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DOCS shift to rst - Hello Reshape SSD C++ Sample & Hello Reshape SSD Python* Sample (#16662)

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# Hello Reshape SSD C++ Sample {#openvino_inference_engine_samples_hello_reshape_ssd_README}
This sample demonstrates how to do synchronous inference of object detection models using [input reshape feature](../../../docs/OV_Runtime_UG/ShapeInference.md).
@sphinxdirective
This sample demonstrates how to do synchronous inference of object detection models using :doc:`input reshape feature <openvino_docs_OV_UG_ShapeInference>`.
Models with only one 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 |
+----------------------------------+-------------------------------------------------------------+------------------------------------------------+
| Feature | API | Description |
+==================================+=============================================================+================================================+
| Node operations | ``ov::Node::get_type_info``, | Get a node info |
| | ``ngraph::op::DetectionOutput::get_type_info_static``, | |
| | ``ov::Output::get_any_name``, | |
| | ``ov::Output::get_shape`` | |
+----------------------------------+-------------------------------------------------------------+------------------------------------------------+
| Model Operations | ``ov::Model::get_ops``, | Get model nodes, reshape input |
| | ``ov::Model::reshape`` | |
+----------------------------------+-------------------------------------------------------------+------------------------------------------------+
| Tensor Operations | ``ov::Tensor::data`` | Get a tensor data |
+----------------------------------+-------------------------------------------------------------+------------------------------------------------+
| Preprocessing | ``ov::preprocess::PreProcessSteps::convert_element_type``, | Model input preprocessing |
| | ``ov::preprocess::PreProcessSteps::convert_layout`` | |
+----------------------------------+-------------------------------------------------------------+------------------------------------------------+
Basic OpenVINO™ Runtime API is covered by [Hello Classification C++ sample](../hello_classification/README.md).
Basic OpenVINO™ Runtime API is covered by :doc:`Hello Classification C++ sample <openvino_inference_engine_samples_hello_classification_README>`.
| Options | Values |
| :--- | :--- |
| Validated Models | [person-detection-retail-0013](@ref omz_models_model_person_detection_retail_0013) |
| Model Format | OpenVINO™ toolkit Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [Python](../../../samples/python/hello_reshape_ssd/README.md) |
+----------------------------------+---------------------------------------------------------------------------------------------+
| Options | Values |
+==================================+=============================================================================================+
| Validated Models | :doc:`person-detection-retail-0013 <omz_models_model_person_detection_retail_0013>` |
+----------------------------------+---------------------------------------------------------------------------------------------+
| Model Format | OpenVINO™ toolkit Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx) |
+----------------------------------+---------------------------------------------------------------------------------------------+
| Supported devices | :doc:`All <openvino_docs_OV_UG_supported_plugins_Supported_Devices>` |
+----------------------------------+---------------------------------------------------------------------------------------------+
| Other language realization | :doc:`Python <openvino_inference_engine_ie_bridges_python_sample_hello_reshape_ssd_README>` |
+----------------------------------+---------------------------------------------------------------------------------------------+
## How It Works
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](../../../docs/OV_Runtime_UG/integrate_with_your_application.md) section of "Integrate OpenVINO™ Runtime with Your Application" guide.
You can see the explicit description of each sample step at :doc:`Integration Steps <openvino_docs_OV_UG_Integrate_OV_with_your_application>` section of "Integrate OpenVINO™ Runtime with Your Application" guide.
## Building
Building
########
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in OpenVINO™ Toolkit Samples guide.
To build the sample, please use instructions available at :doc:`Build the Sample Applications <openvino_docs_OV_UG_Samples_Overview>` section in OpenVINO™ Toolkit Samples guide.
## Running
Running
#######
```
hello_reshape_ssd <path_to_model> <path_to_image> <device>
```
.. code-block:: console
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](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
- You can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
> **NOTES**:
>
> - 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 the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Embedding Preprocessing Computation](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
>
> - 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 Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
>
> - The sample accepts models in ONNX format (\*.onnx) that do not require preprocessing.
