1 | // Aseprite Render Library |
2 | // Copyright (c) 2020 Igara Studio S.A. |
3 | // Copyright (c) 2001-2015 David Capello |
4 | // |
5 | // This file is released under the terms of the MIT license. |
6 | // Read LICENSE.txt for more information. |
7 | |
8 | #ifndef RENDER_COLOR_HISTOGRAM_H_INCLUDED |
9 | #define RENDER_COLOR_HISTOGRAM_H_INCLUDED |
10 | #pragma once |
11 | |
12 | #include <limits> |
13 | #include <vector> |
14 | |
15 | #include "doc/color.h" |
16 | #include "doc/image.h" |
17 | #include "doc/image_traits.h" |
18 | #include "doc/palette.h" |
19 | |
20 | #include "render/median_cut.h" |
21 | |
22 | namespace render { |
23 | using namespace doc; |
24 | |
25 | template<int RBits, // Number of bits for each component in the histogram |
26 | int GBits, |
27 | int BBits, |
28 | int ABits> |
29 | class ColorHistogram { |
30 | public: |
31 | // Number of elements in histogram for each RGB component |
32 | enum { |
33 | RElements = 1 << RBits, |
34 | GElements = 1 << GBits, |
35 | BElements = 1 << BBits, |
36 | AElements = 1 << ABits |
37 | }; |
38 | |
39 | ColorHistogram() |
40 | : m_histogram(RElements*GElements*BElements*AElements, 0) |
41 | , m_useHighPrecision(true) { |
42 | } |
43 | |
44 | // Returns the number of points in the specified histogram |
45 | // entry. Each rgba-index is in the range of the histogram, e.g. |
46 | // r=[0,RElements), g=[0,GElements), etc. |
47 | std::size_t at(int r, int g, int b, int a) const { |
48 | return m_histogram[histogramIndex(r, g, b, a)]; |
49 | } |
50 | |
51 | // Add the specified "color" in the histogram as many times as the |
52 | // specified value in "count". |
53 | void addSamples(doc::color_t color, std::size_t count = 1) { |
54 | int i = histogramIndex(color); |
55 | |
56 | if (m_histogram[i] < std::numeric_limits<std::size_t>::max()-count) // Avoid overflow |
57 | m_histogram[i] += count; |
58 | else |
59 | m_histogram[i] = std::numeric_limits<std::size_t>::max(); |
60 | |
61 | // Accurate colors are used only for less than 256 colors. If the |
62 | // image has more than 256 colors the m_histogram is used |
63 | // instead. |
64 | if (m_useHighPrecision) { |
65 | std::vector<doc::color_t>::iterator it = |
66 | std::find(m_highPrecision.begin(), m_highPrecision.end(), color); |
67 | |
68 | // The color is not in the high-precision table |
69 | if (it == m_highPrecision.end()) { |
70 | if (m_highPrecision.size() < 256) { |
71 | m_highPrecision.push_back(color); |
72 | } |
73 | else { |
74 | // In this case we reach the limit for the high-precision histogram. |
75 | m_useHighPrecision = false; |
76 | } |
77 | } |
78 | } |
79 | } |
80 | |
81 | // Creates a set of entries for the given palette in the given range |
82 | // with the more important colors in the histogram. Returns the |
83 | // number of used entries in the palette (maybe the range [from,to] |
84 | // is more than necessary). |
85 | int createOptimizedPalette(Palette* palette) { |
86 | // Can we use the high-precision table? |
87 | if (m_useHighPrecision && int(m_highPrecision.size()) <= palette->size()) { |
88 | for (int i=0; i<(int)m_highPrecision.size(); ++i) |
89 | palette->setEntry(i, m_highPrecision[i]); |
90 | |
91 | return m_highPrecision.size(); |
92 | } |
93 | // OK, we have to use the histogram and some algorithm (like |
94 | // median-cut) to quantize "optimal" colors. |
95 | else { |
96 | std::vector<doc::color_t> result; |
97 | median_cut(*this, palette->size(), result); |
98 | |
99 | for (int i=0; i<(int)result.size(); ++i) |
100 | palette->setEntry(i, result[i]); |
101 | |
102 | return result.size(); |
103 | } |
104 | } |
105 | |
106 | bool isHighPrecision() { return m_useHighPrecision; } |
107 | int highPrecisionSize() { return m_highPrecision.size(); } |
108 | |
109 | private: |
110 | // Converts input color in a index for the histogram. It reduces |
111 | // each 8-bit component to the resolution given in the template |
112 | // parameters. |
113 | std::size_t histogramIndex(doc::color_t color) const { |
114 | return histogramIndex((rgba_getr(color) >> (8 - RBits)), |
115 | (rgba_getg(color) >> (8 - GBits)), |
116 | (rgba_getb(color) >> (8 - BBits)), |
117 | (rgba_geta(color) >> (8 - ABits))); |
118 | } |
119 | |
120 | std::size_t histogramIndex(int r, int g, int b, int a) const { |
121 | return |
122 | r |
123 | | (g << RBits) |
124 | | (b << (RBits+GBits)) |
125 | | (a << (RBits+GBits+BBits)); |
126 | } |
127 | |
128 | // 3D histogram (the index in the histogram is calculated through histogramIndex() function). |
129 | std::vector<std::size_t> m_histogram; |
130 | |
131 | // High precision histogram to create an accurate palette if RGB |
132 | // source images contains less than 256 colors. |
133 | std::vector<doc::color_t> m_highPrecision; |
134 | |
135 | // True if we can use m_highPrecision still (it means that the |
136 | // number of different samples is less than 256 colors still). |
137 | bool m_useHighPrecision; |
138 | }; |
139 | |
140 | } // namespace render |
141 | |
142 | #endif |
143 | |