Which are the steps to extract contours/shapes from depth videos? - image-processing

I am trying to create a histogram of the depth videos (converted to grayscale first) in order to apply a threshold to keep only highest values, and then do some dilation in order to extract contours. Apparently I am stuck, and besides that i don't know if what I am thinking is the right way to extract contours from depth videos.
In the following code I got stuck in the point of applying the threshold. I think that iam applying it in the wrong way. Which is the correct to apply a threshold in this situation in order to obtain a black and white image?
Any suggestions or links of tutorials would be awesome!!!
Thank you very much!
int bins = 256;
int hsize[] = {bins};
//max and min value of the histogram
float max_value = 0, min_value = 0;
float value;
int normalized;
//ranges - grayscale 0 to 256
float xranges[] = { 0, 256 };
float* ranges[] = { xranges };
//image is the actual source from input depth video
gray = cvCreateImage( cvGetSize(image), 8, 1 );
cvCvtColor( image, gray, CV_BGR2GRAY );
cvNamedWindow("original",1);
cvNamedWindow("gray",1);
cvNamedWindow("histogram",1);
cvNamedWindow("black & white",1);
IplImage* planes[] = { gray };
//get the histogram and some info about it
hist = cvCreateHist( 1, hsize, CV_HIST_ARRAY, ranges,1);
cvCalcHist( planes, hist, 0, NULL);
cvGetMinMaxHistValue( hist, &min_value, &max_value);
printf("min: %f, max: %f\n", min_value, max_value);
imgHistogram = cvCreateImage(cvSize(bins, image->height),8,1);
cvRectangle(imgHistogram, cvPoint(0,0), cvPoint(256,image->height), CV_RGB(255,255,255),-1);
//I think that here i have messed up things :( Any suggestions ???
bw_img = cvCreateImage(cvGetSize(imgHistogram), IPL_DEPTH_8U, 1);
cvThreshold(imgHistogram, bw_img, 150, 255, CV_THRESH_BINARY);
//draw the histogram
for(int i=0; i < bins; i++){
value = cvQueryHistValue_1D( hist, i);
normalized = cvRound(value*image->height/max_value);
cvLine(imgHistogram,cvPoint(i,image->height), cvPoint(i,image->height-normalized), CV_RGB(0,0,0));
}
//show the image results
cvShowImage( "original", image );
cvShowImage( "gray", gray );
cvShowImage( "histogram", imgHistogram );
cvShowImage( "balck & white", bw_img);

Related

Distinguish rock scences using opencv

I am struggling with finding the appropriate contour algorithm for a low quality image. The example image shows a rock scene:
What I am trying to achieve is to find contours arround features such as:
light areas
dark areas
grey1 areas
grey2 areas
etc. until grey-n areas
(The number of areas shall be a parameter of choice)
I do not want to take a simple binary-threshold but rather use some sort of contour-finding (for example watershed or other). The major feature-lines shall be kept, noise within a feature-are can be flattened.
The result of my code can be seen on the images to the right.
Unfortunately, as you can easily tell, the colors do not really represent the original large-scale image features! For example: check out the two areas that I circled with red - these features are almost completely flooded with another color. What I imagine is that at least the very light and the very dark areas are covered by its own color.
cv::Mat cv_src = cv::imread(argv[1]);
cv::Mat output;
cv::Mat cv_src_gray;
cv::cvtColor(cv_src, cv_src_gray, cv::COLOR_RGB2GRAY);
double clipLimit = 0.1;
cv::Size titleGridSize = cv::Size(8,8);
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(clipLimit, titleGridSize);
clahe->apply(cv_src_gray, output);
cv::equalizeHist(output, output);
cv::cvtColor(output, cv_src, cv::COLOR_GRAY2RGB);
// Create binary image from source image
cv::Mat bw;
cv::cvtColor(cv_src, bw, cv::COLOR_BGR2GRAY);
cv::threshold(bw, bw, 180, 255, cv::THRESH_BINARY);
// Perform the distance transform algorithm
cv::Mat dist;
cv::distanceTransform(bw, dist, cv::DIST_L2, CV_32F);
// Normalize the distance image for range = {0.0, 1.0}
cv::normalize(dist, dist, 0, 1., cv::NORM_MINMAX);
// Threshold to obtain the peaks
cv::threshold(dist, dist, .2, 1., cv::THRESH_BINARY);
// Create the CV_8U version of the distance image
cv::Mat dist_8u;
dist.convertTo(dist_8u, CV_8U);
// Find total markers
std::vector<std::vector<cv::Point> > contours;
cv::findContours(dist_8u, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
int ncomp = contours.size();
// Create the marker image for the watershed algorithm
cv::Mat markers = cv::Mat::zeros(dist.size(), CV_32S);
// Draw the foreground markers
for (int i = 0; i < ncomp; i++)
cv::drawContours(markers, contours, i, cv::Scalar::all(i+1), -1);
// Draw the background marker
cv::circle(markers, cv::Point(5,5), 3, CV_RGB(255,255,255), -1);
// Perform the watershed algorithm
cv::watershed(cv_src, markers);
// Generate random colors
std::vector<cv::Vec3b> colors;
for (int i = 0; i < ncomp; i++)
{
int b = cv::theRNG().uniform(0, 255);
int g = cv::theRNG().uniform(0, 255);
int r = cv::theRNG().uniform(0, 255);
colors.push_back(cv::Vec3b((uchar)b, (uchar)g, (uchar)r));
}
// Create the result image
cv::Mat dst = cv::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) = cv::Vec3b(0,0,0);
}
}
// Show me what you got
imshow("final_result", dst);
I think you can use a simple clustering such as k-means for this, then examine the cluster centers (or the mean and standard deviations of each cluster). I quickly tried it in matlab.
im = imread('tvBqt.jpg');
gr = rgb2gray(im);
x = double(gr(:));
idx = kmeans(x, 4);
cl = reshape(idx, 600, 472);
figure,
subplot(1, 2, 1), imshow(gr, []), title('original')
subplot(1, 2, 2), imshow(label2rgb(cl), []), title('clustered')
The result:
You could try using SLIC Superpixels. I tried it and showed some good results. You could vary the parameters to get better clustering.
SLIC Superpixels
SLIC Superpixels with OpenCV C++
SLIC Superpixels with OpenCV Python

