void Init_openvxruby()
{
context = vxCreateContext();
vx_status status = vxQueryContext(context, VX_QUERY_CONTEXT_IMPLEMENTATION, implementation, sizeof(implementation));
if (status != VX_SUCCESS)
{
REXT_PRINT("context = "VX_FMT_REF" status = %d\n", context, status);
strncpy(implementation, "khronos.sample (bad)", sizeof(implementation));
}
rb_mOpenVXString = rb_str_new2(implementation);
rubyext_modules(Qnil, modules, dimof(modules));
// there is no "unload"
}
int main(void) {
vx_context context = vxCreateContext();
vx_uint8 value = 8;
vx_graph graph = vxCreateGraph(context);
vx_image images[] = {
vxCreateUniformImage(context, 640, 480, VX_DF_IMAGE_U8, &value),
vxCreateImage(context, 640, 480, VX_DF_IMAGE_U8)
};
vx_image intermediate_images[] = {
vxCreateVirtualImage(graph, 0, 0, VX_DF_IMAGE_S16)
};
/*Create the depth conversion nodes*/
vx_uint32 uint_value2 = 0;
vx_scalar vx_value2 = vxCreateScalar(context, VX_TYPE_INT32, &uint_value2);
vx_uint32 uint_value1 = 0;
vx_scalar vx_value1 = vxCreateScalar(context, VX_TYPE_INT32, &uint_value1);
/* The order in which these two nodes are created should not matter. */
vxConvertDepthNode(graph, intermediate_images[0], images[1],
VX_CONVERT_POLICY_SATURATE, vx_value2);
vxConvertDepthNode(graph, images[0], intermediate_images[0],
VX_CONVERT_POLICY_SATURATE, vx_value1);
vx_status status = vxVerifyGraph(graph);
if (status == VX_SUCCESS) {
status = vxProcessGraph(graph);
}
if (status != VX_SUCCESS) {
fprintf(stderr, "badness\n");
abort ();
}
exit (0);
}
////////
// main() has all the OpenVX application code for this exercise.
// Command-line usage:
// % exercise3 [<video-sequence>|<camera-device-number>]
// When neither video sequence nor camera device number is specified,
// it defaults to the video sequence in "PETS09-S1-L1-View001.avi".
int main( int argc, char * argv[] )
{
// Get default video sequence when nothing is specified on command-line and
// instantiate OpenCV GUI module for reading input RGB images and displaying
// the image with OpenVX results
const char * video_sequence = argv[1];
CGuiModule gui( video_sequence );
// Try grab first video frame from the sequence using cv::VideoCapture
// and check if video frame is available
if( !gui.Grab() )
{
printf( "ERROR: input has no video\n" );
return 1;
}
////////
// Set the application configuration parameters. Note that input video
// sequence is an 8-bit RGB image with dimensions given by gui.GetWidth()
// and gui.GetHeight(). The parameters for the tensors are:
// tensor_dims - 3 dimensions of tensor [3 x <width> x <height>]
// tensor_input_fixed_point_pos - fixed-point position for input tensor
// tensor_output_fixed_point_pos - fixed-point position for output tensor
vx_uint32 width = gui.GetWidth();
vx_uint32 height = gui.GetHeight();
vx_size tensor_dims[3] = { width, height, 3 }; // 3 channels (RGB)
vx_uint8 tensor_input_fixed_point_pos = 5; // Q10.5: input[-128..127] will be mapped to -4..3.96875
vx_uint8 tensor_output_fixed_point_pos = 7; // Q8.7: output[-1..1] will be mapped to -128 to 128
////////
// Create the OpenVX context and make sure returned context is valid and
// register the log_callback to receive messages from OpenVX framework.
vx_context context = vxCreateContext();
ERROR_CHECK_OBJECT( context );
vxRegisterLogCallback( context, log_callback, vx_false_e );
////////
// Register user kernels with the context.
//
// TODO STEP 05:********
// 1. Register user kernel with context by calling your implementation of "registerUserKernel()".
// ERROR_CHECK_STATUS( registerUserKernel( context ) );
////////
// Create OpenVX tensor objects for input and output
//
// TODO STEP 06:********
// 1. Create tensor objects using tensor_dims, tensor_input_fixed_point_pos, and
// tensor_output_fixed_point_pos
// vx_tensor input_tensor = vxCreateTensor( context, 3, tensor_dims, VX_TYPE_INT16, tensor_input_fixed_point_pos );
// vx_tensor output_tensor = vxCreateTensor( context, /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( input_tensor );
// ERROR_CHECK_OBJECT( output_tensor );
////////
// Create, build, and verify the graph with user kernel node.
//
// TODO STEP 07:********
// 1. Build a graph with just one node created using userTensorCosNode()
// vx_graph graph = vxCreateGraph( context );
// ERROR_CHECK_OBJECT( graph );
// vx_node cos_node = userTensorCosNode( graph, /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( cos_node );
// ERROR_CHECK_STATUS( vxReleaseNode( &cos_node ) );
// ERROR_CHECK_STATUS( vxVerifyGraph( graph ) );
////////
// Process the video sequence frame by frame until the end of sequence or aborted.
cv::Mat bgrMatForOutputDisplay( height, width, CV_8UC3 );
for( int frame_index = 0; !gui.AbortRequested(); frame_index++ )
{
////////
// Copy input RGB frame from OpenCV into input_tensor with UINT8 to Q10.5 (INT16) conversion.
// In order to do this, vxMapTensorPatch API (see "vx_ext_amd.h").
