I want to smooth a histogram.
Therefore I tried to smooth the internal matrix of cvHistogram.
typedef struct CvHistogram
{
int type;
CvArr* bins;
float thresh[CV_MAX_DIM][2]; /* for uniform histograms */
float** thresh2; /* for non-uniform histograms */
CvMatND mat; /* embedded matrix header for array histograms */
}
I tried to smooth the matrix like this:
cvCalcHist( planes, hist, 0, 0 ); // Compute histogram
(...)
// smooth histogram with Gaussian Filter
cvSmooth( hist->mat, hist_img, CV_GAUSSIAN, 3, 3, 0, 0 );
Unfortunately, this is not working because cvSmooth needs a CvMat as input instead of a CvMatND. I couldn't transform CvMatND into CvMat (CvMatND is 2-dim in my case).
Is there anybody who can help me? Thanks.
You can use the same basic algorithm used for Mean filter, just calculating the average.
for(int i = 1; i < NBins - 1; ++i)
{
hist[i] = (hist[i - 1] + hist[i] + hist[i + 1]) / 3;
}
Optionally you can use a slightly more flexible algorithm allowing you to easily change the window size.
int winSize = 5;
int winMidSize = winSize / 2;
for(int i = winMidSize; i < NBins - winMidSize; ++i)
{
float mean = 0;
for(int j = i - winMidSize; j <= (i + winMidSize); ++j)
{
mean += hist[j];
}
hist[i] = mean / winSize;
}
But bear in mind that this is just one simple technique.
If you really want to do it using OpenCv tools, I recommend you access the openCv forum: http://tech.groups.yahoo.com/group/OpenCV/join
You can dramatically change the "smoothness" of a histogram by changing the number of bins you use. A good rule of thumb is to have sqrt(n) bins if you have n data points. You might try applying this heuristic to your histogram and see if you get a better result.
Related
I want to create a 1D plot from an image. Then I want to determine the maxima and their distances to each other in c++.
I am looking for some tips on how I could approach this.
I load the image as cv::Mat. In opencv I have searched, but only found the histogram function, which is wrong. I want to get a cross section of the image - from left to right.
does anyone have an idea ?
Well I have the following picture:
From this I want to create a 1D plot like in the following picture (I created the plot in ImageJ).
Here you can see the maxima (I could refine it with "smooth").
I want to determine the positions of these maxima and then the distances between them.
I have to get to the 1D plot somehow. I suppose I can get to the maxima with a derivation?
++++++++++ UPDATE ++++++++++
Now i wrote this to get an 1D Plot:
cv::Mat img= cv::imread(imgFile.toStdString(), cv::IMREAD_ANYDEPTH | cv::IMREAD_COLOR);
cv::cvtColor(img, img, cv::COLOR_BGR2GRAY);
uint8_t* data = img.data;
int width = img.cols;
int height = img.rows;
int stride = img.step;
std::vector<double> vPlot(width, 0);
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
uint8_t val = data[ i * stride + j];
vPlot[j]=vPlot[j] + val;
}
}
std::ofstream file;
file.open("path\\plot.csv");
for(int i = 0; i < vPlot.size(); i++){
file << vPlot[i];
file << ";";
}
file.close();
When i plot this in excel i got this:
Thats looks not so smooth as in ImageJ. Did i something wrong?
I need it like in the Plot of ImageJ - more smooth.
ok I got it:
for (int i = 0; i < vPlot.size(); i++) {
vPlot[i] = vPlot[i] / height;
}
Ok but i don't know how to get the maxima an distances.
When i have the local maxima (i don't know how), i can calculate the distance between them with the index of the vetcor elements.
Has anybody an idea to get the local Maxima out of the vector, that I plot above ?
