vc6 7.34.0

V-Nova VC-6 codec Python bindings.

V-Nova VC-6 codec python module

Usage

Main codec functionality is inside the codec module that can be imported as follow:

from vnova.vc6_opencl import codec as vc6codec

or if you have installed the CUDA codec:

from vnova.vc6_cuda import codec as vc6codec

Then you can create codecs and start transcoding. For complete examples see provided sample codes

# setup encoder and decoder instances
    encoder = vc6codec.EncoderSync(
        1920, 1080, vc6codec.CodecBackendType.CPU, vc6codec.PictureFormat.RGB_8, vc6codec.ImageMemoryType.CPU)
    encoder.set_generic_preset(vc6codec.EncoderGenericPreset.LOSSLESS)
    # Using double resolution to demonstrate reconfiguration later
    decoder = vc6codec.DecoderSync(1920, 1080, vc6codec.CodecBackendType.CPU, vc6codec.PictureFormat.RGB_8, vc6codec.ImageMemoryType.CPU)
    # encode file to memory
    encoded_image = encoder.read("example_1920x1080_rgb8.rgb")
    # decode memory to memory
    decoder.write(encoded_image.memoryview, "reconstruction_example_1920x1080_rgb8.rgb")
    encoded_image.release()

GPU memory output

In case of cuda package (vc6_cuda), decoder output can be a device memory. To enable this feature create the decoder with specifying GPU_DEVICE as the output memory type. With that, the output images will have __cuda_array_interface__ and can be used with other libraries like cupy, pytorch and nvimagecodec.

    import cupy
    # setup GPU decoder instances with CUDA device output
    decoder = vc6codec.DecoderSync(1920, 1080, vc6codec.CodecBackendType.GPU, vc6codec.PictureFormat.RGB_8, vc6codec.ImageMemoryType.CUDA_DEVICE)
    # decode from file
    decoded_image = decoder.read("example_1920x1080_rgb8.vc6")
    # Make a cupy array from decoded image, download to cpu and write to file
    cuarray = cupy.asarray(decoded_image)
    with open("reconstruction_example_1920x1080_rgb8.rgb", "wb") as decoded_file:
        decoded_file.write(cuarray.get())

Both for sync and async decoders, accessing __cuda_array_interface__ is blocking and implicitly waits on the result to be ready in the image.

The __cuda_array_interface__ always contains a one-dimension data of unsigned-8bit type like the CPU version. Adjusting dimensions (or the type in case of 10-bit formats) is up to the user.

Environment variables

Variables OCL_BIN_LOC and OCL_DEVICE can be set to be used as gpu binary cache location and hint to selecting target GPU respectively. For more details refer to VC6-SDK documentation.

export OCL_BIN_LOC=./tmp/clbin
export OCL_DEVICE=nvidia

Variable CUDA_BIN_LOC serves the same purpose for CUDA version