OpenCV gabor filtering producing different results than Matlab code with same exact kernel

I've created a Gabor filter with the following code:

Mat gabor(double sigma, double theta) {

  int nstds = 3;
  int kernel_size = nstds * sigma * 2 - 1;

  if(!kernel_size % 2) {
    kernel_size++;
  }

  int hks = (kernel_size - 1) / 2;

  theta = theta * CV_PI / 180;
  sigma = sigma;

  double x_theta;
  double y_theta;
  double gamma = 1;
  double del_phi = 0.8;
  double v = sqrt(2 * log(2)) * ((pow(2, del_phi) + 1) / (pow(2, del_phi) - 1));
  double psi = -exp(-0.5 * pow(v, 2));
  double fs = (1 / (CV_PI * sigma)) * sqrt(0.5 * log(2)) * ((pow(2, del_phi) + 1) / (pow(2, del_phi) - 1));
  double lambda = 1 / fs;
  double p = gamma / (2 * CV_PI * pow(sigma, 2));

  Mat kernel(kernel_size, kernel_size, CV_32F);

  for(int y = -hks; y <= hks; y++) {
    for(int x = -hks; x <= hks; x++) {

      x_theta = x * cos(theta) + y * sin(theta);
      y_theta = -x * sin(theta) + y * cos(theta);

      kernel.at<float>(hks+y, hks+x) = 
        (float)
          exp(-0.5 * (pow(x_theta, 2.0) + pow(y_theta, 2.0)) / pow(sigma, 2.0)) * 
          cos(2.0 * CV_PI * x_theta / lambda + psi);
    }
  }

  return kernel;
}

The code above creates a gabor kernel that is identical to my kernel created in Matlab. I apply the gabor filter with the conv2 function in Matlab and filter2D in OpenCV. I understand that filter2D isn't strictly convolution, but when I flip the kernels and change the anchor, etc, nothing helps. The Matlab output looks good but the OpenCV output looks bad. Why?

Matlab (Good):

OpenCV (Bad):

I'm using filter2D(preproc, filtered, CV_64FC1, kernel); to produce the OpenCV output. How come the OpenCV looks all zebra-stripy and the Matlab looks like what I want? Again, the kernel Mat values are exactly the same (I ported the OpenCV kernel into Matlab and compared and they are 99.99% similar).