Project import generated by Copybara.
GitOrigin-RevId: d8caa66de45839696f5bd0786ad3bfbcb9cff632
This commit is contained in:
MediaPipe Team
@@ -0,0 +1,191 @@
|
||||
---
|
||||
layout: default
|
||||
title: MediaPipe on Android
|
||||
parent: Getting Started
|
||||
has_children: true
|
||||
has_toc: false
|
||||
nav_order: 1
|
||||
---
|
||||
|
||||
# MediaPipe on Android
|
||||
{: .no_toc }
|
||||
|
||||
1. TOC
|
||||
{:toc}
|
||||
---
|
||||
|
||||
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.
|
||||
|
||||
## Building Android example apps
|
||||
|
||||
### Prerequisite
|
||||
|
||||
* Install MediaPipe following these [instructions](./install.md).
|
||||
* Setup Java Runtime.
|
||||
* Setup Android SDK release 28.0.3 and above.
|
||||
* Setup Android NDK r18b and above.
|
||||
|
||||
MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see
|
||||
below for Android Studio setup). However, if you prefer using MediaPipe without
|
||||
Android Studio, please run
|
||||
[`setup_android_sdk_and_ndk.sh`](https://github.com/google/mediapipe/blob/master/setup_android_sdk_and_ndk.sh)
|
||||
to download and setup Android SDK and NDK before building any Android example
|
||||
apps.
|
||||
|
||||
If Android SDK and NDK are already installed (e.g., by Android Studio), set
|
||||
$ANDROID_HOME and $ANDROID_NDK_HOME to point to the installed SDK and NDK.
|
||||
|
||||
```bash
|
||||
export ANDROID_HOME=<path to the Android SDK>
|
||||
export ANDROID_NDK_HOME=<path to the Android NDK>
|
||||
```
|
||||
|
||||
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.
|
||||
|
||||
1. To build an Android example app, build against the corresponding
|
||||
`android_binary` build target. For instance, for
|
||||
[MediaPipe Hands](../solutions/hands.md) the target is `handtrackinggpu` in
|
||||
the
|
||||
[BUILD](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/BUILD)
|
||||
file:
|
||||
|
||||
Note: To reduce the binary size, consider appending `--linkopt="-s"` to the
|
||||
command below to strip symbols.
|
||||
|
||||
```bash
|
||||
bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu:handtrackinggpu
|
||||
```
|
||||
|
||||
2. Install it on a device with:
|
||||
|
||||
```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.
|
||||
@@ -1,8 +1,9 @@
|
||||
---
|
||||
layout: default
|
||||
title: MediaPipe Android Archive
|
||||
parent: Getting Started
|
||||
nav_order: 7
|
||||
parent: MediaPipe on Android
|
||||
grand_parent: Getting Started
|
||||
nav_order: 2
|
||||
---
|
||||
|
||||
# MediaPipe Android Archive
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
layout: default
|
||||
title: Building MediaPipe Examples
|
||||
parent: Getting Started
|
||||
nav_order: 2
|
||||
nav_exclude: true
|
||||
---
|
||||
|
||||
# Building MediaPipe Examples
|
||||
@@ -12,464 +12,22 @@ nav_order: 2
|
||||
{:toc}
|
||||
---
|
||||
|
||||
## Android
|
||||
### Android
|
||||
|
||||
### Prerequisite
|
||||
Please see these [instructions](./android.md).
|
||||
|
||||
* Java Runtime.
|
||||
* Android SDK release 28.0.3 and above.
|
||||
* Android NDK r18b and above.
|
||||
### iOS
|
||||
|
||||
MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see
|
||||
below for Android Studio setup). However, if you prefer using MediaPipe without
|
||||
Android Studio, please run
|
||||
[`setup_android_sdk_and_ndk.sh`](https://github.com/google/mediapipe/blob/master/setup_android_sdk_and_ndk.sh)
|
||||
to download and setup Android SDK and NDK before building any Android example
|
||||
apps.
|
||||
Please see these [instructions](./ios.md).
|
||||
|
||||
If Android SDK and NDK are already installed (e.g., by Android Studio), set
|
||||
$ANDROID_HOME and $ANDROID_NDK_HOME to point to the installed SDK and NDK.
|
||||
### Python
|
||||
|
||||
```bash
|
||||
export ANDROID_HOME=<path to the Android SDK>
|
||||
export ANDROID_NDK_HOME=<path to the Android NDK>
|
||||
```
|
||||
Please see these [instructions](./python.md).
|
||||
|
||||
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.
|
||||
### JavaScript
|
||||
|
||||
Please verify all the necessary packages are installed.
|
||||
Please see these [instructions](./javascript.md).
|
||||
|
||||
* 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
|
||||
### C++
|
||||
|
||||
### 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.
|
||||
|
||||
1. To build an Android example app, build against the corresponding
|
||||
`android_binary` build target. For instance, for
|
||||
[MediaPipe Hands](../solutions/hands.md) the target is `handtrackinggpu` in
|
||||
the
|
||||
[BUILD](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/BUILD)
|
||||
file:
|
||||
|
||||
Note: To reduce the binary size, consider appending `--linkopt="-s"` to the
|
||||
command below to strip symbols.
|
||||
|
||||
```bash
|
||||
bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu:handtrackinggpu
|
||||
```
|
||||
|
||||
2. Install it on a device with:
|
||||
|
||||
```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.
|
||||
|
||||
## iOS
|
||||
|
||||
### Prerequisite
|
||||
|
||||
1. Install [Xcode](https://developer.apple.com/xcode/), then install the
|
||||
Command Line Tools using:
|
||||
|
||||
```bash
|
||||
xcode-select --install
|
||||
```
|
||||
|
||||
2. Install [Bazel](https://bazel.build/).
|
||||
|
||||
We recommend using [Homebrew](https://brew.sh/) to get the latest version.
|
||||
|
||||
3. Set Python 3.7 as the default Python version and install the Python "six"
|
||||
library. This is needed for TensorFlow.
|
||||
|
||||
```bash
|
||||
pip3 install --user six
|
||||
```
|
||||
|
||||
4. Clone the MediaPipe repository.
|
||||
|
||||
```bash
|
||||
git clone https://github.com/google/mediapipe.git
|
||||
```
|
||||
|
||||
### Set up a bundle ID prefix
|
||||
|
||||
All iOS apps must have a bundle ID, and you must have a provisioning profile
|
||||
that lets you install an app with that ID onto your phone. To avoid clashes
|
||||
between different MediaPipe users, you need to configure a unique prefix for the
|
||||
bundle IDs of our iOS demo apps.
|
||||
|
||||
If you have a custom provisioning profile, see
|
||||
[Custom provisioning](#custom-provisioning) below.
|
||||
|
||||
Otherwise, run this command to generate a unique prefix:
|
||||
|
||||
```bash
|
||||
python3 mediapipe/examples/ios/link_local_profiles.py
|
||||
```
|
||||
|
||||
### Create an Xcode project
|
||||
|
||||
This allows you to edit and debug one of the example apps in Xcode. It also
|
||||
allows you to make use of automatic provisioning (see later section).
|
||||
|
||||
1. We will use a tool called [Tulsi](https://tulsi.bazel.build/) for generating
|
||||
Xcode projects from Bazel build configurations.
|
||||
|
||||
```bash
|
||||
# cd out of the mediapipe directory, then:
|
||||
git clone https://github.com/bazelbuild/tulsi.git
|
||||
cd tulsi
|
||||
# remove Xcode version from Tulsi's .bazelrc (see http://github.com/bazelbuild/tulsi#building-and-installing):
|
||||
sed -i .orig '/xcode_version/d' .bazelrc
|
||||
# build and run Tulsi:
|
||||
sh build_and_run.sh
|
||||
```
|
||||
|
||||
This will install `Tulsi.app` inside the `Applications` directory in your
|
||||
home directory.
|
||||
|
||||
2. Open `mediapipe/Mediapipe.tulsiproj` using the Tulsi app.
|
||||
|
||||
Tip: If Tulsi displays an error saying "Bazel could not be found", press the
|
||||
"Bazel..." button in the Packages tab and select the `bazel` executable in
|
||||
your homebrew `/bin/` directory.
|
||||
|
||||
3. Select the MediaPipe config in the Configs tab, then press the Generate
|
||||
button below. You will be asked for a location to save the Xcode project.
|
||||
Once the project is generated, it will be opened in Xcode.
|
||||
|
||||
If you get an error about bundle IDs, see the
|
||||
[previous section](#set-up-a-bundle-id-prefix).
|
||||
|
||||
### Set up provisioning
|
||||
|
||||
To install applications on an iOS device, you need a provisioning profile. There
|
||||
are two options:
|
||||
|
||||
1. Automatic provisioning. This allows you to build and install an app to your
|
||||
personal device. The provisining profile is managed by Xcode, and has to be
|
||||
updated often (it is valid for about a week).
|
||||
|
||||
2. Custom provisioning. This uses a provisioning profile associated with an
|
||||
Apple developer account. These profiles have a longer validity period and
|
||||
can target multiple devices, but you need a paid developer account with
|
||||
Apple to obtain one.
|
||||
|
||||
#### Automatic provisioning
|
||||
|
||||
1. Create an Xcode project for MediaPipe, as discussed
|
||||
[earlier](#create-an-xcode-project).
|
||||
|
||||
2. In the project navigator in the left sidebar, select the "Mediapipe"
|
||||
project.
|
||||
|
||||
3. Select one of the application targets, e.g. HandTrackingGpuApp.
|
||||
|
||||
4. Select the "Signing & Capabilities" tab.
|
||||
|
||||
5. Check "Automatically manage signing", and confirm the dialog box.
|
||||
|
||||
6. Select "_Your Name_ (Personal Team)" in the Team pop-up menu.
|
||||
|
||||
7. This set-up needs to be done once for each application you want to install.
|
||||
Repeat steps 3-6 as needed.
|
||||
|
||||
This generates provisioning profiles for each app you have selected. Now we need
|
||||
to tell Bazel to use them. We have provided a script to make this easier.
|
||||
|
||||
1. In the terminal, to the `mediapipe` directory where you cloned the
|
||||
repository.
|
||||
|
||||
2. Run this command:
|
||||
|
||||
```bash
|
||||
python3 mediapipe/examples/ios/link_local_profiles.py
|
||||
```
|
||||
|
||||
This will find and link the provisioning profile for all applications for which
|
||||
you have enabled automatic provisioning in Xcode.
|
||||
|
||||
Note: once a profile expires, Xcode will generate a new one; you must then run
|
||||
this script again to link the updated profiles.
|
||||
|
||||
#### Custom provisioning
|
||||
|
||||
1. Obtain a provisioning profile from Apple.
|
||||
|
||||
Tip: You can use this command to see the provisioning profiles you have
|
||||
previously downloaded using Xcode: `open ~/Library/MobileDevice/"Provisioning
|
||||
Profiles"`. If there are none, generate and download a profile on
|
||||
[Apple's developer site](https://developer.apple.com/account/resources/).
|
||||
|
||||
1. Symlink or copy your provisioning profile to
|
||||
`mediapipe/mediapipe/provisioning_profile.mobileprovision`.
|
||||
|
||||
```bash
|
||||
cd mediapipe
|
||||
ln -s ~/Downloads/MyProvisioningProfile.mobileprovision mediapipe/provisioning_profile.mobileprovision
|
||||
```
|
||||
|
||||
Note: if you had previously set up automatic provisioning, you should remove the
|
||||
`provisioning_profile.mobileprovision` symlink in each example's directory,
|
||||
since it will take precedence over the common one. You can also overwrite it
|
||||
with you own profile if you need a different profile for different apps.
|
||||
|
||||
1. Open `mediapipe/examples/ios/bundle_id.bzl`, and change the
|
||||
`BUNDLE_ID_PREFIX` to a prefix associated with your provisioning profile.
|
||||
|
||||
### Build and run an app using Xcode
|
||||
|
||||
1. Create the Xcode project, and make sure you have set up either automatic or
|
||||
custom provisioning.
|
||||
|
||||
2. You can now select any of the MediaPipe demos in the target menu, and build
|
||||
and run them as normal.
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
Tip: On the device, in Settings > General > Device Management, make sure the
|
||||
developer (yourself) is trusted.
|
||||
|
||||
### Build an app using the command line
|
||||
|
||||
1. Make sure you have set up either automatic or custom provisioning.
|
||||
|
||||
2. Using [MediaPipe Hands](../solutions/hands.md) for example, run:
|
||||
|
||||
```bash
|
||||
bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp
|
||||
```
|
||||
|
||||
You may see a permission request from `codesign` in order to sign the app.
|
||||
|
||||
Tip: If you are using custom provisioning, you can run this
|
||||
[script](https://github.com/google/mediapipe/blob/master/build_ios_examples.sh)
|
||||
to build all MediaPipe iOS example apps.
|
||||
|
||||
3. In Xcode, open the `Devices and Simulators` window (command-shift-2).
|
||||
|
||||
4. Make sure your device is connected. You will see a list of installed apps.
|
||||
Press the "+" button under the list, and select the `.ipa` file built by
|
||||
Bazel.
|
||||
|
||||
5. You can now run the app on your device.
|
||||
|
||||
Tip: On the device, in Settings > General > Device Management, make sure the
|
||||
developer (yourself) is trusted.
|
||||
|
||||
## Desktop
|
||||
|
||||
### Option 1: Running on CPU
|
||||
|
||||
1. To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
|
||||
|
||||
```bash
|
||||
bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu
|
||||
```
|
||||
|
||||
2. To run the application:
|
||||
|
||||
```bash
|
||||
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_cpu \
|
||||
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
|
||||
```
|
||||
|
||||
This will open up your webcam as long as it is connected and on. Any errors
|
||||
is likely due to your webcam being not accessible.
|
||||
|
||||
### Option 2: Running on GPU
|
||||
|
||||
Note: This currently works only on Linux, and please first follow
|
||||
[OpenGL ES Setup on Linux Desktop](./gpu_support.md#opengl-es-setup-on-linux-desktop).
|
||||
|
||||
1. To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
|
||||
|
||||
```bash
|
||||
bazel build -c opt --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 \
|
||||
mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu
|
||||
```
|
||||
|
||||
2. To run the application:
|
||||
|
||||
```bash
|
||||
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_gpu \
|
||||
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
|
||||
```
|
||||
|
||||
This will open up your webcam as long as it is connected and on. Any errors
|
||||
is likely due to your webcam being not accessible, or GPU drivers not setup
|
||||
properly.
|
||||
|
||||
## Python
|
||||
|
||||
MediaPipe Python package is available on
|
||||
[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
|
||||
install mediapipe` on Linux and macOS, as described in, for instance,
|
||||
[Python section in MediaPipe Pose](../solutions/pose.md#python) and in this
|
||||
[colab](https://mediapipe.page.link/pose_py_colab).
|
||||
|
||||
Follow the steps below only if you have local changes and need to build the
|
||||
Python package from source. Otherwise, we strongly encourage our users to simply
|
||||
run `pip install mediapipe`, more convenient and much faster.
|
||||
|
||||
1. Make sure that Bazel and OpenCV are correctly installed and configured for
|
||||
MediaPipe. Please see [Installation](./install.md) for how to setup Bazel
|
||||
and OpenCV for MediaPipe on Linux and macOS.
|
||||
|
||||
2. Install the following dependencies.
|
||||
|
||||
```bash
|
||||
# Debian or Ubuntu
|
||||
$ sudo apt install python3-dev
|
||||
$ sudo apt install python3-venv
|
||||
$ sudo apt install -y protobuf-compiler
|
||||
```
|
||||
|
||||
```bash
|
||||
# macOS
|
||||
$ brew install protobuf
|
||||
```
|
||||
|
||||
3. Activate a Python virtual environment.
|
||||
|
||||
```bash
|
||||
$ python3 -m venv mp_env && source mp_env/bin/activate
|
||||
```
|
||||
|
||||
4. In the virtual environment, go to the MediaPipe repo directory.
|
||||
|
||||
5. Install the required Python packages.
|
||||
|
||||
```bash
|
||||
(mp_env)mediapipe$ pip3 install -r requirements.txt
|
||||
```
|
||||
|
||||
6. Generate and install MediaPipe package.
|
||||
|
||||
```bash
|
||||
(mp_env)mediapipe$ python3 setup.py gen_protos
|
||||
(mp_env)mediapipe$ python3 setup.py install --link-opencv
|
||||
```
|
||||
Please see these [instructions](./cpp.md).
|
||||
|
||||
@@ -0,0 +1,62 @@
|
||||
---
|
||||
layout: default
|
||||
title: MediaPipe in C++
|
||||
parent: Getting Started
|
||||
has_children: true
|
||||
has_toc: false
|
||||
nav_order: 5
|
||||
---
|
||||
|
||||
# MediaPipe in C++
|
||||
{: .no_toc }
|
||||
|
||||
1. TOC
|
||||
{:toc}
|
||||
---
|
||||
|
||||
Please follow instructions below to build C++ command-line example apps in the
|
||||
supported MediaPipe [solutions](../solutions/solutions.md). To learn more about
|
||||
these example apps, start from [Hello World! in C++](./hello_world_cpp.md).