- You can use :doc:`public <omz_models_group_public>` or :doc:`Intel's <omz_models_group_intel>` pre-trained models from the Open Model Zoo. The models can be downloaded using the :doc:`Model Downloader <omz_tools_downloader>`.
- You can use images from the media files collection available at `the storage <https://storage.openvinotoolkit.org/data/test_data>`__.
### Example
.. 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 the Model Optimizer tool with ``--reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- 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
```
.. code-block:: console
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>
```
.. code-block:: console
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
```
omz_downloader --name person-detection-retail-0013
## Sample Output
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:
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.
.. code-block:: console
```
[ 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
omz_converter --name <model_name>
This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
```
4. Perform inference of ``person_detection.bmp`` using ``person-detection-retail-0013`` model on a ``GPU``, for example:
## See Also
.. code-block:: console
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.
.. code-block:: console
[ 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
########
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective
- [Integrate the OpenVINO™ Runtime with Your Application](../../../docs/OV_Runtime_UG/integrate_with_your_application.md)
- [Using OpenVINO™ Toolkit Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

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# Hello Reshape SSD Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_hello_reshape_ssd_README}
This sample demonstrates how to do synchronous inference of object detection models using [Shape Inference feature](../../../docs/OV_Runtime_UG/ShapeInference.md).
@sphinxdirective
This sample demonstrates how to do synchronous inference of object detection models using :doc:`Shape Inference feature <openvino_docs_OV_UG_ShapeInference>`.
Models with only 1 input and output are supported.
The following Python API is used in the application:
| Feature | API | Description |
| :--------------- | :---------------------------------------------------------------------------------------------------------------------------------------- | :---------------- |
| Model Operations | [openvino.runtime.Model.reshape], [openvino.runtime.Model.input], [openvino.runtime.Output.get_any_name], [openvino.runtime.PartialShape] | Managing of model |
+------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+
| Feature | API | Description |
+====================================+================================================================================================================================================================================+======================================+
| Model Operations | `openvino.runtime.Model.reshape <https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.Model.html#openvino.runtime.Model.reshape>`__ , | Managing of model |
| | `openvino.runtime.Model.input <https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.Model.html#openvino.runtime.Model.input>`__ , | |
| | `openvino.runtime.Output.get_any_name <https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.Output.html#openvino.runtime.Output.get_any_name>`__ , | |
| | `openvino.runtime.PartialShape <https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.PartialShape.html>`__ | |
+------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+
Basic OpenVINO™ Runtime API is covered by [Hello Classification Python* Sample](../hello_classification/README.md).
Basic OpenVINO™ Runtime API is covered by :doc:`Hello Classification Python* Sample <openvino_inference_engine_ie_bridges_python_sample_hello_classification_README>`.
| Options | Values |
| :------------------------- | :----------------------------------------------------------------------- |
| Validated Models | [mobilenet-ssd](@ref omz_models_model_mobilenet_ssd) |
| Validated Layout | NCHW |
| Model Format | OpenVINO™ toolkit Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_reshape_ssd/README.md) |
+------------------------------------+---------------------------------------------------------------------------+
| Options | Values |
+====================================+===========================================================================+
| Validated Models | :doc:`mobilenet-ssd <omz_models_model_mobilenet_ssd>` |
+------------------------------------+---------------------------------------------------------------------------+
| Validated Layout | NCHW |
+------------------------------------+---------------------------------------------------------------------------+
| Model Format | OpenVINO™ toolkit Intermediate Representation (.xml + .bin), ONNX (.onnx) |
+------------------------------------+---------------------------------------------------------------------------+
| Supported devices | :doc:`All <openvino_docs_OV_UG_supported_plugins_Supported_Devices>` |
+------------------------------------+---------------------------------------------------------------------------+
| Other language realization | :doc:`C++ <openvino_inference_engine_samples_hello_reshape_ssd_README>` |
+------------------------------------+---------------------------------------------------------------------------+
## How It Works
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.
