33 lines
2.8 KiB
Markdown
33 lines
2.8 KiB
Markdown
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# Hello NV12 Input Classification C Sample
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This sample demonstrates how to execute an inference of image classification networks like AlexNet with images in NV12 color format using Synchronous Inference Request API.
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Hello NV12 Input Classification C Sample demonstrates how to use the NV12 automatic input pre-processing API in your applications.
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For more detailed information on how this sample works, check the dedicated [article](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/hello-nv12-input-classification.html)
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## Requirements
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| Options | Values |
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| ----------------------------| ---------------------------------------------------------------------------------------------------------------------|
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| Model Format | OpenVINO Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx) |
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| Validated images | An uncompressed image in the NV12 color format - \*.yuv |
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| Supported devices | [All](https://docs.openvino.ai/2025/documentation/compatibility-and-support/supported-devices.html) |
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| Other language realization | [C++](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/hello-nv12-input-classification.html) |
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The following C++ API is used in the application:
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| Feature | API | Description |
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| --------------------------| ----------------------------------------------------------|--------------------------------------------------------|
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| Node Operations | ``ov_port_get_any_name`` | Get a layer name |
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| Infer Request Operations | ``ov_infer_request_set_tensor``, | Operate with tensors |
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| | ``ov_infer_request_get_output_tensor_by_index`` | |
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| Preprocessing | ``ov_preprocess_input_tensor_info_set_color_format``, | Change the color format of the input data |
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| | ``ov_preprocess_preprocess_steps_convert_element_type``, | |
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| | ``ov_preprocess_preprocess_steps_convert_color`` | |
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Basic OpenVINO API is covered by [Hello Classification C sample](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/hello-classification.html).
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