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
|
/********************************************************
Author: Tess Zacharias
********************************************************/
#include <numeric>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/opencv.hpp"
#include <iostream>
using namespace cv;
using namespace std;
extern "C"
{
#include "api_scilab.h"
#include "Scierror.h"
#include "BOOL.h"
#include <localization.h>
#include "sciprint.h"
#include "../common.h"
int opencv_watershed(char *fname, unsigned long fname_len)
{
SciErr sciErr;
int intErr = 0;
//checking input argument
CheckInputArgument(pvApiCtx, 1, 1);
CheckOutputArgument(pvApiCtx, 1, 1) ;
Mat src;
retrieveImage(src, 1);
// Create binary image from source image
Mat bw;
cvtColor(src, bw, CV_BGR2GRAY);
threshold(bw, bw, 40, 255, CV_THRESH_BINARY);
// Perform the distance transform algorithm
Mat dist;
distanceTransform(bw, dist, CV_DIST_L2, 3);
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
normalize(dist, dist, 0, 1., cv::NORM_MINMAX);
//imshow("dist", dist);
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
threshold(dist, dist, .5, 1., CV_THRESH_BINARY);
//imshow("dist2", dist);
// Create the CV_8U version of the distance image
// It is needed for cv::findContours()
Mat dist_8u;
dist.convertTo(dist_8u, CV_8U);
// Find total markers
vector<vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
int ncomp = contours.size();
// Create the marker image for the watershed algorithm
Mat markers = Mat::zeros(dist.size(), CV_32SC1);
// Draw the foreground markers
for (int i = 0; i < ncomp; i++)
drawContours(markers, contours, i, Scalar::all(i+1), -1);
// Draw the background marker
circle(markers, cv::Point(5,5), 3, CV_RGB(255,255,255), -1);
//imshow("markers", markers*10000);
// Perform the watershed algorithm
watershed(src, markers);
// Generate random colors
vector<Vec3b> colors;
for (int i = 0; i < ncomp; i++)
{
int b = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int r = theRNG().uniform(0, 255);
colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
// Create the result image
Mat dst = Mat::zeros(markers.size(), CV_8UC3);
// Fill labeled objects with random colors
for (int i = 0; i < markers.rows; i++)
{
for (int j = 0; j < markers.cols; j++)
{
int index = markers.at<int>(i,j);
if (index > 0 && index <= ncomp)
dst.at<cv::Vec3b>(i,j) = colors[index-1];
else
dst.at<cv::Vec3b>(i,j) = Vec3b(0,0,0);
}
}
//imshow("dst", dst);
string tempstring = type2str(dst.type());
char *checker;
checker = (char *)malloc(tempstring.size() + 1);
memcpy(checker, tempstring.c_str(), tempstring.size() + 1);
returnImage(checker,dst,1);
free(checker);
//Assigning the list as the Output Variable
AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1;
//Returning the Output Variables as arguments to the Scilab environment
ReturnArguments(pvApiCtx);
return 0;
}
}
|
