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
|
Back Projection {#tutorial_back_projection}
===============
Goal
----
In this tutorial you will learn:
- What is Back Projection and why it is useful
- How to use the OpenCV function @ref cv::calcBackProject to calculate Back Projection
- How to mix different channels of an image by using the OpenCV function @ref cv::mixChannels
Theory
------
### What is Back Projection?
- Back Projection is a way of recording how well the pixels of a given image fit the distribution
of pixels in a histogram model.
- To make it simpler: For Back Projection, you calculate the histogram model of a feature and then
use it to find this feature in an image.
- Application example: If you have a histogram of flesh color (say, a Hue-Saturation histogram ),
then you can use it to find flesh color areas in an image:
### How does it work?
- We explain this by using the skin example:
- Let's say you have gotten a skin histogram (Hue-Saturation) based on the image below. The
histogram besides is going to be our *model histogram* (which we know represents a sample of
skin tonality). You applied some mask to capture only the histogram of the skin area:
- Now, let's imagine that you get another hand image (Test Image) like the one below: (with its
respective histogram):
- What we want to do is to use our *model histogram* (that we know represents a skin tonality) to
detect skin areas in our Test Image. Here are the steps
-# In each pixel of our Test Image (i.e. \f$p(i,j)\f$ ), collect the data and find the
correspondent bin location for that pixel (i.e. \f$( h_{i,j}, s_{i,j} )\f$ ).
-# Lookup the *model histogram* in the correspondent bin - \f$( h_{i,j}, s_{i,j} )\f$ - and read
the bin value.
-# Store this bin value in a new image (*BackProjection*). Also, you may consider to normalize
the *model histogram* first, so the output for the Test Image can be visible for you.
-# Applying the steps above, we get the following BackProjection image for our Test Image:
-# In terms of statistics, the values stored in *BackProjection* represent the *probability*
that a pixel in *Test Image* belongs to a skin area, based on the *model histogram* that we
use. For instance in our Test image, the brighter areas are more probable to be skin area
(as they actually are), whereas the darker areas have less probability (notice that these
"dark" areas belong to surfaces that have some shadow on it, which in turns affects the
detection).
Code
----
- **What does this program do?**
- Loads an image
- Convert the original to HSV format and separate only *Hue* channel to be used for the
Histogram (using the OpenCV function @ref cv::mixChannels )
- Let the user to enter the number of bins to be used in the calculation of the histogram.
- Calculate the histogram (and update it if the bins change) and the backprojection of the
same image.
- Display the backprojection and the histogram in windows.
- **Downloadable code**:
-# Click
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp)
for the basic version (explained in this tutorial).
-# For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the
skin area) you can check the [improved
demo](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp)
-# ...or you can always check out the classical
[camshiftdemo](https://github.com/opencv/opencv/tree/master/samples/cpp/camshiftdemo.cpp)
in samples.
- **Code at glance:**
@include samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp
Explanation
-----------
-# Declare the matrices to store our images and initialize the number of bins to be used by our
histogram:
@code{.cpp}
Mat src; Mat hsv; Mat hue;
int bins = 25;
@endcode
-# Read the input image and transform it to HSV format:
@code{.cpp}
src = imread( argv[1], 1 );
cvtColor( src, hsv, COLOR_BGR2HSV );
@endcode
-# For this tutorial, we will use only the Hue value for our 1-D histogram (check out the fancier
code in the links above if you want to use the more standard H-S histogram, which yields better
results):
@code{.cpp}
hue.create( hsv.size(), hsv.depth() );
int ch[] = { 0, 0 };
mixChannels( &hsv, 1, &hue, 1, ch, 1 );
@endcode
as you see, we use the function @ref cv::mixChannels to get only the channel 0 (Hue) from
the hsv image. It gets the following parameters:
- **&hsv:** The source array from which the channels will be copied
- **1:** The number of source arrays
- **&hue:** The destination array of the copied channels
- **1:** The number of destination arrays
- **ch[] = {0,0}:** The array of index pairs indicating how the channels are copied. In this
case, the Hue(0) channel of &hsv is being copied to the 0 channel of &hue (1-channel)
- **1:** Number of index pairs
-# Create a Trackbar for the user to enter the bin values. Any change on the Trackbar means a call
to the **Hist_and_Backproj** callback function.
@code{.cpp}
char* window_image = "Source image";
namedWindow( window_image, WINDOW_AUTOSIZE );
createTrackbar("* Hue bins: ", window_image, &bins, 180, Hist_and_Backproj );
Hist_and_Backproj(0, 0);
@endcode
-# Show the image and wait for the user to exit the program:
@code{.cpp}
imshow( window_image, src );
waitKey(0);
return 0;
@endcode
-# **Hist_and_Backproj function:** Initialize the arguments needed for @ref cv::calcHist . The
number of bins comes from the Trackbar:
@code{.cpp}
void Hist_and_Backproj(int, void* )
{
MatND hist;
int histSize = MAX( bins, 2 );
float hue_range[] = { 0, 180 };
const float* ranges = { hue_range };
@endcode
-# Calculate the Histogram and normalize it to the range \f$[0,255]\f$
@code{.cpp}
calcHist( &hue, 1, 0, Mat(), hist, 1, &histSize, &ranges, true, false );
normalize( hist, hist, 0, 255, NORM_MINMAX, -1, Mat() );
@endcode
-# Get the Backprojection of the same image by calling the function @ref cv::calcBackProject
@code{.cpp}
MatND backproj;
calcBackProject( &hue, 1, 0, hist, backproj, &ranges, 1, true );
@endcode
all the arguments are known (the same as used to calculate the histogram), only we add the
backproj matrix, which will store the backprojection of the source image (&hue)
-# Display backproj:
@code{.cpp}
imshow( "BackProj", backproj );
@endcode
-# Draw the 1-D Hue histogram of the image:
@code{.cpp}
int w = 400; int h = 400;
int bin_w = cvRound( (double) w / histSize );
Mat histImg = Mat::zeros( w, h, CV_8UC3 );
for( int i = 0; i < bins; i ++ )
{ rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ), Scalar( 0, 0, 255 ), -1 ); }
imshow( "Histogram", histImg );
@endcode
Results
-------
Here are the output by using a sample image ( guess what? Another hand ). You can play with the
bin values and you will observe how it affects the results:
|