//
// TODO STEP 08:********
// 1. Use vxMapTensorPatch API for access to input tensor object for writing
// 2. Copy UINT8 data from OpenCV RGB image to tensor object
// 3. Use vxUnmapTensorPatch API to return control of buffer back to framework
vx_uint8 * cv_rgb_image_buffer = gui.GetBuffer();
vx_size rgb_stride = gui.GetStride();
// vx_size zeros[3] = { 0 };
// vx_size tensor_stride[3];
// vx_map_id map_id;
// vx_uint8 * buf;
// ERROR_CHECK_STATUS( vxMapTensorPatch( input_tensor,
// 3, /* Fill in parameters */
// &map_id, tensor_stride,
// (void **)&buf, VX_WRITE_ONLY, VX_MEMORY_TYPE_HOST, 0 ) );
// for( vx_size c = 0; c < 3; c++ )
// {
// for( vx_size y = 0; y < height; y++ )
// {
// const vx_uint8 * img = cv_rgb_image_buffer + y * rgb_stride + c;
// vx_int16 * inp = (vx_int16 *)(buf + y * tensor_stride[1] + c * tensor_stride[2]);
// for( vx_size x = 0; x < width; x++ )
// {
// // convert 0..255 to Q10.5 [-4..3.96875 range] fixed-point format
// inp[x] = (vx_int16)img[x * 3] - 128;
// }
// }
// }
// ERROR_CHECK_STATUS( vxUnmapTensorPatch( input_tensor, map_id ) );
////////
// Now that input tensor is ready, just run the graph.
//
// TODO STEP 09:********
// 1. Call vxProcessGraph to execute the tensor_cos kernel in graph
// ERROR_CHECK_STATUS( vxProcessGraph( graph ) );
////////
// Display the output tensor object as RGB image
//
// TODO STEP 10:********
// 1. Use vxMapTensorPatch API for access to output tensor object for reading
// 2. Copy tensor object data into OpenCV RGB image
// 3. Use vxUnmapTensorPatch API to return control of buffer back to framework
// ERROR_CHECK_STATUS( vxMapTensorPatch( output_tensor,
// 3, zeros, tensor_dims,
// &map_id, tensor_stride,
// (void **)&buf, VX_WRITE_ONLY, VX_MEMORY_TYPE_HOST, 0 ) );
// vx_uint8 * cv_bgr_image_buffer = bgrMatForOutputDisplay.data;
// vx_size bgr_stride = bgrMatForOutputDisplay.step;
// for( vx_size c = 0; c < 3; c++ )
// {
// for( vx_size y = 0; y < height; y++ )
// {
// const vx_int16 * out = (const vx_int16 *)(buf + y * tensor_stride[1] + c * tensor_stride[2]);
// vx_uint8 * img = cv_bgr_image_buffer + y * bgr_stride + (2 - c); // (2 - c) for RGB to BGR conversion
// for( vx_size x = 0; x < width; x++ )
// {
// // scale convert Q8.7 [-1..1 range] fixed-point format to 0..255 with saturation
// vx_int16 value = out[x] + 128;
// value = value > 255 ? 255 : value; // saturation needed
// img[x * 3] = (vx_uint8)value;
// }
// }
// }
//#if ENABLE_DISPLAY
// cv::imshow( "Cosine", bgrMatForOutputDisplay );
//#endif
// ERROR_CHECK_STATUS( vxUnmapTensorPatch( output_tensor, map_id ) );
////////
// Display the results and grab the next input RGB frame for the next iteration.
char text[128];
sprintf( text, "Keyboard ESC/Q-Quit SPACE-Pause [FRAME %d] [fixed_point_pos input:%d output:%d]", frame_index, tensor_input_fixed_point_pos, tensor_output_fixed_point_pos );
gui.DrawText( 0, 16, text );
gui.Show();
if( !gui.Grab() )
{
// Terminate the processing loop if the end of sequence is detected.
gui.WaitForKey();
break;
}
}
////////********
// To release an OpenVX object, you need to call vxRelease<Object> API which takes a pointer to the object.
// If the release operation is successful, the OpenVX framework will reset the object to NULL.
//
// TODO STEP 11:****
// 1. Release graph and tensor objects
// ERROR_CHECK_STATUS( vxReleaseGraph( &graph ) );
// ERROR_CHECK_STATUS( vxReleaseTensor( &input_tensor ) );
// ERROR_CHECK_STATUS( vxReleaseTensor( &output_tensor ) );
ERROR_CHECK_STATUS( vxReleaseContext( &context ) );
return 0;
}
////////
// main() has all the OpenVX application code for this exercise.
// Command-line usage:
// % solution_exercise2 [<video-sequence>|<camera-device-number>]
// When neither video sequence nor camera device number is specified,
// it defaults to the video sequence in "PETS09-S1-L1-View001.avi".
int main( int argc, char * argv[] )
{
// Get default video sequence when nothing is specified on command-line and
// instantiate OpenCV GUI module for reading input RGB images and displaying
// the image with OpenVX results.
const char * video_sequence = argv[1];
CGuiModule gui( video_sequence );
// Try to grab the first video frame from the sequence using cv::VideoCapture
// and check if a video frame is available.
if( !gui.Grab() )
{
printf( "ERROR: input has no video\n" );
return 1;
}
////////
// Set the application configuration parameters. Note that input video
// sequence is an 8-bit RGB image with dimensions given by gui.GetWidth()
// and gui.GetHeight(). The parameters for the Harris corners algorithm are:
// max_keypoint_count - maximum number of keypoints to track
// harris_strength_thresh - minimum threshold score to keep a corner
// (computed using the normalized Sobel kernel)
// harris_min_distance - radial L2 distance for non-max suppression
// harris_k_sensitivity - sensitivity threshold k from the Harris-Stephens
// harris_gradient_size - window size for gradient computation
// harris_block_size - block window size used to compute the
// Harris corner score
// lk_pyramid_levels - number of pyramid levels for LK optical flow
// lk_termination - can be VX_TERM_CRITERIA_ITERATIONS or
// VX_TERM_CRITERIA_EPSILON or
// VX_TERM_CRITERIA_BOTH
// lk_epsilon - error for terminating the algorithm
// lk_num_iterations - number of iterations
// lk_use_initial_estimate - turn on/off use of initial estimates
// lk_window_dimension - size of window on which to perform the algorithm
vx_uint32 width = gui.GetWidth();
vx_uint32 height = gui.GetHeight();
vx_size max_keypoint_count = 10000;
vx_float32 harris_strength_thresh = 0.0005f;
vx_float32 harris_min_distance = 5.0f;
vx_float32 harris_k_sensitivity = 0.04f;
vx_int32 harris_gradient_size = 3;
vx_int32 harris_block_size = 3;
vx_uint32 lk_pyramid_levels = 6;
vx_float32 lk_pyramid_scale = VX_SCALE_PYRAMID_HALF;
vx_enum lk_termination = VX_TERM_CRITERIA_BOTH;
vx_float32 lk_epsilon = 0.01f;
vx_uint32 lk_num_iterations = 5;
vx_bool lk_use_initial_estimate = vx_false_e;
vx_uint32 lk_window_dimension = 6;
////////
// Create the OpenVX context and make sure the returned context is valid and
// register the log_callback to receive messages from OpenVX framework.
