整理去年做的小项目，纪念我的图像处理入门。

因为要在DSP上实现，所以完全手工C代码垒起来的，还要保证和PC端跑的结果一样，觉得可能特殊场景会有用，上传github，没有依赖任何库；

格式注释什么的暂时没有整理，没有优化，能用，但是可能不够快，也没有考虑内存开销的优化，直接就在DDR2/SRAM上跑的。

Harris c代码实现    因为要匹配，最后对检测到的角点做了SAD（效率高）

matlab代码网上down的，自己添加下采样和匹配改了个面目全非。

DSP输出：

Harris start: Sample = 1 , Region = 8

max R = 32487164.72,(247, 3)

1 Harris: 178, 64

2 Harris: 186, 83

3 Harris: 191, 85

4 Harris: 195, 86

5 Harris:  67, 88

6 Harris: 217, 90

7 Harris:  58, 91

8 Harris:  52, 95

9 Harris: 241, 98

10 Harris:  17,112

11 Harris:   9,114

12 Harris: 217,115

13 Harris:  43,119

14 Harris: 233,119

15 Harris: 239,123

16 Harris: 228,126

17 Harris:  11,128

18 Harris: 234,130

19 Harris:  64,133

20 Harris: 240,133

21 Harris:  61,135

22 Harris: 225,135

23 Harris: 233,138

24 Harris: 204,139

25 Harris: 176,140

26 Harris: 119,141

27 Harris: 147,141

28 Harris:  60,142

29 Harris:  90,142

30 Harris:  30,143

31 Harris: 241,144

32 Harris: 238,145

33 Harris: 225,150

34 Harris: 233,151

35 Harris: 167,153

36 Harris: 201,153

37 Harris: 134,154

38 Harris: 100,155

39 Harris:  66,156

40 Harris: 221,156

41 Harris:  31,157

42 Harris: 239,163

43 Harris:  24,168

44 Harris: 237,174

45 Harris:  34,179

46 Harris:  65,183

47 Harris: 241,185

48 Harris:  11,188

49 Harris:  40,191

50 Harris:  70,191

51 Harris:  12,200

52 Harris:  30,200

53 Harris:  26,202

54 Harris:  46,207

55 Harris: 165,210

56 Harris: 222,213

57 Harris:  93,215

58 Harris: 218,217

59 Harris: 163,222

60 Harris:  11,224

61 Harris: 233,226

62 Harris: 227,230

63 Harris:  15,232

64 Harris:  18,234

65 Harris: 239,240

66 Harris:  16,243

67 Harris:  31,244

68 Harris:  23,245

69 Harris: 194,245

70 Harris: 210,246

71 Harris: 233,246

max R = 8346880.08,(1, 113)

