Hello NV12 Input Classification C Sample

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.

Hello NV12 Input Classification C Sample demonstrates how to use the NV12 automatic input pre-processing API in your applications.



Validated Models


Model Format

Inference Engine Intermediate Representation (*.xml + *.bin), ONNX (*.onnx)

Validated images

An uncompressed image in the NV12 color format - *.yuv

Supported devices


Other language realization





Node Operations


Get a layer name

Infer Request Operations

ov_infer_request_set_tensor, ov_infer_request_get_output_tensor_by_index

Operate with tensors


ov_preprocess_input_tensor_info_set_color_format, ov_preprocess_preprocess_steps_convert_element_type, ov_preprocess_preprocess_steps_convert_color

Change the color format of the input data

Basic Inference Engine API is covered by Hello Classification C sample.

// Copyright (C) 2018-2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0

#include <stdbool.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include "openvino/c/openvino.h"

 * @brief Struct to store infer results
struct infer_result {
    size_t class_id;
    float probability;

 * @brief Sort result by probability
 * @param struct with infer results to sort
 * @param result_size of the struct
 * @return none
int compare(const void* a, const void* b) {
    const struct infer_result* sa = (const struct infer_result*)a;
    const struct infer_result* sb = (const struct infer_result*)b;
    if (sa->probability < sb->probability) {
        return 1;
    } else if ((sa->probability == sb->probability) && (sa->class_id > sb->class_id)) {
        return 1;
    } else if (sa->probability > sb->probability) {
        return -1;
    return 0;
void infer_result_sort(struct infer_result* results, size_t result_size) {
    qsort(results, result_size, sizeof(struct infer_result), compare);

 * @brief Convert output tensor to infer result struct for processing results
 * @param tensor of output tensor
 * @param result_size of the infer result
 * @return struct infer_result
struct infer_result* tensor_to_infer_result(ov_tensor_t* tensor, size_t* result_size) {
    ov_status_e status = ov_tensor_get_size(tensor, result_size);
    if (status != OK)
        return NULL;

    struct infer_result* results = (struct infer_result*)malloc(sizeof(struct infer_result) * (*result_size));
    if (!results)
        return NULL;

    void* data = NULL;
    status = ov_tensor_data(tensor, &data);
    if (status != OK) {
        return NULL;

    float* float_data = (float*)(data);
    for (size_t i = 0; i < *result_size; ++i) {
        results[i].class_id = i;
        results[i].probability = float_data[i];

    return results;

 * @brief Print results of infer
 * @param results of the infer results
 * @param result_size of the struct of classification results
 * @param img_path image path
 * @return none
void print_infer_result(struct infer_result* results, size_t result_size, const char* img_path) {
    printf("\nImage %s\n", img_path);
    printf("\nclassid probability\n");
    printf("------- -----------\n");
    for (size_t i = 0; i < result_size; ++i) {
        printf("%zu       %f\n", results[i].class_id, results[i].probability);

void print_model_input_output_info(ov_model_t* model) {
    char* friendly_name = NULL;
    ov_model_get_friendly_name(model, &friendly_name);
    printf("[INFO] model name: %s \n", friendly_name);

 * @brief Check image has supported width and height
 * @param string image size in WIDTHxHEIGHT format
 * @param pointer to image width
 * @param pointer to image height
 * @return bool status True(success) or False(fail)

bool is_supported_image_size(const char* size_str, size_t* width, size_t* height) {
    const char* _size = size_str;
    size_t _width = 0, _height = 0;
    while (_size && *_size != 'x' && *_size != '\0') {
        if ((*_size <= '9') && (*_size >= '0')) {
            _width = (_width * 10) + (*_size - '0');
        } else {
            goto err;

    if (_size)

    while (_size && *_size != '\0') {
        if ((*_size <= '9') && (*_size >= '0')) {
            _height = (_height * 10) + (*_size - '0');
        } else {
            goto err;

    if (_width > 0 && _height > 0) {
        if (_width % 2 == 0 && _height % 2 == 0) {
            *width = _width;
            *height = _height;
            return true;
        } else {
            printf("Unsupported image size, width and height must be even numbers \n");
            return false;
    } else {
        goto err;
    printf("Incorrect format of image size parameter, expected WIDTHxHEIGHT, "
           "actual: %s\n",
    return false;

size_t read_image_from_file(const char* img_path, unsigned char* img_data, size_t size) {
    FILE* fp = fopen(img_path, "rb");
    size_t read_size = 0;

    if (fp) {
        fseek(fp, 0, SEEK_END);
        if ((size_t)ftell(fp) >= size) {
            fseek(fp, 0, SEEK_SET);
            read_size = fread(img_data, 1, size, fp);

    return read_size;

