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Author SHA1 Message Date
MediaPipe TeamandSebastian Schmidt 6cdc6443b6 Project import generated by Copybara.
GitOrigin-RevId: afbe148020a643ac800e34d91dc515995a9abb5c
2022-09-09 01:38:52 +00:00
MediaPipe TeamandSebastian Schmidt b65602fd31 Project import generated by Copybara.
GitOrigin-RevId: 777962478d88650e311af635e3ac3fa58e5a530b
2022-09-09 01:35:36 +00:00
MediaPipe TeamandSebastian Schmidt 06c30f1931 Project import generated by Copybara.
GitOrigin-RevId: ca9878d6e9c5beb87512e7536b200c55d150ede8
2022-09-08 19:08:07 +00:00
MediaPipe TeamandSebastian Schmidt ebec590cfe Project import generated by Copybara.
GitOrigin-RevId: e207bb2a1b26cd799055d7735ed35ad2f0e56b83
2022-09-07 20:55:41 +00:00
MediaPipe TeamandSebastian Schmidt d3f98334bf Project import generated by Copybara.
GitOrigin-RevId: 3ce19771d2586aeb611fff75bb7627721cf5d36b
2022-09-07 17:47:04 +00:00
MediaPipe TeamandSebastian Schmidt 4dc4b19ddb Project import generated by Copybara.
GitOrigin-RevId: 1e13be30e2c6838d4a2ff768a39c414bc80534bb
2022-09-06 21:46:17 +00:00
MediaPipe TeamandSebastian Schmidt 63e679d99c Project import generated by Copybara.
GitOrigin-RevId: 0f2489d226f1e2a5d718a8b9efe5e8198ba4ab3b
2022-06-29 18:45:02 +00:00
MediaPipe TeamandSebastian Schmidt c688862570 Project import generated by Copybara.
GitOrigin-RevId: 6e5aa035cd1f6a9333962df5d3ab97a05bd5744e
2022-06-28 12:11:05 +00:00
MediaPipe Teamandjqtang 4a20e9909d Project import generated by Copybara.
GitOrigin-RevId: b66251317fbebfbb8e1f2ddc64ea5da84bceb7e5
2022-05-06 17:05:30 -07:00
MediaPipe Teamandschmidt-sebastian 7fb37c80e8 Project import generated by Copybara.
GitOrigin-RevId: 19a829ffd755edb43e54d20c0e7b9348512d5108
2022-05-05 19:57:20 +00:00
MediaPipe Teamandjqtang c6c80c3745 Project import generated by Copybara.
GitOrigin-RevId: 17113e259b160929c49262e7aa78ac22d228f9fc
2022-03-22 17:48:17 -07:00
MediaPipe Teamandjqtang cc6a2f7af6 Project import generated by Copybara.
GitOrigin-RevId: 73d686c40057684f8bfaca285368bf1813f9fc26
2022-03-21 12:12:39 -07:00
MediaPipe Teamandjqtang e6c19885c6 Project import generated by Copybara.
GitOrigin-RevId: bb059a0721c92e8154d33ce8057b3915a25b3d7d
2021-12-13 15:56:02 -08:00
MediaPipe Teamandchuoling cf101e62a9 Project import generated by Copybara.
GitOrigin-RevId: 7e1d382a1788ebd8412c5626581b4c4cf2fe75ea
2021-11-16 14:32:04 -05:00
MediaPipe Teamandchuoling f4e7f6cc48 Project import generated by Copybara.
GitOrigin-RevId: 412b20ea6bc8e49ba5b50798a6114ad6173ff073
2021-11-04 01:53:16 -04:00
MediaPipe Teamandjqtang d4bb35fe5a Project import generated by Copybara.
GitOrigin-RevId: d4a11282d20fe4d2e137f9032cf349750030dcb9
2021-11-03 17:27:30 -07:00
MediaPipe Teamandchuoling 1faeaae7e5 Project import generated by Copybara.
GitOrigin-RevId: bbbbcb4f5174dea33525729ede47c770069157cd
2021-10-18 17:00:29 -04:00
MediaPipe Teamandjqtang 33d683c671 Project import generated by Copybara.
GitOrigin-RevId: 373e3ac1e5839befd95bf7d73ceff3c5f1171969
2021-10-06 14:27:49 -07:00
MediaPipe Teamandchuoling 137e1cc763 Project import generated by Copybara.
GitOrigin-RevId: 283c1a295de0a53e47d7a94996bda0c52dcfd677
2021-09-13 21:35:51 -04:00
MediaPipe Teamandjqtang 6abec128ed Project import generated by Copybara.
GitOrigin-RevId: f4b1fe3f15810450fb6539e733f6a260d3ee082c
2021-09-01 18:15:31 -07:00
MediaPipe Teamandjqtang 710fb3de58 Project import generated by Copybara.
GitOrigin-RevId: 1610e588e497817fae2d9a458093ab6a370e2972
2021-08-18 17:45:46 -07:00
MediaPipe Teamandchuoling b899d17f18 Project import generated by Copybara.
GitOrigin-RevId: 8e1da4611d93ccb7d9674713157d43be0348d98f
2021-07-27 22:36:23 -04:00
MediaPipe Teamandchuoling 50c92c6623 Project import generated by Copybara.
GitOrigin-RevId: 27c70b5fe62ab71189d358ca122ee4b19c817a8f
2021-07-27 19:36:32 -04:00
MediaPipe Teamandjqtang 374f5e2e7e Project import generated by Copybara.
GitOrigin-RevId: 65b427572550bd9c5bc5f053eeea0f44340d5673
2021-06-28 10:17:10 -07:00
MediaPipe Teamandchuoling 139237092f Project import generated by Copybara.
GitOrigin-RevId: 33adfdf31f3a5cbf9edc07ee1ea583e95080bdc5
2021-06-24 17:55:26 -04:00
MediaPipe Teamandchuoling b544a314b3 Project import generated by Copybara.
GitOrigin-RevId: ec25bf2e416c3689477e82946fb69de2e53b9161
2021-06-10 01:38:18 -04:00
MediaPipe Teamandchuoling b48d72e43f Project import generated by Copybara.
GitOrigin-RevId: 1e221238b0bc717115c8152ad3092da3309a63a1
2021-06-03 17:32:02 -04:00
MediaPipe Teamandchuoling 8b57bf879b Project import generated by Copybara.
GitOrigin-RevId: 08c2016a4df5aef571b464a4d4491f38c6b2af10
2021-06-03 17:04:35 -04:00
MediaPipe Teamandchuoling ae05ad04b3 Project import generated by Copybara.
GitOrigin-RevId: 016275ca4057540b2370ed4531dbc81eb92caae2
2021-05-11 01:00:51 -04:00
MediaPipe Teamandchuoling 017c1dc7ea Project import generated by Copybara.
GitOrigin-RevId: 2146b10f0a498f665f246e16033b686c7947b92d
2021-05-10 16:42:02 -04:00
MediaPipe Teamandchuoling a9b643e0f5 Project import generated by Copybara.
GitOrigin-RevId: ff83882955f1a1e2a043ff4e71278be9d7217bbe
2021-05-05 14:56:16 -04:00
1502 changed files with 128713 additions and 13616 deletions
+8
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@@ -32,6 +32,9 @@ build:macos --copt=-w
# Sets the default Apple platform to macOS.
build --apple_platform_type=macos
# Compile ObjC++ files with C++17
build --per_file_copt=.*\.mm\$@-std=c++17
# Allow debugging with XCODE
build --apple_generate_dsym
@@ -58,6 +61,7 @@ build:android_arm64 --fat_apk_cpu=arm64-v8a
# iOS configs.
build:ios --apple_platform_type=ios
build:ios --copt=-fno-aligned-allocation
build:ios_i386 --config=ios
build:ios_i386 --cpu=ios_i386
@@ -87,6 +91,10 @@ build:darwin_x86_64 --apple_platform_type=macos
build:darwin_x86_64 --macos_minimum_os=10.12
build:darwin_x86_64 --cpu=darwin_x86_64
build:darwin_arm64 --apple_platform_type=macos
build:darwin_arm64 --macos_minimum_os=10.16
build:darwin_arm64 --cpu=darwin_arm64
# This bazelrc file is meant to be written by a setup script.
try-import %workspace%/.configure.bazelrc
+1 -1
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@@ -1 +1 @@
3.7.2
5.2.0
@@ -0,0 +1,27 @@
---
name: "Build/Installation Issue"
about: Use this template for build/installation issues
labels: type:build/install
---
<em>Please make sure that this is a build/installation issue and also refer to the [troubleshooting](https://google.github.io/mediapipe/getting_started/troubleshooting.html) documentation before raising any issues.</em>
**System information** (Please provide as much relevant information as possible)
- OS Platform and Distribution (e.g. Linux Ubuntu 16.04, Android 11, iOS 14.4):
- Compiler version (e.g. gcc/g++ 8 /Apple clang version 12.0.0):
- Programming Language and version ( e.g. C++ 14, Python 3.6, Java ):
- Installed using virtualenv? pip? Conda? (if python):
- [MediaPipe version](https://github.com/google/mediapipe/releases):
- Bazel version:
- XCode and Tulsi versions (if iOS):
- Android SDK and NDK versions (if android):
- Android [AAR](https://google.github.io/mediapipe/getting_started/android_archive_library.html) ( if android):
- OpenCV version (if running on desktop):
**Describe the problem**:
**[Provide the exact sequence of commands / steps that you executed before running into the problem](https://google.github.io/mediapipe/getting_started/getting_started.html):**
**Complete Logs:**
Include Complete Log information or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached:
@@ -0,0 +1,26 @@
---
name: "Solution Issue"
about: Use this template for assistance with a specific mediapipe solution, such as "Pose" or "Iris", including inference model usage/training, solution-specific calculators, etc.
labels: type:support
---
<em>Please make sure that this is a [solution](https://google.github.io/mediapipe/solutions/solutions.html) issue.<em>
**System information** (Please provide as much relevant information as possible)
- Have I written custom code (as opposed to using a stock example script provided in Mediapipe):
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
- [MediaPipe version](https://github.com/google/mediapipe/releases):
- Bazel version:
- Solution (e.g. FaceMesh, Pose, Holistic):
- Programming Language and version ( e.g. C++, Python, Java):
**Describe the expected behavior:**
**Standalone code you may have used to try to get what you need :**
If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/repo link /any notebook:
**Other info / Complete Logs :**
Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached:
@@ -0,0 +1,51 @@
---
name: "Documentation Issue"
about: Use this template for documentation related issues
labels: type:docs
---
Thank you for submitting a MediaPipe documentation issue.
The MediaPipe docs are open source! To get involved, read the documentation Contributor Guide
## URL(s) with the issue:
Please provide a link to the documentation entry, for example: https://github.com/google/mediapipe/blob/master/docs/solutions/face_mesh.md#models
## Description of issue (what needs changing):
Kinds of documentation problems:
### Clear description
For example, why should someone use this method? How is it useful?
### Correct links
Is the link to the source code correct?
### Parameters defined
Are all parameters defined and formatted correctly?
### Returns defined
Are return values defined?
### Raises listed and defined
Are the errors defined? For example,
### Usage example
Is there a usage example?
See the API guide:
on how to write testable usage examples.
### Request visuals, if applicable
Are there currently visuals? If not, will it clarify the content?
### Submit a pull request?
Are you planning to also submit a pull request to fix the issue? See the docs
https://github.com/google/mediapipe/blob/master/CONTRIBUTING.md
+32
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@@ -0,0 +1,32 @@
---
name: "Bug Issue"
about: Use this template for reporting a bug
labels: type:bug
---
<em>Please make sure that this is a bug and also refer to the [troubleshooting](https://google.github.io/mediapipe/getting_started/troubleshooting.html), FAQ documentation before raising any issues.</em>
**System information** (Please provide as much relevant information as possible)
- Have I written custom code (as opposed to using a stock example script provided in MediaPipe):
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
- Browser and version (e.g. Google Chrome, Safari) if the issue happens on browser:
- Programming Language and version ( e.g. C++, Python, Java):
- [MediaPipe version](https://github.com/google/mediapipe/releases):
- Bazel version (if compiling from source):
- Solution ( e.g. FaceMesh, Pose, Holistic ):
- Android Studio, NDK, SDK versions (if issue is related to building in Android environment):
- Xcode & Tulsi version (if issue is related to building for iOS):
**Describe the current behavior:**
**Describe the expected behavior:**
**Standalone code to reproduce the issue:**
Provide a reproducible test case that is the bare minimum necessary to replicate the problem. If possible, please share a link to Colab/repo link /any notebook:
**Other info / Complete Logs :**
Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached
@@ -0,0 +1,24 @@
---
name: "Feature Request"
about: Use this template for raising a feature request
labels: type:feature
---
<em>Please make sure that this is a feature request.</em>
**System information** (Please provide as much relevant information as possible)
- MediaPipe Solution (you are using):
- Programming language : C++/typescript/Python/Objective C/Android Java
- Are you willing to contribute it (Yes/No):
**Describe the feature and the current behavior/state:**
**Will this change the current api? How?**
**Who will benefit with this feature?**
**Please specify the use cases for this feature:**
**Any Other info:**
+12
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@@ -0,0 +1,12 @@
---
name: "Other Issue"
about: Use this template for any other non-support related issues.
labels: type:others
---
This template is for miscellaneous issues not covered by the other issue categories
For questions on how to work with MediaPipe, or support for problems that are not verified bugs in MediaPipe, please go to [StackOverflow](https://stackoverflow.com/questions/tagged/mediapipe) and [Slack](https://mediapipe.page.link/joinslack) communities.
