DETECTION AND OBFUSCATION OF DISPLAY SCREENS IN AUGMENTED REALITY CONTENT

The subject technology receives first image data captured by a camera of an eyewear device. The subject technology detects, using a machine learning model, a representation of a display screen in the first image data. The subject technology selects at least a portion of the representation of the display screen. The subject technology adjusts a visual appearance of the portion of the representation of the display screen. The subject technology causes display of the adjusted visual appearance using a display system of the eyewear device.

BACKGROUND

With the increased use of digital images, affordability of portable computing devices, availability of increased capacity of digital storage media, and increased bandwidth and accessibility of network connections, digital images have become a part of the daily life for an increasing number of people.

Some electronics-enabled eyewear devices, such as so-called smart glasses, allow users to interact with virtual content while a user is engaged in sonic activity. Users wear the eyewear devices and can view a real-world environment through the eyewear devices while interacting with virtual content that is displayed by the eyewear devices.

DETAILED DESCRIPTION

Users with a range of interests from various locations can capture digital images of various subjects and make captured images available to others via networks, such as the Internet. To enhance users' experiences with digital images and provide various features, enabling computing devices to perform image processing operations on various objects and/or features captured in a wide range of changing conditions (e.g., changes in image scales, noises, lighting, movement, or geometric distortion) can be challenging and computationally intensive.

Augmented reality technology aims to bridge a gap between virtual environments and a real world environment by providing an enhanced real world environment that is augmented with electronic information. As a result, the electronic information appears to be part of the real world environment as perceived by a user. In an example, augmented reality technology further provides a user interface to interact with the electronic information that is overlaid in the enhanced real world environment.

A augmented reality (AR) system enables real and virtual environments to be combined in varying degrees to facilitate interactions from a user in a real time manner. Such an AR system, as described herein, therefore can include various possible combinations of real and virtual environments, including augmented reality that primarily includes real elements and is closer to a real environment than a virtual environment (e.g., without real elements). In this manner, a real environment can be connected with a virtual environment by the AR system. A user immersed in an AR environment can navigate through such an environment and the AR system can track the user's viewpoint to provide a visualization based on how the user is situated in the environment. Augmented reality (AR) experiences can be provided in a messaging client application (or the messaging system) as described in embodiments herein.

Embodiments of the subject technology described herein enable various operations involving AR content for capturing and modifying such content with a given electronic device, such as a wearable headset device a given eyewear device) and a mobile computing device.

Messaging systems are frequently utilized and are increasingly leveraged by users of mobile computing devices, in various settings, to provide different types of functionality in a convenient manner. As described herein, the subject messaging system comprises practical applications that provide improvements in capturing image data and rendering AR content (e.g., images, videos, and the Like) based on the captured image data by at least providing technical improvements with capturing image data using power and resource constrained electronic devices. Such improvements in capturing image data are enabled by techniques provided by the subject technology, which reduce latency and increase efficiency in processing captured image data thereby also reducing power consumption in the capturing devices.

As discussed further herein, the subject infrastructure supports the creation and sharing of interactive media, referred to herein as messages including 3D content or AR effects, throughout various components of a messaging system. In example embodiments described herein, messages can enter the system from a live camera or via from storage (e.g., where messages including 3D content and/or AR effects are stored in memory or a database). The subject system supports motion sensor input, and loading of external effects and asset data.

As referred to herein, the phrase “augmented reality experience,” “augmented reality content item,” “augmented reality content generator” includes or refers to various image processing operations corresponding to an image modification, filter, AR content generators, media overlay, transformation, and the like, and additionally can include playback of audio or music content during presentation of AR content or media content, as described further herein.

FIG. 1is a block diagram showing an example messaging system100for exchanging data (e.g., messages and associated content) over a network. The messaging system100includes multiple instances of a client device102, each of which hosts a number of applications including a messaging client application104. Each messaging client application104is communicatively coupled to other instances of the messaging client application104and a messaging server system108via a network106(e.g., the Internet).

A messaging client application104is able to communicate and exchange data with another messaging client application104and with the messaging server system108via the network106. The data exchanged between messaging client application104, and between a messaging client application104and the messaging server system108, includes functions (e.g., commands to invoke functions) as well as payload data text, audio, video or other multimedia data).

The messaging system100includes an eyewear device150, which hosts an eyewear system160, among other applications. The eyewear device150is communicatively coupled to the client device102via the network106(which may include via a dedicated short-range communication path, such as a Bluetooth™ or Wi-Fi direct connection).

The eyewear device150may be a head mounted portable system, worn by a user, that includes a display system capable of presenting a visualization of an augmented reality environment to the user (e.g., head mounted display device). The eyewear device150may be powered with a battery of some kind. In an example, the display system controlled by the eyewear system160of the eyewear device150provides a stereoscopic presentation of the augmented reality environment, enabling a three-dimensional visual display of a rendering of a particular scene, to the user. Further, the eyewear device150may include various sensors including, but not limited to, cameras, image sensors, touch sensors, microphones, inertial measurement units (IMU), heart rate, temperature, among other types of sensors. Moreover, the eyewear device150may include hardware elements that can receive user input such as hardware buttons or switches. User input detected by such sensors and/or hardware elements correspond to various input modalities to initiate a particular operation(s). For example, such input modalities may include, but not limited to, facial tracking, eye tracking (e.g., gaze direction), hand tracking, gesture tracking, biometric readings (e.g., heart rate, pulse, pupil dilation, breath, temperature, electroencephalogram, olfactory), recognizing speech or audio (e.g., particular hotwords), and activating buttons or switches, etc.

The eyewear device150may be communicatively coupled to a base device such as the client device102. Such a base device may, in general, include more computing resources and/or available power in comparison with the eyewear device150. In an example, the eyewear device150may operate in various modes. For instance, the eyewear device150can operate in a standalone mode independent of any base device.

The eyewear device150may also operate in a wireless tethered mode (e.g., connected via a wireless connection with a base device such as client device102), working in conjunction with a given base device. When the eyewear device150operates in the wireless tethered mode, a least a portion of processing user inputs and/or rendering the augmented reality environment may be offloaded to the base device thereby reducing processing burdens on the eyewear device150. For instance, in an implementation, the eyewear device150works in conjunction with the client device102to generate an augmented reality environment including physical and/or virtual objects that enables different forms of interaction (e.g., visual, auditory, and/or physical or tactile interaction) between the user and the generated augmented reality environment in a real-time manner. In an example, the eyewear device150provides a rendering of a scene corresponding to the augmented reality environment that can be perceived by the user and interacted with in a real-time manner. Additionally, as part of presenting the rendered scene, the eyewear device150may provide sound, haptic, or tactile feedback to the user. The content of a given rendered scene may be dependent on available processing capability, network availability and capacity, available battery power, and current system workload.

In an implementation, the eyewear system160generates a message including a recording of a real environment and generates an augmented reality environment including two-dimensional (2D) video for sharing and playback. In another implementation, the eyewear system160generates a message, and subsequently generates a three-dimensional (3D) representation merging information from all sensors and/or combining recording with other users' messages (e.g., different point of views (POVs)). It is further appreciated that the client device102can also generate such augmented reality environments either working in conjunction with the eyewear device150or independently of the eyewear device150.

