快速高斯模糊算法

轉(zhuǎn)載: http://www.cnblogs.com/tntmonks/p/4899649.html

?

剛才發(fā)現(xiàn)一份快速高斯模糊的實(shí)現(xiàn)。

源地址為：http://incubator.quasimondo.com/processing/gaussian_blur_1.php

作者信息為:Fast Gaussian Blur v1.3by Mario Klingemann <http://incubator.quasimondo.com> processing源碼: http://incubator.quasimondo.com/processing/fastblur.pde 效果圖：

轉(zhuǎn)為C語(yǔ)言實(shí)現(xiàn)版本。代碼如下: + View Code?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149

// Fast Gaussian Blur v1.3 // by Mario Klingemann <http://incubator.quasimondo.com> // C version updated and performance optimization by tntmonks(http://tntmonks.cnblogs.com) // One of my first steps with Processing. I am a fan // of blurring. Especially as you can use blurred images // as a base for other effects. So this is something I // might get back to in later experiments. // // What you see is an attempt to implement a Gaussian Blur algorithm // which is exact but fast. I think that this one should be // relatively fast because it uses a special trick by first // making a horizontal blur on the original image and afterwards // making a vertical blur on the pre-processed image. This // is a mathematical correct thing to do and reduces the // calculation a lot. // // In order to avoid the overhead of function calls I unrolled // the whole convolution routine in one method. This may not // look nice, but brings a huge performance boost. // // // v1.1: I replaced some multiplications by additions //?????? and added aome minor pre-caclulations. //?????? Also add correct rounding for float->int conversion // // v1.2: I completely got rid of all floating point calculations //?????? and speeded up the whole process by using a //?????? precalculated multiplication table. Unfortunately //?????? a precalculated division table was becoming too //?????? huge. But maybe there is some way to even speed //?????? up the divisions. // // v1.3: Fixed a bug that caused blurs that start at y>0 //?? to go wrong. Thanks to Jeroen Schellekens for //?????? finding it! void GaussianBlur(unsigned char* img, unsigned? int x, unsigned int y, unsigned int w, unsigned int h, unsigned int comp, unsigned int radius) { ????unsigned int i, j ; ????radius = min(max(1, radius), 248); ????unsigned int kernelSize = 1 + radius * 2; ????unsigned int* kernel = (unsigned int*)malloc(kernelSize* sizeof(unsigned int)); ????memset(kernel, 0, kernelSize* sizeof(unsigned int)); ????unsigned int(*mult)[256] = (unsigned int(*)[256])malloc(kernelSize * 256 * sizeof(unsigned int)); ????memset(mult, 0, kernelSize * 256 * sizeof(unsigned int)); ????unsigned??? int sum = 0; ????for (i = 1; i < radius; i++){ ????????unsigned int szi = radius - i; ????????kernel[radius + i] = kernel[szi] = szi*szi; ????????sum += kernel[szi] + kernel[szi]; ????????for (j = 0; j < 256; j++){ ????????????mult[radius + i][j] = mult[szi][j] = kernel[szi] * j; ????????} ????} ????kernel[radius] = radius*radius; ????sum += kernel[radius]; ????for (j = 0; j < 256; j++){ ?????????? ????????mult[radius][j] = kernel[radius] * j; ????} ????unsigned int?? cr, cg, cb; ????unsigned int?? xl, yl, yi, ym, riw; ????unsigned int?? read, ri, p,?? n; ????unsigned??? int imgWidth = w; ????unsigned??? int imgHeight = h; ????unsigned??? int imageSize = imgWidth*imgHeight; ????unsigned char * rgb = (unsigned char *)malloc(sizeof(unsigned char) * imageSize * 3); ????unsigned char * r = rgb; ????unsigned char * g = rgb + imageSize; ????unsigned char * b = rgb + imageSize * 2; ????unsigned char * rgb2 = (unsigned char *)malloc(sizeof(unsigned char) * imageSize * 3); ????unsigned char * r2 = rgb2; ????unsigned char * g2 = rgb2 + imageSize; ????unsigned char * b2 = rgb2 + imageSize * 2; ?? ????for (size_t yh = 0; yh < imgHeight; ++yh) { ?? ????????for (size_t xw = 0; xw < imgWidth; ++xw) { ????????????n = xw + yh* imgWidth; ????????????p = n*comp; ????????????r[n] = img[p]; ????????????g[n] = img[p + 1]; ????????????b[n] = img[p + 2]; ????????} ????} ????? ????x = max(0, x); ????y = max(0, y); ????w = x + w - max(0, (x + w) - imgWidth); ????h = y + h - max(0, (y + h) - imgHeight); ????yi = y*imgWidth; ?? ????for (yl = y; yl < h; yl++){ ?? ????????for (xl = x; xl < w; xl++){ ????????????cb = cg = cr = sum = 0; ????????????ri = xl - radius; ????????????for (i = 0; i < kernelSize; i++){ ????????????????read = ri + i; ????????????????if (read >= x && read < w) ????????????????{ ????????????????????read += yi; ????????????????????cr += mult[i][r[read]]; ????????????????????cg += mult[i][g[read]]; ????????????????????cb += mult[i][b[read]]; ????????????????????sum += kernel[i]; ????????????????} ????????????} ????????????ri = yi + xl; ????????????r2[ri] = cr / sum; ????????????g2[ri] = cg / sum; ????????????b2[ri] = cb / sum; ????????} ????????yi += imgWidth; ????} ????yi = y*imgWidth; ?? ????for (yl = y; yl < h; yl++){ ????????ym = yl - radius; ????????riw = ym*imgWidth; ????????for (xl = x; xl < w; xl++){ ????????????cb = cg = cr = sum = 0; ????????????ri = ym; ????????????read = xl + riw; ????????????for (i = 0; i < kernelSize; i++){ ????????????????if (ri < h && ri >= y) ????????????????{ ????????????????????cr += mult[i][r2[read]]; ????????????????????cg += mult[i][g2[read]]; ????????????????????cb += mult[i][b2[read]]; ????????????????????sum += kernel[i]; ????????????????} ????????????????ri++; ????????????????read += imgWidth; ????????????} ????????????p = (xl + yi)*comp; ????????????img[p] = (unsigned char)(cr / sum); ????????????img[p + 1] = (unsigned char)(cg / sum); ????????????img[p + 2] = (unsigned char)(cb / sum); ????????} ????????yi += imgWidth; ????} ????free(rgb); ????free(rgb2); ????free(kernel); ????free(mult); }

