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Easy-to-use central source-of-truth your company's media assets
The short explainer video below is just an appetizer of all the DAM use cases you will be able to implement within the Asset Hub:
Learn all you need to know to get started with uploading and managing assets within the Asset Hub from:
through Manage assets,
Enrich assets with Metadata and Tags,
Organize assets in static Folders or dynamic Collections,
Search & find assets with #filters or complex #search queries,
Collaborate with assets between users and,
Share assets internally as well as externally to finally,
Download assets in various target formats
The asset management modal is the go-to place to manage and collaborate on an asset. You can access it in 3 ways:
Double-click on an asset
via the Manage menu in the contextual (right-click) menu
via the Manage menu in the action bar
4 tabs are available for managing the asset:
This tab shows details about various activities performed on an asset:
Rename
Move
Ownership change
Visibility changes
Metadata changes
Tags change
Labels change
New comment
Sharing & permissions change
This tab allows users to view and add written comments or graphical annotations on assets for better collaboration. Various features such as liking, answering, quoting another user or team are available.
The Details tab displays all descriptive information of the asset such as technical details, product-related information, metadata and embedded metadata. The fields can be edited by clicking on the icon.
Field | Description |
---|
If are enabled, the Product reference and Product position can be set and viewed here.
Custom metadata fields can be edited and viewed here. See for details on how to create the metadata structure.
Embedded IPTC/XMP metadata are displayed here. Requires the Extract embedded metadata post process to be enabled under for this section to display.
Based on the versioning behavior configured on your token (see setting), this tab will show the version history of an asset and allows to restore a previous version.
Furthermore, users can or .
Name | The file name |
Title | Multi-lingual title of the asset |
Description | Multi-lingual description of the asset |
Tags |
Labels |
Orientation | Portrait or landscape |
Main colors |
Leverage the Filerobot Image Editor to edit images directly in the browser
In Filerobot you can crop, resize, apply different filters and add watermarks to your images. You can save the results as a variant of the main image or an entirely new asset.
The Edit image button opens the Image Editor.
The Image Editor is a feature-rich inline image editor that supports the most commonly used image editing function of image editing software:
When you are ready with the Editing, click on "Save".
Filerobot is an AI-powered Digital Asset Management (DAM) platform with integrated image and video optimizers to store, organize and accelerate media galeries online
Filerobot can be implement either as or as a combination of:
Collaborative DAM for marketing, content and creative teams to upload, manage and transform marketing assets in one central source-of-truth
Headless DAM for organizing, optimizing and delivering millions of media assets via API and integrating into your tech stack easily
Media Asset Widget for providing a fast and modern upload experience and delivering media galleries on any web application
Filerobot is architected to scale and manage thousands to hundreds of millions of assets per customer tenant.
Filerobot is a modular platform build around 5 main components:
Filerobot File System: A scalable cloud-based file system to store and organize files across the globe
Web applications such as the #the-asset-hub, the #filerobot-media-asset-widget-fmaw, the #sharebox and #airbox to offer multiple entry and media distribution points for content administrators, contributors and consumers
ASK Filerobot: AI-powered automations to enrich and moderate assets and generate variants
Media optimizers to transform and optimize images, videos and any static content on-the-fly and accelerate over CDN
A multi-CDN delivery infrastructure to accelerate asset loading times worldwide
Through a variety of APIs, plugins and connectors, Filerobot is designed to be integrated in an ecosystem of 3rd party applications such as PIM, CMS, ERP, booking platforms, and more.
Filerobot relies on a multi-tenant, scalable and flexible file system serving as foundation to an intuitive for enabling DAM workflows and integrations within your Content Operations.
AI-powered uploads automatically tag assets for better classification, data enrichment and search while content moderation algorithms automate User-Generated Content (UGC) flows.
Filerobot is designed to be the central Hub for all brand and product images, videos as well as static assets in your organization. The Filerobot Media Asset Widget provides interfaces to your internal and external stakeholders to upload, categorize, collaborate and deliver fast and beautiful media assets via multiple channels to global audiences.
The Filerobot Media Asset Widget is a versatile file uploader and media gallery seamlessly integrated into an easy-to-use modal or inline widget. As a window to the Filerobot DAM, it empowers users to enjoy accelerated uploads through Filerobot's robust content ingestion network and reverse CDN as well customizable galleries.
The FMAW offers advanced capabilities such as inline image editing, search, filtering, bulk management of tags and metadata.
It is an ideal solution for building interactive media upload and management experiences in your web or mobile applications.
The Sharebox is Filerobot's external sharing feature that allows to securely share multiple assets directly from the DAM with external parties. Select the files for sharing and create a password-protected Sharebox with optional password protection and expiration date. All active Shareboxes are accessible within the shared tab in the Asset Hub, allowing you to manage them in one place.
The Airbox grants external users to upload assets directly into specific folders in the DAM using public links. Password-protection and expiration date are optionally available.
ASK Filerobot is the hub running all Filerobot's ML and AI capabiltiies. Upon asset upload, ASK Filerobot intelligently extracts information from your media assets with AI and grants you the flexibility to initiate various tasks.
These post-processing capabilities can be done in 2 ways:
Asynchronous processes allow you to avoid waiting for the process to complete during the upload
Synchronous processes empower you to validate assets based on pre-defined criteria
This versatile feature streamlines your asset management process to suit specific business needs.
Filerobot integrates 3 advanced image, video, and static file optimizers, empowering you to transform your media assets on-the-fly or through a background process after uploading.
Image optimization: Filerobot will save you hours in image processing tasks by manipulating and transforming your images, optimized for web delivery.
Video optimization: Filerobot's adaptive streaming and video transcoding features ensure your videos load fast anywhere and on any device. Filerobot can transcode your videos in multiple target resolutions and generate the playlist (manifest) for your DASH or HLS video players.
Static content: With the Filerobot CDN, Filerobot efficiently accelerates the delivery of all types of static content, including PDFs, CSS and JS minification.
Filerobot collaborates with numerous Content delivery Network (CDN) providers that boast an extensive global network of thousands of Points-of-Presence (PoP). This strategic partnership allows Filerobot to efficiently cache media assets as close as possible to the viewers, resulting in significantly faster loading times of assets regardless of the channel, device and user location.
Multi-lingual tags associated to the asset. Tags can be added manually (free input or from tag dictionnary) or automatically via AI. See .
Labels are advanced tags with a dedicated page to view labeled assets. See .
Image's main colors. Requires the post process to be activated.
Feature | Available options: |
---|---|
Adjust
Custom - contains predefined width&height cropping values, including standard sizes for the most popular Social media banners, logos, stories, etc
Rotate - option to rotate the image per 90 degrees
Flip X - flip the image horizontally
Flip Y - flip the image vertically
Resize
An option to specify the cropping values for width&height manually. You can drag and resize the cropping area with the mouse.
Finetune
Brightness - control the overall lightness or darkness of your photo. Slide right for lighter images and left for darker tones.
Contrast - determine the distinction between light and dark areas in your image. Higher contrast can evoke energy and intensity, while lower contrast might create a more subdued or nostalgic atmosphere.
HSV - it stays for "Hue, Saturation, Value". Hue determines the color like green, red, blue, etc. Moving the slider left or right will shift the colors along the color wheel. Saturation controls the intensity or purity of a color. Higher saturation makes colors more vibrant, while lower saturation makes them more muted. Value (also known as brightness) affects the lightness or darkness of a color. Adjusting HSV can dramatically influence the emotional tone of your photo.
Blur - reduce the sharpness and clarity of the entire image.
Warmth - control the balance between warm tones (such as reds, oranges, and yellows) and cool tones (such as blues and greens) in your photos. Warmth adjustments can evoke feelings of coziness, nostalgia, or vibrancy, depending on the desired outcome.
Filters
Select from more than 40 filters to enhance your images.
Watermark
Add watermark - add an already uploaded watermark image to your asset
Upload watermark - choose an image that to be applied as a watermark
Add as text - create a watermark of your choice, applying it as a free text to your image
Draw
Sketching tool
Metadata fields are attributes used to describe an asset for further categorization, grouping and retrieval
Filerobot distinguishes two types on metadata fields:
Embedded metadata: EXIF/IPTC metadata embedded by the camera or the graphical editor software
Custom metadata: metadata fields created in Filerobot to enrich assets
Filerobot allows the creation of simple or complex metadata structures, aka taxonomies, with each metadata field capable of supporting multiple dimensions, such as translations, currencies, distribution channels or any other variant required by your use case.
Metadata can be viewed and edited during the upload process, in bulk post-upload or individually for each asset in the asset management modal, see Manage assets.
Assets can then be searched by metadata field values and dynamic Collections created based on metadata field values.
Let's get started!
Transformations are dynamically generated variations of an origin asset
Unlike versions or edited images, transformations do not create separate files in the DAM.
Transformations are generated via URL query parameters and can be created graphically via the Filerobot Image Editor for single assets or programmatically in bulk.
There are 2 ways to use transformations once created:
Download them to your local computer for further re-use. Various formats are available when downloading a transformation.
Deliver over optimized (JPEG, WebP or AVIF) CDN URLs
When you are using Filerobot DAM, you will notice that we introduce the concept of File Version vs. File Transformation.
In order to create a new image transformation, you can open the transformation panel within the Asset Management modal:
Here the user has the option to create a new transformation by clicking on + Add transformation box. The Image Editor pops up and allows the user to create and save a transformation graphically:
Once the transformation is saved, you can hover over it to either download it directly or copy an optimized CDN link to your clipboard for further distribution:
For example, the optimized CDN link to the feed portrait transformation for Instagram looks like:
The query parameters suffixed to the image CDN URL trigger Filerobot to generate the transformation on-the-fly and cache it in the CDN.
Unlike single transformations which are created using the Image Editor for each asset, custom presets are the way to go when it comes to generating transformations automatically in bulk.
Navigate to the Settings / Project / Process / Delivery URLs page and scroll to the Dynamic asset transformation presets section to create a preset:
To create the new Preset, click the "+ Add preset" button and pick a file to work on. Then, an Image Editor will pop up, letting you crop the test image as you like, add a watermark, or apply a certain effect.
When you are ready, save the changes. Filerobot will generate a Preset, containing the chosen operations and filters. You can always review the exact configuration and rewrite it using the "..." menu -> Edit or Edit in Image Editor.
For each Preset, you can set the "Autogenerate" option and the transformation will apply automatically whenever you upload new images.
Additionally, you can select only specific Presets (image transformations) to be generated during image upload.
The custom preset created above can be used with the query parameter p (for "preset") to generate the transformation:
Embedded metadata may contain for example the GPS coordinates of where the picture was taken, the author, the camera, the lens, ... Filerobot can extract these metadata, including ones from custom namespaces (for example model name, product category) and map it to custom metadata.
To enable the extraction of embedded metadata, navigate to Settings > Development > Automations and enable the Extract embedded metadata post process.
Once enabled, Filerobot will extract embedded metadata from asset upon upload and display them in the Details tab of the asset management modal:
To leverage search and collections grouping based on embedded metadata, contact our support team to enable the mapping between embedded metadata and custom metadata.
The users have the option to define metadata structures, create new fields and group them into different categories. This kind of metadata is defined per Filerobot project (token) and applies to all assets in it. You can have different metadata taxonomies if you have multiple Filerobot tokens.
See Managing metadata.
Coming soon!
Advanced search and filtering
The search bar allows user to use simple search with keywords
Advanced search is activated by typing the @
sign and selecting one of the dedicated search operator (filename,...). This allows user to search in specific fields - file, metadata, asset properties, etc.
User can also use the Context dropdown to narrow down the search scope to a specific folder or to Favourites.
Assets can be filtered by their creation or last modification date using pre-defined or custom time frames
With the format filter, user can choose from a list of asset types or file formats depending on the content of the Filerobot container
Filtering by filesize by entering a size range
This option allows user to filter by selecting one or more tags in the container
The metadata filter allows user to select multiple metadata fields as well as multiple values for each field and apply them as filters
The image group of filters lets user filter by image dimensions/resolution, contents, color or aspect ratio
Labels are used for categorizing and describing items, allowing for flexible grouping and easy Search. One asset can have multiple labels.
Labels allow users to organise their assets in a more structured and permanent way than Tags. They are static - unlike the dynamic Collections whose assets change through applicable filters - and are similar in principle to the labels on physical products (drinks, foods, clothes).
Users can still modify a given label and/or change which assets fall under it.
The system also allows users to view and search both the labels and their applicable assets.
The Labels page can be accessed from the main menu on the Library page:
You can create named, colour-coded labels by clicking on + Add label
button. This allows you to list and manipulate all assets assigned to a specific label in a single view.
Title - please choose a meaningful name for your new label
Colour selector - you can choose a color for your label by clicking on the pre-defined colors or specifying its RGB code in the field bellow
Pinned - if this checkbox is marked, then the new label will appear on the top of the labels list. On Sort the system will sort first the Pinned labels and then will show the other ones.
In order to open the Configuration window, allowing you to manage your labels, you need to click on the "Settings" icon in the left panel of the page:
That will open the "Manage labels" window where you have options to Search, Add new labels, Edit the current configuration or Delete existing labels.
If you want to delete a label, it is a good practice to ensure that the number of the current assets assigned to it, is "0".
Invoking the context menu on an individual or multiple selected assets and choosing the Labels
sub-menu lets the user search and apply the available labels:
If, during the Search, no exact match is found, the user has the option to create a new label and apply it to the asset immediately:
This function is available on the Labels page when selecting a file and opening its context menu:
The user can select multiple files too and use the ...
icon in the Navigation bar in order to access the same option.
In both cases, in order to be able to detach a label, the user should have a Developer, Manager, Admin or Owner User Role.
You can search by specific search criteria inside the pool of all assets assigned to a specific label, using the main Search box:
In case you are interested in finding all assets assigned to a specific label, you can search for this label's name in the Search box at the left panel and then just click on the result:
Filerobot provides multiple options for organizing your assets in order to facilitate the Search, collaboration and sharing of ideas.
Structure | Description |
---|---|
Folders
Most people are familiar with the concept of folders, making it easier for users to grasp and navigate through this structure
Collections
The collections organize files using specific metadata criteria, allowing for automated update of Collections content
Labels
The labels are categories that might contain multiple files, gathered manually together
My favorites
Adds files temporarily to "My favourites" mostly with the purpose of easily finding and accessing them later on
"My Favorites" are typically used to quickly access frequently visited assets. They provide a way to create a personalized flat list of preferred items for easy access.
Users can mark assets as favorites and view / search these assets under the My favorites submenu for quick access. Each asset can be marked as favorite or not and each user can access their list of favorite assets via Menu > My favorites:
The user can add an asset to My favorites list by simply clicking on the heart icon at the bottom right corner of the asset:
An asset whose icon is colored in purple is already added to this list, while an asset whose icon is in grey is not part of My favorites.
Users can add multiple assets in bulk to My favorites list by selecting them and then opening ...
-> Add to -> My favorites in the Navigation bar:
In the same ways, an asset or a group of assets that are already added to My favorites list can be removed from there.
Users, permissions and comments
Filerobot's multi-users and multi-tennant model allows users to upload, manage, collaborate on and distribute digital content in different projects via the Asset Hub.
The commenting system allows you to place annotations on images for easier communication.
