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Video Motion Vector HowTo
==========================
Beignet now supports cl_intel_accelerator and cl_intel_motion_estimation, which are
Khronos official extensions. It provides a hardware acceleration of video motion
vector to users.
Supported hardware platform
---------------------------
Only 3rd Generation Intel Core Processors is supported for vme now. We will consider
to support more platforms if necessary.
Steps
-----
In order to use video motion estimation provided by Beignet in your program, please follow
the steps as below:
- Create a cl_accelerator_intel object using extension API clCreateAcceleratorINTEL, like
this:
_accelerator_type_intel accelerator_type = CL_ACCELERATOR_TYPE_MOTION_ESTIMATION_INTEL;
cl_motion_estimation_desc_intel vmedesc = {CL_ME_MB_TYPE_16x16_INTEL,
CL_ME_SUBPIXEL_MODE_INTEGER_INTEL,
CL_ME_SAD_ADJUST_MODE_NONE_INTEL,
CL_ME_SEARCH_PATH_RADIUS_16_12_INTEL
};
- Invoke clCreateProgramWithBuiltInKernels to create a program object with built-in kernels
information, and invoke clCreateKernel to create a kernel object whose kernel name is
block_motion_estimate_intel.
- The prototype of built-in kernel block_motion_estimate_intel is as following:
_kernel void
block_motion_estimate_intel
(
accelerator_intel_t accelerator,
__read_only image2d_t src_image,
__read_only image2d_t ref_image,
__global short2 * prediction_motion_vector_buffer,
__global short2 * motion_vector_buffer,
__global ushort * residuals
);
So you should create related objects and setup these kernel arguments by clSetKernelArg.
Create source and reference image object, on which you want to do video motion estimation.
The image_channel_order should be CL_R and image_channel_data_type should be CL_UNORM_INT8.
Create a buffer object to get the motion vector result. This motion vector buffer representing
a vector field of pixel block motion vectors, stored linearly in row-major order. The elements
(pixels) of this image contain a motion vector for the corresponding pixel block, with its x/y
components packed as two 16-bit integer values. Each component is encoded as a S13.2 fixed
point value(two's complement).
- Use clEnqueueNDRangeKernel to enqueue this kernel. The only thing you need to setup is global_work_size:
global_work_size[0] equal to width of source image, global_work_size[1] equal to height of source
image.
- Use clEnqueueReadBuffer or clEnqueueMapBuffer to get motion vector result.
Sample code
-----------
We have developed an utest case of using video motion vector in utests/builtin_kernel_block_motion_estimate_intel.cpp.
Please go through it for details.
More references
---------------
<https://www.khronos.org/registry/cl/extensions/intel/cl_intel_accelerator.txt>
<https://www.khronos.org/registry/cl/extensions/intel/cl_intel_motion_estimation.txt>
<https://software.intel.com/en-us/articles/intro-to-motion-estimation-extension-for-opencl>
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