The eyewear system160automatically or selectively moves augmented reality or virtual reality content from one virtual position to another as the user moves around the eyewear device150. For example, the user or wearer of the eyewear device150may initially be looking at a first portion of a real-world environment (e.g., a first room in a house). The user may provide input (e.g., using a client device102or a voice activated or touch activated interface of the eyewear device150) to launch or access virtual content that includes one or more objects.

The messaging server system108provides server-side functionality via the network106to a particular messaging client application104. While certain functions of the messaging system100are described herein as being performed by either a messaging client application104or by the messaging server system108, the location of certain functionality either within the messaging client application104or the messaging server system108is a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the messaging server system108, but to later migrate this technology and functionality to the messaging client application104where a client device102has a sufficient processing capacity.

The Application Program Interface (API) server110receives and transmits message data (e.g., commands and message payloads) between the client device102and the application server112. Specifically, the Application Program Interface (API) server110provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client application104in order to invoke functionality of the application server112. The Application Program Interface (API) server110exposes various functions supported by the application server112, including account registration, login functionality, the sending of messages, via the application server112, from a particular messaging client application104to another messaging client application104, the sending of media files (e.g., images or video) from a messaging client application104to the messaging server application114, and for possible access by another messaging client application104, the setting of a collection of media data (e.g., story), the retrieval of a list of friends of a user of a client device102, the retrieval of such collections, the retrieval of messages and content, the adding and deletion of friends to a social graph, the location of friends within a social graph, and opening an application event (e.g., relating to the messaging client application104).

The application server112also includes an image processing system116that is dedicated to performing various image processing operations, typically with respect to images or video received within the payload of a message at the messaging server application114.

The social network system122supports various social networking functions services, and makes these functions and services available to the messaging server application114. To this end, the social network system122maintains and accesses an entity graph304(as shown inFIG. 3) within the database120. Examples of functions and services supported by the social network system122include the identification of other users of the messaging system100with which a particular user has relationships or is ‘following’, and also the identification of other entities and interests of a particular user.

The application server112is communicatively coupled to a database server118, which facilitates access to a database120in which is stored data associated with messages processed by the messaging server application114.

FIG. 2is block diagram illustrating further details regarding the messaging system100, according to example embodiments. Specifically, the messaging system100is shown to comprise the messaging client application104and the application server112, which in turn embody a number of some subsystems, namely an ephemeral timer system202, a collection management system204and an annotation system206.

The ephemeral timer system202is responsible for enforcing the temporary access to content permitted by the messaging client application104and the messaging server application114. To this end, the ephemeral timer system202incorporates a number of timers that, based on duration and display parameters associated with a message, or collection of messages (e.g., a story), selectively display and enable access to messages and associated content via the messaging client application104. Further details regarding the operation of the ephemeral timer system202are provided below.

The collection management system204is responsible for managing collections of media (e.g., collections of text, image video and audio data). In some examples, a collection of content (e.g., messages, including images, video, text and audio) may be organized into an ‘event gallery’ or an ‘event story,’ Such a collection may be made available for a specified time period, such as the duration of an event to which the content relates. For example, content relating to a music concert may be made available as a ‘story’ for the duration of that music concert. The collection management system204may also be responsible for publishing an icon that provides notification of the existence of a particular collection to the user interface of the messaging client application104.

The collection management system204further more includes a curation interface208that allows a collection manager to manage and curate a particular collection of content. For example, the curation interface208enables an event organizer to curate a collection of content relating to a specific event (e.g., delete inappropriate content or redundant messages). Additionally, the collection management system204employs machine vision (or image recognition technology) and content rules to automatically curate a content collection. In certain embodiments, compensation may be paid to a user for inclusion of user-generated content into a collection. In such cases, the curation interface208operates to automatically make payments to such users for the use of their content.

In one example embodiment, the annotation system206provides a user-based publication platform that enables users to select a geolocation on a map, and upload content associated with the selected geolocation. The user may also specify circumstances under which a particular media overlay should be offered to other users. The annotation system206generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system206provides a merchant-based publication platform that enables merchants to select a particular media overlay associated with a geolocation via a bidding process. For example, the annotation system206associates the media overlay of a highest bidding merchant with a corresponding geolocation for a predefined amount of time.

FIG. 3is a schematic diagram illustrating data structures300which may be stored in the database120of the messaging server system108, according to certain example embodiments. While the content of the database120is shown to comprise a number of tables, it will be appreciated that the data could be stored in other types of data structures (e.g., as an object-oriented database)

The database120includes message data stored within a message table314. The entity table302stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302may include individuals, corporate entities, organizations, objects, places, events, etc. Regardless of type, any entity regarding which the messaging server system108stores data may be a recognized entity. Each entity is provided with a unique identifier, as well as an entity type identifier (not shown).

The entity graph304furthermore stores information regarding relationships and associations between entities. Such relationships may be social, professional (e.g., work at a common corporation or organization) interested-based or activity-based, merely for example.

The database120also stores annotation data, in the example form of filters, in an annotation table312. Filters for which data is stored within the annotation table312are associated with and applied to videos (for which data is stored in a video table310) and/or images (for which data is stored in an image table308). Filters, in one example, are overlays that are displayed as overlaid on an image or video during presentation to a recipient user. Filters may be of varies types, including user-selected filters from a gallery of filters presented to a sending user by the messaging client application104when the sending user is composing a message. Other types of filters include geolocation filters (also known as geo-filters) which may be presented to a sending user based on geographic location. For example, geolocation filters specific to a neighborhood or special location may be presented within a user interface by the messaging client application104, based on geolocation information determined by a GPS unit of the client device102. Another type of filer is a data filer, which may be selectively presented to a sending user by the messaging client application104, based on other inputs or information gathered by the client device102during the message creation process. Example of data filters include current temperature at a specific location, a current speed at which a sending user is traveling, battery life for a client device102, or the current time.

Other annotation data that may be stored within the image table308are augmented reality content generators (e.g., corresponding to applying AR content generators, augmented reality experiences, or augmented reality content items). An augmented reality content generator may be a real-time special effect and sound that may be added to an image or a video.

As described above, augmented reality content generators, augmented reality content items, overlays, image transformations, AR images and similar terms refer to modifications that may be made to videos or images. This includes real-time modification which modifies an image as it is captured using a device sensor and then displayed on a screen of the device with the modifications. This also includes modifications to stored content, such as video clips in a gallery that may be modified. For example, in a device with access to multiple augmented reality content generators, a user can use a single video clip with multiple augmented reality content generators to see how the different augmented reality content generators will modify the stored clip. For example, multiple augmented reality content generators that apply different pseudorandom movement models can be applied to the same content by selecting different augmented reality content generators for the content. Similarly, real-time video capture may be used with an illustrated modification to show how video images currently being captured by sensors of a device would modify the captured data. Such data may simply be displayed on the screen and not stored in memory, or the content captured by the device sensors may be recorded and stored in memory with or without the modifications (or both). In some systems, a preview feature can show how different augmented reality content generators will look within different windows in a display at the same time. This can, for example, enable multiple windows with different pseudorandom animations to be viewed on a display at the same time.

Data and various systems using augmented reality content generators or other such transform systems to modify content using this data can thus involve detection of objects (e.g., faces, hands, bodies, cats, dogs, surfaces, objects, etc.), tracking of such objects as they leave, enter, and move around the field of view in video frames, and the modification or transformation of such objects as they are tracked. In various embodiments, different methods for achieving such transformations may be used. For example, some embodiments may involve generating a three-dimensional mesh model of the object or objects, and using transformations and animated textures of the model within the video to achieve the transformation. In other embodiments, tracking of points on an object may be used to place an image or texture (which may be two dimensional or three dimensional) at the tracked position. In still further embodiments, neural network analysis of video frames may be used to place images, models, or textures in content (e.g., images or frames of video). Augmented reality content generators thus refer both to the images, models, and textures used to create transformations in content, as well as to additional modeling and analysis information needed to achieve such transformations with object detection, tracking, and placement.

In some example embodiments, a graphical processing pipeline architecture is provided that enables different augmented reality experiences (e.g., AR content generators) to be applied in corresponding different layers. Such a graphical processing pipeline provides an extensible rendering engine for providing multiple augmented reality experiences that are included in a composite media (e.g., image or video) or composite AR content for rendering by the messaging client application104(or the messaging system100).

As mentioned above, the video table310stores video data which, in one embodiment, is associated with messages for which records are maintained within the message table314. Similarly, the image table308stores image data associated with messages for which message data is stored in the entity table302. The entity table302may associate various annotations from the annotation table312with various images and videos stored in the image table308and the video table310.

A collection may also constitute a ‘live story,’ which is a collection of content from multiple users that is created manually, automatically, or using a combination of manual and automatic techniques. For example, a ‘live story’ may constitute a curated stream of user-submitted content from varies locations and events. Users whose client devices have location services enabled and are at a common location event at a particular time may, for example, be presented with an option, via a user interface of the messaging client application104, to contribute content to a particular live story. The live story may be identified to the user by the messaging client application104, based on his or her location. The end result is a ‘live story’ told from a community perspective.

FIG. 4is a schematic diagram illustrating a structure of a message400, according to some embodiments, generated by a messaging client application104or the eyewear system160for communication to a further messaging client application104or the messaging server application114. The content of a particular message400is used to populate the message table314stored within the database120, accessible by the messaging server application114. Similarly, the content of a message400is stored in memory as ‘in-transit’ or ‘in-flight’ data of the client device102or the application server112. The message400is shown to include the following components:

A message identifier402: a unique identifier that identifies the message400.

A message text payload404: text, to be generated by a user via a user interface of the client device102and that is included in the message400.

A message image payload406: image data, captured by a camera component of a client device102or retrieved from a memory component of a client device102, and that is included in the message400.

A message video payload408: video data, captured by a camera component or retrieved from a memory component of the client device102and that is included in the message400.

A message audio payload410: audio data, captured by a microphone or retrieved from a memory component of the client device102, and that is included in the message400.

A message annotations412: annotation data (e.g., filters, stickers or other enhancements) that represents annotations to be applied to message image payload406, message video payload408, or message audio payload410of the message400.

A message duration parameter414: parameter value indicating, in seconds, the amount of time for which content of the message (e.g., the message image payload406, message video payload408, message audio payload410) is to be presented or made accessible to a user via the messaging client application104.

A message geolocation parameter416: geolocation data (e.g., latitudinal and longitudinal coordinates) associated with the content payload of the message. Multiple message geolocation parameter416values may be included in the payload, each of these parameter values being associated with respect to content items included in the content (e.g., a specific image into within the message image payload406, or a specific video in the message video payload408).

A message story identifier418: identifier values identifying one or more content collections (e.g., ‘stories’) with which a particular content item in the message image payload406of the message400is associated. For example, multiple images within the message image payload406may each be associated with multiple content collections using identifier values.

A message tag420: each message400may be tagged with multiple tags, each of which is indicative of the subject matter of content included in the message payload. For example, where a particular image included in the message image payload406depicts an animal (e.g., a lion), a tag value may be included within the message tag420that is indicative of the relevant animal. Tag values may be generated manually, based on user input, or may be automatically generated using, for example, image recognition.

A message sender identifier422: an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of a user of the client device102on which the message400was generated and from which the message400was sent

A message receiver identifier424: an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of a user of the client device102to which the message400is addressed,

The contents (e.g., values) of the various components of message400may be pointers to locations in tables within which content data values are stored. For example, an image value in the message image payload406may be a pointer to (or address of) a location within an image table308. Similarly, values within the message video payload408may point to data stored within a video table310, values stored within the message annotations412may point to data stored in an annotation table312, values stored within the message story identifier418may point to data stored in a story table306, and values stored within the message sender identifier422and the message receiver identifier424may point to user records stored within an entity table302.

FIG. 5shows a front perspective view of an eyewear device150in the form of a pair of smart glasses that include a eyewear system160according to one example embodiment. The eyewear device150includes a body503comprising a front piece or frame506and a pair of temples509connected to the frame506for supporting the frame506in position on a user's face when the eyewear device150is worn. The frame506can be made from any suitable material such as plastics or metal, including any suitable shape memory alloy.

The eyewear device150includes a pair of optical elements in the form of a pair of lenses512held by corresponding optical element holders in the form of a pair of rims515forming part of the frame506. The rims515are connected by a bridge518. In other embodiments, one or both of the optical elements can be a display, a display assembly, or a lens and display combination.

The frame506includes a pair of end pieces521defining lateral end portions of the frame506. In this example, a variety of electronics components are housed in one or both of the end pieces521. The temples509are coupled to the respective end pieces521. In this example, the temples509are coupled to the frame506by respective hinges so as to be hingedly movable between a wearable mode and a collapsed mode in which the temples509are pivoted towards the frame506to lie substantially flat against it. In other embodiments, the temples509can be coupled to the frame506by any suitable means, or can be rigidly or fixedly secured to the frame506so as to be integral therewith.

Each of the temples509that includes a front portion of that is coupled to the frame506and any suitable rear portion for coupling to the ear of the user, such as the curves or cute piece illustrated in the example embodiment ofFIG. 5. In some embodiments, the frame506is formed of a single piece of material, so as to have a unitary or monolithic construction. In some embodiments, the whole of the body503(including both the frame506and the temples509) can be of the unitary or monolithic construction.

The eyewear device150has onboard electronics components including a computing device, such as a computer524, or low power processor, which can in different embodiments be of any suitable type so as to be carried by the body503. In some embodiments, the computer524is at least partially housed in one or both of the temples509. In the present embodiment, various components of the computer524are housed in the lateral end pieces521of the frame506. The computer524includes one or more processors with memory (e.g., a volatile storage device, such as random access memory or registers), a storage device (e.g., a non-volatile storage device), wireless communication circuitry (e.g., BLE communication devices and/or WiFi direct devices), and a power source. The computer524comprises low-power circuitry, high-speed circuitry, and, in some embodiments, a display processor. Various embodiments may include these elements in different configurations or integrated together in different ways.

The computer524additionally includes a battery527or other suitable portable power supply. In one embodiment, the battery527is disposed in one of the temples509. In the eyewear device150shown inFIG. 5, the battery527is shown as being disposed in one of the end pieces521, being electrically coupled to the remainder of the computer524housed in the corresponding end piece521.

The eyewear device150is camera-enabled, in this example comprising a camera530mounted in one of the end pieces521and facing forwards so as to be aligned more or less with the direction of view of a wearer of the eyewear device150. The camera530is configured to capture digital images (also referred to herein as digital photographs or pictures) as well as digital video content. Operation of the camera530is controlled by a camera controller provided by the computer524, image data representative of images or video captured by the camera530being temporarily stored on a memory forming part of the computer524. In some embodiments, the eyewear device150can have a pair of cameras530, e.g. housed by the respective end pieces521.

As will be described in greater detail below, the onboard computer524and the lenses512are configured together to provide a eyewear system160that automatically and selectively re-centers virtual content to bring the virtual content to within view of the lenses512by moving the virtual content from a first virtual location to a second virtual location. Specifically, the lenses512can display virtual content or one or more virtual objects. This makes it appear to the user that the virtual content is integrated within a real-world environment that the user views through the lenses512. In some embodiments, the virtual content is received from the client device102. In some embodiments, the virtual content is received directly from the application server112.

The eyewear device150includes an accelerometer and a touch interface and a voice command system. Based on input received by the eyewear device150from the accelerometer and a touch interface and the voice command system, the eyewear device150can control user interaction with the virtual content. In one example, the user interaction can control playback of content that is presented on the lenses512. In another example, the user interaction can navigate through a playlist or music or video library. In another example, the user interaction can navigate through a conversation the user is involved in, such as by scrolling through various three-dimensional or two-dimensional conversation elements (e.g., chat bubbles) and selecting individual conversation elements to respond to generate messages to transmit to participants of the conversation.

The eyewear system160(which can be implemented by the computer524) assigns virtual content to virtual locations. The eyewear system160monitors the current virtual location that is within view of a real-world environment. The eyewear system160retrieves virtual content for display that is within a specified range of the current virtual location that is within view. As the eyewear device150is moved around to be directed to a new portion of the real-world environment, associated with a different set of virtual locations, the eyewear system160excludes any virtual content that is not within range of the different set of virtual locations. For example, as the eyewear device150is moved around to be directed to a new portion of the real-world environment that does not overlap with the previously displayed portion of the real-world environment, the eyewear system160excludes any virtual content that is not within range of the different set of virtual locations.

The eyewear system160can receive a request to bring virtual content into a current view. In response, the eyewear system160updates the virtual location assigned and associated with the virtual content to be the virtual location that is associated with the current view of the real-world environment. As a result, the virtual content is now moved from being out of view to be included in the current view to allow the user to interact with the virtual content. In some cases, the user can only interact with virtual content that is within view of the lenses512. If the user moves around to face another direction resulting in the virtual content going out of view, the user input no longer can control or interact with the previously displayed virtual content until the virtual content is brought back into view.

The eyewear device150further includes one or more communication devices, such as Bluetooth low energy (BLE) communication interface. Such BLE communication interface enables the eyewear device150to communicate wirelessly with the client device102. Other forms of wireless communication can also be employed instead of, or in addition to, the BLE communication interface, such as a WiFi direct interface. The BLE communication interface implements a standard number of BLE communication protocols.

A first of the communications protocols implemented by the BLE interface of the eyewear device150enables an unencrypted link to be established between the eyewear device150and the client device102. In this first protocol, the link-layer communication (the physical interface or medium) between the eyewear device150and the client device102includes unencrypted data. In this first protocol, the application layer (the communication layer operating on the physically exchanged data) encrypts and decrypts data that is physically exchanged in unencrypted form over the link layer of the BLE communication interface. In this way, data exchanged over the physical layer can freely be read by an eavesdropping device, but the eavesdropping device will not be able to decipher the data that is exchanged without performing a decryption operation in the application. layer.

A second of the communications protocols implemented by the BLE interface of the eyewear device150enables an encrypted link to be established between the eyewear device150and the client device102. In this second protocol, the link-layer communication (the physical interface) between the eyewear device150and the client device102receives data from the application layer and adds a first type of encryption to the data before exchanging the data over the physical medium. In this second protocol, the application layer (the communication layer operating on the physically exchanged data) may or may not use a second type of encryption to encrypt and decrypt data that is physically exchanged in encrypted form, using the first type of encryption, over the link layer of the BLE communication interface. Namely, data can be first encrypted by the application layer and then be further encrypted by the physical layer before being exchanged over the physical medium. Following the exchange over the physical medium, the data is then decrypted by the physical layer and then decrypted again (e.g., using a different type of encryption) by the application layer. In this way, data exchanged over the physical layer cannot be read by an eavesdropping device as the data is encrypted in the physical medium.

In some embodiments, the client device102communicates with the eyewear device150using the first protocol to exchange images or videos or virtual content between the messaging client104and the eyewear device150.

As described above, media overlays, such as AR content generators, overlays, image transformations, AR images and similar terms refer to modifications that may be made to videos or images. This includes real-time modification which modifies an image as it is captured using a device sensor and then displayed on a screen of the device with the modifications. This also includes modifications to stored content, such as video clips in a gallery that may be modified. For example, in a device with access to multiple media overlays (e.g., AR content generators), a user can use a single video clip with multiple AR content generators to see how the different AR content generators will modify the stored clip. For example, multiple AR content generators that apply different pseudorandom movement models can be applied to the same content by selecting different AR content generators for the content. Similarly, real-time video capture may be used with an illustrated modification to show how video images currently being captured by sensors of a device would modify the captured data. Such data may simply be displayed on the screen and not stored in memory, or the content captured by the device sensors may be recorded and stored in memory with or without the modifications (or both). In some systems, a preview feature can show how different AR content generators will look within different windows in a display at the same time. This can, for example, enable multiple windows with different pseudorandom animations to be viewed on a display at the same time.

Data and various systems to use AR content generators or other such transform systems to modify content using this data can thus involve detection of objects (e.g. faces, hands, bodies, cats, dogs, surfaces, objects, etc.), tracking of such objects as they leave, enter, and move around the field of view in video frames, and the modification or transformation of such objects as they are tracked. In various embodiments, different methods for achieving such transformations may be used. For example, some embodiments may involve generating a three-dimensional mesh model of the object or objects, and using transformations and animated textures of the model within the video to achieve the transformation. In other embodiments, tracking of points on an object may be used to place an image or texture (which may be two dimensional or three dimensional) at the tracked position. In still further embodiments, neural network analysis of video frames may be used to place images, models, or textures in content (e.g. images or frames of video). Lens data thus refers both to the images, models, and textures used to create transformations in content, as well as to additional modeling and analysis information needed to achieve such transformations with object detection, tracking, and placement.

In some example embodiments, a graphical processing pipeline architecture is provided that enables different media overlays to be applied in corresponding different layers. Such a graphical processing pipeline provides an extensible rendering engine for providing multiple augmented reality content generators that are included in a composite media (e.g., image or video) or composite AR content for rendering by the messaging client application104(or the messaging system100).

