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MediaPipe Team
2020-08-05 20:27:31 -04:00
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parent bdfdaef305
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docs/framework_concepts/calculators.md
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@@ -405,8 +405,4 @@ packets (bottom) based on its series of input packets (top).
| ![Graph using |
: PacketClonerCalculator](../images/packet_cloner_calculator.png) :
| :--------------------------------------------------------------------------: |
| *Each time it receives a packet on its TICK input stream, the |
: PacketClonerCalculator outputs the most recent packet from each of its input :
: streams. The sequence of output packets (bottom) is determined by the :
: sequence of input packets (top) and their timestamps. The timestamps are :
: shown along the right side of the diagram.* :
| *Each time it receives a packet on its TICK input stream, the PacketClonerCalculator outputs the most recent packet from each of its input streams. The sequence of output packets (bottom) is determined by the sequence of input packets (top) and their timestamps. The timestamps are shown along the right side of the diagram.* |
docs/getting_started/building_examples.md
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@@ -280,16 +280,16 @@ are two options:
2. In the project navigator in the left sidebar, select the "Mediapipe"
project.
3. Select the "Signing & Capabilities" tab.
3. Select one of the application targets, e.g. HandTrackingGpuApp.
4. Select one of the application targets, e.g. HandTrackingGpuApp.
4. Select the "Signing & Capabilities" tab.
5. Check "Automatically manage signing", and confirm the dialog box.
6. Select "_Your Name_ (Personal Team)" in the Team pop-up menu.
7. This set-up needs to be done once for each application you want to install.
Repeat steps 4-6 as needed.
Repeat steps 3-6 as needed.
This generates provisioning profiles for each app you have selected. Now we need
to tell Bazel to use them. We have provided a script to make this easier.
@@ -390,9 +390,6 @@ developer (yourself) is trusted.
bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu
```
This will open up your webcam as long as it is connected and on. Any errors
is likely due to your webcam being not accessible.
2. To run the application:
```bash
@@ -400,6 +397,9 @@ developer (yourself) is trusted.
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
```
This will open up your webcam as long as it is connected and on. Any errors
is likely due to your webcam being not accessible.
### Option 2: Running on GPU
Note: This currently works only on Linux, and please first follow
@@ -412,13 +412,13 @@ Note: This currently works only on Linux, and please first follow
mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu
```
This will open up your webcam as long as it is connected and on. Any errors
is likely due to your webcam being not accessible, or GPU drivers not setup
properly.
2. To run the application:
```bash
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_gpu \
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
```
This will open up your webcam as long as it is connected and on. Any errors
is likely due to your webcam being not accessible, or GPU drivers not setup
properly.
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docs/index.md
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@@ -22,13 +22,13 @@ desktop/cloud, web and IoT devices.
## ML solutions in MediaPipe
Face Detection | Face Mesh | Hands | Hair Segmentation
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :---------------:
[![face_detection](images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![hand](images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![hair_segmentation](images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation)
Face Detection | Face Mesh | Iris | Hands
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :---:
[![face_detection](images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![iris](images/mobile/iris_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/iris) | [![hand](images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands)
Object Detection | Box Tracking | Objectron | KNIFT
:----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
[![object_detection](images/mobile/object_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/object_detection) | [![box_tracking](images/mobile/object_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/box_tracking) | [![objectron](images/mobile/objectron_chair_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/objectron) | [![knift](images/mobile/template_matching_android_cpu_small.gif)](https://google.github.io/mediapipe/solutions/knift)
Hair Segmentation | Object Detection | Box Tracking | Objectron | KNIFT
:-------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
[![hair_segmentation](images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation) | [![object_detection](images/mobile/object_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/object_detection) | [![box_tracking](images/mobile/object_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/box_tracking) | [![objectron](images/mobile/objectron_chair_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/objectron) | [![knift](images/mobile/template_matching_android_cpu_small.gif)](https://google.github.io/mediapipe/solutions/knift)
<!-- []() in the first cell is needed to preserve table formatting in GitHub Pages. -->
<!-- Whenever this table is updated, paste a copy to solutions/solutions.md. -->
@@ -37,6 +37,7 @@ Object Detection
:---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---:
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | |
[Iris](https://google.github.io/mediapipe/solutions/iris) | ✅ | ✅ | ✅ | ✅ |
[Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅
@@ -63,6 +64,8 @@ never leaves your device.
![visualizer_runner](images/visualizer_runner.png)
* [MediaPipe Face Detection](https://viz.mediapipe.dev/demo/face_detection)
* [MediaPipe Iris](https://viz.mediapipe.dev/demo/iris_tracking)
* [MediaPipe Iris: Depth-from-Iris](https://viz.mediapipe.dev/demo/iris_depth)
* [MediaPipe Hands](https://viz.mediapipe.dev/demo/hand_tracking)
* [MediaPipe Hands (palm/hand detection only)](https://viz.mediapipe.dev/demo/hand_detection)
* [MediaPipe Hair Segmentation](https://viz.mediapipe.dev/demo/hair_segmentation)
@@ -83,6 +86,8 @@ run code search using
## Publications
* [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation from a Single
Image](https://mediapipe.page.link/iris-blog) in Google AI Blog
* [MediaPipe KNIFT: Template-based feature matching](https://developers.googleblog.com/2020/04/mediapipe-knift-template-based-feature-matching.html)
in Google Developers Blog
* [Alfred Camera: Smart camera features using MediaPipe](https://developers.googleblog.com/2020/03/alfred-camera-smart-camera-features-using-mediapipe.html)
docs/solutions/autoflip.md
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layout: default
title: AutoFlip (Saliency-aware Video Cropping)
parent: Solutions
nav_order: 9
nav_order: 10
---
# AutoFlip: Saliency-aware Video Cropping
docs/solutions/box_tracking.md
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@@ -2,7 +2,7 @@
layout: default
title: Box Tracking
parent: Solutions
nav_order: 6
nav_order: 7
---
# MediaPipe Box Tracking
docs/solutions/face_mesh.md
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@@ -153,8 +153,8 @@ it, in the graph file modify the option of `ConstantSidePacketCalculator`.
[Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://arxiv.org/abs/1907.06724)
([poster](https://docs.google.com/presentation/d/1-LWwOMO9TzEVdrZ1CS1ndJzciRHfYDJfbSxH_ke_JRg/present?slide=id.g5986dd4b4c_4_212))
* Face detection model:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite)
* Face landmark mode:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_landmark.tflite),
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front.tflite)
* Face landmark model:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark.tflite),
[TF.js model](https://tfhub.dev/mediapipe/facemesh/1)
* [Model card](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)
docs/solutions/hair_segmentation.md
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layout: default
title: Hair Segmentation
parent: Solutions
nav_order: 4
nav_order: 5
---
# MediaPipe Hair Segmentation
docs/solutions/hands.md
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layout: default
title: Hands
parent: Solutions
nav_order: 3
nav_order: 4
---
# MediaPipe Hands
docs/solutions/iris.md
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@@ -0,0 +1,204 @@
---
layout: default
title: Iris
parent: Solutions
nav_order: 3
---
# MediaPipe Iris
{: .no_toc }
1. TOC
{:toc}
---
## Overview
A wide range of real-world applications, including computational photography
(glint reflection) and augmented reality effects (virtual avatars) rely on
accurately tracking the iris within an eye. This is a challenging task to solve
on mobile devices, due to the limited computing resources, variable light
conditions and the presence of occlusions, such as hair or people squinting.
Iris tracking can also be utilized to determine the metric distance of the
camera to the user. This can improve a variety of use cases, ranging from
virtual try-on of properly sized glasses and hats to accessibility features that
adopt the font size depending on the viewer’s distance. Often, sophisticated
specialized hardware is employed to compute the metric distance, limiting the
range of devices on which the solution could be applied.