Extensive role-based permission model and user teams ensures access only to relevant resources.
Allows you to collaborate by adding timestamped comments with annotations on each asset.
Deliver Filerobot assets via CDN
All public assets in your container can be shared or published in a webpage or a web application by using the CDN link. This link allows you to take advantage of all avaliable media optimizers via URL parameters. More details are available in the media optimizer Introduction article.
CDN links can deliver only public assets. For more details, please see the Publish assets section.
This link is also available in the #publish-modal for all public assets.
Collaboration with third parties
Filerobot offers a mechanism to collaborate with third parties by allowing them to upload and download assets to your container via dedicated URLs.
Allows third parties to upload assets to your container. Supports password protection and activaton scheduling.
For sharing specific assets with third parties. Supports password protection and activaton scheduling.
User roles, permissions and teams
Filerobot's role-based permission model allows you to create users with different privileges, setting who can access and add assets as well as contribute by editing and annotating assets, or access billing information and analytics.
The Asset Hub allows you to grant mulitple users different privileges to manage, collaborate on and distribute digital content.
Filerobot offers the following user roles:
Filerobot allows you to group users in teams for easier role and permission management.
Role | Description |
---|
| View-only access to assets. |
| Allows access to the assets in the project(s) they are invited to - no access to configuration management and company details. |
| Grants access to the assets, stats & logs, as well as process and acceleration settings. Can use Security Templates (cannot create or edit one) in the respective project(s) they are invited to. |
| Has all Developer permissions plus:
The Manager role grants no access to the company bank account settings or invoicing information. |
| Grants access to the billing and invoicing systems only (no access to the container's assets). |
| Full access - the only restriction is that the administrator cannot manage other administrators. |
| Full access - only one Owner per company is allowed. |
To create a new team, you can use the Add team button. You can also add an avatar to the team, if you wish. |
You can add members to each team from the existing users or invite them by email. |
Asset visibility settings
Each asset in your Filerobot container can be either private or public.
Public assets are accessible by everybody via their CDN links while private ones are avaliable only to users and teams with the appropriate permissions.
This modal allows you to change the visibility settings for each asset or a selection of assets. By default, the visibiliy is inherited from the parent folder or the container. The container visibility can be defined in the Storage settings, #default-asset-visibility section.
Please note that if you edit the permissions of a selection of assets via the Publish modal, you will override the visibility of all selected assets without being able to see their previous settings.
If the visibility is set to public, you can copy the CDN link from this modal.
You can access the Publish modal from the context menu - Share / Publish.
Third-party asset sharing
Sharebox allows you to share assets with third parties without access to Filerobot Asset Hub.
To create a Sharebox, you need to select the assets you would like to share and open the Share assets modal from from the top bar or the context menu - Share / Via Sharebox.
Each Sharebox can optionally be password protected and have an expiration date. Then, you just need to share the Sharebox link.
You can view, modify or delete Sharebox instances from the left menu in your library, Sharebox:
Similar to a headless CMS or a PIM, a Headless DAM decouples the media asset management back office from the web or mobile interfaces used to publish assets
A Headless DAM helps decouple the creative process from the workflows required to publish media asset on the web and mobile. For example, creative teams or app users will feed media assets into Filerobot via the The Filerobot Asset Hub or the Filerobot Media Asset Widget, while developers will use Filerobot's Headless DAM capabilities to publish the media assets on the target web or mobile applications.
A headless DAM is a DAM that allows the creation of custom frontends or galleries to provide an integrated upload experience or display media asset galleries in flexible ways.
A Headless DAM integrates well in a composable architecture, for example for an E-Commerce project with an ERP, a PIM and a CMS.
A Headless DAM enables publishers on various channels to upload, process, and publish digital assets with the least amount of work and maximum effectiveness. Headless DAM is one of the finest options for brands and companies who wish to distribute massive volumes of digital assets across numerous platforms.
With customizable user interfaces, cross-platform data exchange, and streamlined workflows, Headless DAM offers users a quick and fast way to manage their digital assets.
To use the Filerobot DAM API, you will need to create an API key from Settings > Developers > API keys. This API key must be passed as X-Filerobot-Key header in all API requests.
Each API key is associated with a set of permissions governing which actions a key is allowed to perform on the Filerobot File System.
Access the full API reference in Postman below:
The Filerobot Adobe CC embeds the Filerobot Media Asset Widget (FMAW) plugin inside the following supported Adobe applications:
Adobe Photoshop
Adobe Illustrator
Adobe InDesign
No more copy-pasting media assets from your local drive to Filerobot, everything happens from within your Creative Cloud application.
Step 1. Download the extension files from the link and unzip them.
(use the following in your browser if you can't download from the above URL link)
Step 2. Copy the unzipped folder content into the folder for your Creative Cloud version:
If you have Photoshop CC 2014, CC 2015, CC 2015.5, CC 2017 or above:
Mac OS: /Library/Application Support/Adobe/CEP/extensions/
Windows x64: C:/Program Files (x86)/Common Files/Adobe/CEP/extensions/
If you have Photoshop CC:
Mac OS: /Library/Application Support/Adobe/CEPServiceManager4/extensions/
Windows x64: C:/Program Files (x86)/Common Files/Adobe/CEPServiceManager4/extensions/
You have to create the folder if it does not already exist.
Step 3. Restart Photoshop and you will find the Filerobot widget/extension at Window > Extensions > extension_name.
First, you need to log in to your Filerobot account from the Widget.
Then, you can select the Company and Project you would like to access (for multi-project Filerobot accounts).
You can browse your container and its folder structure directly from the widget and use the search and sort functions. You can also create folders.
You can upload images from Photoshop directly to your Filerobot container. Downloaded images from Filerobot can be inserted as a new file or as a new layer in existing Photoshop project.
...
...
Third-party product integration
In the realm of ecosystem integration, seamless connectivity plays a crucial role in facilitating communication and interaction between different third-party components.
This introductory guide aims to clarify the distinctions we do among the two main types of software connections: plugins and connectors (aka applications).
Plugins are software components designed to extend the functionality of a larger application. They allow developers to add specific features or behaviors to an existing software system without modifying its core codebase. Plugins are usually loaded dynamically at runtime, making it easier to add or remove them without affecting the main application's stability.
Plugins provide a modular approach to integration, enabling third-party developers to integrate seamlessly Filerobot into the host application/software, simply using our Filerobot Media Asset Widget to replace or complement the media gallery used by the third party software/tool.
Applications, also known as standalone connectors or adapters, are complete and self-contained entities designed to perform specific tasks or provide a range of functionalities to users. Unlike plugins and libraries, applications are intended to be independent and tightly integrated.
Our applications have their own user interfaces (UI) and may interact with other applications or services independently. They will always be performing a range of additional functions like programatic synchronisation and similar advanced features.
In summary, plugins extend the functionality of existing applications with a more passive role, while applications/connectors facilitate interoperability and seamless synchronisation between different systems by being placed in between them and actively intervening.
In any case, a manual integration will always be possible by the use of our Filerobot Media Asset Widget, whose code/package is linked in this documentation.
Filerobot Magento Plugin
The Filerobot Plugin is an extension which adds Asset Management to the Magento Admin (Product Images, Tinymce 4 WYSWYG) and shows it on the Front-end (Product listing page, Product detail page, Minicart, Cart Page, and Checkout Page).
There are 3 simple steps for enabling the plugin on your Magento 2 website:
Obtain a Filerobot token (request it here);
Install the Filerobot module for Magento 2;
Add your security configuration parameters to access your Filerobot library.
Please note that the plugin will make some changes in the Admin section.
Currently, the plugin only supports the Text Component in Page Builder; other components will be supported in future versions.
TinyMCE Image (Insert/Edit) default function will be disabled. You can change the image size by scaling (drag and drop function) or delete it and add a new one with the size you like;
The plugin will disable the default upload function in Product Edit Page. Instead, every asset will be managed by Filerobot (single source of truth).
The plugin works well with the Magento Luma default theme. If you use a different theme, please check the manual integration below to get the product images.
You can register for a demo if you don't already have a Filerobot account.
Filerobot supports Open Source and Commerce Edition from version 2 onwards.
If you have a question or need assistance, feel free to contact our support.
To be able to install the module by Composer, you need to get a copy of the module on the Magento Marketplace, or directly from Github. Then, unzip the source code to app/code folder.
Enable and install the following modules in Magento:
php bin/magento module:enable Scaleflex_Filerobot php bin/magento setup:upgrade php bin/magento setup:static-content:deploy
Once the steps listed above are completed, you need to enter your Filerobot token and security template into the Filerobot module configuration the Magento admin interface: Stores > Configuration > Filerobot By Scaleflex > Filerobot integration.
If your token and security template id is verified, please activate the module by selecting Yes in the Filerobot Enable dropdown then save again.
After all is done, you will be asked to flush your Magento cache.
You will not be able to activate the plugin until the token and security template id are correct.
Filerobot Token: Your Filerobot token from the Asset Hub interface;
Security Template Identifier: Security template ID, found in the Developers top menu;
Filerobot upload directory: This is the top storage folder for your assets. The default value is /magento
.
Please create the folder in Filerobot first to prevent an error. If you want to change it, you need to check if the folder exists in your Filerobot container.
Please click on Icon Filerobot on TinyMCE:
Then choose which images you want to insert -> transform:
You will be able to select and insert one or more images:
If you want to change the image size, please choose one image at time and click Insert. After that, you will be able to set the desired image dimensions.
If you want to change the dimensions of an already inserted image, you can use Tinymce Image size drag and drop.
Go to Product Edit Page in Images and Video Tab, then click Image Manager. Then you can use the file manager like TinyMCE above.
The Canva plugin allows you to create using all the power of Canva along with any existing templates and designs you already have saved, and transport your final render into your Filerobot library.
Simply follow the below steps:
Click the Upload button (1), and select the Canva option (2)
Then login using you usual credentials in Canva and create at will
Finally, click the "Publish" button (3) on the top right corner of the Canva interface
This will transport your creation as a regular asset, added in the current folder with a simple click on "upload" (4) -after any transformation you could wish to execute from the "settings" side panel-
Filerobot Contentstack App
The features of the Filerobot Contentstack App include:
Asset Manager widget in Contentstack Content Management;
Multiple file type support;
Metadata sync: asset metadata from Filerobot will show on your Rest/GraphQL response.
There are 3 simple steps for enabling the App on your Contentstack instance
(prerequisite) Have a Contentstack Account;
Install the Filerobot App from the Marketplace;
Add your security configuration parameters to access your Filerobot library.
Sign up to Contentstack.
Updating
Security Template Identifier: Security template ID, found in the Developers top menu;
Container: Your Filerobot token from the Asset Hub interface;
Custom Field
JSON Rich Text Editor
Step 2. Select Scaleflex Filerobot
Step 3. Save and Close
Step 4. On Your Entry open Filerobot and choose image
Custom Field
JSON Rich Text Editor
Obtain a Filerobot token (request it );
if you don't already have a Filerobot account.
Step 1. On Content Type Menu
Step 3. Save and Close
Step 4. On Your Entry open Filerobot and choose image Step 5. Publish
Step 1. On Content Type Menu
Step 2. Select Scaleflex Filerobot
Step 5. Publish
Filerobot Opencart plugin
The Filerobot Plugin is an extension which adds Asset Management to Opencart.
There are 3 simple steps for enabling the Filerobot plugin on your Opencart admin:
Obtain a Filerobot token (request it here);
Install the Filerobot module for Opencart;
Add your security configuration parameters to access your Filerobot library.
Currently, the plugin supports the default WYSIWYG editor from Opencart - Summernote, but the native "Image resize" function is disabled. You can change the image size by removing the previous one then add it with new dimensions (a dedicated function will be added in the next version).
Register for a demo if you don't already have a Filerobot account.
Upload the admin
folder in upload/admin
to your Opencart source code.
Login to the Admin Dashboard, navigate to Extensions/Installers and upload the scaleflex_filerobot.ocmod.zip
file. Then, go to Modification and click Reload to update the installer.
Navigate to Extensions/Extensions, choose Modules from the drop-down menu, select Filerobot and update all configurations.
Filerobot Token: Your Filerobot token from the Asset Hub interface;
Security Template Identifier: Security template ID, found in the Developers top menu;
Filerobot upload directory: This is the top storage folder for your assets.
Please create the folder in Filerobot first to prevent error.
You need to enable the extension in order to use Filerobot in all pages.
With Summernote:
With Image field:
If you are on Summernote: You can select multiple images for insertion.
If you are in an Image Field: You should select only one. If you selected multiple items, it will use the last one, as Opencart's Image Field inserts only one image at a time.
This extension is 100% compatible with the default theme.
If you use another theme (stock or custom), you should verify that the image is indeed from Filerobot before returning data to the view. An examples for banners would be:
Filerobot Contentful App
The features of the Filerobot Contentful App include:
Asset Manager widget in Contentful Content Management;
Multiple file type support;
Metadata sync: asset metadata from Filerobot will show on your Rest/GraphQL response.
There are 3 simple steps for enabling the App on your Contentful instance
(prerequisite) Have a Contentful Account;
Obtain a Filerobot token (request it here);
Install the Filerobot App from the Marketplace;
Add your security configuration parameters to access your Filerobot library.
Register for a demo if you don't already have a Filerobot account.
Sign up to Contentful.
Contentful is 100% Cloud Native, so you have to sign up for a hosted CMS at https://be.contentful.com and you will get a link to your space:
Find it here https://www.contentful.com/marketplace/ and install it or click install now on https://www.contentful.com/marketplace/app/scaleflex-filerobot/.
Filerobot Token: Your Filerobot token from the Asset Hub interface;
Security Template Identifier: Security template ID, found in the Developers top menu;
Filerobot upload directory: This is the top storage folder for your assets;
Assign to Fields: The app supports only the JSON Object Field, you can choose which field will be used by the app.
With the Asset Manager (1) button you can open the FMAW (Filerobot Media Asset Widget):
You can upload new assets with the Upload button (2):
or choose assets existing in your container (3) and Add (4) them:
You can delete a specific asset ot clear all of them (5):
Filerobot Drupal module
Step 2: Extract the zip file in /modules
;
Step 3: In the Drual admin, navigate to Extend / find Filerobot by Scaleflex / Install
Step 1: Run composer require 'drupal/filerobot_by_scaleflex:^1.0'
Step 2: In the Drupal admin, navigate to Extend / find Filerobot by Scaleflex / Install
Activation: You can enable or disable the module;
Token: Your token from the Filerobot Asset Hub;
CNAME: If you have a CNAME configure in Filerobot Asset Hub, you can enter it in this field;
Security Template Identifier: To load the Filerobot Widget or Filerobot Image Editor, you need to create a Security Template in your Filerobot Asset Hub first. This will grant your Drupal instance the necessary credentials to access your storage;
Filerobot upload directory: The directory in your Filerobot account where the files will be stored.
You need to click Update widget to load the image after choosing the image from the Filerobot widget.
Filerobot x Opencart v4 plugin
The Filerobot Plugin is an extension which adds Asset Management to Opencart.
There are 3 simple steps for enabling the Filerobot plugin on your Opencart admin:
Install the Filerobot module for Opencart;
Add your security configuration parameters to access your Filerobot library.
