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I have installed Opencv 3.1.0 along with Opencv_contrib module with the aim of reading caffe models into Opencv. I followed the official Opencv installation documentation and with a change in Cmake command, I was able to build it successfully on Ubuntu 14.04lts.

When I type the following command to run the sample program -

g++ -o 1 ssd_object_detection.cpp  `pkg-config --cflags --libs opencv`

I get the error below.

ssd_object_detection.cpp:1:27: fatal error: opencv2/dnn.hpp: No such file or directory
 #include <opencv2/dnn.hpp>
                           ^
compilation terminated.

I looked at the opencv_contrib github repo and they mentioned that you have to run it with a linker to the specific module which you want. So my command then became-

g++ -o 1 ssd_object_detection.cpp  `pkg-config --cflags --libs opencv` -lopencv_dnn

But I still get the same error. Does this mean that my dnn module hasn't been built properly? The error is from the header file opencv2/dnn.hpp not being recognised but I don't know why?

The sample program is from this link- sample program to load caffe model and run

Output of cmake-

cmake -D CMAKE_BUILD_TYPE=Release -D OPENCV_EXTRA_MODULES=~/opt/opencv_contrib/modules -D CMAKE_INSTALL_PREFIX=/usr/local ..
-- The CXX compiler identification is GNU 4.8.4
-- The C compiler identification is GNU 4.8.4
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Detected version of GNU GCC: 48 (408)
-- Performing Test HAVE_CXX_FSIGNED_CHAR
-- Performing Test HAVE_CXX_FSIGNED_CHAR - Success
-- Performing Test HAVE_C_FSIGNED_CHAR
-- Performing Test HAVE_C_FSIGNED_CHAR - Success
-- Performing Test HAVE_CXX_W
-- Performing Test HAVE_CXX_W - Success
-- Performing Test HAVE_C_W
-- Performing Test HAVE_C_W - Success
-- Performing Test HAVE_CXX_WALL
-- Performing Test HAVE_CXX_WALL - Success
-- Performing Test HAVE_C_WALL
-- Performing Test HAVE_C_WALL - Success
-- Performing Test HAVE_CXX_WERROR_RETURN_TYPE
-- Performing Test HAVE_CXX_WERROR_RETURN_TYPE - Success
-- Performing Test HAVE_C_WERROR_RETURN_TYPE
-- Performing Test HAVE_C_WERROR_RETURN_TYPE - Success
-- Performing Test HAVE_CXX_WERROR_NON_VIRTUAL_DTOR
-- Performing Test HAVE_CXX_WERROR_NON_VIRTUAL_DTOR - Success
-- Performing Test HAVE_C_WERROR_NON_VIRTUAL_DTOR
-- Performing Test HAVE_C_WERROR_NON_VIRTUAL_DTOR - Success
-- Performing Test HAVE_CXX_WERROR_ADDRESS
-- Performing Test HAVE_CXX_WERROR_ADDRESS - Success
-- Performing Test HAVE_C_WERROR_ADDRESS
-- Performing Test HAVE_C_WERROR_ADDRESS - Success
-- Performing Test HAVE_CXX_WERROR_SEQUENCE_POINT
-- Performing Test HAVE_CXX_WERROR_SEQUENCE_POINT - Success
-- Performing Test HAVE_C_WERROR_SEQUENCE_POINT
-- Performing Test HAVE_C_WERROR_SEQUENCE_POINT - Success
-- Performing Test HAVE_CXX_WFORMAT
-- Performing Test HAVE_CXX_WFORMAT - Success
-- Performing Test HAVE_C_WFORMAT
-- Performing Test HAVE_C_WFORMAT - Success
-- Performing Test HAVE_CXX_WERROR_FORMAT_SECURITY
-- Performing Test HAVE_CXX_WERROR_FORMAT_SECURITY - Success
-- Performing Test HAVE_C_WERROR_FORMAT_SECURITY
-- Performing Test HAVE_C_WERROR_FORMAT_SECURITY - Success
-- Performing Test HAVE_CXX_WMISSING_DECLARATIONS
-- Performing Test HAVE_CXX_WMISSING_DECLARATIONS - Success
-- Performing Test HAVE_C_WMISSING_DECLARATIONS
-- Performing Test HAVE_C_WMISSING_DECLARATIONS - Success
-- Performing Test HAVE_CXX_WMISSING_PROTOTYPES
-- Performing Test HAVE_CXX_WMISSING_PROTOTYPES - Failed
-- Performing Test HAVE_C_WMISSING_PROTOTYPES
-- Performing Test HAVE_C_WMISSING_PROTOTYPES - Success
-- Performing Test HAVE_CXX_WSTRICT_PROTOTYPES
-- Performing Test HAVE_CXX_WSTRICT_PROTOTYPES - Failed
-- Performing Test HAVE_C_WSTRICT_PROTOTYPES
-- Performing Test HAVE_C_WSTRICT_PROTOTYPES - Success
-- Performing Test HAVE_CXX_WUNDEF
-- Performing Test HAVE_CXX_WUNDEF - Success
-- Performing Test HAVE_C_WUNDEF
-- Performing Test HAVE_C_WUNDEF - Success
-- Performing Test HAVE_CXX_WINIT_SELF
-- Performing Test HAVE_CXX_WINIT_SELF - Success
-- Performing Test HAVE_C_WINIT_SELF
-- Performing Test HAVE_C_WINIT_SELF - Success
-- Performing Test HAVE_CXX_WPOINTER_ARITH
-- Performing Test ...

