talksik/mediapipe
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Project import generated by Copybara.

Browse Source 
GitOrigin-RevId: 612e50bb8db2ec3dc1c30049372d87a80c3848db
This commit is contained in:
MediaPipe Team
2020-08-30 19:52:55 -04:00
committed by chuoling
parent a7225b938a
commit c0124fb83c
248 changed files with 5225 additions and 1914 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/_config.yml
+2 -2
View File
@@ -22,8 +22,8 @@ aux_links:
# Footer content appears at the bottom of every page's main content
footer_content: "&copy; 2020 GOOGLE LLC | <a href=\"https://policies.google.com/privacy\">PRIVACY POLICY</a> | <a href=\"https://policies.google.com/terms\">TERMS OF SERVICE</a>"
# Color scheme currently only supports "dark" or nil (default)
color_scheme: nil
# Color scheme currently only supports "dark", "light"/nil (default), or a custom scheme that you define
color_scheme: mediapipe
# Google Analytics Tracking (optional)
ga_tracking: UA-140696581-2
docs/_sass/color_schemes/mediapipe.scss
+1
View File
@@ -0,0 +1 @@
$link-color: #0097A7;
docs/getting_started/building_examples.md
+46 -33
View File
@@ -425,7 +425,47 @@ Note: This currently works only on Linux, and please first follow
## Python
### Prerequisite
MediaPipe Python package is available on
[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
install mediapipe` on Linux and macOS, as described below in
[Run in python interpreter](#run-in-python-interpreter) and in this
[colab](https://mediapipe.page.link/mp-py-colab).
### Run in Python interpreter
Using [MediaPipe Pose](../solutions/pose.md) as an example:
```bash
# Activate a Python virtual environment.
$ python3 -m venv mp_env && source mp_env/bin/activate
# Install MediaPipe Python package
(mp_env)$ pip install mediapipe
# Run in Python interpreter
(mp_env)$ python3
>>> import mediapipe as mp
>>> pose_tracker = mp.examples.UpperBodyPoseTracker()
# For image input
>>> pose_landmarks, _ = pose_tracker.run(input_file='/path/to/input/file', output_file='/path/to/output/file')
>>> pose_landmarks, annotated_image = pose_tracker.run(input_file='/path/to/file')
# For live camera input
# (Press Esc within the output image window to stop the run or let it self terminate after 30 seconds.)
>>> pose_tracker.run_live()
# Close the tracker.
>>> pose_tracker.close()
```
Tip: Use command `deactivate` to exit the Python virtual environment.
### Building Python package from source
Follow these steps only if you have local changes and need to build the Python
package from source. Otherwise, we strongly encourage our users to simply run
`pip install mediapipe`, more convenient and much faster.
1. Make sure that Bazel and OpenCV are correctly installed and configured for
MediaPipe. Please see [Installation](./install.md) for how to setup Bazel
@@ -445,50 +485,23 @@ Note: This currently works only on Linux, and please first follow
$ brew install protobuf
```
### Set up Python virtual environment.
1. Activate a Python virtual environment.
3. Activate a Python virtual environment.
```bash
$ python3 -m venv mp_env && source mp_env/bin/activate
```
2. In the virtual environment, go to the MediaPipe repo directory.
4. In the virtual environment, go to the MediaPipe repo directory.
3. Install the required Python packages.
5. Install the required Python packages.
```bash
(mp_env)mediapipe$ pip3 install -r requirements.txt
```
4. Generate and install MediaPipe package.
6. Generate and install MediaPipe package.
```bash
(mp_env)mediapipe$ python3 setup.py gen_protos
(mp_env)mediapipe$ python3 setup.py install
(mp_env)mediapipe$ python3 setup.py install --link-opencv
```
### Run in Python interpreter
Make sure you are not in the MediaPipe repo directory.
