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README.md

Holoscan utility scripts

This folder includes the following scripts:

Note: these will be included in the SDK installation at /opt/nvidia/holoscan/bin

aja_build.sh

Builds the AJA SDK with the proper flags and optionally loads the driver.

convert_gxf_entities_to_images.py

Takes in the encoded GXF tensor files generated by the video_stream_recorder and export raw frames in .png files.

Prerequisites

pip install numpy~=1.21 pillow

This script depends on gxf_entity_codec.py which is located in the same folder.

Usage

The command below will read the racerx.gxf_entities and racerx.gxf_index files from the existing racerx dataset under data/racerx (which is a 854x480 video with framerate 25fps and 3 channels) and convert them to PNG files.

python3 scripts/convert_gxf_entities_to_images.py --directory data/racerx --basename racerx

Use --outputdir to specify the directory where the files will be created. Use --outputname to specify a different output name than the default tensor prefix.

convert_gxf_entities_to_video.py

Takes in the encoded GXF tensor files generated by the video_stream_recorder (.gxf_entities and .gxf_index) and emit the raw video feed.

Prerequisites

pip install numpy~=1.21

This script depends on gxf_entity_codec.py which is located in the same folder.

Usage

The command below will read the racerx.gxf_entities and racerx.gxf_index files from the existing racerx dataset under data/racerx (which is a 854x480 video with framerate 25fps and 3 channels) and use ffmpeg to encode the emitted video stream to a video file, converted_video.mp4:

python3 scripts/convert_gxf_entities_to_video.py --directory data/racerx --basename racerx | ffmpeg -f rawvideo -pix_fmt rgb24 -s 854x480 -r 25 -i - -f mp4 -vcodec libx264 -pix_fmt yuv420p -r 25 -y racerx-medium.mp4

convert_video_to_gxf_entities.py

Takes in a raw video feed and emits encoded GXF tensor files entities for playback with the video_stream_replayer operator. The tensors will be saved with metadata indicating that the data should be copied to the GPU on read.

Prerequisites

pip install numpy~=1.21

This script depends on gxf_entity_codec.py which is located in the same folder.

Usage

Example usage converting the output of a tool like ffmpeg to encoded GXF tensors:

ffmpeg -i video_1920x1080.avi -pix_fmt rgb24 -f rawvideo pipe:1 | python3 scripts/convert_video_to_gxf_entities.py --width 1920 --height 1080 --channels 3 --framerate 30

Above command will create two files: tensor.gxf_entities and tensor.gxf_index from the video_1920x1080.avi video file. Use --directory to specify the directory where the files will be created. Use --basename to specify a different output name than the default tensor

When working with long videos, it can be desired to limit the disc space usage. In this case, ffmpeg can be used to convert only a portion of the video. Use -ss option to set the start time. Use -t option to set the duration or the -to option to set the end time.

Examples:

ffmpeg -ss 00:00:05 -i video_1920x1080.avi -t 00:00:05 -pix_fmt rgb24 -f rawvideo pipe:1 | python3 scripts/convert_video_to_gxf_entities.py --width 1920 --height 1080 --channels 3 --framerate 30
ffmpeg -ss 00:00:05 -i video_1920x1080.avi -to 00:00:10 -pix_fmt rgb24 -f rawvideo pipe:1 | python3 scripts/convert_video_to_gxf_entities.py --width 1920 --height 1080 --channels 3 --framerate 30

Above commands will parse the video starting at 00:00:05 and ending at 00:00:10.

ctest_time_comparison.py

This script compares time from different tests to make sure they match expected timing comparison.

Usage

python3 ./scripts/ctest_time_comparison.py <filename> "TEST1" "LESS" "TEST2"

download_ngc_data

Download and unzip datasets from NGC. This can optionally run a script to convert video files to GXF tensor files compatible with the video_stream_replayer operator.

Prerequisites

  • wget or curl to download datasets using curl. Note that as of Jan 2025, download full zip archives from NGC is not supported.
  • NGC CLI to download datasets using the <org>/[team]/<name>:<version> format (useful if the above fails or for private registries)

Usage

The example below will download and unzip the RacerX video from NGC:

./scripts/download_ngc_data --url nvidia/clara-holoscan/holoscan_racerx_video:20231009

Use --help for more options such as output dir/name or conversion to GXF tensor files.

generate_extension_uuids.py

Provides a set of UUIDs to be used by GXF_EXT_FACTORY_SET_INFO and GXF_EXT_FACTORY_ADD to declare a new GXF extension.

python3 scripts/generate_extension_uuids.py

generate_gxf_manifest.py

Generates a GXF extension registry manifest. Refer to Graph Composer Registry documentation for registry details.

Holoscan SDK provides the CMake function generate_gxf_registry_manifest to call this script each time a target is updated. You can also call this script directly for manual testing with your own Holoscan GXF extensions.

Note that GXF manifests are not portable and typically include filepaths relative to the build environment. All extensions must be available in the local environment to use this script.

The script accepts a number of optional arguments, including manifest content, extension search paths, a custom Python .pickle database path, and more. See generate_gxf_manifest.py -h for help.

The GXF registry CLI need not be present in the environment to generate an extension manifest.

Usage

python3 scripts/generate_gxf_manifest.py \
    --output <my_manifest.yaml>
    --name <MyHoloscanExtension> \
    --extension-library <path/to/my_extension.so> \
    --uuid <uuid> \
    --version <version> \
    --extension-dependencies [libgxf_std.so,libgxf_ucx.so,...]
    ...

graph_surgeon.py

When converting a model from PyTorch to ONNX, it is likely that the input of the model is in the form NCHW (batch, channels, height, width), and needs to be converted to NHWC (batch, height, width, channels). This script performs the conversion and generates a modified model. Note that this script modifies the names of the output by appending _old to the input and output.

Usage

python3 scripts/graph_surgeon.py input_model.onnx output_model.onnx

gxf_entity_codec.py

Utility classes used by convert_gxf_entities_to_images.py, convert_gxf_entities_to_video.py and convert_video_to_gxf_entities.py.

video_validation.py

This script converts GXF tensor files to frame images to compare each frame with a set of baselines. The difference between them is computed using SSD (Sum of Square difference) for each pixel and an average is reported for a frame.

Prerequisites

pip install numpy~=1.21 pillow

This script depends on convert_gxf_entity_to_images.py which is located in the same folder.

Usage

See python3 ./scripts/video_validation.py --help