If you are reporting a vulnerability, please use the [dedicated reporting process](https://github.com/google/mediapipe/security).
+18
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@@ -0,0 +1,18 @@
# Copyright 2021 The MediaPipe Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# A list of assignees
assignees:
- sureshdagooglecom
+34
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@@ -0,0 +1,34 @@
# Copyright 2021 The MediaPipe Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
#
# This file was assembled from multiple pieces, whose use is documented
# throughout. Please refer to the TensorFlow dockerfiles documentation
# for more information.
# Number of days of inactivity before an Issue or Pull Request becomes stale
daysUntilStale: 7
# Number of days of inactivity before a stale Issue or Pull Request is closed
daysUntilClose: 7
# Only issues or pull requests with all of these labels are checked if stale. Defaults to `[]` (disabled)
onlyLabels:
- stat:awaiting response
# Comment to post when marking as stale. Set to `false` to disable
markComment: >
This issue has been automatically marked as stale because it has not had
recent activity. It will be closed if no further activity occurs. Thank you.
# Comment to post when removing the stale label. Set to `false` to disable
unmarkComment: false
closeComment: >
Closing as stale. Please reopen if you'd like to work on this further.
+1 -1
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@@ -5,7 +5,7 @@
* Bug fixes
* Documentation fixes
For new feature additions (e.g., new graphs and calculators), we are currently not planning to accept new feature pull requests into the MediaPipe repository. Instead, we like to get contributors to create their own repositories of the new feature and list it at [Awesome MediaPipe](https://mediapipe.org). This will allow contributors to more quickly get their code out to the community.
For new feature additions (e.g., new graphs and calculators), we are currently not planning to accept new feature pull requests into the MediaPipe repository. Instead, we like to get contributors to create their own repositories of the new feature and list it at [Awesome MediaPipe](https://mediapipe.page.link/awesome-mediapipe). This will allow contributors to more quickly get their code out to the community.
Before sending your pull requests, make sure you followed this list.
+8 -3
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@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
FROM ubuntu:18.04
FROM ubuntu:20.04
MAINTAINER <[email protected]>
@@ -23,6 +23,7 @@ ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
gcc-8 g++-8 \
ca-certificates \
curl \
ffmpeg \
@@ -41,20 +42,24 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
software-properties-common && \
add-apt-repository -y ppa:openjdk-r/ppa && \
apt-get update && apt-get install -y openjdk-8-jdk && \
apt-get install -y mesa-common-dev libegl1-mesa-dev libgles2-mesa-dev && \
apt-get install -y mesa-utils && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 100 --slave /usr/bin/g++ g++ /usr/bin/g++-8
RUN pip3 install --upgrade setuptools
RUN pip3 install wheel
RUN pip3 install future
RUN pip3 install absl-py numpy opencv-contrib-python protobuf==3.20.1
RUN pip3 install six==1.14.0
RUN pip3 install tensorflow==1.14.0
RUN pip3 install tensorflow==2.2.0
RUN pip3 install tf_slim
RUN ln -s /usr/bin/python3 /usr/bin/python
# Install bazel
ARG BAZEL_VERSION=3.7.2
ARG BAZEL_VERSION=5.2.0
RUN mkdir /bazel && \
wget --no-check-certificate -O /bazel/installer.sh "https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/b\
azel-${BAZEL_VERSION}-installer-linux-x86_64.sh" && \
+1 -7
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@@ -7,10 +7,4 @@ include MANIFEST.in
include README.md
include requirements.txt
recursive-include mediapipe/modules *.tflite *.txt *.binarypb
exclude mediapipe/modules/objectron/object_detection_3d_chair_1stage.tflite
exclude mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite
exclude mediapipe/modules/objectron/object_detection_3d_sneakers.tflite
exclude mediapipe/modules/objectron/object_detection_3d_chair.tflite
exclude mediapipe/modules/objectron/object_detection_3d_camera.tflite
exclude mediapipe/modules/objectron/object_detection_3d_cup.tflite
recursive-include mediapipe/modules *.txt
+30 -46
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@@ -4,7 +4,7 @@ title: Home
nav_order: 1
---
![MediaPipe](docs/images/mediapipe_small.png)
![MediaPipe](https://mediapipe.dev/images/mediapipe_small.png)
--------------------------------------------------------------------------------
@@ -13,21 +13,21 @@ nav_order: 1
[MediaPipe](https://google.github.io/mediapipe/) offers cross-platform, customizable
ML solutions for live and streaming media.
![accelerated.png](docs/images/accelerated_small.png) | ![cross_platform.png](docs/images/cross_platform_small.png)
![accelerated.png](https://mediapipe.dev/images/accelerated_small.png) | ![cross_platform.png](https://mediapipe.dev/images/cross_platform_small.png)
:------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------:
***End-to-End acceleration***: *Built-in fast ML inference and processing accelerated even on common hardware* | ***Build once, deploy anywhere***: *Unified solution works across Android, iOS, desktop/cloud, web and IoT*
![ready_to_use.png](docs/images/ready_to_use_small.png) | ![open_source.png](docs/images/open_source_small.png)
![ready_to_use.png](https://mediapipe.dev/images/ready_to_use_small.png) | ![open_source.png](https://mediapipe.dev/images/open_source_small.png)
***Ready-to-use solutions***: *Cutting-edge ML solutions demonstrating full power of the framework* | ***Free and open source***: *Framework and solutions both under Apache 2.0, fully extensible and customizable*
## ML solutions in MediaPipe
Face Detection | Face Mesh | Iris | Hands | Pose | Holistic
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :------:
[![face_detection](docs/images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](docs/images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![iris](docs/images/mobile/iris_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/iris) | [![hand](docs/images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![pose](docs/images/mobile/pose_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/pose) | [![hair_segmentation](docs/images/mobile/holistic_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/holistic)
[![face_detection](https://mediapipe.dev/images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](https://mediapipe.dev/images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![iris](https://mediapipe.dev/images/mobile/iris_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/iris) | [![hand](https://mediapipe.dev/images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![pose](https://mediapipe.dev/images/mobile/pose_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/pose) | [![hair_segmentation](https://mediapipe.dev/images/mobile/holistic_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/holistic)
Hair Segmentation | Object Detection | Box Tracking | Instant Motion Tracking | Objectron | KNIFT
:-------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
[![hair_segmentation](docs/images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation) | [![object_detection](docs/images/mobile/object_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/object_detection) | [![box_tracking](docs/images/mobile/object_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/box_tracking) | [![instant_motion_tracking](docs/images/mobile/instant_motion_tracking_android_small.gif)](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | [![objectron](docs/images/mobile/objectron_chair_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/objectron) | [![knift](docs/images/mobile/template_matching_android_cpu_small.gif)](https://google.github.io/mediapipe/solutions/knift)
[![hair_segmentation](https://mediapipe.dev/images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation) | [![object_detection](https://mediapipe.dev/images/mobile/object_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/object_detection) | [![box_tracking](https://mediapipe.dev/images/mobile/object_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/box_tracking) | [![instant_motion_tracking](https://mediapipe.dev/images/mobile/instant_motion_tracking_android_small.gif)](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | [![objectron](https://mediapipe.dev/images/mobile/objectron_chair_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/objectron) | [![knift](https://mediapipe.dev/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. -->
@@ -40,11 +40,12 @@ Hair Segmentation
[Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ | ✅ |
[Pose](https://google.github.io/mediapipe/solutions/pose) | ✅ | ✅ | ✅ | ✅ | ✅ |
[Holistic](https://google.github.io/mediapipe/solutions/holistic) | ✅ | ✅ | ✅ | ✅ | ✅ |
[Selfie Segmentation](https://google.github.io/mediapipe/solutions/selfie_segmentation) | ✅ | ✅ | ✅ | ✅ | ✅ |
[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) | ✅ | | ✅ | ✅ | |
[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) | | | ✅ | | |
@@ -54,46 +55,22 @@ See also
[MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
for ML models released in MediaPipe.
## MediaPipe in Python
MediaPipe offers customizable Python solutions as a prebuilt Python package on
[PyPI](https://pypi.org/project/mediapipe/), which can be installed simply with
`pip install mediapipe`. It also provides tools for users to build their own
solutions. Please see
[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python)
for more info.
## MediaPipe on the Web
MediaPipe on the Web is an effort to run the same ML solutions built for mobile
and desktop also in web browsers. The official API is under construction, but
the core technology has been proven effective. Please see
[MediaPipe on the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html)
in Google Developers Blog for details.
You can use the following links to load a demo in the MediaPipe Visualizer, and
over there click the "Runner" icon in the top bar like shown below. The demos
use your webcam video as input, which is processed all locally in real-time and
never leaves your device.
![visualizer_runner](docs/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 Pose](https://viz.mediapipe.dev/demo/pose_tracking)
* [MediaPipe Hair Segmentation](https://viz.mediapipe.dev/demo/hair_segmentation)
## Getting started
Learn how to [install](https://google.github.io/mediapipe/getting_started/install)
MediaPipe and
[build example applications](https://google.github.io/mediapipe/getting_started/building_examples),
and start exploring our ready-to-use
[solutions](https://google.github.io/mediapipe/solutions/solutions) that you can
further extend and customize.
To start using MediaPipe
[solutions](https://google.github.io/mediapipe/solutions/solutions) with only a few
lines code, see example code and demos in
[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python) and
[MediaPipe in JavaScript](https://google.github.io/mediapipe/getting_started/javascript).
To use MediaPipe in C++, Android and iOS, which allow further customization of
the [solutions](https://google.github.io/mediapipe/solutions/solutions) as well as
building your own, learn how to
[install](https://google.github.io/mediapipe/getting_started/install) MediaPipe and
start building example applications in
[C++](https://google.github.io/mediapipe/getting_started/cpp),
[Android](https://google.github.io/mediapipe/getting_started/android) and
[iOS](https://google.github.io/mediapipe/getting_started/ios).
The source code is hosted in the
[MediaPipe Github repository](https://github.com/google/mediapipe), and you can
@@ -102,6 +79,13 @@ run code search using
## Publications
* [Bringing artworks to life with AR](https://developers.googleblog.com/2021/07/bringing-artworks-to-life-with-ar.html)
in Google Developers Blog
* [Prosthesis control via Mirru App using MediaPipe hand tracking](https://developers.googleblog.com/2021/05/control-your-mirru-prosthesis-with-mediapipe-hand-tracking.html)
in Google Developers Blog
* [SignAll SDK: Sign language interface using MediaPipe is now available for
developers](https://developers.googleblog.com/2021/04/signall-sdk-sign-language-interface-using-mediapipe-now-available.html)
in Google Developers Blog
* [MediaPipe Holistic - Simultaneous Face, Hand and Pose Prediction, on Device](https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html)
in Google AI Blog
* [Background Features in Google Meet, Powered by Web ML](https://ai.googleblog.com/2020/10/background-features-in-google-meet.html)
@@ -152,8 +136,8 @@ run code search using
## Community
* [Awesome MediaPipe](https://mediapipe.org) - A curated list of awesome
MediaPipe related frameworks, libraries and software
* [Awesome MediaPipe](https://mediapipe.page.link/awesome-mediapipe) - A
curated list of awesome MediaPipe related frameworks, libraries and software
* [Slack community](https://mediapipe.page.link/joinslack) for MediaPipe users
* [Discuss](https://groups.google.com/forum/#!forum/mediapipe) - General
community discussion around MediaPipe
+157 -55
View File
@@ -2,6 +2,12 @@ workspace(name = "mediapipe")
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# Protobuf expects an //external:python_headers target
bind(
name = "python_headers",
actual = "@local_config_python//:python_headers",
)
http_archive(
name = "bazel_skylib",
type = "tar.gz",
@@ -16,11 +22,11 @@ bazel_skylib_workspace()
load("@bazel_skylib//lib:versions.bzl", "versions")
versions.check(minimum_bazel_version = "3.7.2")
# ABSL cpp library lts_2020_09_23
# ABSL cpp library lts_2021_03_24, patch 2.
http_archive(
name = "com_google_absl",
urls = [
"https://github.com/abseil/abseil-cpp/archive/20200923.tar.gz",
"https://github.com/abseil/abseil-cpp/archive/refs/tags/20210324.2.tar.gz",
],
# Remove after https://github.com/abseil/abseil-cpp/issues/326 is solved.
patches = [
@@ -29,14 +35,15 @@ http_archive(
patch_args = [
"-p1",
],
strip_prefix = "abseil-cpp-20200923",
sha256 = "b3744a4f7a249d5eaf2309daad597631ce77ea62e0fc6abffbab4b4c3dc0fc08"
strip_prefix = "abseil-cpp-20210324.2",
sha256 = "59b862f50e710277f8ede96f083a5bb8d7c9595376146838b9580be90374ee1f"
)
http_archive(
name = "rules_cc",
strip_prefix = "rules_cc-master",
urls = ["https://github.com/bazelbuild/rules_cc/archive/master.zip"],
strip_prefix = "rules_cc-2f8c04c04462ab83c545ab14c0da68c3b4c96191",
# The commit can be updated if the build passes. Last updated 6/23/22.
urls = ["https://github.com/bazelbuild/rules_cc/archive/2f8c04c04462ab83c545ab14c0da68c3b4c96191.zip"],
)
http_archive(
@@ -53,26 +60,20 @@ rules_foreign_cc_dependencies()
all_content = """filegroup(name = "all", srcs = glob(["**"]), visibility = ["//visibility:public"])"""