Login to Admin Dashboard
Go to Extensions > Installer and upload the file filerobot.ocmod.zip
Install Filerobot plugin
Navigate to Extensions/Extensions, choose Modules from the drop-down menu, select Filerobot and update all configurations.
Filerobot Token: Your Filerobot token from the Asset Hub interface;
Security Template Identifier: Security template ID, found in the Developers top menu;
Filerobot upload directory: This is the top storage folder for your assets.
Please create the folder in Filerobot first to prevent error.
You need to enable the extension in order to use Filerobot in all pages.
With Ckeditor:
With Image field:
If you are on Ckeditor: You can select multiple images for insertion.
If you are in an Image Field: You should select only one. If you selected multiple items, it will use the last one, as Opencart's Image Field inserts only one image at a time.
This extension is 100% compatible with the default theme.
If you use another theme (stock or custom), you should verify that the image is indeed from Filerobot before returning data to the view. An examples for banners would be:
Filerobot Shopware module
Step 2: Rename the Zip file with the name filerobot.zip
Step 3: In Admin go to Extensions / My extensions / Upload extension
Step 1: In Admin go to Extensions / Store / Catalog and search for "Filerobot by Scaleflex":
Step 2: Select the extension and choose Add extension:
Step 3: Go to Extensions / My extensions / Filerobot by Scaleflex - Digital Asset Management, Media Optimisation and Acceleration / choose Configuration:
Step 4: Update the configuration and activate the Module and your site is ready to go:
Admin access key ID: Need to generate in "Setting" to call API in Admin Dashboard;
Admin secret access key: Need to generate in "Setting" to call API in Admin Dashboard;
Folder Id: This key will auto generate when the plugin activate;
Activation: Enable/Disable the module;
Token: Please enter your Filerobot token here (eg: abcdefgh);
CNAME: Enter the CNAME as per the configuration done in your Filerobot Asset Hub interface, once validated and the SSL certificate is accepted. (Or leave blank if none);
Security Template Identifier: To load the Filerobot Widget or Filerobot Image Editor, you need to create a Security Template in your Filerobot Asset Hub first, in order for your Shopware instantiation of the Filerobot Widget to obtain proper credentials and access your storage;
Filerobot upload directory: The directory in your Filerobot account, where the files will be stored.
The step to get "Admin access key ID" and "Admin secret access key":
Step 1: Download the latest version from ;
Obtain a Filerobot token (request it );
if you don't already have a Filerobot account.
if you don't already have a Filerobot account.
Step 1: Download the latest version
Filerobot Sylius plugin
Register for a demo if you don't already have a Filerobot account.
Open a command console, enter your project directory and execute the following command to download the latest stable version of this bundle:
This command requires you to have Composer installed globally, as explained in the installation chapter of the Composer documentation.
Then, enable the plugin by adding it to the list of registered plugins/bundles in config/bundles.php file of your project:
To configure the Filerobot Sylius plugin, the following components need to be updated:
Create file in config/routes/scaleflex_sylius_filerobot.yaml
with the following content:
Change form theme {% form_theme form '@ScaleflexSyliusFilerobotPlugin/Admin/Form/imagesTheme.html.twig' %}
in your templates/bundles/SyliusAdminBundle/Product/Tab/_media.html.twig
Change grid thumbnail column template
Create a file config/packages/scaleflex_filerobot.yaml
and add content bellow
Navigate to Configuration / Scaleflex Filerobot
You can enable the plugin only if both token and security template id are correct.
Activation: Enable/Disable Filerobot plugin
Filerobot Token: Your Filerobot token, unique Filerobot Project and gallery identifier
Security Template Identifier: Your security template ID, found in the "Developers" section of your Filerobot admin
Filerobot upload directory: (sub)folder path to your asset's gallery, eg. /my_sylius_folder
The Filerobot plugin offers various Twig methods and filters to assist developers:
is_filerobot(image_path)
: Twig function which checks if an image is stored in Filerobot;
image_path|filerobot('sylius_shop_product_thumbnail')
: A resizing Twig filter; More filters can be added in in config/package/scaleflex_filerobot.yaml
.
and in Twig
We have some default size follow Sylius default, you can override it in filter config above
If you use Scaleflex Filerobot on existing File you have to check the path is filerobot or not, if not use the default way
Example with Sylius default
templates/bundles/SyliusShopBundle/Product/_mainImage.html.twig
templates/bundles/SyliusShopBundle/Product/Show/_mainImage.html.twig
:
Filerobot Strapi plugin
To get Filerobot integrated you will need to setup these 2 components:
Filerobot plugin: https://github.com/scaleflex/strapi-plugin-filerobot/blob/v4/README.md;
Filerobot upload-provider: https://github.com/scaleflex/strapi-provider-upload-filerobot/blob/v4/README.md.
Benefits:
All your media will be hosted on Scaleflex's Filerobot platform;
You have the option to syncronize all your existing media to Filerobot;
You will have access to Filerobot Media Asset Widget (FMAW), where you can upload media to Filerobot and select media from Filerobot.
Additional workflows or enrichments can be performed from the Filerobot Hub UI
Register for a demo if you don't already have a Filerobot account.
Install the Filerobot plugin and you should see Filerobot by Scaleflex in the left menu.
The following options are available to you:
Then, install the Filerobot upload provider. Every media you upload will have Filerobot URLs:
Upload media to Filerobot:
Select media from Filerobot:
Beginning with 2 local images
and 4 images on Filerobot
Synchronize
Results
Please note that this plugin is directly developed and maintained by the Uniform team.
This integration allows business users to build personalized landing pages and applications by repurposing content from your Scaleflex Filerobot digital asset management system. It also enables developers to use their preferred front-end tools to build these applications.
Make assets housed in Scaleflex Filerobot available to the Uniform asset library by adding this integration.
Log into Scaleflex Filerobot Hub.
Note Container Id string to the left from your account icon at the top right corner. You will need this value in the next section.
Click on the Settings icon top right corner of the screen.
Scroll to the tab Development section.
Click on Security templates link.
Create a new template if not created yet.
Note the Secret Key of your template. You will need this value in the next section.
In Uniform, open your project.
Navigate to the Integrations tab.
Scroll to the section Browse Integrations.
Click Scaleflex Filerobot.
Click Add to project.
Enter the required values:
Click Save.
After you install the Scaleflex Filerobot integration, new asset library becomes available.
You can manage your Scaleflex Filerobot assets from inside Uniform. Uniform leverages its Media Widget to provide access to your library throughout your project.
You can insert assets directly into your Uniform Canvas components using asset parameters.
The Filerobot Adobe CC embeds the FMAW plugin inside following supported Adobe applications:
Adobe Photoshop
Adobe Illustrator
No more copy-pasting media assets from your local drive to Filerobot, everything happens from your Adobe application.
Step 2. Copy the unzipped folder content into the folder for your Creative Cloud version:
If you have Photoshop CC 2014, CC 2015, CC 2015.5, CC 2017 or above:
Mac OS: /Library/Application Support/Adobe/CEP/extensions/
Windows x64: C:/Program Files (x86)/Common Files/Adobe/CEP/extensions/
If you have Photoshop CC:
Mac OS: /Library/Application Support/Adobe/CEPServiceManager4/extensions/
Windows x64: C:/Program Files (x86)/Common Files/Adobe/CEPServiceManager4/extensions/
You have to create the folder if it does not already exist.
Step 3. Restart Photoshop and you will find the Filerobot widget/extension at Window > Extensions > extension_name.
First, you need to log in to your Filerobot account from the Widget.
Then, you can select the Company and Project you would like to access (for multi-project Filerobot accounts).
You can browse your container and its folder structure directly from the widget and use the search and sort functions. You can also create folders.
You can upload images from Photoshop directly to your Filerobot container. Downloaded images from Filerobot can be inserted as a new file or as a new layer in existing Photoshop project.
...
...
Filerobot Akeneo connector app
The Filerobot Akeneo app offers the following features:
One Filerobot token supports only one Akeneo EE Instance. One Akeneo EE Instance however can use multiples Filerobot tokens
Metadata sync locales and code of metadata need to match.
Asset Manager
Medialink Type only: Link Sync
Akeneo Entity Reference
Image Type: Binary sync
Text type: Link sync
Size setting
Support at family level (Asset Manager), Entity level (Entity Reference) for specific Scope and Locale
Metadata sync to Attribute
Support Global
Support value per locale
Your Akeneo plan needs to be Enterprise Edition.
You need to login to Filerobot Hub first
After login to Akeneo, navigate to Connect / App Store and Find the Filerobot by Scaleflex App
Then click Connect: You will redirected to the Filerobot Asset Hub and will be able choose which token you want to use for your Akeneo EE instance
Each Token can be used for only one Akeneo EE Instance
Multiple Akeneo instance can use the same Token
After selecting your token and clicking Active, you will redirected back to Akeneo
Then please Confirm that you give App access to the Akeneo EE instance
After confirmation, you will be redirected back to Filerobot
Choose to go to Setting
View the Asset Library
1. General Settings
General setting
Enable / Disable: If disabled, you cannot use Akeneo Sync feature on Filerobot
Default size: Default image size will be used if there are no config related to each asset
Default function: Crop or Fit
Please click Apply change to update configuration
After updating the settings, please refresh the Filerobot browser page
Size settings: please see the size section below
Sync all setting / Best fit for newcomers
How does it work?
Can be duplicated
Priority: Bottom to Top, and it will apply the first matching setting only
Example: An Asset with these information:
Scope: commerce / Locale: en_US / Family: filerobotmedia
Will try to match setting following the steps bellow:
First try: Type: Asset / Family: filerobotmedia / Scope: commerce / Locale: en_US
Second try: Type: Asset / Family: All Items / Scope: commerce / Locale: en_US
Third try: Type: Global / Scope: commerce / Locale: en_US
Use default setting
Add new
(1) To add new size setting Click Add new size on the top right
Explanation
(1) Type, currently The app support 3 types
Global: Affect all asset include entity and asset
Asset: Asset Family
Entity: Entity Reference
(2) If we choose Asset or Entity, we can also choose which attribute family(asset manager) or entity(entity reference) will be applied this setting
We can also choose All Items to apply to all Family / Entity
(3) / (4) Scope and Locale
To choose locale you muse choose a scope first / can choose No Scope
There are 4 cases can happen:
(5) / (6) Function and Size
Function: Crop and Fit
Size of asset to be resized
Must follow format: widthxheight, example: 300x400
Edit / Delete
(1) Click on Edit on each setting
Same as Add new
(2) You can Delete by click Delete button on opened Modal
(1) On each Asset / Click Detail
(2) You can choose which type to sync
Entity: Entity reference
Asset: Asset Manager
(3) On Sync Asset Tab
Choose Family or Entity
Choose attribute(For entity support two types: Text(Link sync) and Image(Binary Sync)
There are 4 cases can happen:
Value per channel and value per locale are both false: Can sync without choose scopes or locales
Value per locale is true, value per channel is false: Must choose at least one locale
Value per locale is false, value per channel is true: Must choose at least one scope
Value per channel and value per locale are both true: Must choose at least one scope and locale
Entity Code(Entity Ref): Code -> You can search for old code, or create new one.
(4) To check sync history, Click Sync Logs
(5) You can see which locales and scopes were synced by click View on Each item
Only support attribute with following settings:
Value per locale: true / Value per channel: false
Value per locale: false / value per channel: false
Configuration step:
(1) Go to store config
(2) In Regional variants, Add new group
(3) Option setting
(3.1) Name of Group - Can be anything you want
(3.2) / (3.3) Code of Locale / Must match the setting in Akeneo / See picture bellow
Metadata setting
(1) Click to manage, Metadata tab, you can add new Group then Add new field
(2.1) API Value must match (3.1) Attribute code(in Entity / Asset Manager Family)
If (3.2) is uncheck then (2.2) is None else (2.2) must specific a Regional variants group as we did above with Same setting as Akeneo locales
How to sync
After configuration, everything is automatic for the mapping between two systems
On each Asset detail(1) you can see Metadata tab(2), You can change value by locale or value only(if value per locale is false)(3) , then click Done(4) to save the value
You must close the asset popup and open again to make new metadata available to sync(5)
Uniform setting | Value |
---|---|
Step 1. the extension files from the link bellow and unzip them.
You need an active Filerobot account. if you don't already have one;
Navigate to Settings / Developement / Akeneo PIM to open the Setting page. Alternatively, you can follow this
The App | Akeneo |
---|
Container Id
Your Filerobot container id (aka token).
Security templates secret key
Security templates are meant to be used by frontend applications calling the Filerobot DAM API directly or via the Filerobot Media Asset Widget.
No Scope + No Locale | Value per locale: False Value per channel: False |
Scope + No Locale | Value per locale: False Value per channel: True |
No Scope + Locale | Value per locale: True Value per channel: False |
Scope + Locale | Value per locale: True Value per channel: True |
Filerobot Pabbly integration
Pabbly connects more than 800+ software products for seamless real-time data transfer. Their simple integration platform allows anyone to seamlessly integrate Filerobot with other applications to automate workflows.
You can request for demo if you don't have a Filerobot Account.
After logging in Filerobot, you need to login to Pabbly Connect.
In the Actions seaction, search for Filerobot by Scaleflex.
Choose Action Event or Trigger Event from the drop down input and confirm with Connect:
You can find your token and the API key in Filerobot Asset Hub: Developers / API Secret Keys.
Once you save your Token and API key, you don't need to enter it again for another action event by selecting "Select Existing Connection" option.
Triggers are events that occur in FIlerobot and that events data are sent to Pabbly. Filerobot Pabbly connector has the following triggers:
Filerobot Pabbly connector has the following actions and corresponding inputs:
Have a Kontent CMS setup. https://kontent.ai/
Add custom element
Content model (left hand side) > Create new > Custom element (right hand side). You will then have the below view.
**Hosted code URL: *https://cdn.scaleflex.it/plugins/filerobot-kontent/v2/index.html?func=proxy
Parameters:
note: "dir" element is optional, by default will go to the root folder of your library. Do not add "/" at the start & end of the directory string.
Create new content with this Custom Element
Content & asset (left hand side) > Create new > Chose type: FMAW. Chose and add some images, publish. You will then have the below view.
The images below are the images added in from FMAW into a Kontent content-item. When they are added, they will have Filerobot-URLs (instead of their Strapi CMS URLs).
Tutorial: https://kontent.ai/learn/tutorials/develop-apps/build-strong-foundation/set-up-preview/
Go to Settings > API Keys. For this, you will definitely need your Project ID. You may also need your API Key (depends on what you want to do, but for simple things Project ID is enough).
Write a client app that utilizes the Delivery API
Recall that Kontent have 3 set of APIs that you can interact with
Delivery APIs in plain HTTPS form can be downloaded from https://kontent.ai/learn/tutorials/develop-apps/get-started/postman-collection/ and imported into POSTMAN
For the 3 content items that comes out of the box, Kontent already have an example client app, which we can imitate.
This example app was made by using React JS https://github.com/kontent-ai/sample-app-react
Hosted on various Github Pages.
After writing and hosting your client app, go to Settings > Preview URLs and define your Preview URL formats.
