79 lines
2.6 KiB
Markdown
79 lines
2.6 KiB
Markdown
---
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layout: default
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title: Object Classification
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parent: Solutions
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nav_order: TODO
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---
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# MediaPipe Object Classification
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{: .no_toc }
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1. TOC
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{:toc}
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---
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## Example Apps
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Note: To visualize a graph, copy the graph and paste it into
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[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
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to visualize its associated subgraphs, please see
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[visualizer documentation](../tools/visualizer.md).
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<!-- ### Mobile
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Please first see general instructions for
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[iOS](../getting_started/building_examples.md#ios) on how to build MediaPipe examples.
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#### GPU Pipeline
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* iOS target:
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[`mediapipe/examples/ios/objectdetectiongpu:ObjectDetectionGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/objectdetectiongpu/BUILD)
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-->
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### Desktop
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#### Live Camera Input
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Please first see general instructions for
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[desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe examples.
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* Graph:
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[`mediapipe/graphs/object_classification/object_classification_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_classification/object_classification_desktop_live.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/object_classification:object_classification_pytorch_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/object_classification/BUILD)
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#### Video File Input
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* With a PyTorch Model
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This uses a MobileNetv2 trace model from PyTorch Hub. To fetch and prepare it, run:
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```bash
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python mediapipe/models/trace_mobilenetv2.py
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```
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The pipeline is implemented in this
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[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_classification/object_classification_desktop_live.pbtxt).
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To build the application, run:
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```bash
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bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/object_classification:object_classification_pytorch_cpu
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```
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To run the application, replace `<input video path>` and `<output video
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path>` in the command below with your own paths:
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Tip: You can find a test video available in
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`mediapipe/examples/desktop/object_detection`.
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```
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GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/object_classification/object_classification_pytorch_cpu \
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--calculator_graph_config_file=mediapipe/graphs/object_classification/object_classification_desktop_live.pbtxt \
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--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
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```
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<!--
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## Resources
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* [Models and model cards](./models.md#object_detection)
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--> |