Hello Classification Sample

This sample demonstrates how to do inference of image classification models using Synchronous Inference Request API. Before using the sample, refer to the following requirements:

• Models with only one input and output are supported.

• The sample accepts any file format supported by core.read_model.

• To build the sample, use instructions available at Build the Sample Applications section in “Get Started with Samples” guide.

How It Works

At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image to the OpenVINO™ Runtime plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.

Python

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import logging as log
import sys

import cv2
import numpy as np
import openvino as ov


def main():
    log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)

    # Parsing and validation of input arguments
    if len(sys.argv) != 4:
        log.info(f'Usage: {sys.argv[0]} <path_to_model> <path_to_image> <device_name>')
        return 1

    model_path = sys.argv[1]
    image_path = sys.argv[2]
    device_name = sys.argv[3]

# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
    log.info('Creating OpenVINO Runtime Core')
    core = ov.Core()

# --------------------------- Step 2. Read a model --------------------------------------------------------------------
    log.info(f'Reading the model: {model_path}')
    # (.xml and .bin files) or (.onnx file)
    model = core.read_model(model_path)

    if len(model.inputs) != 1:
        log.error('Sample supports only single input topologies')
        return -1

    if len(model.outputs) != 1:
        log.error('Sample supports only single output topologies')
        return -1

# --------------------------- Step 3. Set up input --------------------------------------------------------------------
    # Read input image
    image = cv2.imread(image_path)
    # Add N dimension
    input_tensor = np.expand_dims(image, 0)

# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
    ppp = ov.preprocess.PrePostProcessor(model)

    _, h, w, _ = input_tensor.shape

    # 1) Set input tensor information:
    # - input() provides information about a single model input
    # - reuse precision and shape from already available `input_tensor`
    # - layout of data is 'NHWC'
    ppp.input().tensor() \
        .set_shape(input_tensor.shape) \
        .set_element_type(ov.Type.u8) \
        .set_layout(ov.Layout('NHWC'))  # noqa: ECE001, N400

    # 2) Adding explicit preprocessing steps:
    # - apply linear resize from tensor spatial dims to model spatial dims
    ppp.input().preprocess().resize(ov.preprocess.ResizeAlgorithm.RESIZE_LINEAR)

    # 3) Here we suppose model has 'NCHW' layout for input
    ppp.input().model().set_layout(ov.Layout('NCHW'))

    # 4) Set output tensor information:
    # - precision of tensor is supposed to be 'f32'
    ppp.output().tensor().set_element_type(ov.Type.f32)

    # 5) Apply preprocessing modifying the original 'model'
    model = ppp.build()

# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
    log.info('Loading the model to the plugin')
    compiled_model = core.compile_model(model, device_name)

# --------------------------- Step 6. Create infer request and do inference synchronously -----------------------------
    log.info('Starting inference in synchronous mode')
    results = compiled_model.infer_new_request({0: input_tensor})

# --------------------------- Step 7. Process output ------------------------------------------------------------------
    predictions = next(iter(results.values()))

    # Change a shape of a numpy.ndarray with results to get another one with one dimension
    probs = predictions.reshape(-1)

    # Get an array of 10 class IDs in descending order of probability
    top_10 = np.argsort(probs)[-10:][::-1]

    header = 'class_id probability'

    log.info(f'Image path: {image_path}')
    log.info('Top 10 results: ')
    log.info(header)
    log.info('-' * len(header))

    for class_id in top_10:
        probability_indent = ' ' * (len('class_id') - len(str(class_id)) + 1)
        log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}')

    log.info('')

# ----------------------------------------------------------------------------------------------------------------------
    log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
    return 0


if __name__ == '__main__':
    sys.exit(main())

C++

// Copyright (C) 2018-2024 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#include <iterator>
#include <memory>
#include <sstream>
#include <string>
#include <vector>

// clang-format off
#include "openvino/openvino.hpp"

#include "samples/args_helper.hpp"
#include "samples/common.hpp"
#include "samples/classification_results.h"
#include "samples/slog.hpp"
#include "format_reader_ptr.h"
// clang-format on

/**
 * @brief Main with support Unicode paths, wide strings
 */
int tmain(int argc, tchar* argv[]) {
    try {
        // -------- Get OpenVINO runtime version --------
        slog::info << ov::get_openvino_version() << slog::endl;