|
||||
|
||||
## Building C++ command-line example apps
|
||||
|
||||
### Option 1: Running on CPU
|
||||
|
||||
1. To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
|
||||
|
||||
```bash
|
||||
bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu
|
||||
```
|
||||
|
||||
2. To run the application:
|
||||
|
||||
```bash
|
||||
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_cpu \
|
||||
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
|
||||
```
|
||||
|
||||
This will open up your webcam as long as it is connected and on. Any errors
|
||||
is likely due to your webcam being not accessible.
|
||||
|
||||
### Option 2: Running on GPU
|
||||
|
||||
Note: This currently works only on Linux, and please first follow
|
||||
[OpenGL ES Setup on Linux Desktop](./gpu_support.md#opengl-es-setup-on-linux-desktop).
|
||||
|
||||
1. To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
|
||||
|
||||
```bash
|
||||
bazel build -c opt --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 \
|
||||
mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu
|
||||
```
|
||||
|
||||
2. To run the application:
|
||||
|
||||
```bash
|
||||
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_gpu \
|
||||
--calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
|
||||
```
|
||||
|
||||
This will open up your webcam as long as it is connected and on. Any errors
|
||||
is likely due to your webcam being not accessible, or GPU drivers not setup
|
||||
properly.
|
||||
@@ -2,7 +2,7 @@
|
||||
layout: default
|
||||
title: GPU Support
|
||||
parent: Getting Started
|
||||
nav_order: 6
|
||||
nav_order: 7
|
||||
---
|
||||
|
||||
# GPU Support
|
||||
|
||||
@@ -1,8 +1,9 @@
|
||||
---
|
||||
layout: default
|
||||
title: Hello World! on Android
|
||||
parent: Getting Started
|
||||
nav_order: 3
|
||||
parent: MediaPipe on Android
|
||||
grand_parent: Getting Started
|
||||
nav_order: 1
|
||||
---
|
||||
|
||||
# Hello World! on Android
|
||||
@@ -496,7 +497,7 @@ CameraHelper.CameraFacing cameraFacing =
|
||||
applicationInfo.metaData.getBoolean("cameraFacingFront", false)
|
||||
? CameraHelper.CameraFacing.FRONT
|
||||
: CameraHelper.CameraFacing.BACK;
|
||||
cameraHelper.startCamera(this, cameraFacing, /*surfaceTexture=*/ null);
|
||||
cameraHelper.startCamera(this, cameraFacing, /*unusedSurfaceTexture=*/ null);
|
||||
```
|
||||
|
||||
At this point, the application should build successfully. However, when you run
|
||||
|
||||
+5
-4
@@ -1,11 +1,12 @@
|
||||
---
|
||||
layout: default
|
||||
title: Hello World! on Desktop (C++)
|
||||
parent: Getting Started
|
||||
nav_order: 5
|
||||
title: Hello World! in C++
|
||||
parent: MediaPipe in C++
|
||||
grand_parent: Getting Started
|
||||
nav_order: 1
|
||||
---
|
||||
|
||||
# Hello World! on Desktop (C++)
|
||||
# Hello World! in C++
|
||||
{: .no_toc }
|
||||
|
||||
1. TOC
|
||||
@@ -1,8 +1,9 @@
|
||||
---
|
||||
layout: default
|
||||
title: Hello World! on iOS
|
||||
parent: Getting Started
|
||||
nav_order: 4
|
||||
parent: MediaPipe on iOS
|
||||
grand_parent: Getting Started
|
||||
nav_order: 1
|
||||
---
|
||||
|
||||
# Hello World! on iOS
|
||||
@@ -193,8 +194,7 @@ bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/helloworld:HelloWor
|
||||
|
||||
Then, go back to XCode, open Window > Devices and Simulators, select your
|
||||
device, and add the `.ipa` file generated by the command above to your device.
|
||||
Here is the document on [setting up and compiling](./building_examples.md#ios)
|
||||
iOS MediaPipe apps.
|
||||
Here is the document on [setting up and compiling](./ios.md) iOS MediaPipe apps.
|
||||
|
||||
Open the application on your device. Since it is empty, it should display a
|
||||
blank white screen.
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
layout: default
|
||||
title: Installation
|
||||
parent: Getting Started
|
||||
nav_order: 1
|
||||
nav_order: 6
|
||||
---
|
||||
|
||||
# Installation
|
||||
@@ -23,32 +23,21 @@ Note: To make Mediapipe work with TensorFlow, please set Python 3.7 as the
|
||||
default Python version and install the Python "six" library by running `pip3
|
||||
install --user six`.
|
||||
|
||||
Note: To build and run Android example apps, see these
|
||||
[instructions](./building_examples.md#android). To build and run iOS example
|
||||
apps, see these [instructions](./building_examples.md#ios).
|
||||
|
||||
## Installing on Debian and Ubuntu
|
||||
|
||||
1. Checkout MediaPipe repository.
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/google/mediapipe.git
|
||||
|
||||
# Change directory into MediaPipe root directory
|
||||
$ cd mediapipe
|
||||
```
|
||||
|
||||
2. Install Bazel.
|
||||
1. Install Bazel.
|
||||
|
||||
Follow the official
|
||||
[Bazel documentation](https://docs.bazel.build/versions/master/install-ubuntu.html)
|
||||
to install Bazel 3.4 or higher.
|
||||
|
||||
For Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu, Bazel needs to
|
||||
be built from source.
|
||||
For Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu only, Bazel needs
|
||||
to be built from source:
|
||||
|
||||
```bash
|
||||
# For Bazel 3.4.0
|
||||
mkdir $HOME/bazel-3.4.0
|
||||
cd $HOME/bazel-3.4.0
|
||||
wget https://github.com/bazelbuild/bazel/releases/download/3.4.0/bazel-3.4.0-dist.zip
|
||||
sudo apt-get install build-essential openjdk-8-jdk python zip unzip
|
||||
unzip bazel-3.4.0-dist.zip
|
||||
@@ -56,6 +45,16 @@ apps, see these [instructions](./building_examples.md#ios).
|
||||
sudo cp output/bazel /usr/local/bin/
|
||||
```
|
||||
|
||||
2. Checkout MediaPipe repository.
|
||||
|
||||
```bash
|
||||
$ cd $HOME
|
||||
$ git clone https://github.com/google/mediapipe.git
|
||||
|
||||
# Change directory into MediaPipe root directory
|
||||
$ cd mediapipe
|
||||
```
|
||||
|
||||
3. Install OpenCV and FFmpeg.
|
||||
|
||||
Option 1. Use package manager tool to install the pre-compiled OpenCV
|
||||
@@ -174,7 +173,7 @@ apps, see these [instructions](./building_examples.md#ios).
|
||||
# when building GPU examples.
|
||||
```
|
||||
|
||||
5. Run the [Hello World desktop example](./hello_world_desktop.md).
|
||||
5. Run the [Hello World! in C++ example](./hello_world_cpp.md).
|
||||
|
||||
```bash
|
||||
$ export GLOG_logtostderr=1
|
||||
@@ -208,7 +207,13 @@ build issues.
|
||||
|
||||
**Disclaimer**: Running MediaPipe on CentOS is experimental.
|
||||
|
||||
1. Checkout MediaPipe repository.
|
||||
1. Install Bazel.
|
||||
|
||||
Follow the official
|
||||
[Bazel documentation](https://docs.bazel.build/versions/master/install-redhat.html)
|
||||
to install Bazel 3.4 or higher.
|
||||
|
||||
2. Checkout MediaPipe repository.
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/google/mediapipe.git
|
||||
@@ -217,12 +222,6 @@ build issues.
|
||||
$ cd mediapipe
|
||||
```
|
||||
|
||||
2. Install Bazel.
|
||||
|
||||
Follow the official
|
||||
[Bazel documentation](https://docs.bazel.build/versions/master/install-redhat.html)
|
||||
to install Bazel 3.4 or higher.
|
||||
|
||||
3. Install OpenCV.
|
||||
|
||||
Option 1. Use package manager tool to install the pre-compiled version.
|
||||
@@ -304,7 +303,7 @@ build issues.
|
||||
)
|
||||
```
|
||||
|
||||
4. Run the [Hello World desktop example](./hello_world_desktop.md).
|
||||
4. Run the [Hello World! in C++ example](./hello_world_cpp.md).
|
||||
|
||||
```bash
|
||||
$ export GLOG_logtostderr=1
|
||||
@@ -337,15 +336,7 @@ build issues.
|
||||
* Install [Xcode](https://developer.apple.com/xcode/) and its Command Line
|
||||
Tools by `xcode-select --install`.
|
||||
|
||||
2. Checkout MediaPipe repository.
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/google/mediapipe.git
|
||||
|
||||
$ cd mediapipe
|
||||
```
|
||||
|
||||
3. Install Bazel.
|
||||
2. Install Bazel.
|
||||
|
||||
Option 1. Use package manager tool to install Bazel
|
||||
|
||||
@@ -358,6 +349,14 @@ build issues.
|
||||
[Bazel documentation](https://docs.bazel.build/versions/master/install-os-x.html#install-with-installer-mac-os-x)
|
||||
to install Bazel 3.4 or higher.
|
||||
|
||||
3. Checkout MediaPipe repository.
|
||||
|
||||
```bash
|
||||
$ git clone https://github.com/google/mediapipe.git
|
||||
|
||||
$ cd mediapipe
|
||||
```
|
||||
|
||||
4. Install OpenCV and FFmpeg.
|
||||
|
||||
Option 1. Use HomeBrew package manager tool to install the pre-compiled
|
||||
@@ -439,7 +438,7 @@ build issues.
|
||||
$ pip3 install --user six
|
||||
```
|
||||
|
||||
6. Run the [Hello World desktop example](./hello_world_desktop.md).
|
||||
6. Run the [Hello World! in C++ example](./hello_world_cpp.md).
|
||||
|
||||
```bash
|
||||
$ export GLOG_logtostderr=1
|
||||
@@ -540,7 +539,7 @@ next section.
|
||||
)
|
||||
```
|
||||
|
||||
9. Run the [Hello World desktop example](./hello_world_desktop.md).
|
||||
9. Run the [Hello World! in C++ example](./hello_world_cpp.md).
|
||||
|
||||
Note: For building MediaPipe on Windows, please add `--action_env
|
||||
PYTHON_BIN_PATH="C://path//to//python.exe"` to the build command.
|
||||
@@ -673,7 +672,7 @@ cameras. Alternatively, you use a video file as input.
|
||||
)
|
||||
```
|
||||
|
||||
8. Run the [Hello World desktop example](./hello_world_desktop.md).
|
||||
8. Run the [Hello World! in C++ example](./hello_world_cpp.md).
|
||||
|
||||
```bash
|
||||
username@DESKTOP-TMVLBJ1:~/mediapipe$ export GLOG_logtostderr=1
|
||||
@@ -729,7 +728,7 @@ This will use a Docker image that will isolate mediapipe's installation from the
|
||||
# Successfully tagged mediapipe:latest
|
||||
```
|
||||
|
||||
3. Run the [Hello World desktop example](./hello_world_desktop.md).
|
||||
3. Run the [Hello World! in C++ example](./hello_world_cpp.md).
|
||||
|
||||
```bash
|
||||
$ docker run -it --name mediapipe mediapipe:latest
|
||||
|
||||
@@ -0,0 +1,222 @@
|
||||
---
|
||||
layout: default
|
||||
title: MediaPipe on iOS
|
||||
parent: Getting Started
|
||||
has_children: true
|
||||
has_toc: false
|
||||
nav_order: 2
|
||||
---
|
||||
|
||||
# MediaPipe on iOS
|
||||
{: .no_toc }
|
||||
|
||||
1. TOC
|
||||
{:toc}
|
||||
---
|
||||
|
||||
Please follow instructions below to build iOS example apps in the supported
|
||||
MediaPipe [solutions](../solutions/solutions.md). To learn more about these
|
||||
example apps, start from, start from
|
||||
[Hello World! on iOS](./hello_world_ios.md).
|
||||
|
||||
## Building iOS example apps
|
||||
|
||||
### Prerequisite
|
||||
|
||||
1. Install MediaPipe following these [instructions](./install.md).
|
||||
|
||||
2. Install [Xcode](https://developer.apple.com/xcode/), then install the
|
||||
Command Line Tools using:
|
||||
|
||||
```bash
|
||||
xcode-select --install
|
||||
```
|
||||
|
||||
3. Install [Bazel](https://bazel.build/).
|
||||
|
||||
We recommend using [Homebrew](https://brew.sh/) to get the latest version.
|
||||
|
||||
4. Set Python 3.7 as the default Python version and install the Python "six"
|
||||
library. This is needed for TensorFlow.
|
||||
|
||||
```bash
|
||||
pip3 install --user six
|
||||
```
|
||||
|
||||
5. Clone the MediaPipe repository.
|
||||
|
||||
```bash
|
||||
git clone https://github.com/google/mediapipe.git
|
||||
```
|
||||
|
||||
### Set up a bundle ID prefix
|
||||
|
||||
All iOS apps must have a bundle ID, and you must have a provisioning profile
|
||||
that lets you install an app with that ID onto your phone. To avoid clashes
|
||||
between different MediaPipe users, you need to configure a unique prefix for the
|
||||
bundle IDs of our iOS demo apps.
|
||||
|
||||
If you have a custom provisioning profile, see
|
||||
[Custom provisioning](#custom-provisioning) below.
|
||||
|
||||
Otherwise, run this command to generate a unique prefix:
|
||||
|
||||
```bash
|
||||
python3 mediapipe/examples/ios/link_local_profiles.py
|
||||
```
|
||||
|
||||
### Create an Xcode project
|
||||
|
||||
This allows you to edit and debug one of the example apps in Xcode. It also
|
||||
allows you to make use of automatic provisioning (see later section).
|
||||
|
||||
1. We will use a tool called [Tulsi](https://tulsi.bazel.build/) for generating
|
||||
Xcode projects from Bazel build configurations.
|
||||
|
||||
```bash
|
||||
# cd out of the mediapipe directory, then:
|
||||
git clone https://github.com/bazelbuild/tulsi.git
|
||||
cd tulsi
|
||||
# remove Xcode version from Tulsi's .bazelrc (see http://github.com/bazelbuild/tulsi#building-and-installing):
|
||||
sed -i .orig '/xcode_version/d' .bazelrc
|
||||
# build and run Tulsi:
|
||||
sh build_and_run.sh
|
||||
```
|
||||
|
||||
This will install `Tulsi.app` inside the `Applications` directory in your
|
||||
home directory.
|
||||
|
||||
2. Open `mediapipe/Mediapipe.tulsiproj` using the Tulsi app.
|
||||
|
||||
Tip: If Tulsi displays an error saying "Bazel could not be found", press the
|
||||
"Bazel..." button in the Packages tab and select the `bazel` executable in
|
||||
your homebrew `/bin/` directory.
|
||||
|
||||
3. Select the MediaPipe config in the Configs tab, then press the Generate
|
||||
button below. You will be asked for a location to save the Xcode project.
|
||||
Once the project is generated, it will be opened in Xcode.
|
||||
|
||||
If you get an error about bundle IDs, see the
|
||||
[previous section](#set-up-a-bundle-id-prefix).
|
||||
|
||||
### Set up provisioning
|
||||
|
||||
To install applications on an iOS device, you need a provisioning profile. There
|
||||
are two options:
|
||||
|
||||
1. Automatic provisioning. This allows you to build and install an app to your
|
||||
personal device. The provisining profile is managed by Xcode, and has to be
|
||||
updated often (it is valid for about a week).
|
||||
|
||||
2. Custom provisioning. This uses a provisioning profile associated with an
|
||||
Apple developer account. These profiles have a longer validity period and
|
||||
can target multiple devices, but you need a paid developer account with
|
||||
Apple to obtain one.
|
||||
|
||||
#### Automatic provisioning
|
||||
|
||||
1. Create an Xcode project for MediaPipe, as discussed
|
||||
[earlier](#create-an-xcode-project).
|
||||
|
||||
2. In the project navigator in the left sidebar, select the "Mediapipe"
|
||||
project.
|
||||
|
||||
3. Select one of the application targets, e.g. HandTrackingGpuApp.
|
||||
|
||||
4. Select the "Signing & Capabilities" tab.
|
||||
|
||||
5. Check "Automatically manage signing", and confirm the dialog box.
|
||||
|
||||
6. Select "_Your Name_ (Personal Team)" in the Team pop-up menu.
|
||||
|
||||
7. This set-up needs to be done once for each application you want to install.
|
||||
Repeat steps 3-6 as needed.
|
||||
|
||||
This generates provisioning profiles for each app you have selected. Now we need
|
||||
to tell Bazel to use them. We have provided a script to make this easier.
|
||||
|
||||
1. In the terminal, to the `mediapipe` directory where you cloned the
|
||||
repository.
|
||||
|
||||
2. Run this command:
|
||||
|
||||
```bash
|
||||
python3 mediapipe/examples/ios/link_local_profiles.py
|
||||
```
|
||||
|
||||
This will find and link the provisioning profile for all applications for which
|
||||
you have enabled automatic provisioning in Xcode.
|
||||
|
||||
Note: once a profile expires, Xcode will generate a new one; you must then run
|
||||
this script again to link the updated profiles.
|
||||
|
||||
#### Custom provisioning
|
||||
|
||||
1. Obtain a provisioning profile from Apple.
|
||||
|
||||
Tip: You can use this command to see the provisioning profiles you have
|
||||
previously downloaded using Xcode: `open ~/Library/MobileDevice/"Provisioning
|
||||
Profiles"`. If there are none, generate and download a profile on
|
||||
[Apple's developer site](https://developer.apple.com/account/resources/).
|
||||
|
||||
1. Symlink or copy your provisioning profile to
|
||||
`mediapipe/mediapipe/provisioning_profile.mobileprovision`.
|
||||
|
||||
```bash
|
||||
cd mediapipe
|
||||
ln -s ~/Downloads/MyProvisioningProfile.mobileprovision mediapipe/provisioning_profile.mobileprovision
|
||||
```
|
||||
|
||||
Note: if you had previously set up automatic provisioning, you should remove the
|
||||
`provisioning_profile.mobileprovision` symlink in each example's directory,
|
||||
since it will take precedence over the common one. You can also overwrite it
|
||||
with you own profile if you need a different profile for different apps.
|
||||
|
||||
1. Open `mediapipe/examples/ios/bundle_id.bzl`, and change the
|
||||
`BUNDLE_ID_PREFIX` to a prefix associated with your provisioning profile.
|
||||
|
||||
### Build and run an app using Xcode
|
||||
|
||||
1. Create the Xcode project, and make sure you have set up either automatic or
|
||||
custom provisioning.
|
||||
|
||||
2. You can now select any of the MediaPipe demos in the target menu, and build
|
||||
and run them as normal.