As discussed herein, the subject infrastructure supports the creation and sharing of interactive messages with interactive effects throughout various components of the messaging system100. In an example, to provide such interactive effects, a given interactive message may include image data along with 2D data, or 3D data. The infrastructure as described herein enables other forms of 3D and interactive media (e.g., 2D media content) to be provided across the subject system, which allows for such interactive media to be shared across the messaging system100and alongside photo and video messages. In example embodiments described herein, messages can enter the system from a live camera or via from storage (e.g., where messages with 2D or 3D content or augmented reality (AR) effects (e.g., 3D effects, or other interactive effects are stored in memory or a database). In an example of an interactive message with 3D data, the subject system supports motion sensor input and manages the sending and storage of 3D data, and loading of external effects and asset data.

As mentioned above, an interactive message includes an image in combination with a 2D effect, or a 3D effect and depth data. In an example embodiment, a message is rendered using the subject system to visualize the spatial detail/geometry of what the camera sees, in addition to a traditional image texture. When a viewer interacts with this message by moving a client device, the movement triggers corresponding changes in the perspective the image and geometry are rendered at to the viewer.

In an embodiment, the subject system provides AR effects (which may include 3D effects using 3D data, or interactive 2D effects that do not use 3D data) that work in conjunction with other components of the system to provide particles, shaders, 2D assets and 3D geometry that can inhabit different 3D-planes within messages. The AR effects as described herein, in an example, are rendered in a real-time manner for the user.

As mentioned herein, a gyro-based interaction refers to a type of interaction in which a given client device's rotation is used as an input to change an aspect of the effect (e.g., rotating phone along x-axis in order to change the color of a light in the scene).

As mentioned herein, an augmented reality content generator refers to a real-time special effect and/or sound that may be added to a message and modifies image and/or 3D data with an AR effects and/other 3D content such as 3D animated graphical elements, 3D objects (e.g., non- animated), and the like.

The following discussion relates to example data that is stored in connection with such a message in accordance to some embodiments.

FIG. 6is a schematic diagram illustrating a structure of the message annotations412, as described above inFIG. 4, including additional information corresponding to a given message, according to some embodiments, generated by the messaging client application104or the eyewear system160.

In an embodiment, the content of a particular message400, as shown inFIG. 3, including the additional data shown inFIG. 6is used to populate the message table314stored within the database120for a given message, which is then accessible by the messaging client application104. As illustrated inFIG. 6, message annotations412includes the following components corresponding to various data:augmented reality (AR) content identifier652: identifier of an AR content generator utilized in the messagemessage identifier654: identifier of the messageasset identifiers656: a set of identifiers for assets in the message. For example, respective asset identifiers can be included for assets that are determined by the particular AR content generator. In an embodiment, such assets are created by the AR content generator on the sender side client device, uploaded to the messaging server application114, and utilized on the receiver side client device in order to recreate the message. Examples of typical assets include:The original still RGB image(s) captured by the cameraThe post-processed image(s) with AR content generator effects applied to the original imagea augmented reality (AR) content metadata658: additional metadata associated with the AR content generator corresponding to the AR identifier652, such as:AR content generator category: corresponding to a type or classification for a particular AR content generatorAR content generator carousel indexcarousel group: This can be populated and utilized when eligible post-capture AR content generators are inserted into a carousel interface. In an implementation, a new value “AR_DEFAULT_GROUP” (e.g., a default group assigned to an AR content generator can be added to the list of valid group names.capture metadata660corresponding to additional metadata, such as:camera image metadatacamera intrinsic datafocal lengthprincipal pointother camera information (e.g., camera position)sensor informationgyroscopic sensor dataposition sensor dataaccelerometer sensor dataother sensor datalocation sensor data

FIG. 7is a block diagram illustrating various modules of an eyewear system160, according to certain example embodiments. The eyewear system160is shown as including an AR content recording system700. As further shown, the AR content recording system700includes a camera module702, a capture module704, an image data processing module706, a rendering module708, and a content recording module710. The various modules of the AR content recording system700are configured to communicate with each other (e.g., via a bus, shared memory, or a switch). Any one or more of these modules may be implemented using one or more computer processors720(e.g., by configuring such one or more computer processors to perform functions described for that module) and hence may include one or more of the computer processors720(e.g., a set of processors provided by the eyewear device150).

Any one or more of the modules described may be implemented using hardware alone (e.g., one or more of the computer processors720of a machine (e.g., machine1100) or a combination of hardware and software. For example, any described module of the eyewear system160may physically include an arrangement of one or more of the computer processors720(e.g., a subset of or among the one or more computer processors of the machine (e.g., machine1100) configured to perform the operations described herein for that module. As another example, any module of the AR content recording system700may include software, hardware, or both, that configure an arrangement of one or more computer processors720(e.g., among the one or more computer processors of the machine (e.g., machine1100) to perform the operations described herein for that module. Accordingly, different modules of the AR content recording system700may include and configure different arrangements of such computer processors720or a single arrangement of such computer processors720at different points in time. Moreover, any two or more modules of the eyewear system160may be combined into a single module, and the functions described herein for a single module may be subdivided among multiple modules. Furthermore, according to various example embodiments, modules described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices.

The camera module702performs camera related operations, including functionality for operations involving one or more cameras of the eyewear device150. In an example, camera module702can access camera functionality across different processes that are executing on the eyewear device150, determining surfaces for face or surface tracking, responding to various requests (e.g., involving image data of a particular resolution or format) for camera data or image data (e.g., frames) from such processes, providing metadata to such processes that are consuming the requested camera data or image data. As mentioned herein, a “process” or “computing process” can refer to an instance of a computer program that is being executed by one or more threads of a given processor(s).

As mentioned herein, surface tracking refers to operations for tracking one or more representations of surfaces corresponding to planes (e.g., a given horizontal plane, a floor, a table) in the input frame. In an example, surface tracking is accomplished using hit testing and/or ray casting techniques. Hit testing, in an example, determines whether a selected point (e.g., pixel or set of pixels) in the input frame intersects with a surface or plane of a representation of a physical object in the input frame. Ray casting, in an example, utilizes a Cartesian based coordinate system (e.g., x and y coordinates) and projects a ray (e.g., vector) into the camera's view of the world, as captured in the input frame, to detect planes that the ray intersects.

As further illustrated, the camera module702receives the input frame (or alternatively a duplicate of the input frame in an embodiment). The camera module702can include various tracking functionality based on a type of object to track. In an example, the camera module702includes tracking capabilities for surface tracking, face tracking, object tracking, and the like. In an implementation, the camera module702may only execute one of each of a plurality of tracking processes at a time for facilitating the management of computing resources at the client device102or eyewear device150. In addition, the camera module702may perform one or more object recognition or detection operations on the input frame.

As referred to herein, tracking refers to operations for determining spatial properties (e.g., position and/or orientation) of a given object (or portion thereof) during a post-processing stage. In an implementation, during tracking, the object's position and orientation are measured in a continuous manner. Different objects may be tracked, such as a user's head, eyes, or limbs, surfaces, or other objects. Tracking involves dynamic sensing and measuring to enable virtual objects and/or effects to be rendered with respect to physical objects in a three-dimensional space corresponding to a scene (e.g., the input frame). Thus, the camera module702determines metrics corresponding to at least the relative position and orientation of one or more physical objects in the input frame and includes these metrics in tracking data which is provided to the rendering module708. In an example, the camera module702updates (e.g., track over time) such metrics from frame to subsequent frame.