I am trying to implement sobel edge detection from scratch but my output can't seem to match with OpenCV's sobel function. I performed correlation on the image with the sobel operator in both x and y directions and then computed gradient magnitude as square root of sum of squares of magnitudes in both x & y direction. I believe the problem is how I assign the threshold for edge detection.

Images-

1.Original Image-

2.My implementation of Sobel Edge Detection -

3.Opencv Sobel edge function output -

Code-

#include <iostream>
#include <bits/stdc++.h>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/opencv.hpp"
#include "opencv2/imgproc/imgproc.hpp" 
#include <opencv2/objdetect/objdetect.hpp>
#include <math.h>

using namespace cv; 
using namespace std; 

int main()
{
// Reading image
Mat img = imread("1.jpg");


// Blurring and Converting to grayscale
Mat img_gray,image_blur;
GaussianBlur( img, image_blur, Size(5,5), 3, 3);
    cvtColor(image_blur,img_gray,CV_RGB2GRAY);

int cols = img_gray.cols;
int rows = img_gray.rows;

// Creating sobel operator in x direction
int sobel_x[3][3] = {-1,0,1,-2,0,2,-1,0,1};
// Creating sobel operator in y direction
int sobel_y[3][3] = {-1,-2,-1,0,0,0,1,2,1};


int radius = 1;

// Handle border issues
Mat _src;
copyMakeBorder(img_gray, _src, radius, radius, radius, radius, BORDER_REFLECT101);

// Create output matrix
Mat gradient_x = img_gray.clone();
Mat gradient_y = img_gray.clone();
Mat gradient_f = img_gray.clone();

int max=0;

// Correlation loop in x direction 

// Iterate on image 
for (int r = radius; r < _src.rows - radius; ++r)
{
    for (int c = radius; c < _src.cols - radius; ++c)
    {
        int s = 0;

        // Iterate on kernel
        for (int i = -radius; i <= radius; ++i)
        {
            for (int j = -radius; j <= radius; ++j)
            {
                s += _src.at<uchar>(r + i, c + j) * sobel_x[i + radius][j + radius];
            }
        }
        gradient_x.at<uchar>(r - radius, c - radius) = s/30;

        /*if(s>200)
            gradient.at<uchar>(r - radius, c - radius) = 255;
        else
            gradient.at<uchar>(r - radius, c - radius) = 0;
        */    
    }
}


// Conrrelation loop in y direction 

// Iterate on image 
for (int r = radius; r < _src.rows - radius; ++r)
{
    for (int c = radius; c < _src.cols - radius; ++c)
    {
        int s = 0;

        // Iterate on kernel
        for (int i = -radius; i <= radius; ++i)
        {
            for (int j = -radius; j <= radius; ++j)
            {
                s += _src.at<uchar>(r + i, c + j) * sobel_y[i + radius][j + radius];
            }
        }

        gradient_y.at<uchar>(r - radius, c - radius) = s/30;

        /*if(s>200)
            gradient.at<uchar>(r - radius, c - radius) = 255;
        else
            gradient.at<uchar>(r - radius, c - radius) = 0;
        */    
    }
}




