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Annotation 6394255904012

A landmark is a point of interest within a face.

Ориентир - это точка интереса в лице.

1. [wı`ðın] n редк.
находящийся внутри (конверта, пакета и т. п.)

the within complaint — прилагаемая (к письму) жалоба
revolt from within — протест внутри страны

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Face Detection Concepts | ML Kit | Google Developers
n be identified as being the same person. Note that this isn't a form of face recognition; face tracking only makes inferences based on the position and motion of the faces in a video sequence. A landmark is a point of interest within a face. The left eye, right eye, and base of the nose are all examples of landmarks. ML Kit provides the ability to find landmarks on a detected face. A contour is a set of points that follow t

Annotation 6394830261516

change the flutterMode

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Visual Studio Code - Flutter
ter’s build modes. Open the launch.json file in VS Code. If you do not have a launch.json file, go to the Run view in VS Code and click create a launch.json file. In the configurations section, change the flutterMode property to the build mode you want to target. For example, if you want to run in debug mode, your launch.json might look like this: content_copy "configurations": [ { "name": "Flutter"

Annotation 6394833669388

in a separate process from the test itself.

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flutter_driver library - Dart API
ort.stdio_stepper webdriver.sync_core webdriver.sync_io description flutter_driver library Provides API to test Flutter applications that run on real devices and emulators. The application runs in a separate process from the test itself. This is Flutter's version of Selenium WebDriver (generic web), Protractor (Angular), Espresso (Android) or Earl Gray (iOS). Classes Ancestor A Flutter Driver finder that finds an ancest

Annotation 6394836815116

or video. When a face is detected it has an associated position

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Face Detection Concepts | ML Kit | Google Developers
ion Concepts bookmark Table of contents Face orientation Landmarks Contours Classification Minimum Face Size Next Steps Face detection locates human faces in visual media such as digital images or video. When a face is detected it has an associated position, size, and orientation; and it can be searched for landmarks such as the eyes and nose. Here are some of the terms that we use regarding the face detection feature of ML Kit: Face track

Annotation 6394839698700

Track faces across video frames Get an identifier for each unique detected face. The identifier is consistent across invocations, so you can perform image manipulation on a particular person in a video stream.

Отслеживайте границы по видео-каркасам Получите идентификатор для каждого уникального обнаруженного лица. Идентификатор согласуется в результате вызовов, поэтому вы можете выполнять манипулирование изображениями на конкретном человеке в видеопотоке.

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Face Detection | ML Kit | Google Developers
ours of facial features Get the contours of detected faces and their eyes, eyebrows, lips, and nose. Recognize facial expressions Determine whether a person is smiling or has their eyes closed. Track faces across video frames Get an identifier for each unique detected face. The identifier is consistent across invocations, so you can perform image manipulation on a particular person in a video stream. Process video frames in real time Face detection is performed on the device, and is fast enough to be used in real-time applications, such as video manipulation. Example results Example

Annotation 6394842582284

frame to frame

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Face Detection Concepts | ML Kit | Google Developers
that we use regarding the face detection feature of ML Kit: Face tracking extends face detection to video sequences. Any face that appears in a video for any length of time can be tracked from frame to frame. This means a face detected in consecutive video frames can be identified as being the same person. Note that this isn't a form of face recognition; face tracking only makes inferences

Annotation 6394844155148

in consecutive video frames

В последовательных видеокадрах

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Face Detection Concepts | ML Kit | Google Developers
eature of ML Kit: Face tracking extends face detection to video sequences. Any face that appears in a video for any length of time can be tracked from frame to frame. This means a face detected in consecutive video frames can be identified as being the same person. Note that this isn't a form of face recognition; face tracking only makes inferences based on the position and motion of the faces in a video

Annotation 6394846514444

sequence

последовательность

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Face Detection Concepts | ML Kit | Google Developers
frames can be identified as being the same person. Note that this isn't a form of face recognition; face tracking only makes inferences based on the position and motion of the faces in a video sequence. A landmark is a point of interest within a face. The left eye, right eye, and base of the nose are all examples of landmarks. ML Kit provides the ability to find landmarks on a detecte

Annotation 6394849660172

together.

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Face Detection Concepts | ML Kit | Google Developers
o the camera. ML Kit doesn't report the Euler X, Euler Y or Euler Z angle of a detected face when LANDMARK_MODE_NONE, CONTOUR_MODE_ALL, CLASSIFICATION_MODE_NONE and PERFORMANCE_MODE_FASTare set together. Landmarks A landmark is a point of interest within a face. The left eye, right eye, and nose base are all examples of landmarks. ML Kit detects faces without looking for landmarks. Land

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that can be detected given the Euler Y angle of an associated face:

которые могут быть обнаружены, учитывая Угол EULER Y связанного лица:

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Face Detection Concepts | ML Kit | Google Developers
xamples of landmarks. ML Kit detects faces without looking for landmarks. Landmark detection is an optional step that is disabled by default. The following table summarizes all of the landmarks that can be detected given the Euler Y angle of an associated face: Euler Y angle Detectable landmarks < -36 degrees left eye, left mouth, left ear, nose base, left cheek -36 degrees to -12 degrees left mouth, nose base, bottom mouth, right eye, left

Annotation 6394853592332

left eye

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Face Detection Concepts | ML Kit | Google Developers
disabled by default. The following table summarizes all of the landmarks that can be detected given the Euler Y angle of an associated face: Euler Y angle Detectable landmarks < -36 degrees left eye, left mouth, left ear, nose base, left cheek -36 degrees to -12 degrees left mouth, nose base, bottom mouth, right eye, left eye, left cheek, left ear tip -12 degrees to 12 degrees righ

Annotation 6394855165196

right eye

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Face Detection Concepts | ML Kit | Google Developers
se, left cheek, right cheek, left mouth, right mouth, bottom mouth 12 degrees to 36 degrees right mouth, nose base, bottom mouth, left eye, right eye, right cheek, right ear tip > 36 degrees right eye, right mouth, right ear, nose base, right cheek Each detected landmark includes its associated position in the image. Contours A contour is a set of points that represent the shape of a

Annotation 6394856738060

right eye, left eye

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Face Detection Concepts | ML Kit | Google Developers
ees left eye, left mouth, left ear, nose base, left cheek -36 degrees to -12 degrees left mouth, nose base, bottom mouth, right eye, left eye, left cheek, left ear tip -12 degrees to 12 degrees right eye, left eye, nose base, left cheek, right cheek, left mouth, right mouth, bottom mouth 12 degrees to 36 degrees right mouth, nose base, bottom mouth, left eye, right eye, right cheek, right ear tip

Annotation 6394858310924

but detection will take longer;

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Face Detection Concepts | ML Kit | Google Developers
for is roughly 10% of the width of the image being searched. The minimum face size is a performance vs. accuracy trade-off: setting the minimum size smaller lets the detector find smaller faces but detection will take longer; setting it larger might exclude smaller faces but will run faster. The minimum face size is not a hard limit; the detector may find faces slightly smaller than specified. Next Steps Use

Annotation 6394859883788

it larger might exclude smaller faces but will run faster.

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Face Detection Concepts | ML Kit | Google Developers
mage being searched. The minimum face size is a performance vs. accuracy trade-off: setting the minimum size smaller lets the detector find smaller faces but detection will take longer; setting it larger might exclude smaller faces but will run faster. The minimum face size is not a hard limit; the detector may find faces slightly smaller than specified. Next Steps Use face detection in your iOS or Android app: iOS Android Rate and re

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill