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I want to detect the very minimal movement of a conveyor belt using image evaluation (Resolution: 31x512, image rate: 1000 per second.). The moment of belt-start is important for me.
If I do cv::absdiff between two subsequent images, I obtain very noisy result:
According to the mechanical rotation sensor of the motor, the movement starts here:
I tried to threshold the abs-diff image with a cascade of erosion and dilation, but I could detect the earliest change more than second too late in this image:
Is it possible to find the change earlier?
Here is the sequence of the Images without changes (according to motor sensor):
In this sequence the movement begins in the middle image:
Looks like I've found a solution which works in MY case.
Instead of comparing the image changes in space-domain, the cross-correlation should be applied:
I convert both images to DFT, multiply DFT-Mats and convert back. The max pixel value is the center of the correlation. As long as the images are same, the max-pix remains in the same position and moves otherwise.
The actual working code uses 3 images, 2 DFT multiplication result between images 1,2 and 2,3:
Mat img1_( 512, 32, CV_16UC1 );
Mat img2_( 512, 32, CV_16UC1 );
Mat img3_( 512, 32, CV_16UC1 );
//read the data in the images wohever you want. I read from MHD-file
//Set ROI (if required)
Mat img1 = img1_(cv::Rect(0,200,32,100));
Mat img2 = img2_(cv::Rect(0,200,32,100));
Mat img3 = img3_(cv::Rect(0,200,32,100));
//Float mats for DFT
Mat img1f;
Mat img2f;
Mat img3f;
//DFT and produtcts mats
Mat dft1,dft2,dft3,dftproduct,dftproduct2;
//Calculate DFT of both images
img1.convertTo(img1f, CV_32FC1);
cv::dft(img1f, dft1);
img2.convertTo(img3f, CV_32FC1);
cv::dft(img3f, dft3);
img3.convertTo(img2f, CV_32FC1);
cv::dft(img2f, dft2);
//Multiply DFT Mats
cv::mulSpectrums(dft1,dft2,dftproduct,true);
cv::mulSpectrums(dft2,dft3,dftproduct2,true);
//Convert back to space domain
cv::Mat result,result2;
cv::idft(dftproduct,result);
cv::idft(dftproduct2,result2);
//Not sure if required, I needed it for visualizing
cv::normalize( result, result, 0, 255, NORM_MINMAX, CV_8UC1);
cv::normalize( result2, result2, 0, 255, NORM_MINMAX, CV_8UC1);
//Find maxima positions
double dummy;
Point locdummy; Point maxLoc1; Point maxLoc2;
cv::minMaxLoc(result, &dummy, &dummy, &locdummy, &maxLoc1);
cv::minMaxLoc(result2, &dummy, &dummy, &locdummy, &maxLoc2);
//Calculate products simply fot having one value to compare
int maxlocProd1 = maxLoc1.x*maxLoc1.y;
int maxlocProd2 = maxLoc2.x*maxLoc2.y;
//Calculate absolute difference of the products. Not 0 means movement
int absPosDiff = std::abs(maxlocProd2-maxlocProd1);
if ( absPosDiff>0 )
{
std::cout << id<< std::endl;
break;
}
I'm wondering how to plot a 2d histogram of an HSV Mat in opencv c++. My current code attempting to display it fails miserably. I've looked around on how to plot histograms and all the ones I've found were those plotting them as independent 1d histograms.
Here's my current output with the number of hue bins being 30 and saturation bins being 32:
Here's another output with the number of hue bins being 7 and saturaation bins being 5:
I would like it to look more like the result here
http://docs.opencv.org/doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.html
I also noticed whenever I do cout << Hist.size it gives me 50x50. Am I to understand that just means the first dimension of the array is 250 in size?
Also, how does one sort the histogram from highest to lowest (or vice versa) value frequency? That is another problem I am trying to solve.
