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aggregate_metadata.json

@@ -671,7 +671,7 @@
                 "workdir": "holohub_bin"
             }
         },
-        "readme": "# Simple CV-CUDA application\n\nThis application demonstrates seamless interoperability between Holoscan tensors and CV-CUDA tensors. The image processing pipeline is just a simple flip of the video orientation.\n\nNote that the C++ version of this application currently requires extra code to handle zero-copy conversion back and forth between CV-CUDA and Holoscan tensor types. On the Python side, the conversion is trivial due to the support for the [DLPack Python specification](https://dmlc.github.io/dlpack/latest/python_spec.html) in both CV-CUDA and Holoscan. We hope to make C++ interoperability with CV-CUDA easier in the future.\n\n# Using the docker file\n\nThis application requires a compiled version of [CV-CUDA](https://github.com/CVCUDA/CV-CUDA).\nFor simplicity a DockerFile is available. To generate the container run:\n\n```bash\n./dev_container build --docker_file ./applications/cvcuda_basic/Dockerfile\n```\n\nThe C++ version of the application can then be built by launching this container and using the provided `run` script.\n\n```bash\n./dev_container launch\n./run build cvcuda_basic\n```\n\n# Running the Application\n\nThis application uses the endoscopy dataset as an example. The `run build` command above will automatically download it. This application is then run inside the container.\n\n```bash\n./dev_container launch\n```\n\nThe Python version of the simple CV-CUDA pipeline example can be run via\n```\npython applications/cvcuda_basic/python/cvcuda_basic.py --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic python\n```\n\nThe C++ version of the simple CV-CUDA pipeline example can then be run via\n```\n./build/applications/cvcuda_basic/cpp/cvcuda_basic --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic cpp\n```\n",
+        "readme": "# Simple CV-CUDA application\n\nThis application demonstrates seamless interoperability between Holoscan tensors and CV-CUDA tensors. The image processing pipeline is just a simple flip of the video orientation.\n\nNote that the C++ version of this application currently requires extra code to handle conversion\nback and forth between CV-CUDA and Holoscan tensor types. On the Python side, the conversion is\ntrivial due to the support for the [DLPack Python\nspecification](https://dmlc.github.io/dlpack/latest/python_spec.html) in both CV-CUDA and Holoscan.\nWe provide two [operators](../../operators/cvcuda_holoscan_interop/README.md) to handle the\ninteroperability between CVCUDA and Holoscan tensors.\n\n# Using the docker file\n\nThis application requires a compiled version of [CV-CUDA](https://github.com/CVCUDA/CV-CUDA).\nFor simplicity a DockerFile is available. To generate the container run:\n\n```bash\n./dev_container build --docker_file ./applications/cvcuda_basic/Dockerfile\n```\n\nThe C++ version of the application can then be built by launching this container and using the provided `run` script.\n\n```bash\n./dev_container launch\n./run build cvcuda_basic\n```\n\n# Running the Application\n\nThis application uses the endoscopy dataset as an example. The `run build` command above will automatically download it. This application is then run inside the container.\n\n```bash\n./dev_container launch\n```\n\nThe Python version of the simple CV-CUDA pipeline example can be run via\n```\npython applications/cvcuda_basic/python/cvcuda_basic.py --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic python\n```\n\nThe C++ version of the simple CV-CUDA pipeline example can then be run via\n```\n./build/applications/cvcuda_basic/cpp/cvcuda_basic --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic cpp\n```\n",
         "application_name": "cvcuda_basic",
         "source_folder": "applications"
     },
@@ -719,7 +719,7 @@
                 "workdir": "holohub_bin"
             }
         },
-        "readme": "# Simple CV-CUDA application\n\nThis application demonstrates seamless interoperability between Holoscan tensors and CV-CUDA tensors. The image processing pipeline is just a simple flip of the video orientation.\n\nNote that the C++ version of this application currently requires extra code to handle zero-copy conversion back and forth between CV-CUDA and Holoscan tensor types. On the Python side, the conversion is trivial due to the support for the [DLPack Python specification](https://dmlc.github.io/dlpack/latest/python_spec.html) in both CV-CUDA and Holoscan. We hope to make C++ interoperability with CV-CUDA easier in the future.\n\n# Using the docker file\n\nThis application requires a compiled version of [CV-CUDA](https://github.com/CVCUDA/CV-CUDA).\nFor simplicity a DockerFile is available. To generate the container run:\n\n```bash\n./dev_container build --docker_file ./applications/cvcuda_basic/Dockerfile\n```\n\nThe C++ version of the application can then be built by launching this container and using the provided `run` script.\n\n```bash\n./dev_container launch\n./run build cvcuda_basic\n```\n\n# Running the Application\n\nThis application uses the endoscopy dataset as an example. The `run build` command above will automatically download it. This application is then run inside the container.\n\n```bash\n./dev_container launch\n```\n\nThe Python version of the simple CV-CUDA pipeline example can be run via\n```\npython applications/cvcuda_basic/python/cvcuda_basic.py --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic python\n```\n\nThe C++ version of the simple CV-CUDA pipeline example can then be run via\n```\n./build/applications/cvcuda_basic/cpp/cvcuda_basic --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic cpp\n```\n",
+        "readme": "# Simple CV-CUDA application\n\nThis application demonstrates seamless interoperability between Holoscan tensors and CV-CUDA tensors. The image processing pipeline is just a simple flip of the video orientation.\n\nNote that the C++ version of this application currently requires extra code to handle conversion\nback and forth between CV-CUDA and Holoscan tensor types. On the Python side, the conversion is\ntrivial due to the support for the [DLPack Python\nspecification](https://dmlc.github.io/dlpack/latest/python_spec.html) in both CV-CUDA and Holoscan.\nWe provide two [operators](../../operators/cvcuda_holoscan_interop/README.md) to handle the\ninteroperability between CVCUDA and Holoscan tensors.\n\n# Using the docker file\n\nThis application requires a compiled version of [CV-CUDA](https://github.com/CVCUDA/CV-CUDA).\nFor simplicity a DockerFile is available. To generate the container run:\n\n```bash\n./dev_container build --docker_file ./applications/cvcuda_basic/Dockerfile\n```\n\nThe C++ version of the application can then be built by launching this container and using the provided `run` script.\n\n```bash\n./dev_container launch\n./run build cvcuda_basic\n```\n\n# Running the Application\n\nThis application uses the endoscopy dataset as an example. The `run build` command above will automatically download it. This application is then run inside the container.\n\n```bash\n./dev_container launch\n```\n\nThe Python version of the simple CV-CUDA pipeline example can be run via\n```\npython applications/cvcuda_basic/python/cvcuda_basic.py --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic python\n```\n\nThe C++ version of the simple CV-CUDA pipeline example can then be run via\n```\n./build/applications/cvcuda_basic/cpp/cvcuda_basic --data=/workspace/holohub/data/endoscopy\n```\n\nor using the run script\n\n```bash\n./run launch cvcuda_basic cpp\n```\n",
         "application_name": "cvcuda_basic",
         "source_folder": "applications"
     },