In an implementation, the camera module702provides, as output, tracking data (e.g., metadata) corresponding to the aforementioned metrics (e.g., position and orientation). In some instances, the camera module702includes logic for shape recognition, edge detection, or any other suitable object detection mechanism. The object of interest may also be determined by the camera module702to be an example of a predetermined object type, matching shapes, edges, or landmarks within a range to an object type of a set of predetermined object types.

In an implementation, the camera module702can utilize techniques which combines information from the device's motion sensors (e.g., accelerometer and gyroscope sensors, and the like) with an analysis of the scene provided in the input frame. For example, the camera module702detects features in the input frame, and as a result, tracks differences in respective positions of such features across several input frames using information derived at least in part on data from the motion sensors of the device.

As mentioned herein, face tracking refers to operations for tracking representations of facial features, such as portions of a user's face, in the input frame. In some embodiments, the camera module702includes facial tracking logic to identify all or a portion of a face within the one or more images and track landmarks of the face across the set of images of the video stream. As mentioned herein, object tracking refers to tracking a representation of a physical object in the input frame.

In an embodiment, the camera module702utilizes machine learning techniques to detect whether a physical object, corresponding to a representation of display screen, is included in captured image data (e.g., from a current field of view of the eyewear device150). Embodiments of this are discussed in more detail below inFIG. 8and.FIG. 9.

In an example, the camera module702utilizes a machine learning model such a neural network is utilized for detecting a representation of a display screen in the image data. A neural network model can refer to a feedforward deep neural network that is implemented to approximate a function f: Models in this regard are referred to as feedforward because information flows through the function being evaluated from an input x, through one or more intermediate operations used to define f, and finally to an output y. Feedforward deep neural networks are called networks because they may be represented by connecting together different operations. A model of the feedforward deep neural networks may be represented as a graph representing how the operations are connected together from an input layer, through one or more hidden layers, and finally to an output layer. Each node in such a graph represents an operation to be performed in an example. It is appreciated, however, that other types of neural networks are contemplated by the implementations described herein. For example, a recurrent neural network such as a long short-term memory (LSTM) neural network may be provided for annotation, or a convolutional neural network (CNN) may be utilized.

In an example, for computer vision techniques of the subject technology, the camera module702utilizes a convolutional neural network model to detect a representation of a display screen (or other applicable objects) in the image data. Such a convolutional neural network (CNN) can be trained using training data which includes thousands or millions of images of display screens such that the trained CNN can be provided with input data (e.g., image or video data) and perform tasks to detect the presence of a display screen(s) in the input data. A convolution operation involves finding local patterns in the input data, such as image data. Such patterns that are learned by the CNN therefore can be recognized in any other part of the image data, which advantageously provides translation invariant capabilities. For example, an image of a display screen viewed from the side can still produce a correct classification of a display screen as if the display screen was viewed frontally. Similarly, in cases of occlusion when an object (e.g., display screen) to be detected is partially blocked from view, the CNN is still able to detect the object in the image data.

In an embodiment, the camera module702acts as an intermediary between other components of the AR content recording system700and the capture module704. As mentioned above, the camera module702can receive requests for captured image data from the image data processing module706. The camera module702can also receive requests for the captured image data from the content recording module710. The camera module702can forward such requests to the capture module704for processing.

The capture module704captures images (which may also include depth data) captured by one or more cameras of eyewear device150(e.g., in response to the aforementioned requests from other components). For example, an image is a photograph captured by an optical sensor (e.g., camera) of the eyewear device150. An image includes one or more real-world features, such as a user's face or real-world object(s) detected in the image. In some embodiments, an image includes metadata describing the image. Each captured image can be included in a data structure mentioned herein as a “frame”, which can include the raw image data along with metadata and other information. In an embodiment, capture module704can send captured image data and metadata as (captured) frames to one or more components of the AR content recording system700. The sending of the captured frames can occur asynchronously, which may cause synchronization issues as one component might receive and process a given frame slightly before or after another component receives and processes the same frame. In applications for rendering AR effects and AR environments, such synchronization issues can result in a perceived lag from the viewpoint of the user (e.g., a glitch or perception of non-responsiveness), which reduces and detracts from the immersive experience of the AR environments. As discussed further below, embodiments of the subject technology therefore enables generating time information for each captured frame (e.g., timestamps) to facilitate synchronization of operations and improve rendering of AR effects and AR environments which a presented to the viewing user of the eyewear device150,

The image data processing module706generates tracking data and other metadata for captured image data, including metadata associated with operations for generating AR content and AR effects applied to the captured image data. The image data processing module706performs operations on the received image data. For example, various image processing operations are performed by the image data processing module706. The image data processing module706performs various operations based on algorithms or techniques that correspond to animations and/or providing visual and/or auditory effects to the received image data. In an embodiment, a given augmented reality content generator can utilize the image data processing module706to perform operations as part of generating AR content and AR effects which is then provided to a rendering process to render such AR content and AR effects (e.g., including 2D effects or 3D effects) and the like.

The rendering module708performs rendering of AR content for display by the eyewear system160based on data provided by at least one of the aforementioned modules. In an example, the rendering module708utilizes a graphical processing pipeline to perform graphical operations to render the AR content for display. The rendering module708implements, in an example, an extensible rendering engine which supports multiple image processing operations corresponding to respective augmented reality content generators. In an example, the rendering module708can receive a composite AR content item for rendering on a display provided by eyewear device150.

In some implementations, the rendering module708provide a graphics system that renders two-dimensional (2D) objects or objects from a three-dimensional (3D) world (real or imaginary) onto a 2D display screen. Such a graphics system (e.g., one included on the eyewear device150) includes a graphics processing unit (GPU) in some implementations for performing image processing operations and rendering graphical elements for display.

In an implementation, the GPU includes a logical graphical processing pipeline, which can receive a representation of a 2D or 3D scene and provide an output of a bitmap that represents a 2D image for display. Existing application programming interfaces (APIs) have implemented graphical pipeline models. Examples of such APIs include the Open Graphics Library (OPENGL) API and the METAL API. The graphical processing pipeline includes a number of stages to convert a group of vertices, textures, buffers, and state information into an image frame on the screen. In an implementation, one of the stages of the graphical processing pipeline is a shader, which may be utilized as part of a particular augmented reality content generator that is applied to an input frame (e.g., image or video). A shader can be implemented as code running on a specialized processing unit, also referred to as a shader unit or shader processor, usually executing several computing threads, programmed to generate appropriate levels of color and/or special effects to fragments being rendered. For example, a vertex shader processes attributes (position, texture coordinates, color, etc.) of a vertex, and a pixel shader processes attributes (texture values, color, z-depth and alpha value) of a pixel. In some instances, a pixel shader is referred to as a fragment shader.

It is to be appreciated that other types of shader processes may be provided. In an example, a particular sampling rate is utilized, within the graphical processing pipeline, for rendering an entire frame, and/or pixel shading is performed at a particular per-pixel rate. In this manner, a given electronic device (e.g., the eyewear device150) operates the graphical processing pipeline to convert information corresponding to objects into a bitmap that can be displayed by the electronic device.