1 Harris: 137,  9

2 Harris: 154, 10

3 Harris: 140, 24

4 Harris: 171, 64

5 Harris: 175, 65

6 Harris: 148, 67

7 Harris:  49, 75

8 Harris:  88, 80

9 Harris: 174, 82

10 Harris: 117, 83

11 Harris: 178, 83

12 Harris: 184, 83

13 Harris:  80, 84

14 Harris: 188, 85

15 Harris: 104, 86

16 Harris: 193, 86

17 Harris:  69, 87

18 Harris:  63, 88

19 Harris: 205, 89

20 Harris: 104, 90

21 Harris: 212, 90

22 Harris:  55, 91

23 Harris:  67, 93

24 Harris:  48, 94

25 Harris:  42, 95

26 Harris: 175, 96

27 Harris:  38, 97

28 Harris: 122, 97

29 Harris: 236, 98

30 Harris:  32, 99

31 Harris: 205,104

32 Harris:  13,105

33 Harris:  44,105

34 Harris: 197,105

35 Harris: 212,105

36 Harris:  44,107

37 Harris:  34,110

38 Harris: 225,110

39 Harris: 199,111

40 Harris:  10,112

41 Harris:  65,112

42 Harris: 204,112

43 Harris:  83,114

44 Harris: 210,115

45 Harris: 217,117

46 Harris:  35,119

47 Harris: 226,119

48 Harris: 245,120

49 Harris: 192,123

50 Harris: 231,123

51 Harris: 219,125

52 Harris: 228,127

53 Harris: 185,129

54 Harris: 225,131

55 Harris:  54,132

56 Harris: 230,132

57 Harris: 208,134

58 Harris:  46,135

59 Harris:  51,135

60 Harris: 215,135

61 Harris: 201,136

62 Harris: 194,139

63 Harris: 222,139

64 Harris: 137,140

65 Harris: 165,140

66 Harris: 183,140

67 Harris: 108,141

68 Harris:  19,142

69 Harris:  34,142

70 Harris:  49,142

71 Harris:  79,142

72 Harris: 118,142

73 Harris: 203,142

74 Harris: 214,143

75 Harris: 230,144

76 Harris: 178,145

77 Harris: 227,145

78 Harris: 238,145

79 Harris: 163,146

80 Harris: 155,147

81 Harris: 180,149

82 Harris: 213,150

83 Harris: 221,151

84 Harris:  61,152

85 Harris: 154,153

86 Harris: 188,153

87 Harris: 121,154

88 Harris: 232,154

89 Harris:  87,155

90 Harris:  53,156

91 Harris: 208,156

92 Harris:  18,157

93 Harris:  74,157

94 Harris: 189,159

95 Harris: 161,162

96 Harris: 127,163

97 Harris: 166,163

98 Harris: 225,163

99 Harris: 184,164

100 Harris:  33,166

101 Harris:  52,166

102 Harris:  58,168

103 Harris:   9,169

104 Harris:  41,169

105 Harris: 218,170

106 Harris:  27,171

107 Harris:  56,172

108 Harris:  17,173

109 Harris: 135,173

110 Harris:  25,174

111 Harris:  66,174

112 Harris:  50,175

113 Harris: 222,175

114 Harris:  98,176

115 Harris:  56,177

116 Harris: 234,177

117 Harris:  17,178

118 Harris:  29,178

119 Harris: 116,179

120 Harris:  44,180

121 Harris: 131,180

122 Harris: 244,180

123 Harris: 234,181

124 Harris:  32,182

125 Harris:  14,183

126 Harris:  48,183

127 Harris: 167,183

128 Harris: 131,184

129 Harris: 140,184

130 Harris: 241,184

131 Harris: 215,186

132 Harris:  19,187

133 Harris: 223,187

134 Harris: 148,188

135 Harris:  27,189

136 Harris:  23,190

137 Harris:  31,191

138 Harris:  52,191

139 Harris: 120,192

140 Harris: 147,194

141 Harris: 167,195

142 Harris: 187,195

143 Harris:  34,197

144 Harris: 220,197

145 Harris: 193,198

146 Harris:  29,199

147 Harris: 225,199

148 Harris: 180,200

149 Harris: 189,200

150 Harris: 144,201

151 Harris:  16,203

152 Harris: 110,203

153 Harris: 147,203

154 Harris: 230,203

155 Harris:   9,204

156 Harris:  88,205

157 Harris: 182,205

158 Harris: 191,205

159 Harris: 206,206

160 Harris:  26,207

161 Harris: 144,210

162 Harris: 244,210

163 Harris: 194,211

164 Harris:  56,212

165 Harris: 202,212

166 Harris:  95,213

167 Harris:  12,215

168 Harris:  31,215

169 Harris:  72,215

170 Harris: 182,216

171 Harris: 196,216

172 Harris: 104,217

173 Harris: 112,217

174 Harris:  99,219

175 Harris: 141,222

176 Harris: 233,222

177 Harris: 200,223

178 Harris:   9,224

179 Harris: 210,226

180 Harris:  15,229

181 Harris: 147,229

182 Harris: 204,230

183 Harris:  47,234

184 Harris: 126,234

185 Harris: 139,234

186 Harris:   9,235

187 Harris:  53,236

188 Harris: 212,238

189 Harris: 129,239

190 Harris: 206,239

191 Harris:  27,240

192 Harris:  12,244

193 Harris: 190,244

194 Harris: 244,244