# GoogleTest/GoogleMock framework. Used by most unit-tests.
# Last updated 2020-06-30.
# Last updated 2021-07-02.
http_archive(
name = "com_google_googletest",
urls = ["https://github.com/google/googletest/archive/aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e.zip"],
patches = [
# fix for https://github.com/google/googletest/issues/2817
"@//third_party:com_google_googletest_9d580ea80592189e6d44fa35bcf9cdea8bf620d6.diff"
],
patch_args = [
"-p1",
],
strip_prefix = "googletest-aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e",
sha256 = "04a1751f94244307cebe695a69cc945f9387a80b0ef1af21394a490697c5c895",
urls = ["https://github.com/google/googletest/archive/4ec4cd23f486bf70efcc5d2caa40f24368f752e3.zip"],
strip_prefix = "googletest-4ec4cd23f486bf70efcc5d2caa40f24368f752e3",
sha256 = "de682ea824bfffba05b4e33b67431c247397d6175962534305136aa06f92e049",
)
# Google Benchmark library.
# Google Benchmark library v1.6.1 released on 2022-01-10.
http_archive(
name = "com_google_benchmark",
urls = ["https://github.com/google/benchmark/archive/master.zip"],
strip_prefix = "benchmark-master",
urls = ["https://github.com/google/benchmark/archive/refs/tags/v1.6.1.tar.gz"],
strip_prefix = "benchmark-1.6.1",
sha256 = "6132883bc8c9b0df5375b16ab520fac1a85dc9e4cf5be59480448ece74b278d4",
build_file = "@//third_party:benchmark.BUILD",
)
@@ -129,16 +130,16 @@ http_archive(
# ...but the Java download is currently broken, so we use the "source" download.
http_archive(
name = "com_google_protobuf_javalite",
sha256 = "a79d19dcdf9139fa4b81206e318e33d245c4c9da1ffed21c87288ed4380426f9",
strip_prefix = "protobuf-3.11.4",
urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.11.4.tar.gz"],
sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
strip_prefix = "protobuf-3.19.1",
urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
)
http_archive(
name = "com_google_protobuf",
sha256 = "a79d19dcdf9139fa4b81206e318e33d245c4c9da1ffed21c87288ed4380426f9",
strip_prefix = "protobuf-3.11.4",
urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.11.4.tar.gz"],
sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
strip_prefix = "protobuf-3.19.1",
urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
patches = [
"@//third_party:com_google_protobuf_fixes.diff"
],
@@ -147,12 +148,50 @@ http_archive(
],
)
load("//third_party/flatbuffers:workspace.bzl", flatbuffers = "repo")
flatbuffers()
http_archive(
name = "com_google_audio_tools",
strip_prefix = "multichannel-audio-tools-master",
urls = ["https://github.com/google/multichannel-audio-tools/archive/master.zip"],
)
# sentencepiece
http_archive(
name = "com_google_sentencepiece",
strip_prefix = "sentencepiece-1.0.0",
sha256 = "c05901f30a1d0ed64cbcf40eba08e48894e1b0e985777217b7c9036cac631346",
urls = [
"https://github.com/google/sentencepiece/archive/1.0.0.zip",
],
repo_mapping = {"@com_google_glog" : "@com_github_glog_glog"},
)
http_archive(
name = "org_tensorflow_text",
sha256 = "f64647276f7288d1b1fe4c89581d51404d0ce4ae97f2bcc4c19bd667549adca8",
strip_prefix = "text-2.2.0",
urls = [
"https://github.com/tensorflow/text/archive/v2.2.0.zip",
],
patches = [
"//third_party:tensorflow_text_remove_tf_deps.diff",
"//third_party:tensorflow_text_a0f49e63.diff",
],
patch_args = ["-p1"],
repo_mapping = {"@com_google_re2": "@com_googlesource_code_re2"},
)
http_archive(
name = "com_googlesource_code_re2",
sha256 = "e06b718c129f4019d6e7aa8b7631bee38d3d450dd980246bfaf493eb7db67868",
strip_prefix = "re2-fe4a310131c37f9a7e7f7816fa6ce2a8b27d65a8",
urls = [
"https://github.com/google/re2/archive/fe4a310131c37f9a7e7f7816fa6ce2a8b27d65a8.tar.gz",
],
)
# 2020-07-09
http_archive(
name = "pybind11_bazel",
@@ -161,28 +200,38 @@ http_archive(
sha256 = "75922da3a1bdb417d820398eb03d4e9bd067c4905a4246d35a44c01d62154d91",
)
# Point to the commit that deprecates the usage of Eigen::MappedSparseMatrix.
http_archive(
name = "pybind11",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/pybind/pybind11/archive/v2.4.3.tar.gz",
"https://github.com/pybind/pybind11/archive/v2.4.3.tar.gz",
"https://github.com/pybind/pybind11/archive/70a58c577eaf067748c2ec31bfd0b0a614cffba6.zip",
],
sha256 = "1eed57bc6863190e35637290f97a20c81cfe4d9090ac0a24f3bbf08f265eb71d",
strip_prefix = "pybind11-2.4.3",
sha256 = "b971842fab1b5b8f3815a2302331782b7d137fef0e06502422bc4bc360f4956c",
strip_prefix = "pybind11-70a58c577eaf067748c2ec31bfd0b0a614cffba6",
build_file = "@pybind11_bazel//:pybind11.BUILD",
)
http_archive(
name = "pybind11_protobuf",
sha256 = "baa1f53568283630a5055c85f0898b8810f7a6431bd01bbaedd32b4c1defbcb1",
strip_prefix = "pybind11_protobuf-3594106f2df3d725e65015ffb4c7886d6eeee683",
urls = [
"https://github.com/pybind/pybind11_protobuf/archive/3594106f2df3d725e65015ffb4c7886d6eeee683.tar.gz",
],
)
# Point to the commit that deprecates the usage of Eigen::MappedSparseMatrix.
http_archive(
name = "ceres_solver",
url = "https://github.com/ceres-solver/ceres-solver/archive/2.0.0.zip",
url = "https://github.com/ceres-solver/ceres-solver/archive/123fba61cf2611a3c8bddc9d91416db26b10b558.zip",
patches = [
"@//third_party:ceres_solver_compatibility_fixes.diff"
],
patch_args = [
"-p1",
],
strip_prefix = "ceres-solver-2.0.0",
sha256 = "db12d37b4cebb26353ae5b7746c7985e00877baa8e7b12dc4d3a1512252fff3b"
strip_prefix = "ceres-solver-123fba61cf2611a3c8bddc9d91416db26b10b558",
sha256 = "8b7b16ceb363420e0fd499576daf73fa338adb0b1449f58bea7862766baa1ac7"
)
http_archive(
@@ -207,7 +256,10 @@ new_local_repository(
new_local_repository(
name = "macos_opencv",
build_file = "@//third_party:opencv_macos.BUILD",
path = "/usr/local/opt/opencv@3",
# For local MacOS builds, the path should point to an opencv@3 installation.
# If you edit the path here, you will also need to update the corresponding
# prefix in "opencv_macos.BUILD".
path = "/usr/local",
)
new_local_repository(
@@ -242,21 +294,26 @@ http_archive(
url = "https://github.com/opencv/opencv/releases/download/3.2.0/opencv-3.2.0-ios-framework.zip",
)
# You may run setup_android.sh to install Android SDK and NDK.
android_ndk_repository(
name = "androidndk",
)
android_sdk_repository(
name = "androidsdk",
http_archive(
name = "stblib",
strip_prefix = "stb-b42009b3b9d4ca35bc703f5310eedc74f584be58",
sha256 = "13a99ad430e930907f5611325ec384168a958bf7610e63e60e2fd8e7b7379610",
urls = ["https://github.com/nothings/stb/archive/b42009b3b9d4ca35bc703f5310eedc74f584be58.tar.gz"],
build_file = "@//third_party:stblib.BUILD",
patches = [
"@//third_party:stb_image_impl.diff"
],
patch_args = [
"-p1",
],
)
# iOS basic build deps.
http_archive(
name = "build_bazel_rules_apple",
sha256 = "7a7afdd4869bb201c9352eed2daf37294d42b093579b70423490c1b4d4f6ce42",
url = "https://github.com/bazelbuild/rules_apple/releases/download/0.19.0/rules_apple.0.19.0.tar.gz",
sha256 = "77e8bf6fda706f420a55874ae6ee4df0c9d95da6c7838228b26910fc82eea5a2",
url = "https://github.com/bazelbuild/rules_apple/releases/download/0.32.0/rules_apple.0.32.0.tar.gz",
patches = [
# Bypass checking ios unit test runner when building MP ios applications.
"@//third_party:build_bazel_rules_apple_bypass_test_runner_check.diff"
@@ -282,10 +339,9 @@ swift_rules_dependencies()
http_archive(
name = "build_bazel_apple_support",
sha256 = "122ebf7fe7d1c8e938af6aeaee0efe788a3a2449ece5a8d6a428cb18d6f88033",
sha256 = "741366f79d900c11e11d8efd6cc6c66a31bfb2451178b58e0b5edc6f1db17b35",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/apple_support/releases/download/0.7.1/apple_support.0.7.1.tar.gz",
"https://github.com/bazelbuild/apple_support/releases/download/0.7.1/apple_support.0.7.1.tar.gz",
"https://github.com/bazelbuild/apple_support/releases/download/0.10.0/apple_support.0.10.0.tar.gz"
],
)
@@ -324,7 +380,10 @@ load("@rules_jvm_external//:defs.bzl", "maven_install")
maven_install(
artifacts = [
"androidx.concurrent:concurrent-futures:1.0.0-alpha03",
"androidx.lifecycle:lifecycle-common:2.2.0",
"androidx.lifecycle:lifecycle-common:2.3.1",
"androidx.activity:activity:1.2.2",
"androidx.exifinterface:exifinterface:1.3.3",
"androidx.fragment:fragment:1.3.4",
"androidx.annotation:annotation:aar:1.1.0",
"androidx.appcompat:appcompat:aar:1.1.0-rc01",
"androidx.camera:camera-core:1.0.0-beta10",
@@ -337,9 +396,14 @@ maven_install(
"androidx.test.espresso:espresso-core:3.1.1",
"com.github.bumptech.glide:glide:4.11.0",
"com.google.android.material:material:aar:1.0.0-rc01",
"com.google.code.findbugs:jsr305:3.0.2",
"com.google.flogger:flogger-system-backend:0.3.1",
"com.google.flogger:flogger:0.3.1",
"com.google.auto.value:auto-value:1.8.1",
"com.google.auto.value:auto-value-annotations:1.8.1",
"com.google.code.findbugs:jsr305:latest.release",
"com.google.android.datatransport:transport-api:3.0.0",
"com.google.android.datatransport:transport-backend-cct:3.1.0",
"com.google.android.datatransport:transport-runtime:3.1.0",
"com.google.flogger:flogger-system-backend:0.6",
"com.google.flogger:flogger:0.6",
"com.google.guava:guava:27.0.1-android",
"com.google.guava:listenablefuture:1.0",
"junit:junit:4.12",
@@ -366,10 +430,29 @@ http_archive(
],
)
# Tensorflow repo should always go after the other external dependencies.
# 2021-03-25
_TENSORFLOW_GIT_COMMIT = "c67f68021824410ebe9f18513b8856ac1c6d4887"
_TENSORFLOW_SHA256= "fd07d0b39422dc435e268c5e53b2646a8b4b1e3151b87837b43f86068faae87f"
# Load Zlib before initializing TensorFlow to guarantee that the target
# @zlib//:mini_zlib is available
http_archive(
name = "zlib",
build_file = "//third_party:zlib.BUILD",
sha256 = "c3e5e9fdd5004dcb542feda5ee4f0ff0744628baf8ed2dd5d66f8ca1197cb1a1",
strip_prefix = "zlib-1.2.11",
urls = [
"http://mirror.bazel.build/zlib.net/fossils/zlib-1.2.11.tar.gz",
"http://zlib.net/fossils/zlib-1.2.11.tar.gz", # 2017-01-15
],
patches = [
"@//third_party:zlib.diff",
],
patch_args = [
"-p1",
],
)