        // -------- Parsing and validation of input arguments --------
        if (argc != 4) {
            slog::info << "Usage : " << TSTRING2STRING(argv[0]) << " <path_to_model> <path_to_image> <device_name>"
                       << slog::endl;
            return EXIT_FAILURE;
        }

        const std::string args = TSTRING2STRING(argv[0]);
        const std::string model_path = TSTRING2STRING(argv[1]);
        const std::string image_path = TSTRING2STRING(argv[2]);
        const std::string device_name = TSTRING2STRING(argv[3]);

        // -------- Step 1. Initialize OpenVINO Runtime Core --------
        ov::Core core;

        // -------- Step 2. Read a model --------
        slog::info << "Loading model files: " << model_path << slog::endl;
        std::shared_ptr<ov::Model> model = core.read_model(model_path);
        printInputAndOutputsInfo(*model);

        OPENVINO_ASSERT(model->inputs().size() == 1, "Sample supports models with 1 input only");
        OPENVINO_ASSERT(model->outputs().size() == 1, "Sample supports models with 1 output only");

        // -------- Step 3. Set up input

        // Read input image to a tensor and set it to an infer request
        // without resize and layout conversions
        FormatReader::ReaderPtr reader(image_path.c_str());
        if (reader.get() == nullptr) {
            std::stringstream ss;
            ss << "Image " + image_path + " cannot be read!";
            throw std::logic_error(ss.str());
        }

        ov::element::Type input_type = ov::element::u8;
        ov::Shape input_shape = {1, reader->height(), reader->width(), 3};
        std::shared_ptr<unsigned char> input_data = reader->getData();

        // just wrap image data by ov::Tensor without allocating of new memory
        ov::Tensor input_tensor = ov::Tensor(input_type, input_shape, input_data.get());

        const ov::Layout tensor_layout{"NHWC"};

        // -------- Step 4. Configure preprocessing --------

        ov::preprocess::PrePostProcessor ppp(model);

        // 1) Set input tensor information:
        // - input() provides information about a single model input
        // - reuse precision and shape from already available `input_tensor`
        // - layout of data is 'NHWC'
        ppp.input().tensor().set_shape(input_shape).set_element_type(input_type).set_layout(tensor_layout);
        // 2) Adding explicit preprocessing steps:
        // - convert layout to 'NCHW' (from 'NHWC' specified above at tensor layout)
        // - apply linear resize from tensor spatial dims to model spatial dims
        ppp.input().preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
        // 4) Suppose model has 'NCHW' layout for input
        ppp.input().model().set_layout("NCHW");
        // 5) Set output tensor information:
        // - precision of tensor is supposed to be 'f32'
        ppp.output().tensor().set_element_type(ov::element::f32);

        // 6) Apply preprocessing modifying the original 'model'
        model = ppp.build();

        // -------- Step 5. Loading a model to the device --------
        ov::CompiledModel compiled_model = core.compile_model(model, device_name);

        // -------- Step 6. Create an infer request --------
        ov::InferRequest infer_request = compiled_model.create_infer_request();
        // -----------------------------------------------------------------------------------------------------

        // -------- Step 7. Prepare input --------
        infer_request.set_input_tensor(input_tensor);

        // -------- Step 8. Do inference synchronously --------
        infer_request.infer();

        // -------- Step 9. Process output
        const ov::Tensor& output_tensor = infer_request.get_output_tensor();

        // Print classification results
        ClassificationResult classification_result(output_tensor, {image_path});
        classification_result.show();
        // -----------------------------------------------------------------------------------------------------
    } catch (const std::exception& ex) {
        std::cerr << ex.what() << std::endl;
        return EXIT_FAILURE;
    }

    return EXIT_SUCCESS;
}

C

// Copyright (C) 2018-2024 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#include <opencv_c_wrapper.h>
#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_shape_t output_shape = {0};
    ov_status_e status = ov_tensor_get_shape(tensor, &output_shape);
    if (status != OK)
        return NULL;

    *result_size = output_shape.dims[1];

    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) {
        free(results);
        return NULL;
    }
    float* float_data = (float*)(data);

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

    ov_shape_free(&output_shape);
    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");
    size_t i;
    for (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);
    ov_free(friendly_name);
}

#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 != 4) {
        printf("Usage : ./hello_classification_c <path_to_model> <path_to_image> "
               "<device_name>\n");
        return EXIT_FAILURE;
    }