As a result, the program creates an output image, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/integrate_with_your_application.md) section of "Integrate OpenVINO™ Runtime with Your Application" guide.
each sample step at :doc:`Integration Steps <openvino_docs_OV_UG_Integrate_OV_with_your_application>` section of "Integrate OpenVINO™ Runtime with Your Application" guide.
## Running
Running
#######
```
python hello_reshape_ssd.py <path_to_model> <path_to_image> <device_name>
```
.. code-block:: console
python hello_reshape_ssd.py <path_to_model> <path_to_image> <device_name>
To run the sample, you need to specify a model and image:
- You can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
- You can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
> **NOTES**:
>
> - 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 the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Embedding Preprocessing Computation](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
>
> - 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 Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
>
> - The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
- You can use :doc:`public <omz_models_group_public>` or :doc:`Intel's <omz_models_group_intel>` pre-trained models from the Open Model Zoo. The models can be downloaded using the :doc:`Model Downloader <omz_tools_downloader>`.
- You can use images from the media files collection available at `the storage <https://storage.openvinotoolkit.org/data/test_data>`.
### Example
.. note::
1. Install the `openvino-dev` Python package to use Open Model Zoo Tools:
```
python -m pip install openvino-dev[caffe]
```
- 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 the Model Optimizer tool with ``--reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
Example
+++++++
1. Install the ``openvino-dev`` Python package to use Open Model Zoo Tools:
.. code-block:: console
python -m pip install openvino-dev[caffe]
2. Download a pre-trained model:
```
omz_downloader --name mobilenet-ssd
```
.. code-block:: console
omz_downloader --name mobilenet-ssd
3. If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:
```
omz_converter --name mobilenet-ssd
```
4. Perform inference of `banana.jpg` using `ssdlite_mobilenet_v2` model on a `GPU`, for example:
```
python hello_reshape_ssd.py mobilenet-ssd.xml banana.jpg GPU
```
.. code-block:: console
## Sample Output
omz_converter --name mobilenet-ssd
4. Perform inference of ``banana.jpg`` using ``ssdlite_mobilenet_v2`` model on a ``GPU``, for example:
.. code-block:: console
python hello_reshape_ssd.py mobilenet-ssd.xml banana.jpg GPU
Sample Output
#############
The sample application logs each step in a standard output stream and creates an output image, drawing bounding boxes for inference results with an over 50% confidence.
```
[ INFO ] Creating OpenVINO Runtime Core
[ INFO ] Reading the model: C:/test_data/models/mobilenet-ssd.xml
[ INFO ] Reshaping the model to the height and width of the input image
[ INFO ] Loading the model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Found: class_id = 52, confidence = 0.98, coords = (21, 98), (276, 210)
[ INFO ] Image out.bmp was created!
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
```
.. code-block:: console
## See Also
[ INFO ] Creating OpenVINO Runtime Core
[ INFO ] Reading the model: C:/test_data/models/mobilenet-ssd.xml
[ INFO ] Reshaping the model to the height and width of the input image
[ INFO ] Loading the model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Found: class_id = 52, confidence = 0.98, coords = (21, 98), (276, 210)
[ INFO ] Image out.bmp was created!
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
- [Integrate the OpenVINO™ Runtime with Your Application](../../../docs/OV_Runtime_UG/integrate_with_your_application.md)
- [Using OpenVINO™ Toolkit Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
See Also
########
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective
[openvino.runtime.Model.reshape]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.Model.html#openvino.runtime.Model.reshape
[openvino.runtime.Model.input]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.Model.html#openvino.runtime.Model.input
[openvino.runtime.Output.get_any_name]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.Output.html#openvino.runtime.Output.get_any_name
[openvino.runtime.PartialShape]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.PartialShape.html