vx_context context = vxCreateContext();
ERROR_CHECK_OBJECT( context );
vxRegisterLogCallback( context, log_callback, vx_false_e );
////////
// Create OpenVX image object for input RGB image.
vx_image input_rgb_image = vxCreateImage( context, width, height, VX_DF_IMAGE_RGB );
ERROR_CHECK_OBJECT( input_rgb_image );
////////********
// OpenVX optical flow functionality requires pyramids of the current input
// image and the previous image. It also requires keypoints that correspond
// to the previous pyramid and will output updated keypoints into
// another keypoint array. To be able to toggle between the current and
// the previous buffers, you need to use OpenVX delay objects and vxAgeDelay().
// Create OpenVX pyramid and array object exemplars and create OpenVX delay
// objects for both to hold two of each. Note that the exemplar objects are not
// needed once the delay objects are created.
//
// TODO STEP 01:********
// 1. Use vxCreatePyramid API to create a pyramid exemplar with the
// same dimensions as the input image, VX_DF_IMAGE_U8 as image format,
// lk_pyramid_levels as levels, and lk_pyramid_scale as scale.
// We gave code for this in comments.
// 2. Use vxCreateArray API to create an array exemplar with
// keypoint data type with num_keypoint_count as capacity.
// You need to add missing parameters to code in comments.
// 3. Use vxCreateDelay API to create delay objects for pyramid and
// keypoint array using the exemplars created using the two steps above.
// Use 2 delay slots for both of the delay objects.
// We gave code for one in comments; do similar for the other.
// 4. Release the pyramid and keypoint array exemplar objects.
// We gave code for one in comments; do similar for the other.
// 5. Use ERROR_CHECK_OBJECT/STATUS macros for proper error checking.
// We gave few error checks; do similar for the others.
// vx_pyramid pyramidExemplar = vxCreatePyramid( context, lk_pyramid_levels,
// lk_pyramid_scale, width, height, VX_DF_IMAGE_U8 );
// ERROR_CHECK_OBJECT( pyramidExemplar );
// vx_delay pyramidDelay = vxCreateDelay( context, ( vx_reference )pyramidExemplar, 2 );
// ERROR_CHECK_OBJECT( pyramidDelay );
// ERROR_CHECK_STATUS( vxReleasePyramid( &pyramidExemplar ) );
// vx_array keypointsExemplar = vxCreateArray( /* Fill in parameters */ );
// vx_delay keypointsDelay = vxCreateDelay( /* Fill in parameters */ );
////////********
// An object from a delay slot can be accessed using vxGetReferenceFromDelay API.
// You need to use index = 0 for the current object and index = -1 for the previous object.
//
// TODO STEP 02:********
// 1. Use vxGetReferenceFromDelay API to get the current and previous
// pyramid objects from pyramid delay object. Note that you need
// to typecast the vx_reference object to vx_pyramid.
// We gave code for one in comments; do similar for the other.
// 2. Similarly, get the current and previous keypoint array objects from
// the keypoint delay object.
// We gave code for one in comments; do similar for the other.
// 3. Use ERROR_CHECK_OBJECT for proper error checking.
// We gave one error check; do similar for the others.
// vx_pyramid currentPyramid = ( vx_pyramid ) vxGetReferenceFromDelay( pyramidDelay, 0 );
// vx_pyramid previousPyramid = ( vx_pyramid ) vxGetReferenceFromDelay( /* Fill in parameters */ );
// vx_array currentKeypoints = ( vx_array ) vxGetReferenceFromDelay( /* Fill in parameters */ );
// vx_array previousKeypoints = ( vx_array ) vxGetReferenceFromDelay( keypointsDelay, -1 );
// ERROR_CHECK_OBJECT( currentPyramid );
////////********
// Harris and optical flow algorithms require their own graph objects.
// The Harris graph needs to extract gray scale image out of input RGB,
// compute an initial set of keypoints, and compute an initial pyramid for use
// by the optical flow graph.
//
// TODO STEP 03:********
// 1. Create two graph objects: one for the Harris corner detector and
// the other for feature tracking using optical flow using the
// vxCreateGraph API.
// We gave code for one graph; do similar for the other.
// 2. Use ERROR_CHECK_OBJECT to check the objects.
// We gave one error check; do similar for the other.
// vx_graph graphHarris = vxCreateGraph( context );
// vx_graph graphTrack = /* Fill in here */;
// ERROR_CHECK_OBJECT( graphHarris );
////////********
// Harris and pyramid computation expect input to be an 8-bit image.
// Given that input is an RGB image, it is best to extract a gray image
// from RGB image, which requires two steps:
// - perform RGB to IYUV color conversion
// - extract Y channel from IYUV image
// This requires two intermediate OpenVX image objects. Since you don't
// need to access these objects from the application, they can be virtual
// objects that can be created using the vxCreateVirtualImage API.
//
// TODO STEP 04:********
// 1. Create an IYUV image and a U8 image (for Y channel) with the same
// dimensions as the input RGB image. Note that the image formats for
// IYUV and U8 images are VX_DF_IMAGE_IYUV and VX_DF_IMAGE_U8.
// Note that virtual objects are specific to a graph, so you
// need to create two sets, one for each graph.
// We gave one fully in comments and you need to fill in missing
// parameters for the others.
