1 | // ----------------------------------------------------------------------- |
---|
2 | // zscale.cc: Get a suitable pixel value scaling for displaying a 2D |
---|
3 | // image in greyscale. |
---|
4 | // ----------------------------------------------------------------------- |
---|
5 | // Copyright (C) 2006, Matthew Whiting, ATNF |
---|
6 | // |
---|
7 | // This program is free software; you can redistribute it and/or modify it |
---|
8 | // under the terms of the GNU General Public License as published by the |
---|
9 | // Free Software Foundation; either version 2 of the License, or (at your |
---|
10 | // option) any later version. |
---|
11 | // |
---|
12 | // Duchamp is distributed in the hope that it will be useful, but WITHOUT |
---|
13 | // ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
---|
14 | // FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
---|
15 | // for more details. |
---|
16 | // |
---|
17 | // You should have received a copy of the GNU General Public License |
---|
18 | // along with Duchamp; if not, write to the Free Software Foundation, |
---|
19 | // Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA |
---|
20 | // |
---|
21 | // Correspondence concerning Duchamp may be directed to: |
---|
22 | // Internet email: Matthew.Whiting [at] atnf.csiro.au |
---|
23 | // Postal address: Dr. Matthew Whiting |
---|
24 | // Australia Telescope National Facility, CSIRO |
---|
25 | // PO Box 76 |
---|
26 | // Epping NSW 1710 |
---|
27 | // AUSTRALIA |
---|
28 | // ----------------------------------------------------------------------- |
---|
29 | #include <iostream> |
---|
30 | #include <algorithm> |
---|
31 | #include <duchamp/Utils/utils.hh> |
---|
32 | const int nsample=1000; |
---|
33 | // const int width=300; |
---|
34 | const int width=150; |
---|
35 | const float contrast=0.25; |
---|
36 | |
---|
37 | void zscale(long imagesize, float *image, float &z1, float &z2) |
---|
38 | { |
---|
39 | |
---|
40 | float *smallarray = new float[nsample]; |
---|
41 | float *ct = new float[nsample]; |
---|
42 | long size=0; |
---|
43 | |
---|
44 | float mean=0.,sd=0.; |
---|
45 | for(int i=0;i<imagesize;i++) mean+=image[i]; |
---|
46 | mean /= float(imagesize); |
---|
47 | for(int i=0;i<imagesize;i++) sd+=(image[i]-mean)*(image[i]-mean); |
---|
48 | sd /= float(imagesize); |
---|
49 | |
---|
50 | |
---|
51 | if(imagesize>nsample){ |
---|
52 | int step = (imagesize / nsample) + 1; |
---|
53 | for(int i=0;i<imagesize;i++){ |
---|
54 | if((i%step)==0){ |
---|
55 | smallarray[size] = image[i]; |
---|
56 | ct[size]=(float)size+1.; |
---|
57 | size++; |
---|
58 | } |
---|
59 | } |
---|
60 | } |
---|
61 | else{ |
---|
62 | for(int i=0;i<imagesize;i++){ |
---|
63 | smallarray[i] = image[i]; |
---|
64 | ct[i] = float(i+1); |
---|
65 | } |
---|
66 | size=imagesize; |
---|
67 | } |
---|
68 | |
---|
69 | std::sort(smallarray,smallarray+size); |
---|
70 | |
---|
71 | /* fit a linear slope to the centre of the cumulative distribution */ |
---|
72 | long midpt = size/2; |
---|
73 | long imin = midpt - width; |
---|
74 | float slope,intercept,errSlope,errIntercept,r; |
---|
75 | if(size<2*width) |
---|
76 | linear_regression(size,ct,smallarray,0,size-1,slope,errSlope, |
---|
77 | intercept,errIntercept,r); |
---|
78 | else linear_regression(size,ct,smallarray,imin,imin+2*width,slope,errSlope, |
---|
79 | intercept,errIntercept,r); |
---|
80 | |
---|
81 | z1 = smallarray[midpt] + (slope/contrast)*(float)(1-midpt); |
---|
82 | z2 = smallarray[midpt] + (slope/contrast)*(float)(nsample-midpt); |
---|
83 | |
---|
84 | if(z1==z2){ |
---|
85 | |
---|
86 | if(z1!=0) z2 = z1 * 1.05; |
---|
87 | else z2 = z1+0.01; |
---|
88 | |
---|
89 | } |
---|
90 | |
---|
91 | delete [] smallarray; |
---|
92 | delete [] ct; |
---|
93 | |
---|
94 | } |
---|
95 | |
---|
96 | |
---|
97 | void zscale(long imagesize, float *image, float &z1, float &z2, float blankVal) |
---|
98 | { |
---|
99 | float *newimage = new float[imagesize]; |
---|
100 | int newsize=0; |
---|
101 | for(int i=0;i<imagesize;i++) if(image[i]!=blankVal) newimage[newsize++] = image[i]; |
---|
102 | |
---|
103 | float *smallarray = new float[nsample]; |
---|
104 | float *ct = new float[nsample]; |
---|
105 | long size=0; |
---|
106 | |
---|
107 | float mean=0.,sd=0.; |
---|
108 | for(int i=0;i<newsize;i++) mean+=newimage[i]; |
---|
109 | mean /= float(newsize); |
---|
110 | for(int i=0;i<newsize;i++) sd+=(newimage[i]-mean)*(newimage[i]-mean); |
---|
111 | sd /= float(newsize); |
---|
112 | |
---|
113 | |
---|
114 | if(newsize>nsample){ |
---|
115 | int step = (newsize / nsample) + 1; |
---|
116 | for(int i=0;i<newsize;i++){ |
---|
117 | if((i%step)==0){ |
---|
118 | smallarray[size] = newimage[i]; |
---|
119 | ct[size]=(float)size+1.; |
---|
120 | size++; |
---|
121 | } |
---|
122 | } |
---|
123 | } |
---|
124 | else{ |
---|
125 | for(int i=0;i<newsize;i++){ |
---|
126 | smallarray[i] = newimage[i]; |
---|
127 | ct[i] = float(i+1); |
---|
128 | } |
---|
129 | size=newsize; |
---|
130 | } |
---|
131 | |
---|
132 | std::sort(smallarray,smallarray+size); |
---|
133 | |
---|
134 | /* fit a linear slope to the centre of the cumulative distribution */ |
---|
135 | long midpt = size/2; |
---|
136 | long imin = midpt - width; |
---|
137 | float slope,intercept,errSlope,errIntercept,r; |
---|
138 | if(size<2*width) |
---|
139 | linear_regression(size,ct,smallarray,0,size-1,slope,errSlope, |
---|
140 | intercept,errIntercept,r); |
---|
141 | else linear_regression(size,ct,smallarray,imin,imin+2*width,slope,errSlope, |
---|
142 | intercept,errIntercept,r); |
---|
143 | |
---|
144 | z1 = smallarray[midpt] + (slope/contrast)*(float)(1-midpt); |
---|
145 | z2 = smallarray[midpt] + (slope/contrast)*(float)(nsample-midpt); |
---|
146 | |
---|
147 | if(z1==z2){ |
---|
148 | |
---|
149 | if(z1!=0) z2 = z1 * 1.05; |
---|
150 | else z2 = z1+0.01; |
---|
151 | |
---|
152 | } |
---|
153 | |
---|
154 | delete [] newimage; |
---|
155 | delete [] smallarray; |
---|
156 | delete [] ct; |
---|
157 | |
---|
158 | |
---|
159 | } |
---|
160 | |
---|
161 | |
---|