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
Tip: On the device, in Settings > General > Device Management, make sure the
|
||||
developer (yourself) is trusted.
|
||||
|
||||
### Build an app using the command line
|
||||
|
||||
1. Make sure you have set up either automatic or custom provisioning.
|
||||
|
||||
2. Using [MediaPipe Hands](../solutions/hands.md) for example, run:
|
||||
|
||||
```bash
|
||||
bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp
|
||||
```
|
||||
|
||||
You may see a permission request from `codesign` in order to sign the app.
|
||||
|
||||
Tip: If you are using custom provisioning, you can run this
|
||||
[script](https://github.com/google/mediapipe/blob/master/build_ios_examples.sh)
|
||||
to build all MediaPipe iOS example apps.
|
||||
|
||||
3. In Xcode, open the `Devices and Simulators` window (command-shift-2).
|
||||
|
||||
4. Make sure your device is connected. You will see a list of installed apps.
|
||||
Press the "+" button under the list, and select the `.ipa` file built by
|
||||
Bazel.
|
||||
|
||||
5. You can now run the app on your device.
|
||||
|
||||
Tip: On the device, in Settings > General > Device Management, make sure the
|
||||
developer (yourself) is trusted.
|
||||
@@ -0,0 +1,88 @@
|
||||
---
|
||||
layout: default
|
||||
title: MediaPipe in JavaScript
|
||||
parent: Getting Started
|
||||
nav_order: 4
|
||||
---
|
||||
|
||||
# MediaPipe in JavaScript
|
||||
{: .no_toc }
|
||||
|
||||
1. TOC
|
||||
{:toc}
|
||||
---
|
||||
|
||||
## Ready-to-use JavaScript Solutions
|
||||
|
||||
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]
|
||||
[Hands][H-pg] | [@mediapipe/hands][H-npm] | [mediapipe.dev/demo/hands][H-demo]
|
||||
[Pose][P-pg] | [@mediapipe/pose][P-npm] | [mediapipe.dev/demo/pose][P-demo]
|
||||
[Holistic][Ho-pg] | [@mediapipe/holistic][Ho-npm] | [mediapipe.dev/demo/holistic][Ho-demo]
|
||||
|
||||
Click on a solution link above for more information, including API and code
|
||||
snippets.
|
||||
|
||||
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:
|
||||
|
||||
* [@mediapipe/drawing_utils][draw-npm] - Utilities to draw landmarks and
|
||||
connectors.
|
||||
* [@mediapipe/camera_utils][cam-npm] - Utilities to operate the camera.
|
||||
* [@mediapipe/control_utils][ctrl-npm] - Utilities to show sliders and FPS
|
||||
widgets.
|
||||
|
||||
Note: See these demos and more at [MediaPipe on CodePen][codepen]
|
||||
|
||||
All of these solutions are staged in [NPM][npm]. You can install any package
|
||||
locally with `npm install`. Example:
|
||||
|
||||
```
|
||||
npm install @mediapipe/holistic.
|
||||
```
|
||||
|
||||
If you would rather not stage these locally, you can rely on a CDN (e.g.,
|
||||
[jsDelivr](https://www.jsdelivr.com/)). This will allow you to add scripts
|
||||
directly to your HTML:
|
||||
|
||||
```
|
||||
<head>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]/drawing_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]/holistic.js" crossorigin="anonymous"></script>
|
||||
</head>
|
||||
```
|
||||
|
||||
Note: You can specify version numbers to both NPM and jsdelivr. They are
|
||||
structured as `<major>.<minor>.<build>`. To prevent breaking changes from
|
||||
affecting your work, restrict your request to a `<minor>` number. e.g.,
|
||||
`@mediapipe/[email protected]`.
|
||||
|
||||
[Ho-pg]: ../solutions/holistic#javascript-solution-api
|
||||
[F-pg]: ../solutions/face_mesh#javascript-solution-api
|
||||
[H-pg]: ../solutions/hands#javascript-solution-api
|
||||
[P-pg]: ../solutions/pose#javascript-solution-api
|
||||
[Ho-npm]: https://www.npmjs.com/package/@mediapipe/holistic
|
||||
[F-npm]: https://www.npmjs.com/package/@mediapipe/face_mesh
|
||||
[H-npm]: https://www.npmjs.com/package/@mediapipe/hands
|
||||
[P-npm]: https://www.npmjs.com/package/@mediapipe/pose
|
||||
[draw-npm]: https://www.npmjs.com/package/@mediapipe/pose
|
||||
[cam-npm]: https://www.npmjs.com/package/@mediapipe/pose
|
||||
[ctrl-npm]: https://www.npmjs.com/package/@mediapipe/pose
|
||||
[Ho-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/holistic
|
||||
[F-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/face_mesh
|
||||
[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
|
||||
[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
|
||||
[H-demo]: https://mediapipe.dev/demo/hands
|
||||
[P-demo]: https://mediapipe.dev/demo/pose
|
||||
[npm]: https://www.npmjs.com/package/@mediapipe
|
||||
[codepen]: https://code.mediapipe.dev/codepen
|
||||
@@ -0,0 +1,120 @@
|
||||
---
|
||||
layout: default
|
||||
title: MediaPipe in Python
|
||||
parent: Getting Started
|
||||
has_children: true
|
||||
has_toc: false
|
||||
nav_order: 3
|
||||
---
|
||||
|
||||
# MediaPipe in Python
|
||||
{: .no_toc }
|
||||
|
||||
1. TOC
|
||||
{:toc}
|
||||
---
|
||||
|
||||
## Ready-to-use Python Solutions
|
||||
|
||||
MediaPipe offers ready-to-use yet customizable Python solutions as a prebuilt
|
||||
Python package. MediaPipe Python package is available on
|
||||
[PyPI](https://pypi.org/project/mediapipe/) for Linux, macOS and Windows.
|
||||
|
||||
You can, for instance, activate a Python virtual environment:
|
||||
|
||||
```bash
|
||||
$ python3 -m venv mp_env && source mp_env/bin/activate
|
||||
```
|
||||
|
||||
Install MediaPipe Python package and start Python intepreter:
|
||||
|
||||
```bash
|
||||
(mp_env)$ pip install mediapipe
|
||||
(mp_env)$ python3
|
||||
```
|
||||
|
||||
In Python interpreter, import the package and start using one of the solutions:
|
||||
|
||||
```python
|
||||
import mediapipe as mp
|
||||
mp_face_mesh = mp.solutions.face_mesh
|
||||
```
|
||||
|
||||
Tip: Use command `deactivate` to later exit the Python virtual environment.
|
||||
|
||||
To learn more about configuration options and usage examples, please find
|
||||
details in each solution via the links below:
|
||||
|
||||
* [MediaPipe Face Mesh](../solutions/face_mesh#python-solution-api)
|
||||
* [MediaPipe Hands](../solutions/hands#python-solution-api)
|
||||
* [MediaPipe Pose](../solutions/pose#python-solution-api)
|
||||
* [MediaPipe Holistic](../solutions/holistic#python-solution-api)
|
||||
|
||||
## MediaPipe on Google Colab
|
||||
|
||||
* [MediaPipe Face Mesh Colab](https://mediapipe.page.link/face_mesh_py_colab)
|
||||
* [MediaPipe Hands Colab](https://mediapipe.page.link/hands_py_colab)
|
||||
* [MediaPipe Pose Colab](https://mediapipe.page.link/pose_py_colab)
|
||||
* [MediaPipe Holistic Colab](https://mediapipe.page.link/holistic_py_colab)
|
||||
|
||||
## MediaPipe Python Framework
|
||||
|
||||
The ready-to-use solutions are built upon the MediaPipe Python framework, which
|
||||
can be used by advanced users to run their own MediaPipe graphs in Python.
|
||||
Please see [here](./python_framework.md) for more info.
|
||||
|
||||
## Building MediaPipe Python Package
|
||||
|
||||
Follow the steps below only if you have local changes and need to build the
|
||||
Python package from source. Otherwise, we strongly encourage our users to simply
|
||||
run `pip install mediapipe` to use the ready-to-use solutions, more convenient
|
||||
and much faster.
|
||||
|
||||
1. Make sure that Bazel and OpenCV are correctly installed and configured for
|
||||
MediaPipe. Please see [Installation](./install.md) for how to setup Bazel
|
||||
and OpenCV for MediaPipe on Linux and macOS.
|
||||
|
||||
2. Install the following dependencies.
|
||||
|
||||
Debian or Ubuntu:
|
||||
|
||||
```bash
|
||||
$ sudo apt install python3-dev
|
||||
$ sudo apt install python3-venv
|
||||
$ sudo apt install -y protobuf-compiler
|
||||
```
|
||||
|
||||
macOS:
|
||||
|
||||
```bash
|
||||
$ brew install protobuf
|
||||
```
|
||||
|
||||
Windows:
|
||||
|
||||
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.
|
||||
|
||||
3. Activate a Python virtual environment.
|
||||
|
||||
```bash
|
||||
$ python3 -m venv mp_env && source mp_env/bin/activate
|
||||
```
|
||||
|
||||
4. In the virtual environment, go to the MediaPipe repo directory.
|
||||
|
||||
5. Install the required Python packages.
|
||||
|
||||
```bash
|
||||
(mp_env)mediapipe$ pip3 install -r requirements.txt
|
||||
```
|
||||
|
||||
6. Generate and install MediaPipe package.
|
||||
|
||||
```bash
|
||||
(mp_env)mediapipe$ python3 setup.py gen_protos
|
||||
(mp_env)mediapipe$ python3 setup.py install --link-opencv
|
||||
```
|
||||
@@ -0,0 +1,268 @@
|
||||
---
|
||||
layout: default
|
||||
title: MediaPipe Python Framework
|
||||
parent: MediaPipe in Python
|
||||
grand_parent: Getting Started
|
||||
nav_order: 1
|
||||
---
|
||||
|
||||
# MediaPipe Python Framework
|
||||
{: .no_toc }
|
||||
|
||||
1. TOC
|
||||
{:toc}
|
||||
---
|
||||
|
||||
The MediaPipe Python framework grants direct access to the core components of
|
||||
the MediaPipe C++ framework such as Timestamp, Packet, and CalculatorGraph,
|
||||
whereas the
|
||||
[ready-to-use Python solutions](./python.md#ready-to-use-python-solutions) hide
|
||||
the technical details of the framework and simply return the readable model
|
||||
inference results back to the callers.
|
||||
|
||||
MediaPipe framework sits on top of
|
||||
[the pybind11 library](https://pybind11.readthedocs.io/en/stable/index.html).
|
||||
The C++ core framework is exposed in Python via a C++/Python language binding.
|
||||
The content below assumes that the reader already has a basic understanding of
|
||||
the MediaPipe C++ framework. Otherwise, you can find useful information in
|
||||
[Framework Concepts](../framework_concepts/framework_concepts.md).
|
||||
|
||||
### Packet
|
||||
|
||||
The packet is the basic data flow unit in MediaPipe. A packet consists of a
|
||||
numeric timestamp and a shared pointer to an immutable payload. In Python, a
|
||||
MediaPipe packet can be created by calling one of the packet creator methods in
|
||||
the
|
||||
[`mp.packet_creator`](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/packet_creator.cc)
|
||||
module. Correspondingly, the packet payload can be retrieved by using one of the
|
||||
packet getter methods in the
|
||||
[`mp.packet_getter`](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/packet_getter.cc)
|
||||
module. Note that the packet payload becomes **immutable** after packet
|
||||
creation. Thus, the modification of the retrieved packet content doesn't affect
|
||||
the actual payload in the packet. MediaPipe framework Python API supports the
|
||||
most commonly used data types of MediaPipe (e.g., ImageFrame, Matrix, Protocol
|
||||
Buffers, and the primitive data types) in the core binding. The comprehensive
|
||||
table below shows the type mappings between the Python and the C++ data type
|
||||
along with the packet creator and the content getter method for each data type
|
||||
supported by the MediaPipe Python framework API.
|
||||
|
||||
Python Data Type | C++ Data Type | Packet Creator | Content Getter
|
||||
------------------------------------ | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------
|
||||
bool | bool | create_bool(True) | get_bool(packet)
|
||||
int or np.intc | int_t | create_int(1) | get_int(packet)
|
||||
int or np.int8 | int8_t | create_int8(2**7-1) | get_int(packet)
|
||||
int or np.int16 | int16_t | create_int16(2**15-1) | get_int(packet)
|
||||
int or np.int32 | int32_t | create_int32(2**31-1) | get_int(packet)
|
||||
int or np.int64 | int64_t | create_int64(2**63-1) | get_int(packet)
|
||||
int or np.uint8 | uint8_t | create_uint8(2**8-1) | get_uint(packet)
|
||||
int or np.uint16 | uint16_t | create_uint16(2**16-1) | get_uint(packet)
|
||||
int or np.uint32 | uint32_t | create_uint32(2**32-1) | get_uint(packet)
|
||||
int or np.uint64 | uint64_t | create_uint64(2**64-1) | get_uint(packet)
|
||||
float or np.float32 | float | create_float(1.1) | get_float(packet)
|
||||
float or np.double | double | create_double(1.1) | get_float(packet)
|
||||
str (UTF-8) | std::string | create_string('abc') | get_str(packet)
|
||||
bytes | std::string | create_string(b'\xd0\xd0\xd0') | get_bytes(packet)
|
||||
mp.Packet | mp::Packet | create_packet(p) | get_packet(packet)
|
||||
List\[bool\] | std::vector\<bool\> | create_bool_vector(\[True, False\]) | get_bool_list(packet)
|
||||
List\[int\] or List\[np.intc\] | int\[\] | create_int_array(\[1, 2, 3\]) | get_int_list(packet, size=10)
|
||||
List\[int\] or List\[np.intc\] | std::vector\<int\> | create_int_vector(\[1, 2, 3\]) | get_int_list(packet)
|
||||
List\[float\] or List\[np.float\] | float\[\] | create_float_arrary(\[0.1, 0.2\]) | get_float_list(packet, size=10)
|
||||
List\[float\] or List\[np.float\] | std::vector\<float\> | create_float_vector(\[0.1, 0.2\]) | get_float_list(packet, size=10)
|
||||
List\[str\] | std::vector\<std::string\> | create_string_vector(\['a'\]) | get_str_list(packet)
|
||||
List\[mp.Packet\] | std::vector\<mp::Packet\> | create_packet_vector(<br> \[packet1, packet2\]) | get_packet_list(p)
|
||||
Mapping\[str, Packet\] | std::map<std::string, Packet> | create_string_to_packet_map(<br> {'a': packet1, 'b': packet2}) | get_str_to_packet_dict(packet)
|
||||
np.ndarray<br>(cv.mat and PIL.Image) | mp::ImageFrame | create_image_frame(<br> format=ImageFormat.SRGB,<br> 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)
|
||||
|
||||
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
|
||||
with MediaPipe, you may extend the Packet API for a new data type in the
|
||||
following steps:
|
||||
|
||||
1. Write the pybind11
|
||||
[class binding code](https://pybind11.readthedocs.io/en/stable/advanced/classes.html)
|
||||
or
|
||||
[a custom type caster](https://pybind11.readthedocs.io/en/stable/advanced/cast/custom.html?highlight=custom%20type%20caster)
|
||||
for the custom type in a cc file.