Using [MediaPipe Pose](../solutions/pose.md) as an example:
```bash
(mp_env)$ python3
>>> import mediapipe as mp
>>> pose_tracker = mp.examples.UpperBodyPoseTracker()
# For image input
>>> pose_landmarks, _ = pose_tracker.run(input_file='/path/to/input/file', output_file='/path/to/output/file')
>>> pose_landmarks, annotated_image = pose_tracker.run(input_file='/path/to/file')
# For live camera input
# (Press Esc within the output image window to stop the run or let it self terminate after 30 seconds.)
>>> pose_tracker.run_live()
# Close the tracker.
>>> pose_tracker.close()
```
Tip: Use command `deactivate` to exit the Python virtual environment.
docs/images/mobile/instant_motion_tracking_android_small.gif
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 925 KiB

docs/index.md
+24 -21
View File
@@ -22,32 +22,33 @@ desktop/cloud, web and IoT devices.
## ML solutions in MediaPipe
Face Detection | Face Mesh | Iris 🆕 | Hands | Pose 🆕
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :----:
[![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) | [![pose](images/mobile/pose_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/pose)
Face Detection | Face Mesh | Iris | Hands | Pose | 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) | [![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) | [![pose](images/mobile/pose_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/pose) | [![hair_segmentation](images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation)
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)
Object Detection | Box Tracking | Instant Motion 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) | [![instant_motion_tracking](images/mobile/instant_motion_tracking_android_small.gif)](https://google.github.io/mediapipe/solutions/instant_motion_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. -->
[]() | Android | iOS | Desktop | Python | 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) | ✅ | ✅ | ✅ | | ✅ |
[Pose](https://google.github.io/mediapipe/solutions/pose) 🆕 | ✅ | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
[]() | Android | iOS | Desktop | Python | 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) | ✅ | ✅ | ✅ | | ✅ |
[Pose](https://google.github.io/mediapipe/solutions/pose) | ✅ | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | ✅ | | | | |
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
## MediaPipe on the Web
@@ -88,6 +89,8 @@ run code search using
## Publications
* [Instant Motion Tracking With MediaPipe](https://mediapipe.page.link/instant-motion-tracking-blog)
in Google Developers Blog
* [BlazePose - On-device Real-time Body Pose Tracking](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
in Google AI Blog
* [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation](https://ai.googleblog.com/2020/08/mediapipe-iris-real-time-iris-tracking.html)
docs/solutions/autoflip.md
+1 -1
View File
@@ -2,7 +2,7 @@
layout: default
title: AutoFlip (Saliency-aware Video Cropping)
parent: Solutions
nav_order: 11
nav_order: 12
---
# AutoFlip: Saliency-aware Video Cropping
docs/solutions/instant_motion_tracking.md
+122
View File
@@ -0,0 +1,122 @@
---
layout: default
title: Instant Motion Tracking
parent: Solutions
nav_order: 9
---
# MediaPipe Instant Motion Tracking
{: .no_toc }
1. TOC
{:toc}
---
## Overview
Augmented Reality (AR) technology creates fun, engaging, and immersive user
experiences. The ability to perform AR tracking across devices and platforms,
without initialization, remains important to power AR applications at scale.
MediaPipe Instant Motion Tracking provides AR tracking across devices and
platforms without initialization or calibration. It is built upon the
[MediaPipe Box Tracking](./box_tracking.md) solution. With Instant Motion
Tracking, you can easily place virtual 2D and 3D content on static or moving
surfaces, allowing them to seamlessly interact with the real-world environment.
![instant_motion_tracking_android_small](../images/mobile/instant_motion_tracking_android_small.gif) |
:-----------------------------------------------------------------------: |
*Fig 1. Instant Motion Tracking is used to augment the world with a 3D sticker.* |
## Pipeline
The Instant Motion Tracking pipeline is implemented as a MediaPipe
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt),
which internally utilizes a
[RegionTrackingSubgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt)
in order to perform anchor tracking for each individual 3D sticker.
We first use a
[StickerManagerCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/calculators/sticker_manager_calculator.cc)
to prepare the individual sticker data for the rest of the application. This
information is then sent to the
[RegionTrackingSubgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt)
that performs 3D region tracking for sticker placement and rendering. Once
acquired, our tracked sticker regions are sent with user transformations (i.e.
gestures from the user to rotate and zoom the sticker) and IMU data to the
[MatricesManagerCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/calculators/matrices_manager_calculator.cc),
which turns all our sticker transformation data into a set of model matrices.
This data is handled directly by our
[GlAnimationOverlayCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc)
as an input stream, which will render the provided texture and object file using
our matrix specifications. The output of
[GlAnimationOverlayCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc)
is a video stream depicting the virtual 3D content rendered on top of the real
world, creating immersive AR experiences for users.