See how Kontent defined their preview URLS for their example app
Codename
, URLSlug
& ItemId
are obtained as follows:
Once you done all the above, you can preview by clicking Preview
https://scaleflex.zendesk.com/hc/en-gb/requests/new or hello@scaleflex.com
Filerobot Zapier integration
Zapier connects over 3,000 different tools, applications, and services you need to manage your business. Their simple integration platform allows anyone to seamlessly integrate Filerobot with other applications to automate workflows.
The Filerobot integration allows to:
Setup a trigger when a new file is uploaded to Filerobot (useful for setting up notifications, transforming uploaded files or using other tools)
Upload new files to Filerobot
Search for existing files and folders
Create folders
Start a new Zap and add a tool to start the trigger with. It can be Filerobot or any other tool. Filerobot can either start the Zap or execute the actions for the trigger coming from other tools, as shown in the example below.
Once the Filerobot trigger/action is added, you will be asked to "Choose account" to connect to Filerobot.
New browser window will pop up asking for authentication details to Filerobot Asset Hub.
Filerobot Token can be found in the Filerobot Asset Hub project selector and next to the project title. While Filerobot API Secret Key is available in the Developers menu in the Filerobot Asset Hub. Make sure the API Secret Key has the minimum required permission for the actions of the Zap.
Once done, you have successfully connected your Filerobot account to your Zapier account.
After the account is successfully configured and authenticated, you will be able to set up the triggers and actions for execution.
When selecting the Folder Path, if you leave it as a default option, it will only do actions on the Home folder of your Filerobot account. When looking for a specific folder, you can add the folder path manually or use the search function for exiting folders in the Home folder path.
Trigger Name | Description |
---|---|
Action Name | Description | Inputs |
---|---|---|
Transform files in Filerobot (this is done by adding to the file CDN link)
File Upload
It is fired when a new file is uploaded to Filerobot.File details are sent.
File Upload
Used to upload remote hosted files to filerobot
Origin File Url,File name
File Delete
Delete file from Filerobot
File UUID
File Rename
Used to rename file.
File UUID,File new name
Folder Create
Create new folder in Filerobot.
Folder path
Folder Delete
Delete folder in Filerobot
Folder UUID
Folder Rename
Rename folder in Filerobot
Folder UUID,Folder new name
Folder List
Lists all folders details
(N.A.)
Prismic Headless CMS integration
Instead of using the /list
endpoint (API documentation), this API supports Prismic's format directly.
This endpoint lists the assets with the following settings:
50 elements per pages
ordered by last_update
GET {API_SECRET_KEY}@api.filerobot.com/{TOKEN}/integration/prismic/assets
Copy-paste the cURL request in your Terminal to see the API in action.
The response format will look like this:
Where:
results_size is the total counting of the listing result; and could be easily understood as the sum of size(results)
of all pages, while each page contains a maximum of 50 files.
image_url is fixed to be resized to 100x100
description would be returned as a concatenation from blob->meta->search
and blob->meta->tags
and the parsed list of directories and sub-directories. In case the file doesn't have this field (and is at the root folder), it defaults to No description for this file is available
.
Parameter | Description |
---|---|
API_SECRET_KEY
can be generated in the Asset hub (documentation)
TOKEN
your Filerobot token
folder
the path to the folder you would like to list default: /
page
the result page number, starting from 1 default: 1
Filerobot Shopify app
The Filerobot x Shopify App can be installed from the Shopify App Store.
Create a support request to be helped adding the App to your store.
Have a Filerobot Asset Hub account, with the Filerobot App installed. If you don't have an account yet, please contact us to get one;
Create the Security Template Identifier (SEC) to use the app (Setting > Security templates > Add template)
You already have a store on Shopify.
From Shopify, open the Filerobot app in the Apps menu item and click the button “Configuration”
Fill-in your Filerobot token, CNAME (not required), Security Template Identifier (SEC), Upload Directory (not required, the default will be "/"), and “Save”
Add Filerobot snippets to your theme: In modal Configuration, click the button “Update Snippets” to update or create new Filerobot snippets in your theme store.
Add product webhooks for action create/update/delete, that will check and update images to Filerobot Hub when the user has action create/update/delete.
In Shopify, open the Filerobot app from the Apps menu item.
Click the button “Sync All Product Media”, the process will run in the background for a few minutes depending on the number of images on your store, the process will download all product images from Shopify and store them on Filerobot Library.
Fill in your product name you need to "Sync"
You can add Filerobot images to your site by editing your theme's code.
The snippets provide the functionality to display images on your site
filerobot-image-tag.liquid: Builds an image tag with multiple sources to render product images on your site lazily, applying transformations.
filerobot-product-media.liquid: Uses filerobot-image-tag to render product images on your site, with optional transformations.
filerobot-url.liquid: This will help you build a Filerobot URL to deliver an image on your site with any transformations that you want to apply.
You can use these snippets in your theme's code, referencing them in other snippets or sections.
You can use filerobot-image-tag.liquid or filerobot-url.liquid to display Filerobot image on your page
First we need assign variable fr_metafields
Example:
To render image tag
Example:
To get Filerobot image url
Example:
Note: Read more about attribute optimizer following this LINK
A versatile command-line interface tool for seamless server-side interaction with the headless DAM API
The Filerobot CLI is a command-line interface tool designed to interact with the Filerobot API. It enables server-side use and provides a convenient way to perform various operations on digital assets, such as uploading, downloading, deleting, moving and renaming files. Additionally, it allows users to list and query asset information, including tags and metadata.
The Filerobot CLI provides all the functionality of Filerobot and its APIs. It can be used from the terminal or in a script to access the features of Filerobot in a convenient way. Using the tool, all media assets can be managed via simple commands on macOS or Linux.
For example, you can perform Admin and Upload API operations by typing commands directly into a terminal. This saves you time as you do not need to set up a development environment. You can also build scripts with multiple CLI commands to implement more complex processes and workflows.
Complete documentation of the tool is available on its GitHub page:
What follows is a brief introduction and a quick-start guide to get you up to speed in no time.
The Filerobot CLI provides a range of use cases that demonstrate its versatility:
Bulk upload - Upload a large number of assets to the DAM in a single command, saving time and effort;
Automated workflows - Incorporate the CLI application into server-side scripts or workflows to perform scheduled asset management tasks;
Asset manipulation - Rename, move or delete assets based on specific criteria;
Metadata management - Query and update asset metadata;
Backup and recovery - Download assets from the DAM for backup or recovery purposes;
Integration with other systems - Integrate the CLI into third-party tools or systems for seamless asset management.
The features of Filerobot CLI include:
Uploading/Downloading multiple assets;
Batch asset deletion;
Listing assets;
Querying asset information such as metadata, tags, etc.;
Moving assets between folders;
Renaming assets.
The following Filerobot APIs are supported: Delete, Download, Inspect, List, Move, Product, Rename and Upload.
Complete information about the Filerobot API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find details about the API endpoints, together with all required request parameters, so you know how to interact with them.
Filerobot API can be installed by running one of the following commands depending on your operating system. They download the latest release of the tool from the GitHub page, move it to an appropriate directory and make it available for use.
You can confirm that the installation was successful by running:
The version of the tool should appear on the screen:
Configuring the CLI requires a Filerobot Token (mytoken) and API Secret Key (mysupersecretkey) both of which are available from the Filerobot Asset Hub.
The following commands are available:
Additional info for any command can be accessed using filerobot [command] --help
.
Inspect the current token and key:
List the contents of a directory:
Inspect a specific resource:
Download a file by UUID:
Upload a file to a specific folder:
Upload all .jpg images in the current directory:
Command | Description |
---|---|
config
sets the token and key provided by Scaleflex, required to use the tool
delete
deletes a file by UUID
download
downloads a file by path or UUID
help
displays the help info for a command
inspect
gets file information by UUID
list
lists files and directories from your Filerobot store
move
moves files to a new folder. The folder is created if it doesn't exist
product
allows access to the Product API functionalities
rename
renames a file by UUID.
upload
uploads a file to a specific folder
version
prints the version number of Filerobot CLI
Some of the key capabilities of the Widget are:
Single and multi-file uploads into a Filerobot storage container (project)
Optimized for mobile
Upload via Drag & Drop or Copy & Paste
Upload from 3rd party storages such as Google Drive, Dropbox, OneDrive, Facebook, Instagram
Webcam and screencast integration to upload real-time videos and screen recordings
File explorer and media gallery with file management capabilities (folder creation, file move, rename, ...)
Zipped download of multiple files
File versioning with history, version browsing
File and media asset sharing via accelerated CDN links
Media gallery with powerful search capabilities based on tags and customizable metadata (taxonomy)
AI-based tagging of images
Embedded Filerobot Image Editor for inline image editing
Image annotator and comments for collaboration
Image variant generator with customizable template to generate optimal sizes for social media posts (example)
Native support of Blurhash from our friends at Wolt for compact representation of a placeholder for an image
Post-upload video transcoding for delivering HLS & DASH playlists for adaptive streaming
On-the-fly image resizing via Cloudimage
A demo page of the FMAW with all modules enabled is available here.
A modern asset uploader, picker and modular gallery with embedded image editor to deliver modern media asset-centric experiences
The FMAW is a file uploader and media gallery in one easy-to-integrate modal or inline widget. It is the storefront of the Filerobot DAM and enables accelerated uploads through Filerobot's content ingestion network and reverse CDN.
The modular architecture of the FMAW allows to enable only the needed modules (see #od_05f3da56) to address multiple use cases for accessing and managing your media asset library from your web or mobile application.
It is optimized for mobile and can be used to upload media recorded with a mobile device camera.
Automate and enhance asset management with custom tasks, triggered on asset upload
Upload post processes are actions that are triggered for a given asset upon its upload into Filerobot. Post-processing involves running various algorithms and ML models on an asset (e.g., detecting if it contains inappropriate content, counting the number of faces in an image, determining the dominant colors, removing the image background, and many others).
Post process tasks are automated actions that can be performed in two modes:
Synchronous - The results are evaluated before accepting the upload and returned in the response. The use of this mode is generally preferred in user-generated content (UGC) scenarios where user-uploaded images and videos must be moderated automatically based on a number of pre-defined criteria.
Asynchronous - The results are evaluated in the background (in a non-blocking manner) after the file has been uploaded. In some cases, depending on the specified post process type, the result will be appended to the asset's information, tags or metadata.
The following table is a summary of all available post process actions:
Post processes marked with a * require ASK Filerobot credits in order to be executed
Additional details are available on the tasks' respective pages in the ASK Filerobot section
The post process settings can be accessed from the Developers / Automations / Post processing menu.
When a post process automation task is activated, it will run automatically on each upload. If not activated, the task can be manually triggered for certain uploads via the Upload API using the postprocess parameter (e.g., postprocess=remove-background
).
It is possible to specify trigger rules when setting up a post process. These are conditions which must be met in order to activate the related task. If left empty, the task will be run on each upload.
Trigger rules are evaluated no matter if the task is run manually or automatically.
The following variables may be used to construct rules:
As mentioned, the synchronous tasks provide a mechanism to validate uploads using validation rules. Possible actions when conditions have been met are:
Refuse - The upload is rejected.
Notice - A notice is issued, added to the file information and returned in the API response.
Setting up many synchronous tasks might slow down the upload process significantly.
Process | API name | Supported modes | Description |
---|---|---|---|
Process | API name | Supported modes | Description |
---|---|---|---|
Property | Asset type | Description | Example |
---|---|---|---|
Process | Validation rule | Example |
---|---|---|
Calculate blurhash
blurhash
async
Generates a blurhash (compact representation that can be used as a placeholder) of the image. The result is added to the asset's information.
Count faces
face-count
sync / async
Returns the number of face(s) detected in the image. Can be used for classification or to ensure the presence / absence of face(s) upon upload.
Extract brand logo *
brand-detect
sync / async
Detects the presence of logos from a database containing thousand of popular brands. The list of recognized brands is included in the file information.
Extract dominant colors
dominant-color
sync / async
Analyzes the image and returns a palette consisting of the most prominent colors in the image.
Index for face clustering *
face-clustering
async
Adds the image to an index for face clustering.
Index for image similarity search
image-similarities
async
Adds the image to an index for similarity search.
Not Safe For Work *
nsfw
sync / async
Detects adult or explicit content within the image.
Plate number blurring *
plate-blurring
async
Finds and automatically blurs vehicle license plate numbers in the image.
Recognize number plate *
plate-recognition
sync / async
Detects and recognizes car license plates in the image. The result is included in the asset information.
Recognize text (OCR) *
ocr
sync / async
Extracts any text readable in the image.
Remove artifacts (Quality improvement) *
remove-artifacts
async
Removes any JPEG artifacts and improves the quality of the image.
Remove background *
remove-background
sync / async
Removes the background of the image. Useful for products, portraits, etc.
Scene classification *
scene-classification
sync / async
Detects scene categories, attributes and type of environment in the image.
Tag with AI *
autotag
async
Performs automatic tag generation for the image.
Validate image properties
image-properties
sync
Validates if the image meets any number of predefined criteria.
Compress video *
compress
async
Changes the video bitrate and resolution.
Convert video *
convert
async
Converts the video file to another format.
Transcode video *
transcode
sync
Converts the video into adaptive streaming formats (HLS/DASH).
Trim video *
trim
async
Trims the video file based on provided time intervals.
Validate video properties
video-properties
sync
Validates if the video meets any number of predefined criteria.
Image/Video width (px)
images / videos
Horizontal image dimensions / frame size (in pixels).
Image width (px) is smaller than 500
Image/Video height (px)
images / videos
Vertical image dimensions / frame size (in pixels).
Video height (px) is larger or equal than 200
Image format
images
The file type of the image.
Image format contains PNG, JPEG
File size (B)
images / videos
The size of the asset in bytes.
File size (B) is larger than 5000000
Video bitrate (bps)
videos
The video bitrate in bits per second.
Video bitrate (bps) is smaller than 1500000
Video duration (s)
videos
The video duration in seconds.
Video duration (s) is larger than 60
Upload folder path
images / videos
The location in which the asset is getting uploaded.
Upload folder path starts with '/users'
Count faces
Number of faces
Number of faces is smaller than 1 => Refuse
Not Safe For Work
NSFW
Drawing
Hentai
Neutral
Porn
NSFW is larger than 0.70 => Refuse
Recognize number plate
Number of characters in number plate
Number plate content
Number plate content does not start with 'C' => Notice
Recognize text (OCR)
Text contains
Text contains 'Copyright' => Refuse
Validate image properties
Image/Video width (px)
Image/Video height (px)
Image format
File size (B)
Upload folder path
Image width (px) is smaller than 500 => Notice
Validate video properties
Image/Video width (px)
Image/Video height (px)
File size (B)
Video bitrate (bps)
Video duration (s)
Upload folder path
Video bitrate (bps) is smaller than 800000 => Refuse
Automations allow you to apply automatic actions (background removal, auto-tagging, brand recognition, etc.) to assets during or after the upload process. It is also possible to send the uploaded asset's details to external API endpoints.
Machine learning models that analyze the content of an image and assign it to predefined categories or labels
Machine learning models, particularly classification models, play a crucial role in digital asset management. Classification models are algorithms that learn patterns and relationships in data to assign predefined categories or labels to new, unseen data. They can be used to categorize and organize assets, such as images, videos, or documents, based on their content or characteristics.