|
||||
|
||||
```c++
|
||||
#include "path/to/my_type/header/file.h"
|
||||
#include "pybind11/pybind11.h"
|
||||
|
||||
namespace py = pybind11;
|
||||
|
||||
PYBIND11_MODULE(my_type_binding, m) {
|
||||
// Write binding code or a custom type caster for MyType.
|
||||
py::class_<MyType>(m, "MyType")
|
||||
.def(py::init<>())
|
||||
.def(...);
|
||||
}
|
||||
```
|
||||
|
||||
2. Create a new packet creator and getter method of the custom type in a
|
||||
separate cc file.
|
||||
|
||||
```c++
|
||||
#include "path/to/my_type/header/file.h"
|
||||
#include "mediapipe/framework/packet.h"
|
||||
#include "pybind11/pybind11.h"
|
||||
|
||||
namespace mediapipe {
|
||||
namespace py = pybind11;
|
||||
|
||||
PYBIND11_MODULE(my_packet_methods, m) {
|
||||
m.def(
|
||||
"create_my_type",
|
||||
[](const MyType& my_type) { return MakePacket<MyType>(my_type); });
|
||||
|
||||
m.def(
|
||||
"get_my_type",
|
||||
[](const Packet& packet) {
|
||||
if(!packet.ValidateAsType<MyType>().ok()) {
|
||||
PyErr_SetString(PyExc_ValueError, "Packet data type mismatch.");
|
||||
return py::error_already_set();
|
||||
}
|
||||
return packet.Get<MyType>();
|
||||
});
|
||||
} // namespace mediapipe
|
||||
```
|
||||
|
||||
3. Add two bazel build rules for the custom type binding and the new packet
|
||||
methods in the BUILD file.
|
||||
|
||||
```
|
||||
load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
|
||||
|
||||
pybind_extension(
|
||||
name = "my_type_binding",
|
||||
srcs = ["my_type_binding.cc"],
|
||||
deps = [":my_type"],
|
||||
)
|
||||
|
||||
pybind_extension(
|
||||
name = "my_packet_methods",
|
||||
srcs = ["my_packet_methods.cc"],
|
||||
deps = [
|
||||
":my_type",
|
||||
"//mediapipe/framework:packet"
|
||||
],
|
||||
)
|
||||
```
|
||||
|
||||
4. Build the pybind extension targets (with the suffix .so) by Bazel and move the generated dynamic libraries into one of the $LD_LIBRARY_PATH dirs.
|
||||
|
||||
5. Use the binding modules in Python.
|
||||
|
||||
```python
|
||||
import my_type_binding
|
||||
import my_packet_methods
|
||||
|
||||
packet = my_packet_methods.create_my_type(my_type_binding.MyType())
|
||||
my_type = my_packet_methods.get_my_type(packet)
|
||||
```
|
||||
|
||||
### Timestamp
|
||||
|
||||
Each packet contains a timestamp that is in units of microseconds. In Python,
|
||||
the Packet API provides a convenience method `packet.at()` to define the numeric
|
||||
timestamp of a packet. More generally, `packet.timestamp` is the packet class
|
||||
property for accessing the underlying timestamp. To convert an Unix epoch to a
|
||||
MediaPipe timestamp,
|
||||
[the Timestamp API](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/timestamp.cc)
|
||||
offers a method `mp.Timestamp.from_seconds()` for this purpose.
|
||||
|
||||
### ImageFrame
|
||||
|
||||
ImageFrame is the container for storing an image or a video frame. Formats
|
||||
supported by ImageFrame are listed in
|
||||
[the ImageFormat enum](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/image_frame.cc#l=170).
|
||||
Pixels are encoded row-major with interleaved color components, and ImageFrame
|
||||
supports uint8, uint16, and float as its data types. MediaPipe provides
|
||||
[an ImageFrame Python API](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/image_frame.cc)
|
||||
to access the ImageFrame C++ class. In Python, the easiest way to retrieve the
|
||||
pixel data is to call `image_frame.numpy_view()` to get a numpy ndarray. Note
|
||||
that the returned numpy ndarray, a reference to the internal pixel data, is
|
||||
unwritable. If the callers need to modify the numpy ndarray, it's required to
|
||||
explicitly call a copy operation to obtain a copy. When MediaPipe takes a numpy
|
||||
ndarray to make an ImageFrame, it assumes that the data is stored contiguously.
|
||||
Correspondingly, the pixel data of an ImageFrame will be realigned to be
|
||||
contiguous when it's returned to the Python side.
|
||||
|
||||
### Graph
|
||||
|
||||
In MediaPipe, all processing takes places within the context of a
|
||||
CalculatorGraph.
|
||||
[The CalculatorGraph Python API](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/calculator_graph.cc)
|
||||
is a direct binding to the C++ CalculatorGraph class. The major difference is
|
||||
the CalculatorGraph Python API raises a Python error instead of returning a
|
||||
non-OK Status when an error occurs. Therefore, as a Python user, you can handle
|
||||
the exceptions as you normally do. The life cycle of a CalculatorGraph contains
|
||||
three stages: initialization and setup, graph run, and graph shutdown.
|
||||
|
||||
1. Initialize a CalculatorGraph with a CalculatorGraphConfig protobuf or binary
|
||||
protobuf file, and provide callback method(s) to observe the output
|
||||
stream(s).
|
||||
|
||||
Option 1. Initialize a CalculatorGraph with a CalculatorGraphConfig protobuf
|
||||
or its text representation, and observe the output stream(s):
|
||||
|
||||
```python
|
||||
import mediapipe as mp
|
||||
|
||||
config_text = """
|
||||
input_stream: 'in_stream'
|
||||
output_stream: 'out_stream'
|
||||
node {
|
||||
calculator: 'PassThroughCalculator'
|
||||
input_stream: 'in_stream'
|
||||
output_stream: 'out_stream'
|
||||
}
|
||||
"""
|
||||
graph = mp.CalculatorGraph(graph_config=config_text)
|
||||
output_packets = []
|
||||
graph.observe_output_stream(
|
||||
'out_stream',
|
||||
lambda stream_name, packet:
|
||||
output_packets.append(mp.packet_getter.get_str(packet)))
|
||||
```
|
||||
|
||||
Option 2. Initialize a CalculatorGraph with with a binary protobuf file, and
|
||||
observe the output stream(s).
|
||||
|
||||
```python
|
||||
import mediapipe as mp
|
||||
# resources dependency
|
||||
|
||||
graph = mp.CalculatorGraph(
|
||||
binary_graph=os.path.join(
|
||||
resources.GetRunfilesDir(), 'path/to/your/graph.binarypb'))
|
||||
graph.observe_output_stream(
|
||||
'out_stream',
|
||||
lambda stream_name, packet: print(f'Get {packet} from {stream_name}'))
|
||||
```
|
||||
|
||||
2. Start the graph run and feed packets into the graph.
|
||||
|
||||
```python
|
||||
graph.start_run()
|
||||
|
||||
graph.add_packet_to_input_stream(
|
||||
'in_stream', mp.packet_creator.create_str('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,
|
||||
data=rgb_img).at(1))
|
||||
```
|
||||
|
||||
3. Close the graph after finish. You may restart the graph for another graph
|
||||
run after the call to `close()`.
|
||||
|
||||
```python
|
||||
graph.close()
|
||||
```
|
||||
|
||||
The Python script can be run by your local Python runtime.
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 156 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 297 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 4.5 MiB |
Binary file not shown.
|
After Width: | Height: | Size: 617 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 120 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 224 KiB After Width: | Height: | Size: 90 KiB |
+31
-32
@@ -21,33 +21,34 @@ ML solutions for live and streaming media.
|
||||
|
||||
## ML solutions in MediaPipe
|
||||
|
||||
Face Detection | Face Mesh | Iris | Hands | Pose | Hair Segmentation
|
||||
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :---------------:
|
||||
[](https://google.github.io/mediapipe/solutions/face_detection) | [](https://google.github.io/mediapipe/solutions/face_mesh) | [](https://google.github.io/mediapipe/solutions/iris) | [](https://google.github.io/mediapipe/solutions/hands) | [](https://google.github.io/mediapipe/solutions/pose) | [](https://google.github.io/mediapipe/solutions/hair_segmentation)
|
||||
Face Detection | Face Mesh | Iris | Hands | Pose | Holistic
|
||||
:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :------:
|
||||
[](https://google.github.io/mediapipe/solutions/face_detection) | [](https://google.github.io/mediapipe/solutions/face_mesh) | [](https://google.github.io/mediapipe/solutions/iris) | [](https://google.github.io/mediapipe/solutions/hands) | [](https://google.github.io/mediapipe/solutions/pose) | [](https://google.github.io/mediapipe/solutions/holistic)
|
||||
|
||||
Object Detection | Box Tracking | Instant Motion Tracking | Objectron | KNIFT
|
||||
:----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
|
||||
[](https://google.github.io/mediapipe/solutions/object_detection) | [](https://google.github.io/mediapipe/solutions/box_tracking) | [](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | [](https://google.github.io/mediapipe/solutions/objectron) | [](https://google.github.io/mediapipe/solutions/knift)
|
||||
Hair Segmentation | Object Detection | Box Tracking | Instant Motion Tracking | Objectron | KNIFT
|
||||
:-------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
|
||||
[](https://google.github.io/mediapipe/solutions/hair_segmentation) | [](https://google.github.io/mediapipe/solutions/object_detection) | [](https://google.github.io/mediapipe/solutions/box_tracking) | [](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | [](https://google.github.io/mediapipe/solutions/objectron) | [](https://google.github.io/mediapipe/solutions/knift)
|
||||
|
||||
<!-- []() in the first cell is needed to preserve table formatting in GitHub Pages. -->
|
||||
<!-- Whenever this table is updated, paste a copy to solutions/solutions.md. -->
|
||||
|
||||
[]() | Android | iOS | Desktop | Python | Web | Coral
|
||||
:---------------------------------------------------------------------------------------- | :-----: | :-: | :-----: | :----: | :-: | :---:
|
||||
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | | ✅ | ✅
|
||||
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | ✅ | |
|
||||
[Iris](https://google.github.io/mediapipe/solutions/iris) | ✅ | ✅ | ✅ | | ✅ |
|
||||
[Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Pose](https://google.github.io/mediapipe/solutions/pose) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | ✅ |
|
||||
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
|
||||
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
|
||||
[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | ✅ | | | | |
|
||||
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
|
||||
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
|
||||
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
|
||||
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
|
||||
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
|
||||
[]() | [Android](https://google.github.io/mediapipe/getting_started/android) | [iOS](https://google.github.io/mediapipe/getting_started/ios) | [C++](https://google.github.io/mediapipe/getting_started/cpp) | [Python](https://google.github.io/mediapipe/getting_started/python) | [JS](https://google.github.io/mediapipe/getting_started/javascript) | [Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
|
||||
:---------------------------------------------------------------------------------------- | :-------------------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------------: | :-----------------------------------------------------------: | :--------------------------------------------------------------------:
|
||||
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | | | ✅
|
||||
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Iris](https://google.github.io/mediapipe/solutions/iris) | ✅ | ✅ | ✅ | | |
|
||||
[Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Pose](https://google.github.io/mediapipe/solutions/pose) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Holistic](https://google.github.io/mediapipe/solutions/holistic) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | |
|
||||
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
|
||||
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
|
||||
[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | ✅ | | | | |
|
||||
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
|
||||
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
|
||||
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
|
||||
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
|
||||
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
|
||||
|
||||
See also
|
||||
[MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
|
||||
@@ -55,16 +56,12 @@ for ML models released in MediaPipe.
|
||||
|
||||
## MediaPipe in Python
|
||||
|
||||
MediaPipe Python package is available on
|
||||
[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
|
||||
install mediapipe` on Linux and macOS, as described in:
|
||||
|
||||
* [MediaPipe Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh#python)
|
||||
([colab](https://mediapipe.page.link/face_mesh_py_colab))
|
||||
* [MediaPipe Hands](https://google.github.io/mediapipe/solutions/hands#python)
|
||||
([colab](https://mediapipe.page.link/hands_py_colab))
|
||||
* [MediaPipe Pose](https://google.github.io/mediapipe/solutions/pose#python)
|
||||
([colab](https://mediapipe.page.link/pose_py_colab))
|
||||
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.md)
|
||||
for more info.
|
||||
|
||||
## MediaPipe on the Web
|
||||
|
||||
@@ -105,6 +102,8 @@ run code search using
|
||||
|
||||
## Publications
|
||||
|
||||
* [Background Features in Google Meet, Powered by Web ML](https://ai.googleblog.com/2020/10/background-features-in-google-meet.html)
|
||||
in Google AI Blog
|
||||
* [MediaPipe 3D Face Transform](https://developers.googleblog.com/2020/09/mediapipe-3d-face-transform.html)
|
||||
in Google Developers Blog
|
||||
* [Instant Motion Tracking With MediaPipe](https://developers.googleblog.com/2020/08/instant-motion-tracking-with-mediapipe.html)
|
||||
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: AutoFlip (Saliency-aware Video Cropping)
|
||||
parent: Solutions
|
||||
nav_order: 12
|
||||
nav_order: 13
|
||||
---
|
||||
|
||||
# AutoFlip: Saliency-aware Video Cropping
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: Box Tracking
|
||||
parent: Solutions
|
||||
nav_order: 8
|
||||
nav_order: 9
|
||||
---
|
||||
|
||||
# MediaPipe Box Tracking
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -105,9 +111,8 @@ new detections to remove obsolete or duplicated boxes.
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
|
||||
and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
|
||||
examples.
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
|
||||
@@ -8,8 +8,14 @@ nav_order: 1
|
||||
# MediaPipe Face Detection
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -36,9 +42,8 @@ section.
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
|
||||
and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
|
||||
examples.
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
|
||||
+223
-95
@@ -8,8 +8,14 @@ nav_order: 2
|
||||
# MediaPipe Face Mesh
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -206,11 +212,222 @@ The effect renderer is implemented as a MediaPipe
|
||||
| :---------------------------------------------------------------------: |
|
||||
| *Fig 4. An example of face effects rendered by the Face Geometry Effect Renderer.* |
|
||||
|
||||
## Solution APIs
|
||||
|
||||
### Configuration Options
|
||||
|
||||
Naming style and availability may differ slightly across platforms/languages.
|
||||
|
||||
#### static_image_mode
|
||||
|
||||
If set to `false`, the solution treats the input images as a video stream. It
|
||||
will try to detect faces in the first input images, and upon a successful
|
||||
detection further localizes the face landmarks. In subsequent images, once all
|
||||
[max_num_faces](#max_num_faces) faces are detected and the corresponding face
|
||||
landmarks are localized, it simply tracks those landmarks without invoking
|
||||
another detection until it loses track of any of the faces. This reduces latency
|
||||
and is ideal for processing video frames. If set to `true`, face detection runs
|
||||
on every input image, ideal for processing a batch of static, possibly
|
||||
unrelated, images. Default to `false`.
|
||||
|
||||
#### max_num_faces
|
||||
|
||||
Maximum number of faces to detect. Default to `1`.
|
||||
|
||||
#### min_detection_confidence
|
||||
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the face detection model for the
|
||||
detection to be considered successful. Default to `0.5`.
|
||||
|
||||
#### min_tracking_confidence
|
||||
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
|
||||
face landmarks to be considered tracked successfully, or otherwise face
|
||||
detection will be invoked automatically on the next input image. Setting it to a
|
||||
higher value can increase robustness of the solution, at the expense of a higher
|
||||
latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
|
||||
face detection simply runs on every image. Default to `0.5`.
|
||||
|
||||
### Output
|
||||
|
||||
Naming style may differ slightly across platforms/languages.
|
||||
|
||||
#### multi_face_landmarks
|
||||
|
||||
Collection of detected/tracked faces, where each face is represented as a list
|
||||
of 468 face landmarks and each landmark is composed of `x`, `y` and `z`. `x` and
|
||||
`y` are normalized to `[0.0, 1.0]` by the image width and height respectively.
|
||||
`z` represents the landmark depth with the depth at center of the head being the
|
||||
origin, and the smaller the value the closer the landmark is to the camera. The
|
||||
magnitude of `z` uses roughly the same scale as `x`.
|
||||
|
||||
### Python Solution API
|
||||
|
||||
Please first follow general [instructions](../getting_started/python.md) to
|
||||
install MediaPipe Python package, then learn more in the companion [Colab] and
|
||||
the following usage example.