## Using Instant Motion Tracking
With the Instant Motion Tracking MediaPipe [graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt),
an application can create an interactive and realistic AR experience by
specifying the required input streams, side packets, and output streams.
The input streams are the following:
* Input Video (GpuBuffer): Video frames to render augmented stickers onto.
* Rotation Matrix (9-element Float Array): The 3x3 row-major rotation
matrix from the device IMU to determine proper orientation of the device.
* Sticker Proto String (String): A string representing the
serialized [sticker buffer protobuf message](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.proto),
containing a list of all stickers and their attributes.
* Each sticker in the Protobuffer has a unique ID to find associated
anchors and transforms, an initial anchor placement in a normalized [0.0, 1.0]
3D space, a user rotation and user scaling transform on the sticker,
and an integer indicating which type of objects to render for the
sticker (e.g. 3D asset or GIF).
* Sticker Sentinel (Integer): When an anchor must be initially placed or
repositioned, this value must be changed to the ID of the anchor to reset from
the sticker buffer protobuf message. If no valid ID is provided, the system
will simply maintain tracking.
Side packets are also an integral part of the Instant Motion Tracking solution
to provide device-specific information for the rendering system:
* Field of View (Float): The field of view of the camera in radians.
* Aspect Ratio (Float): The aspect ratio (width / height) of the camera frames
(this ratio corresponds to the image frames themselves, not necessarily the
screen bounds).
* Object Asset (String): The
[GlAnimationOverlayCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc)
must be provided with an associated asset file name pointing to the 3D model
to render in the viewfinder.
* (Optional) Texture (ImageFrame on Android, GpuBuffer on iOS): Textures for
the
[GlAnimationOverlayCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc)
can be provided either via an input stream (dynamic texturing) or as a side
packet (unchanging texture).
The rendering system for the Instant Motion Tracking is powered by OpenGL. For
more information regarding the structure of model matrices and OpenGL rendering,
please visit [OpenGL Wiki](https://www.khronos.org/opengl/wiki/). With the
specifications above, the Instant Motion Tracking capabilities can be adapted to
any device that is able to run the MediaPipe framework with a working IMU system
and connected camera.
## Example Apps
Please first see general instructions for
[Android](../getting_started/building_examples.md#android) on how to build
MediaPipe examples.
* Graph: [mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt)
* Android target (or download prebuilt [ARM64 APK](https://drive.google.com/file/d/1KnaBBoKpCHR73nOBJ4fL_YdWVTAcwe6L/view?usp=sharing)):
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/instantmotiontracking:instantmotiontracking`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/instantmotiontracking/BUILD)
## Resources
* Google Developers Blog:
[Instant Motion Tracking With MediaPipe](https://mediapipe.page.link/instant-motion-tracking-blog)
* Google AI Blog:
[The Instant Motion Tracking Behind Motion Stills AR](https://ai.googleblog.com/2018/02/the-instant-motion-tracking-behind.html)
* Paper:
[Instant Motion Tracking and Its Applications to Augmented Reality](https://arxiv.org/abs/1907.06796)
docs/solutions/iris.md
+6 -1
View File
@@ -55,7 +55,7 @@ that uses a
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)
[iris landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/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
@@ -72,6 +72,11 @@ Note: To visualize a graph, copy the graph and paste it into
to visualize its associated subgraphs, please see
[visualizer documentation](../tools/visualizer.md).
The output of the pipeline is a set of 478 3D landmarks, including 468 face
landmarks from [MediaPipe Face Mesh](./face_mesh.md), with those around the eyes
further refined (see Fig 2), and 10 additional iris landmarks appended at the
end (5 for each eye, and see Fig 2 also).