// 2. Use ERROR_CHECK_OBJECT to check the objects.
// We gave one error check in comments; do similar for others.
// vx_image harris_yuv_image = vxCreateVirtualImage( graphHarris, width, height, VX_DF_IMAGE_IYUV );
// vx_image harris_luma_image = vxCreateVirtualImage( graphHarris, /* Fill in parameters */ );
// vx_image opticalflow_yuv_image = vxCreateVirtualImage( graphTrack, /* Fill in parameters */ );
// vx_image opticalflow_luma_image = vxCreateVirtualImage( /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( harris_yuv_image );
////////********
// The Harris corner detector and optical flow nodes (see "VX/vx_nodes.h")
// take strength_thresh, min_distance, sensitivity, epsilon,
// num_iterations, and use_initial_estimate parameters as scalar
// data objects. So, you need to create scalar objects with the corresponding
// configuration parameters.
//
// TODO STEP 05:********
// 1. Create scalar data objects of VX_TYPE_FLOAT32 for strength_thresh,
// min_distance, sensitivity, and epsilon. Set their
// initial values to harris_strength_thresh, harris_min_distance,
// harris_k_sensitivity, and lk_epsilon.
// We gave code full code for one scalar in comments; fill in
// missing arguments for other ones.
// 2. Similarly, create scalar objects for num_iterations and
// use_initial_estimate with initial values: lk_num_iterations and
// lk_use_initial_estimate. Make sure to use proper data types for
// these parameters.
// We gave code full code for one scalar in comments; fill in
// missing arguments for the other.
// 3. Use ERROR_CHECK_OBJECT to check proper creation of objects.
// We gave the error check for one scalar; do similar for other 5 scalars.
// vx_scalar strength_thresh = NULL; // vxCreateScalar( context, VX_TYPE_FLOAT32, &harris_strength_thresh );
// vx_scalar min_distance = NULL; // vxCreateScalar( context, /* Fill in parameters */ );
// vx_scalar sensitivity = NULL; // vxCreateScalar( /* Fill in parameters */ );
// vx_scalar epsilon = NULL; // vxCreateScalar( /* Fill in parameters */ );
// vx_scalar num_iterations = NULL; // vxCreateScalar( context, VX_TYPE_UINT32, /* Fill in parameter */ );
// vx_scalar use_initial_estimate = NULL; // vxCreateScalar( context, VX_TYPE_BOOL, &lk_use_initial_estimate );
// ERROR_CHECK_OBJECT( strength_thresh );
////////********
// Now all the objects have been created for building the graphs.
// First, build a graph that performs Harris corner detection and initial pyramid computation.
// See "VX/vx_nodes.h" for APIs how to add nodes into a graph.
//
// TODO STEP 06:********
// 1. Use vxColorConvertNode and vxChannelExtractNode APIs to get gray
// scale image for Harris and Pyramid computation from the input
// RGB image. Add these nodes into Harris graph.
// We gave code in comments with a missing parameter for you to fill in.
// 2. Use vxGaussianPyramidNode API to add pyramid computation node.
// You need to use the current pyramid from the pyramid delay object.
// We gave code in comments with a missing parameter for you to fill in.
// 3. Use vxHarrisCornersNode API to add a Harris corners node.
// You need to use the current keypoints from keypoints delay object.
// We gave code in comments with few missing parameters for you to fill in.
// 4. Use ERROR_CHECK_OBJECT to check proper creation of objects.
// 5. Release node and virtual objects immediately since the graph
// retains references to them.
// 6. Call vxVerifyGraph to check for any errors in the graph.
// Fill in missing parameter in commented code.
// vx_node nodesHarris[] =
// {
// vxColorConvertNode( graphHarris, input_rgb_image, harris_yuv_image ),
// vxChannelExtractNode( graphHarris, /* Fill in parameter */, VX_CHANNEL_Y, harris_luma_image ),
// vxGaussianPyramidNode( graphHarris, /* Fill in parameter */, currentPyramid ),
// vxHarrisCornersNode( graphHarris, /* Fill in missing parameters */, currentKeypoints, NULL )
// };
// for( vx_size i = 0; i < sizeof( nodesHarris ) / sizeof( nodesHarris[0] ); i++ )
// {
// ERROR_CHECK_OBJECT( nodesHarris[i] );
// ERROR_CHECK_STATUS( vxReleaseNode( &nodesHarris[i] ) );
// }
// ERROR_CHECK_STATUS( vxReleaseImage( &harris_yuv_image ) );
// ERROR_CHECK_STATUS( vxReleaseImage( &harris_luma_image ) );
// ERROR_CHECK_STATUS( vxVerifyGraph( /* Fill in parameter */ ) );
////////********
// Now, build a graph that performs pyramid computation and feature
// tracking using optical flow.
//
// TODO STEP 07:********
// 1. Use vxColorConvertNode and vxChannelExtractNode APIs to get a gray
// scale image for Harris and Pyramid computation from the input
// RGB image. Add these nodes into Harris graph.
// We gave the code in comments for color convert node; do similar
// one for the channel extract node.
// 2. Use vxGaussianPyramidNode API to add pyramid computation node.
// You need to use the current pyramid from the pyramid delay object.
// Most of the code is given in the comments; fill in the missing parameter.
// 3. Use vxOpticalFlowPyrLKNode API to add an optical flow node. You need to
// use the current and previous keypoints from the keypoints delay object.
// Fill in the missing parameters in commented code.
// 4. Use ERROR_CHECK_OBJECT to check proper creation of objects.
// 5. Release node and virtual objects immediately since the graph
// retains references to them.
// 6. Call vxVerifyGraph to check for any errors in the graph.
// Fill in the missing parameter in commented code.