|
||||
|
||||
Supported configuration options:
|
||||
|
||||
* [static_image_mode](#static_image_mode)
|
||||
* [max_num_faces](#max_num_faces)
|
||||
* [min_detection_confidence](#min_detection_confidence)
|
||||
* [min_tracking_confidence](#min_tracking_confidence)
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import mediapipe as mp
|
||||
mp_drawing = mp.solutions.drawing_utils
|
||||
mp_face_mesh = mp.solutions.face_mesh
|
||||
|
||||
# For static images:
|
||||
face_mesh = mp_face_mesh.FaceMesh(
|
||||
static_image_mode=True,
|
||||
max_num_faces=1,
|
||||
min_detection_confidence=0.5)
|
||||
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
|
||||
for idx, file in enumerate(file_list):
|
||||
image = cv2.imread(file)
|
||||
# Convert the BGR image to RGB before processing.
|
||||
results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
|
||||
|
||||
# Print and draw face mesh landmarks on the image.
|
||||
if not results.multi_face_landmarks:
|
||||
continue
|
||||
annotated_image = image.copy()
|
||||
for face_landmarks in results.multi_face_landmarks:
|
||||
print('face_landmarks:', face_landmarks)
|
||||
mp_drawing.draw_landmarks(
|
||||
image=annotated_image,
|
||||
landmark_list=face_landmarks,
|
||||
connections=mp_face_mesh.FACE_CONNECTIONS,
|
||||
landmark_drawing_spec=drawing_spec,
|
||||
connection_drawing_spec=drawing_spec)
|
||||
cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
|
||||
face_mesh.close()
|
||||
|
||||
# For webcam input:
|
||||
face_mesh = mp_face_mesh.FaceMesh(
|
||||
min_detection_confidence=0.5, min_tracking_confidence=0.5)
|
||||
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
|
||||
cap = cv2.VideoCapture(0)
|
||||
while cap.isOpened():
|
||||
success, image = cap.read()
|
||||
if not success:
|
||||
print("Ignoring empty camera frame.")
|
||||
# If loading a video, use 'break' instead of 'continue'.
|
||||
continue
|
||||
|
||||
# Flip the image horizontally for a later selfie-view display, and convert
|
||||
# the BGR image to RGB.
|
||||
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
|
||||
# To improve performance, optionally mark the image as not writeable to
|
||||
# pass by reference.
|
||||
image.flags.writeable = False
|
||||
results = face_mesh.process(image)
|
||||
|
||||
# Draw the face mesh annotations on the image.
|
||||
image.flags.writeable = True
|
||||
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
||||
if results.multi_face_landmarks:
|
||||
for face_landmarks in results.multi_face_landmarks:
|
||||
mp_drawing.draw_landmarks(
|
||||
image=image,
|
||||
landmark_list=face_landmarks,
|
||||
connections=mp_face_mesh.FACE_CONNECTIONS,
|
||||
landmark_drawing_spec=drawing_spec,
|
||||
connection_drawing_spec=drawing_spec)
|
||||
cv2.imshow('MediaPipe FaceMesh', image)
|
||||
if cv2.waitKey(5) & 0xFF == 27:
|
||||
break
|
||||
face_mesh.close()
|
||||
cap.release()
|
||||
```
|
||||
|
||||
### JavaScript Solution API
|
||||
|
||||
Please first see general [introduction](../getting_started/javascript.md) on
|
||||
MediaPipe in JavaScript, then learn more in the companion [web demo] and the
|
||||
following usage example.
|
||||
|
||||
Supported configuration options:
|
||||
|
||||
* [maxNumFaces](#max_num_faces)
|
||||
* [minDetectionConfidence](#min_detection_confidence)
|
||||
* [minTrackingConfidence](#min_tracking_confidence)
|
||||
|
||||
```html
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh/face_mesh.js" crossorigin="anonymous"></script>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="container">
|
||||
<video class="input_video"></video>
|
||||
<canvas class="output_canvas" width="1280px" height="720px"></canvas>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
```javascript
|
||||
<script type="module">
|
||||
const videoElement = document.getElementsByClassName('input_video')[0];
|
||||
const canvasElement = document.getElementsByClassName('output_canvas')[0];
|
||||
const canvasCtx = canvasElement.getContext('2d');
|
||||
|
||||
function onResults(results) {
|
||||
canvasCtx.save();
|
||||
canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
|
||||
canvasCtx.drawImage(
|
||||
results.image, 0, 0, canvasElement.width, canvasElement.height);
|
||||
if (results.multiFaceLandmarks) {
|
||||
for (const landmarks of results.multiFaceLandmarks) {
|
||||
drawConnectors(canvasCtx, landmarks, FACEMESH_TESSELATION,
|
||||
{color: '#C0C0C070', lineWidth: 1});
|
||||
drawConnectors(canvasCtx, landmarks, FACEMESH_RIGHT_EYE, {color: '#FF3030'});
|
||||
drawConnectors(canvasCtx, landmarks, FACEMESH_RIGHT_EYEBROW, {color: '#FF3030'});
|
||||
drawConnectors(canvasCtx, landmarks, FACEMESH_LEFT_EYE, {color: '#30FF30'});
|
||||
drawConnectors(canvasCtx, landmarks, FACEMESH_LEFT_EYEBROW, {color: '#30FF30'});
|
||||
drawConnectors(canvasCtx, landmarks, FACEMESH_FACE_OVAL, {color: '#E0E0E0'});
|
||||
drawConnectors(canvasCtx, landmarks, FACEMESH_LIPS, {color: '#E0E0E0'});
|
||||
}
|
||||
}
|
||||
canvasCtx.restore();
|
||||
}
|
||||
|
||||
const faceMesh = new FaceMesh({locateFile: (file) => {
|
||||
return `https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh/${file}`;
|
||||
}});
|
||||
faceMesh.setOptions({
|
||||
maxNumFaces: 1,
|
||||
minDetectionConfidence: 0.5,
|
||||
minTrackingConfidence: 0.5
|
||||
});
|
||||
faceMesh.onResults(onResults);
|
||||
|
||||
const camera = new Camera(videoElement, {
|
||||
onFrame: async () => {
|
||||
await faceMesh.send({image: videoElement});
|
||||
},
|
||||
width: 1280,
|
||||
height: 720
|
||||
});
|
||||
camera.start();
|
||||
</script>
|
||||
```
|
||||
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios) and
|
||||
[desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe examples.
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
@@ -254,99 +471,6 @@ and for iOS modify `kNumFaces` in
|
||||
Tip: Maximum number of faces to detect/process is set to 1 by default. To change
|
||||
it, in the graph file modify the option of `ConstantSidePacketCalculator`.
|
||||
|
||||
#### Python
|
||||
|
||||
MediaPipe Python package is available on
|
||||
[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
|
||||
install mediapipe` on Linux and macOS, as described below and in this
|
||||
[colab](https://mediapipe.page.link/face_mesh_py_colab). If you do need to build
|
||||
the Python package from source, see
|
||||
[additional instructions](../getting_started/building_examples.md#python).
|
||||
|
||||
Activate a Python virtual environment:
|
||||
|
||||
```bash
|
||||
$ python3 -m venv mp_env && source mp_env/bin/activate
|
||||
```
|
||||
|
||||
Install MediaPipe Python package:
|
||||
|
||||
```bash
|
||||
(mp_env)$ pip install mediapipe
|
||||
```
|
||||
|
||||
Run the following Python code:
|
||||
|
||||
<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/face_mesh.py and copy it over. -->
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import mediapipe as mp
|
||||
mp_drawing = mp.solutions.drawing_utils
|
||||
mp_face_mesh = mp.solutions.face_mesh
|
||||
|
||||
# For static images:
|
||||
face_mesh = mp_face_mesh.FaceMesh(
|
||||
static_image_mode=True,
|
||||
max_num_faces=1,
|
||||
min_detection_confidence=0.5)
|
||||
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
|
||||
for idx, file in enumerate(file_list):
|
||||
image = cv2.imread(file)
|
||||
# Convert the BGR image to RGB before processing.
|
||||
results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
|
||||
|
||||
# Print and draw face mesh landmarks on the image.
|
||||
if not results.multi_face_landmarks:
|
||||
continue
|
||||
annotated_image = image.copy()
|
||||
for face_landmarks in results.multi_face_landmarks:
|
||||
print('face_landmarks:', face_landmarks)
|
||||
mp_drawing.draw_landmarks(
|
||||
image=annotated_image,
|
||||
landmark_list=face_landmarks,
|
||||
connections=mp_face_mesh.FACE_CONNECTIONS,
|
||||
landmark_drawing_spec=drawing_spec,
|
||||
connection_drawing_spec=drawing_spec)
|
||||
cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', image)
|
||||
face_mesh.close()
|
||||
|
||||
# For webcam input:
|
||||
face_mesh = mp_face_mesh.FaceMesh(
|
||||
min_detection_confidence=0.5, min_tracking_confidence=0.5)
|
||||
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
|
||||
cap = cv2.VideoCapture(0)
|
||||
while cap.isOpened():
|
||||
success, image = cap.read()
|
||||
if not success:
|
||||
break
|
||||
|
||||
# Flip the image horizontally for a later selfie-view display, and convert
|
||||
# the BGR image to RGB.
|
||||
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
|
||||
# To improve performance, optionally mark the image as not writeable to
|
||||
# pass by reference.
|
||||
image.flags.writeable = False
|
||||
results = face_mesh.process(image)
|
||||
|
||||
# Draw the face mesh annotations on the image.
|
||||
image.flags.writeable = True
|
||||
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
||||
if results.multi_face_landmarks:
|
||||
for face_landmarks in results.multi_face_landmarks:
|
||||
mp_drawing.draw_landmarks(
|
||||
image=image,
|
||||
landmark_list=face_landmarks,
|
||||
connections=mp_face_mesh.FACE_CONNECTIONS,
|
||||
landmark_drawing_spec=drawing_spec,
|
||||
connection_drawing_spec=drawing_spec)
|
||||
cv2.imshow('MediaPipe FaceMesh', image)
|
||||
if cv2.waitKey(5) & 0xFF == 27:
|
||||
break
|
||||
face_mesh.close()
|
||||
cap.release()
|
||||
```
|
||||
|
||||
### Face Effect Example
|
||||
|
||||
Face effect example showcases real-time mobile face effect application use case
|
||||
@@ -379,3 +503,7 @@ only works for a single face. For visual reference, please refer to *Fig. 4*.
|
||||
[OBJ](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/data/canonical_face_model.obj),
|
||||
[UV visualization](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png)
|
||||
* [Models and model cards](./models.md#face_mesh)
|
||||
|
||||
[Colab]:https://mediapipe.page.link/face_mesh_py_colab
|
||||
|
||||
[web demo]:https://code.mediapipe.dev/codepen/face_mesh
|
||||
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: Hair Segmentation
|
||||
parent: Solutions
|
||||
nav_order: 6
|
||||
nav_order: 7
|
||||
---
|
||||
|
||||
# MediaPipe Hair Segmentation
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
|
||||
@@ -17,9 +23,8 @@ nav_order: 6
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
|
||||
and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
|
||||
examples.
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
|
||||
+238
-97
@@ -8,8 +8,14 @@ nav_order: 4
|
||||
# MediaPipe Hands
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -126,16 +132,239 @@ and provide additional supervision on the nature of hand geometry, we also
|
||||
render a high-quality synthetic hand model over various backgrounds and map it
|
||||
to the corresponding 3D coordinates.
|
||||
|
||||
 |
|
||||
:--------------------------------------------------------: |
|
||||
*Fig 2. 21 hand landmarks.* |
|
||||
|
||||
|  |
|
||||
| :-------------------------------------------------------------------------: |
|
||||
| *Fig 2. Top: Aligned hand crops passed to the tracking network with ground truth annotation. Bottom: Rendered synthetic hand images with ground truth annotation.* |
|
||||
| *Fig 3. Top: Aligned hand crops passed to the tracking network with ground |
|
||||
: truth annotation. Bottom\: Rendered synthetic hand images with ground truth :
|
||||
: annotation.* :
|
||||
|
||||
## Solution APIs
|
||||
|
||||
### Configuration Options
|
||||
|
||||
Naming style and availability may differ slightly across platforms/languages.
|
||||
|
||||
#### static_image_mode
|
||||
|
||||
If set to `false`, the solution treats the input images as a video stream. It
|
||||
will try to detect hands in the first input images, and upon a successful
|
||||
detection further localizes the hand landmarks. In subsequent images, once all
|
||||
[max_num_hands](#max_num_hands) hands are detected and the corresponding hand
|
||||
landmarks are localized, it simply tracks those landmarks without invoking
|
||||
another detection until it loses track of any of the hands. This reduces latency
|
||||
and is ideal for processing video frames. If set to `true`, hand detection runs
|
||||
on every input image, ideal for processing a batch of static, possibly
|
||||
unrelated, images. Default to `false`.
|
||||
|
||||
#### max_num_hands
|
||||
|
||||
Maximum number of hands to detect. Default to `2`.
|
||||
|
||||
#### min_detection_confidence
|
||||
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the hand detection model for the
|
||||
detection to be considered successful. Default to `0.5`.
|
||||
|
||||
#### min_tracking_confidence:
|
||||
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
|
||||
hand landmarks to be considered tracked successfully, or otherwise hand
|
||||
detection will be invoked automatically on the next input image. Setting it to a
|
||||
higher value can increase robustness of the solution, at the expense of a higher
|
||||
latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
|
||||
hand detection simply runs on every image. Default to `0.5`.
|
||||
|
||||
### Output
|
||||
|
||||
Naming style may differ slightly across platforms/languages.
|
||||
|
||||
#### multi_hand_landmarks
|
||||
|
||||
Collection of detected/tracked hands, where each hand is represented as a list
|
||||
of 21 hand landmarks and each landmark is composed of `x`, `y` and `z`. `x` and
|
||||
`y` are normalized to `[0.0, 1.0]` by the image width and height respectively.
|
||||
`z` represents the landmark depth with the depth at the wrist being the origin,
|
||||
and the smaller the value the closer the landmark is to the camera. The
|
||||
magnitude of `z` uses roughly the same scale as `x`.
|
||||
|
||||
#### multi_handedness
|
||||
|
||||
Collection of handedness of the detected/tracked hands (i.e. is it a left or
|
||||
right hand). Each hand is composed of `label` and `score`. `label` is a string
|
||||
of value either `"Left"` or `"Right"`. `score` is the estimated probability of
|
||||
the predicted handedness and is always greater than or equal to `0.5` (and the
|
||||
opposite handedness has an estimated probability of `1 - score`).
|
||||
|
||||
Note that handedness is determined assuming the input image is mirrored, i.e.,
|
||||
taken with a front-facing/selfie camera with images flipped horizontally. If it
|
||||
is not the case, please swap the handedness output in the application.
|
||||
|
||||
### Python Solution API
|
||||
|
||||
Please first follow general [instructions](../getting_started/python.md) to
|
||||
install MediaPipe Python package, then learn more in the companion [Colab] and
|
||||
the following usage example.
|
||||
|
||||
Supported configuration options:
|
||||
|
||||
* [static_image_mode](#static_image_mode)
|
||||
* [max_num_hands](#max_num_hands)
|
||||
* [min_detection_confidence](#min_detection_confidence)
|
||||
* [min_tracking_confidence](#min_tracking_confidence)
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import mediapipe as mp
|
||||
mp_drawing = mp.solutions.drawing_utils
|
||||
mp_hands = mp.solutions.hands
|
||||
|
||||
# For static images:
|
||||
hands = mp_hands.Hands(
|
||||
static_image_mode=True,
|
||||
max_num_hands=2,
|
||||
min_detection_confidence=0.5)
|
||||
for idx, file in enumerate(file_list):
|
||||
# Read an image, flip it around y-axis for correct handedness output (see
|
||||
# above).
|
||||
image = cv2.flip(cv2.imread(file), 1)
|
||||
# Convert the BGR image to RGB before processing.
|
||||
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
|
||||
|
||||
# Print handedness and draw hand landmarks on the image.
|
||||
print('Handedness:', results.multi_handedness)
|
||||
if not results.multi_hand_landmarks:
|
||||
continue
|
||||
image_hight, image_width, _ = image.shape
|
||||
annotated_image = image.copy()
|
||||
for hand_landmarks in results.multi_hand_landmarks:
|
||||
print('hand_landmarks:', hand_landmarks)
|
||||
print(
|
||||
f'Index finger tip coordinates: (',
|
||||
f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
|
||||
f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_hight})'
|
||||
)
|
||||
mp_drawing.draw_landmarks(
|
||||
annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
|
||||
cv2.imwrite(
|
||||
'/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
|
||||
hands.close()
|
||||
|
||||
# For webcam input:
|
||||
hands = mp_hands.Hands(
|
||||
min_detection_confidence=0.5, min_tracking_confidence=0.5)
|
||||
cap = cv2.VideoCapture(0)
|
||||
while cap.isOpened():
|
||||
success, image = cap.read()
|
||||
if not success:
|
||||
print("Ignoring empty camera frame.")
|
||||
# If loading a video, use 'break' instead of 'continue'.
|
||||
continue
|
||||
|
||||
# Flip the image horizontally for a later selfie-view display, and convert
|
||||
# the BGR image to RGB.
|
||||
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
|
||||
# To improve performance, optionally mark the image as not writeable to
|
||||
# pass by reference.
|
||||
image.flags.writeable = False
|
||||
results = hands.process(image)
|
||||
|
||||
# Draw the hand annotations on the image.
|
||||
image.flags.writeable = True
|
||||
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
||||
if results.multi_hand_landmarks:
|
||||
for hand_landmarks in results.multi_hand_landmarks:
|
||||
mp_drawing.draw_landmarks(
|
||||
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
|
||||
cv2.imshow('MediaPipe Hands', image)
|
||||
if cv2.waitKey(5) & 0xFF == 27:
|
||||
break
|
||||
hands.close()
|
||||
cap.release()
|
||||
```
|
||||
|
||||
### JavaScript Solution API
|
||||
|
||||
Please first see general [introduction](../getting_started/javascript.md) on
|
||||
MediaPipe in JavaScript, then learn more in the companion [web demo] and a
|
||||
[fun application], and the following usage example.