195 Harris: 235,245

196 Harris:  29,247

197 Harris: 210,247

Matched points: 29

 0: matchedCorner[174] (163,222,141,222) d: 0.03975

 1: matchedCorner[160] (165,210,144,210) d: 0.05183

 2: matchedCorner[168] ( 93,215, 72,215) d: 0.05275

 3: matchedCorner[84] (167,153,154,153) d: 0.05445

 4: matchedCorner[89] ( 66,156, 53,156) d: 0.05949

 5: matchedCorner[137] ( 70,191, 52,191) d: 0.05978

 6: matchedCorner[125] ( 65,183, 48,183) d: 0.06032

 7: matchedCorner[69] ( 60,142, 49,142) d: 0.06281

 8: matchedCorner[91] ( 31,157, 18,157) d: 0.06552

 9: matchedCorner[159] ( 46,207, 26,207) d: 0.06596

10: matchedCorner[13] (191, 85,188, 85) d: 0.06981

11: matchedCorner[178] (233,226,210,226) d: 0.07079

12: matchedCorner[70] ( 90,142, 79,142) d: 0.07585

13: matchedCorner[135] ( 40,191, 23,190) d: 0.07670

14: matchedCorner[46] (233,119,226,119) d: 0.07697

15: matchedCorner[67] ( 30,143, 19,142) d: 0.07735

16: matchedCorner[64] (176,140,165,140) d: 0.07751

17: matchedCorner[181] (227,230,204,230) d: 0.07766

18: matchedCorner[58] ( 61,135, 51,135) d: 0.07878

19: matchedCorner[11] (186, 83,184, 83) d: 0.08188

20: matchedCorner[45] ( 43,119, 35,119) d: 0.08230

21: matchedCorner[54] ( 64,133, 54,132) d: 0.08530

22: matchedCorner[86] (134,154,121,154) d: 0.08576

23: matchedCorner[49] (239,123,231,123) d: 0.08604

24: matchedCorner[88] (100,155, 87,155) d: 0.09002

25: matchedCorner[63] (147,141,137,140) d: 0.09699

26: matchedCorner[82] (233,151,221,151) d: 0.09712

27: matchedCorner[90] (221,156,208,156) d: 0.09803

28: matchedCorner[176] (218,217,200,223) d: 0.09966

29: matchedCorner[43] (217,115,210,115) d: 0.10124

30: matchedCorner[97] (239,163,225,163) d: 0.10264

31: matchedCorner[15] (195, 86,193, 86) d: 0.10453

32: matchedCorner[81] (225,150,213,150) d: 0.10457

33: matchedCorner[39] ( 17,112, 10,112) d: 0.10529

34: matchedCorner[61] (204,139,194,139) d: 0.10692

35: matchedCorner[85] (201,153,188,153) d: 0.10697

36: matchedCorner[20] (217, 90,212, 90) d: 0.10785

37: matchedCorner[66] (119,141,108,141) d: 0.10903

38: matchedCorner[96] ( 24,168,166,163) d: 0.11074

39: matchedCorner[116] ( 34,179, 17,178) d: 0.11335

40: matchedCorner[17] ( 67, 88, 63, 88) d: 0.11478

41: matchedCorner[37] (194,245,225,110) d: 0.11824

42: matchedCorner[74] (241,144,230,144) d: 0.11879

43: matchedCorner[59] (225,135,215,135) d: 0.12120

44: matchedCorner[21] ( 58, 91, 55, 91) d: 0.12153

45: matchedCorner[76] (238,145,227,145) d: 0.12230

46: matchedCorner[ 3] (178, 64,171, 64) d: 0.12550

47: matchedCorner[28] (241, 98,236, 98) d: 0.12557

48: matchedCorner[27] ( 11,188,122, 97) d: 0.13124

49: matchedCorner[23] ( 52, 95, 48, 94) d: 0.14120

50: matchedCorner[164] (222,213,202,212) d: 0.14147

51: matchedCorner[37] ( 30,200,225,110) d: 0.14359

52: matchedCorner[71] ( 11,128,118,142) d: 0.14817

53: matchedCorner[165] ( 18,234, 95,213) d: 0.14993

54: matchedCorner[55] (240,133,230,132) d: 0.15049

55: matchedCorner[188] (241,185,129,239) d: 0.15167

56: matchedCorner[112] (237,174,222,175) d: 0.15237

57: matchedCorner[53] (234,130,225,131) d: 0.15975

58: matchedCorner[94] ( 12,200,161,162) d: 0.16515

59: matchedCorner[130] (210,246,215,186) d: 0.16592

60: matchedCorner[171] ( 15,232,104,217) d: 0.17298

61: matchedCorner[62] (233,138,222,139) d: 0.17425

62: matchedCorner[77] ( 26,202,238,145) d: 0.17961

63: matchedCorner[187] (239,240,212,238) d: 0.18350

64: matchedCorner[157] (  9,114,191,205) d: 0.18354

65: matchedCorner[19] ( 31,244,104, 90) d: 0.18516

66: matchedCorner[196] (233,246,210,247) d: 0.19022

67: matchedCorner[50] (228,126,219,125) d: 0.19145

68: matchedCorner[174] ( 11,224,141,222) d: 0.19151

69: matchedCorner[167] ( 16,243, 31,215) d: 0.22552

70: matchedCorner[163] ( 23,245, 56,212) d: 0.23696

Harris end!

SURF c代码实现    为了利用积分图最后求特征向量的时候没有旋转图像，基本是参考OpenCV和网上down到的一份matlab以及C#拼起来的

matlab代码可能来自这里，记不清楚了，总之是网上不知名大神共享的，当时连OpenCV都没用过的小白我如获至宝...

DSP输出：

OpenSURF test:

Cycle to get ipt1 is 0

Number of key points in pic1:103

Cycle to describe ipt1 is 0

Number of key points in pic2:128

Cycle to get and describe ipt2 is 0

Time to matched: 0 ms

 0: matchedIpt[95] ( 85.99, 85.17, 82.86, 85.30) d: 0.00260

 1: matchedIpt[70] ( 47.06, 95.99, 42.83, 95.91) d: 0.00359

 2: matchedIpt[82] ( 67.96,194.75, 49.92,194.57) d: 0.00455

 3: matchedIpt[13] (214.44,125.26,205.99,125.16) d: 0.00532

 4: matchedIpt[69] (202.97, 92.88,198.14, 92.96) d: 0.00570

 5: matchedIpt[69] (205.31, 93.83,198.14, 92.96) d: 0.00662

 6: matchedIpt[ 5] ( 35.38, 99.79, 29.98, 99.51) d: 0.00670

 7: matchedIpt[54] ( 61.49,217.59, 38.79,217.87) d: 0.00806

 8: matchedIpt[35] (152.79,179.11,136.93,179.25) d: 0.00806

 9: matchedIpt[38] ( 58.68,183.02, 41.89,182.71) d: 0.00832

10: matchedIpt[92] ( 72.85,239.35, 48.47,239.21) d: 0.00904

11: matchedIpt[80] ( 73.11,186.04, 55.19,186.07) d: 0.00931

12: matchedIpt[ 3] (231.97, 93.13,227.17, 93.04) d: 0.00975

13: matchedIpt[25] (132.31,160.72,118.63,160.50) d: 0.01006

14: matchedIpt[30] (149.93,167.20,134.69,166.84) d: 0.01027

15: matchedIpt[ 8] ( 94.27,111.85, 87.44,111.56) d: 0.01030

16: matchedIpt[94] (122.94, 83.84,119.90, 83.62) d: 0.01062

17: matchedIpt[105] (176.30,191.17,157.86,191.50) d: 0.01115

18: matchedIpt[31] (223.54,168.70,208.99,168.44) d: 0.01183

19: matchedIpt[12] ( 46.80,120.28, 38.81,119.73) d: 0.01211

20: matchedIpt[71] (224.16,100.35,219.76,100.07) d: 0.01308

21: matchedIpt[44] (186.16,195.73,166.90,195.93) d: 0.01309

22: matchedIpt[77] (213.58,172.32,198.76,172.91) d: 0.01331

23: matchedIpt[107] (115.48,203.99, 95.71,203.96) d: 0.01420

24: matchedIpt[20] (169.80,146.23,158.25,146.20) d: 0.01550

25: matchedIpt[ 7] (232.83,101.16,227.14,100.89) d: 0.01680

26: matchedIpt[ 9] ( 86.30,113.77, 79.05,113.92) d: 0.01721

27: matchedIpt[66] (122.35, 83.45,119.29, 83.40) d: 0.01780

28: matchedIpt[ 6] ( 27.53,101.58, 22.73,101.44) d: 0.01916

29: matchedIpt[ 1] ( 60.16, 90.06, 56.41, 89.71) d: 0.01963

30: matchedIpt[37] (137.53,181.55,120.93,181.54) d: 0.01971

31: matchedIpt[58] (118.01,226.98, 94.54,227.09) d: 0.02034

32: matchedIpt[112] (171.38,220.73,149.69,221.25) d: 0.02042

33: matchedIpt[17] (230.51,135.97,219.99,135.96) d: 0.02077

34: matchedIpt[61] (110.96,230.61, 87.16,230.17) d: 0.02178

35: matchedIpt[125] (169.41,123.85,166.05,124.65) d: 0.02278

36: matchedIpt[52] ( 65.81,216.05, 46.05,215.23) d: 0.02281