// vx_node nodesTrack[] =
// {
// vxColorConvertNode( graphTrack, input_rgb_image, opticalflow_yuv_image ),
// vxChannelExtractNode( graphTrack, /* Fill in parameters */ ),
// vxGaussianPyramidNode( graphTrack, /* Fill in parameter */, currentPyramid ),
// vxOpticalFlowPyrLKNode( graphTrack, /* Fill in parameters */ )
// };
// for( vx_size i = 0; i < sizeof( nodesTrack ) / sizeof( nodesTrack[0] ); i++ )
// {
// ERROR_CHECK_OBJECT( nodesTrack[i] );
// ERROR_CHECK_STATUS( vxReleaseNode( &nodesTrack[i] ) );
// }
// ERROR_CHECK_STATUS( vxReleaseImage( &opticalflow_yuv_image ) );
// ERROR_CHECK_STATUS( vxReleaseImage( &opticalflow_luma_image ) );
// ERROR_CHECK_STATUS( vxVerifyGraph( /* Fill in parameter */ ) );
////////
// Process the video sequence frame by frame until the end of sequence or aborted.
for( int frame_index = 0; !gui.AbortRequested(); frame_index++ )
{
////////
// Copy the input RGB frame from OpenCV to OpenVX.
// In order to do this, you need to use vxAccessImagePatch and vxCommitImagePatch APIs.
// See "VX/vx_api.h" for the description of these APIs.
vx_rectangle_t cv_rgb_image_region;
cv_rgb_image_region.start_x = 0;
cv_rgb_image_region.start_y = 0;
cv_rgb_image_region.end_x = width;
cv_rgb_image_region.end_y = height;
vx_imagepatch_addressing_t cv_rgb_image_layout;
cv_rgb_image_layout.stride_x = 3;
cv_rgb_image_layout.stride_y = gui.GetStride();
vx_uint8 * cv_rgb_image_buffer = gui.GetBuffer();
ERROR_CHECK_STATUS( vxAccessImagePatch( input_rgb_image, &cv_rgb_image_region, 0,
&cv_rgb_image_layout, ( void ** )&cv_rgb_image_buffer, VX_WRITE_ONLY ) );
ERROR_CHECK_STATUS( vxCommitImagePatch( input_rgb_image, &cv_rgb_image_region, 0,
&cv_rgb_image_layout, cv_rgb_image_buffer ) );
////////********
// Now that input RGB image is ready, just run a graph.
// Run Harris at the beginning to initialize the previous keypoints.
//
// TODO STEP 08:********
// 1. Run a graph using vxProcessGraph API. Select Harris graph
// if the frame_index == 0 (i.e., the first frame of the video
// sequence), otherwise, select the feature tracking graph.
// 2. Use ERROR_CHECK_STATUS for error checking.
////////********
// To mark the keypoints in display, you need to access the output
// keypoint array and draw each item on the output window using gui.DrawArrow().
//
// TODO STEP 09:********
// 1. Use vxGetReferenceFromDelay API to get the current and previous
// keypoints array objects from the keypoints delay object.
// Make sure to typecast the vx_reference object to vx_array.
// We gave one for the previous previous keypoint array in comments;
// do a similar one for the current keypoint array.
// 2. OpenVX array object has an attribute that keeps the current
// number of items in the array. The name of the attribute is
// VX_ARRAY_ATTRIBUTE_NUMITEMS and its value is of type vx_size.
// Use vxQueryArray API to get number of keypoints in the
// current keypoint array data object, representing number of
// corners detected in the input RGB image.
// IMPORTANT: Read number of items into "num_corners"
// because this variable is displayed by code segment below.
// We gave most part of this statement in comment; just fill in the
// missing parameter.
// 3. The data items in output keypoint array are of type
// vx_keypoint_t (see "VX/vx_types.h"). To access the array
// buffer, use vxAccessArrayRange with start index = 0,
// end index = number of items in the array, and usage mode =
// VX_READ_ONLY. Note that the stride returned by this access
// call is not guaranteed to be sizeof(vx_keypoint_t).
// Also make sure that num_corners is > 0, because
// vxAccessArrayRange expects end index > 0.
// We gave the code for previous keypoint array in comment;
// do similar one for the current keypoint array.
// 4. For each item in the keypoint buffer, use vxArrayItem to
// access an individual keypoint and draw a marker at (x,y)
// using gui.DrawArrow() if tracking_status field of keypoint
// is non-zero. Also count number of keypoints with
// non-zero tracking_status into "num_tracking" variable.
// We gave most of the code; fill in the missing parameters and uncomment.
// 5. Hand the control of output keypoint buffer over back to
// OpenVX framework by calling vxCommitArrayRange API.
// We gave the code for previous keypoint array in comment;
// do similar one for the current keypoint array.
// 6. Use ERROR_CHECK_STATUS for error checking.
vx_size num_corners = 0, num_tracking = 0;
// previousKeypoints = ( vx_array )vxGetReferenceFromDelay( keypointsDelay, -1 );
// currentKeypoints = ( vx_array )vxGetReferenceFromDelay( /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( currentKeypoints );
// ERROR_CHECK_OBJECT( previousKeypoints );
// ERROR_CHECK_STATUS( vxQueryArray( previousKeypoints, /* Fill in parameter */, &num_corners, sizeof( num_corners ) ) );
if( num_corners > 0 )
{
vx_size kp_old_stride, kp_new_stride;
vx_keypoint_t * kp_old_buf = NULL, * kp_new_buf = NULL;
// ERROR_CHECK_STATUS( vxAccessArrayRange( previousKeypoints, 0, num_corners,
// &kp_old_stride, ( void ** ) &kp_old_buf, VX_READ_ONLY ) );
// ERROR_CHECK_STATUS( vxAccessArrayRange( /* Fill in parameters */ );
for( vx_size i = 0; i < num_corners; i++ )
{
// vx_keypoint_t * kp_old = &vxArrayItem( vx_keypoint_t, kp_old_buf, i, kp_old_stride );
// vx_keypoint_t * kp_new = &vxArrayItem( /* Fill in parameters */ );
// if( kp_new->tracking_status )
// {
// num_tracking++;
// gui.DrawArrow( kp_old->x, kp_old->y, kp_new->x, kp_new->y );
// }
}
// ERROR_CHECK_STATUS( vxCommitArrayRange( previousKeypoints, 0, num_corners, kp_old_buf ) );
// ERROR_CHECK_STATUS( vxCommitArrayRange( /* Fill in parameters */ ) );
}
////////********
// Flip the current and previous pyramid and keypoints in the delay objects.