|
||||
|
||||
Supported configuration options:
|
||||
|
||||
* [maxNumHands](#max_num_hands)
|
||||
* [minDetectionConfidence](#min_detection_confidence)
|
||||
* [minTrackingConfidence](#min_tracking_confidence)
|
||||
|
||||
```html
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/hands/hands.js" crossorigin="anonymous"></script>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="container">
|
||||
<video class="input_video"></video>
|
||||
<canvas class="output_canvas" width="1280px" height="720px"></canvas>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
```javascript
|
||||
<script type="module">
|
||||
const videoElement = document.getElementsByClassName('input_video')[0];
|
||||
const canvasElement = document.getElementsByClassName('output_canvas')[0];
|
||||
const canvasCtx = canvasElement.getContext('2d');
|
||||
|
||||
function onResults(results) {
|
||||
canvasCtx.save();
|
||||
canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
|
||||
canvasCtx.drawImage(
|
||||
results.image, 0, 0, canvasElement.width, canvasElement.height);
|
||||
if (results.multiHandLandmarks) {
|
||||
for (const landmarks of results.multiHandLandmarks) {
|
||||
drawConnectors(canvasCtx, landmarks, HAND_CONNECTIONS,
|
||||
{color: '#00FF00', lineWidth: 5});
|
||||
drawLandmarks(canvasCtx, landmarks, {color: '#FF0000', lineWidth: 2});
|
||||
}
|
||||
}
|
||||
canvasCtx.restore();
|
||||
}
|
||||
|
||||
const hands = new Hands({locateFile: (file) => {
|
||||
return `https://cdn.jsdelivr.net/npm/@mediapipe/hands/${file}`;
|
||||
}});
|
||||
hands.setOptions({
|
||||
maxNumHands: 2,
|
||||
minDetectionConfidence: 0.5,
|
||||
minTrackingConfidence: 0.5
|
||||
});
|
||||
hands.onResults(onResults);
|
||||
|
||||
const camera = new Camera(videoElement, {
|
||||
onFrame: async () => {
|
||||
await hands.send({image: videoElement});
|
||||
},
|
||||
width: 1280,
|
||||
height: 720
|
||||
});
|
||||
camera.start();
|
||||
</script>
|
||||
```
|
||||
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
|
||||
and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
|
||||
examples.
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
@@ -186,99 +415,6 @@ and for iOS modify `kNumHands` in
|
||||
Tip: Maximum number of hands to detect/process is set to 2 by default. To change
|
||||
it, in the graph file modify the option of `ConstantSidePacketCalculator`.
|
||||
|
||||
### Python
|
||||
|
||||
MediaPipe Python package is available on
|
||||
[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
|
||||
install mediapipe` on Linux and macOS, as described below and in this
|
||||
[colab](https://mediapipe.page.link/hands_py_colab). If you do need to build the
|
||||
Python package from source, see
|
||||
[additional instructions](../getting_started/building_examples.md#python).
|
||||
|
||||
Activate a Python virtual environment:
|
||||
|
||||
```bash
|
||||
$ python3 -m venv mp_env && source mp_env/bin/activate
|
||||
```
|
||||
|
||||
Install MediaPipe Python package:
|
||||
|
||||
```bash
|
||||
(mp_env)$ pip install mediapipe
|
||||
```
|
||||
|
||||
Run the following Python code:
|
||||
|
||||
<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/hands.py and copy it over. -->
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import mediapipe as mp
|
||||
mp_drawing = mp.solutions.drawing_utils
|
||||
mp_hands = mp.solutions.hands
|
||||
|
||||
# For static images:
|
||||
hands = mp_hands.Hands(
|
||||
static_image_mode=True,
|
||||
max_num_hands=2,
|
||||
min_detection_confidence=0.7)
|
||||
for idx, file in enumerate(file_list):
|
||||
# Read an image, flip it around y-axis for correct handedness output (see
|
||||
# above).
|
||||
image = cv2.flip(cv2.imread(file), 1)
|
||||
# Convert the BGR image to RGB before processing.
|
||||
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
|
||||
|
||||
# Print handedness and draw hand landmarks on the image.
|
||||
print('handedness:', results.multi_handedness)
|
||||
if not results.multi_hand_landmarks:
|
||||
continue
|
||||
annotated_image = image.copy()
|
||||
for hand_landmarks in results.multi_hand_landmarks:
|
||||
print('hand_landmarks:', hand_landmarks)
|
||||
mp_drawing.draw_landmarks(
|
||||
annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
|
||||
cv2.imwrite(
|
||||
'/tmp/annotated_image' + str(idx) + '.png', cv2.flip(image, 1))
|
||||
hands.close()
|
||||
|
||||
# For webcam input:
|
||||
hands = mp_hands.Hands(
|
||||
min_detection_confidence=0.7, min_tracking_confidence=0.5)
|
||||
cap = cv2.VideoCapture(0)
|
||||
while cap.isOpened():
|
||||
success, image = cap.read()
|
||||
if not success:
|
||||
break
|
||||
|
||||
# Flip the image horizontally for a later selfie-view display, and convert
|
||||
# the BGR image to RGB.
|
||||
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
|
||||
# To improve performance, optionally mark the image as not writeable to
|
||||
# pass by reference.
|
||||
image.flags.writeable = False
|
||||
results = hands.process(image)
|
||||
|
||||
# Draw the hand annotations on the image.
|
||||
image.flags.writeable = True
|
||||
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
||||
if results.multi_hand_landmarks:
|
||||
for hand_landmarks in results.multi_hand_landmarks:
|
||||
mp_drawing.draw_landmarks(
|
||||
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
|
||||
cv2.imshow('MediaPipe Hands', image)
|
||||
if cv2.waitKey(5) & 0xFF == 27:
|
||||
break
|
||||
hands.close()
|
||||
cap.release()
|
||||
```
|
||||
|
||||
Tip: Use command `deactivate` to exit the Python virtual environment.
|
||||
|
||||
### Web
|
||||
|
||||
Please refer to [these instructions](../index.md#mediapipe-on-the-web).
|
||||
|
||||
## Resources
|
||||
|
||||
* Google AI Blog:
|
||||
@@ -289,3 +425,8 @@ Please refer to [these instructions](../index.md#mediapipe-on-the-web).
|
||||
[MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)
|
||||
([presentation](https://www.youtube.com/watch?v=I-UOrvxxXEk))
|
||||
* [Models and model cards](./models.md#hands)
|
||||
|
||||
[Colab]:https://mediapipe.page.link/hands_py_colab
|
||||
|
||||
[web demo]:https://code.mediapipe.dev/codepen/hands
|
||||
[fun application]:https://code.mediapipe.dev/codepen/defrost
|
||||
|
||||
@@ -0,0 +1,413 @@
|
||||
---
|
||||
layout: default
|
||||
title: Holistic
|
||||
parent: Solutions
|
||||
nav_order: 6
|
||||
---
|
||||
|
||||
# MediaPipe Holistic
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Live perception of simultaneous [human pose](./pose.md),
|
||||
[face landmarks](./face_mesh.md), and [hand tracking](./hands.md) in real-time
|
||||
on mobile devices can enable various modern life applications: fitness and sport
|
||||
analysis, gesture control and sign language recognition, augmented reality
|
||||
try-on and effects. MediaPipe already offers fast and accurate, yet separate,
|
||||
solutions for these tasks. Combining them all in real-time into a semantically
|
||||
consistent end-to-end solution is a uniquely difficult problem requiring
|
||||
simultaneous inference of multiple, dependent neural networks.
|
||||
|
||||
 |
|
||||
:----------------------------------------------------------------------------------------------------: |
|
||||
*Fig 1. Example of MediaPipe Holistic.* |
|
||||
|
||||
## ML Pipeline
|
||||
|
||||
The MediaPipe Holistic pipeline integrates separate models for
|
||||
[pose](./pose.md), [face](./face_mesh.md) and [hand](./hands.md) components,
|
||||
each of which are optimized for their particular domain. However, because of
|
||||
their different specializations, the input to one component is not well-suited
|
||||
for the others. The pose estimation model, for example, takes a lower, fixed
|
||||
resolution video frame (256x256) as input. But if one were to crop the hand and
|
||||
face regions from that image to pass to their respective models, the image
|
||||
resolution would be too low for accurate articulation. Therefore, we designed
|
||||
MediaPipe Holistic as a multi-stage pipeline, which treats the different regions
|
||||
using a region appropriate image resolution.
|
||||
|
||||
First, we estimate the human pose (top of Fig 2) with [BlazePose](./pose.md)’s
|
||||
pose detector and subsequent landmark model. Then, using the inferred pose
|
||||
landmarks we derive three regions of interest (ROI) crops for each hand (2x) and
|
||||
the face, and employ a re-crop model to improve the ROI. We then crop the
|
||||
full-resolution input frame to these ROIs and apply task-specific face and hand
|
||||
models to estimate their corresponding landmarks. Finally, we merge all
|
||||
landmarks with those of the pose model to yield the full 540+ landmarks.
|
||||
|
||||
 |
|
||||
:------------------------------------------------------------------------------: |
|
||||
*Fig 2. MediaPipe Holistic Pipeline Overview.* |
|
||||
|
||||
To streamline the identification of ROIs for face and hands, we utilize a
|
||||
tracking approach similar to the one we use for standalone
|
||||
[face](./face_mesh.md) and [hand](./hands.md) pipelines. It assumes that the
|
||||
object doesn't move significantly between frames and uses estimation from the
|
||||
previous frame as a guide to the object region on the current one. However,
|
||||
during fast movements, the tracker can lose the target, which requires the
|
||||
detector to re-localize it in the image. MediaPipe Holistic uses
|
||||
[pose](./pose.md) prediction (on every frame) as an additional ROI prior to
|
||||
reduce the response time of the pipeline when reacting to fast movements. This
|
||||
also enables the model to retain semantic consistency across the body and its
|
||||
parts by preventing a mixup between left and right hands or body parts of one
|
||||
person in the frame with another.
|
||||
|
||||
In addition, the resolution of the input frame to the pose model is low enough
|
||||
that the resulting ROIs for face and hands are still too inaccurate to guide the
|
||||
re-cropping of those regions, which require a precise input crop to remain
|
||||
lightweight. To close this accuracy gap we use lightweight face and hand re-crop
|
||||
models that play the role of
|
||||
[spatial transformers](https://arxiv.org/abs/1506.02025) and cost only ~10% of
|
||||
corresponding model's inference time.
|
||||
|
||||
The pipeline is implemented as a MediaPipe
|
||||
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt)
|
||||
that uses a
|
||||
[holistic landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt)
|
||||
from the
|
||||
[holistic landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark)
|
||||
and renders using a dedicated
|
||||
[holistic renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt).
|
||||
The
|
||||
[holistic landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt)
|
||||
internally uses a
|
||||
[pose landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark)
|
||||
,
|
||||
[hand landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark)
|
||||
and
|
||||
[face landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/).
|
||||
Please check them for implementation details.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
to visualize its associated subgraphs, please see
|
||||
[visualizer documentation](../tools/visualizer.md).
|
||||
|
||||
## Models
|
||||
|
||||
### Landmark Models
|
||||
|
||||
MediaPipe Holistic utilizes the pose, face and hand landmark models in
|
||||
[MediaPipe Pose](./pose.md), [MediaPipe Face Mesh](./face_mesh.md) and
|
||||
[MediaPipe Hands](./hands.md) respectively to generate a total of 543 landmarks
|
||||
(33 pose landmarks, 468 face landmarks, and 21 hand landmarks per hand).
|
||||
|
||||
### Hand Recrop Model
|
||||
|
||||
For cases when the accuracy of the pose model is low enough that the resulting
|
||||
ROIs for hands are still too inaccurate we run the additional lightweight hand
|
||||
re-crop model that play the role of
|
||||
[spatial transformer](https://arxiv.org/abs/1506.02025) and cost only ~10% of
|
||||
hand model inference time.
|
||||
|
||||
## Solution APIs
|
||||
|
||||
### Cross-platform Configuration Options
|
||||
|
||||
Naming style and availability may differ slightly across platforms/languages.
|
||||
|
||||
#### static_image_mode
|
||||
|
||||
If set to `false`, the solution treats the input images as a video stream. It
|
||||
will try to detect the most prominent person in the very first images, and upon
|
||||
a successful detection further localizes the pose and other landmarks. In
|
||||
subsequent images, it then simply tracks those landmarks without invoking
|
||||
another detection until it loses track, on reducing computation and latency. If
|
||||
set to `true`, person detection runs every input image, ideal for processing a
|
||||
batch of static, possibly unrelated, images. Default to `false`.
|
||||
|
||||
#### upper_body_only
|
||||
|
||||
If set to `true`, the solution outputs only the 25 upper-body pose landmarks
|
||||
(535 in total) instead of the full set of 33 pose landmarks (543 in total). Note
|
||||
that upper-body-only prediction may be more accurate for use cases where the
|
||||
lower-body parts are mostly out of view. Default to `false`.
|
||||
|
||||
#### smooth_landmarks
|
||||
|
||||
If set to `true`, the solution filters pose landmarks across different input
|
||||
images to reduce jitter, but ignored if [static_image_mode](#static_image_mode)
|
||||
is also set to `true`. Default to `true`.
|
||||
|
||||
#### min_detection_confidence
|
||||
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the person-detection model for the
|
||||
detection to be considered successful. Default to `0.5`.
|
||||
|
||||
#### min_tracking_confidence
|
||||
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
|
||||
pose landmarks to be considered tracked successfully, or otherwise person
|
||||
detection will be invoked automatically on the next input image. Setting it to a
|
||||
higher value can increase robustness of the solution, at the expense of a higher
|
||||
latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
|
||||
person detection simply runs on every image. Default to `0.5`.
|
||||
|
||||
### Output
|
||||
|
||||
Naming style may differ slightly across platforms/languages.
|
||||
|
||||
#### pose_landmarks
|
||||
|
||||
A list of pose landmarks. Each landmark consists of the following:
|
||||
|
||||
* `x` and `y`: Landmark coordinates normalized to `[0.0, 1.0]` by the image
|
||||
width and height respectively.
|
||||
* `z`: Should be discarded as currently the model is not fully trained to
|
||||
predict depth, but this is something on the roadmap.
|
||||
* `visibility`: A value in `[0.0, 1.0]` indicating the likelihood of the
|
||||
landmark being visible (present and not occluded) in the image.
|
||||
|
||||
#### face_landmarks
|
||||
|
||||
A list of 468 face landmarks. Each landmark consists of `x`, `y` and `z`. `x`
|
||||
and `y` are normalized to `[0.0, 1.0]` by the image width and height
|
||||
respectively. `z` represents the landmark depth with the depth at center of the
|
||||
head being the origin, and the smaller the value the closer the landmark is to
|
||||
the camera. The magnitude of `z` uses roughly the same scale as `x`.
|
||||
|
||||
#### left_hand_landmarks
|
||||
|
||||
A list of 21 hand landmarks on the left hand. Each landmark consists of `x`, `y`
|
||||
and `z`. `x` and `y` are normalized to `[0.0, 1.0]` by the image width and
|
||||
height respectively. `z` represents the landmark depth with the depth at the
|
||||
wrist being the origin, and the smaller the value the closer the landmark is to
|
||||
the camera. The magnitude of `z` uses roughly the same scale as `x`.
|
||||
|
||||
#### right_hand_landmarks
|
||||
|
||||
A list of 21 hand landmarks on the right hand, in the same representation as
|
||||
[left_hand_landmarks](#left_hand_landmarks).
|
||||
|
||||
### Python Solution API
|
||||
|
||||
Please first follow general [instructions](../getting_started/python.md) to
|
||||
install MediaPipe Python package, then learn more in the companion [Colab] and
|
||||
the following usage example.
