Hello Reshape SSD C++ Sample

This sample demonstrates how to do synchronous inference of object detection models using input reshape feature. Models with only one input and output are supported.

Requirements

Options	Values
Validated Models	person-detection-retail-0013
Model Format	OpenVINO™ toolkit Intermediate Representation (*.xml + *.bin), ONNX (*.onnx)
Supported devices	All
Other language realization	Python

C++ API

The following C++ API is used in the application:

Feature	API	Description
Node operations	ov::Node::get_type_info, ngraph::op::DetectionOutput::get_type_info_static, ov::Output::get_any_name, ov::Output::get_shape	Get a node info
Model Operations	ov::Model::get_ops, ov::Model::reshape	Get model nodes, reshape input
Tensor Operations	ov::Tensor::data	Get a tensor data
Preprocessing	ov::preprocess::PreProcessSteps::convert_element_type, ov::preprocess::PreProcessSteps::convert_layout	Model input preprocessing

Basic OpenVINO™ Runtime API is covered by Hello Classification C++ sample.

Sample Code

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

#include <memory>
#include <string>
#include <vector>

// clang-format off
#include "openvino/openvino.hpp"
#include "openvino/opsets/opset9.hpp"

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

// thickness of a line (in pixels) to be used for bounding boxes
constexpr int BBOX_THICKNESS = 2;

using namespace ov::preprocess;

int main(int argc, char* argv[]) {
    try {
        // -------- Get OpenVINO runtime version -----------------------------
        slog::info << ov::get_openvino_version() << slog::endl;

        // --------------------------- Parsing and validation of input arguments
        if (argc != 4) {
            std::cout << "Usage : " << argv[0] << " <path_to_model> <path_to_image> <device>" << std::endl;
            return EXIT_FAILURE;
        }
        const std::string model_path{argv[1]};
        const std::string image_path{argv[2]};
        const std::string device_name{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);

        // Step 3. Validate model inputs and outputs
        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");

        // SSD has an additional post-processing DetectionOutput layer that simplifies output filtering,
        // try to find it.
        const ov::NodeVector ops = model->get_ops();
        const auto it = std::find_if(ops.begin(), ops.end(), [](const std::shared_ptr<ov::Node>& node) {
            return std::string{node->get_type_name()} ==
                   std::string{ov::opset9::DetectionOutput::get_type_info_static().name};
        });
        if (it == ops.end()) {
            throw std::logic_error("model does not contain DetectionOutput layer");
        }
        // -------------------------------------------------------------------

        // Step 4. Read input image

        // Read input image without resize
        FormatReader::ReaderPtr reader(image_path.c_str());
        if (reader.get() == nullptr) {
            std::cout << "Image " + image_path + " cannot be read!" << std::endl;
            return 1;
        }

        std::shared_ptr<unsigned char> image_data = reader->getData();
        size_t image_channels = 3;
        size_t image_width = reader->width();
        size_t image_height = reader->height();
        // -------------------------------------------------------------------

        // Step 5. Reshape model to image size and batch size
        // assume model layout NCHW
        const ov::Layout model_layout{"NCHW"};

        ov::Shape tensor_shape = model->input().get_shape();

        size_t batch_size = 1;

        tensor_shape[ov::layout::batch_idx(model_layout)] = batch_size;
        tensor_shape[ov::layout::channels_idx(model_layout)] = image_channels;
        tensor_shape[ov::layout::height_idx(model_layout)] = image_height;
        tensor_shape[ov::layout::width_idx(model_layout)] = image_width;

        std::cout << "Reshape network to the image size = [" << image_height << "x" << image_width << "] " << std::endl;
        model->reshape({{model->input().get_any_name(), tensor_shape}});
        printInputAndOutputsInfo(*model);
        // -------------------------------------------------------------------

        // Step 6. Configure model preprocessing
        const ov::Layout tensor_layout{"NHWC"};

        // clang-format off
        ov::preprocess::PrePostProcessor ppp = ov::preprocess::PrePostProcessor(model);

        // 1) input() with no args assumes a model has a single input
        ov::preprocess::InputInfo& input_info = ppp.input();
        // 2) Set input tensor information:
        // - precision of tensor is supposed to be 'u8'
        // - layout of data is 'NHWC'
        input_info.tensor().
              set_element_type(ov::element::u8).
              set_layout(tensor_layout);
        // 3) Adding explicit preprocessing steps:
        // - convert u8 to f32
        // - convert layout to 'NCHW' (from 'NHWC' specified above at tensor layout)
        ppp.input().preprocess().
            convert_element_type(ov::element::f32).
            convert_layout("NCHW");
        // 4) Here we suppose model has 'NCHW' layout for input
        input_info.model().set_layout("NCHW");
        // 5) output () with no args assumes a model has a single output
        ov::preprocess::OutputInfo& output_info = ppp.output();
        // 6) declare output element type as FP32
        output_info.tensor().set_element_type(ov::element::f32);

        // 7) Apply preprocessing modifing the original 'model'
        model = ppp.build();
        // clang-format on
        // -------------------------------------------------------------------

        // Step 7. Loading a model to the device
        ov::CompiledModel compiled_model = core.compile_model(model, device_name);
        // -------------------------------------------------------------------

        // Step 8. Create an infer request
        ov::InferRequest infer_request = compiled_model.create_infer_request();

        // Step 9. Fill model with input data
        ov::Tensor input_tensor = infer_request.get_input_tensor();

