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Merge pull request #5360 from maloel/rs-dnn-vino
Add rs-dnn-vino sample
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‎examples/readme.md‎

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|[Apriltag Pose](./pose-apriltag)|C++|Demonstrates how to compute [Apriltag](https://github.com/AprilRobotics/apriltag/tree/3.1.1) pose from T265 fisheye image stream. | :star::star: |[![Motion Tracking - T260 and SLAM](https://img.shields.io/badge/-Tracking-0e2356.svg)](../doc/t265.md#examples-and-tools)|
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|[AR-Basic](./ar-basic)|C++|Shows how to use pose and fisheye frames to display a simple virtual object on the fisheye image | :star::star: |[![Motion Tracking - T260 and SLAM](https://img.shields.io/badge/-Tracking-0e2356.svg)](../doc/t265.md#examples-and-tools)|
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|[DNN](../wrappers/opencv/dnn)| C++ & [OpenCV](https://github.com/IntelRealSense/librealsense/tree/master/wrappers/opencv#getting-started) | Intel RealSense camera used for real-time object-detection | :star::star: | [![Depth Sensing - Structured Light, Stereo and L500](https://img.shields.io/badge/-Depth-5bc3ff.svg)](./depth.md) |
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|[DNN](../wrappers/openvino/dnn)| C++ & [OpenVINO](https://github.com/IntelRealSense/librealsense/tree/master/wrappers/openvino) | Intel RealSense camera used for real-time object-detection | :star::star: | [![Depth Sensing - Structured Light, Stereo and L500](https://img.shields.io/badge/-Depth-5bc3ff.svg)](./depth.md) |
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|[Tracking and Depth](./tracking-and-depth)| C++ | Shows how to use the tracking camera T265 together with a depth camera to display a 3D pointcloud with respect to a static reference frame | :star::star: | [![Depth Sensing - Structured Light, Stereo and L500](https://img.shields.io/badge/-Depth-5bc3ff.svg)](./depth.md) [![Motion Tracking - T260 and SLAM](https://img.shields.io/badge/-Tracking-0e2356.svg)](../doc/t265.md#examples-and-tools)
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|[Trajectory](./trajectory)| C++ | Shows how to calculate and render 3D trajectory based on pose data from a tracking camera | :star::star::star: | [![Motion Tracking - T260 and SLAM](https://img.shields.io/badge/-Tracking-0e2356.svg)](../doc/t265.md#examples-and-tools)
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|[Software Device](./software-device)| C++ | Shows how to create a custom `rs2::device` | :star::star::star: | [![Depth Sensing - Structured Light, Stereo and L500](https://img.shields.io/badge/-Depth-5bc3ff.svg)](./depth.md) [![Motion Tracking - T260 and SLAM](https://img.shields.io/badge/-Tracking-0e2356.svg)](../doc/t265.md#examples-and-tools) |

‎wrappers/openvino/CMakeLists.txt‎

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set(OPENVINO_FILES
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../rs-vino/base-detection.cpp
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../rs-vino/base-detection.h
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../rs-vino/face-detection.cpp
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../rs-vino/face-detection.h
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../rs-vino/detected-face.cpp
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../rs-vino/detected-face.h
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../rs-vino/object-detection.cpp
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../rs-vino/object-detection.h
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../rs-vino/detected-object.cpp
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../rs-vino/detected-object.h
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../rs-vino/openvino-helpers.h
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${IE_ROOT_DIR}/src/extension/ext_list.hpp
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)
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file(DOWNLOAD "${OPENVINO_MODEL_SRC_URL}/${filename}" "${path}"
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EXPECTED_HASH SHA1=${sha1}
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STATUS status)
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list(GET status 0 error_code)
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if (NOT ${error_code} EQUAL 0)
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message(FATAL_ERROR "Error (${status}) downloading: ${OPENVINO_MODEL_SRC_URL}/${filename}")
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endif()
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endif()
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endfunction()
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# List all the specific examples for OpenVINO
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add_subdirectory(face)
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add_subdirectory(dnn)

‎wrappers/openvino/dnn/CMakeLists.txt‎

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cmake_minimum_required(VERSION 3.1.0)
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project(RealSenseVinoDnnExample)
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# OpenCV is required here. You can simply use the version that's supplied with
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# OpenVINO (${INTEL_OPENVINO_DIR}/opencv/cmake).
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if(NOT DEFINED OpenCV_DIR OR NOT IS_DIRECTORY ${OpenCV_DIR})
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set(OpenCV_DIR "" CACHE PATH "The path to the OpenCV Toolkit build directory")
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message( FATAL_ERROR "OpenVINO examples require OpenCV; specify OpenCV_DIR" )
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endif()
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find_package(OpenCV REQUIRED)
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get_property(deps VARIABLE PROPERTY DEPENDENCIES)
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set(DEPENDENCIES ${deps} ${OpenCV_LIBS})
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include_directories( ../../opencv )
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add_executable(rs-dnn-vino
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rs-dnn-vino.cpp
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${OPENVINO_FILES}
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${ELPP_FILES}
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)
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source_group("OpenVINO" FILES ${OPENVINO_FILES})
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source_group("EasyLogging++" FILES ${ELPP_FILES})
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set_property(TARGET rs-dnn-vino PROPERTY CXX_STANDARD 11)
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target_link_libraries(rs-dnn-vino ${DEPENDENCIES})
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set_target_properties (rs-dnn-vino PROPERTIES
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FOLDER "Examples/openvino"
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)
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install(
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TARGETS
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rs-dnn-vino
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RUNTIME DESTINATION
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${CMAKE_INSTALL_PREFIX}/bin
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)
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# Download model files -- these will go into build/wrappers/openvino/dnn, which is also where the sample
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# is run from in Visual Studio
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dl_vino_model( "README.txt" "789e144d6cafa379c8437d2a6860829b6d935a8d" )
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dl_vino_model( "mobilenet-ssd.xml" "483ba684dd53ba138550377075e928b2008418bb" )
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dl_vino_model( "mobilenet-ssd.bin" "3b687f0b9519b0d296c23d5f038a8623b5aeb49b" )
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dl_vino_model( "mobilenet-ssd.labels" "b11e85afe8cff3172ad9b62d904bbc8013b3ddbf" )

‎wrappers/openvino/dnn/readme.md‎

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# rs-dnn-vino Sample
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## Overview
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This example demonstrates OpenVINO™ toolkit integration with object detection, using
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basic depth information to approximate distance.
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<p align="center"><img src="rs-dnn-vino.jpg" alt="screenshot"/></p>
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The same exact neural network is used here as in the OpenCV DNN sample, for
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comparison.
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## Requirements
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A camera with both depth and RGB sensors is required.
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This sample makes use of OpenCV. You can use the OpenCV that is packaged
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with OpenVINO by pointing OpenCV_DIR to `${INTEL_OPENVINO_DIR}/opencv/cmake`.
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## Implementation
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We can reuse the `openvino_helpers::object_detection` code we used in the
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[rs-face-vino example](../face), but we now provide it with a different model
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aimed at object rather than face detection. You can see it now recognizes a
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`person` rather than each face.
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There is a single trained model with two Intermediate Representation files
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(`mobilenet-ssd.xml` and `.bin`) provided with the sample. The sample
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will, however, load any model present in the current directory and is able to
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switch between them at runtime, allowing some experimentation.
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> The `object_detection` class does have requirements from the model: it is
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> expected to have **a single input and output!!!** (bounding box, classification,
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> confidence, etc.), and will be rejected otherwise.
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> You can see the inputs and outputs of a model listed in the .xml file. Search
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> for a layer with `type="Input"` to find the inputs. Similarly, the expected
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> output layer is of `type="DetectionOutput"`.
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> Some neural networks (e.g., the version of Faster R-CNN available with
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> OpenVINO) have two inputs, adding an additional layer for more information.
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> Some effort was made to support such models. Feel free to experiment.
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Each model can optionally provide a `.labels` classification file to help map
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the output "label" integer into a human-recognizable name such as "person",
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"bottle", etc.
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These are not provided by the OpenVINO model zoo and need to be created
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manually according to the classes used when training the model.
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See the format in `mobilenet-ssd.labels` for an example: one line per
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classification, starting at 0 (which is expected to be the background).
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## Speed
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The MobileNet models are intended for use on mobile devices and so their
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performance is high and they are suitable for use on the CPU. More advanced
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models can be more accurate or provide better classification but may require
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acceleration using a GPU or other device.

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