Now o wrote this to find the maxima:
// find maxima
std::vector<int> idxMax;
int flag = 0;
for(int i = 1; i < avg.size(); i++){
double diff = avg[i] - avg[i-1];
if(diff < 0){
if(flag>0){
idxMax.push_back(i);
flag = -1;
}
}
if(diff >= 0){
if(flag<=0){
flag = 1;
}
}
}
But more maxima are found than wanted. The length of the vector varies and also the number of peaks. These can be close together or far away. They are also not always the same height, as can be seen in the picture
I'm learning OpenCV (C++) and as a simple practice, I designed a simple effect which makes some of image pixels black or white. I want each pixel to be edited at most once; so I added address of all pixels to a vector. But it made my code very slow; specially for large images or high amounts of effect. Here is my code:
void effect1(Mat& img, float amount) // 100 ≥ amount ≥ 0
{
vector<uchar*> addresses;
int channels = img.channels();
uchar* lastAddress = img.ptr<uchar>(0) + img.total() * channels;
for (uchar* i = img.ptr<uchar>(0); i < lastAddress; i += channels) addresses.push_back(i); //Fast Enough
size_t count = img.total() * amount / 100 / 2;
for (size_t i = 0; i < count; i++)
{
size_t addressIndex = xor128() % addresses.size(); //Fast Enough, xor128() is a fast random number generator
for (size_t j = 0; j < channels; j++)
{
*(addresses[addressIndex] + j) = 255;
} //Fast Enough
addresses.erase(addresses.begin() + addressIndex); // MAKES CODE EXTREMELY SLOW
}
for (size_t i = 0; i < count; i++)
{
size_t addressIndex = xor128() % addresses.size(); //Fast Enough, xor128() is a fast random number generator
for (size_t j = 0; j < channels; j++)
{
*(addresses[addressIndex] + j) = 0;
} //Fast Enough
addresses.erase(addresses.begin() + addressIndex); // MAKES CODE EXTREMELY SLOW
}
}
I think rearranging vector items after erasing an item is what makes my code slow (if I remove addresses.erase, code will run fast).
Is there any fast method to select each random item from a collection (or a number range) at most once?
Also: I'm pretty sure such effect already exists. Does anyone know the name of it?
This answer assumes you have a random bit generator function, since std::random_shuffle requires that. I don't know how xor128 works, so I'll use the functionality of the <random> library.
If we have a population of N items, and we want to select groups of size j and k randomly from that population with no overlap, we can write down the index of each item on a card, shuffle the deck, draw j cards, and then draw k cards. Everything left over is discarded. We can achieve this with the <random> library. Answer pending on how to incorporate a custom PRNG like you implemented with xor128.
This assumes that random_device won't work on your system (many compilers implement it in a way that it will always return the same sequence) so we seed the random generator with current time like the good old fashioned srand our mother used to make.
Untested since I don't know how to use OpenCV. Anyone with a lick of experience with that please edit as appropriate.
#include <ctime> // for std::time
#include <numeric> // for std::iota
#include <random>
#include <vector>
void effect1(Mat& img, float amount, std::mt19937 g) // 0.0 ≥ amount ≥ 1.00
{
std::vector<cv::Size> ind(img.total());
std::iota(ind.begin(), ind.end(), 0); // fills with 0, 1, 2, ...
std::random_shuffle(ind.begin(), ind.end(), g);
cv::Size count = img.total() * amount;
auto white = get_white<Mat>(); // template function to return this matrix' concept of white
// could easily replace with cv::Vec3d(255,255,255)
// if all your matrices are 3 channel?
auto black = get_black<Mat>(); // same but... opposite
auto end = ind.begin() + count;
for (auto it = ind.begin(), it != end; ++it)
{
img.at(*it) = white;
}
end = (ind.begin() + 2 * count) > ind.end() ?
ind.end() :
ind.begin() + 2 * count;
for (auto it = ind.begin() + count; it != end; ++it)
{
img.at(*it) = black;
}
}
int main()
{
std::mt19937 g(std::time(nullptr)); // you normally see this seeded with random_device
// but that's broken on some implementations
// adjust as necessary for your needs
cv::Mat mat = ... // make your cv objects
effect1(mat, 0.1, g);
// display it here
}
Another approach
Instead of shuffling indices and drawing cards from a deck, assume each pixel has a random probability of switching to white, switching to black, or staying the same. If your amount is 0.4, then select a random number between 0.0 and 1.0, any result between 0.0 and 0.4 flips the pixel black, and betwen 0.4 and 0.8 flips it white, otherwise it stays the same.
