97 lines
3.0 KiB
Markdown
97 lines
3.0 KiB
Markdown
---
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nav_exclude: true
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---
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# Examples
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Below are code samples on how to run MediaPipe on both mobile and desktop. We
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currently support MediaPipe APIs on mobile for Android only but will add support
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for Objective-C shortly.
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## Mobile
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### [Hello World! on Android](./getting_started/hello_world_android.md)
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This should be the first mobile Android example users go through in detail. It
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teaches the following:
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* Introduction of a simple MediaPipe graph running on mobile GPUs for
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[Sobel edge detection](https://en.wikipedia.org/wiki/Sobel_operator).
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* Building a simple baseline Android application that displays "Hello World!".
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* Adding camera preview support into the baseline application using the
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Android [CameraX] API.
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* Incorporating the Sobel edge detection graph to process the live camera
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preview and display the processed video in real-time.
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[Sobel edge detection]:https://en.wikipedia.org/wiki/Sobel_operator
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[CameraX]:https://developer.android.com/training/camerax
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### [Hello World! on iOS](./getting_started/hello_world_ios.md)
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This is the iOS version of Sobel edge detection example.
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### [Face Detection](./solutions/face_detection.md)
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### [Face Mesh](./solutions/face_mesh.md)
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### [Hand](./solutions/hand.md)
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### [Hair Segmentation](./solutions/hair_segmentation.md)
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### [Object Detection](./solutions/object_detection.md)
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### [Box Tracking](./solutions/box_tracking.md)
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### [Objectron: 3D Object Detection](./solutions/objectron.md)
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### [KNIFT: Template-based Feature Matching](./solutions/knift.md)
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## Desktop
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### [Hello World for C++](./getting_started/hello_world_desktop.md)
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This shows how to run a simple graph using the MediaPipe C++ APIs.
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### [Face Detection](./solutions/face_detection.md)
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### [Face Mesh](./solutions/face_mesh.md)
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### [Hand](./solutions/hand.md)
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### [Hair Segmentation](./solutions/hair_segmentation.md)
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### [Object Detection](./solutions/object_detection.md)
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### [Box Tracking](./solutions/box_tracking.md)
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### [AutoFlip - Semantic-aware Video Cropping](./solutions/autoflip.md)
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### [Preparing Data Sets with MediaSequence](./solutions/media_sequence.md)
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This shows how to use MediaPipe for media processing to prepare video data sets
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for training a TensorFlow model.
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### [Feature Extraction and Model Inference for YouTube-8M Challenge](./solutions/youtube_8m.md)
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This shows how to use MediaPipe to prepare training data for the YouTube-8M
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Challenge and do the model inference with the baseline model.
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## Google Coral (ML acceleration with Google EdgeTPU)
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### [Face Detection](./solutions/face_detection.md)
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### [Object Detection](./solutions/object_detection.md)
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## Web Browser
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See more details [here](./getting_started/web.md) and
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[Google Developer blog post](https://mediapipe.page.link/webdevblog).
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### [Face Detection in Browser](https://viz.mediapipe.dev/demo/face_detection)
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### [Hand Detection in Browser](https://viz.mediapipe.dev/demo/hand_detection)
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### [Hand Tracking in Browser](https://viz.mediapipe.dev/demo/hand_tracking)
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### [Hair Segmentation in Browser](https://viz.mediapipe.dev/demo/hair_segmentation)
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