The content recording module710sends a request(s) to the camera module702to initiate recording of image data by one or more cameras provided by the eyewear device150. In an embodiment, the camera module702acts as intermediary between other components in the AR content recording system. For example, the camera module can receive a request from the content recording module710to initiate recording, and forward the request to the capture module704for processing. The capture module704, upon receiving the request from the camera module702, performs operations to initiate image data capture by the camera(s) provided by the eyewear device150. Captured image data, including timestamp information for each frame from the captured image data, can then be sent to the content recording module710for processing. In an example, the content recording module710can perform operations to process captured image data for rendering by the rendering module708.

In an embodiment, components of the AR content recording system700can communicate using an inter-process communication (IPC) protocol. In an embodiment, components of the AR content recording system700can communicate through an API provided by the AR content recording system700.

In an embodiment, the camera module702receives a signal or command (or a request) to stop recording of image data (e.g., sent from the content recording module710). In response, the camera module702sends a request to the capture module704to stop capturing image data. The capture module704, in response to the request to stop recording, complies with the request and ceases further operations to capture image data using one or more cameras of the eyewear device150. The camera module702, after receiving the signal or command to stop recording, can also asynchronously send a signal to the image data processing module706that recording of image data (e.g., capture of image data by the capture module704) has (requested to be) stopped. The image data processing module706, after receiving the signal, performs operations to complete or finish image processing operations, including performing operations to generate metadata related to AR content items and AR effects. Such metadata can then be sent to the capture module704, which then generates a composite AR content item, including the metadata. The composite AR content item can be received by the rendering module708and rendered for display on a display device provided by the eyewear device150.

FIG. 8illustrates examples of AR content in which a display screen (e.g., included with a given electronic device) is detected in a field of view of a user while using the eyewear device150.

As shown in a first AR environment800, a field of view810includes an object (e.g., a rug) that is indicated with AR content815that provides an annotation that the current object in the field of view810is not detected as a display screen. In an embodiment, the visual appearance of the current object in the field of view810is adjusted (e.g., increasing brightness or luminosity, changing a color value, and the like).

In an example, other types of adjustments can be performed in the field of view810. For example, the current object can be decreased in brightness or luminosity, blurred, color inverted, converted to grayscale, and the like.

As shown in a second AR environment850, a field of view860includes an object870(e.g., a rug) that is overlaid with AR content865that provides an annotation that the current object in the field of view810is detected as a display screen. In this example, a portion of the field of view860is selected corresponding to the detected display screen, and the visual appearance of the portion is adjusted (e.g., lowering brightness or luminosity).

In an embodiment, as the user changes the field of view displayed by a display system of the eyewear device150, the visual appearance of the AR environment can be adjusted to either increase a brightness or luminosity of object that are not considered a display screen, or lower a brightness or luminosity of an object that is detected to be a display screen (while retaining or forgo modifying other objects in the field of view).

In an embodiment, as mentioned above, the camera module702utilizes a machine learning model, such as a CNN to detect an object in input image data, such as a representation of a display screen. For example, an input image data (e.g., provided by the capture module704) is fed to the CNN, and the input is interpreted as a tensor of a number of dimensions (e.g., 4 dimensions). In this example, a first axis represents a batch, in this case 1, a second axis represents a height of the image data, a third axis represents a width of the image data and the fourth axis represents the number of color channels. Representing an image in this manner uses the channels last convention, such as the number of channels that appear in the fourth dimension (N×H×W×C), Further, an alternative first channel convention can be provided which places the number of channels immediately after the batch axis (N×C×H×W). For an input image, the number of channels is 3 if it is a color image representing the red, green, blue (RGB) channels, respectively, and the number of channels is 1 for a monochrome image.

The CNN utilized by the camera module702then performs a convolution operation, involving a number of filters, on the image data to generate corresponding feature maps. In this example, such feature maps represent the features automatically learned by the CNN. A subsequent operation involves pooling, which involves downsizing feature maps to a lower size. Max pooling or average pooling may be utilized in an example. Max pooling includes choosing the maximum features in a slide window, and average pooling includes averaging features. The down-sampled feature maps are then convolved further and passed deeper into the CNN. In the CNN, earlier layers detect simple features such as edges while later layers combine these features detected earlier into complex features such as patterns, object parts, and the like. Moving deeper in the CNN, the size of the image data reduces while the depth (e.g., number of channels) increases. In the CNN, the advanced feature maps are fed into a fully connected neural network comprising dense layers and activation functions. A final prediction (e.g., whether the image data includes a representation of a display screen) is then determined by the CNN using a similar process provided in a dense neural network.

FIG. 9is a flowchart illustrating a method900, according to certain example embodiments. The method900may be embodied in computer-readable instructions for execution by one or more computer processors such that the operations of the method900may be performed in part or in whole by the eyewear device150, particularly with respect to respective components of the AR content recording system700described above inFIG. 7; accordingly, the method900is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method900may be deployed on various other hardware configurations and the method900is not intended to be limited to the AR content recording system700.

At operation902, the camera module702receives first image data captured by a camera of an eyewear device.

At operation904, the camera module702detects, using a machine learning model, a representation of a display screen in the first image data.

At operation906, the image data processing module706selects at least a portion of the representation of the display screen.

At operation908, the image data processing module706adjusts a visual appearance of the portion of the representation of the display screen.

At operation910, the rendering module708causes display of the adjusted visual appearance using a display system of the eyewear device.

In embodiments, the image data processing module706detects, using the machine learning model, the representation of the display screen comprises performing a objection detection process on the first image data to determine the representation of the display screen, wherein the machine learning model determines a prediction of the representation of the display screen being included in the first image data.

In embodiments, the machine learning model comprises a convolutional neural network (CNN).

In embodiments, the CNN determines a region of interest where the representation of the display screen is present, the region of interest comprising a portion of the first image data.

In embodiments, the region of interest comprises a candidate bounding box including the representation of the display screen.

In embodiments, the CNN generates a feature map based on the region of interest and provides a vector of values corresponding to the feature map as an output, the vector including respective values describing contents of the region of interest.

In embodiments, the feature map includes a set of features, and a classifier model generates a classification of the set of features from the feature map, the classification comprising a display screen object.

In embodiments, the image data processing module706adjusts the visual appearance of the portion of the representation of the display screen by generating a representation of a bounding box around the representation of the display screen, the representation of the box including a set of pixels corresponding to at least four sides. modifying the set of pixels by at least changing a first color value of a first pixel to a second color value, where the second color value is different than the first color value, and modifying a second set of pixels corresponding to the representation of the display screen by at least changing a first luminosity value of a third pixel to a second luminosity value, wherein the second luminosity value is greater than the first luminosity value.

In embodiments, the image data processing module706modifies the set of pixels by at least generating a message indicating that a screen has been detected.

In embodiments, the image data processing module706receives second image data captured by the camera, the second image data being received upon movement of the camera of the eyewear device based on a change of a position of a head of a user wearing the eyewear device, detects that the representation of the display screen is no longer present in the second image data, and modifies the second image data to indicate that the representation of the display screen is no longer present, the modifying comprising reducing a portion of the second image data to decrease at least one luminosity value of a pixel from the second image data.