# TensorFlow repo should always go after the other external dependencies.
# TF on 2022-08-10.
_TENSORFLOW_GIT_COMMIT = "af1d5bc4fbb66d9e6cc1cf89503014a99233583b"
_TENSORFLOW_SHA256 = "f85a5443264fc58a12d136ca6a30774b5bc25ceaf7d114d97f252351b3c3a2cb"
http_archive(
name = "org_tensorflow",
urls = [
@@ -377,7 +460,8 @@ http_archive(
],
patches = [
"@//third_party:org_tensorflow_compatibility_fixes.diff",
"@//third_party:org_tensorflow_objc_cxx17.diff",
# Diff is generated with a script, don't update it manually.
"@//third_party:org_tensorflow_custom_ops.diff",
],
patch_args = [
"-p1",
@@ -390,3 +474,21 @@ load("@org_tensorflow//tensorflow:workspace3.bzl", "tf_workspace3")
tf_workspace3()
load("@org_tensorflow//tensorflow:workspace2.bzl", "tf_workspace2")
tf_workspace2()
# Edge TPU
http_archive(
name = "libedgetpu",
sha256 = "14d5527a943a25bc648c28a9961f954f70ba4d79c0a9ca5ae226e1831d72fe80",
strip_prefix = "libedgetpu-3164995622300286ef2bb14d7fdc2792dae045b7",
urls = [
"https://github.com/google-coral/libedgetpu/archive/3164995622300286ef2bb14d7fdc2792dae045b7.tar.gz"
],
)
load("@libedgetpu//:workspace.bzl", "libedgetpu_dependencies")
libedgetpu_dependencies()
load("@coral_crosstool//:configure.bzl", "cc_crosstool")
cc_crosstool(name = "crosstool")
load("//third_party:external_files.bzl", "external_files")
external_files()
+2 -2
View File
@@ -109,7 +109,7 @@ for app in ${apps}; do
if [[ ${category} != "shoe" ]]; then
bazel_flags_extended+=(--define ${category}=true)
fi
bazel "${bazel_flags_extended[@]}"
bazelisk "${bazel_flags_extended[@]}"
cp -f "${bin}" "${apk}"
fi
apks+=(${apk})
@@ -120,7 +120,7 @@ for app in ${apps}; do
if [[ ${app_name} == "templatematchingcpu" ]]; then
switch_to_opencv_4
fi
bazel "${bazel_flags[@]}"
bazelisk "${bazel_flags[@]}"
cp -f "${bin}" "${apk}"
if [[ ${app_name} == "templatematchingcpu" ]]; then
switch_to_opencv_3
+5 -4
View File
@@ -17,15 +17,15 @@
# Script to build/run all MediaPipe desktop example apps (with webcam input).
#
# To build and run all apps and store them in out_dir:
# $ ./build_ios_examples.sh -d out_dir
# $ ./build_desktop_examples.sh -d out_dir
# Omitting -d and the associated directory saves all generated apps in the
# current directory.
# To build all apps and store them in out_dir:
# $ ./build_ios_examples.sh -d out_dir -b
# $ ./build_desktop_examples.sh -d out_dir -b
# Omitting -d and the associated directory saves all generated apps in the
# current directory.
# To run all apps already stored in out_dir:
# $ ./build_ios_examples.sh -d out_dir -r
# $ ./build_desktop_examples.sh -d out_dir -r
# Omitting -d and the associated directory assumes all apps are in the current
# directory.
@@ -83,7 +83,7 @@ for app in ${apps}; do
bazel_flags=("${default_bazel_flags[@]}")
bazel_flags+=(${target})
bazel "${bazel_flags[@]}"
bazelisk "${bazel_flags[@]}"
cp -f "${bin_dir}/${app}/"*"_cpu" "${out_dir}"
fi
if [[ $build_only == false ]]; then
@@ -97,6 +97,7 @@ for app in ${apps}; do
if [[ ${target_name} == "holistic_tracking" ||
${target_name} == "iris_tracking" ||
${target_name} == "pose_tracking" ||
${target_name} == "selfie_segmentation" ||
${target_name} == "upper_body_pose_tracking" ]]; then
graph_suffix="cpu"
else
+1 -1
View File
@@ -71,7 +71,7 @@ for app in ${apps}; do
bazel_flags+=(--linkopt=-s)
fi
bazel "${bazel_flags[@]}"
bazelisk "${bazel_flags[@]}"
cp -f "${bin_dir}/${app}/"*".ipa" "${out_dir}"
fi
done
+1 -1
View File
@@ -20,7 +20,7 @@ aux_links:
- "//github.com/google/mediapipe"
# 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>"
footer_content: "&copy; 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", "light"/nil (default), or a custom scheme that you define
color_scheme: mediapipe
+68 -11
View File
@@ -133,7 +133,7 @@ write outputs. After Close returns, the calculator is destroyed.
Calculators with no inputs are referred to as sources. A source calculator
continues to have `Process()` called as long as it returns an `Ok` status. A
source calculator indicates that it is exhausted by returning a stop status
(i.e. MediaPipe::tool::StatusStop).
(i.e. [`mediaPipe::tool::StatusStop()`](https://github.com/google/mediapipe/tree/master/mediapipe/framework/tool/status_util.cc).).
## Identifying inputs and outputs
@@ -187,7 +187,7 @@ node {
```
In the calculator implementation, inputs and outputs are also identified by tag
name and index number. In the function below input are output are identified:
name and index number. In the function below input and output are identified:
* By index number: The combined input stream is identified simply by index
`0`.
@@ -248,12 +248,70 @@ absl::Status MyCalculator::Process() {
}
```
## Calculator options
Calculators accept processing parameters through (1) input stream packets (2)
input side packets, and (3) calculator options. Calculator options, if
specified, appear as literal values in the `node_options` field of the
`CalculatorGraphConfiguration.Node` message.
```
node {
calculator: "TfLiteInferenceCalculator"
input_stream: "TENSORS:main_model_input"
output_stream: "TENSORS:main_model_output"
node_options: {
[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
model_path: "mediapipe/models/detection_model.tflite"
}
}
}
```
The `node_options` field accepts the proto3 syntax. Alternatively, calculator
options can be specified in the `options` field using proto2 syntax.
```
node {
calculator: "TfLiteInferenceCalculator"
input_stream: "TENSORS:main_model_input"
output_stream: "TENSORS:main_model_output"
node_options: {
[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
model_path: "mediapipe/models/detection_model.tflite"
}
}
}
```
Not all calculators accept calcuator options. In order to accept options, a
calculator will normally define a new protobuf message type to represent its
options, such as `PacketClonerCalculatorOptions`. The calculator will then
read that protobuf message in its `CalculatorBase::Open` method, and possibly
also in its `CalculatorBase::GetContract` function or its
`CalculatorBase::Process` method. Normally, the new protobuf message type will
be defined as a protobuf schema using a ".proto" file and a
`mediapipe_proto_library()` build rule.
```
mediapipe_proto_library(
name = "packet_cloner_calculator_proto",
srcs = ["packet_cloner_calculator.proto"],
visibility = ["//visibility:public"],
deps = [
"//mediapipe/framework:calculator_options_proto",
"//mediapipe/framework:calculator_proto",
],
)
```
## Example calculator
This section discusses the implementation of `PacketClonerCalculator`, which
does a relatively simple job, and is used in many calculator graphs.
`PacketClonerCalculator` simply produces a copy of its most recent input
packets on demand.
`PacketClonerCalculator` simply produces a copy of its most recent input packets
on demand.
`PacketClonerCalculator` is useful when the timestamps of arriving data packets
are not aligned perfectly. Suppose we have a room with a microphone, light
@@ -279,8 +337,8 @@ input streams:
imageframe of video data representing video collected from camera in the
room with timestamp.
Below is the implementation of the `PacketClonerCalculator`. You can see
the `GetContract()`, `Open()`, and `Process()` methods as well as the instance
Below is the implementation of the `PacketClonerCalculator`. You can see the
`GetContract()`, `Open()`, and `Process()` methods as well as the instance
variable `current_` which holds the most recent input packets.
```c++
@@ -355,7 +413,6 @@ class PacketClonerCalculator : public CalculatorBase {
current_[i].At(cc->InputTimestamp()));
// Add a packet to output stream of index i a packet from inputstream i
// with timestamp common to all present inputs
//
} else {
cc->Outputs().Index(i).SetNextTimestampBound(
cc->InputTimestamp().NextAllowedInStream());
@@ -382,7 +439,7 @@ defined your calculator class, register it with a macro invocation
REGISTER_CALCULATOR(calculator_class_name).
Below is a trivial MediaPipe graph that has 3 input streams, 1 node
(PacketClonerCalculator) and 3 output streams.
(PacketClonerCalculator) and 2 output streams.
```proto
input_stream: "room_mic_signal"
@@ -402,6 +459,6 @@ node {
The diagram below shows how the `PacketClonerCalculator` defines its output
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.* |
![Graph using PacketClonerCalculator](https://mediapipe.dev/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.* |
@@ -110,3 +110,12 @@ Other policies are also available, implemented using a separate kind of
component known as an InputStreamHandler.
See [Synchronization](synchronization.md) for more details.
### Real-time streams
MediaPipe calculator graphs are often used to process streams of video or audio
frames for interactive applications. Normally, each Calculator runs as soon as
all of its input packets for a given timestamp become available. Calculators
used in real-time graphs need to define output timestamp bounds based on input
timestamp bounds in order to allow downstream calculators to be scheduled
promptly. See [Real-time Streams](realtime_streams.md) for details.
+1 -1
View File
@@ -149,7 +149,7 @@ When possible, these calculators use platform-specific functionality to share da
The below diagram shows the data flow in a mobile application that captures video from the camera, runs it through a MediaPipe graph, and renders the output on the screen in real time. The dashed line indicates which parts are inside the MediaPipe graph proper. This application runs a Canny edge-detection filter on the CPU using OpenCV, and overlays it on top of the original video using the GPU.
![How GPU calculators interact](../images/gpu_example_graph.png)
![How GPU calculators interact](https://mediapipe.dev/images/gpu_example_graph.png)
Video frames from the camera are fed into the graph as `GpuBuffer` packets. The
input stream is accessed by two calculators in parallel.
+3 -3
View File
@@ -83,12 +83,12 @@ Below is an example of how to create a subgraph named `TwoPassThroughSubgraph`.
output_stream: "out3"
node {
calculator: "PassThroughculator"
calculator: "PassThroughCalculator"
input_stream: "out1"
output_stream: "out2"
}
node {
calculator: "PassThroughculator"
calculator: "PassThroughCalculator"
input_stream: "out2"
output_stream: "out3"
}
@@ -159,7 +159,7 @@ Please use the `CalculatorGraphTest.Cycle` unit test in
below is the cyclic graph in the test. The `sum` output of the adder is the sum
of the integers generated by the integer source calculator.
![a cyclic graph that adds a stream of integers](../images/cyclic_integer_sum_graph.svg "A cyclic graph")
![a cyclic graph that adds a stream of integers](https://mediapipe.dev/images/cyclic_integer_sum_graph.svg "A cyclic graph")
This simple graph illustrates all the issues in supporting cyclic graphs.
+186
View File
@@ -0,0 +1,186 @@
---
layout: default
title: Real-time Streams
parent: Framework Concepts
nav_order: 6
---
# Real-time Streams
{: .no_toc }
1. TOC
{:toc}
---
## Real-time timestamps
MediaPipe calculator graphs are often used to process streams of video or audio
frames for interactive applications. The MediaPipe framework requires only that
successive packets be assigned monotonically increasing timestamps. By
convention, real-time calculators and graphs use the recording time or the
presentation time of each frame as its timestamp, with each timestamp indicating
the microseconds since `Jan/1/1970:00:00:00`. This allows packets from various
sources to be processed in a globally consistent sequence.
## Real-time scheduling
Normally, each Calculator runs as soon as all of its input packets for a given
timestamp become available. Normally, this happens when the calculator has
finished processing the previous frame, and each of the calculators producing
its inputs have finished processing the current frame. The MediaPipe scheduler
invokes each calculator as soon as these conditions are met. See
[Synchronization](synchronization.md) for more details.
## Timestamp bounds
When a calculator does not produce any output packets for a given timestamp, it
can instead output a "timestamp bound" indicating that no packet will be
produced for that timestamp. This indication is necessary to allow downstream
calculators to run at that timestamp, even though no packet has arrived for
certain streams for that timestamp. This is especially important for real-time
graphs in interactive applications, where it is crucial that each calculator
begin processing as soon as possible.
Consider a graph like the following:
```
node {
calculator: "A"
input_stream: "alpha_in"
output_stream: "alpha"
}
node {
calculator: "B"
input_stream: "alpha"
input_stream: "foo"
output_stream: "beta"
}
```
Suppose: at timestamp `T`, node `A` doesn't send a packet in its output stream
`alpha`. Node `B` gets a packet in `foo` at timestamp `T` and is waiting for a
packet in `alpha` at timestamp `T`. If `A` doesn't send `B` a timestamp bound
update for `alpha`, `B` will keep waiting for a packet to arrive in `alpha`.
Meanwhile, the packet queue of `foo` will accumulate packets at `T`, `T+1` and
so on.