    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_preprocess_output_info_t* output_info = NULL;
    ov_preprocess_output_tensor_info_t* output_tensor_info = 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;
    ov_layout_t* input_layout = NULL;
    ov_layout_t* model_layout = NULL;
    ov_shape_t input_shape;
    ov_output_const_port_t* output_port = NULL;
    ov_output_const_port_t* input_port = NULL;

    // -------- Get OpenVINO runtime version --------
    ov_version_t version;
    CHECK_STATUS(ov_get_openvino_version(&version));
    printf("---- OpenVINO INFO----\n");
    printf("Description : %s \n", version.description);
    printf("Build number: %s \n", version.buildNumber);
    ov_version_free(&version);

    // -------- Parsing and validation of input arguments --------
    const char* input_model = argv[1];
    const char* input_image_path = argv[2];
    const char* device_name = argv[3];

    // -------- Step 1. Initialize OpenVINO Runtime Core --------
    CHECK_STATUS(ov_core_create(&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));
    print_model_input_output_info(model);

    CHECK_STATUS(ov_model_const_output(model, &output_port));
    if (!output_port) {
        fprintf(stderr, "[ERROR] Sample supports models with 1 output only %d\n", __LINE__);
        goto err;
    }

    CHECK_STATUS(ov_model_const_input(model, &input_port));
    if (!input_port) {
        fprintf(stderr, "[ERROR] Sample supports models with 1 input only %d\n", __LINE__);
        goto err;
    }

    // -------- Step 3. Set up input
    c_mat_t img;
    image_read(input_image_path, &img);
    ov_element_type_e input_type = U8;
    int64_t dims[4] = {1, (size_t)img.mat_height, (size_t)img.mat_width, 3};
    ov_shape_create(4, dims, &input_shape);
    CHECK_STATUS(ov_tensor_create_from_host_ptr(input_type, input_shape, img.mat_data, &tensor));

    // -------- Step 4. Configure preprocessing --------
    CHECK_STATUS(ov_preprocess_prepostprocessor_create(model, &preprocess));
    CHECK_STATUS(ov_preprocess_prepostprocessor_get_input_info_by_index(preprocess, 0, &input_info));

    CHECK_STATUS(ov_preprocess_input_info_get_tensor_info(input_info, &input_tensor_info));
    CHECK_STATUS(ov_preprocess_input_tensor_info_set_from(input_tensor_info, tensor));

    const char* input_layout_desc = "NHWC";
    CHECK_STATUS(ov_layout_create(input_layout_desc, &input_layout));
    CHECK_STATUS(ov_preprocess_input_tensor_info_set_layout(input_tensor_info, input_layout));

    CHECK_STATUS(ov_preprocess_input_info_get_preprocess_steps(input_info, &input_process));
    CHECK_STATUS(ov_preprocess_preprocess_steps_resize(input_process, RESIZE_LINEAR));
    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));

    CHECK_STATUS(ov_preprocess_prepostprocessor_get_output_info_by_index(preprocess, 0, &output_info));
    CHECK_STATUS(ov_preprocess_output_info_get_tensor_info(output_info, &output_tensor_info));
    CHECK_STATUS(ov_preprocess_output_set_element_type(output_tensor_info, F32));

    CHECK_STATUS(ov_preprocess_prepostprocessor_build(preprocess, &new_model));

    // -------- Step 5. Loading a model to the device --------
    CHECK_STATUS(ov_core_compile_model(core, new_model, device_name, 0, &compiled_model));

    // -------- Step 6. Create an infer request --------
    CHECK_STATUS(ov_compiled_model_create_infer_request(compiled_model, &infer_request));

    // -------- Step 7. Prepare input --------
    CHECK_STATUS(ov_infer_request_set_input_tensor_by_index(infer_request, 0, tensor));

    // -------- Step 8. Do inference synchronously --------
    CHECK_STATUS(ov_infer_request_infer(infer_request));