//
// TODO STEP 10:********
// 1. Use vxAgeDelay API to flip the current and previous buffers in delay objects.
// You need to call vxAgeDelay for both two delay objects.
// 2. Use ERROR_CHECK_STATUS for error checking.
// ERROR_CHECK_STATUS( vxAgeDelay( /* Fill in parameter */ ) );
// ERROR_CHECK_STATUS( vxAgeDelay( /* Fill in parameter */ ) );
////////
// Display the results and grab the next input RGB frame for the next iteration.
char text[128];
sprintf( text, "Keyboard ESC/Q-Quit SPACE-Pause [FRAME %d]", frame_index );
gui.DrawText( 0, 16, text );
sprintf( text, "Number of Corners: %d [tracking %d]", ( int )num_corners, ( int )num_tracking );
gui.DrawText( 0, 36, text );
gui.Show();
if( !gui.Grab() )
{
// Terminate the processing loop if the end of sequence is detected.
gui.WaitForKey();
break;
}
}
////////********
// Query graph performance using VX_GRAPH_ATTRIBUTE_PERFORMANCE and print timing
// in milliseconds. Note that time units of vx_perf_t fields are nanoseconds.
//
// TODO STEP 11:********
// 1. Use vxQueryGraph API with VX_GRAPH_ATTRIBUTE_PERFORMANCE to query graph performance.
// We gave the attribute query for one graph in comments. Do the same for the second graph.
// 2. Print the average and min execution times in milliseconds. Use the printf in comments.
// vx_perf_t perfHarris = { 0 }, perfTrack = { 0 };
// ERROR_CHECK_STATUS( vxQueryGraph( graphHarris, VX_GRAPH_ATTRIBUTE_PERFORMANCE, &perfHarris, sizeof( perfHarris ) ) );
// ERROR_CHECK_STATUS( vxQueryGraph( /* Fill in parameters here for get performance of the other graph */ );
// printf( "GraphName NumFrames Avg(ms) Min(ms)\n"
// "Harris %9d %7.3f %7.3f\n"
// "Track %9d %7.3f %7.3f\n",
// ( int )perfHarris.num, ( float )perfHarris.avg * 1e-6f, ( float )perfHarris.min * 1e-6f,
// ( int )perfTrack.num, ( float )perfTrack.avg * 1e-6f, ( float )perfTrack.min * 1e-6f );
////////********
// Release all the OpenVX objects created in this exercise, and make the context as the last one to release.
// To release an OpenVX object, you need to call vxRelease<Object> API which takes a pointer to the object.
// If the release operation is successful, the OpenVX framework will reset the object to NULL.
//
// TODO STEP 12:********
// 1. For releasing all other objects use vxRelease<Object> APIs.
// You have to release 2 graph objects, 1 image object, 2 delay objects,
// 6 scalar objects, and 1 context object.
// 2. Use ERROR_CHECK_STATUS for error checking.
// ERROR_CHECK_STATUS( vxReleaseContext( &context ) );
return 0;
}
/*! \brief The graph factory example.
* \ingroup group_example
*/
int main(int argc, char *argv[])
{
vx_status status = VX_SUCCESS;
vx_context context = vxCreateContext();
if (context)
{
vx_image images[] = {
vxCreateImage(context, 640, 480, VX_DF_IMAGE_U8),
vxCreateImage(context, 640, 480, VX_DF_IMAGE_S16),
};
vx_graph graph = vxGraphFactory(context, VX_GRAPH_FACTORY_EDGE);
if (graph)
{
vx_uint32 p, num = 0;
status |= vxQueryGraph(graph, VX_GRAPH_ATTRIBUTE_NUMPARAMETERS, &num, sizeof(num));
if (status == VX_SUCCESS)
{
printf("There are %u parameters to this graph!\n", num);
for (p = 0; p < num; p++)
{
vx_parameter param = vxGetGraphParameterByIndex(graph, p);
if (param)
{
vx_enum dir = 0;
vx_enum type = 0;
status |= vxQueryParameter(param, VX_PARAMETER_ATTRIBUTE_DIRECTION, &dir, sizeof(dir));
status |= vxQueryParameter(param, VX_PARAMETER_ATTRIBUTE_TYPE, &type, sizeof(type));
printf("graph.parameter[%u] dir:%d type:%08x\n", p, dir, type);
vxReleaseParameter(¶m);
}
else
{
printf("Invalid parameter retrieved from graph!\n");
}
}
status |= vxSetGraphParameterByIndex(graph, 0, (vx_reference)images[0]);
status |= vxSetGraphParameterByIndex(graph, 1, (vx_reference)images[1]);
}
status |= vxVerifyGraph(graph);
if (status == VX_SUCCESS)
{
status = vxProcessGraph(graph);
if (status == VX_SUCCESS)
{
printf("Ran Graph!\n");
}
}
vxReleaseGraph(&graph);
}
else
{
printf("Failed to create graph!\n");
}
vxReleaseContext(&context);
}
else
{
printf("failed to create context!\n");
}
return status;
}
vxContext()
{
ctx = vxCreateContext();
vxErr::check(ctx);
}
int main(int argc, char *argv[]) {
vx_status status = VX_FAILURE;
vx_context context = vxCreateContext();
if (argc < 2) {
usage(argv[0]);
goto relCtx;
}
vx_char *srcfilename = argv[1];
printf("src img: %s\n", srcfilename);
FILE *fp = fopen(srcfilename, "r");
if (!fp) {
goto relCtx;
}
char pgmstr[1024];
unsigned int n;
n = fread(pgmstr, 1, sizeof(pgmstr), fp);
if (n != sizeof(pgmstr)) {
goto relClose;
}
const char delim = '\n';
const char *token = NULL;
unsigned int width, height;
