Cropping API
Imagga Auto Cropping API helps you automatically generate meaningful thumbnails for large collections of images in any size and ratio. The technology analyzes your image composition and determines the best regions to crop to preserve the most relevant part of the image within the size predefined by you. Perfect for creating content-aware thumbnails, managing predefined layouts, or automated generation of fixed-size previews for e-commerce.
Create multiple thumbnails keeping the main object of interest untacked. Automatically with the help of AI smart cropping.
We will detect the main object of interest on the image and crop around it, keeping it as intact as possible for any size and ratio.
Besides cropping around the areas of interest, we can manage the appropriate scaling of all image copies you need to create.
Easily integrate the API into your application or e-commerce project. You have complete control over the thumbnail sizes, cropping, and resizing options.
Our API will scale with your needs and let you customize the desired ratios and resolutions of all images you need to process. Hassle-free, fast, and efficient.
Perfect solution for e-commerce websites and online marketplaces where you need multiple thumbnails and image sizes for any of your pages. We can create the required image sizes in real-time so that you can get your products and offerings online in no time.
Many different resolutions, aspect ratios, thumbnails, and views of the same image are needed to provide the right user experience in your image-centric mobile application. Our smart cropping will make you forget about the inconvenient central cropping.
If your data team uploads a lot of image content to your system, they must adjust and crop thousands of photos daily. This can be hard for them and also requires time that could otherwise be spent on doing something more creative and productive. Our smart cropping solves this problem automatically.
FotoWare makes any content searchable by assigning keywords to files. However, an overlapping pain point for users was to populate their collections with relevant descriptive keywords. Auto-tagging was an obvious solution to solve this problem.
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