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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
|
// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SEGMENT_ImAGE_H__
#define DLIB_SEGMENT_ImAGE_H__
#include "segment_image_abstract.h"
#include "../algs.h"
#include <vector>
#include "../geometry.h"
#include "../disjoint_subsets.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
namespace impl
{
struct graph_image_segmentation_data
{
graph_image_segmentation_data() : component_size(1), internal_diff(0) {}
unsigned long component_size;
unsigned short internal_diff;
};
template <typename T>
inline T edge_diff(
const T& a,
const T& b
)
{
if (a > b)
return a - b;
else
return b - a;
}
struct segment_image_edge_data
{
segment_image_edge_data (){}
segment_image_edge_data (
const rectangle& rect,
const point& p1,
const point& p2,
const unsigned short& diff_
) :
idx1(p1.y()*rect.width() + p1.x()),
idx2(p2.y()*rect.width() + p2.x()),
diff(diff_)
{}
unsigned long idx1;
unsigned long idx2;
unsigned short diff;
};
}
// ----------------------------------------------------------------------------------------
template <
typename in_image_type,
typename out_image_type
>
void segment_image (
const in_image_type& in_img,
out_image_type& out_img,
const unsigned long k = 200,
const unsigned long min_diff = 0
)
{
using namespace dlib::impl;
typedef typename in_image_type::type ptype;
// make sure requires clause is not broken
DLIB_ASSERT(is_same_object(in_img, out_img) == false,
"\t void segment_image()"
<< "\n\t The input images can't be the same object."
);
COMPILE_TIME_ASSERT(is_unsigned_type<ptype>::value && sizeof(ptype) <= 2);
COMPILE_TIME_ASSERT(is_unsigned_type<typename out_image_type::type>::value);
out_img.set_size(in_img.nr(), in_img.nc());
// don't bother doing anything if the image is too small
if (in_img.nr() < 2 || in_img.nc() < 2)
{
assign_all_pixels(out_img,0);
return;
}
disjoint_subsets sets;
sets.set_size(in_img.size());
std::vector<graph_image_segmentation_data> data(in_img.size());
std::vector<unsigned long> counts(std::numeric_limits<ptype>::max()+1, 0);
border_enumerator be(get_rect(in_img), 1);
// we are going to do a radix sort on the edge weights. So the first step
// is to accumulate them into count.
const rectangle area = get_rect(in_img);
while (be.move_next())
{
const long r = be.element().y();
const long c = be.element().x();
const ptype pix = in_img[r][c];
if (area.contains(c-1,r)) counts[edge_diff(pix, in_img[r ][c-1])] += 1;
if (area.contains(c+1,r)) counts[edge_diff(pix, in_img[r ][c+1])] += 1;
if (area.contains(c-1,r-1)) counts[edge_diff(pix, in_img[r-1][c-1])] += 1;
if (area.contains(c ,r-1)) counts[edge_diff(pix, in_img[r-1][c ])] += 1;
if (area.contains(c+1,r-1)) counts[edge_diff(pix, in_img[r-1][c+1])] += 1;
if (area.contains(c-1,r+1)) counts[edge_diff(pix, in_img[r+1][c-1])] += 1;
if (area.contains(c ,r+1)) counts[edge_diff(pix, in_img[r+1][c ])] += 1;
if (area.contains(c+1,r+1)) counts[edge_diff(pix, in_img[r+1][c+1])] += 1;
}
for (long r = 1; r+1 < in_img.nr(); ++r)
{
for (long c = 1; c+1 < in_img.nc(); ++c)
{
const ptype pix = in_img[r][c];
counts[edge_diff(pix, in_img[r ][c-1])] += 1;
counts[edge_diff(pix, in_img[r ][c+1])] += 1;
counts[edge_diff(pix, in_img[r-1][c-1])] += 1;
counts[edge_diff(pix, in_img[r-1][c ])] += 1;
counts[edge_diff(pix, in_img[r-1][c+1])] += 1;
counts[edge_diff(pix, in_img[r+1][c-1])] += 1;
counts[edge_diff(pix, in_img[r+1][c ])] += 1;
counts[edge_diff(pix, in_img[r+1][c+1])] += 1;
}
}
const unsigned long num_edges = shrink_rect(area,1).area()*8 + in_img.nr()*2*5 - 8 + (in_img.nc()-2)*2*5;
std::vector<segment_image_edge_data> sorted_edges(num_edges);
// integrate counts. The idea is to have sorted_edges[counts[i]] be the location that edges
// with an edge_diff of i go. So counts[0] == 0, counts[1] == number of 0 edge diff edges, etc.