MediaPipe Iris is a ML solution for accurate iris estimation, able to track
landmarks involving the iris, pupil and the eye contours using a single RGB
camera, in real-time, without the need for specialized hardware. Through use of
iris landmarks, the solution is also able to determine the metric distance
between the subject and the camera with relative error less than 10%. Note that
iris tracking does not infer the location at which people are looking, nor does
it provide any form of identity recognition. With the cross-platfrom capability
of the MediaPipe framework, MediaPipe Iris can run on most modern
[mobile phones](#mobile), [desktops/laptops](#desktop) and even on the
[web](#web).
![iris_tracking_example.gif](../images/mobile/iris_tracking_example.gif) |
:------------------------------------------------------------------------: |
*Fig 1. Example of MediaPipe Iris: eyelid (red) and iris (blue) contours.* |
## ML Pipeline
The first step in the pipeline leverages [MediaPipe Face Mesh](./face_mesh.md),
which generates a mesh of the approximate face geometry. From this mesh, we
isolate the eye region in the original image for use in the subsequent iris
tracking step.
The pipeline is implemented as a MediaPipe
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt)
that uses a
[face landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt)
from the
[face landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark),
an
[iris landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_tracking/iris_landmark_left_and_right_gpu.pbtxt)
from the
[iris landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark),
and renders using a dedicated
[iris-and-depth renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_gpu.pbtxt).
The
[face landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt)
internally uses a
[face detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front_gpu.pbtxt)
from the
[face detection module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection).
Note: To visualize a graph, copy the graph and paste it into
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
to visualize its associated subgraphs, please see
[visualizer documentation](../tools/visualizer.md).
## Models
### Face Detection Model
The face detector is the same [bazelFace](https://arxiv.org/abs/1907.05047)
model used in [MediaPipe Face Detection](./face_detection.md).
### Face Landmark Model
The face landmark model is the same as in [MediaPipe Face Mesh](./face_mesh.md).
You can also find more details in this
[paper](https://arxiv.org/abs/1907.06724).
### Iris Landmark Model
The iris model takes an image patch of the eye region and estimates both the eye
landmarks (along the eyelid) and iris landmarks (along ths iris contour). You
can find more details in this [paper](https://arxiv.org/abs/2006.11341).
![iris_tracking_eye_and_iris_landmarks.png](../images/mobile/iris_tracking_eye_and_iris_landmarks.png) |
:----------------------------------------------------------------------------------------------------: |
*Fig 2. Eye landmarks (red) and iris landmarks (green).* |
## Depth-from-Iris
MediaPipe Iris is able to determine the metric distance of a subject to the
camera with less than 10% error, without requiring any specialized hardware.
This is done by relying on the fact that the horizontal iris diameter of the
human eye remains roughly constant at 11.7±0.5 mm across a wide population,
along with some simple geometric arguments. For more details please refer to our
[Google AI Blog post](https://mediapipe.page.link/iris-blog).
![iris_tracking_depth_from_iris.gif](../images/mobile/iris_tracking_depth_from_iris.gif) |
:--------------------------------------------------------------------------------------------: |
*Fig 3. (Left) MediaPipe Iris predicting metric distance in cm on a Pixel 2 from iris tracking without use of a depth sensor. (Right) Ground-truth depth.* |
## Example Apps
Please first see general instructions for
[Android](../getting_started/building_examples.md#android),
[iOS](../getting_started/building_examples.md#ios) and
[desktop](../getting_started/building_examples.md#desktop) on how to build
MediaPipe examples.
Note: To visualize a graph, copy the graph and paste it into
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
to visualize its associated subgraphs, please see
[visualizer documentation](../tools/visualizer.md).