// PGM P5 magic string
token = strtok(pgmstr, &delim);
// PGM author
token = strtok(NULL, &delim);
// PGM image size
token = strtok(NULL, &delim);
sscanf(token, "%u %u", &width, &height);
printf("width:%u height:%u\n", width, height);
status = vxGetStatus((vx_reference)context);
if (status != VX_SUCCESS) {
fprintf(stderr, "error: vxCreateContext\n");
goto relClose;
}
vx_rectangle_t rect = {1, 1, width + 1, height + 1};
vx_uint32 i = 0;
vx_image images[] = {
vxCreateImage(context, width + 2, height + 2, VX_DF_IMAGE_U8), // 0:input
vxCreateImageFromROI(images[0], &rect), // 1:ROI input
vxCreateImage(context, width, height, VX_DF_IMAGE_U8), // 2:box
vxCreateImage(context, width, height, VX_DF_IMAGE_U8), // 3:gaussian
vxCreateImage(context, width, height, VX_DF_IMAGE_U8), // 4:alpha
vxCreateImage(context, width, height, VX_DF_IMAGE_S16),// 5:add
};
vx_float32 a = 0.5f;
vx_scalar alpha = vxCreateScalar(context, VX_TYPE_FLOAT32, &a);
status |= vxLoadKernels(context, "openvx-tiling");
status |= vxLoadKernels(context, "openvx-debug");
if (status != VX_SUCCESS) {
fprintf(stderr, "error: vxLoadKernels %d\n", status);
goto relImg;
}
vx_graph graph = vxCreateGraph(context);
status = vxGetStatus((vx_reference)context);
if (status != VX_SUCCESS) {
fprintf(stderr, "error: vxGetStatus\n");
goto relKern;
}
ax_node_t axnodes[] = {
{ vxFReadImageNode(graph, srcfilename, images[1]), "Read" },
{ vxTilingBoxNode(graph, images[1], images[2], 5, 5), "Box" },
{ vxFWriteImageNode(graph, images[2], "ot_box.pgm"), "Write" },
{ vxTilingGaussianNode(graph, images[1], images[3]), "Gaussian" },
{ vxFWriteImageNode(graph, images[3], "ot_gauss.pgm"), "Write" },
{ vxTilingAlphaNode(graph, images[1], alpha, images[4]), "Alpha" },
{ vxFWriteImageNode(graph, images[4], "ot_alpha.pgm"), "Write" },
{ vxTilingAddNode(graph, images[1], images[4], images[5]), "Add" },
{ vxFWriteImageNode(graph, images[5], "ot_add.pgm"), "Write" },
};
for (i = 0; i < dimof(axnodes); i++) {
if (axnodes[i].node == 0) {
fprintf(stderr, "error: Failed to create node[%u]\n", i);
status = VX_ERROR_INVALID_NODE;
goto relNod;
}
}
status = vxVerifyGraph(graph);
if (status != VX_SUCCESS) {
fprintf(stderr, "error: vxVerifyGraph %d\n", status);
goto relNod;
}
status = vxProcessGraph(graph);
if (status != VX_SUCCESS) {
fprintf(stderr, "error: vxProcessGraph %d\n", status);
goto relNod;
}
// perf timings
vx_perf_t perf_node;
vx_perf_t perf_graph;
vxQueryGraph(graph, VX_GRAPH_ATTRIBUTE_PERFORMANCE, &perf_graph, sizeof(perf_graph));
axPrintPerf("Graph", &perf_graph);
for (i = 0; i < dimof(axnodes); ++i) {
vxQueryNode(axnodes[i].node, VX_NODE_ATTRIBUTE_PERFORMANCE, &perf_node, sizeof(perf_node));
axPrintPerf(axnodes[i].name, &perf_node);
}
relNod:
for (i = 0; i < dimof(axnodes); i++) {
vxReleaseNode(&axnodes[i].node);
}
vxReleaseGraph(&graph);
relKern:
relImg:
for (i = 0; i < dimof(images); i++) {
vxReleaseImage(&images[i]);
}
relClose:
fclose(fp);
relCtx:
vxReleaseContext(&context);
printf("%s::main() returns = %d\n", argv[0], status);
return (int)status;
}
int main(int argc, char *argv[])
{
vx_status status = VX_SUCCESS;
vx_context context = vxCreateContext();
if (vxGetStatus((vx_reference)context) == VX_SUCCESS)
{
vx_char implementation[VX_MAX_IMPLEMENTATION_NAME];
vx_char *extensions = NULL;
vx_int32 m, modules = 0;
vx_uint32 k, kernels = 0;
vx_uint32 p, parameters = 0;
vx_uint32 a = 0;
vx_uint16 vendor, version;
vx_size size = 0;
vx_kernel_info_t *table = NULL;
// take each arg as a module name to load
for (m = 1; m < argc; m++)
{
if (vxLoadKernels(context, argv[m]) != VX_SUCCESS)
printf("Failed to load module %s\n", argv[m]);
else
printf("Loaded module %s\n", argv[m]);
}
vxPrintAllLog(context);
vxRegisterHelperAsLogReader(context);
vxQueryContext(context, VX_CONTEXT_VENDOR_ID, &vendor, sizeof(vendor));
vxQueryContext(context, VX_CONTEXT_VERSION, &version, sizeof(version));
vxQueryContext(context, VX_CONTEXT_IMPLEMENTATION, implementation, sizeof(implementation));
vxQueryContext(context, VX_CONTEXT_MODULES, &modules, sizeof(modules));
vxQueryContext(context, VX_CONTEXT_EXTENSIONS_SIZE, &size, sizeof(size));
printf("implementation=%s (%02x:%02x) has %u modules\n", implementation, vendor, version, modules);
extensions = malloc(size);
if (extensions)
{
vx_char *line = extensions, *token = NULL;
vxQueryContext(context, VX_CONTEXT_EXTENSIONS, extensions, size);
do {
token = strtok(line, " ");
if (token)
printf("extension: %s\n", token);
line = NULL;
} while (token);
free(extensions);
}
status = vxQueryContext(context, VX_CONTEXT_UNIQUE_KERNELS, &kernels, sizeof(kernels));
