Python API samples#

The following samples demonstrates the use of nvImageCodec Python API:

• Basics
  • Passing decoding parameters
  • Passing encoding parameters
  • Support of __cuda_array_interface__
  • Support of __array_interface__
• Batch processing
• Code stream and partial (ROI) decoding
  • Out of bounds region decoding
  • Exploiting Tile Geometry
• Sliding window decoding with tile cache
  • Tile access heatmap
  • Choosing the cache capacity
• CuPy interoperability
  • DLPack
  • Specifying sample_format and color_spec
• cuCIM interoperability
• CV-CUDA interoperability
  • Resnet50 classification example from cv-cuda
• Tensorflow interoperability
• Torch interoperability
  • Interoperability between nvImageCodec and Torch using DLPack
  • Interoperability between nvImageCodec and Torch using __cuda_array_interface__
  • Simplified version by passing troch tensor directly to write function
• Multi-image
• Advanced multi-image (SubIFDs and Pagination)
  • Setup
  • Part 1: SubIFD Access (Thumbnails)
  • Part 2: Pagination
  • Part 3: SubIFDs in OME-TIFF (Pyramid Levels)
• Metadata extraction
  • Geo
  • Aperio
  • Philips
  • Leica
  • Trestle
  • Ventana
  • Generic tiff tag reading
  • List all available TIFF tags
  • Use PIL.TiffTags together with nvImageCodec generic Tiff tag reading
• Transcode
  • Lossless uint8
  • Lossless uint16
  • Lossless int16
  • Lossess uint16 with alpha channel
  • Lossy uint8
• Accelerated DICOM decoding and encoding
  • Getting Started with pydicom Plugin
  • Advanced Batch Decoding
  • Transcoding to HTJ2K and Multi-Frame
  • Overview
  • Supported Transfer Syntaxes
• Reuse resources (Image and CodeStream)
  • Reusing Image for multiple decoding operations
  • Reusing CodeStream for multiple encoding operations
  • Reusing externally managed buffers (from CuPy or NumPy)

Please see also CV-CUDA samples which use nvImageCodec.