//Calculating gradient magnitude
for(int i=0; i<gradient_f.rows; i++)
{
    for(int j=0; j<gradient_f.cols; j++)
    {
        gradient_f.at<uchar>(i,j) = sqrt( pow(gradient_x.at<uchar>(i,j),2) + pow(gradient_y.at<uchar>(i,j),2) );  

         if(gradient_f.at<uchar>(i,j) >240)
            gradient_f.at<uchar>(i,j) = 100;
        else
            gradient_f.at<uchar>(i,j) = 0;
    }
}




imshow("grad magnitude",gradient_f);
waitKey(0);




cv::Mat Gx, Gy; int ksize=3;
Mat abs_grad_x, abs_grad_y;
cv::Sobel(img_gray, Gx, CV_8U, 1, 0, ksize);
convertScaleAbs( Gx, abs_grad_x );
cv ...

I am trying to implement the Canny edge detection algorithm from scratch with the help of OpenCV. I am facing a problem implementing the Non-Maximum Suppression step which helps to thin the edges.

My logic is to first compute the intensity gradient vector, then group it in either 0,45,90,135 degrees direction and then try to find local maxima. The method to find this local maxima is by making sure that current pixel is greater than succeeding and preceeding pixel in same direction. If not, I assign value of zero to this pixel. Using this logic, I'm still not able to thin the edges. I feel the error is when I'm computing the intensity gradient vector for each pixel.

Here is my code-

#include <iostream>
#include <bits/stdc++.h>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/opencv.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv2/objdetect/objdetect.hpp>
#include <math.h>

using namespace cv;  
using namespace std; 

int main()
{
// Reading image
Mat img = imread("1.jpg");

// Displaying image
//imshow("Original Image",img);
//waitKey(0);

// Converting to grayscale
Mat img_gray,image_gray;
cvtColor(img,image_gray,CV_RGB2GRAY);
GaussianBlur( image_gray, img_gray, Size(15,15), 3, 3);


// Displaying grayscale image
imshow("Original Image",img_gray);
waitKey(0);


int cols = img_gray.cols;
int rows = img_gray.rows;

// Creating sobel operator in x direction
int sobel_x[3][3] = {-1,0,1,-2,0,2,-1,0,1};
// Creating sobel operator in y direction
int sobel_y[3][3] = {1,2,1,0,0,0,-1,-2,-1};


int radius = 1;

// Handle border issues
Mat _src;
copyMakeBorder(img_gray, _src, radius, radius, radius, radius, BORDER_REFLECT101);

// Create output matrix
Mat gradient_x = img_gray.clone();
Mat gradient_y = img_gray.clone();
Mat gradient_f = img_gray.clone();
Mat gradient_mag = img_gray.clone();

// Conrrelation loop in x direction 

// Iterate on image 
for (int r = radius; r < _src.rows - radius; ++r)
{
    for (int c = radius; c < _src.cols - radius; ++c)
    {
        int s = 0;

        // Iterate on kernel
        for (int i = -radius; i <= radius; ++i)
        {
            for (int j = -radius; j <= radius; ++j)
            {
                s += _src.at<uchar>(r + i, c + j) * sobel_x[i + radius][j + radius];
            }
        }
        gradient_x.at<uchar>(r - radius, c - radius) = s/8;

        /*if(s>200)
            gradient.at<uchar>(r - radius, c - radius) = 255;
        else
            gradient.at<uchar>(r - radius, c - radius) = 0;
        */    
    }
}

// Conrrelation loop in y direction 

// Iterate on image 
for (int r = radius; r < _src.rows - radius; ++r)
{
    for (int c = radius; c < _src.cols - radius; ++c)
    {
        int s = 0;

        // Iterate on kernel
        for (int i = -radius; i <= radius; ++i)
        {
            for (int j = -radius; j <= radius; ++j)
            {
                s += _src.at<uchar>(r + i, c + j) * sobel_y[i + radius][j + radius];
            }
        }
        gradient_y.at<uchar>(r - radius, c - radius) = s/8;

        /*if(s>200)
            gradient.at<uchar>(r - radius, c - radius) = 255;
        else
            gradient.at<uchar>(r - radius, c - radius) = 0;
        */    
    }
}

///cout<<endl<<"max:"<<max;
//cout<<img_gray.rows;
//cout<<endl<<_src.rows;
cout<<endl<<gradient_x.rows;
cout<<endl<<gradient_y.rows;
cout<<endl<<gradient_f.rows<<gradient_f.cols;   


//Calculating gradient magnitude
for(int i=0; i ...