if (status != VX_SUCCESS) goto exit;
printf("There are %u kernels\n", kernels);
size = kernels * sizeof(vx_kernel_info_t);
table = malloc(size);
status = vxQueryContext(context, VX_CONTEXT_UNIQUE_KERNEL_TABLE, table, size);
for (k = 0; k < kernels && table != NULL && status == VX_SUCCESS; k++)
{
vx_kernel kernel = vxGetKernelByEnum(context, table[k].enumeration);
if (kernel && vxGetStatus((vx_reference)kernel) == VX_SUCCESS)
{
status = vxQueryKernel(kernel, VX_KERNEL_PARAMETERS, ¶meters, sizeof(parameters));
printf("\t\tkernel[%u]=%s has %u parameters (%d)\n",
table[k].enumeration,
table[k].name,
parameters,
status);
for (p = 0; p < parameters; p++)
{
vx_parameter parameter = vxGetKernelParameterByIndex(kernel, p);
vx_enum type = VX_TYPE_INVALID, dir = VX_INPUT;
vx_uint32 tIdx, dIdx;
status = VX_SUCCESS;
status |= vxQueryParameter(parameter, VX_PARAMETER_TYPE, &type, sizeof(type));
status |= vxQueryParameter(parameter, VX_PARAMETER_DIRECTION, &dir, sizeof(dir));
for (tIdx = 0; tIdx < dimof(parameter_names); tIdx++)
if (parameter_names[tIdx].tenum == type)
break;
for (dIdx = 0; dIdx < dimof(direction_names); dIdx++)
if (direction_names[dIdx].tenum == dir)
break;
if (status == VX_SUCCESS)
printf("\t\t\tparameter[%u] type:%s dir:%s\n", p,
parameter_names[tIdx].name,
direction_names[dIdx].name);
vxReleaseParameter(¶meter);
}
for (a = 0; a < dimof(attribute_names); a++)
{
switch (attribute_names[a].type)
{
case VX_TYPE_SIZE:
{
vx_size value = 0;
if (VX_SUCCESS == vxQueryKernel(kernel, attribute_names[a].tenum, &value, sizeof(value)))
printf("\t\t\tattribute[%u] %s = "VX_FMT_SIZE"\n",
attribute_names[a].tenum & VX_ATTRIBUTE_ID_MASK,
attribute_names[a].name,
value);
break;
}
case VX_TYPE_UINT32:
{
vx_uint32 value = 0;
if (VX_SUCCESS == vxQueryKernel(kernel, attribute_names[a].tenum, &value, sizeof(value)))
printf("\t\t\tattribute[%u] %s = %u\n",
attribute_names[a].tenum & VX_ATTRIBUTE_ID_MASK,
attribute_names[a].name,
value);
break;
}
default:
break;
}
}
vxReleaseKernel(&kernel);
}
else
{
printf("ERROR: kernel %s is invalid (%d) !\n", table[k].name, status);
}
}
for (m = 1; m < argc; m++)
{
if (vxUnloadKernels(context, argv[m]) != VX_SUCCESS)
printf("Failed to unload module %s\n", argv[m]);
else
printf("Unloaded module %s\n", argv[m]);
}
exit:
if (table) free(table);
vxReleaseContext(&context);
}
return 0;
}
/*!
* \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;
}
//**************************************************************************
// EXPORTED FUNCTIONS
//**************************************************************************
static void Initialize(JNIEnv *env, jobject obj)
{
vx_context context = vxCreateContext();
SetHandle(env, obj, ContextClass, handleName, (jlong)context);
}
int main(int argc, char **argv)
{
int i;
vx_status status;
vx_set_debug_zone(VX_ZONE_ERROR);
//vx_set_debug_zone(VX_ZONE_WARNING);
//vx_set_debug_zone(VX_ZONE_INFO);
vx_context context = vxCreateContext();
CHECK_NOT_NULL(context, "vxCreateContext");
printf("Success create vx_context!!\n\n");
vxInitLog(&helper_log);
vxRegisterLogCallback(context, &vxHelperLogCallback, vx_false_e);
Mat src = imread(SRC_IMG_NAME);
CHECK_NOT_NULL(src.data, "imread");
resize(src, src, Size(IMG_WIDTH,IMG_HEIGHT));
cvtColor(src, src, CV_RGB2GRAY);
for(i=0; i<1; i++)
{
Mat result_cv(IMG_HEIGHT,IMG_WIDTH,CV_8UC1);
Mat result_vx(IMG_HEIGHT,IMG_WIDTH,CV_8UC1);
printf("Start to run not_box3x3_graph()\n");
not_box3x3_cv(src.clone(), result_cv);
status = not_box3x3_graph(context, src.clone(), result_vx);
printf("Return from not_box3x3_graph() result_vx: %d\n", status);
if(verify_result(result_cv, result_vx))
printf("Verify passed!!\n");
else
printf("Verify fail!!\n");
printf("\n");
//imwrite("not_box3x3_cv.jpg",result_cv);
//imwrite("not_box3x3_vx.jpg",result_vx);
printf("Start to run not_not_graph()\n");
not_not_cv(src.clone(), result_cv);
status = not_not_graph(context, src.clone(), result_vx);
printf("Return from not_not_graph() result_vx: %d\n", status);
if(verify_result(result_cv, result_vx))
printf("Verify passed!!\n");
else
printf("Verify fail!!\n");
printf("\n");
printf("Start to run not_graph()\n");
not_cv(src.clone(), result_cv);
status = not_graph(context, src.clone(), result_vx);
printf("Return from not_not_graph() result_vx: %d\n", status);
if(verify_result(result_cv, result_vx))
printf("Verify passed!!\n");
else
printf("Verify fail!!\n");
printf("\n");
//imwrite("result_cv.jpg",result_cv);
//imwrite("result_vx.jpg",result_vx);
}
status = vxReleaseContext(&context);
CHECK_STATUS(status, "vxReleaseContext");
printf("%s done!!\n", argv[0]);
return 0;
}