectors: list) -> list:
+        size = domain / sectors
+        rem = domain % sectors
+
+        if rem == 0:
+            return [int(size)] * sectors
+
+        size = math.ceil(size)
+        rem = domain % size
+        output_sectors = [int(size)] * (sectors - 1)
+
+        if self.remainder == lt.Remainder.Float:
+            size = domain / sectors
+            output_sectors = [size] * sectors
+        elif self.remainder == lt.Remainder.DistributeFront:
+            output_sectors.append(int(rem))
+        elif self.remainder == lt.Remainder.DistributeBack:
+            output_sectors.insert(0, int(rem))
+        else:
+            raise ValueError
+
+        return output_sectors
+
+    def map(self, domain: tuple, rang: tuple, x: Number) -> Number:
+        m = (rang[1] - rang[0]) / (domain[1] - domain[0])
+        b = rang[0] - m * domain[0]
+        return m * x + b
diff --git a/utils/tuning/libtuning/smoothing.py b/utils/tuning/libtuning/smoothing.py
new file mode 100644
index 00000000..b8a5a242
--- /dev/null
+++ b/utils/tuning/libtuning/smoothing.py
@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+#
+# smoothing.py - Wrapper for cv2 smoothing functions to enable duck-typing
+
+import cv2
+
+
+# @brief Wrapper for cv2 smoothing functions so that they can be duck-typed
+class Smoothing(object):
+    def __init__(self):
+        pass
+
+    def smoothing(self, src):
+        raise NotImplementedError
+
+
+class MedianBlur(Smoothing):
+    def __init__(self, ksize):
+        self.ksize = ksize
+
+    def smoothing(self, src):
+        return cv2.medianBlur(src.astype('float32'), self.ksize).astype('float64')
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