To output a packet on a stream, a calculator uses the API functions
`CalculatorContext::Outputs` and `OutputStream::Add`. To instead output a
timestamp bound on a stream, a calculator can use the API functions
`CalculatorContext::Outputs` and `CalculatorContext::SetNextTimestampBound`. The
specified bound is the lowest allowable timestamp for the next packet on the
specified output stream. When no packet is output, a calculator will typically
do something like:
```
cc->Outputs().Tag("output_frame").SetNextTimestampBound(
cc->InputTimestamp().NextAllowedInStream());
```
The function `Timestamp::NextAllowedInStream` returns the successive timestamp.
For example, `Timestamp(1).NextAllowedInStream() == Timestamp(2)`.
## Propagating timestamp bounds
Calculators that will be used in real-time graphs need to define output
timestamp bounds based on input timestamp bounds in order to allow downstream
calculators to be scheduled promptly. A common pattern is for calculators to
output packets with the same timestamps as their input packets. In this case,
simply outputting a packet on every call to `Calculator::Process` is sufficient
to define output timestamp bounds.
However, calculators are not required to follow this common pattern for output
timestamps, they are only required to choose monotonically increasing output
timestamps. As a result, certain calculators must calculate timestamp bounds
explicitly. MediaPipe provides several tools for computing appropriate timestamp
bound for each calculator.
1\. **SetNextTimestampBound()** can be used to specify the timestamp bound, `t +
1`, for an output stream.
```
cc->Outputs.Tag("OUT").SetNextTimestampBound(t.NextAllowedInStream());
```
Alternatively, an empty packet with timestamp `t` can be produced to specify the
timestamp bound `t + 1`.
```
cc->Outputs.Tag("OUT").Add(Packet(), t);
```
The timestamp bound of an input stream is indicated by the packet or the empty
packet on the input stream.
```
Timestamp bound = cc->Inputs().Tag("IN").Value().Timestamp();
```
2\. **TimestampOffset()** can be specified in order to automatically copy the
timestamp bound from input streams to output streams.
```
cc->SetTimestampOffset(0);
```
This setting has the advantage of propagating timestamp bounds automatically,
even when only timestamp bounds arrive and Calculator::Process is not invoked.
3\. **ProcessTimestampBounds()** can be specified in order to invoke
`Calculator::Process` for each new "settled timestamp", where the "settled
timestamp" is the new highest timestamp below the current timestamp bounds.
Without `ProcessTimestampBounds()`, `Calculator::Process` is invoked only with
one or more arriving packets.
```
cc->SetProcessTimestampBounds(true);
```
This setting allows a calculator to perform its own timestamp bounds calculation
and propagation, even when only input timestamps are updated. It can be used to
replicate the effect of `TimestampOffset()`, but it can also be used to
calculate a timestamp bound that takes into account additional factors.
For example, in order to replicate `SetTimestampOffset(0)`, a calculator could
do the following:
```
absl::Status Open(CalculatorContext* cc) {
cc->SetProcessTimestampBounds(true);
}
absl::Status Process(CalculatorContext* cc) {
cc->Outputs.Tag("OUT").SetNextTimestampBound(
cc->InputTimestamp().NextAllowedInStream());
}
```
## Scheduling of Calculator::Open and Calculator::Close
`Calculator::Open` is invoked when all required input side-packets have been
produced. Input side-packets can be provided by the enclosing application or by
"side-packet calculators" inside the graph. Side-packets can be specified from
outside the graph using the API's `CalculatorGraph::Initialize` and
`CalculatorGraph::StartRun`. Side packets can be specified by calculators within
the graph using `CalculatorGraphConfig::OutputSidePackets` and
`OutputSidePacket::Set`.
Calculator::Close is invoked when all of the input streams have become `Done` by
being closed or reaching timestamp bound `Timestamp::Done`.
**Note:** If the graph finishes all pending calculator execution and becomes
`Done`, before some streams become `Done`, then MediaPipe will invoke the
remaining calls to `Calculator::Close`, so that every calculator can produce its
final outputs.
The use of `TimestampOffset` has some implications for `Calculator::Close`. A
calculator specifying `SetTimestampOffset(0)` will by design signal that all of
its output streams have reached `Timestamp::Done` when all of its input streams
have reached `Timestamp::Done`, and therefore no further outputs are possible.
This prevents such a calculator from emitting any packets during
`Calculator::Close`. If a calculator needs to produce a summary packet during
`Calculator::Close`, `Calculator::Process` must specify timestamp bounds such
that at least one timestamp (such as `Timestamp::Max`) remains available during
`Calculator::Close`. This means that such a calculator normally cannot rely upon
`SetTimestampOffset(0)` and must instead specify timestamp bounds explicitly
using `SetNextTimestampBounds()`.
+2 -2
View File
@@ -169,7 +169,7 @@ behavior depending on resource constraints.
[`CalculatorBase`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator_base.h
[`DefaultInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/default_input_stream_handler.h
[`SyncSetInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/sync_set_input_stream_handler.h
[`ImmediateInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/immediate_input_stream_handler.h
[`SyncSetInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/sync_set_input_stream_handler.cc
[`ImmediateInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/immediate_input_stream_handler.cc
[`CalculatorGraphConfig::max_queue_size`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator.proto
[`FlowLimiterCalculator`]: https://github.com/google/mediapipe/tree/master/mediapipe/calculators/core/flow_limiter_calculator.cc
+17 -121
View File
@@ -16,18 +16,20 @@ nav_order: 1
Please follow instructions below to build Android example apps in the supported
MediaPipe [solutions](../solutions/solutions.md). To learn more about these
example apps, start from [Hello World! on Android](./hello_world_android.md). To
incorporate MediaPipe into an existing Android Studio project, see these
[instructions](./android_archive_library.md) that use Android Archive (AAR) and
Gradle.
example apps, start from [Hello World! on Android](./hello_world_android.md).
## Building Android example apps
To incorporate MediaPipe into Android Studio projects, see these
[instructions](./android_solutions.md) to use the MediaPipe Android Solution
APIs (currently in alpha) that are now available in
[Google's Maven Repository](https://maven.google.com/web/index.html?#com.google.mediapipe).
## Building Android example apps with Bazel
### Prerequisite
* Install MediaPipe following these [instructions](./install.md).
* Setup Java Runtime.
* Setup Android SDK release 28.0.3 and above.
* Setup Android SDK release 30.0.0 and above.
* Setup Android NDK version between 18 and 21.
MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see
@@ -45,22 +47,21 @@ export ANDROID_HOME=<path to the Android SDK>
export ANDROID_NDK_HOME=<path to the Android NDK>
```
and add android_ndk_repository() and android_sdk_repository() rules into the
[`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE) file as
the following:
```bash
$ echo "android_sdk_repository(name = \"androidsdk\")" >> WORKSPACE
$ echo "android_ndk_repository(name = \"androidndk\", api_level=21)" >> WORKSPACE
```
In order to use MediaPipe on earlier Android versions, MediaPipe needs to switch
to a lower Android API level. You can achieve this by specifying `api_level =
$YOUR_INTENDED_API_LEVEL` in android_ndk_repository() and/or
android_sdk_repository() in the
[`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE) file.
Please verify all the necessary packages are installed.
* Android SDK Platform API Level 28 or 29
* Android SDK Build-Tools 28 or 29
* Android SDK Platform-Tools 28 or 29
* Android SDK Tools 26.1.1
* Android NDK 17c or above
### Option 1: Build with Bazel in Command Line
Tip: You can run this
[script](https://github.com/google/mediapipe/blob/master/build_android_examples.sh)
to build (and install) all MediaPipe Android example apps.
@@ -84,108 +85,3 @@ to build (and install) all MediaPipe Android example apps.
```bash
adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/handtrackinggpu.apk
```
### Option 2: Build with Bazel in Android Studio
The MediaPipe project can be imported into Android Studio using the Bazel
plugins. This allows the MediaPipe examples to be built and modified in Android
Studio.
To incorporate MediaPipe into an existing Android Studio project, see these
[instructions](./android_archive_library.md) that use Android Archive (AAR) and
Gradle.
The steps below use Android Studio 3.5 to build and install a MediaPipe example
app:
1. Install and launch Android Studio 3.5.
2. Select `Configure` -> `SDK Manager` -> `SDK Platforms`.
* Verify that Android SDK Platform API Level 28 or 29 is installed.
* Take note of the Android SDK Location, e.g.,
`/usr/local/home/Android/Sdk`.
3. Select `Configure` -> `SDK Manager` -> `SDK Tools`.
* Verify that Android SDK Build-Tools 28 or 29 is installed.
* Verify that Android SDK Platform-Tools 28 or 29 is installed.
* Verify that Android SDK Tools 26.1.1 is installed.
* Verify that Android NDK 17c or above is installed.
* Take note of the Android NDK Location, e.g.,
`/usr/local/home/Android/Sdk/ndk-bundle` or
`/usr/local/home/Android/Sdk/ndk/20.0.5594570`.
4. Set environment variables `$ANDROID_HOME` and `$ANDROID_NDK_HOME` to point
to the installed SDK and NDK.
```bash
export ANDROID_HOME=/usr/local/home/Android/Sdk
# If the NDK libraries are installed by a previous version of Android Studio, do
export ANDROID_NDK_HOME=/usr/local/home/Android/Sdk/ndk-bundle
# If the NDK libraries are installed by Android Studio 3.5, do
export ANDROID_NDK_HOME=/usr/local/home/Android/Sdk/ndk/<version number>
```
5. Select `Configure` -> `Plugins` to install `Bazel`.
6. On Linux, select `File` -> `Settings` -> `Bazel settings`. On macos, select
`Android Studio` -> `Preferences` -> `Bazel settings`. Then, modify `Bazel
binary location` to be the same as the output of `$ which bazel`.
7. Select `Import Bazel Project`.
* Select `Workspace`: `/path/to/mediapipe` and select `Next`.
* Select `Generate from BUILD file`: `/path/to/mediapipe/BUILD` and select
`Next`.
* Modify `Project View` to be the following and select `Finish`.
```
directories:
# read project settings, e.g., .bazelrc
.
-mediapipe/objc
-mediapipe/examples/ios
targets:
//mediapipe/examples/android/...:all
//mediapipe/java/...:all
android_sdk_platform: android-29
sync_flags:
--host_crosstool_top=@bazel_tools//tools/cpp:toolchain
```
8. Select `Bazel` -> `Sync` -> `Sync project with Build files`.
Note: Even after doing step 4, if you still see the error: `"no such package
'@androidsdk//': Either the path attribute of android_sdk_repository or the
ANDROID_HOME environment variable must be set."`, please modify the
[`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE)
file to point to your SDK and NDK library locations, as below:
```
android_sdk_repository(
name = "androidsdk",
path = "/path/to/android/sdk"
)
android_ndk_repository(
name = "androidndk",
path = "/path/to/android/ndk"
)
```
9. Connect an Android device to the workstation.
10. Select `Run...` -> `Edit Configurations...`.
* Select `Templates` -> `Bazel Command`.
* Enter Target Expression:
`//mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu:handtrackinggpu`
* Enter Bazel command: `mobile-install`.
* Enter Bazel flags: `-c opt --config=android_arm64`.
* Press the `[+]` button to add the new configuration.
* Select `Run` to run the example app on the connected Android device.
+51 -40
View File
@@ -3,7 +3,7 @@ layout: default
title: MediaPipe Android Archive
parent: MediaPipe on Android
grand_parent: Getting Started
nav_order: 2
nav_order: 3
---
# MediaPipe Android Archive
@@ -37,7 +37,7 @@ each project.
load("//mediapipe/java/com/google/mediapipe:mediapipe_aar.bzl", "mediapipe_aar")
mediapipe_aar(
name = "mp_face_detection_aar",
name = "mediapipe_face_detection",
calculators = ["//mediapipe/graphs/face_detection:mobile_calculators"],
)
```
@@ -45,26 +45,49 @@ each project.
2. Run the Bazel build command to generate the AAR.
```bash
bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
--fat_apk_cpu=arm64-v8a,armeabi-v7a --strip=ALWAYS \
//path/to/the/aar/build/file:aar_name
bazel build -c opt --strip=ALWAYS \
--host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
--fat_apk_cpu=arm64-v8a,armeabi-v7a \
--legacy_whole_archive=0 \
--features=-legacy_whole_archive \
--copt=-fvisibility=hidden \
--copt=-ffunction-sections \
--copt=-fdata-sections \
--copt=-fstack-protector \
--copt=-Oz \
--copt=-fomit-frame-pointer \
--copt=-DABSL_MIN_LOG_LEVEL=2 \
--linkopt=-Wl,--gc-sections,--strip-all \
//path/to/the/aar/build/file:aar_name.aar
```
For the face detection AAR target we made in the step 1, run:
For the face detection AAR target we made in step 1, run:
```bash
bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --fat_apk_cpu=arm64-v8a,armeabi-v7a \
//mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar
bazel build -c opt --strip=ALWAYS \
--host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
--fat_apk_cpu=arm64-v8a,armeabi-v7a \
--legacy_whole_archive=0 \
--features=-legacy_whole_archive \
--copt=-fvisibility=hidden \
--copt=-ffunction-sections \
--copt=-fdata-sections \
--copt=-fstack-protector \
--copt=-Oz \
--copt=-fomit-frame-pointer \
--copt=-DABSL_MIN_LOG_LEVEL=2 \
--linkopt=-Wl,--gc-sections,--strip-all \
//mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar
# It should print:
# Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar up-to-date:
# bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
# Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar up-to-date:
# bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
```
3. (Optional) Save the AAR to your preferred location.