        // copy NHWC data from image to tensor with batch
        unsigned char* image_data_ptr = image_data.get();
        unsigned char* tensor_data_ptr = input_tensor.data<unsigned char>();
        size_t image_size = image_width * image_height * image_channels;
        for (size_t i = 0; i < image_size; i++) {
            tensor_data_ptr[i] = image_data_ptr[i];
        }
        // -------------------------------------------------------------------

        // Step 10. Do inference synchronously
        infer_request.infer();

        // Step 11. Get output data from the model
        ov::Tensor output_tensor = infer_request.get_output_tensor();

        ov::Shape output_shape = model->output().get_shape();
        const size_t ssd_object_count = output_shape[2];
        const size_t ssd_object_size = output_shape[3];

        const float* detections = output_tensor.data<const float>();
        // -------------------------------------------------------------------

        std::vector<int> boxes;
        std::vector<int> classes;

        // Step 12. Parse SSD output
        for (size_t object = 0; object < ssd_object_count; object++) {
            int image_id = static_cast<int>(detections[object * ssd_object_size + 0]);
            if (image_id < 0) {
                break;
            }

            // detection, has the format: [image_id, label, conf, x_min, y_min, x_max, y_max]
            int label = static_cast<int>(detections[object * ssd_object_size + 1]);
            float confidence = detections[object * ssd_object_size + 2];
            int xmin = static_cast<int>(detections[object * ssd_object_size + 3] * image_width);
            int ymin = static_cast<int>(detections[object * ssd_object_size + 4] * image_height);
            int xmax = static_cast<int>(detections[object * ssd_object_size + 5] * image_width);
            int ymax = static_cast<int>(detections[object * ssd_object_size + 6] * image_height);

            if (confidence > 0.5f) {
                // collect only objects with >50% probability
                classes.push_back(label);
                boxes.push_back(xmin);
                boxes.push_back(ymin);
                boxes.push_back(xmax - xmin);
                boxes.push_back(ymax - ymin);

                std::cout << "[" << object << "," << label << "] element, prob = " << confidence << ",    (" << xmin
                          << "," << ymin << ")-(" << xmax << "," << ymax << ")" << std::endl;
            }
        }

        // draw bounding boxes on the image
        addRectangles(image_data.get(), image_height, image_width, boxes, classes, BBOX_THICKNESS);

        const std::string image_name = "hello_reshape_ssd_output.bmp";
        if (writeOutputBmp(image_name, image_data.get(), image_height, image_width)) {
            std::cout << "The resulting image was saved in the file: " + image_name << std::endl;
        } else {
            throw std::logic_error(std::string("Can't create a file: ") + image_name);
        }

    } catch (const std::exception& ex) {
        std::cerr << ex.what() << std::endl;
        return EXIT_FAILURE;
    }
    std::cout << std::endl
              << "This sample is an API example, for any performance measurements "
                 "please use the dedicated benchmark_app tool"
              << std::endl;
    return EXIT_SUCCESS;
}

How It Works

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

Building

To build the sample, please use instructions available at Build the Sample Applications section in OpenVINO™ Toolkit Samples guide.

Running

hello_reshape_ssd <path_to_model> <path_to_image> <device>

To run the sample, you need to 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.

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 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 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. Install openvino-dev python package if you don’t have it to use Open Model Zoo Tools:

   python -m pip install openvino-dev
   

2. Download a pre-trained model using:

   omz_downloader --name person-detection-retail-0013
   

3. person-detection-retail-0013 does not need to be converted, because it is already in necessary format, so you can skip this step. If you want to use another model that is not in the IR or ONNX format, you can convert it using the model converter script:

   omz_converter --name <model_name>
   

4. Perform inference of person_detection.bmp using person-detection-retail-0013 model on a GPU, for example:

   hello_reshape_ssd person-detection-retail-0013.xml person_detection.bmp GPU
   

Sample Output

The application renders an image with detected objects enclosed in rectangles. It outputs the list of classes of the detected objects along with the respective confidence values and the coordinates of the rectangles to the standard output stream.

[ INFO ] OpenVINO Runtime version ......... <version>
[ INFO ] Build ........... <build>
[ INFO ]
[ INFO ] Loading model files: \models\person-detection-retail-0013.xml
[ INFO ] model name: ResMobNet_v4 (LReLU) with single SSD head
[ INFO ]     inputs
[ INFO ]         input name: data
[ INFO ]         input type: f32
[ INFO ]         input shape: {1, 3, 320, 544}
[ INFO ]     outputs
[ INFO ]         output name: detection_out
[ INFO ]         output type: f32
[ INFO ]         output shape: {1, 1, 200, 7}
Reshape network to the image size = [960x1699]
[ INFO ] model name: ResMobNet_v4 (LReLU) with single SSD head
[ INFO ]     inputs
[ INFO ]         input name: data
[ INFO ]         input type: f32
[ INFO ]         input shape: {1, 3, 960, 1699}
[ INFO ]     outputs
[ INFO ]         output name: detection_out
[ INFO ]         output type: f32
[ INFO ]         output shape: {1, 1, 200, 7}
[0,1] element, prob = 0.716309,    (852,187)-(983,520)
The resulting image was saved in the file: hello_reshape_ssd_output.bmp

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

See Also

• Integrate the OpenVINO™ Runtime with Your Application

• Using OpenVINO™ Toolkit Samples

• Model Downloader

• Convert a Model