Classification models are typically trained on labeled datasets, where each data instance is associated with a known category or label. During the training process, the model learns to recognize patterns and features in the input data that differentiate one category from another. Once trained, the model can classify new, unseen data based on the learned patterns.
The following pages describe the models that are available in ASK Filerobot:
Automatic assignment of relevant tags and keywords to the input image
An ML model that detects logos of popular brands visible in the input image
An ML model that extracts the most prominent colors from an image
A combination of ML models that accurately detect human faces and predict crucial characteristics like facial landmarks, expression, ethnicity, age and gender
A system that detects human faces, extracts feature vectors and clusters similar faces to efficiently group images based on the individuals present in them
An ML model that detects and recognizes vehicle registration plate numbers
A machine learning algorithm that utilizes optical character recognition techniques to accurately identify and extract text from images or scanned documents
An ML model classifying an image as pure product image or application image, showing the product in use
An ML model that accurately classifies images, offering appropriate scene categories and attributes
An ML model that identifies sport activities depicted in images, empowering diverse industries with precise content organization and targeted applications
An ML model that detects logos of popular brands, visible in the input image
In today's digital landscape, brands are a vital aspect of any business. A brand logo serves as a symbol of identity, recognition, and trust. Managing and protecting brand assets is essential for maintaining brand consistency, ensuring compliance, and maximizing brand visibility. Our model is designed to streamline this process by automating the detection and categorization of brand logos within your digital assets, facilitating effective brand asset management.
The Brand recognizer is powered by state-of-the-art machine learning algorithms, and trained on vast datasets containing diverse brands from a wide selection of industries. This enables the model to accurately recognize and detect brand logos with varying sizes, orientations, and backgrounds within the provided images.
The model supports a vast number of popular brands and enables efficient brand analytics and monitoring, providing valuable insights and efficient brand management within our service.
The following are some use cases, suitable for our Brand recognition model:
Brand monitoring and compliance - By identifying brand logos in user-generated content, the model assists in brand monitoring, ensuring compliance with brand guidelines and protecting brand integrity.
Brand analytics - The model provides insights into brand visibility and presence by analyzing the frequency and distribution of detected brand logos across digital assets. This information can be used for brand performance evaluation and marketing strategies.
Competition Analysis - By recognizing competitor brand logos within the digital assets, the model aids in conducting competitive analysis, identifying market trends, and evaluating brand positioning.
Image Retrieval - The Brand Recognizer enables efficient searching and retrieval of digital assets by logo-based queries. Users can locate specific assets based on recognized brands.
Digital rights management - Copyright and licensing agreements can be enforced with automatic logo detection and tracking. This ensures compliance with intellectual property regulations.
An up-to-date reference with all API endpoints is available here:
Input image | Input image |
---|---|
Output preview | Output preview |
---|---|
API response | API response |
---|---|
A machine-learning algorithm that utilizes optical character recognition techniques to accurately identify and extract text from images or scanned documents
The OCR ML model is an integral component of our service, providing robust optical character recognition capabilities with support for multiple languages.
The model is designed to convert text present in images or scanned documents into editable and searchable data. It leverages the power of machine learning techniques to accurately recognize characters and words, enabling efficient text extraction and analysis.
It examines the input, tries to detect any text fragments present in the image and recognizes the characters in those fragments according to the specified language. Detected fragments with a good enough confidence level are returned as text strings.
The OCR service can be useful for multiple use cases, including:
Text extraction and indexing - The model extracts text from images or scanned documents, enabling efficient indexing and searching of digital assets. Users can find images or documents based on specific keywords or phrases mentioned within the text content.
Document digitization - Important information can be preserved by converting physical documents into digital formats.
Language translation - Combined with language translation capabilities, OCR can facilitate multilingual asset management, enabling users to search and translate text in various languages.
Improved accessibility - Converting text within images or scanned documents into machine-readable format enhances accessibility for visually impaired individuals, enabling screen readers or assistive technologies to interpret the content.
An up-to-date reference with all API endpoints is available here:
A ML model that evaluates the technical quality of images by analyzing their visual attributes
The quality of digital content is a critical factor in delivering satisfactory user experience and achieving branding goals. Our Image quality assessment (IQA) model is a machine learning solution designed to evaluate the visual quality of images.
When an image is fed into the system, the model processes it to assess various quality attributes and generates a quality score that reflects a weighted combination of those attributes, providing a precise assessment of the image's technical quality.
The attributes include multiple factors such as sharpness, blurriness, noise, dynamic range, contrast, distortion, etc. By providing a thorough image quality assessment, the model greatly enhances the management of digital assets and offers indispensable aid in content selection and optimization.
Image quality assessment finds application in various domains and industries where image quality is a top priority:
Stock photography - Stock image providers can use the model to rate and categorize the images in their libraries, thus helping customers find ones with superior quality for their projects.
E-commerce - E-commerce platforms can ensure product images meet quality standards, improving the visual appeal of product listings and driving sales.
Publishing - Media outlets and publishing companies can automate the assessment of images for articles so their publications contain high-quality assets.
Advertising - Advertising agencies can use more easily evaluate the technical quality of campaign visuals, helping maintain brand consistency and professionalism.
Digital marketing - Marketers can optimize their social media and online content for greater impact and engagement.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
An ML model that detects and recognizes vehicle registration plate numbers
The Number plate recognizer is a machine-learning model that detects and recognizes vehicle registration plate numbers in images.
It is developed and trained to detect vehicle license plates, recognize the characters on the plate, and return them as text strings. The model takes in an input image containing a vehicle and outputs the alphanumeric number present on the vehicle's registration plate in text form.
The process of license plate recognition consists of the following steps:
License plate detection;
Pre-processing the resulting image from Step 1 (warping, deskewing) to prepare it for optical character recognition (OCR);
Passing the image from Step 2 through an OCR engine and receiving the recognized characters.
Some typical use cases for this model include:
Efficient vehicle cataloging - Vehicles can automatically be cataloged in a database. When new vehicle images are uploaded, the model can extract the registration plate numbers and associate them with the corresponding vehicle records, allowing for quick and accurate identification and retrieval of vehicle assets.
Smart searching - Users can leverage plate number recognition to perform targeted searches and easily locate specific vehicles, thus saving time and effort.
Automated metadata - Vehicle assets can be tagged with relevant metadata based on their registration plate numbers. This metadata may include vehicle make, model, year, and other information associated with the recognized plate.
Regulatory compliance - The model can aid in ensuring compliance with legal requirements related to vehicle registration and documentation.
An up-to-date reference with all API endpoints is available here:
An ML model that identifies sport activities depicted in images, empowering diverse industries with precise content organization and targeted applications
The Sport classifier is a state-of-the-art artificial intelligence service. Its primary function is to analyze and classify images based on the sports activities they depict. Utilizing cutting-edge deep learning techniques and neural networks, this model can accurately identify various sports and provide valuable insights for content organization and a wide range of applications.
The model offers a diverse set of sports categories, including but not limited to football, basketball, tennis, swimming, gymnastics, baseball and many more. It ensures precise tagging and categorization of sports-related images, making it an indispensable tool for media agencies and content managers.
Sports classification can be applied in multiple domains, including:
Sports media coverage - By quickly identifying the sport depicted in an image, journalists and editors can efficiently organize their image archives and provide real-time coverage during sporting events, enhancing storytelling and enriching their publications.
Sports analytics - The model can aid in automating the process of collecting data by categorizing images from games and training sessions.
Sports product marketing - Sports brands and retailers can utilize the model to optimize their marketing efforts. Sorting images of athletes engaging in specific sports can aid in the creation of targeted campaigns.
Search enhancement - Precise sport categorization can optimize the process of searching and filtering assets, enabling users to find relevant images for their projects with ease.
An up-to-date reference with all API endpoints is available here:
Precise classification of property-related images across various industries.
Efficient categorization of digital content is essential for organizing and retrieving assets effectively. The Property classification model is an advanced machine learning (ML) solution designed to identify and classify the type of real estate property depicted within an image.
The model analyzes the input image and determines if the displayed realty falls into one of the following property classes:
Apartment
Bathroom
Bedroom
Dining
Garage
House
Industrial
Kitchen
Living
Office
The Property classification feature is based on state-of-the-art computer vision techniques and deep learning algorithms that interpret the image data and identify patterns that are indicative of the property type depicted. This allows the model to accurately predict the predefined category that the image is part of, enabling precise classification. By doing this, this model significantly enhances the management of digital assets, streamlining content organization and retrieval for various industries.
Property classification finds utility in a variety of use cases, all aimed at improving digital asset management and content organization:
Real estate listings - Real estate agencies can use the model to automatically classify and organize property images for listing websites, making it easier for potential buyers or renters to find properties of interest.
Property management - Property management companies can organize images of rental properties, simplifying property management and tenant communication.
Interior design - Interior design platforms can categorize images of furniture and home decoration products, thus enhancing the online shopping experience for customers.
Hospitality industry - The model can help hotels and hospitality businesses sort images of their rooms, facilities and dining areas for marketing and reservation platforms.
E-commerce product classification - E-commerce platforms can use the model to automatically categorize products based on the type of setting they are intended for (e.g., kitchen appliances, bedroom furniture).
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
An ML model that accurately classifies images, offering appropriate scene categories and attributes
The Scene classifier is a cutting-edge artificial intelligence system integrated into our service. Its primary function is to analyze and classify images based on their environmental context and distinguish between indoor and outdoor scenes. Leveraging the power of deep learning and neural networks, this model can accurately determine the setting of an image and provide insights about various scene categories, that the image has been determined to be part of.
The model offers a rich set of scene categories and attributes to help users understand the context of their images better. Some of the scene categories include landscapes, beaches, mountains, forests, interiors, exteriors, etc. It can also identify specific attributes like natural lighting, artificial lighting, crowded spaces, open spaces, urban settings, rural settings and more.
Scene classification can be appropriate in the following use cases:
Content organization - The Scene classifier plays a vital role in automatically organizing large collections of images. By accurately determining whether an image is taken indoors or outdoors, users can easily filter and group their assets based on environmental context, streamlining content management and retrieval.
Marketing campaigns - Marketing teams can sort and analyze images that are relevant to specific campaigns. For instance, for an outdoor adventure campaign, the system can help select images of landscapes, mountains, and beaches, thus ensuring consistent and fitting visuals.
Search optimization - Scene classification adds a new layer of metadata to the images. This enhances search capabilities, enabling efficient searching for specific scenes or environments.
An up-to-date reference with all API endpoints is available here:
Input image | API response |
---|---|
Input image | API response |
---|---|
Product image | API response |
---|
Input image | Input image |
---|
API response | API response |
---|
Input image | API response |
---|
Input image | Input image |
---|
API response | API response |
---|
Enhance content management with general-purpose visual and language understanding
Bridging the gap between visual and textual content is a crucial step in unlocking the full potential of digital assets. The Image-to-text ML model is an advanced solution designed to do just that by providing general-purpose visual and language understanding.
The model leverages state-of-the-art natural language processing and computer vision techniques to facilitate the understanding of images and textual data. When a user submits an image and an accompanying textual prompt (typically in the form of a question regarding the image), the model processes the visual and textual data, identifying objects, context and relationships within the image, and generates a relevant response.
Users can pose a wide range of questions, from object recognition and content analysis to more complex queries related to the image. The output is a properly constructed natural language answer that provides insights or information pertaining to the submitted data.
Our Image-to-text functionality is a versatile tool that gives customers the ability to extract insights, enrich content and enhance the overall management of digital assets.
The Image-to-text functionality is powerful enough to be applied across a spectrum of industries and domains, such as:
Content tagging - Customers can automatically generate descriptive metadata for images, simplifying the organization and retrieval of digital assets.
E-commerce and product catalogs - E-commerce platforms can utilize the model to answer user queries about product images, providing detailed information and enhancing the shopping experience.
Media and entertainment - Media companies can analyze and describe scenes, characters and objects in images, aiding in content categorization and analysis.
Educational content - Educational institutions can enhance e-learning platforms by automatically generating explanations and descriptions for visual content in course materials.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
An ML model that accurately separates foreground subjects from backgrounds, enabling easy and efficient generation of transparent assets
The Background remover is an advanced machine-learning model, designed to make background removal an effortless and time-saving process. By leveraging the power of deep learning algorithms, it can accurately separate foreground subjects from their backgrounds, resulting in high-quality transparent assets.
The model is built on a foundation of state-of-the-art deep learning techniques, particularly focused on semantic segmentation. It is trained on extensive datasets containing a diverse range of images, ensuring robustness and adaptability to handle various edge cases and complexities. By utilizing a multi-layered neural network architecture, it processes each pixel in an image to classify it as part of the foreground or background.
The model undergoes a rigorous training process, learning to identify different object shapes, fine details and semi-transparent elements in the images. As a result, it can accurately separate foreground subjects from their backgrounds, even in challenging scenarios.
Automatic background removal can be a game-changer for many workflows. By transforming product photos, portraits and creative artworks into transparent assets in a matter of seconds, it eliminates any fiddling with tedious image editing software or outsourcing the task to graphic designers. Professional-grade background removal can be achieved effortlessly, saving valuable time and resources.
Automatic background removal can prove invaluable for multiple use cases, such as:
E-commerce product catalogs - The model ensures consistent, visually appealing product images that seamlessly blend into any website or marketing material and can streamline any e-commerce business.
Portrait photography - The background remover offers a quick and efficient way to remove distracting backgrounds from portrait shots, enabling better focus on the subject's features and expressions.
Design projects - Designers can explore boundless creative possibilities by easily overlaying graphics, text or new backgrounds, allowing for eye-catching collages, posters, and social media posts.
Presentations and marketing materials - Creation of professional presentations by placing images on any background, ensuring a clean and polished look that captivates the audience.
Image localization - The model facilitates localization for global audiences by enabling easy background replacement to suit different cultural contexts and brand aesthetics.
An up-to-date reference with all API endpoints is available here:
Input image | Input prompt | API response |
---|---|---|
Input image | Input image |
---|---|
Output preview | Output preview |
---|---|
API response | API response |
---|---|
Automatic and accurate blurring of license plate numbers in images to protect privacy and comply with data regulations
License plate blurring is the process of obscuring the license plates of vehicles in images so they become unreadable. Such a feature can be really useful, especially as online privacy is becoming a concern for many. It can be used to prevent the identification of vehicles by automatically blurring their license plates.
The Number plate anonymizer is an advanced ML model, designed to protect privacy and comply with data protection regulations. It efficiently detects vehicle registration plates within images and automatically applies a precise blur filter, rendering the plate numbers and characters illegible, while preserving the integrity of the surrounding content.
Automatic plate blurring can be useful across various scenarios:
Security - The model can be utilized to automatically blur license plates in user images, protecting the privacy of individuals and vehicles by ensuring that sensitive information remains confidential.
Public image galleries - Any visible license plates are automatically blurred, adhering to privacy regulations and protecting personal data.
Vehicle sales and auctions - In online vehicle sales or auction platforms, license plates in vehicle images can be blurred, thus safeguarding the identities of sellers and buyers.
Insurance and claims - Insurance companies can anonymize sensitive information on damaged vehicles in images used for claims processing.