```bash
cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
/absolute/path/to/your/preferred/location
```
@@ -75,11 +98,11 @@ each project.
2. Copy the AAR into app/libs.
```bash
cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
/path/to/your/app/libs/
```
![Screenshot](../images/mobile/aar_location.png)
![Screenshot](https://mediapipe.dev/images/mobile/aar_location.png)
3. Make app/src/main/assets and copy assets (graph, model, and etc) into
app/src/main/assets.
@@ -89,32 +112,17 @@ each project.
and copy
[the binary graph](https://github.com/google/mediapipe/blob/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/BUILD#L41)
and
[the face detection tflite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front.tflite).
[the face detection tflite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range.tflite).
```bash
bazel build -c opt mediapipe/mediapipe/graphs/face_detection:mobile_gpu_binary_graph
cp bazel-bin/mediapipe/graphs/face_detection/mobile_gpu.binarypb /path/to/your/app/src/main/assets/
cp mediapipe/modules/face_detection/face_detection_front.tflite /path/to/your/app/src/main/assets/
bazel build -c opt mediapipe/graphs/face_detection:face_detection_mobile_gpu_binary_graph
cp bazel-bin/mediapipe/graphs/face_detection/face_detection_mobile_gpu.binarypb /path/to/your/app/src/main/assets/
cp mediapipe/modules/face_detection/face_detection_short_range.tflite /path/to/your/app/src/main/assets/
```
![Screenshot](../images/mobile/assets_location.png)
![Screenshot](https://mediapipe.dev/images/mobile/assets_location.png)
4. Make app/src/main/jniLibs and copy OpenCV JNI libraries into
app/src/main/jniLibs.
MediaPipe depends on OpenCV, you will need to copy the precompiled OpenCV so
files into app/src/main/jniLibs. You can download the official OpenCV
Android SDK from
[here](https://github.com/opencv/opencv/releases/download/3.4.3/opencv-3.4.3-android-sdk.zip)
and run:
```bash
cp -R ~/Downloads/OpenCV-android-sdk/sdk/native/libs/arm* /path/to/your/app/src/main/jniLibs/
```
![Screenshot](../images/mobile/android_studio_opencv_location.png)
5. Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
4. Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
```
dependencies {
@@ -125,21 +133,24 @@ each project.
androidTestImplementation 'androidx.test.ext:junit:1.1.0'
androidTestImplementation 'androidx.test.espresso:espresso-core:3.1.1'
// MediaPipe deps
implementation 'com.google.flogger:flogger:0.3.1'
implementation 'com.google.flogger:flogger-system-backend:0.3.1'
implementation 'com.google.code.findbugs:jsr305:3.0.2'
implementation 'com.google.flogger:flogger:latest.release'
implementation 'com.google.flogger:flogger-system-backend:latest.release'
implementation 'com.google.code.findbugs:jsr305:latest.release'
implementation 'com.google.guava:guava:27.0.1-android'
implementation 'com.google.guava:guava:27.0.1-android'
implementation 'com.google.protobuf:protobuf-java:3.11.4'
implementation 'com.google.protobuf:protobuf-javalite:3.19.1'
// CameraX core library
def camerax_version = "1.0.0-beta10"
implementation "androidx.camera:camera-core:$camerax_version"
implementation "androidx.camera:camera-camera2:$camerax_version"
implementation "androidx.camera:camera-lifecycle:$camerax_version"
// AutoValue
def auto_value_version = "1.8.1"
implementation "com.google.auto.value:auto-value-annotations:$auto_value_version"
annotationProcessor "com.google.auto.value:auto-value:$auto_value_version"
}
```
6. Follow our Android app examples to use MediaPipe in Android Studio for your
5. Follow our Android app examples to use MediaPipe in Android Studio for your
use case. If you are looking for an example, a face detection example can be
found
[here](https://github.com/jiuqiant/mediapipe_face_detection_aar_example) and
+131
View File
@@ -0,0 +1,131 @@
---
layout: default
title: MediaPipe Android Solutions
parent: MediaPipe on Android
grand_parent: Getting Started
nav_order: 2
---
# MediaPipe Android Solutions
{: .no_toc }
1. TOC
{:toc}
---
MediaPipe Android Solution APIs (currently in alpha) are available in:
* [MediaPipe Face Detection](../solutions/face_detection#android-solution-api)
* [MediaPipe Face Mesh](../solutions/face_mesh#android-solution-api)
* [MediaPipe Hands](../solutions/hands#android-solution-api)
## Incorporation in Android Studio
Prebuilt packages of Android Solution APIs can be found in
[Google's Maven Repository](https://maven.google.com/web/index.html?#com.google.mediapipe).
To incorporate them into an Android Studio project, add the following into the
project's Gradle dependencies:
```
dependencies {
// MediaPipe solution-core is the foundation of any MediaPipe Solutions.
implementation 'com.google.mediapipe:solution-core:latest.release'
// Optional: MediaPipe Face Detection Solution.
implementation 'com.google.mediapipe:facedetection:latest.release'
// Optional: MediaPipe Face Mesh Solution.
implementation 'com.google.mediapipe:facemesh:latest.release'
// Optional: MediaPipe Hands Solution.
implementation 'com.google.mediapipe:hands:latest.release'
}
```
If you need further customization, instead of using the prebuilt maven packages
consider building a MediaPipe Android Archive library locally from source by
following these [instructions](./android_archive_library.md).
## Building solution example apps
Detailed usage examples of the Android Solution APIs can be found in the
[source code](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions)
of the solution example apps.
To build these apps:
1. Open Android Studio Arctic Fox on Linux, macOS, or Windows.
2. Import mediapipe/examples/android/solutions directory into Android Studio.
![Screenshot](https://mediapipe.dev/images/import_mp_android_studio_project.png)
3. For Windows users, run `create_win_symlinks.bat` as administrator to create
res directory symlinks.
![Screenshot](https://mediapipe.dev/images/run_create_win_symlinks.png)
4. Select "File" -> "Sync Project with Gradle Files" to sync project.
5. Run solution example app in Android Studio.
![Screenshot](https://mediapipe.dev/images/run_android_solution_app.png)
6. (Optional) Run solutions on CPU.
MediaPipe solution example apps run the pipeline and model inference on GPU
by default. If needed, for example to run the apps on Android Emulator, set
the `RUN_ON_GPU` boolean variable to `false` in the app's
`MainActivity.java` to run the pipeline and model inference on CPU.
## MediaPipe Solution APIs Terms of Service
Last modified: November 12, 2021
Use of MediaPipe Solution APIs is subject to the
[Google APIs Terms of Service](https://developers.google.com/terms),
[Google API Services User Data Policy](https://developers.google.com/terms/api-services-user-data-policy),
and the terms below. Please check back from time to time as these terms and
policies are occasionally updated.
**Privacy**
When you use MediaPipe Solution APIs, processing of the input data (e.g. images,
video, text) fully happens on-device, and **MediaPipe does not send that input
data to Google servers**. As a result, you can use our APIs for processing data
that should not leave the device.
MediaPipe Android Solution APIs will contact Google servers from time to time in
order to receive things like bug fixes, updated models, and hardware accelerator
compatibility information. MediaPipe Android Solution APIs also send metrics
about the performance and utilization of the APIs in your app to Google. Google
uses this metrics data to measure performance, API usage, debug, maintain and
improve the APIs, and detect misuse or abuse, as further described in our
[Privacy Policy](https://policies.google.com/privacy).
**You are responsible for obtaining informed consent from your app users about
Googles processing of MediaPipe metrics data as required by applicable law.**
Data we collect may include the following, across all MediaPipe Android Solution
APIs:
- Device information (such as manufacturer, model, OS version and build) and
available ML hardware accelerators (GPU and DSP). Used for diagnostics and
usage analytics.
- App identification information (package name / bundle id, app version). Used
for diagnostics and usage analytics.
- API configuration (such as image format, resolution, and MediaPipe version
used). Used for diagnostics and usage analytics.
- Event type (such as initialize, download model, update, run, and detection).
Used for diagnostics and usage analytics.
- Error codes. Used for diagnostics.
- Performance metrics. Used for diagnostics.
- Per-installation identifiers that do not uniquely identify a user or
physical device. Used for operation of remote configuration and usage
analytics.
- Network request sender IP addresses. Used for remote configuration
diagnostics. Collected IP addresses are retained temporarily.
+1 -1
View File
@@ -103,7 +103,7 @@ monotonically increasing timestamps. By convention, realtime calculators and
graphs use the recording time or the presentation time as the timestamp for each
packet, with each timestamp representing microseconds since
`Jan/1/1970:00:00:00`. This allows packets from various sources to be processed
in a gloablly consistent order.
in a globally consistent order.
Normally for offline processing, every input packet is processed and processing
continues as long as necessary. For online processing, it is often necessary to
+15
View File
@@ -59,6 +59,21 @@ OpenGL ES profile shading language version string: OpenGL ES GLSL ES 3.20
OpenGL ES profile extensions:
```
If you have connected to your computer through SSH and find when you probe for
GPU information you see the output:
```bash
glxinfo | grep -i opengl
Error: unable to open display
```
Try re-establishing your SSH connection with the `-X` option and try again. For
example:
```bash
ssh -X <user>@<host>
```
*Notice the ES 3.20 text above.*
You need to see ES 3.1 or greater printed in order to perform TFLite inference
+7 -8
View File
@@ -27,12 +27,12 @@ graph on Android.
A simple camera app for real-time Sobel edge detection applied to a live video
stream on an Android device.
![edge_detection_android_gpu_gif](../images/mobile/edge_detection_android_gpu.gif)
![edge_detection_android_gpu_gif](https://mediapipe.dev/images/mobile/edge_detection_android_gpu.gif)
## Setup
1. Install MediaPipe on your system, see [MediaPipe installation guide] for
details.
1. Install MediaPipe on your system, see
[MediaPipe installation guide](./install.md) for details.
2. Install Android Development SDK and Android NDK. See how to do so also in
[MediaPipe installation guide].
3. Enable [developer options] on your Android device.
@@ -69,7 +69,7 @@ node: {
A visualization of the graph is shown below:
![edge_detection_mobile_gpu](../images/mobile/edge_detection_mobile_gpu.png)
![edge_detection_mobile_gpu](https://mediapipe.dev/images/mobile/edge_detection_mobile_gpu.png)
This graph has a single input stream named `input_video` for all incoming frames
that will be provided by your device's camera.
@@ -260,7 +260,7 @@ adb install bazel-bin/$APPLICATION_PATH/helloworld.apk
Open the application on your device. It should display a screen with the text
`Hello World!`.
![bazel_hello_world_android](../images/mobile/bazel_hello_world_android.png)
![bazel_hello_world_android](https://mediapipe.dev/images/mobile/bazel_hello_world_android.png)
## Using the camera via `CameraX`
@@ -377,7 +377,7 @@ Add the following line in the `$APPLICATION_PATH/res/values/strings.xml` file:
When the user doesn't grant camera permission, the screen will now look like
this:
![missing_camera_permission_android](../images/mobile/missing_camera_permission_android.png)
![missing_camera_permission_android](https://mediapipe.dev/images/mobile/missing_camera_permission_android.png)
Now, we will add the [`SurfaceTexture`] and [`SurfaceView`] objects to
`MainActivity`:
@@ -753,7 +753,7 @@ And that's it! You should now be able to successfully build and run the
application on the device and see Sobel edge detection running on a live camera
feed! Congrats!
![edge_detection_android_gpu_gif](../images/mobile/edge_detection_android_gpu.gif)
![edge_detection_android_gpu_gif](https://mediapipe.dev/images/mobile/edge_detection_android_gpu.gif)
If you ran into any issues, please see the full code of the tutorial
[here](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic).
@@ -770,7 +770,6 @@ If you ran into any issues, please see the full code of the tutorial
[`ExternalTextureConverter`]:https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/components/ExternalTextureConverter.java
[`FrameLayout`]:https://developer.android.com/reference/android/widget/FrameLayout
[`FrameProcessor`]:https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/components/FrameProcessor.java
[MediaPipe installation guide]:./install.md
[`PermissionHelper`]: https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/components/PermissionHelper.java
[`SurfaceHolder.Callback`]:https://developer.android.com/reference/android/view/SurfaceHolder.Callback.html
[`SurfaceView`]:https://developer.android.com/reference/android/view/SurfaceView
+1 -1
View File
@@ -85,7 +85,7 @@ nav_order: 1
This graph consists of 1 graph input stream (`in`) and 1 graph output stream
(`out`), and 2 [`PassThroughCalculator`]s connected serially.
![hello_world graph](../images/hello_world.png)
![hello_world graph](https://mediapipe.dev/images/hello_world.png)
4. Before running the graph, an `OutputStreamPoller` object is connected to the
output stream in order to later retrieve the graph output, and a graph run
+36 -9
View File
@@ -27,12 +27,12 @@ on iOS.
A simple camera app for real-time Sobel edge detection applied to a live video
stream on an iOS device.
