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60 lines
2.4 KiB
60 lines
2.4 KiB
2 months ago
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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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