　　快速高斯模糊算法




剛才發(fā)現(xiàn)一份快速高斯模糊的實(shí)現(xiàn)。

源地址為：http://incubator.quasimondo.com/processing/gaussian_blur_1.php
作者信息為:
Fast Gaussian Blur v1.3
by Mario Klingemann <http://incubator.quasimondo.com>
processing源碼: http://incubator.quasimondo.com/processing/fastblur.pde
效果圖：




轉(zhuǎn)為C語(yǔ)言實(shí)現(xiàn)版本。

代碼如下:







1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149


// Fast Gaussian Blur v1.3

// by Mario Klingemann <http://incubator.quasimondo.com>

// C version updated and performance optimization by tntmonks(http://tntmonks.cnblogs.com)

// One of my first steps with Processing. I am a fan

// of blurring. Especially as you can use blurred images

// as a base for other effects. So this is something I

// might get back to in later experiments.

//

// What you see is an attempt to implement a Gaussian Blur algorithm

// which is exact but fast. I think that this one should be

// relatively fast because it uses a special trick by first

// making a horizontal blur on the original image and afterwards

// making a vertical blur on the pre-processed image. This

// is a mathematical correct thing to do and reduces the

// calculation a lot.

//

// In order to avoid the overhead of function calls I unrolled

// the whole convolution routine in one method. This may not

// look nice, but brings a huge performance boost.

//

//

// v1.1: I replaced some multiplications by additions

// and added aome minor pre-caclulations.

// Also add correct rounding for float->int conversion

//

// v1.2: I completely got rid of all floating point calculations

// and speeded up the whole process by using a

// precalculated multiplication table. Unfortunately

// a precalculated division table was becoming too

// huge. But maybe there is some way to even speed

// up the divisions.