### Mobile
* Graph:
[`mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt)
* Android target:
[(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1cywcNtqk764TlZf1lvSTV4F3NGB2aL1R/view?usp=sharing)
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/iristrackinggpu:iristrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/iristrackinggpu/BUILD)
* iOS target:
[`mediapipe/examples/ios/iristrackinggpu:IrisTrackingGpuApp`](http:/mediapipe/examples/ios/iristrackinggpu/BUILD)
### Desktop
#### Live Camera Input
Please first see general instructions for
[desktop](../getting_started/building_examples.md#desktop) on how to build
MediaPipe examples.
* Running on CPU
* Graph:
[`mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt)
* Target:
[`mediapipe/examples/desktop/iris_tracking:iris_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/iris_tracking/BUILD)
* Running on GPU
* Graph:
[`mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt)
* Target:
[`mediapipe/examples/desktop/iris_tracking:iris_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/iris_tracking/BUILD)
#### Video File Input
1. To build the application, run:
```bash
bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/iris_tracking:iris_tracking_cpu_video_input
```
2. To run the application, replace `<input video path>` and `<output video
path>` in the command below with your own paths:
```
bazel-bin/mediapipe/examples/desktop/iris_tracking/iris_tracking_cpu_video_input \
--calculator_graph_config_file=mediapipe/graphs/iris_tracking/iris_tracking_cpu_video_input.pbtxt \
--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
```
#### Single-image Depth Estimation
1. To build the application, run:
```bash
bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/iris_tracking:iris_depth_from_image_desktop
```
2. To run the application, replace `<input image path>` and `<output image
path>` in the command below with your own paths:
```bash
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/iris_tracking/iris_depth_from_image_desktop \
--input_image_path=<input image path> --output_image_path=<output image path>
```
### Web
Please refer to [these instructions](../index.md#mediapipe-on-the-web).
## Resources
* Google AI Blog: [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation
from a Single Image](https://mediapipe.page.link/iris-blog)
* Paper:
[Real-time Pupil Tracking from Monocular Video for Digital Puppetry](https://arxiv.org/abs/2006.11341)
([presentation](https://youtu.be/cIhXkiiapQI))
* Face detection model:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front.tflite)
* Face landmark model:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark.tflite),
[TF.js model](https://tfhub.dev/mediapipe/facemesh/1)
* Iris landmark model:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark.tflite)
* [Model card](https://mediapipe.page.link/iris-mc)
docs/solutions/knift.md
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layout: default
title: KNIFT (Template-based Feature Matching)
parent: Solutions
nav_order: 8
nav_order: 9
---
# MediaPipe KNIFT
docs/solutions/media_sequence.md
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layout: default
title: Dataset Preparation with MediaSequence
parent: Solutions
nav_order: 10
nav_order: 11
---
# Dataset Preparation with MediaSequence
docs/solutions/object_detection.md
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layout: default
title: Object Detection
parent: Solutions
nav_order: 5
nav_order: 6
---
# MediaPipe Object Detection
@@ -95,8 +95,8 @@ Please first see general instructions for
```
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite \
--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt \
--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt \
--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
```
* With a TensorFlow Model
@@ -131,8 +131,8 @@ Please first see general instructions for
```bash
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite \
--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt \
--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt \
--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
```
### Coral
docs/solutions/objectron.md
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layout: default
title: Objectron (3D Object Detection)
parent: Solutions
nav_order: 7
nav_order: 8
---
# MediaPipe Objectron
docs/solutions/solutions.md
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---
<!-- []() in the first cell is needed to preserve table formatting in GitHub Pages. -->
<!-- Whenever this table is updated, paste a copy to ../index.md. -->
<!-- Whenever this table is updated, paste a copy to ../external_index.md. -->
[]() | Android | iOS | Desktop | Web | Coral
:---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :-: | :---:
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | ✅ | ✅
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | |
[Iris](https://google.github.io/mediapipe/solutions/iris) | ✅ | ✅ | ✅ | ✅ |
[Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | ✅
docs/solutions/youtube_8m.md
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layout: default
title: YouTube-8M Feature Extraction and Model Inference
parent: Solutions
nav_order: 11
nav_order: 12
---
# YouTube-8M Feature Extraction and Model Inference