OpenCV calculate distance (stereo vision)

For my project I am using parts of the next code: link.
To track objects of a specific color I implemented this method:
My question is: How can I calculate the distance to the tracked colored objects?
Thank you in advance!
*The application calls the method for the left and right frame. This is not efficient...
**I need to calculate detectedObject.Zcor
DetectedObject Detect(IplImage *frame)
{
//Track object (left frame and right frame)
//Calculate average position
//Show X,Y,Z coordinate and detected color
color_image = frame;
imgThreshold = cvCreateImage(cvSize(color_image->width,color_image->height), IPL_DEPTH_8U, 1);
cvInitFont(&font, CV_FONT_HERSHEY_PLAIN, 1, 1, 0, 1.4f, CV_AA);
imgdraw = cvCreateImage(cvGetSize(color_image),8,3);
cvSetZero(imgdraw);
cvFlip(color_image, color_image, 1);
cvSmooth(color_image, color_image, CV_GAUSSIAN, 3, 0);
threshold = getThreshold(color_image);
cvErode(threshold, threshold, NULL, 3);
cvDilate(threshold, threshold, NULL, 10);
imgThreshold = cvCloneImage(threshold);
storage = cvCreateMemStorage(0);
contours = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvPoint), storage);
cvFindContours(threshold, storage, &contours, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE, cvPoint(0,0));
final = cvCreateImage(cvGetSize(color_image),8,3);
for(; contours!=0; contours = contours->h_next)
{
CvRect rect = cvBoundingRect(contours, 0);
cvRectangle(color_image,
cvPoint(rect.x, rect.y),
cvPoint(rect.x+rect.width, rect.y+rect.height),
cvScalar(0,0,255,0),
2,8,0);
string s = to_string(rect.x) + "," + to_string(rect.y);
char const* pchar = s.c_str();
cvPutText(frame, pchar, cvPoint(rect.x, rect.y), &font, cvScalar(0,0,255,0));
detectedObject.Xcor = rect.x;
detectedObject.Ycor = rect.y;
}
cvShowImage("Threshold", imgThreshold);
cvAdd(final,imgdraw,final);
detectedObject.Zcor = 0;
return detectedObject;
}
For depth estimation you will need a calibrated stereo pair (known camera matrices for both the left and the right cameras). Then, using the camera matrices and corresponding points/contours in the stereo pair, you can compute depth.

Blur planes of 3D cv::Mat1f

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.