![edge_detection_ios_gpu_gif](../images/mobile/edge_detection_ios_gpu.gif)
![edge_detection_ios_gpu_gif](https://mediapipe.dev/images/mobile/edge_detection_ios_gpu.gif)
## Setup
1. Install MediaPipe on your system, see [MediaPipe installation guide] for
details.
1. Install MediaPipe on your system, see
[MediaPipe installation guide](./install.md) for details.
2. Setup your iOS device for development.
3. Setup [Bazel] on your system to build and deploy the iOS app.
@@ -67,7 +67,7 @@ node: {
A visualization of the graph is shown below:
![edge_detection_mobile_gpu](../images/mobile/edge_detection_mobile_gpu.png)
![edge_detection_mobile_gpu](https://mediapipe.dev/images/mobile/edge_detection_mobile_gpu.png)
This graph has a single input stream named `input_video` for all incoming frames
that will be provided by your device's camera.
@@ -113,6 +113,10 @@ bazel to build the iOS application. The content of the
5. `Main.storyboard` and `Launch.storyboard`
6. `Assets.xcassets` directory.
Note: In newer versions of Xcode, you may see additional files `SceneDelegate.h`
and `SceneDelegate.m`. Make sure to copy them too and add them to the `BUILD`
file mentioned below.
Copy these files to a directory named `HelloWorld` to a location that can access
the MediaPipe source code. For example, the source code of the application that
we will build in this tutorial is located in
@@ -127,7 +131,7 @@ Create a `BUILD` file in the `$APPLICATION_PATH` and add the following build
rules:
```
MIN_IOS_VERSION = "10.0"
MIN_IOS_VERSION = "11.0"
load(
"@build_bazel_rules_apple//apple:ios.bzl",
@@ -247,6 +251,12 @@ We need to get frames from the `_cameraSource` into our application
`MPPInputSourceDelegate`. So our application `ViewController` can be a delegate
of `_cameraSource`.
Update the interface definition of `ViewController` accordingly:
```
@interface ViewController () <MPPInputSourceDelegate>
```
To handle camera setup and process incoming frames, we should use a queue
different from the main queue. Add the following to the implementation block of
the `ViewController`:
@@ -288,6 +298,12 @@ utility called `MPPLayerRenderer` to display images on the screen. This utility
can be used to display `CVPixelBufferRef` objects, which is the type of the
images provided by `MPPCameraInputSource` to its delegates.
In `ViewController.m`, add the following import line:
```
#import "mediapipe/objc/MPPLayerRenderer.h"
```
To display images of the screen, we need to add a new `UIView` object called
`_liveView` to the `ViewController`.
@@ -411,6 +427,12 @@ Objective-C++.
### Use the graph in `ViewController`
In `ViewController.m`, add the following import line:
```
#import "mediapipe/objc/MPPGraph.h"
```
Declare a static constant with the name of the graph, the input stream and the
output stream:
@@ -549,10 +571,16 @@ method to receive packets on this output stream and display them on the screen:
}
```
Update the interface definition of `ViewController` with `MPPGraphDelegate`:
```
@interface ViewController () <MPPGraphDelegate, MPPInputSourceDelegate>
```
And that is all! Build and run the app on your iOS device. You should see the
results of running the edge detection graph on a live video feed. Congrats!
![edge_detection_ios_gpu_gif](../images/mobile/edge_detection_ios_gpu.gif)
![edge_detection_ios_gpu_gif](https://mediapipe.dev/images/mobile/edge_detection_ios_gpu.gif)
Please note that the iOS examples now use a [common] template app. The code in
this tutorial is used in the [common] template app. The [helloworld] app has the
@@ -560,6 +588,5 @@ appropriate `BUILD` file dependencies for the edge detection graph.
[Bazel]:https://bazel.build/
[`edge_detection_mobile_gpu.pbtxt`]:https://github.com/google/mediapipe/tree/master/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
[MediaPipe installation guide]:./install.md
[common]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/common)
[helloworld]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld)
[common]:https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/common
[helloworld]:https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld
+163 -78
View File
@@ -43,104 +43,189 @@ install --user six`.
3. Install OpenCV and FFmpeg.
Option 1. Use package manager tool to install the pre-compiled OpenCV
libraries. FFmpeg will be installed via libopencv-video-dev.
**Option 1**. Use package manager tool to install the pre-compiled OpenCV
libraries. FFmpeg will be installed via `libopencv-video-dev`.
Note: Debian 9 and Ubuntu 16.04 provide OpenCV 2.4.9. You may want to take
option 2 or 3 to install OpenCV 3 or above.
OS | OpenCV
-------------------- | ------
Debian 9 (stretch) | 2.4
Debian 10 (buster) | 3.2
Debian 11 (bullseye) | 4.5
Ubuntu 16.04 LTS | 2.4
Ubuntu 18.04 LTS | 3.2
Ubuntu 20.04 LTS | 4.2
Ubuntu 20.04 LTS | 4.2
Ubuntu 21.04 | 4.5
```bash
$ sudo apt-get install libopencv-core-dev libopencv-highgui-dev \
libopencv-calib3d-dev libopencv-features2d-dev \
libopencv-imgproc-dev libopencv-video-dev
$ sudo apt-get install -y \
libopencv-core-dev \
libopencv-highgui-dev \
libopencv-calib3d-dev \
libopencv-features2d-dev \
libopencv-imgproc-dev \
libopencv-video-dev
```
Debian 9 and Ubuntu 18.04 install the packages in
`/usr/lib/x86_64-linux-gnu`. MediaPipe's [`opencv_linux.BUILD`] and
[`ffmpeg_linux.BUILD`] are configured for this library path. Ubuntu 20.04
may install the OpenCV and FFmpeg packages in `/usr/local`, Please follow
the option 3 below to modify the [`WORKSPACE`], [`opencv_linux.BUILD`] and
[`ffmpeg_linux.BUILD`] files accordingly.
Moreover, for Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu, the
library path needs to be modified like the following:
MediaPipe's [`opencv_linux.BUILD`] and [`WORKSPACE`] are already configured
for OpenCV 2/3 and should work correctly on any architecture:
```bash
sed -i "s/x86_64-linux-gnu/aarch64-linux-gnu/g" third_party/opencv_linux.BUILD
# WORKSPACE
new_local_repository(
name = "linux_opencv",
build_file = "@//third_party:opencv_linux.BUILD",
path = "/usr",
)
# opencv_linux.BUILD for OpenCV 2/3 installed from Debian package
cc_library(
name = "opencv",
linkopts = [
"-l:libopencv_core.so",
"-l:libopencv_calib3d.so",
"-l:libopencv_features2d.so",
"-l:libopencv_highgui.so",
"-l:libopencv_imgcodecs.so",
"-l:libopencv_imgproc.so",
"-l:libopencv_video.so",
"-l:libopencv_videoio.so",
],
)
```
Option 2. Run [`setup_opencv.sh`] to automatically build OpenCV from source
and modify MediaPipe's OpenCV config.
For OpenCV 4 you need to modify [`opencv_linux.BUILD`] taking into account
current architecture:
Option 3. Follow OpenCV's
```bash
# WORKSPACE
new_local_repository(
name = "linux_opencv",
build_file = "@//third_party:opencv_linux.BUILD",
path = "/usr",
)
# opencv_linux.BUILD for OpenCV 4 installed from Debian package
cc_library(
name = "opencv",
hdrs = glob([
# Uncomment according to your multiarch value (gcc -print-multiarch):
# "include/aarch64-linux-gnu/opencv4/opencv2/cvconfig.h",
# "include/arm-linux-gnueabihf/opencv4/opencv2/cvconfig.h",
# "include/x86_64-linux-gnu/opencv4/opencv2/cvconfig.h",
"include/opencv4/opencv2/**/*.h*",
]),
includes = [
# Uncomment according to your multiarch value (gcc -print-multiarch):
# "include/aarch64-linux-gnu/opencv4/",
# "include/arm-linux-gnueabihf/opencv4/",
# "include/x86_64-linux-gnu/opencv4/",
"include/opencv4/",
],
linkopts = [
"-l:libopencv_core.so",
"-l:libopencv_calib3d.so",
"-l:libopencv_features2d.so",
"-l:libopencv_highgui.so",
"-l:libopencv_imgcodecs.so",
"-l:libopencv_imgproc.so",
"-l:libopencv_video.so",
"-l:libopencv_videoio.so",
],
)
```
**Option 2**. Run [`setup_opencv.sh`] to automatically build OpenCV from
source and modify MediaPipe's OpenCV config. This option will do all steps
defined in Option 3 automatically.
**Option 3**. Follow OpenCV's
[documentation](https://docs.opencv.org/3.4.6/d7/d9f/tutorial_linux_install.html)
to manually build OpenCV from source code.
Note: You may need to modify [`WORKSPACE`], [`opencv_linux.BUILD`] and
[`ffmpeg_linux.BUILD`] to point MediaPipe to your own OpenCV and FFmpeg
libraries. For example if OpenCV and FFmpeg are both manually installed in
"/usr/local/", you will need to update: (1) the "linux_opencv" and
"linux_ffmpeg" new_local_repository rules in [`WORKSPACE`], (2) the "opencv"
cc_library rule in [`opencv_linux.BUILD`], and (3) the "libffmpeg"
cc_library rule in [`ffmpeg_linux.BUILD`]. These 3 changes are shown below:
You may need to modify [`WORKSPACE`] and [`opencv_linux.BUILD`] to point
MediaPipe to your own OpenCV libraries. Assume OpenCV would be installed to
`/usr/local/` which is recommended by default.
OpenCV 2/3 setup:
```bash
# WORKSPACE
new_local_repository(
name = "linux_opencv",
build_file = "@//third_party:opencv_linux.BUILD",
path = "/usr/local",
name = "linux_opencv",
build_file = "@//third_party:opencv_linux.BUILD",
path = "/usr/local",
)
# opencv_linux.BUILD for OpenCV 2/3 installed to /usr/local
cc_library(
name = "opencv",
linkopts = [
"-L/usr/local/lib",
"-l:libopencv_core.so",
"-l:libopencv_calib3d.so",
"-l:libopencv_features2d.so",
"-l:libopencv_highgui.so",
"-l:libopencv_imgcodecs.so",
"-l:libopencv_imgproc.so",
"-l:libopencv_video.so",
"-l:libopencv_videoio.so",
],
)
```
OpenCV 4 setup:
```bash
# WORKSPACE
new_local_repository(
name = "linux_ffmpeg",
build_file = "@//third_party:ffmpeg_linux.BUILD",
path = "/usr/local",
name = "linux_opencv",
build_file = "@//third_party:opencv_linux.BUILD",
path = "/usr/local",
)
# opencv_linux.BUILD for OpenCV 4 installed to /usr/local
cc_library(
name = "opencv",
srcs = glob(
[
"lib/libopencv_core.so",
"lib/libopencv_highgui.so",
"lib/libopencv_imgcodecs.so",
"lib/libopencv_imgproc.so",
"lib/libopencv_video.so",
"lib/libopencv_videoio.so",
],
),
hdrs = glob([
# For OpenCV 3.x
"include/opencv2/**/*.h*",
# For OpenCV 4.x
# "include/opencv4/opencv2/**/*.h*",
]),
includes = [
# For OpenCV 3.x
"include/",
# For OpenCV 4.x
# "include/opencv4/",
],
linkstatic = 1,
visibility = ["//visibility:public"],
name = "opencv",
hdrs = glob([
"include/opencv4/opencv2/**/*.h*",
]),
includes = [
"include/opencv4/",
],
linkopts = [
"-L/usr/local/lib",
"-l:libopencv_core.so",
"-l:libopencv_calib3d.so",
"-l:libopencv_features2d.so",
"-l:libopencv_highgui.so",
"-l:libopencv_imgcodecs.so",
"-l:libopencv_imgproc.so",
"-l:libopencv_video.so",
"-l:libopencv_videoio.so",
],
)
```
Current FFmpeg setup is defined in [`ffmpeg_linux.BUILD`] and should work
for any architecture:
```bash
# WORKSPACE
new_local_repository(
name = "linux_ffmpeg",
build_file = "@//third_party:ffmpeg_linux.BUILD",
path = "/usr"
)
# ffmpeg_linux.BUILD for FFmpeg installed from Debian package
cc_library(
name = "libffmpeg",
srcs = glob(
[
"lib/libav*.so",
],
),
hdrs = glob(["include/libav*/*.h"]),
includes = ["include"],
linkopts = [
"-lavcodec",
"-lavformat",
"-lavutil",
],
linkstatic = 1,
visibility = ["//visibility:public"],
name = "libffmpeg",
linkopts = [
"-l:libavcodec.so",
"-l:libavformat.so",
"-l:libavutil.so",
],
)
```
@@ -471,7 +556,7 @@ next section.
4. Install Visual C++ Build Tools 2019 and WinSDK
Go to
[the VisualStudio website](ttps://visualstudio.microsoft.com/visual-cpp-build-tools),
[the VisualStudio website](https://visualstudio.microsoft.com/visual-cpp-build-tools),
download build tools, and install Microsoft Visual C++ 2019 Redistributable
and Microsoft Build Tools 2019.
@@ -484,7 +569,7 @@ next section.
