/*!
* \brief An example of an super resolution algorithm.
* \ingroup group_example
*/
int example_super_resolution(int argc, char *argv[])
{
vx_status status = VX_SUCCESS;
vx_uint32 image_index = 0, max_num_images = 4;
vx_uint32 width = 640;
vx_uint32 i = 0;
vx_uint32 winSize = 32;
vx_uint32 height = 480;
vx_int32 sens_thresh = 20;
vx_float32 alpha = 0.2f;
vx_float32 tau = 0.5f;
vx_enum criteria = VX_TERM_CRITERIA_BOTH; // lk params
vx_float32 epsilon = 0.01;
vx_int32 num_iterations = 10;
vx_bool use_initial_estimate = vx_true_e;
vx_int32 min_distance = 5; // harris params
vx_float32 sensitivity = 0.04;
vx_int32 gradient_size = 3;
vx_int32 block_size = 3;
vx_context context = vxCreateContext();
vx_scalar alpha_s = vxCreateScalar(context, VX_TYPE_FLOAT32, &alpha);
vx_scalar tau_s = vxCreateScalar(context, VX_TYPE_FLOAT32, &tau);
vx_matrix matrix_forward = vxCreateMatrix(context, VX_TYPE_FLOAT32, 3, 3);
vx_matrix matrix_backwords = vxCreateMatrix(context, VX_TYPE_FLOAT32, 3, 3);
vx_array old_features = vxCreateArray(context, VX_TYPE_KEYPOINT, 1000);
vx_array new_features = vxCreateArray(context, VX_TYPE_KEYPOINT, 1000);
vx_scalar epsilon_s = vxCreateScalar(context, VX_TYPE_FLOAT32, &epsilon);
vx_scalar num_iterations_s = vxCreateScalar(context, VX_TYPE_INT32, &num_iterations);
vx_scalar use_initial_estimate_s = vxCreateScalar(context, VX_TYPE_BOOL, &use_initial_estimate);
vx_scalar min_distance_s = vxCreateScalar(context, VX_TYPE_INT32, &min_distance);
vx_scalar sensitivity_s = vxCreateScalar(context, VX_TYPE_FLOAT32, &sensitivity);
vx_scalar sens_thresh_s = vxCreateScalar(context, VX_TYPE_INT32, &sens_thresh);
vx_scalar num_corners = vxCreateScalar(context, VX_TYPE_SIZE, NULL);
if (vxGetStatus((vx_reference)context) == VX_SUCCESS)
{
vx_image images[] =
{ vxCreateImage(context, width, height, VX_DF_IMAGE_UYVY), // index 0:
vxCreateImage(context, width, height, VX_DF_IMAGE_U8), // index 1: Get Y channel
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 2: scale up to high res.
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 3: back wrap: transform to the original Image.
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 4: guassian blur
vxCreateImage(context, width, height, VX_DF_IMAGE_U8), // index 5: scale down
vxCreateImage(context, width, height, VX_DF_IMAGE_S16), // index 6: Subtract the transformed Image with original moved Image
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_S16), // index 7: Scale Up the delta image.
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_S16), // index 8: Guassian blur the delta Image
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_S16), // index 9: forward wrap: tranform the deltas back to the high res Image
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 10: accumulate sum?
vxCreateImage(context, width, height, VX_DF_IMAGE_U8), // index 11: Get U channel
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 12: scale up to high res.
vxCreateImage(context, width, height, VX_DF_IMAGE_U8), // index 13: Get V channel
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 14: scale up to high res.