unsigned long prev = counts[0];
for (unsigned long i = 1; i < counts.size(); ++i)
{
const unsigned long temp = counts[i];
counts[i] += counts[i-1];
counts[i-1] -= prev;
prev = temp;
}
counts[counts.size()-1] -= prev;
// now build a sorted list of all the edges
be.reset();
while(be.move_next())
{
const long r = be.element().y();
const long c = be.element().x();
const point p(c,r);
const ptype pix = in_img[r][c];
if (area.contains(c-1,r))
{
const ptype diff = edge_diff(pix, in_img[r ][c-1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c-1,r),diff);
}
if (area.contains(c+1,r))
{
const ptype diff = edge_diff(pix, in_img[r ][c+1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c+1,r),diff);
}
if (area.contains(c-1,r-1))
{
const ptype diff = edge_diff(pix, in_img[r-1][c-1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c-1,r-1),diff);
}
if (area.contains(c ,r-1))
{
const ptype diff = edge_diff(pix, in_img[r-1][c ]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c ,r-1),diff);
}
if (area.contains(c+1,r-1))
{
const ptype diff = edge_diff(pix, in_img[r-1][c+1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c+1,r-1),diff);
}
if (area.contains(c-1,r+1))
{
const ptype diff = edge_diff(pix, in_img[r+1][c-1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c-1,r+1),diff);
}
if (area.contains(c ,r+1))
{
const ptype diff = edge_diff(pix, in_img[r+1][c ]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c ,r+1),diff);
}
if (area.contains(c+1,r+1))
{
const ptype diff = edge_diff(pix, in_img[r+1][c+1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c+1,r+1),diff);
}
}
// same thing as the above loop but now we do it on the interior of the image and therefore
// don't have to include the boundary checking if statements used above.
for (long r = 1; r+1 < in_img.nr(); ++r)
{
for (long c = 1; c+1 < in_img.nc(); ++c)
{
const point p(c,r);
const ptype pix = in_img[r][c];
ptype diff;
diff = edge_diff(pix, in_img[r ][c-1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c-1,r),diff);
diff = edge_diff(pix, in_img[r ][c+1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c+1,r),diff);
diff = edge_diff(pix, in_img[r-1][c-1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c-1,r-1),diff);
diff = edge_diff(pix, in_img[r-1][c ]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c ,r-1),diff);
diff = edge_diff(pix, in_img[r-1][c+1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c+1,r-1),diff);
diff = edge_diff(pix, in_img[r+1][c-1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c-1,r+1),diff);
diff = edge_diff(pix, in_img[r+1][c ]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c ,r+1),diff);
diff = edge_diff(pix, in_img[r+1][c+1]);
sorted_edges[counts[diff]++] = segment_image_edge_data(area,p,point(c+1,r+1),diff);
}
}
// now start connecting blobs together to make a minimum spanning tree.
for (unsigned long i = 0; i < sorted_edges.size(); ++i)
{
const unsigned long idx1 = sorted_edges[i].idx1;
const unsigned long idx2 = sorted_edges[i].idx2;
unsigned long set1 = sets.find_set(idx1);
unsigned long set2 = sets.find_set(idx2);
if (set1 != set2)
{
const ptype diff = sorted_edges[i].diff;
const ptype tau1 = k/data[set1].component_size;
const ptype tau2 = k/data[set2].component_size;
const ptype mint = std::min(data[set1].internal_diff + tau1,
data[set2].internal_diff + tau2);
if (diff <= std::max<ptype>(mint,min_diff))
{
const unsigned long new_set = sets.merge_sets(set1, set2);
data[new_set].component_size = data[set1].component_size + data[set2].component_size;
data[new_set].internal_diff = diff;
}
}
}
unsigned long idx = 0;
for (long r = 0; r < out_img.nr(); ++r)
{
for (long c = 0; c < out_img.nc(); ++c)
{
out_img[r][c] = sets.find_set(idx++);
}
}
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SEGMENT_ImAGE_H__
|