//

// v1.3: Fixed a bug that caused blurs that start at y>0

// to go wrong. Thanks to Jeroen Schellekens for

// finding it!



void GaussianBlur(unsigned char* img, unsigned int x, unsigned int y, unsigned int w, unsigned int h, unsigned int comp, unsigned int radius)

{

unsigned int i, j ;

radius = min(max(1, radius), 248);

unsigned int kernelSize = 1 + radius * 2;

unsigned int* kernel = (unsigned int*)malloc(kernelSize* sizeof(unsigned int));

memset(kernel, 0, kernelSize* sizeof(unsigned int));

unsigned int(*mult)[256] = (unsigned int(*)[256])malloc(kernelSize * 256 * sizeof(unsigned int));

memset(mult, 0, kernelSize * 256 * sizeof(unsigned int));

unsigned int sum = 0;

for (i = 1; i < radius; i++){

unsigned int szi = radius - i;

kernel[radius + i] = kernel[szi] = szi*szi;

sum += kernel[szi] + kernel[szi];

for (j = 0; j < 256; j++){

mult[radius + i][j] = mult[szi][j] = kernel[szi] * j;

}

}

kernel[radius] = radius*radius;

sum += kernel[radius];

for (j = 0; j < 256; j++){



mult[radius][j] = kernel[radius] * j;

}



unsigned int cr, cg, cb;

unsigned int xl, yl, yi, ym, riw;

unsigned int read, ri, p, n;

unsigned int imgWidth = w;

unsigned int imgHeight = h;

unsigned int imageSize = imgWidth*imgHeight;

unsigned char * rgb = (unsigned char *)malloc(sizeof(unsigned char) * imageSize * 3);

unsigned char * r = rgb;

unsigned char * g = rgb + imageSize;

unsigned char * b = rgb + imageSize * 2;

unsigned char * rgb2 = (unsigned char *)malloc(sizeof(unsigned char) * imageSize * 3);

unsigned char * r2 = rgb2;

unsigned char * g2 = rgb2 + imageSize;

unsigned char * b2 = rgb2 + imageSize * 2;



for (size_t yh = 0; yh < imgHeight; ++yh) {



for (size_t xw = 0; xw < imgWidth; ++xw) {

n = xw + yh* imgWidth;

p = n*comp;

r[n] = img[p];

g[n] = img[p + 1];

b[n] = img[p + 2];

}

}



x = max(0, x);

y = max(0, y);

w = x + w - max(0, (x + w) - imgWidth);

h = y + h - max(0, (y + h) - imgHeight);

yi = y*imgWidth;



for (yl = y; yl < h; yl++){



for (xl = x; xl < w; xl++){

cb = cg = cr = sum = 0;

ri = xl - radius;

for (i = 0; i < kernelSize; i++){

read = ri + i;

if (read >= x && read < w)

{

read += yi;

cr += mult[i][r[read]];

cg += mult[i][g[read]];

cb += mult[i][b[read]];

sum += kernel[i];

}

}

ri = yi + xl;

r2[ri] = cr / sum;

g2[ri] = cg / sum;

b2[ri] = cb / sum;

}

yi += imgWidth;

}

yi = y*imgWidth;



for (yl = y; yl < h; yl++){

ym = yl - radius;

riw = ym*imgWidth;

for (xl = x; xl < w; xl++){

cb = cg = cr = sum = 0;

ri = ym;

read = xl + riw;

for (i = 0; i < kernelSize; i++){

if (ri < h && ri >= y)

{

cr += mult[i][r2[read]];

cg += mult[i][g2[read]];

cb += mult[i][b2[read]];

sum += kernel[i];

}

ri++;

read += imgWidth;

}

p = (xl + yi)*comp;

img[p] = (unsigned char)(cr / sum);

img[p + 1] = (unsigned char)(cg / sum);

img[p + 2] = (unsigned char)(cb / sum);

}

yi += imgWidth;

}



free(rgb);

free(rgb2);

free(kernel);

free(mult);

}


　　

總結(jié)

以上是生活随笔為你收集整理的快速高斯模糊算法的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺(jué)得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。