npu.lib package

Subpackages

• npu.lib.applications package
  • Submodules
  • npu.lib.applications.videoapps module
    • ColorDetectVideoProcessing
      • ColorDetectVideoProcessing.rtps
      • ColorDetectVideoProcessing.start()
    • ColorThresholdVideoProcessing
      • ColorThresholdVideoProcessing.rtps
    • DenoiseDPVideoProcessing
    • DenoiseTPVideoProcessing
    • EdgeDetectVideoProcessing
      • EdgeDetectVideoProcessing.rtps
      • EdgeDetectVideoProcessing.start()
    • ScaledColorThresholdVideoProcessing
      • ScaledColorThresholdVideoProcessing.rtps
    • VideoApplication
      • VideoApplication.cam_h
      • VideoApplication.cam_w
      • VideoApplication.resolution
      • VideoApplication.rtps
      • VideoApplication.start()
      • VideoApplication.stop()
      • VideoApplication.videores
    • pxtype
      • pxtype.GRAY
      • pxtype.RGBA
  • Module contents
    • Applications
• npu.lib.cached package
  • Module contents
    • Cached
• npu.lib.kernels package
  • Submodules
  • npu.lib.kernels.addweighted module
    • AddWeighted
      • AddWeighted.behavioralfx()
    • AddWeightedScalar
  • npu.lib.kernels.bitwiseand module
    • BitwiseAnd
      • BitwiseAnd.behavioralfx()
    • BitwiseAndScalar
  • npu.lib.kernels.bitwiseor module
    • BitwiseOr
      • BitwiseOr.behavioralfx()
    • BitwiseOrScalar
  • npu.lib.kernels.filter2d module
    • Filter2d
      • Filter2d.behavioralfx()
    • Filter2dScalar
  • npu.lib.kernels.gray2rgba module
    • Gray2Rgba
      • Gray2Rgba.behavioralfx()
    • Gray2RgbaScalar
  • npu.lib.kernels.inrange module
    • InRange
      • InRange.behavioralfx()
  • npu.lib.kernels.inverse module
    • Inverse
      • Inverse.behavioralfx()
  • npu.lib.kernels.kernelgenerator module
    • KernelObjCall
  • npu.lib.kernels.median module
    • Median
      • Median.behavioralfx()
    • MedianScalar
  • npu.lib.kernels.plusn module
    • Plus1
      • Plus1.behavioralfx()
    • PlusN
      • PlusN.behavioralfx()
  • npu.lib.kernels.rgba2gray module
    • Rgba2Gray
      • Rgba2Gray.behavioralfx()
    • Rgba2GrayScalar
  • npu.lib.kernels.rgba2hue module
    • Rgba2Hue
      • Rgba2Hue.behavioralfx()
    • Rgba2HueScalar
  • npu.lib.kernels.rgba_inverse module
    • RgbaInverse
      • RgbaInverse.behavioralfx()
  • npu.lib.kernels.threshold module
    • RgbaRtpThres
      • RgbaRtpThres.behavioralfx()
    • ThresholdGrayscale
      • ThresholdGrayscale.behavioralfx()
    • ThresholdRgba
      • ThresholdRgba.behavioralfx()
  • Module contents
    • Kernels

Module contents

Library of prebuilts

The NPU submodule npu.lib contains prebuilt binaries for various vision applications, optimized C++ kernel sources and pre-defined graphs to help quickly build custom applications.

Applications

Prebuilt applications can be run as-is with no additional build steps required.

You can list the available videoapps with

from npu.utils import videoapps videoapps()

Example running edge detect

from npu.lib import EdgeDetectVideoProcessing

app = EdgeDetectVideoProcessing() app.start()

Kernels

List the available kernels with

from npu.utils import aiekernels aiekernels()

Graphs

Pre-defined graphs are classes that inherit functionality from AppBuilder and make it simple to insert your own kernel into an NPU application without worrying too much about setting up the graph for dataflow.

Example using an RGB720pBuilder class with a custom kernel object

from npu.lib.graphs.graph_1ct import RGB720pBuilder app_builder = RGB720pBuilder(kernel=passthrough)

This graph is specifically designed to work with 720p RGBA data, in applications which have one input and one output buffer.