## Models
### Face Detection Model
docs/solutions/knift.md
+1 -1
View File
@@ -2,7 +2,7 @@
layout: default
title: KNIFT (Template-based Feature Matching)
parent: Solutions
nav_order: 10
nav_order: 11
---
# MediaPipe KNIFT
docs/solutions/media_sequence.md
+1 -1
View File
@@ -2,7 +2,7 @@
layout: default
title: Dataset Preparation with MediaSequence
parent: Solutions
nav_order: 12
nav_order: 13
---
# Dataset Preparation with MediaSequence
docs/solutions/objectron.md
+2 -2
View File
@@ -2,7 +2,7 @@
layout: default
title: Objectron (3D Object Detection)
parent: Solutions
nav_order: 9
nav_order: 10
---
# MediaPipe Objectron
@@ -161,7 +161,7 @@ to visualize its associated subgraphs, please see
### Objectron for Shoes
* Graph:
[`mediapipe/graphs/hair_segmentation/hair_segmentation_mobile_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/shoe_classic_occlusion_tracking.pbtxt)
[`mediapipe/graphs/object_detection_3d/shoe_classic_occlusion_tracking.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/shoe_classic_occlusion_tracking.pbtxt)
* Android target:
[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1S0K4hbWt3o31FfQ4QU3Rz7IHrvOUMx1d)
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetection3d:objectdetection3d`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetection3d/BUILD)
docs/solutions/pose.md
+18 -2
View File
@@ -142,10 +142,21 @@ MediaPipe examples.
### Python
Please first see general instructions for
[Python](../getting_started/building_examples.md#python) examples.
MediaPipe Python package is available on
[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
install mediapipe` on Linux and macOS, as described below and in this
[colab](https://mediapipe.page.link/mp-py-colab). If you do need to build the
Python package from source, see
[additional instructions](../getting_started/building_examples.md#python).
```bash
# Activate a Python virtual environment.
$ python3 -m venv mp_env && source mp_env/bin/activate
# Install MediaPipe Python package
(mp_env)$ pip install mediapipe
# Run in Python interpreter
(mp_env)$ python3
>>> import mediapipe as mp
>>> pose_tracker = mp.examples.UpperBodyPoseTracker()
@@ -153,6 +164,9 @@ Please first see general instructions for
# For image input
>>> pose_landmarks, _ = pose_tracker.run(input_file='/path/to/input/file', output_file='/path/to/output/file')
>>> pose_landmarks, annotated_image = pose_tracker.run(input_file='/path/to/file')
# To print out the pose landmarks, you can simply do "print(pose_landmarks)".
# However, the data points can be more accessible with the following approach.
>>> [print('x is', data_point.x, 'y is', data_point.y, 'z is', data_point.z, 'visibility is', data_point.visibility) for data_point in pose_landmarks.landmark]
# For live camera input
# (Press Esc within the output image window to stop the run or let it self terminate after 30 seconds.)
@@ -162,6 +176,8 @@ Please first see general instructions for
>>> pose_tracker.close()
```
Tip: Use command `deactivate` to exit the Python virtual environment.
### Web
Please refer to [these instructions](../index.md#mediapipe-on-the-web).
docs/solutions/solutions.md
+16 -15
View File
@@ -16,18 +16,19 @@ has_toc: false
<!-- []() in the first cell is needed to preserve table formatting in GitHub Pages. -->
<!-- Whenever this table is updated, paste a copy to ../external_index.md. -->
[]() | Android | iOS | Desktop | Python | 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) | ✅ | ✅ | ✅ | | ✅ |
[Pose](https://google.github.io/mediapipe/solutions/pose) 🆕 | ✅ | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
[]() | Android | iOS | Desktop | Python | 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) | ✅ | ✅ | ✅ | | ✅ |
[Pose](https://google.github.io/mediapipe/solutions/pose) | ✅ | ✅ | ✅ | ✅ | ✅ |
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | ✅ |
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | ✅ | | | | |
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
docs/solutions/youtube_8m.md
+1 -1
View File
@@ -2,7 +2,7 @@
layout: default
title: YouTube-8M Feature Extraction and Model Inference
parent: Solutions
nav_order: 13
nav_order: 14
---
# YouTube-8M Feature Extraction and Model Inference
docs/tools/tracing_and_profiling.md
+4 -1
View File
@@ -144,10 +144,13 @@ we record ten intervals of half a second each. This can be overridden by adding
```bash
profiler_config {
trace_enabled: true
trace_log_path: "/sdcard/profiles"
trace_log_path: "/sdcard/profiles/"
}
```
Note: The forward slash at the end of the `trace_log_path` is necessary for
indicating that `profiles` is a directory (that *should* exist).
* Download the trace files from the device.
```bash