An up-to-date reference with all API endpoints is available here:
An ML model that efficiently detects and removes compression artifacts, enhances image quality while preserving vital visual elements
The Artifact remover is specifically designed to detect and eliminate a wide array of artifacts, primarily caused by heavy lossy compression, ensuring a remarkable enhancement of image quality and the preservation of essential visual elements.
The model is based on deep learning techniques that analyze and learn from vast and diverse datasets of images featuring various compression artifacts. Through this extensive training, the model becomes proficient in recognizing specific patterns and distortions linked to aggressive compression methods.
Once it receives an image, the model efficiently identifies the presence of compression artifacts and applies sophisticated image restoration algorithms to remove or reduce them. The restoration process restores crucial details, textures, and sharpness, resulting in an image with heightened clarity and visual appeal.
Automatic artifact removal and quality improvement find valuable applications in various domains:
E-commerce platforms - In the world of online retail, image quality plays a crucial role in customer engagement and purchasing decisions. The model ensures that product images are of top-notch quality by removing compression artifacts, thus improving the overall shopping experience by delivering visually appealing product showcases.
Digital advertising - High-quality visuals are essential for successful digital advertising campaigns. Captivating ad campaigns with artifact-free images can boost engagement and strengthen the brand message.
Archives and galleries - Historical archives and art galleries often house valuable images that may have undergone degradation due to outdated compression techniques. Restoring such images ensures the preservation of their visual authenticity.
Printing and publishing - In print media image quality is crucial. By employing the model to remove compression artifacts, publishers can achieve clear, vivid images that resonate with readers and convey their intended message effectively.
An up-to-date reference with all API endpoints is available here:
Input image | Input image |
---|---|
Output preview | Output preview |
---|---|
API response | API response |
---|---|
Input image | Input image |
---|
Output preview | Output preview |
---|
API response | API response |
---|
Automation of content analysis for appropriateness, facilitating efficient content moderation
Content moderation models are machine learning algorithms designed to analyze and assess various types of digital content and determine its appropriateness based on predefined guidelines or criteria. These models play a crucial role in moderating user-generated content, ensuring compliance with community standards, guidelines or legal requirements.
Their primary goal is to automate the process of content moderation, which can be time-consuming and challenging to handle manually. They employ a combination of natural language processing (NLP), computer vision and audio analysis techniques to analyze and classify content based on multiple factors such as explicit or harmful language, hate speech, offensive imagery, violence, nudity or other criteria defined by the platform or organization.
The training of moderation ML models involves feeding them with large labeled datasets. These datasets serve as examples to teach the model to recognize and classify different types of inappropriate or undesirable content. Through the training process, the model learns patterns, features, and context cues that help it make accurate predictions about the suitability or moderation level of new, unseen content.
By automating the initial content moderation process, they can significantly reduce the manual workload, increase efficiency and provide consistent enforcement of content policies.
Advanced deep learning techniques that generate high-quality and realistic images based on provided text prompts
The Text-to-image generator is a cutting-edge ML model, designed to generate captivating and realistic images based on provided text prompts. It allows users to bring their textual ideas and concepts to life, creating visually stunning assets that complement their digital content library.
To synthesize images from text descriptions, the model first processes the textual input, understanding the context, objects and settings described. Then, it generates high-resolution, visually coherent images that capture the essence of the text prompt.
The model facilitates image creation by empowering users to generate stunning visuals effortlessly. The image synthesis capabilities allow the generator to unlock limitless creative possibilities, making it a valuable tool.
Tex-to-image generation can be a game changer in several use cases, including:
Marketing and advertising - Marketers can visualize and prototype advertising campaigns. By converting taglines into images, they can create compelling visuals that resonate with the target audience, boosting engagement and brand recall.
Product prototyping - Product development teams can visualize concepts and design ideas. By describing product features in text, they can rapidly generate images that represent potential product variations and iterate through designs effectively.
Educational content - Textual descriptions of historical events, scientific concepts or literary settings can be transformed into visually engaging images that enhance the experience for users of e-learning platforms.
Content creation - By providing textual prompts for abstract ideas, designers, artists and content creators can generate unique and imaginative visuals that can be used in digital art, illustrations or graphic designs.
An up-to-date reference with all API endpoints is available here:
Input prompt | Input prompt |
---|---|
Output preview | Output preview |
---|---|
API response | API response |
---|---|
An ML model for adult content detection
The NSFW machine-learning model is an advanced algorithm designed to accurately detect and classify adult content within digital media files. It plays a vital role in ensuring the safe and appropriate use of media assets in various industries.
By employing state-of-the-art computer vision techniques, the model offers powerful capabilities to automatically identify explicit and adult content, enhancing content moderation and compliance processes.
The model utilizes deep learning algorithms trained on extensive datasets to accurately identify and classify adult content, including nudity, explicit imagery and suggestive or provocative materials. It is seamlessly integrated into our service, allowing for efficient and scalable content analysis.
Possible applications of adult content detection include:
Content moderation - The model can automatically filter and moderate user-generated content. It helps prevent the upload of adult or explicit materials, ensuring a safer online environment.
Brand protection - Advertisers can safeguard their reputations and protect their digital assets. By automatically screening media content before publishing or distribution, companies can maintain brand integrity and avoid association with inappropriate or NSFW materials.
Legal compliance - In industries such as publishing, advertising, and e-commerce, the NSFW model can help ensure compliance with legal and regulatory standards. By identifying and flagging adult content, companies can adhere to age restrictions, protect minors from explicit material and avoid legal repercussions.
Content Curation - Content creators can efficiently curate their collections. The automatic tagging and categorization process for assets makes it easier to search and manage content based on its appropriateness for different target audiences.
An up-to-date reference with all API endpoints is available here:
Input image | API response |
---|---|
A ML model that provides a reliable means of verifying the authenticity of property images for real estate websites with user-generated content.
When it comes to Digital Asset Management (DAM) services, the veracity and trustworthiness of digital content are paramount, especially in the real estate sector. The Real estate authenticity verification model is an innovative machine learning solution designed to classify real estate images into two distinct categories:
Real images - Authentic images captured via phones and cameras that provide genuine representations of real estate properties.
Artificial images - Images that are synthetically crafted using specialized software and provide visually appealing but artificial and unnatural portrayals of properties.
This model can play a pivotal role in authenticating the legitimacy of property images, making it an ideal tool for enhancing real estate websites that utilize user-generated content. Authenticity verification can not only improve user trust but also streamline the management of real estate digital assets.
Real estate authenticity verification can be employed in a range of scenarios:
Real estate listings - Online marketplaces for buying and selling properties can use the model to verify the authenticity of user-submitted property images, enhancing the credibility of their listings and reducing the likelihood of fraudulent ones.
Vacation rental - Platforms for vacation rentals can ensure that images accurately represent the properties they advertise, providing peace of mind to travelers.
Property management - Property management companies can authenticate images provided by tenants or property owners, aiding in the transparent documentation of property conditions.
Property valuation - Real estate valuation services can use the model to confirm the authenticity of images when assessing property values.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
Input image | API response |
---|---|
Turning files into assets requires to extract information from them and enrich it with metadata and tags
Upload assets in bulk from your local computer or various external sources
The Filerobot Uploader lets you upload assets in the DAM from a variety of sources:
Your device's storage, camera or screen
Web links
Third party sources like Google Drive, Dropbox, Instagram, and many others.
You can drag & drop, copy & paste or browse to the file's location to select it for upload:
You can transform your assets as part of the upload process.
Once you click the Upload button, a progress icon is shown on each asset, as well as an overall Progress bar underneath the Upload modal, showing the cumulative progress of all assets:
There are 3 possible end states and relevant actions:
File type | Available operations |
---|---|
State | Asset highlighting | Available actions |
---|---|---|
images
Resize - set the maximum width and height for the image to be resized to after upload
Edit image - crop; adjust the brightness, contrast and other attributes; add filters; draw on the image; or add watermarks. See Edit images for more information.
videos
Change the resolution - automatically or to fit a specific device type (desktop, mobile, tablet, etc...)
Transcode - generate HLS and/or DASH manifest files for adaptive streaming. See Transcode for more information.
any type
Rename - by clicking once on the filename or selecting Rename from the context menu
Manage details - by choosing the Manage option from the context menu. See Manage assets for more information.
Add metadata - by pressing the Fill metadata button. See Metadata for more information.
... and of course, remove the file as well as add more files for upload (via Add more button)
Successfully uploaded
See Manage assets to continue
Failed to upload
Remove the file
Retry the upload
Duplicate detected (see Storage for possible system policies on duplicate detection)
Remove the file
Show file location (only available if the Duplicate policy is set to Override)
Incorporating metadata enriches your assets with valuable insights, transforming them into more than just files – they become organized, searchable, and informative resources
Asset metadata can be viewed and managed from the asset management modal.
Users with level Contributor or higher, can edit metadata fields during or after file upload.
You can also edit metadata fields of multiple assets in bulk. For that purpose, select the assets you want to edit metadata of and select the Edit multiple assets option from the contextual menu or navigation bar:
If any metadata field is configured as "Required" in the metadata structure, no metadata changes can be saved if the field value stays empty.
You have 2 ways to retrieve assets by metadata fields:
You can search by a specific metadata field in the search bar, pointing to this field with the special symbol @:
If can combine metadata-based search with free text search.
Alternatively, you can use the Metadata filter to select a field and a search value. Entering multiple metadata fields in the metadata filter will perform an "OR" search.
Metadata variants can be toggled using the toggle.
You can edit the metadata information of an asset through the icon in the top right corner of the screen:
Metadata, as well a Tags can be translated in multiple languages. You can manage languages from the Metadata variants section under Settings > Project > Metadata > (tab) Configuration. Edit the Language variant to enable or disable languages.
Please note the special case for the Language variants:
The topmost option will be the default language for metadata and tags
All language options are to be selected from a drop-down list of supported languages (and not free text entries) - the API keys are the ISO code of the language selected, e.g. en for English
the API key cannot be edited
You can also create additional custom metadata variants to declinate a metadata field in different dimension, such as currencies, file format, etc, ...
Clicking on the button allows to edit languages:
Create a new variant via the button:
To link a variant to a metadata field, navigate to to Settings > Project > Metadata > tab Assets and either create a new metadata field or edit an existing field suing the button.
Tags are pre-set values which can be applied to assets for filtering and classification purposes. Tags can also be generated automatically based on image content using integrated AI-based image recognition algorithms.
You can add tags to assets using the Bulk Edit or Single file edit functionality.
In order to assign a specific tag to multiple assets, you need to select the files and then to choose option "Edit multiple assets".
Then choose "Tags" and you can specify the tags that you would like to apply to your assets.
Clicking on "Regional settings" you can choose the language on which to apply your tags.
You can assign tags to a single file by selecting that file, then clicking on "Manage" and choosing the icon representing "Edit metadata".
At that point, you will get the option to add new tags, choosing them from the "Suggested" list or generating them using AI service (if this feature is enabled for your project). If you start typing a tag that is currently not existing in the Tags list, it will be added to the list and attached to the asset at the same time.
For that purpose, you need to use the Bulk Edit functionality. Please select the files that contain the tag which you want to detach and choose the "Editing multiple assets" option. Then in the Tags section choose "Delete" and specify the tag that you want to remove.
Please select the asset which tags you want to edit, then go to "Manage" and open the "Edit metadata" mode. Now you have the option to remove tags by clicking on the "x" symbol at the top-right corner of every tag or to choose the "Clear all" option so to delete the entire tags list.
In order to search by a specific tag in the Search box, you need to type #name-of-your-tag
or to use the dedicated Tags filter.
In Filerobot Hub you can organize your assets by folders. This view is available in the left panel of the Library under the name "Folders":
It consists of a navigation panel where the folders and their sub-folders are hierarchically ordered and an expanded view where you can preview the content of the current folder. It is available in a Grid view or a List. The view is controlled by the following icon:
If you select a single folder and then click on the "Info" icon at the top right part of the page, you will open a panel showing the folder's details:
In order to create a new folder you can use the + New Folder
button at the top right part of the page. The new folder will be created as a sub-folder of your current folder's location.
Please note that "/" is treated as a special symbol in the folder name. If you specify "Media/Images", the system will create a folder "Media" and a sub-folder "Images" inside it.
If you select a folder and open its context menu, you have the option to download its content. The same action is available from the navigation bar and the download icon at the top right corner of the selected folder. The content is exported as a ZIP archive.
This option is useful in cases when you need to get details about the folder's content.
Please select the folder, open the context menu and choose "More actions -> Export list of files (CSV)". At this step you can choose to export the file details of the entire project, only the files placed directly in the selected folder or the folder+its nested sub-folders.
The information that is exported in the CSV contains:
filename
CDN link
mime-type
size
file uuid
creation date
update date
visibility settings
If you know the name of the folder in which you are interested, you can jump directly into it using the small Search folders box:
If you know that some file is located in a specific folder, you can use the main Search box to specify this folder and then apply additional search criteria in order to find the files you are interested in:
Faceted navigation available in Filerobot products (Widget, Hub and Portal)
At its core, faceted navigation utilizes a faceted classification system to organize information. This system categorizes items based on multiple, independent attributes called facets. Each added facet acts as new filtering layer.
Concept: Faceted navigation falls under the umbrella of parametric search techniques. Instead of relying solely on keyword matching in a traditional search, it allows users to filter based on pre-defined parameters (facets) associated with the asset. This enables a more nuanced, smarter search experience.
Technology: Faceted navigation relies on efficient data structures and algorithms to handle large datasets and filter combinations. In essence, the system needs to quickly identify items that match all selected facet criteria. Search engines like Solr or Elastic Search are popular choices for implementing faceted navigation due to their ability to handle complex filtering operations.
Benefits: The technical aspects of faceted navigation translate into several benefits:
Drill-down refinement: Users can progressively narrow down results by applying multiple facet filters, leading to a precise subset of assets.
Faceted exploration: Faceted browsing allows users to explore different categories and discover new assets they might not have found through a simple keyword search.
Improved efficiency: By filtering irrelevant options, faceted navigation reduces the number of results a user needs to sift through, saving them time and effort.
Faceted navigation is applied for 4 types of attributes (as of May 8th, 2024)
Date (Uploaded and Created date)
Metadata Single-select
Metadata Multi-select
Metadata Boolean
→ Faceted navigation is only available in the "Assets" view (ie. not in the Folder view)
→ Faceted navigation section is built as a side bar at the left side of the UI, replacing entirely the filter bar (shown below, that will then be hidden) in the Assets view.
This type of navigation is activated at a {token} level, and is a global setting for all users, that can be found at the Preference configuration page.
Within a metadata, the operator between 2 values or more is set to OR
Between 2 metadata or more, the operator is set to AND
In details:
Within a metadata
-> For multi-select, we provide 2 operators: AND
and OR
Let assume we are calling query HAS
(equal to IS
but for multi-select we call it HAS
just for gramatical purpose)
We will have 2 options
Metadata HAS
a AND
b -> expected results are files which have both values a and b, file that contains only a or b won't be returned
Metadata HAS
a OR
b -> expected results are files which has either a or b, file that contains both of the values will be returned also
➡️ This option cover the 1st option -> within a multi-select metadata, we will choose HAS-OR
operator to cover more file results
-> For single-select, it will always be OR
when users click in more than one answers as one file can not contain more than one value.