How to find the coordinates of a point w.r.t another point on an image using OpenCV

Today I wrote a program for detecting circles using Hough Transform using OpenCV in C.
The program inputs 3 images, each image contains a fixed small circle and a big circle with variable position. The program then recognizes both the circles and marks the centres of both the circles. Now what I want to do is that in the output image the (x,y) coordinates of the centre of the bigger circle should be displayed with respect to the centre of the fixed smaller circle . Here's the code for 'circle.cpp'
#include <cv.h>
#include <highgui.h>
#include <math.h>
int main(int argc, char** argv)
{
IplImage* img;
int n=3;
char input[21],output[21];
for(int l=1;l<=n;l++)
{
sprintf(input,"Frame%d.jpg",l); // Inputs Images
if( (img=cvLoadImage(input))!= 0)
{
IplImage* gray = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 1 );
IplImage* canny=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
IplImage* rgbcanny=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,3);
CvMemStorage* storage = cvCreateMemStorage(0);
cvCvtColor( img, gray, CV_BGR2GRAY );
cvSmooth( gray, gray, CV_GAUSSIAN, 9, 9 ); // smooth it, otherwise a lot of false circles may be detected
cvCanny(gray,canny,50,100,3);
CvSeq* circles = cvHoughCircles( canny, storage, CV_HOUGH_GRADIENT, 2, gray->height/4, 200, 100 );
int i;
cvCvtColor(canny,rgbcanny,CV_GRAY2BGR);
for( i = 0; i < circles->total; i++ )
{
float* p = (float*)cvGetSeqElem( circles, i );
cvCircle( rgbcanny, cvPoint(cvRound(p[0]),cvRound(p[1])), 3, CV_RGB(0,255,0), -1, 8, 0 );
cvCircle( rgbcanny, cvPoint(cvRound(p[0]),cvRound(p[1])), cvRound(p[2]), CV_RGB(255,0,0), 3, 8, 0 );
}
cvNamedWindow( "circles", 1 );
cvShowImage( "circles", rgbcanny );
//Displays Output images
sprintf(output,"circle%d.jpg",l);
cvSaveImage(output,rgbcanny);
cvWaitKey(0);
}
}
return 0;
}
And here are the input and output images:
Please suggest what changes should I make in the code to display the desired (x,y)coordinates. Thanx a lot :)
Before you show the image, use cvPutText to add the desired text. The parameters of this function are self-explaining. The font should be initialized using cvInitFont.
When you calculate the relative coordinates, keep in mind that in OpenCV, the coordinate system is like this
-----> x
|
|
v
y
just in case you are interested in showing the relative coordinates in a system in which the axes point in another direction.
You should check that the Hough transform has detected exactly two circles. If so, all the data you need is in the circles variable. If (xa,ya) are the coordinates of the bigger circle and (xb,yb) the coordinates of the smaller one, the relative coordinates are (xa-xb,ya-yb).

Segmenting and masking all shades of red from an image using opencv

I am trying to segment all shades of red form an image using hue saturation values and use InRangeS function to create a mask which should have all red areas whitened and all others blacked(a new 1 channel image). Thwn Inpaint them to kind of obscure the segmented portions.
My code is as given.
However I am unable to get an output image, it doesnt segment the desired color range.
Any pointers on my approach and why it isnt working. ?
int main(){
IplImage *img1=cvLoadImage("/home/techrascal/projects/test1/image2.jpeg");
//IplImage *img3;
IplImage *imghsv;
IplImage *img4;
CvSize sz=cvGetSize(img1);
imghsv=cvCreateImage(sz,IPL_DEPTH_8U,3);
img4=cvCreateImage(sz,IPL_DEPTH_8U,1);
int width = img1->width;
int height = img1->height;
int bpp = img1->nChannels;
//int w=img4->width;
//int h=img4->height;
//int bn=img4->nChannels;
cvNamedWindow("original", 1);
cvNamedWindow("hsv",1);
cvNamedWindow("Blurred",1);
int r,g,b;
// create inpaint mask: img 4 will behave as mask
cvCvtColor(img1,imghsv,CV_BGR2HSV);
CvScalar hsv_min = cvScalar(0, 0, 0, 0);
CvScalar hsv_max = cvScalar(255, 0, 0, 0);
//cvShowImage("hsv",imghsv);
cvInRangeS( imghsv, hsv_min, hsv_max, img4 );
cvInpaint(img1, img4, img1, 3,CV_INPAINT_NS );
cvShowImage("Blurred",img1);
cvReleaseImage(&img1);
cvReleaseImage(&imghsv);
cvReleaseImage(&img4);
//cvReleaseImage(&img3);
char d=cvWaitKey(10000);
cvDestroyAllWindows();
return 0;}
Your code logic seems correct but you will definetely need to adjust your hsv range values
(hsv_min and hsv_max).
Read this detailed guide that show you hsv range defined in opencv
http://www.shervinemami.co.cc/colorConversion.html

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