General algorithm:
given probability of flipping -> f
for each pixel in image -> p:
get next random float([0.0, 1.0)) -> r
if r < f
then p <- BLACK
else if r < 2*f
then p <- WHITE
You won't get the same number of white/black pixels each time, but that's randomness! We're generating a random number for each pixel anyway for the shuffling algorithm. This has the same complexity unless I'm mistaken.
Also: I'm pretty sure such effect already exists. Does anyone know the name of it?
The effect you're describing is called salt and pepper noise. There is no direct implementation in OpenCV that I know of though.
I think rearranging vector items after erasing an item is what makes
my code slow (if I remove addresses.erase, code will run fast).
Im not sure why you add your pixels to a vector in your code, it would make much more sense and also be much more performant to directly work on the Mat object and change the pixel value directly. You could use OpenCVs inbuild Mat.at() function to directly change the pixel values to either 0 or 255.
I would create a single loop which generates random indexes in the range of your image dimension and manipulate the image pixels directly. That way you are in O(n) for your noise addition. You could also just search for "OpenCV" and "salt and pepper noise", I am sure there already are a lot of really performant implementations.
I also post a simpler code:
void saltAndPepper(Mat& img, float amount)
{
vector<size_t> pixels(img.total()); // size_t = unsigned long long
uchar channels = img.channels();
iota(pixels.begin(), pixels.end(), 0); // Fill vector with 0, 1, 2, ...
shuffle(pixels.begin(), pixels.end(), mt19937(time(nullptr))); // Shuffle the vector
size_t count = img.total() * amount / 100 / 2;
for (size_t i = 0; i < count; i++)
{
for (size_t j = 0; j < channels; j++) // Set all pixel channels (e.g. Grayscale with 1 channel or BGR with 3 channels) to 255
{
*(img.ptr<uchar>(0) + (pixels[i] * channels) + j) = 255;
}
}
for (size_t i = count; i < count*2; i++)
{
for (size_t j = 0; j < channels; j++) // Set all pixel channels (e.g. Grayscale with 1 channel or BGR with 3 channels) to 0
{
*(img.ptr<uchar>(0) + (pixels[i] * channels) + j) = 0;
}
}
}
I have problem with low contrast image segmentation.
Task is to find surface defects. They are visible (defects are always dark areas) but the contrast of image is very low.
Below two samples.
I have tried enhance contrast and then tresholding:
Mat tmp1 = imread("C:\\framesRoi\\311.bmp",0);
stretchContrast(tmp1);
threshold(tmp1,tmp1,75,255,THRESH_BINARY);
where stretch contrast impl:
int minValue = 255, maxValue = 0;
const int l = sourceImg.cols * sourceImg.rows * sourceImg.channels();
if(sourceImg.isContinuous())
{
uchar* ptr = sourceImg.ptr<uchar>(0);
for(int i = 0; i < l; ++i)
{
if(ptr[i] < minValue)
{
minValue = ptr[i];
}
if(ptr[i] > maxValue)
{
maxValue = ptr[i];
}
}
}
cout<<"min: "<<minValue<<";"<<"max value: "<<maxValue<<endl;
const int magicThreshold = 10;
if(sourceImg.isContinuous())
{
uchar* ptr = sourceImg.ptr<uchar>(0);
for(int i = 0; i < l; ++i)
{
ptr[i] = 255 * (ptr[i]-minValue)/(maxValue - minValue);
}
}
But this approach failed. There are many false detections and not all defects are detected:
Here is zip with test frames: https://dl.dropboxusercontent.com/u/47015140/testFrames.rar
Try clustering the image by gray level using a clustering method such as kmeans. Below I've used kmeans directly on the images without any gray level transformations (using 3 clusters gave me better results). You should be able to improve results by clustering a preprocessed image using methods outlined in the comments.