My current function is as follows.
void Perform_Hist(Mat& MeanShift, Mat& Pyramid_Result, Mat& BackProj){
Mat HSV, Hist;
int histSize[] = {hbins, sbins};
int channels[] = {0, 1};
float hranges[] = {0, 180};
float sranges[] = {0, 256};
const float* ranges[] = {hranges, sranges};
cvtColor(MeanShift, HSV, CV_BGR2HSV);
Mat PyrGray = Pyramid_Result.clone();
calcHist(&HSV, 1, channels, Mat(), Hist, 2, histSize, ranges, true, false);
normalize(Hist, Hist, 0, 255, NORM_MINMAX, -1, Mat());
invert(Hist, Hist, 1);
calcBackProject(&PyrGray, 1, channels, Hist, BackProj, ranges, 1, true);
double maxVal = 0; minMaxLoc(Hist, 0, &maxVal, 0, 0);
int scale = 10;
Mat histImage = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
for(int i = 1; i < hbins * sbins; i++){
line(histImage,
Point(hbins*sbins*(i-1), sbins - cvRound(Hist.at<float>(i-1))),
Point(hbins*sbins*(i-1), sbins - cvRound(Hist.at<float>(i))),
Scalar(255,0,0), 2, 8, 0);
}
imshow (HISTOGRAM, histImage);
}
Did you mean something like this?
it is HSV histogram showed as 3D graph
V is ignored to get to 3D (otherwise it would be 4D graph ...)
if yes then this is how to do it (I do not use OpenCV so adjust it to your needs):
convert source image to HSV
compute histogram ignoring V value
all colors with the same H,S are considered as single color no matter what the V is
you can ignore any other but the V parameter looks like the best choice
draw the graph
first draw ellipse with darker color (HSV base disc)
then for each dot take the corresponding histogram value and draw vertical line with brighter color. Line size is proportional to the histogram value
Here is the C++ code I did this with:
picture pic0,pic1,pic2,zed;
int his[65536];
DWORD w;
int h,s,v,x,y,z,i,n;
double r,a;
color c;
// compute histogram (ignore v)
pic2=pic0; // copy input image pic0 to pic2
pic2.rgb2hsv(); // convert to HSV
for (x=0;x<65536;x++) his[x]=0; // clear histogram
for (y=0;y<pic2.ys;y++) // compute it
for (x=0;x<pic2.xs;x++)
{
c=pic2.p[y][x];
h=c.db[picture::_h];
s=c.db[picture::_s];
w=h+(s<<8); // form 16 bit number from 24bit HSV color
his[w]++; // update color usage count ...
}
for (n=0,x=0;x<65536;x++) if (n<his[x]) n=his[x]; // max probability
// draw the colored HSV base plane and histogram
zed =pic1; zed .clear(999); // zed buffer for 3D
pic1.clear(0); // image of histogram
for (h=0;h<255;h++)
for (s=0;s<255;s++)
{
c.db[picture::_h]=h;
c.db[picture::_s]=s;
c.db[picture::_v]=100; // HSV base darker
c.db[picture::_a]=0;
x=pic1.xs>>1; // HSV base disc position centers on the bottom
y=pic1.ys-100;
a=2.0*M_PI*double(h)/256.0; // disc -> x,y
r=double(s)/256.0;
x+=120.0*r*cos(a); // elipse for 3D ilusion
y+= 50.0*r*sin(a);
z=-y;
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } x++;
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } y++;
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } x--;
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } y--;
w=h+(s<<8); // get histogram index for this color
i=((pic1.ys-150)*his[w])/n;
c.db[picture::_v]=255; // histogram brighter
for (;(i>0)&&(y>0);i--,y--)
{
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } x++;
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } y++;
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } x--;
if (zed.p[y][x].dd>=z){ pic1.p[y][x]=c; zed.p[y][x].dd=z; } y--;
}
}
pic1.hsv2rgb(); // convert to RGB to see correct colors
input image is pic0 (rose), output image is pic1 (histogram graph)
pic2 is the pic0 converted to HSV for histogram computation
zed is the Zed buffer for 3D display avoiding Z sorting ...