37: matchedIpt[87] (168.93,219.45,148.37,220.08) d: 0.02330

38: matchedIpt[110] ( 70.02,219.18, 48.17,218.89) d: 0.02513

39: matchedIpt[32] (211.27,171.33,197.11,171.64) d: 0.02650

40: matchedIpt[115] ( 81.10,228.88, 57.83,229.34) d: 0.02689

41: matchedIpt[96] (231.62, 91.24,226.57, 88.88) d: 0.02925

42: matchedIpt[64] (108.60,243.74, 82.07,243.78) d: 0.03126

43: matchedIpt[22] (140.21,154.63,127.19,154.34) d: 0.03203

44: matchedIpt[95] ( 91.32, 84.99, 82.86, 85.30) d: 0.03230

45: matchedIpt[93] (146.93, 27.16,146.03, 27.53) d: 0.03469

46: matchedIpt[60] (134.77,229.37,110.14,228.31) d: 0.03591

47: matchedIpt[105] (175.91,189.85,157.86,191.50) d: 0.04027

48: matchedIpt[125] (174.17,124.58,166.05,124.65) d: 0.04138

49: matchedIpt[85] (199.25,207.93,178.58,207.88) d: 0.04370

50: matchedIpt[24] ( 37.63,159.41, 24.60,158.93) d: 0.04504

51: matchedIpt[97] (220.12,128.17,211.02,128.12) d: 0.05070

52: matchedIpt[25] (229.83,128.56,118.63,160.50) d: 0.05942

53: matchedIpt[78] (238.84,169.78,238.67,173.68) d: 0.07877

54: matchedIpt[108] (172.92,208.34,151.75,208.93) d: 0.07884

55: matchedIpt[39] (156.11,186.26,147.67,185.59) d: 0.08015

56: matchedIpt[55] (165.94,219.38,144.14,219.32) d: 0.08813

57: matchedIpt[27] (234.62,162.62,220.43,162.56) d: 0.09373

58: matchedIpt[14] (155.06,171.10,228.58,124.93) d: 0.09682

59: matchedIpt[17] ( 38.87,167.80,219.99,135.96) d: 0.09913

60: matchedIpt[10] (218.30,114.22,211.73,114.14) d: 0.10389

61: matchedIpt[121] (227.64,178.41,205.99,178.25) d: 0.10590

62: matchedIpt[38] ( 58.80,196.00, 41.89,182.71) d: 0.10633

63: matchedIpt[34] ( 36.21,174.12, 20.49,173.70) d: 0.10729

64: matchedIpt[121] (229.02,176.50,205.99,178.25) d: 0.11332

65: matchedIpt[14] (236.82,124.87,228.58,124.93) d: 0.11353

66: matchedIpt[120] (207.85,159.31,194.75,159.18) d: 0.11531

67: matchedIpt[114] ( 51.57,229.20, 33.27,226.18) d: 0.11878

68: matchedIpt[71] (224.11,101.66,219.76,100.07) d: 0.11981

69: matchedIpt[99] ( 43.98,156.20, 31.10,155.11) d: 0.12281

70: matchedIpt[99] (241.88,195.45, 31.10,155.11) d: 0.12402

71: matchedIpt[25] (235.89,145.88,118.63,160.50) d: 0.12459

72: matchedIpt[66] (110.62,216.96,119.29, 83.40) d: 0.12702

OpenSURF end!

其实写那些代码的时候根本不懂SURF的原理，依样画葫芦竟然还画的有模有样，当然理论水平还是要跟上去。

斑点检测  尺度空间 特征点方法综述  觉得这个博客特别好，很形象的解释。

大部分参考了《图像局部不变性特征与描述》。

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