For 2 metadata or more
For faceted search, it is an AND operator between multiple metadata as the answers will be more specific and this is the main idea for faceted navigation: trimming down the most correct answers
a. Multi-select metadata "colour" with value: “red”, “white”, “black”
When "red" and "white" are searched for -> expected answers are:
Files that contain value red only
Files that contain value white only
Files that contain both value red and white
b. Single-select metadata "size" with value: “35”, “36”, “37”
When “35” and “36” are searched for -> expected answers are:
files that contain 35 value only
files that contain 36 value only
c. Now, combining the two previous examples
When “red”, “white”, “35”, “36” are searched for -> expected answers have to use AND
operator to encompass the 2 metadata constraints, so answers will be assets that contain:
red and 35,
red and 36,
white and 35,
white and 36,
red and white and 35,
red and white and 36
→ Refining searches further is leading to more accurate and relevant results.
Publishing and collaboration with third parties
Filerobot allows delivery of your assets over and using all Media optimizer capabilities (for use in webpages and web applications). You can also collaborate with non-registered users via secure URLs:
Any public asset can be shared or published via a CDN link
Controls the visibility of each asset/folder or the whole Filerobot container
Allows asset download and upload by external users
Comments with annotations
Images annotations allow you to communicate with fellow contributors and discuss features of the assets. Comments and annotations are timestamped and are available to all users with editing privileges.
The comment section is accessible from the context menu and the preview modal of each asset.
A collection is a group of related assets defined by a set of properties such as meta, tag, file type etc.
Example: A collection of Cars may contain all assets tagged with #car
or having "Car" as a value in the metadata field "Vehicle type".
The main difference between grouping assets via Folders, Labels, My favourites vs Collections is that the membership of assets to a group is dynamic when using collections. This means that assets are automatically added or removed from a collection based on their technical properties and metadata values.
This Collections sub-page can be accessed from the main menu on the Library page:
In the collections list, the collections submenu has the following options:
Name: sort by name ascending or descending
“Sort” icon: if the collections are currently sorted by name ascending then this will sort them descending and vice-versa
“Planet” icon: allows to select the metadata/tags language
Refresh: refreshes the collections list
"Info" icon: if an asset is selected, provides additional details for it
You can do this by clicking on the button + Add collection. A pop-up Configuration panel will open.
Name - please specify a meaningful collection name
Access - by default it is Company. Only users with access to Filerobot Hub can see these assets.
Filter type: Query - the user creating the collection needs to write a search query inside the text area to define filters.
Query - here should be placed the filtering query for the collection
Groups - the groups are an alternative of the Filtering Query. They contain a list of pre-defined collection criteria and can be used in combination with the Query or on its own.
In order to open the Configuration window, allowing you to manage your collections, you need to click on the "Settings" icon in the left panel of the page:
That will open the "Manage collections" window where you have options to Search, Add new collections, Edit the current configuration or Delete existing collections.
The comment systems allows threading and sorting. To keep track of progress, you can mark comments as resolved.
Third-party asset upload and download
Airbox allows non-registered users to upload files by using public links. Airbox instances can optionally have activation and expiration date, as well as password protection.
To create an Airbox instance, you need to select Airbox from the left menu of your Filerobot library.
You can select start and/or expiration date and password:
Filerobot Media library download
You can download assets one by one or from a selection by using the context menu or the selection top bar.
Filerobot allows you to apply transformation on the downloaded assets to crop images, reduce dimensions, convert to another format or compress the files into a single archive.
If need to create profile images or covers for social media, you can use the Social media presets with pre-defined aspect ratio for cropping. The available options include profile pictures, covers, logos, etc. to use in LinkedIn, Facebook and Twitter.
To enhance user flexibility, Filerobot supports a convenient download feature through the use of the query string parameter &download=1.
When appended to the CDN link, this parameter triggers the download of the original asset associated with the link.
Setting | Values | Description |
---|---|---|
Format
JPEG
| WebP
| PNG
| GIF
Output format
Quality
Excellent
| Good
| Fair
Compression quality
Resize:
Width
and height
integer
Limit image dimensions (in pixels)
Crop:
Width
and height
integer
Image crop dimensions (in pixels), available in advanced transformation only
Simple transform: - limit image dimensions; - set compression format and quality.
Advanced transformation: - image cropping - limit image dimensions; - set compression format and quality.
A ML model to identify the presence of visual watermarks in input images
Protection of intellectual property and brand integrity is of great importance to content creators, marketers and organizations. Watermarks, which are often applied to images to prevent unauthorized use, play a significant role in safeguarding digital assets.
The Watermark detection functionality has been developed as a crucial component of our service. This machine learning model is designed to accurately determine whether an input image contains a visual watermark, thus providing customers with the ability to identify and work with their digital assets more effectively.
The employed deep learning algorithms analyze the input image data, extracting unique features and patterns that are indicative of visible watermarks, with the end goal being distinguishing between watermarked and non-watermarked images with high accuracy.
Watermark detection can be employed in a variety of use cases, including:
Licensing compliance - Media agencies can use the feature to ensure that content consumers adhere to licensing agreements, thus preventing unauthorized use of their assets.
Asset management - The model can automatically categorize and tag images, allowing users to quickly locate and retrieve non-watermarked versions of their assets.
Quality control - Design and marketing teams can easily verify that images in promotional materials are free from unintended watermarks, ensuring consistent branding.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
Input image | Input image |
---|---|
API response | API response |
---|---|
Input image | Input image |
---|---|
API response | API response |
---|---|
Input image | Input image |
---|---|
API response | API response |
---|---|
Get alerted when events of importance need your attention
Notifications allow to specify when to be alerted about important events such as asset expiry, being tagged in comments, and many other significant events.
Notifications are set per project and are configured in Settings > Project > Notifications (see Notifications).
The notifications overlay provides the following capabilities:
For example, if a user clicks on an asset expiry notification (whether on the day or for a day in the future), the user will be taken to the Library > Assets page, with the relevant metadata filter applied, displaying the expiring assets (see Search & find assets).
Filerobot Prestashop plugin
Supports the default versions of Product Media and TinyMCE (and not other TinyMCE Plugins);
Supports Prestashop from version 1.7.8.7 onwards.
Register for a demo if you don't already have a Filerobot account.
- Download the Filerobot module
Step 1: Download the latest version Download Latest release of Module
Step 2: Unzip the file and change the folder name to "filerobot"
Step 3: Zip the file with the name "filerobot.zip"
Step 4: In Admin go to Modules/Module Manager -> Upload the Zipped file
- From the Marketplace
Step 1: In Admin go to Modules/Module Catalog and search for "Filerobot by Scaleflex"
Step 2: Click "Install"
Step 3: Go to Modules/Module Manager -> Other Section -> Click on Configure on "Filerobot by Scaleflex"
Step 4: Update the configuration (credentials) and activate the Module, then your site is ready to go.
Activation: Enable/Disable the module
Token: Your Filerobot Token
Security template identifier: Your Filerobot security template (in the top menu Developers)
Filerobot upload directory: The directory where you want to store/manage assets
Product media
Go to Catalog/Product -> Click +
button in Product Images
Choose images and click Insert
Product WYSIWYG
Click the image
icon on WYSIWYG
Then it is the same as for a Product Media click Insert
after choosing your image(s)
Click the bell icon in the header to view any notification.
If there is at least one unread notification, a red dot is added to the bell icon:
Feature | Descriptioon |
---|---|
Number in brackets in the title, e.g. (10)
Tells the number of unread notifications
Elipsis menu (...)
Additional actions, such as Mark all as read
Blue background & blue dot in upper right corner of notification (dot can be toggled)
Unread notification
White background & white (hidden) dot in the upper right corner of notification (dot can be toggled)
Read notification
Notification date
Date when the notification was triggered
Red clock notification icon
Notification for asset(s) expiring today
Yellow clock notification icon
Notification for asset(s) expiring in a number of days (as configured)
Action link
Takes the user to the relevant notification object (asset(s), comment, etc) and marks the notification read
Filerobot Wordpress plugin
The Filerobot WordPress Plugin allows integration of Filerobot with your WordPress website.
The plugin allows you to interact with your WordPress database and upload or access media assets from within the Filerobot DAM.
You only need to have a Filerobot account and install the plugin in your WordPress site.
If you are new to Filerobot, here is a short introduction: https://vimeo.com/616931056
Plugin: https://wordpress.org/plugins/filerobot-digital-asset-management-and-acceleration/
Or
Register for a demo if you don't already have a Filerobot account.
Step 4 - Enter "Filerobot" in the search field and then install the plugin
Step 5 - Activate (it will not modify yet your current configuration or image source & delivery)
Step 6 - Navigate to the Filerobot admin page
Step 7. Fill-in the plugin's settings page with authentication parameters
CNAME: You can set a custom domain. When some clients' images on Filerobot redisplay and they don’t want the Filerobot’s domain to appear in the images' URLs, then it's possible to set it there.
Filerobot token: The token (project id) that you signed up for, from Filerobot.
Security Template Identifier: It’s like a way obtain limited-time passwords. See: Security templates. Behind the scenes, the code would use the Security Template Identifier to obtain a SASS (the “limited-time password”). The SASS would then be used in the auth header for interacting with the Filerobot API (https://developers.scaleflex.com/)
Filerobot upload directory: Which "root" folder you want to upload to on the Filerobot platform.
“Don’t store media assets on WP server” checked means not to store the size-variants of an image on the “local” WP CMS. The original image is however still stored on the WP CMS.
“Use Filerobot Media Asset Widget as gallery” checked means only use FMAW as the Media Gallery/Uploader. The default WP Media Gallery will therefore be hidden.
“Synchronize Filerobot metadata” checked means this option will import the metadata available in Filerobot (tags, etc.) in your image description and alt text to facilitate search. Warning: This option is mandatory for advanced editors (like Elementor, Gutemberg, etc.).
“Sync Post ID” checked means this option will sync all post ID are using the asset to a specific metadata field.
“Sync metadata fields” checked means this option will import the specific metadata fields in Filerobot with the selected metadata fields chosen in the setting. Warning: This setting needs a specific set of metadata in Filerobot to be activated, otherwise will break the synchronization. And “Name of the metadata list in WP database” is required if this option checked.
The “Test connection” button tests if your Filerobot token and Security Template Identifier can connect to your Filerobot asset library.
The “Synchronization status” button tells you how many files still needs to be synchronized from WP CMS to Filerobot platform (the “up”) and how how many files still needs to be synchronized from Filerobot platform to WP CMS (the “down”).
The “Trigger synchronization” button starts the actual synchronization.
For example, before Filerobot is installed and activated, you have these 2 images on your WP CMS and these 2 images on your Filerobot platform:
At this point, the images' URLs still have WP CMS URLs
After you install, activate and configure Filerobot, you can first check the connection to Filerobot. This will confirm if you entered the correct credentials.
Then you can check the status of what is yet to be synchronized:
Then you can do the actual synchronization:
The progress bars will give you information about the current synchronization process
The logs tab will show you more information about the synchronized media assets.
Now you can see that all the images' URLs had became Filerobot URLs
and on Filerobot platform
The next major feature that Filerobot brings is: the Filerobot Media Asset Widget (FMAW) in Media Library
Here you can upload your media assets to the Filerobot platform (instead of WP’s local library)
The next major feature that Filerobot brings is that: anywhere where the Media Manager opens, a FMAW tab will be inserted into it.
The next feature that Filerobot brings is it’s image editor, instead of WP’s image editor
Filerobot also has a cron
Deactivation
When you deactivate Filerobot, all the media assets that has been synchronized to Filerobot will be removed.
From Jan 2022: We enabled the Media files' metadata sync:
FR meta -> WP meta,
FR tags -> WP alt,
FR comments -> WP contents
From Jan 2022: Filerobot plugin only works with these settings:
June 2022: All English. No more Multilingual, language strings, etc …
The Filerobot Wordpress plugin works well with other popular plugins:
Classic Editor
Gutenberg Editor
WooCommerce
Elementor
ACF
... but let us know if you have any compatibility issue with third parties plugins; as WordPress is a very open system, it is always possible that some plugin overwrite part of the configuration.
The Filerobot token is a key concept to understand when working with Filerobot. It identifies your tenant, aka project.
Whether you need to enhance sharing and collaboration around media assets within siloed teams to increase productivity or manage millions of product images, videos and data sheets, Filerobot's multi-tenancy will enable you to address all use cases from a single pane of glass.
Single or multi-brand scenarios?
Single or multi-site setup?
Interactions between multiple internal and external teams as part of your Content
Operations?
Filerobot's scalable multi-tenancy will prevent asset duplication, save time in categorizing and retrieving assets and overall structure your media asset library to set your organization for success when it comes to building engaging and fast digital experiences.
Each tenant is called a Project identified by a Token ({token}
) in the DAM. A project hierarchy can be created with users and teams given various access permissions to a single or multiple projects.
Each new Filerobot account comes with a first token or project. Each project combines a storage container to store assets and a CDN distribution to deliver optimized assets over CDN all around the world.
Some system settings are project-specific while others are company specific.
As a simple rule, all Organisation settings are linked to the company, while all other settings such as Project or Development or are project-specific.
Users can be created at company level and thus access all company projects or at project-level to restrict their access to only specific projects. See Users for more details.
Integrate Hygraph with Filerobot by Scaleflex to bring in and manage your digital assets seamlessly from the DAM as the Single Source of Truth.
With this application, users can upload, store, edit, manage, optimize, enrich and automate their media assets right within their Hygraph UI, saving time and better enhancing their workflow.
Publish your Media Assets directly from within Hygraph with the Filerobot Media Asset Widget:
As Original
: This option is available for all assets. Adds them to the content entry as is as preview or downloadable element.
As Transformation
: This option is only available for image assets. It lets you modify the image format, quality, size, and even crop part of it.
This integration is developed and maintained by our partner Hygraph.
Useful resources:
Trigger API calls on events, automate workflows and collaborate effortlessly
Webhooks are a powerful feature used in modern web development and API integrations. They provide a way for applications to communicate and exchange data in real time.
They enable users to trigger API calls to third-party endpoints upon specific events and send relevant asset's information.
The concept behind webhooks is based on the "push" model of communication, where data is actively sent to a predefined endpoint as opposed to the usual "pull" model, where applications need to request data from a server actively.
In the context of Filerobot, webhooks allow users to integrate external systems, automate workflows and stay informed about changes and more generally actions performed on their assets. When a relevant event takes place, such as a new file upload, file renaming or file movement, the webhook is triggered and a POST request is sent to a third-party endpoint, providing details about the the affected asset.
This mechanism allows seamless connections with other applications which enhances collaboration capabilities.
Using webhooks simplifies the delivery of personalized notifications or alerts to assigned recipients whenever essential actions are executed on critical assets. This facilitates prompt responses and efficient communication.
They can also be integrated with project management tools to automatically create tasks, assign responsibilities and track progress whenever assets are uploaded to or moved within the DAM.
In order to set up the webhook, the following information must be provided:
Hostname - The domain name of the third-party API that will be contacted.
Path - The specific path that together with the Hostname forms the complete target URL (Webhook URL) to which the data will be sent.
A consumer cannot always verify that the webhook it is receiving is actually coming from the expected source. Due to this, there are two authentication methods supported:
Basic authentication - The simplest way to verify a webhook. It makes use of a username
and a password
that are used for authentication when sending the data to to the Webhook URL.