FIG. 10is a block diagram illustrating an example software architecture1006, which may. be used in conjunction with various hardware architectures herein described,FIG. 10is a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein, The software architecture1006may execute on hardware such as machine1100ofFIG. 11that includes, among other things, processors1104, memory1114, and (input/output) I/O components1118. A representative hardware layer1052is illustrated and can represent, for example, the machine1100ofFIG. 11. The representative hardware layer1052includes a processing unit1054having associated executable instructions1004. Executable instructions1004represent the executable instructions of the software architecture1006, including implementation of the methods, components, and so forth described herein. The hardware layer1052also includes memory and/or storage modules memory/storage1056, which also have executable instructions1004. The hardware layer1052may also comprise other hardware1058,

In the example architecture ofFIG. 10, the software architecture1006may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture1006may include layers such as an operating system1002, libraries1020, frameworks/middleware1018, applications1016, and a presentation layer1014. Operationally, the applications1016and/or other components within the layers may invoke API calls1008through the software stack and receive a response as in messages1012to the API calls1008. The layers illustrated are representative in nature and not all software architectures have all layers, For example, some mobile or special purpose operating systems may not provide a frameworks/middleware1018, while others may provide such a layer. Other software architectures may include additional or different layers.

The operating system1002may manage hardware resources and provide common services. The operating system1002may include, for example, a kernel1022, services1024, and drivers1026. The kernel1022may act as an abstraction layer between the hardware and the other software layers. For example, the kernel1022may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services1024may provide other common services for the other software layers. The drivers1026are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers1026include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration.

The libraries1020provide a common infrastructure that is used by the applications1016and/or other components and/or layers. The libraries1020provide functionality that allows other software components to perform tasks in an easier fashion than to interface directly with the underlying operating system1002functionality (e.g., kernel1022, services1024and/or drivers1026). The libraries1020may include system libraries1044(e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, and the like. In addition, the libraries1020may include API libraries1046such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries1020may also include a wide variety of other libraries1048to provide many other APIs to the applications1016and other software components/modules.

The frameworks/middleware1018(also sometimes referred to as middleware) provide a higher-level common infrastructure that may be used by the applications1016and/or other software components/modules. For example, the frameworks/middleware1018may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware1018may provide a broad spectrum of other APIs that may be used by the applications1016and/or other software components/modules, some of which may be specific to a particular operating system1002or platform.

The applications1016include built-in applications1038and/or third-party applications1040. Examples of representative built-in applications1038may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. Third-party applications1040may include an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform, and may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. The third-party applications1040may invoke the API calls1008provided by the mobile operating system (such as operating system1002) to facilitate functionality described herein.

The applications1016may use built in operating system functions (e.g., kernel1022, services1024and/or drivers1026), libraries1020, and frameworks/middleware1018to create user interfaces to interact with users of the system. Alternatively, or additionally, in some systems interactions with a user may occur through a presentation layer, such as presentation layer1014. In these systems, the application/component ‘logic’ can be separated from the aspects of the application/component that interact with a user.

FIG. 11is a block diagram illustrating components of a machine1100, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,FIG. 11shows a diagrammatic representation of the machine1100in the example form of a computer system, within which instructions1110(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine1100to perform any one or more of the methodologies discussed herein may be executed. As such, the instructions1110may be used to implement modules or components described herein. The instructions1110transform the general, non-programmed machine1100into a particular machine1100programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine1100operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine1100may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine1100may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions1110, sequentially or otherwise, that specify actions to be taken by machine1100. Further, while only a single machine1100is illustrated, the term ‘machine’ shall also be taken to include a collection of machines that individually or jointly execute the instructions1110to perform any one or more of the methodologies discussed herein.

The machine1100may include processors1104, including processor1108to processor1112, memory/storage1106, and I/O components1118, which may be configured to communicate with each other such as via a bus1102. The memory/storage1106may include a memory1114, such as a main memory, or other memory storage, and a storage unit1116, both accessible to the processors1104such as via the bus1102. The storage unit1116and memory1114store the instructions1110embodying any one or more of the methodologies or functions described herein. The instructions1110may also reside, completely or partially, within the memory1114, within the storage unit1116, within at least one of the processors1104(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine1100. Accordingly, the memory1114, the storage unit1116, and the memory of processors1104are examples of machine-readable media.

Communication may be implemented using a wide variety of technologies. The I/O components1118may include communication components1140operable to couple the machine1100to a network1132or devices1120via coupling1124and coupling1122, respectively. For example, the communication components1140may include a network interface component or other suitable device to interface with the network1132. In further examples, communication components1140may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices1120may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

The following discussion relates to various terms or phrases that are mentioned throughout the subject disclosure.

‘Signal Medium’ refers to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term ‘signal medium’ shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term ‘modulated data signal’ means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms ‘transmission medium’ and ‘signal medium’ mean the same thing and may be used interchangeably in this disclosure.

‘Processor’ refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor) that manipulates data values according to control signals (e.g., ‘commands’, ‘op codes’, ‘machine code’, etc.) and which produces corresponding output signals that are applied to operate a machine. A processor may, for example, be a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC) or any combination thereof. A processor may further be a multi-core processor having two or more independent processors (sometimes referred to as ‘cores’) that may execute instructions contemporaneously.

‘Machine-Storage Medium’ refers to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions, routines and/or data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks The terms ‘machine-storage medium,’ device-storage medium,' computer-storage medium' mean the same thing and may be used interchangeably in this disclosure. The terms ‘machine-storage media,’ computer-storage media,' and ‘device-storage media’ specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term ‘signal medium.’

‘Carrier Signal’ refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device.

‘Computer-Readable Medium’ refers to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms ‘machine-readable medium,’ computer-readable medium' and ‘device-readable medium’ mean the same thing and may be used interchangeably in this disclosure.

‘Client Device’ refers to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may use to access a network. In the subject disclosure, a client device is also referred to as an ‘electronic device.’

‘Signal Medium’ refers to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term ‘signal medium’ shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term ‘modulated data signal’ means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms ‘transmission medium’ and ‘signal medium’ mean the same thing and may be used interchangeably in this disclosure.

‘Processor’ refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor) that manipulates data values according to control signals (e.g., ‘commands’, ‘op codes’, ‘machine code’, etc.) and which produces corresponding output signals that are applied to operate a machine. A processor may, for example, be a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC) or any combination thereof. A processor may further be a multi-core processor having two or more independent processors (sometimes referred to as ‘cores’) that may execute instructions contemporaneously.

‘Machine-Storage Medium’ refers to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions, routines and/or data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks The terms ‘machine-storage medium,’ device-storage medium,' computer-storage medium' mean the same thing and may be used interchangeably in this disclosure. The terms ‘machine-storage media,’ ‘computer-storage media,’ and ‘device-storage media’ specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term ‘signal medium.’

‘Carrier Signal’ refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device.

‘Computer-Readable Medium’ refers to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms ‘machine-readable medium,’ computer-readable medium' and ‘device-readable medium’ mean the same thing and may be used interchangeably in this disclosure.