vxCreateImage(context, width, height, VX_DF_IMAGE_UYVY), // index 15: output image
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 16: original y image scaled
vxCreateImage(context, width * 2, height * 2, VX_DF_IMAGE_U8), // index 17: difference image for last calculation
};
vx_pyramid pyramid_new = vxCreatePyramid(context, 4, 2, width, height, VX_DF_IMAGE_U8);
vx_pyramid pyramid_old = vxCreatePyramid(context, 4, 2, width, height, VX_DF_IMAGE_U8);
vx_graph graphs[] =
{ vxCreateGraph(context), vxCreateGraph(context), vxCreateGraph(context), vxCreateGraph(context), };
vxLoadKernels(context, "openvx-debug");
if (vxGetStatus((vx_reference)graphs[0]) == VX_SUCCESS)
{
vxChannelExtractNode(graphs[0], images[0], VX_CHANNEL_Y, images[1]); // One iteration of super resolution calculation
vxScaleImageNode(graphs[0], images[1], images[2], VX_INTERPOLATION_TYPE_BILINEAR);
vxWarpPerspectiveNode(graphs[0], images[2], matrix_forward, 0, images[3]);
vxGaussian3x3Node(graphs[0], images[3], images[4]);
vxScaleImageNode(graphs[0], images[4], images[5], VX_INTERPOLATION_TYPE_BILINEAR);
vxSubtractNode(graphs[0], images[5], images[16], VX_CONVERT_POLICY_SATURATE, images[6]);
vxScaleImageNode(graphs[0], images[6], images[7], VX_INTERPOLATION_TYPE_BILINEAR);
vxGaussian3x3Node(graphs[0], images[7], images[8]);
vxWarpPerspectiveNode(graphs[0], images[8], matrix_backwords, 0, images[9]);
vxAccumulateWeightedImageNode(graphs[0], images[9], alpha_s, images[10]);
}
if (vxGetStatus((vx_reference)graphs[1]) == VX_SUCCESS)
{
vxChannelExtractNode(graphs[1], images[0], VX_CHANNEL_Y, images[1]); // One iteration of super resolution calculation
vxGaussianPyramidNode(graphs[1], images[1], pyramid_new);
vxOpticalFlowPyrLKNode(graphs[1], pyramid_old, pyramid_new, old_features, old_features, new_features,
criteria, epsilon_s, num_iterations_s, use_initial_estimate_s, winSize);
}
if (vxGetStatus((vx_reference)graphs[2]) == VX_SUCCESS)
{
vxChannelExtractNode(graphs[2], images[0], VX_CHANNEL_Y, images[1]); // One iteration of super resolution calculation
vxHarrisCornersNode(graphs[2], images[1], sens_thresh_s, min_distance_s, sensitivity_s, gradient_size,
block_size, old_features, num_corners);
vxGaussianPyramidNode(graphs[2], images[1], pyramid_old);
vxScaleImageNode(graphs[2], images[1], images[16], VX_INTERPOLATION_TYPE_BILINEAR);
}
if (vxGetStatus((vx_reference)graphs[3]) == VX_SUCCESS)
{
vxSubtractNode(graphs[3], images[10], images[16], VX_CONVERT_POLICY_SATURATE, images[17]);
vxAccumulateWeightedImageNode(graphs[3], images[17], tau_s, images[16]);
vxChannelExtractNode(graphs[3], images[16], VX_CHANNEL_U, images[11]);
vxScaleImageNode(graphs[3], images[11], images[12], VX_INTERPOLATION_TYPE_BILINEAR); // upscale the u channel
vxChannelExtractNode(graphs[3], images[0], VX_CHANNEL_V, images[13]);
vxScaleImageNode(graphs[3], images[13], images[14], VX_INTERPOLATION_TYPE_BILINEAR); // upscale the v channel
vxChannelCombineNode(graphs[3], images[10], images[12], images[14], 0, images[15]); // recombine the channels
}
status = VX_SUCCESS;
status |= vxVerifyGraph(graphs[0]);
status |= vxVerifyGraph(graphs[1]);
status |= vxVerifyGraph(graphs[2]);
status |= vxVerifyGraph(graphs[3]);
if (status == VX_SUCCESS)
{
/* read the initial image in */
status |= vxuFReadImage(context, "c:\\work\\super_res\\superres_1_UYVY.yuv", images[0]);
/* compute the "old" pyramid */
status |= vxProcessGraph(graphs[2]);
/* for each input image, read it in and run graphs[1] and [0]. */
for (image_index = 1; image_index < max_num_images; image_index++)
{
char filename[256];
sprintf(filename, "c:\\work\\super_res\\superres_%d_UYVY.yuv", image_index + 1);
status |= vxuFReadImage(context, filename, images[0]);
status |= vxProcessGraph(graphs[1]);
userCalculatePerspectiveTransformFromLK(matrix_forward, matrix_backwords, old_features, new_features);
status |= vxProcessGraph(graphs[0]);
}
/* run the final graph */
status |= vxProcessGraph(graphs[3]);
/* save the output */
status |= vxuFWriteImage(context, images[15], "superres_UYVY.yuv");
}
vxReleaseGraph(&graphs[0]);
vxReleaseGraph(&graphs[1]);
vxReleaseGraph(&graphs[2]);
vxReleaseGraph(&graphs[3]);
for (i = 0; i < dimof(images); i++)
{
vxReleaseImage(&images[i]);
}
vxReleasePyramid(&pyramid_new);
vxReleasePyramid(&pyramid_old);
}
vxReleaseMatrix(&matrix_forward);
vxReleaseMatrix(&matrix_backwords);
vxReleaseScalar(&alpha_s);
vxReleaseScalar(&tau_s);
/* Release the context last */
vxReleaseContext(&context);
return status;
}
void fReadImage(vx_context c, const std::string& path, vx_image img)
{
IVX_CHECK_STATUS( vxuFReadImage(c, (vx_char*)path.c_str(), img) );
}
