on running this code an assertion failed error occurred on cmd line window

OPENCV Error : Assertion Failed <scn =="3" ||="" scn="4"> in cv::cvtColor,file:C:(address)

include <stdio.h>

include <iostream>

include "opencv2/core/core.hpp"

include "opencv2/features2d/features2d.hpp"

include "opencv2/highgui/highgui.hpp"

include "opencv2/nonfree/nonfree.hpp"

include "opencv2/calib3d/calib3d.hpp"

include "opencv2/imgproc/imgproc.hpp"

using namespace cv;

/** @function main */ int main() {

// Load the images
Mat image1 = imread("scene11.jpeg");
Mat image2 = imread("scene21.jpeg");
Mat gray_image1;
Mat gray_image2;
// Convert to Grayscale
cvtColor(image1, gray_image1, CV_RGB2GRAY);
cvtColor(image2, gray_image2, CV_RGB2GRAY);

imshow("image1", image2);
imshow("image2", image1);

if (!gray_image1.data || !gray_image2.data)
{
    std::cout << " --(!) Error reading images " << std::endl; return -1;
}

//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 400;

SurfFeatureDetector detector(minHessian);

std::vector< KeyPoint > keypoints_object, keypoints_scene;

detector.detect(gray_image1, keypoints_object);
detector.detect(gray_image2, keypoints_scene);

//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;

Mat descriptors_object, descriptors_scene;

extractor.compute(gray_image1, keypoints_object, descriptors_object);
extractor.compute(gray_image2, keypoints_scene, descriptors_scene);

//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
matcher.match(descriptors_object, descriptors_scene, matches);

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for (int i = 0; i < descriptors_object.rows; i++)
{
    double dist = matches[i].distance;
    if (dist < min_dist) min_dist = dist;
    if (dist > max_dist) max_dist = dist;
}

printf("-- Max dist : %f \n", max_dist);
printf("-- Min dist : %f \n", min_dist);

//-- Use only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;

for (int i = 0; i < descriptors_object.rows; i++)
{
    if (matches[i].distance < 3 * min_dist)
    {
        good_matches.push_back(matches[i]);
    }
}
std::vector< Point2f > obj;
std::vector< Point2f > scene;

for (size_t i = 0; i < good_matches.size(); i++)
{
    //-- Get the keypoints from the good matches
    obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
    scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
}

// Find the Homography Matrix
Mat H = findHomography(obj, scene, CV_RANSAC);
// Use the Homography Matrix to warp the images
cv::Mat result;
warpPerspective(image1, result, H, cv::Size(image1.cols + image2.cols, image1.rows));
cv::Mat half(result, cv::Rect(0, 0, image2.cols, image2.rows));
image2.copyTo(half);
imshow("Result", result);

waitKey(0);
return 0;

}

on running this code an assertion failed error occurred on cmd line window

OPENCV Error : Assertion Failed <scn =="3" ||="" scn="4"> in cv::cvtColor,file:C:(address)

#include <stdio.h>
#include <iostream>

#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;
 
cv;
/** @function main */
int main()
{
 
{
// Load the images
 Mat image1 = imread("scene11.jpeg");
 Mat image2 = imread("scene21.jpeg");
 Mat gray_image1;
 Mat gray_image2;
 // Convert to Grayscale
 cvtColor(image1, gray_image1, CV_RGB2GRAY);
 cvtColor(image2, gray_image2, CV_RGB2GRAY);
 imshow("image1", image2);
 imshow("image2", image1);
 if (!gray_image1.data || !gray_image2.data)
 {
  std::cout << " --(!) Error reading images " << std::endl; return -1;
 }
 //-- Step 1: Detect the keypoints using SURF Detector
 int minHessian = 400;
 SurfFeatureDetector detector(minHessian);
 std::vector< KeyPoint > keypoints_object, keypoints_scene;
 detector.detect(gray_image1, keypoints_object);
 detector.detect(gray_image2, keypoints_scene);
 //-- Step 2: Calculate descriptors (feature vectors)
 SurfDescriptorExtractor extractor;
 Mat descriptors_object, descriptors_scene;
 extractor.compute(gray_image1, keypoints_object, descriptors_object);
 extractor.compute(gray_image2, keypoints_scene, descriptors_scene);
 //-- Step 3: Matching descriptor vectors using FLANN matcher
 FlannBasedMatcher matcher;
 std::vector< DMatch > matches;
 matcher.match(descriptors_object, descriptors_scene, matches);
 double max_dist = 0; double min_dist = 100;
 //-- Quick calculation of max and min distances between keypoints
 for (int i = 0; i < descriptors_object.rows; i++)
 {
  double dist = matches[i].distance;
  if (dist < min_dist) min_dist = dist;
 if (dist > max_dist) max_dist = dist;
 }
 printf("-- Max dist : %f \n", max_dist);
 printf("-- Min dist : %f \n", min_dist);
 //-- Use only "good" matches (i.e. whose distance is less than 3*min_dist )
 std::vector< DMatch > good_matches;
 for (int i = 0; i < descriptors_object.rows; i++)
 {
  if (matches[i].distance < 3 * min_dist)
 {
 good_matches.push_back(matches[i]);
 }
 }
 std::vector< Point2f > obj;
 std::vector< Point2f > scene;
 for (size_t i = 0; i < good_matches.size(); i++)
 {
  //-- Get the keypoints from the good matches
 obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
 scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
 }
 // Find the Homography Matrix
 Mat H = findHomography(obj, scene, CV_RANSAC);
 // Use the Homography Matrix to warp the images
 cv::Mat result;
 warpPerspective(image1, result, H, cv::Size(image1.cols + image2.cols, image1.rows));
 cv::Mat half(result, cv::Rect(0, 0, image2.cols, image2.rows));
 image2.copyTo(half);
 imshow("Result", result);
 waitKey(0);
 return 0;
}
  
}