    // -------- Step 9. Process output
    CHECK_STATUS(ov_infer_request_get_output_tensor_by_index(infer_request, 0, &output_tensor));
    // Print classification results
    size_t results_num;
    results = tensor_to_infer_result(output_tensor, &results_num);
    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 --------
err:
    free(results);
    image_free(&img);
    ov_shape_free(&input_shape);
    ov_output_const_port_free(output_port);
    ov_output_const_port_free(input_port);
    if (output_tensor)
        ov_tensor_free(output_tensor);
    if (infer_request)
        ov_infer_request_free(infer_request);
    if (compiled_model)
        ov_compiled_model_free(compiled_model);
    if (input_layout)
        ov_layout_free(input_layout);
    if (model_layout)
        ov_layout_free(model_layout);
    if (output_tensor_info)
        ov_preprocess_output_tensor_info_free(output_tensor_info);
    if (output_info)
        ov_preprocess_output_info_free(output_info);
    if (p_input_model)
        ov_preprocess_input_model_info_free(p_input_model);
    if (input_process)
        ov_preprocess_preprocess_steps_free(input_process);
    if (input_tensor_info)
        ov_preprocess_input_tensor_info_free(input_tensor_info);
    if (input_info)
        ov_preprocess_input_info_free(input_info);
    if (preprocess)
        ov_preprocess_prepostprocessor_free(preprocess);
    if (new_model)
        ov_model_free(new_model);
    if (tensor)
        ov_tensor_free(tensor);
    if (model)
        ov_model_free(model);
    if (core)
        ov_core_free(core);
    return EXIT_SUCCESS;
}

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

Running

Python

python hello_classification.py <path_to_model> <path_to_image> <device_name>

C++

hello_classification <path_to_model> <path_to_image> <device_name>

C

hello_classification_c <path_to_model> <path_to_image> <device_name>

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

• You can get a model specific for your inference task from one of model repositories, such as TensorFlow Zoo, HuggingFace, or TensorFlow Hub.

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

Note

• By default, OpenVINO™ Toolkit Samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using model conversion API 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 intermediate representation (IR) format (*.xml + *.bin) using the model conversion API.

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

Example

1. Download a pre-trained model.

2. You can convert it by using:

   Python

   import openvino as ov
   
   ov_model = ov.convert_model('./models/alexnet')
   # or, when model is a Python model object
   ov_model = ov.convert_model(alexnet)
   

   CLI

   ovc ./models/alexnet
   

3. Perform inference of an image, using a model on a GPU, for example:

   Python

   python hello_classification.py ./models/alexnet/alexnet.xml ./images/banana.jpg GPU
   

   C++

   hello_classification ./models/googlenet-v1.xml ./images/car.bmp GPU
   

   C

   hello_classification_c alexnet.xml ./opt/intel/openvino/samples/scripts/car.png GPU
   

Sample Output

Python

The sample application logs each step in a standard output stream and outputs top-10 inference results.

[ INFO ] Creating OpenVINO Runtime Core
[ INFO ] Reading the model: /models/alexnet/alexnet.xml
[ INFO ] Loading the model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Image path: /images/banana.jpg
[ INFO ] Top 10 results:
[ INFO ] class_id probability
[ INFO ] --------------------
[ INFO ] 954      0.9703885
[ INFO ] 666      0.0219518
[ INFO ] 659      0.0033120
[ INFO ] 435      0.0008246
[ INFO ] 809      0.0004433
[ INFO ] 502      0.0003852
[ INFO ] 618      0.0002906
[ INFO ] 910      0.0002848
[ INFO ] 951      0.0002427
[ INFO ] 961      0.0002213
[ INFO ]
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

C++

The application outputs top-10 inference results.

[ INFO ] OpenVINO Runtime version ......... <version>
[ INFO ] Build ........... <build>
[ INFO ]
[ INFO ] Loading model files: /models/googlenet-v1.xml
[ INFO ] model name: GoogleNet
[ INFO ]     inputs
[ INFO ]         input name: data
[ INFO ]         input type: f32
[ INFO ]         input shape: {1, 3, 224, 224}
[ INFO ]     outputs
[ INFO ]         output name: prob
[ INFO ]         output type: f32
[ INFO ]         output shape: {1, 1000}

Top 10 results:

Image /images/car.bmp

classid probability
------- -----------
656     0.8139648
654     0.0550537
468     0.0178375
436     0.0165405
705     0.0111694
817     0.0105820
581     0.0086823
575     0.0077515
734     0.0064468
785     0.0043983

C

The application outputs top-10 inference results.

Top 10 results:

Image /opt/intel/openvino/samples/scripts/car.png

classid probability
------- -----------
656       0.666479
654       0.112940
581       0.068487
874       0.033385
436       0.026132
817       0.016731
675       0.010980
511       0.010592
569       0.008178
717       0.006336

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

Additional Resources

• Integrate the OpenVINO™ Runtime with Your Application

• Get Started with Samples

• Using OpenVINO Samples

• Convert a Model

• OpenVINO Runtime C API

• Hello Classification Python Sample on Github

• Hello Classification C++ Sample on Github

• Hello Classification C Sample on Github