Shape of the clusters may slightly vary due to the randomness of kmeans.
Now if you take connected components of the clustered image and calculate the average gray level of those regions, the defects should have a lower average than the other regions.
I did clustering part in Matlab.
im = imread('r2SOV.png');%Uy1Fq r2SOV
gr = im;
size = size(gr);
% perform closing using a 5x5 circular structuring element
sel = strel('disk', 2, 4);
mcl = imclose(gr, sel);
% cluster gray levels using kmeans: using 3 clusters
x = double(mcl(:));
idx = kmeans(x, 3);
cl = reshape(idx, size);
figure, imshow(label2rgb(cl))
As people said in your comment, you can change the brightness in a negative way and push up the contrast.
Moreover, the sharpen filter is also very useful for your case. You can do this in OpenCV.
I think you should try adaptiveThreshold function with a large window.
#include "opencv2/opencv.hpp"
using namespace cv;
int main(int argc,char** argv )
{
Mat im = imread("c:/data/img1.png",0);
cv::namedWindow("ctrl");
int win=62;
int th=2100;
cv::createTrackbar( "win", "ctrl", &win, 500);
cv::createTrackbar( "th", "ctrl", &th, 10000);
while(true)
{
Mat thresh;
medianBlur(im,thresh,15);//helps smooth out smaller noises, which you could also remove by size instead of this way
adaptiveThreshold(thresh,thresh,255,ADAPTIVE_THRESH_MEAN_C,THRESH_BINARY,win*2+1,( th/1000.));
imshow("thresh",thresh);
if(waitKey(1)==27)
exit(0);
}
}
all results here (http://www.datafilehost.com/d/99e3d86c) You might also want to take a look at imagej which implements a bunch of auto-threshold algorithms. I think what you need is something that takes local image information into account.
I'm looking for some pointers on how to sample an image using OpenGL at a list of specified locations. Any links to tutorial or examples similar to the problem below?
At the moment we have a code that calculates the 'output intensity' at a list of specified locations x1,y1, x2,y2, ..., xn,yn applying a Lanczos2 filter to an input image. The amount of locations at the moment is 20 (which is actually the list of phosphene locations in a visual prosthesis) but it will eventually increase up to 256 and GPU processing will certainly accelerate things. The list of locations can't be hardcoded.
So far I have seen how to implement a median filter and alike, but in my case there is no need to compute the convolution with the filter kernel at every image pixel, just at the locations specified.
Handle those values (intesity) in a second texture that has a sample or not bit.
If you use OpenGL, you'll be able to define the ROI (region of interest), the portion of an image to which you want to apply edits or processing, as you describe.
If you go that route, this is how you calculate the median in a pixel neighborhood radius of your choosing using OpenGL ES 2.0/3.0:
kernel vec4 medianUnsharpKernel(sampler u) {
vec4 pixel = unpremultiply(sample(u, samplerCoord(u)));
vec2 xy = destCoord();
int radius = 3;
int bounds = (radius - 1) / 2;
vec4 sum = vec4(0.0);
for (int i = (0 - bounds); i <= bounds; i++)
{
for (int j = (0 - bounds); j <= bounds; j++ )
{
sum += unpremultiply(sample(u, samplerTransform(u, vec2(xy + vec2(i, j)))));
}
}
vec4 mean = vec4(sum / vec4(pow(float(radius), 2.0)));
float mean_avg = float(mean);
float comp_avg = 0.0;
vec4 comp = vec4(0.0);
vec4 median = mean;
for (int i = (0 - bounds); i <= bounds; i++)
{
for (int j = (0 - bounds); j <= bounds; j++ )
{
comp = unpremultiply(sample(u, samplerTransform(u, vec2(xy + vec2(i, j)))));
comp_avg = float(comp);
median = (comp_avg < mean_avg) ? max(median, comp) : median;
}
}
return premultiply(vec4(vec3(abs(pixel.rgb - median.rgb)), 1.0));
}
A brief description of the steps
1. Calculate the mean of the values of the pixels surrounding the source pixel in a 3x3 neighborhood;
2. Find the maximum pixel value of all pixels in the same neighborhood that are less than the mean.
3. [OPTIONAL] Subtract the median pixel value from the source pixel value for edge detection.
If you're using the median value for edge detection, there are a couple of ways to modify the above code for better results, namely, hybrid median filtering and truncated media filtering (a substitute and a better 'mode' filtering). If you're interested, please ask.
I want to access image pixels as float array in opencv. Ive done the following:
Mat input = imread("Lena.jpg",CV_LOAD_IMAGE_GRAYSCALE);
int height = input.rows;
int width = input.cols;
Mat out;
input.convertTo(input, CV_32FC1);
copyMakeBorder(input, input, 3, 3, 3, 3, 0);
out = Mat(height, width, input.type());
float *outdata = (float*)out.data;
float *indata = (float*)input.data;
for(int j = 0; j < height; j++){
for(int i =0; i < width; i++){
outdata[j*width + i] = indata[(j* width + i)];
}
}
normalize(out, out,0,255,NORM_MINMAX,CV_8UC1);
imshow("output", out);
waitKey();
This should return the original image in "out", however, I'm getting some weird image. Can anyone explain whats wrong with the code. I think i need to use some step size (widthStep). Thanks.
the line
copyMakeBorder(input, input, 3, 3, 3, 3, 0);
changes the dimensions of input, it adds 6 rows and 6 columns to the image. That means your height and width variables are holding the wrong values when you define out and try to loop over the values on input.
if you change the order to
copyMakeBorder(input, input, 3, 3, 3, 3, 0);
int height = input.rows;
int width = input.cols;
it should work fine.
Some ideas:
Something like outdata[j*width + i] is a more standard pattern for this sort of thing.
According to the opencv documentation, there is a templated Mat::at(int y, int x) method that allows you to access individual elements of a matrix.
float f = input.at<float>(0, 0);
Note that this requires that your underlying matrix type is float -- it won't do a conversion for you.
Alternatively, you could access the data row-by-row, as in this example that sums up the positive elements of a matrix M of type double:
double sum=0;
for(int i = 0; i < M.rows; i++)
{
const double* Mi = M.ptr<double>(i);
for(int j = 0; j < M.cols; j++)
sum += std::max(Mi[j], 0.);
}
If none of these work, I'd suggest creating a small matrix with known values (e.g. a 2x2 matrix with 1 black pixel and 3 white pixels) and use that to help debug your code.
To really make it apparent what the problem is, imagine a 16 by 16 image. Now think of pixel number 17 in the linear representation.
17 is a prime number. There is no j*i that will index your source image at pixel 17 if the row or column width is 16. Thus elements like 17, 19, 23 and so on will be uninitialized or at best 0, resulting in a "weird" output.
How about pixel 8 in the linear representation? that one in contrast will get hit by your loop four times, i.e. by 1x8, 2x4, 4x2, and 8x1!
The indexing #NateKohl presents in his answer will fix it since he multiplies a row position by the length of the row and then simply walks along the columns.
You can try this loop...
for(int row=0;row<height;row++)
{
for(int col=0;col<width;col++)
{
float float_data = input.at<float>(row,col);
// do some processing with value of float_data
out.at<float>(row,col) = float_data;
}
}
Is there a need to cast the uchar pointers of input and out Mats to float pointers?