|
||||
|
||||
Supported configuration options:
|
||||
|
||||
* [static_image_mode](#static_image_mode)
|
||||
* [upper_body_only](#upper_body_only)
|
||||
* [smooth_landmarks](#smooth_landmarks)
|
||||
* [min_detection_confidence](#min_detection_confidence)
|
||||
* [min_tracking_confidence](#min_tracking_confidence)
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import mediapipe as mp
|
||||
mp_drawing = mp.solutions.drawing_utils
|
||||
mp_holistic = mp.solutions.holistic
|
||||
|
||||
# For static images:
|
||||
holistic = mp_holistic.Holistic(static_image_mode=True)
|
||||
for idx, file in enumerate(file_list):
|
||||
image = cv2.imread(file)
|
||||
image_hight, image_width, _ = image.shape
|
||||
# Convert the BGR image to RGB before processing.
|
||||
results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
|
||||
|
||||
if results.pose_landmarks:
|
||||
print(
|
||||
f'Nose coordinates: ('
|
||||
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
|
||||
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_hight})'
|
||||
)
|
||||
# Draw pose, left and right hands, and face landmarks on the image.
|
||||
annotated_image = image.copy()
|
||||
mp_drawing.draw_landmarks(
|
||||
annotated_image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
|
||||
mp_drawing.draw_landmarks(
|
||||
annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
|
||||
mp_drawing.draw_landmarks(
|
||||
annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
|
||||
mp_drawing.draw_landmarks(
|
||||
annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
|
||||
cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
|
||||
holistic.close()
|
||||
|
||||
# For webcam input:
|
||||
holistic = mp_holistic.Holistic(
|
||||
min_detection_confidence=0.5, min_tracking_confidence=0.5)
|
||||
cap = cv2.VideoCapture(0)
|
||||
while cap.isOpened():
|
||||
success, image = cap.read()
|
||||
if not success:
|
||||
print("Ignoring empty camera frame.")
|
||||
# If loading a video, use 'break' instead of 'continue'.
|
||||
continue
|
||||
|
||||
# Flip the image horizontally for a later selfie-view display, and convert
|
||||
# the BGR image to RGB.
|
||||
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
|
||||
# To improve performance, optionally mark the image as not writeable to
|
||||
# pass by reference.
|
||||
image.flags.writeable = False
|
||||
results = holistic.process(image)
|
||||
|
||||
# Draw landmark annotation on the image.
|
||||
image.flags.writeable = True
|
||||
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
||||
mp_drawing.draw_landmarks(
|
||||
image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
|
||||
mp_drawing.draw_landmarks(
|
||||
image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
|
||||
mp_drawing.draw_landmarks(
|
||||
image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
|
||||
mp_drawing.draw_landmarks(
|
||||
image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
|
||||
cv2.imshow('MediaPipe Holistic', image)
|
||||
if cv2.waitKey(5) & 0xFF == 27:
|
||||
break
|
||||
holistic.close()
|
||||
cap.release()
|
||||
```
|
||||
|
||||
### JavaScript Solution API
|
||||
|
||||
Please first see general [introduction](../getting_started/javascript.md) on
|
||||
MediaPipe in JavaScript, then learn more in the companion [web demo] and the
|
||||
following usage example.
|
||||
|
||||
Supported configuration options:
|
||||
|
||||
* [upperBodyOnly](#upper_body_only)
|
||||
* [smoothLandmarks](#smooth_landmarks)
|
||||
* [minDetectionConfidence](#min_detection_confidence)
|
||||
* [minTrackingConfidence](#min_tracking_confidence)
|
||||
|
||||
```html
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/holistic/holistic.js" crossorigin="anonymous"></script>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="container">
|
||||
<video class="input_video"></video>
|
||||
<canvas class="output_canvas" width="1280px" height="720px"></canvas>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
```javascript
|
||||
<script type="module">
|
||||
const videoElement = document.getElementsByClassName('input_video')[0];
|
||||
const canvasElement = document.getElementsByClassName('output_canvas')[0];
|
||||
const canvasCtx = canvasElement.getContext('2d');
|
||||
|
||||
function onResults(results) {
|
||||
canvasCtx.save();
|
||||
canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
|
||||
canvasCtx.drawImage(
|
||||
results.image, 0, 0, canvasElement.width, canvasElement.height);
|
||||
drawConnectors(canvasCtx, results.poseLandmarks, POSE_CONNECTIONS,
|
||||
{color: '#00FF00', lineWidth: 4});
|
||||
drawLandmarks(canvasCtx, results.poseLandmarks,
|
||||
{color: '#FF0000', lineWidth: 2});
|
||||
drawConnectors(canvasCtx, results.faceLandmarks, FACEMESH_TESSELATION,
|
||||
{color: '#C0C0C070', lineWidth: 1});
|
||||
drawConnectors(canvasCtx, results.leftHandLandmarks, HAND_CONNECTIONS,
|
||||
{color: '#CC0000', lineWidth: 5});
|
||||
drawLandmarks(canvasCtx, results.leftHandLandmarks,
|
||||
{color: '#00FF00', lineWidth: 2});
|
||||
drawConnectors(canvasCtx, results.rightHandLandmarks, HAND_CONNECTIONS,
|
||||
{color: '#00CC00', lineWidth: 5});
|
||||
drawLandmarks(canvasCtx, results.rightHandLandmarks,
|
||||
{color: '#FF0000', lineWidth: 2});
|
||||
canvasCtx.restore();
|
||||
}
|
||||
|
||||
const holistic = new Holistic({locateFile: (file) => {
|
||||
return `https://cdn.jsdelivr.net/npm/@mediapipe/holistic/${file}`;
|
||||
}});
|
||||
holistic.setOptions({
|
||||
upperBodyOnly: false,
|
||||
smoothLandmarks: true,
|
||||
minDetectionConfidence: 0.5,
|
||||
minTrackingConfidence: 0.5
|
||||
});
|
||||
holistic.onResults(onResults);
|
||||
|
||||
const camera = new Camera(videoElement, {
|
||||
onFrame: async () => {
|
||||
await holistic.send({image: videoElement});
|
||||
},
|
||||
width: 1280,
|
||||
height: 720
|
||||
});
|
||||
camera.start();
|
||||
</script>
|
||||
```
|
||||
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md), and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
to visualize its associated subgraphs, please see
|
||||
[visualizer documentation](../tools/visualizer.md).
|
||||
|
||||
### Mobile
|
||||
|
||||
* Graph:
|
||||
[`mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt)
|
||||
* Android target:
|
||||
[(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1o-Trp2GIRitA0OvmZWUQjVMa476xpfgK/view?usp=sharing)
|
||||
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/holistictrackinggpu:holistictrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/holistictrackinggpu/BUILD)
|
||||
* iOS target:
|
||||
[`mediapipe/examples/ios/holistictrackinggpu:HolisticTrackingGpuApp`](http:/mediapipe/examples/ios/holistictrackinggpu/BUILD)
|
||||
|
||||
### Desktop
|
||||
|
||||
Please first see general instructions for [desktop](../getting_started/cpp.md)
|
||||
on how to build MediaPipe examples.
|
||||
|
||||
* Running on CPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/holistic_tracking:holistic_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/holistic_tracking/BUILD)
|
||||
* Running on GPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/holistic_tracking:holistic_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/holistic_tracking/BUILD)
|
||||
|
||||
## Resources
|
||||
|
||||
* Google AI Blog:
|
||||
[MediaPipe Holistic - Simultaneous Face, Hand and Pose Prediction on Device](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
|
||||
* [Models and model cards](./models.md#holistic)
|
||||
|
||||
[Colab]:https://mediapipe.page.link/holistic_py_colab
|
||||
|
||||
[web demo]:https://code.mediapipe.dev/codepen/holistic
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: Instant Motion Tracking
|
||||
parent: Solutions
|
||||
nav_order: 9
|
||||
nav_order: 10
|
||||
---
|
||||
|
||||
# MediaPipe Instant Motion Tracking
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -104,8 +110,7 @@ and connected camera.
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android) on how to build
|
||||
MediaPipe examples.
|
||||
[Android](../getting_started/android.md) on how to build MediaPipe examples.
|
||||
|
||||
* Graph: [mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt)
|
||||
|
||||
|
||||
+10
-7
@@ -8,8 +8,14 @@ nav_order: 3
|
||||
# MediaPipe Iris
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -116,10 +122,8 @@ along with some simple geometric arguments. For more details please refer to our
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android),
|
||||
[iOS](../getting_started/building_examples.md#ios) and
|
||||
[desktop](../getting_started/building_examples.md#desktop) on how to build
|
||||
MediaPipe examples.
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
@@ -140,9 +144,8 @@ to visualize its associated subgraphs, please see
|
||||
|
||||
#### Live Camera Input
|
||||
|
||||
Please first see general instructions for
|
||||
[desktop](../getting_started/building_examples.md#desktop) on how to build
|
||||
MediaPipe examples.
|
||||
Please first see general instructions for [desktop](../getting_started/cpp.md)
|
||||
on how to build MediaPipe examples.
|
||||
|
||||
* Running on CPU
|
||||
* Graph:
|
||||
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: KNIFT (Template-based Feature Matching)
|
||||
parent: Solutions
|
||||
nav_order: 11
|
||||
nav_order: 12
|
||||
---
|
||||
|
||||
# MediaPipe KNIFT
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -67,7 +73,7 @@ you'd like to use your own template images, see
|
||||
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android) on how to build MediaPipe examples.
|
||||
[Android](../getting_started/android.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: Dataset Preparation with MediaSequence
|
||||
parent: Solutions
|
||||
nav_order: 13
|
||||
nav_order: 14
|
||||
---
|
||||
|
||||
# Dataset Preparation with MediaSequence
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
@@ -48,10 +48,17 @@ nav_order: 30
|
||||
|
||||
* Pose detection model:
|
||||
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection.tflite)
|
||||
* Full-body pose landmark model:
|
||||
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_full_body.tflite)
|
||||
* Upper-body pose landmark model:
|
||||
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite)
|
||||
* [Model card](https://mediapipe.page.link/blazepose-mc)
|
||||
|
||||
### [Holistic](https://google.github.io/mediapipe/solutions/holistic)
|
||||
|
||||
* Hand recrop model:
|
||||
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/hand_recrop.tflite)
|
||||
|
||||
### [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation)
|
||||
|
||||
* [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/hair_segmentation.tflite)
|
||||
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: Object Detection
|
||||
parent: Solutions
|
||||
nav_order: 7
|
||||
nav_order: 8
|
||||
---
|
||||
|
||||
# MediaPipe Object Detection
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
|
||||
@@ -24,8 +30,8 @@ to visualize its associated subgraphs, please see
|
||||
### Mobile
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android) and
|
||||
[iOS](../getting_started/building_examples.md#ios) on how to build MediaPipe examples.
|
||||
[Android](../getting_started/android.md) and [iOS](../getting_started/ios.md) on
|
||||
how to build MediaPipe examples.
|
||||
|
||||
#### GPU Pipeline
|
||||
|
||||
@@ -56,8 +62,8 @@ same configuration as the GPU pipeline, runs entirely on CPU.
|
||||
|
||||
#### Live Camera Input
|
||||
|
||||
Please first see general instructions for
|
||||
[desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe examples.
|
||||
Please first see general instructions for [desktop](../getting_started/cpp.md)
|
||||
on how to build MediaPipe examples.
|
||||
|
||||
* Graph:
|
||||
[`mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt)
|
||||
|
||||
@@ -2,21 +2,27 @@
|
||||
layout: default
|
||||
title: Objectron (3D Object Detection)
|
||||
parent: Solutions
|
||||
nav_order: 10
|
||||
nav_order: 11
|
||||
---
|
||||
|
||||
# MediaPipe Objectron
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
MediaPipe Objectron is a mobile real-time 3D object detection solution for
|
||||
everyday objects. It detects objects in 2D images, and estimates their poses
|
||||
through a machine learning (ML) model, trained on a newly created 3D dataset.
|
||||
through a machine learning (ML) model, trained on the [Objectron dataset](https://github.com/google-research-datasets/Objectron).
|
||||
|
||||
 |  |  | 
|
||||
:--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------:
|
||||
@@ -106,7 +112,8 @@ detector does not need to run every frame.
|
||||
*Fig 5. Network architecture and post-processing for two-stage 3D object detection.* |
|
||||
|
||||
We can use any 2D object detector for the first stage. In this solution, we use
|
||||
[TensorFlow Object Detection](https://github.com/tensorflow/models/tree/master/research/object_detection).
|
||||
[TensorFlow Object Detection](https://github.com/tensorflow/models/tree/master/research/object_detection) trained
|
||||
with the [Open Images dataset](https://storage.googleapis.com/openimages/web/index.html).
|
||||
The second stage 3D bounding box predictor we released runs 83FPS on Adreno 650
|
||||
mobile GPU.
|
||||
|
||||
@@ -157,9 +164,9 @@ The Objectron 3D object detection and tracking pipeline is implemented as a
|
||||
MediaPipe
|
||||
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt),
|
||||
which internally uses a
|
||||
[detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/subgraphs/objectron_detection_gpu.pbtxt)
|
||||
[detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt)
|
||||
and a
|
||||
[tracking subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/subgraphs/objectron_tracking_gpu.pbtxt).
|
||||
[tracking subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt).
|
||||
The detection subgraph performs ML inference only once every few frames to
|
||||
reduce computation load, and decodes the output tensor to a FrameAnnotation that
|
||||
contains nine keypoints: the 3D bounding box's center and its eight vertices.
|
||||
@@ -176,13 +183,14 @@ tracking results, based on the area of overlap.
|
||||
|
||||
We also released our [Objectron dataset](http://objectron.dev), with which we
|
||||
trained our 3D object detection models. The technical details of the Objectron
|
||||
dataset, including usage and tutorials, are available on the dataset website.
|
||||
dataset, including usage and tutorials, are available on
|
||||
the [dataset website](https://github.com/google-research-datasets/Objectron/).
|
||||
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android) and
|
||||
[iOS](../getting_started/building_examples.md#ios) on how to build MediaPipe examples.
|
||||
[Android](../getting_started/android.md) and [iOS](../getting_started/ios.md) on
|
||||
how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
@@ -254,7 +262,7 @@ to visualize its associated subgraphs, please see
|
||||
## Resources
|
||||
|
||||
* Google AI Blog:
|
||||
[Announcing the Objectron Dataset](https://mediapipe.page.link/objectron_dataset_ai_blog)
|
||||
[Announcing the Objectron Dataset](https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html)
|
||||
* Google AI Blog:
|
||||
[Real-Time 3D Object Detection on Mobile Devices with MediaPipe](https://ai.googleblog.com/2020/03/real-time-3d-object-detection-on-mobile.html)
|
||||
* Paper: [MobilePose: Real-Time Pose Estimation for Unseen Objects with Weak
|
||||
|
||||
+245
-87
@@ -8,8 +8,14 @@ nav_order: 5
|
||||
# MediaPipe Pose
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
## Overview
|
||||
@@ -20,16 +26,16 @@ gesture control. For example, it can form the basis for yoga, dance, and fitness
|
||||
applications. It can also enable the overlay of digital content and information
|
||||
on top of the physical world in augmented reality.
|
||||
|
||||
MediaPipe Pose is a ML solution for high-fidelity upper-body pose tracking,
|
||||
inferring 25 2D upper-body landmarks from RGB video frames utilizing our
|
||||
MediaPipe Pose is a ML solution for high-fidelity body pose tracking, inferring
|
||||
33 2D landmarks on the whole body (or 25 upper-body landmarks) from RGB video
|
||||
frames utilizing our
|
||||
[BlazePose](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
|
||||
research. Current state-of-the-art approaches rely primarily on powerful desktop
|
||||
research that also powers the
|
||||
[ML Kit Pose Detection API](https://developers.google.com/ml-kit/vision/pose-detection).
|
||||
Current state-of-the-art approaches rely primarily on powerful desktop
|
||||
environments for inference, whereas our method achieves real-time performance on
|
||||
most modern [mobile phones](#mobile), [desktops/laptops](#desktop), in
|
||||
[python](#python) and even on the [web](#web). A variant of MediaPipe Pose that
|
||||
performs full-body pose tracking on mobile phones will be included in an
|
||||
upcoming release of
|
||||
[ML Kit](https://developers.google.com/ml-kit/early-access/pose-detection).
|
||||
[python](#python) and even on the [web](#web).
|
||||
|
||||
 |
|
||||
:--------------------------------------------------------------------------------------------: |
|
||||
@@ -40,23 +46,24 @@ upcoming release of
|
||||
The solution utilizes a two-step detector-tracker ML pipeline, proven to be
|
||||
effective in our [MediaPipe Hands](./hands.md) and
|
||||
[MediaPipe Face Mesh](./face_mesh.md) solutions. Using a detector, the pipeline
|
||||
first locates the pose region-of-interest (ROI) within the frame. The tracker
|
||||
subsequently predicts the pose landmarks within the ROI using the ROI-cropped
|
||||
frame as input. Note that for video use cases the detector is invoked only as
|
||||
needed, i.e., for the very first frame and when the tracker could no longer
|
||||
identify body pose presence in the previous frame. For other frames the pipeline
|
||||
simply derives the ROI from the previous frame’s pose landmarks.
|
||||
first locates the person/pose region-of-interest (ROI) within the frame. The
|
||||
tracker subsequently predicts the pose landmarks within the ROI using the
|
||||
ROI-cropped frame as input. Note that for video use cases the detector is
|
||||
invoked only as needed, i.e., for the very first frame and when the tracker
|
||||
could no longer identify body pose presence in the previous frame. For other
|
||||
frames the pipeline simply derives the ROI from the previous frame’s pose
|
||||
landmarks.
