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Hello Reshape SSD C++ Sample

This sample demonstrates how to do synchronous inference of object detection models using input reshape feature. Models with only 1 input and output are supported.

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 described in Hello Classification C++ sample.

Options

Values

Validated Models

person-detection-retail-0013

Model Format

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

Supported devices

All

Other language realization

Python

How It Works

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

For more information, refer to the explicit description of Integration Steps in the Integrate OpenVINO Runtime with Your Application.

Building

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

Running

Before running the sample, specify a model and an image:

To run the sample, use the following script:

hello_reshape_ssd <path_to_model> <path_to_image> <device>

NOTES :

  • By default, samples and demos in OpenVINO Toolkit expect input with BGR order of channels. If you trained your model to work with RGB order, you need to manually rearrange the default order of channels in the sample or demo application, or reconvert your model, using Model Optimizer 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 OpenVINO Intermediate Representation (OpenVINO IR) format (*.xml + *.bin) by using Model Optimizer.

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

Example

  1. Install the openvino-dev Python package to use Open Model Zoo Tools:

    python -m pip install openvino-dev[caffe,onnx,tensorflow2,pytorch,mxnet]

  2. Download a pre-trained model, using:

    omz_downloader --name person-detection-retail-0013

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

    omz_converter --name <model_name>

  4. Perform inference of the person_detection.bmp image, using the 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 use the dedicated benchmark_app tool
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