#define CHECK_STATUS(return_status)                                                      \
    if (return_status != OK) {                                                           \
        fprintf(stderr, "[ERROR] return status %d, line %d\n", return_status, __LINE__); \
        goto err;                                                                        \

int main(int argc, char** argv) {
    // -------- Check input parameters --------
    if (argc != 5) {
        printf("Usage : ./hello_nv12_input_classification_c <path_to_model> <path_to_image> "
               "<WIDTHxHEIGHT> <device_name>\n");
        return EXIT_FAILURE;

    size_t input_width = 0, input_height = 0, img_size = 0;
    if (!is_supported_image_size(argv[3], &input_width, &input_height)) {
        fprintf(stderr, "ERROR is_supported_image_size, line %d\n", __LINE__);
        return EXIT_FAILURE;
    unsigned char* img_data = NULL;
    ov_core_t* core = NULL;
    ov_model_t* model = NULL;
    ov_tensor_t* tensor = NULL;
    ov_preprocess_prepostprocessor_t* preprocess = NULL;
    ov_preprocess_input_info_t* input_info = NULL;
    ov_model_t* new_model = NULL;
    ov_preprocess_input_tensor_info_t* input_tensor_info = NULL;
    ov_preprocess_preprocess_steps_t* input_process = NULL;
    ov_preprocess_input_model_info_t* p_input_model = NULL;
    ov_compiled_model_t* compiled_model = NULL;
    ov_infer_request_t* infer_request = NULL;
    ov_tensor_t* output_tensor = NULL;
    struct infer_result* results = NULL;
    char* input_tensor_name = NULL;
    char* output_tensor_name = NULL;
    ov_output_const_port_t* input_port = NULL;
    ov_output_const_port_t* output_port = NULL;
    ov_layout_t* model_layout = NULL;
    ov_shape_t input_shape;

    // -------- Get OpenVINO runtime version --------
    ov_version_t version = {.description = NULL, .buildNumber = NULL};
    printf("---- OpenVINO INFO----\n");
    printf("description : %s \n", version.description);
    printf("build number: %s \n", version.buildNumber);

    // -------- Parsing and validation of input arguments --------
    const char* input_model = argv[1];
    const char* input_image_path = argv[2];
    const char* device_name = argv[4];

    // -------- Step 1. Initialize OpenVINO Runtime Core --------

    // -------- Step 2. Read a model --------
    printf("[INFO] Loading model files: %s\n", input_model);
    CHECK_STATUS(ov_core_read_model(core, input_model, NULL, &model));

    CHECK_STATUS(ov_model_const_output(model, &output_port));

    CHECK_STATUS(ov_model_const_input(model, &input_port));

    CHECK_STATUS(ov_port_get_any_name(input_port, &input_tensor_name));
    CHECK_STATUS(ov_port_get_any_name(output_port, &output_tensor_name));

    // -------- Step 3. Configure preprocessing  --------
    CHECK_STATUS(ov_preprocess_prepostprocessor_create(model, &preprocess));

    // 1) Select input with 'input_tensor_name' tensor name
    CHECK_STATUS(ov_preprocess_prepostprocessor_get_input_info_by_name(preprocess, input_tensor_name, &input_info));

    // 2) Set input type
    // - as 'u8' precision
    // - set color format to NV12 (single plane)
    // - static spatial dimensions for resize preprocessing operation
    CHECK_STATUS(ov_preprocess_input_info_get_tensor_info(input_info, &input_tensor_info));
    CHECK_STATUS(ov_preprocess_input_tensor_info_set_element_type(input_tensor_info, U8));
    CHECK_STATUS(ov_preprocess_input_tensor_info_set_color_format(input_tensor_info, NV12_SINGLE_PLANE));
        ov_preprocess_input_tensor_info_set_spatial_static_shape(input_tensor_info, input_height, input_width));

    // 3) Pre-processing steps:
    //    a) Convert to 'float'. This is to have color conversion more accurate
    //    b) Convert to BGR: Assumes that model accepts images in BGR format. For RGB, change it manually
    //    c) Resize image from tensor's dimensions to model ones
    CHECK_STATUS(ov_preprocess_input_info_get_preprocess_steps(input_info, &input_process));
    CHECK_STATUS(ov_preprocess_preprocess_steps_convert_element_type(input_process, F32));
    CHECK_STATUS(ov_preprocess_preprocess_steps_convert_color(input_process, BGR));
    CHECK_STATUS(ov_preprocess_preprocess_steps_resize(input_process, RESIZE_LINEAR));

    // 4) Set model data layout (Assuming model accepts images in NCHW layout)
    CHECK_STATUS(ov_preprocess_input_info_get_model_info(input_info, &p_input_model));

    const char* model_layout_desc = "NCHW";
    CHECK_STATUS(ov_layout_create(model_layout_desc, &model_layout));
    CHECK_STATUS(ov_preprocess_input_model_info_set_layout(p_input_model, model_layout));

    // 5) Apply preprocessing to an input with 'input_tensor_name' name of loaded model
    CHECK_STATUS(ov_preprocess_prepostprocessor_build(preprocess, &new_model));