I use my own picture class for images so some members are:
xs,ys size of image in pixels
p[y][x].dd is pixel at (x,y) position as 32 bit integer type
clear(color) - clears entire image
resize(xs,ys) - resizes image to new resolution
rgb2hsv() and hsv2rgb() ... guess what it does :)
[edit1] your 2D histogram
It looks like you have color coded into 2D array. One axis is H and second is S. So you need to calculate H,S value from array address. If it is linear then for HSV[i][j]:
H=h0+(h1-h0)*i/maxi
S=s0+(s1-s0)*j/maxj
or i,j reversed
h0,h1,s0,s1 are the color ranges
maxi,maxj are the array size
As you can see you also discard V like me so now you have H,S for each cell in histogram 2D array. Where probability is the cell value. Now if you want to draw an image you need to know how to output this (as a 2D graph, 3D, mapping,...). For unsorted 2D graph draw graph where:
x=i+maj*i
y=HSV[i][j]
color=(H,S,V=200);
If you want to sort it then just compute the x axis differently or loop the 2D array in sort order and x just increment
[edit2] update of code and some images
I have repaired the C++ code above (wrong Z value sign, changed Z buffer condition and added bigger points for nicer output). Your 2D array colors can be as this:
Where one axis/index is H, the other S and Value is fixed (I choose 200). If your axises are swapped then just mirror it by y=x I think ...
The color sorting is really just an order in which you pick all the colors from array. for example:
v=200; x=0;
for (h=0;h<256;h++)
for (s=0;s<256;s++,x++)
{
y=HSV[h][s];
// here draw line (x,0)->(x,y) by color hsv2rgb(h,s,v);
}
This is the incrementing way. You can compute x from H,S instead to achieve different sorting or swap the fors (x++ must be in the inner loop)
If you want RGB histogram plot instead see:
how to plot rgb color histogram of image with objective c
I have what should be a simple exercise in OpenCV, but can't seem to get it working. I'm trying to determine the density of edges in a section of an image. This is the process I follow:
1. pull subimage from image
2. use Canny to find edges in subImage
3. threshold to create binary image
4. create histogram for binary image
5. get number of pixels in binary image that are "on" (255)
6. calculate "edge density" as numPixelsOn/totalPixels
I've checked the results of 1,2,and 3 above, and results seem ok. Steps 4 and 5 seem to be giving me trouble.
Here's my code for calculating the histogram:
int histSize = 256; // bin size
float range[] = { 0, 256} ;
const float* histRange = { range };
bool uniform = true;
bool accumulate = false;
Mat hist;
/// Compute the histograms:
calcHist( &gray, 1, 0, Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
This doesn't seem to be working. When I check hist after calling calcHist, it has no data (i.e. data == 0)... or maybe I don't understand what I'm looking at.
Now for accessing the "bins" in the histogram, I've tried a number of things. First I tried this:
uchar* p;
p = hist.ptr<uchar>(0);
double edgePixels = p[255];
I also tried to use:
cvQueryHistValue_1D(hist,255); // #include <opencv2/legacy/compat.hpp>
This wouldn't compile. Gave 2 errors: 'cv::Mat' does not have an overloaded member 'operator ->', and 'bins': is not a member of 'cv::Mat'
I guess I need some help on this.
There is an error in your 3rd param - channels, that should be an array so you should call it like this
int histSize = 256; // bin size
float range[] = { 0, 256} ;
const float* histRange = { range };
bool uniform = true;
bool accumulate = false;
Mat hist;
int channels[] = {0};
/// Compute the histograms:
calcHist( &gray, 1, channels, Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
You should also call:
hist.at<float>(0);
to get your value, OpenCV stores them as floats, this is the reason you're getting 0 when using uchar as uchar is smaller than float and the numbers stores as small enough to not fill the first bites.
I created three dimension matrix for computing of histogram as follows:
// Histogram of HSV image
int const hue_bins = 180; //
int const sat_bins = 256; //
int const val_bins = 4; // Only four bins for V channel!
float const hue_range[2] = {0, 180};
float const sat_range[2] = {0, 256};
float const val_range[2] = {0, 256};
int const hsv_sizes[] = {hue_bins, sat_bins, val_bins};
cv::Mat1f m_tone_frequences(3, hsv_sizes, 0.);
Then I'm using
cv::calcHist
( &image, 1, channels, mask, histogram
, num_channels, hsv_sizes, ranges);
...
cv::calcBackProject
( &image_f, 1, channels, histogram
, backproject, hsv_sizes, 1.0);
and seems it works fine (code is simplified).
Since the histograms are sampled from a single image, it is possible to run into sampling problems (object of interest has narrow color distribution). So I want to apply Gaussian smoothing to "Value" histogram planes.
I'm tried get histogram rows, but it gives me anothed 3D Mat:
cv::Mat1f hrow = histogram.row(0);
// hrow.dims ==3 && hrow.rows == -1 && hrow.cols == -1
and I don't have ideas about processing of it.
I am at a loss to solve this issue because this action should be very simple to do.
Any advice is greatly appreciated.
In Matlab, If A is a matrix, sum(A) treats the columns of A as vectors, returning a row vector of the sums of each column.
sum(Image); How could it be done with OpenCV?
Using cvReduce has worked for me. For example, if you need to store the column-wise sum of a matrix as a row matrix you could do this:
CvMat * MyMat = cvCreateMat(height, width, CV_64FC1);
// Fill in MyMat with some data...
CvMat * ColSum = cvCreateMat(1, MyMat->width, CV_64FC1);
cvReduce(MyMat, ColSum, 0, CV_REDUCE_SUM);
More information is available in the OpenCV documentation.
EDIT after 3 years:
The proper function for this is cv::reduce.
Reduces a matrix to a vector.
The function reduce reduces the matrix to a vector by treating the
matrix rows/columns as a set of 1D vectors and performing the
specified operation on the vectors until a single row/column is
obtained. For example, the function can be used to compute horizontal
and vertical projections of a raster image. In case of REDUCE_MAX and
REDUCE_MIN , the output image should have the same type as the source
one. In case of REDUCE_SUM and REDUCE_AVG , the output may have a
larger element bit-depth to preserve accuracy. And multi-channel
arrays are also supported in these two reduction modes.
OLD:
I've used ROI method: move ROI of height of the image and width 1 from left to right and calculate means.
Mat src = imread(filename, 0);
vector<int> graph( src.cols );
for (int c=0; c<src.cols-1; c++)
{
Mat roi = src( Rect( c,0,1,src.rows ) );
graph[c] = int(mean(roi)[0]);
}
Mat mgraph( 260, src.cols+10, CV_8UC3);
for (int c=0; c<src.cols-1; c++)
{
line( mgraph, Point(c+5,0), Point(c+5,graph[c]), Scalar(255,0,0), 1, CV_AA);
}
imshow("mgraph", mgraph);
imshow("source", src);
EDIT:
Just out of curiosity, I've tried resize to height 1 and the result was almost the same:
Mat test;
cv::resize(src,test,Size( src.cols,1 ));
Mat mgraph1( 260, src.cols+10, CV_8UC3);
for(int c=0; c<test.cols; c++)
{
graph[c] = test.at<uchar>(0,c);
}
for (int c=0; c<src.cols-1; c++)
{
line( mgraph1, Point(c+5,0), Point(c+5,graph[c]), Scalar(255,255,0), 1, CV_AA);
}
imshow("mgraph1", mgraph1);
cvSum respects ROI, so if you move a 1 px wide window over the whole image, you can calculate the sum of each column.
My c++ got a little rusty so I won't provide a code example, though the last time I did this I used OpenCVSharp and it worked fine. However, I'm not sure how efficient this method is.
My math skills are getting rusty too, but shouldn't it be possible to sum all elements in columns in a matrix by multiplying it by a vector of 1s?
For an 8 bit greyscale image, the following should work (I think).
It shouldn't be too hard to expand to different image types.
int imgStep = image->widthStep;
uchar* imageData = (uchar*)image->imageData;
uint result[image->width];
memset(result, 0, sizeof(uchar) * image->width);
for (int col = 0; col < image->width; col++) {
for (int row = 0; row < image->height; row++) {
result[col] += imageData[row * imgStep + col];
}
}
// your desired vector is in result