Option 1. Follow
[the official Bazel documentation](https://docs.bazel.build/versions/master/install-windows.html)
to install Bazel 3.7.2 or higher.
to install Bazel 5.2.0 or higher.
Option 2. Follow the official
[Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
@@ -572,7 +657,7 @@ cameras. Alternatively, you use a video file as input.
Note: Windows' and WSLs adb versions must be the same version, e.g., if WSL
has ADB 1.0.39, you need to download the corresponding Windows ADB from
[here](https://dl.google.com/android/repository/platform-tools_r26.0.1-windows.zip).
[here](https://dl.google.com/android/repository/platform-tools_r30.0.3-windows.zip).
3. Launch WSL.
@@ -711,7 +796,7 @@ This will use a Docker image that will isolate mediapipe's installation from the
```bash
$ docker run -it --name mediapipe mediapipe:latest
root@bca08b91ff63:/mediapipe# GLOG_logtostderr=1 bazel run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world:hello_world
root@bca08b91ff63:/mediapipe# GLOG_logtostderr=1 bazel run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world
# Should print:
# Hello World!
@@ -738,7 +823,7 @@ common build issues.
root@bca08b91ff63:/mediapipe# bash ./setup_android_sdk_and_ndk.sh
# Should print:
# Android NDK is now installed. Consider setting $ANDROID_NDK_HOME environment variable to be /root/Android/Sdk/ndk-bundle/android-ndk-r18b
# Android NDK is now installed. Consider setting $ANDROID_NDK_HOME environment variable to be /root/Android/Sdk/ndk-bundle/android-ndk-r19c
# Set android_ndk_repository and android_sdk_repository in WORKSPACE
# Done
+10 -2
View File
@@ -32,9 +32,14 @@ example apps, start from, start from
xcode-select --install
```
3. Install [Bazel](https://bazel.build/).
3. Install [Bazelisk](https://github.com/bazelbuild/bazelisk)
.
We recommend using [Homebrew](https://brew.sh/) to get the latest version.
We recommend using [Homebrew](https://brew.sh/) to get the latest versions.
```bash
brew install bazelisk
```
4. Set Python 3.7 as the default Python version and install the Python "six"
library. This is needed for TensorFlow.
@@ -187,6 +192,9 @@ Note: When you ask Xcode to run an app, by default it will use the Debug
configuration. Some of our demos are computationally heavy; you may want to use
the Release configuration for better performance.
Note: Due to an imcoptibility caused by one of our dependencies, MediaPipe
cannot be used for apps running on the iPhone Simulator on Apple Silicon (M1).
Tip: To switch build configuration in Xcode, click on the target menu, choose
"Edit Scheme...", select the Run action, and switch the Build Configuration from
Debug to Release. Note that this is set independently for each target.
+25 -17
View File
@@ -16,17 +16,29 @@ nav_order: 4
MediaPipe currently offers the following solutions:
Solution | NPM Package | Example
----------------- | ----------------------------- | -------
[Face Mesh][F-pg] | [@mediapipe/face_mesh][F-npm] | [mediapipe.dev/demo/face_mesh][F-demo]
[Face Detection][Fd-pg] | [@mediapipe/face_detection][Fd-npm] | [mediapipe.dev/demo/face_detection][Fd-demo]
[Hands][H-pg] | [@mediapipe/hands][H-npm] | [mediapipe.dev/demo/hands][H-demo]
[Holistic][Ho-pg] | [@mediapipe/holistic][Ho-npm] | [mediapipe.dev/demo/holistic][Ho-demo]
[Pose][P-pg] | [@mediapipe/pose][P-npm] | [mediapipe.dev/demo/pose][P-demo]
Solution | NPM Package | Example
--------------------------- | --------------------------------------- | -------
[Face Mesh][F-pg] | [@mediapipe/face_mesh][F-npm] | [mediapipe.dev/demo/face_mesh][F-demo]
[Face Detection][Fd-pg] | [@mediapipe/face_detection][Fd-npm] | [mediapipe.dev/demo/face_detection][Fd-demo]
[Hands][H-pg] | [@mediapipe/hands][H-npm] | [mediapipe.dev/demo/hands][H-demo]
[Holistic][Ho-pg] | [@mediapipe/holistic][Ho-npm] | [mediapipe.dev/demo/holistic][Ho-demo]
[Objectron][Ob-pg] | [@mediapipe/objectron][Ob-npm] | [mediapipe.dev/demo/objectron][Ob-demo]
[Pose][P-pg] | [@mediapipe/pose][P-npm] | [mediapipe.dev/demo/pose][P-demo]
[Selfie Segmentation][S-pg] | [@mediapipe/selfie_segmentation][S-npm] | [mediapipe.dev/demo/selfie_segmentation][S-demo]
Click on a solution link above for more information, including API and code
snippets.
### Supported plaforms:
| Browser | Platform | Notes |
| ------- | ----------------------- | -------------------------------------- |
| Chrome | Android / Windows / Mac | Pixel 4 and older unsupported. Fuschia |
| | | unsupported. |
| Chrome | iOS | Camera unavailable in Chrome on iOS. |
| Safari | iPad/iPhone/Mac | iOS and Safari on iPad / iPhone / |
| | | MacBook |
The quickest way to get acclimated is to look at the examples above. Each demo
has a link to a [CodePen][codepen] so that you can edit the code and try it
yourself. We have included a number of utility packages to help you get started:
@@ -66,29 +78,25 @@ affecting your work, restrict your request to a `<minor>` number. e.g.,
[F-pg]: ../solutions/face_mesh#javascript-solution-api
[Fd-pg]: ../solutions/face_detection#javascript-solution-api
[H-pg]: ../solutions/hands#javascript-solution-api
[Ob-pg]: ../solutions/objectron#javascript-solution-api
[P-pg]: ../solutions/pose#javascript-solution-api
[S-pg]: ../solutions/selfie_segmentation#javascript-solution-api
[Ho-npm]: https://www.npmjs.com/package/@mediapipe/holistic
[F-npm]: https://www.npmjs.com/package/@mediapipe/face_mesh
[Fd-npm]: https://www.npmjs.com/package/@mediapipe/face_detection
[H-npm]: https://www.npmjs.com/package/@mediapipe/hands
[Ob-npm]: https://www.npmjs.com/package/@mediapipe/objectron
[P-npm]: https://www.npmjs.com/package/@mediapipe/pose
[S-npm]: https://www.npmjs.com/package/@mediapipe/selfie_segmentation
[draw-npm]: https://www.npmjs.com/package/@mediapipe/drawing_utils
[cam-npm]: https://www.npmjs.com/package/@mediapipe/camera_utils
[ctrl-npm]: https://www.npmjs.com/package/@mediapipe/control_utils
[Ho-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/holistic
[F-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/face_mesh
[Fd-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/face_detection
[H-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/hands
[P-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/pose
[Ho-pen]: https://code.mediapipe.dev/codepen/holistic
[F-pen]: https://code.mediapipe.dev/codepen/face_mesh
[Fd-pen]: https://code.mediapipe.dev/codepen/face_detection
[H-pen]: https://code.mediapipe.dev/codepen/hands
[P-pen]: https://code.mediapipe.dev/codepen/pose
[Ho-demo]: https://mediapipe.dev/demo/holistic
[F-demo]: https://mediapipe.dev/demo/face_mesh
[Fd-demo]: https://mediapipe.dev/demo/face_detection
[H-demo]: https://mediapipe.dev/demo/hands
[Ob-demo]: https://mediapipe.dev/demo/objectron
[P-demo]: https://mediapipe.dev/demo/pose
[S-demo]: https://mediapipe.dev/demo/selfie_segmentation
[npm]: https://www.npmjs.com/package/@mediapipe
[codepen]: https://code.mediapipe.dev/codepen
+6 -7
View File
@@ -26,7 +26,7 @@ You can, for instance, activate a Python virtual environment:
$ python3 -m venv mp_env && source mp_env/bin/activate
```
Install MediaPipe Python package and start Python intepreter:
Install MediaPipe Python package and start Python interpreter:
```bash
(mp_env)$ pip install mediapipe
@@ -51,6 +51,7 @@ details in each solution via the links below:
* [MediaPipe Holistic](../solutions/holistic#python-solution-api)
* [MediaPipe Objectron](../solutions/objectron#python-solution-api)
* [MediaPipe Pose](../solutions/pose#python-solution-api)
* [MediaPipe Selfie Segmentation](../solutions/selfie_segmentation#python-solution-api)
## MediaPipe on Google Colab
@@ -62,6 +63,7 @@ details in each solution via the links below:
* [MediaPipe Pose Colab](https://mediapipe.page.link/pose_py_colab)
* [MediaPipe Pose Classification Colab (Basic)](https://mediapipe.page.link/pose_classification_basic)
* [MediaPipe Pose Classification Colab (Extended)](https://mediapipe.page.link/pose_classification_extended)
* [MediaPipe Selfie Segmentation Colab](https://mediapipe.page.link/selfie_segmentation_py_colab)
## MediaPipe Python Framework
@@ -111,9 +113,8 @@ Nvidia Jetson and Raspberry Pi, please read
Download the latest protoc win64 zip from
[the Protobuf GitHub repo](https://github.com/protocolbuffers/protobuf/releases),
unzip the file, and copy the protoc.exe executable to a preferred
location. Please ensure that location is added into the Path environment
variable.
unzip the file, and copy the protoc.exe executable to a preferred location.
Please ensure that location is added into the Path environment variable.
3. Activate a Python virtual environment.
@@ -129,16 +130,14 @@ Nvidia Jetson and Raspberry Pi, please read
(mp_env)mediapipe$ pip3 install -r requirements.txt
```
6. Generate and install MediaPipe package.
6. Build and install MediaPipe package.
```bash
(mp_env)mediapipe$ python3 setup.py gen_protos
(mp_env)mediapipe$ python3 setup.py install --link-opencv
```
or
```bash
(mp_env)mediapipe$ python3 setup.py gen_protos
(mp_env)mediapipe$ python3 setup.py bdist_wheel
```
+4 -3
View File
@@ -74,7 +74,7 @@ Mapping\[str, Packet\] | std::map<std::string, Packet> | create_st
np.ndarray<br>(cv.mat and PIL.Image) | mp::ImageFrame | create_image_frame(<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format=ImageFormat.SRGB,<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data=mat) | get_image_frame(packet)
np.ndarray | mp::Matrix | create_matrix(data) | get_matrix(packet)
Google Proto Message | Google Proto Message | create_proto(proto) | get_proto(packet)
List\[Proto\] | std::vector\<Proto\> | create_proto_vector(proto_list) | get_proto_list(packet)
List\[Proto\] | std::vector\<Proto\> | n/a | get_proto_list(packet)
It's not uncommon that users create custom C++ classes and and send those into
the graphs and calculators. To allow the custom classes to be used in Python
@@ -126,6 +126,7 @@ following steps:
}
return packet.Get<MyType>();
});
}
} // namespace mediapipe
```
@@ -249,12 +250,12 @@ three stages: initialization and setup, graph run, and graph shutdown.
graph.start_run()
graph.add_packet_to_input_stream(
'in_stream', mp.packet_creator.create_str('abc').at(0))
'in_stream', mp.packet_creator.create_string('abc').at(0))
rgb_img = cv2.cvtColor(cv2.imread('/path/to/your/image.png'), cv2.COLOR_BGR2RGB)
graph.add_packet_to_input_stream(
'in_stream',
mp.packet_creator.create_image_frame(format=mp.ImageFormat.SRGB,
mp.packet_creator.create_image_frame(image_format=mp.ImageFormat.SRGB,
data=rgb_img).at(1))
```
+43
View File
@@ -97,6 +97,49 @@ linux_opencv/macos_opencv/windows_opencv.BUILD files for your local opencv
libraries. [This GitHub issue](https://github.com/google/mediapipe/issues/666)
may also help.
## Python pip install failure
The error message:
```
ERROR: Could not find a version that satisfies the requirement mediapipe
ERROR: No matching distribution found for mediapipe
```
after running `pip install mediapipe` usually indicates that there is no qualified MediaPipe Python for your system.
Please note that MediaPipe Python PyPI officially supports the **64-bit**
version of Python 3.7 to 3.10 on the following OS:
- x86_64 Linux
- x86_64 macOS 10.15+
- amd64 Windows
If the OS is currently supported and you still see this error, please make sure
that both the Python and pip binary are for Python 3.7 to 3.10. Otherwise,
please consider building the MediaPipe Python package locally by following the
instructions [here](python.md#building-mediapipe-python-package).
## Python DLL load failure on Windows
The error message:
```
ImportError: DLL load failed: The specified module could not be found
```
usually indicates that the local Windows system is missing Visual C++
redistributable packages and/or Visual C++ runtime DLLs. This can be solved by
either installing the official
[vc_redist.x64.exe](https://support.microsoft.com/en-us/topic/the-latest-supported-visual-c-downloads-2647da03-1eea-4433-9aff-95f26a218cc0)
or installing the "msvc-runtime" Python package by running
```bash
$ python -m pip install msvc-runtime
```
Please note that the "msvc-runtime" Python package is not released or maintained
by Microsoft.
## Native method not found
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