OAuth2 authentication - A more secure and complex authentication method that allows users to grant limited access to their resources without exposing their credentials. This type of authentication can be used in two ways:
By specifying the Authentication endpoint
, username
and password
to retrieve a temporary token which is then used in the webhook.
By directly entering the Static bearer token
to use every time without generating a temporary one before the request.
The triggers determine the events that need to happen in order to execute the webhook. The following ones are available:
On upload - After a successful asset upload.
On move - When the asset location is changed.
On rename - When the asset is renamed.
On delete - When the asset is deleted.
On change meta - When any metadata field is modified.
On change info - When some asset's information (e.g. title, description, etc.) is changed.
On change tag - When the asset's tags are edited.
On change label - When the asset's labels are modified.
There is a fine-grained control over which specific asset details are sent as part of the request JSON body to the selected API endpoint. This ensures that only the necessary information to handle the event effectively is communicated. Those details are divided into several categories, depending on the asset type:
Filerobot Commercetools connector app
Nginx
PM2
Nodejs > v16.10.0
Certbot
To install the Filerobot module, please follow the steps below:
2. Upload and unzip to the server at your domain path (Ex: /var/www/public_html/)
4. Go to the source folder and run "npm install", "npm run build" (this command will build and create a folder public)
5. Go to the folder public and run "pm2 serve --spa".
Configure the Filerobot module as follow:
For Commercetools project:
Clone file custom-application-config.example.mjs and rename to custom-application-config.mjs
Input your Commercetools configuration
Set your entryPointUriPath at src/constants.js
For Filerobot module:
Clone file filerobot-config.json.example in folder "src" and rename to filerobot-config.json
Input your Filerobot token, SEC, upload directory, and any other options as fitting.
The can be accessed from the menu.
File (All assets) | Video | Image |
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1. Download the latest version
3. Configuration Commercetools & Filerobot plugin
6. Config your domain, port you will run with Nginx and create SSL with Certbot For example, config with port 8080
Get the applicationId and entryPointUriPath of Commercetools
Name | Width | Width |
Size | Height | Height |
Path | Length (in seconds) |
MIME type | Bitrate |
CDN link | HLS/DASH playlist URL |
File UUID |
Installing the FMAW library happens over npm.
The FMAW consists of the Filerobot Core package and additional modules described below. The modules marked as required must be imported for the FMAW to function.
Module
Required
Description
yes
Displays the folder structure and media gallery and provides file management capabilites.
yes
Handles multipart file upload.
no
Displays upload/download progress in a status bar.
no
Displays pop-up messages/statuses related to file operations.
no
Thumbnail generator for different file type previews.
no
Inline image editor with functionalities such as filters, crop, resizing, watermark, collages, etc.... Also used by other features of the Widget such as the variant generator and export function.
no
Inline image annotation and comment feed for people to collaborate on media assets.
no
Captures photos or videos from the device's camera and upload them to the Filerobot storage container.
no
Device screen recorder and uploader
no
Inline image editor prior the actual upload of the image to, for example, upload a resized version of a large image into the storage container.
Framework
Link to code snippet
React
Vanilla (plain) JS
Scaleflex announces the EOL (End Of Life) of the Filerobot Widget release v2
The milestone events, descriptions, and dates are shown below.
Customers with active Support contracts will continue to receive support from the Scaleflex Support, per the terms and conditions specified in the support contract.
In order to avoid any inconvenience or disruption of Services, we recommend to our Customers having implemented the Filerobot Widget v2 to migrate to the v3.
Status
Phase 1 - migration plan layout, no new features developed for v2 (until February 1st, 2023)
Phase 2 - POCs and final version (until August 1st, 2023)
Phase 3 - v2 no longer supported (September 30, 2023)
Step by step tutorial
All Widget functions and APIs for v2 version should remain compatible, however some advanced and new features will not be available unless the Widget used in the custom implementation is migrated to v3.
Be aware that no further developments will be published for the v2 Widget version, from February 1st 2023.
Transforming traditional asset management with Artificial Intelligence
Businesses and organizations are constantly flooded with an overwhelming amount of digital assets. From images and videos to documents and presentations, managing these assets efficiently is crucial for streamlined operations and successful marketing campaigns.
DAM systems such as Filerobot provide a centralized hub for organizing, storing and distributing digital assets. And even though DAM solutions have revolutionized the way digital content is handled, their potential can be further amplified by combining them with Artificial Intelligence (AI). This leads to better asset management, enhanced user experiences and increased productivity.
The integration of AI with DAM is, without a doubt, a game-changer in the world of asset management. The various benefits it offers, including automated tagging, improved search, intelligent content recommendations and efficient content moderation, highlight its enormous potential.
With AI-powered DAM systems, organizations can optimize asset management, deliver better user experiences, and ultimately achieve increased productivity. As technology continues to advance, the relationship between DAM and AI will continue to evolve, empowering businesses to harness the full potential of their digital assets and stay ahead in the competitive digital landscape.
ASK Filerobot is a set of machine-learning models hosted by Scaleflex to accomplish advanced image and video recognition and enrichment tasks. Built with powerful technologies, it powers up productivity and efficiency with AI & ML. By intelligently extracting information from any media assets, it helps clients stay ahead of the competition with faster content workflows.
ASK Filerobot greatly improves Digital Asset Management with market-leading AI & ML automation that create dynamic ways to screen, retrieve and govern digital assets.
The following pages describe the various services that provide automation to solve multiple digital media challenges.
A system that detects human faces, extracts feature vectors and clusters similar faces to efficiently group images based on the individuals present in them
The Face Clustering ML model is an advanced component offered as a part of our service. With its powerful facial detection and feature extraction capabilities, this model fundamentally changes the way images are organized and grouped based on the individuals present in them.
The model accurately detects visible human faces within images, regardless of their positions or orientations. This crucial first step ensures that all relevant faces are identified for subsequent clustering.
For each detected face, the model extracts a high-dimensional feature vector, representing the unique facial characteristics and attributes of that individual. These feature vectors capture critical facial traits while discarding irrelevant information, making them ideal for face-based comparisons.
Leveraging advanced machine learning algorithms, the model clusters all faces (and the respective images they are part of) based on the similarity of their feature vectors. Faces with similar features are grouped, enabling the automatic identification of distinct individuals in image collections.
The face clustering model can categorize faces without the need for explicit training data on individual identities. This flexibility allows the model to adapt to various datasets and expand its clustering capabilities.
Automatic face clustering can have a significant impact on the following use cases:
Event photography management - Event photographs often contain multiple different individuals. The model simplifies the organization of these images by automatically grouping them based on the distinct people present. This streamlines the curation and delivery of photo collections.
Content organization - Automatic face clustering can make it easier for users to navigate and discover images related to specific individuals.
Photo collections - In personal photo libraries, the face clustering model assists in creating dedicated albums or collections for family members and friends, thus saving users valuable time by automatically organizing photos by individual identities.
An up-to-date reference with all API endpoints is available here:
An ML model that extracts the most prominent colors in an image
The model analyzes the colors present in the input image and determines if they fall into several predefined ranges. It then compares the number of pixels in those ranges and outputs color names that were detected as visually dominant.
The pixels of the image are examined to identify the most prominent colors present. Multiple factors such as the distribution and frequency of different color values are taken into account to determine the dominant colors.
To find the color of a pixel, all values in the image are converted to an alternative color representation that is designed to be more closely aligned with the way human vision perceives colors. This enures high-quality results.
Some typical use cases for this model include:
Visual search and filtering - Users can search for images based on specific dominant color criteria. For example, they can search for images dominated by a particular color to find assets that align with a specific theme or aesthetic.
Color-based categorization - The feature facilitates the automatic categorization of images based on their dominant colors. This allows for easier location and grouping of images with similar color schemes.
Branding Enhancement - Teams can utilize the dominant color information to ensure consistent branding and visual coherence. They can search for images that match their brand identity and integrate them into their campaigns.
An up-to-date reference with all API endpoints is available here:
Automatic assignment of relevant tags and keywords to the input image
Harnessing the power of visual assets is crucial for businesses across various industries. However, as digital libraries grow exponentially, manually assigning tags and keywords to images becomes a daunting and time-consuming task. That's where automatic tagging can help.
The Image tagging ML model is a powerful machine learning algorithm integrated into our service. It automatically analyzes the pixel content of images, extracts their features, and assigns relevant tags or keywords to facilitate improved organization and utilization of visual assets. With this model, businesses can unlock a new level of efficiency and productivity.
The process of AI tagging involves object detection techniques that detect objects, scenes, and concepts within the images. Based on this analysis, appropriate tags are assigned to each image, enabling proper asset management and content discovery.
The AI tagging model can provide support for a wide range of typical use cases, including:
Efficient organization - Automatically assigned tags enable efficient organization of visual assets by categorizing them based on specific objects, scenes or concepts. Users can easily navigate through their image library and quickly locate the desired assets.
Accelerated search - Users can retrieve images based on generated tags or keywords. This accelerates the search process, making it effortless to find the required images in large collections.
Content Recommendation - Visually similar assets can be identified based on shared tags. Users can discover related content and leverage the full potential of their visual assets.
Visual content analysis - The model can aid in understanding visual trends, analyzing customer preferences and performing visual data analytics.
An up-to-date reference with all API endpoints is available here:
A set of ML models that accurately detect human faces and predict crucial characteristics like facial landmarks, expression, ethnicity, age, and gender
The Face analyzer consists of several cutting-edge ML models. Its primary function is to detect visible human faces in images and predict some facial characteristics that are deemed important.
Leveraging state-of-the-art deep learning algorithms and neural networks, the Analyzer accurately identifies and analyzes faces, extracting the following information for each face:
position in the image (bounding box);
facial landmarks (coordinates of points that map to specific facial structures on the face);
expression classification (happy, angry, sad, etc.);
ethnicity classification;
age estimation;
gender classification.
Use cases for automatic facial analysis include:
Image tagging and organization - Automatic facial analysis enables users to easily categorize and index images based on expressions, ethnicities, age groups and genders. This streamlines content management, making it easier to locate specific images for various purposes.
Inclusive representation - The Face Analyzer can facilitate inclusive representation in media content. By analyzing facial ethnicities and genders, content creators can ensure diversity and cultural representation in their visual assets, thus promoting inclusivity.
Search Optimization - Automatic tagging and categorization of images based on facial characteristics allow users to find specific faces, expressions, or ethnicities with ease.
An up-to-date reference with all API endpoints is available here:
Please refer to the relevant NPM repository for more detailed instructions:
Product image | API response |
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Input image | Input image |
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API response | API response |
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Input image | Input image |
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Output preview | Output preview |
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API response | API response |
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Some AI models can be used to analyze and enrich video files. |
An ML model classifying an image as pure product image or application image, showing the product in use
The Product type classifier is a machine learning model, specifically designed to classify images into two categories:
Pure product images typically show the product by itself in a clear and uncluttered way. The goal in this case is to focus on and highlight the product features and design.
Product application Images that show the product in use or context, often in a real-world setting. These images are designed to illustrate how the product can be operated, its potential benefits, and how it fits into a larger system or environment.
The model is particularly useful in the context of online stores and businesses dealing with large collections of product images. By leveraging its power, businesses can streamline their digital asset management workflows, organize image libraries, and deliver personalized user experiences.
Some typical use cases include:
Image library organization - Online stores often have vast libraries of product images that need to be effectively organized and categorized. The classifier can automatically sort images based on their content, facilitating easy retrieval and management.
Search enhancement - The model allows for more accurate search and filtering capabilities. Users can easily find images that showcase the product itself or images that demonstrate its usage in real-world contexts.
Content Curation - The classifier aids in curating image galleries and content showcases by providing an automated classification of images. It aids in showcasing product images in relevant contexts, ensuring a coherent and focused presentation.
Product Recommendations - By understanding whether an image focuses on the product or its application, businesses can enhance their recommendation algorithms. The classifier can be used to tailor recommendations based on the user's preference for pure product images or application images.
An up-to-date reference with all API endpoints is available here:
You will find below the list of AI models applied to images. You can also apply multiple models to one image in one API call via the API.
Product image | Application image |
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Product image | API response |
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Application image | API response |
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Unleashing the power of AI to transform your digital assets
A derivative asset refers to a modified or transformed version of the original digital asset. These derivatives are created to meet specific needs, such as different file formats, resolutions or variations in content. The goal is to make the asset more suitable for various platforms or use cases, without altering the integrity of the original.
For instance, a high-resolution image can have derivatives in different resolutions for web, print or mobile applications. Those assets play a crucial role as they enable efficient distribution and utilization across diverse channels and media.
Derivative generation involves the automated or manual process of creating these derivative assets from the original master asset. They can usually be generated on-the-fly or through predefined workflows. Automation reduces the manual effort required to produce derivatives and ensures consistent output quality.
Additionally, derivative generation allows users to access and use the right version of an asset that suits their specific requirements. This functionality streamlines asset management, reduces duplication, and ensures that the right content is efficiently delivered to the right audience.
Derivative generation can be enhanced and accelerated using different machine learning models. ML models, particularly those in the field of computer vision and image processing, can play a significant role in automating the creation of derivative assets.
A Generative AI model is a type of artificial intelligence algorithm designed to generate new content that resembles or is similar to the data it was trained on. Unlike traditional AI models that are used for classification or prediction tasks, generative models focus on creating new data rather than making decisions based on existing data.
They can be trained to recognize and understand different elements of an image, such as objects, faces, backgrounds, and other features. This understanding allows for intelligent resizing, cropping, background removal and other manipulation that produce derivatives without compromising the overall quality and aesthetics. By incorporating such capabilities, the process of derivative generation becomes more accurate and scalable.
Generative AI models have a wide range of applications, including image synthesis, text generation, music composition, video generation, and more. They are particularly valuable in creative fields and content generation tasks where producing new and original content is essential. However, they can also be used in other domains, such as data augmentation for training other machine learning models or in data generation for simulations and testing.
The following sub pages describe some of the derivative generation models offered by ASK Filerobot.
An ML model that automatically counts the number of human faces in an image
Images play a crucial role in various industries, from advertising and marketing to security and analytics. Counting the number of visible human faces in them is a fundamental task for multiple applications.
Manual face counting can be time-consuming and prone to errors. Therefore, our face counting ML model is designed to streamline this process by automating the detection and counting of human faces, providing businesses with valuable insights.
The model is an advanced machine learning algorithm integrated into our service. It automatically analyzes the pixel content of images, extracts their features, and accurately detects and counts the number of human faces present.
Automatic face counting can be valuable in multiple situations, including:
Crowd management - The model assists in crowd management by automatically counting the number of people in specific areas or events. This information can lead to better resource allocation and crowd flow optimization.
Audience measurement - Counting the number of faces in promotional materials enables businesses to analyze audience engagement, measure campaign effectiveness and make data-driven decisions.
Social media analytics - The model can be utilized to quantify the reach and impact of visual content. By counting faces in shared images, businesses can gauge audience interaction, identify trends, and measure the virality of content.
Retail analytics - By counting the number of faces in-store or at specific displays, retailers can gauge customer engagement, optimize store layouts, and measure the effectiveness of merchandising strategies.
An up-to-date reference with all API endpoints is available here:
Input image | API response |
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