|
||||
|
||||
The pipeline is implemented as a MediaPipe
|
||||
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
|
||||
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
|
||||
that uses a
|
||||
[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)
|
||||
[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt)
|
||||
from the
|
||||
[pose landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark)
|
||||
and renders using a dedicated
|
||||
[upper-body pose renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/subgraphs/upper_body_pose_renderer_gpu.pbtxt).
|
||||
[pose renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt).
|
||||
The
|
||||
[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)
|
||||
[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt)
|
||||
internally uses a
|
||||
[pose detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt)
|
||||
from the
|
||||
@@ -69,7 +76,7 @@ to visualize its associated subgraphs, please see
|
||||
|
||||
## Models
|
||||
|
||||
### Pose Detection Model (BlazePose Detector)
|
||||
### Person/pose Detection Model (BlazePose Detector)
|
||||
|
||||
The detector is inspired by our own lightweight
|
||||
[BlazeFace](https://arxiv.org/abs/1907.05047) model, used in
|
||||
@@ -87,84 +94,93 @@ hip midpoints.
|
||||
|
||||
### Pose Landmark Model (BlazePose Tracker)
|
||||
|
||||
The landmark model currently included in MediaPipe Pose predicts the location of
|
||||
25 upper-body landmarks (see figure below), each with `(x, y, z, visibility)`.
|
||||
Note that the `z` value should be discarded as the model is currently not fully
|
||||
trained to predict depth, but this is something we have on the roadmap. The
|
||||
model shares the same architecture as the full-body version that predicts 33
|
||||
landmarks, described in more detail in the
|
||||
[BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
|
||||
and in this [paper](https://arxiv.org/abs/2006.10204).
|
||||
The landmark model in MediaPipe Pose comes in two versions: a full-body model
|
||||
that predicts the location of 33 pose landmarks (see figure below), and an
|
||||
upper-body version that only predicts the first 25. The latter may be more
|
||||
accurate than the former in scenarios where the lower-body parts are mostly out
|
||||
of view.
|
||||
|
||||
 |
|
||||
:------------------------------------------------------------------------------------------------: |
|
||||
*Fig 3. 25 upper-body pose landmarks.* |
|
||||
Please find more detail in the
|
||||
[BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html),
|
||||
this [paper](https://arxiv.org/abs/2006.10204) and
|
||||
[the model card](./models.md#pose), and the attributes in each landmark
|
||||
[below](#pose_landmarks).
|
||||
|
||||
## Example Apps
|
||||
 |
|
||||
:----------------------------------------------------------------------------------------------: |
|
||||
*Fig 3. 33 pose landmarks.* |
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/building_examples.md#android),
|
||||
[iOS](../getting_started/building_examples.md#ios),
|
||||
[desktop](../getting_started/building_examples.md#desktop) and
|
||||
[Python](../getting_started/building_examples.md#python) on how to build
|
||||
MediaPipe examples.
|
||||
## Solution APIs
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
to visualize its associated subgraphs, please see
|
||||
[visualizer documentation](../tools/visualizer.md).
|
||||
### Cross-platform Configuration Options
|
||||
|
||||
### Mobile
|
||||
Naming style and availability may differ slightly across platforms/languages.
|
||||
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
|
||||
* Android target:
|
||||
[(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1uKc6T7KSuA0Mlq2URi5YookHu0U3yoh_/view?usp=sharing)
|
||||
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu:upperbodyposetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu/BUILD)
|
||||
* iOS target:
|
||||
[`mediapipe/examples/ios/upperbodyposetrackinggpu:UpperBodyPoseTrackingGpuApp`](http:/mediapipe/examples/ios/upperbodyposetrackinggpu/BUILD)
|
||||
#### static_image_mode
|
||||
|
||||
### Desktop
|
||||
If set to `false`, the solution treats the input images as a video stream. It
|
||||
will try to detect the most prominent person in the very first images, and upon
|
||||
a successful detection further localizes the pose landmarks. In subsequent
|
||||
images, it then simply tracks those landmarks without invoking another detection
|
||||
until it loses track, on reducing computation and latency. If set to `true`,
|
||||
person detection runs every input image, ideal for processing a batch of static,
|
||||
possibly unrelated, images. Default to `false`.
|
||||
|
||||
Please first see general instructions for
|
||||
[desktop](../getting_started/building_examples.md#desktop) on how to build
|
||||
MediaPipe examples.
|
||||
#### upper_body_only
|
||||
|
||||
* Running on CPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
|
||||
* Running on GPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
|
||||
If set to `true`, the solution outputs only the 25 upper-body pose landmarks.
|
||||
Otherwise, it outputs the full set of 33 pose landmarks. Note that
|
||||
upper-body-only prediction may be more accurate for use cases where the
|
||||
lower-body parts are mostly out of view. Default to `false`.
|
||||
|
||||
### Python
|
||||
#### smooth_landmarks
|
||||
|
||||
MediaPipe Python package is available on
|
||||
[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
|
||||
install mediapipe` on Linux and macOS, as described below and in this
|
||||
[colab](https://mediapipe.page.link/pose_py_colab). If you do need to build the
|
||||
Python package from source, see
|
||||
[additional instructions](../getting_started/building_examples.md#python).
|
||||
If set to `true`, the solution filters pose landmarks across different input
|
||||
images to reduce jitter, but ignored if [static_image_mode](#static_image_mode)
|
||||
is also set to `true`. Default to `true`.
|
||||
|
||||
Activate a Python virtual environment:
|
||||
#### min_detection_confidence
|
||||
|
||||
```bash
|
||||
$ python3 -m venv mp_env && source mp_env/bin/activate
|
||||
```
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the person-detection model for the
|
||||
detection to be considered successful. Default to `0.5`.
|
||||
|
||||
Install MediaPipe Python package:
|
||||
#### min_tracking_confidence
|
||||
|
||||
```bash
|
||||
(mp_env)$ pip install mediapipe
|
||||
```
|
||||
Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
|
||||
pose landmarks to be considered tracked successfully, or otherwise person
|
||||
detection will be invoked automatically on the next input image. Setting it to a
|
||||
higher value can increase robustness of the solution, at the expense of a higher
|
||||
latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
|
||||
person detection simply runs on every image. Default to `0.5`.
|
||||
|
||||
Run the following Python code:
|
||||
### Output
|
||||
|
||||
<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/pose.py and copy it over. -->
|
||||
Naming style may differ slightly across platforms/languages.
|
||||
|
||||
#### pose_landmarks
|
||||
|
||||
A list of pose landmarks. Each lanmark consists of the following:
|
||||
|
||||
* `x` and `y`: Landmark coordinates normalized to `[0.0, 1.0]` by the image
|
||||
width and height respectively.
|
||||
* `z`: Should be discarded as currently the model is not fully trained to
|
||||
predict depth, but this is something on the roadmap.
|
||||
* `visibility`: A value in `[0.0, 1.0]` indicating the likelihood of the
|
||||
landmark being visible (present and not occluded) in the image.
|
||||
|
||||
### Python Solution API
|
||||
|
||||
Please first follow general [instructions](../getting_started/python.md) to
|
||||
install MediaPipe Python package, then learn more in the companion [Colab] and
|
||||
the following usage example.
|
||||
|
||||
Supported configuration options:
|
||||
|
||||
* [static_image_mode](#static_image_mode)
|
||||
* [upper_body_only](#upper_body_only)
|
||||
* [smooth_landmarks](#smooth_landmarks)
|
||||
* [min_detection_confidence](#min_detection_confidence)
|
||||
* [min_tracking_confidence](#min_tracking_confidence)
|
||||
|
||||
```python
|
||||
import cv2
|
||||
@@ -177,17 +193,22 @@ pose = mp_pose.Pose(
|
||||
static_image_mode=True, min_detection_confidence=0.5)
|
||||
for idx, file in enumerate(file_list):
|
||||
image = cv2.imread(file)
|
||||
image_hight, image_width, _ = image.shape
|
||||
# Convert the BGR image to RGB before processing.
|
||||
results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
|
||||
|
||||
# Print and draw pose landmarks on the image.
|
||||
if not results.pose_landmarks:
|
||||
continue
|
||||
print(
|
||||
'nose landmark:',
|
||||
results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE])
|
||||
f'Nose coordinates: ('
|
||||
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
|
||||
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_hight})'
|
||||
)
|
||||
# Draw pose landmarks on the image.
|
||||
annotated_image = image.copy()
|
||||
mp_drawing.draw_landmarks(
|
||||
annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
|
||||
cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', image)
|
||||
cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
|
||||
pose.close()
|
||||
|
||||
# For webcam input:
|
||||
@@ -197,7 +218,9 @@ cap = cv2.VideoCapture(0)
|
||||
while cap.isOpened():
|
||||
success, image = cap.read()
|
||||
if not success:
|
||||
break
|
||||
print("Ignoring empty camera frame.")
|
||||
# If loading a video, use 'break' instead of 'continue'.
|
||||
continue
|
||||
|
||||
# Flip the image horizontally for a later selfie-view display, and convert
|
||||
# the BGR image to RGB.
|
||||
@@ -219,11 +242,142 @@ pose.close()
|
||||
cap.release()
|
||||
```
|
||||
|
||||
Tip: Use command `deactivate` to exit the Python virtual environment.
|
||||
### JavaScript Solution API
|
||||
|
||||
### Web
|
||||
Please first see general [introduction](../getting_started/javascript.md) on
|
||||
MediaPipe in JavaScript, then learn more in the companion [web demo] and the
|
||||
following usage example.
|
||||
|
||||
Please refer to [these instructions](../index.md#mediapipe-on-the-web).
|
||||
Supported configuration options:
|
||||
|
||||
* [upperBodyOnly](#upper_body_only)
|
||||
* [smoothLandmarks](#smooth_landmarks)
|
||||
* [minDetectionConfidence](#min_detection_confidence)
|
||||
* [minTrackingConfidence](#min_tracking_confidence)
|
||||
|
||||
```html
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/pose/pose.js" crossorigin="anonymous"></script>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="container">
|
||||
<video class="input_video"></video>
|
||||
<canvas class="output_canvas" width="1280px" height="720px"></canvas>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
```javascript
|
||||
<script type="module">
|
||||
const videoElement = document.getElementsByClassName('input_video')[0];
|
||||
const canvasElement = document.getElementsByClassName('output_canvas')[0];
|
||||
const canvasCtx = canvasElement.getContext('2d');
|
||||
|
||||
function onResults(results) {
|
||||
canvasCtx.save();
|
||||
canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
|
||||
canvasCtx.drawImage(
|
||||
results.image, 0, 0, canvasElement.width, canvasElement.height);
|
||||
drawConnectors(canvasCtx, results.poseLandmarks, POSE_CONNECTIONS,
|
||||
{color: '#00FF00', lineWidth: 4});
|
||||
drawLandmarks(canvasCtx, results.poseLandmarks,
|
||||
{color: '#FF0000', lineWidth: 2});
|
||||
canvasCtx.restore();
|
||||
}
|
||||
|
||||
const pose = new Pose({locateFile: (file) => {
|
||||
return `https://cdn.jsdelivr.net/npm/@mediapipe/pose/${file}`;
|
||||
}});
|
||||
pose.setOptions({
|
||||
upperBodyOnly: false,
|
||||
smoothLandmarks: true,
|
||||
minDetectionConfidence: 0.5,
|
||||
minTrackingConfidence: 0.5
|
||||
});
|
||||
pose.onResults(onResults);
|
||||
|
||||
const camera = new Camera(videoElement, {
|
||||
onFrame: async () => {
|
||||
await pose.send({image: videoElement});
|
||||
},
|
||||
width: 1280,
|
||||
height: 720
|
||||
});
|
||||
camera.start();
|
||||
</script>
|
||||
```
|
||||
|
||||
## Example Apps
|
||||
|
||||
Please first see general instructions for
|
||||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md), and
|
||||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||||
|
||||
Note: To visualize a graph, copy the graph and paste it into
|
||||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||||
to visualize its associated subgraphs, please see
|
||||
[visualizer documentation](../tools/visualizer.md).
|
||||
|
||||
### Mobile
|
||||
|
||||
#### Main Example
|
||||
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
|
||||
* Android target:
|
||||
[(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/17GFIrqEJS6W8UHKXlYevTtSCLxN9pWlY/view?usp=sharing)
|
||||
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/posetrackinggpu:posetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/posetrackinggpu/BUILD)
|
||||
* iOS target:
|
||||
[`mediapipe/examples/ios/posetrackinggpu:PoseTrackingGpuApp`](http:/mediapipe/examples/ios/posetrackinggpu/BUILD)
|
||||
|
||||
#### Upper-body Only
|
||||
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
|
||||
* Android target:
|
||||
[(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1uKc6T7KSuA0Mlq2URi5YookHu0U3yoh_/view?usp=sharing)
|
||||
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu:upperbodyposetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu/BUILD)
|
||||
* iOS target:
|
||||
[`mediapipe/examples/ios/upperbodyposetrackinggpu:UpperBodyPoseTrackingGpuApp`](http:/mediapipe/examples/ios/upperbodyposetrackinggpu/BUILD)
|
||||
|
||||
### Desktop
|
||||
|
||||
Please first see general instructions for [desktop](../getting_started/cpp.md)
|
||||
on how to build MediaPipe examples.
|
||||
|
||||
#### Main Example
|
||||
|
||||
* Running on CPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/pose_tracking:pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/pose_tracking/BUILD)
|
||||
* Running on GPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/pose_tracking:pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/pose_tracking/BUILD)
|
||||
|
||||
#### Upper-body Only
|
||||
|
||||
* Running on CPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
|
||||
* Running on GPU
|
||||
* Graph:
|
||||
[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
|
||||
* Target:
|
||||
[`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
|
||||
|
||||
## Resources
|
||||
|
||||
@@ -233,3 +387,7 @@ Please refer to [these instructions](../index.md#mediapipe-on-the-web).
|
||||
[BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
|
||||
([presentation](https://youtu.be/YPpUOTRn5tA))
|
||||
* [Models and model cards](./models.md#pose)
|
||||
|
||||
[Colab]:https://mediapipe.page.link/pose_py_colab
|
||||
|
||||
[web demo]:https://code.mediapipe.dev/codepen/pose
|
||||
|
||||
+17
-16
@@ -16,22 +16,23 @@ has_toc: false
|
||||
<!-- []() in the first cell is needed to preserve table formatting in GitHub Pages. -->
|
||||
<!-- Whenever this table is updated, paste a copy to ../external_index.md. -->
|
||||
|
||||
[]() | Android | iOS | Desktop | Python | Web | Coral
|
||||
:---------------------------------------------------------------------------------------- | :-----: | :-: | :-----: | :----: | :-: | :---:
|
||||
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | | ✅ | ✅
|
||||
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | ✅ | |
|
||||
[Iris](https://google.github.io/mediapipe/solutions/iris) | ✅ | ✅ | ✅ | | ✅ |
|
||||
[Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Pose](https://google.github.io/mediapipe/solutions/pose) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | ✅ |
|
||||
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
|
||||
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
|
||||
[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | ✅ | | | | |
|
||||
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
|
||||
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
|
||||
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
|
||||
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
|
||||
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
|
||||
[]() | [Android](https://google.github.io/mediapipe/getting_started/android) | [iOS](https://google.github.io/mediapipe/getting_started/ios) | [C++](https://google.github.io/mediapipe/getting_started/cpp) | [Python](https://google.github.io/mediapipe/getting_started/python) | [JS](https://google.github.io/mediapipe/getting_started/javascript) | [Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
|
||||
:---------------------------------------------------------------------------------------- | :-------------------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------------: | :-----------------------------------------------------------: | :--------------------------------------------------------------------:
|
||||
[Face Detection](https://google.github.io/mediapipe/solutions/face_detection) | ✅ | ✅ | ✅ | | | ✅
|
||||
[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Iris](https://google.github.io/mediapipe/solutions/iris) | ✅ | ✅ | ✅ | | |
|
||||
[Hands](https://google.github.io/mediapipe/solutions/hands) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Pose](https://google.github.io/mediapipe/solutions/pose) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Holistic](https://google.github.io/mediapipe/solutions/holistic) | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅ | | ✅ | | |
|
||||
[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
|
||||
[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
|
||||
[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | ✅ | | | | |
|
||||
[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | | | |
|
||||
[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
|
||||
[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
|
||||
[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
|
||||
[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m) | | | ✅ | | |
|
||||
|
||||
See also
|
||||
[MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
|
||||
|
||||
@@ -2,14 +2,20 @@
|
||||
layout: default
|
||||
title: YouTube-8M Feature Extraction and Model Inference
|
||||
parent: Solutions
|
||||
nav_order: 14
|
||||
nav_order: 15
|
||||
---
|
||||
|
||||
# YouTube-8M Feature Extraction and Model Inference
|
||||
{: .no_toc }
|
||||
|
||||
<details close markdown="block">
|
||||
<summary>
|
||||
Table of contents
|
||||
</summary>
|
||||
{: .text-delta }
|
||||
1. TOC
|
||||
{:toc}
|
||||
</details>
|
||||
---
|
||||
|
||||
MediaPipe is a useful and general framework for media processing that can assist
|
||||
|
||||
Reference in New Issue
Block a user