    // -------- Step 4. Loading a model to the device --------
    CHECK_STATUS(ov_core_compile_model(core, new_model, device_name, 0, &compiled_model));

    // -------- Step 5. Create an infer request --------
    CHECK_STATUS(ov_compiled_model_create_infer_request(compiled_model, &infer_request));

    // -------- Step 6. Prepare input data  --------
    img_size = input_width * (input_height * 3 / 2);
    if (!img_size) {
        fprintf(stderr, "[ERROR] Invalid Image size, line %d\n", __LINE__);
        goto err;
    img_data = (unsigned char*)calloc(img_size, sizeof(unsigned char));
    if (!img_data) {
        fprintf(stderr, "[ERROR] calloc returned NULL, line %d\n", __LINE__);
        goto err;
    if (img_size != read_image_from_file(input_image_path, img_data, img_size)) {
        fprintf(stderr, "[ERROR] Image dimensions not match with NV12 file size, line %d\n", __LINE__);
        goto err;
    ov_element_type_e input_type = U8;
    size_t batch = 1;
    int64_t dims[4] = {batch, input_height * 3 / 2, input_width, 1};
    ov_shape_create(4, dims, &input_shape);
    CHECK_STATUS(ov_tensor_create_from_host_ptr(input_type, input_shape, img_data, &tensor));

    // -------- Step 6. Set input tensor  --------
    // Set the input tensor by tensor name to the InferRequest
    CHECK_STATUS(ov_infer_request_set_tensor(infer_request, input_tensor_name, tensor));

    // -------- Step 7. Do inference --------
    // Running the request synchronously

    // -------- Step 8. Process output --------
    CHECK_STATUS(ov_infer_request_get_output_tensor_by_index(infer_request, 0, &output_tensor));
    // Print classification results
    size_t results_num = 0;
    results = tensor_to_infer_result(output_tensor, &results_num);
    if (!results) {
        goto err;
    infer_result_sort(results, results_num);
    size_t top = 10;
    if (top > results_num) {
        top = results_num;
    printf("\nTop %zu results:\n", top);
    print_infer_result(results, top, input_image_path);

    // -------- free allocated resources --------
    if (output_tensor)
    if (infer_request)
    if (compiled_model)
    if (p_input_model)
    if (input_process)
    if (model_layout)
    if (input_tensor_info)
    if (input_info)
    if (preprocess)
    if (new_model)
    if (tensor)
    if (model)
    if (core)
    return EXIT_SUCCESS;

How It Works

Upon the start-up, the sample application reads command-line parameters, loads specified network and an image in the NV12 color format to an Inference Engine plugin. Then, the sample creates an synchronous inference request object. When inference is done, the application outputs data to the standard output stream.

You can see the explicit description of each sample step at Integration Steps section of “Integrate OpenVINO™ Runtime with Your Application” guide.


To build the sample, please use instructions available at Build the Sample Applications section in Inference Engine Samples guide.


To run the sample, you need specify a model and image:

  • You can use public or Intel’s pre-trained models from the Open Model Zoo. The models can be downloaded using the Model Downloader.

  • You can use images from the media files collection available at the storage.

The sample accepts an uncompressed image in the NV12 color format. To run the sample, you need to convert your BGR/RGB image to NV12. To do this, you can use one of the widely available tools such as FFmpeg* or GStreamer*. The following command shows how to convert an ordinary image into an uncompressed NV12 image using FFmpeg:

ffmpeg -i cat.jpg -pix_fmt nv12 cat.yuv


  • Because the sample reads raw image files, you should provide a correct image size along with the image path. The sample expects the logical size of the image, not the buffer size. For example, for 640x480 BGR/RGB image the corresponding NV12 logical image size is also 640x480, whereas the buffer size is 640x720.

  • By default, this sample expects that network input has BGR channels order. If you trained your model to work with RGB order, you need to reconvert your model using mo with reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Embedding Preprocessing Computation.

  • Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the model conversion API.

  • The sample accepts models in ONNX format (.onnx) that do not require preprocessing.


  1. Download a pre-trained model using Model Downloader:

    python <path_to_omz_tools>/downloader.py --name alexnet
  2. If a model is not in the Inference Engine IR or ONNX format, it must be converted. You can do this using the model converter script:

    python <path_to_omz_tools>/converter.py --name alexnet
  3. Perform inference of NV12 image using alexnet model on a CPU, for example:

    <path_to_sample>/hello_nv12_input_classification_c <path_to_model>/alexnet.xml <path_to_image>/cat.yuv 300x300 CPU

Sample Output

The application outputs top-10 inference results.

Top 10 results:

Image ./cat.yuv

classid probability
------- -----------
435       0.091733
876       0.081725
999       0.069305
587       0.043726
666       0.038957
419       0.032892
285       0.030309
700       0.029941
696       0.021628
855       0.020339

This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool