Cloudinary Blog

Blog posts of 'java' tag - Page 2
Compress an Image Automatically Without Losing Quality

One of the most important things to know about compressing image files is that a smaller file size comes at the cost of a lower image quality. How much lower, and whether low enough to make a difference visually, depends on the image. Compression can be very effective at reducing the size of the image, and besides lowering the costs of storage space and bandwidth, a reduced image size goes a long way to retaining your users’ attention with faster, smaller downloads.

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New interactive web development demo with code samples
Developing a great website and maintaining it can be a challenging and time-consuming task, even for the most talented developer. You need to meet graphic design requirements for any device according to the latest design trends, and constantly find ways to optimize your website performance, for any browser. 
We can save you a lot of time and effort. Cloudinary takes care of the entire image management pipeline: image upload, a rich set of transformation and optimization capabilities, cloud storage, administration and super-fast CDN delivery. 
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How to Zip Photos Dynamically With a Single Line of Code

As a developer, you want to allow your users to download multiple files in a single click. An easy way to download multiple files and share them is to generate a ZIP file. When images are involved, you may also want to normalize the original images before including them in the ZIP file, by scaling them down to the same maximum resolution or converting them to the same format.

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Apply conditions to dynamically transform images.

It's great to have the capability to transform images on the fly by using dynamic URLs to customize the images to fit the graphic design of your site or mobile application. However, what if you want to transform an image depending on a specific image characteristic (like its width or aspect ratio) or its contents (does it contain a face?). What you need is a way to apply a transformation to an image only if a specific condition is met. Take for example a situation where you have allocated space on your page for a user uploaded image with a width and height of 200 pixels. Furthermore, if the image contains a face you would like to zoom in and focus on the face itself, otherwise you would like to fit the entire image into the available space:

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Facial Attribute Detection with Microsoft's Face API

Many of the photos displayed on the internet these days are of people. If your website or mobile application displays photos that include people, you will want to make sure that their faces are included in the delivered images when cropping and transforming them to fit your graphic design and responsive layout. You may even want to further transform an image according to the faces present, for example, adding a harlequin mask overlay on all of their eyes, where each mask is adjusted to the correct size and orientation (although not a typical use case, it's a cool example of using advanced facial attribute detection):

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Automatic Image Tagging and Categorization Using Imagga Api

If you have an application that allows users to upload their own photos, it can be very useful to be able to organize these photos according to their content. This will allow you to categorize the content for displaying to all your users and make your image library searchable. Furthermore, you can also learn more about your users according to the content they upload and find different trends of what people care about. Other added benefits can also include the ability to display matching content to your users according to their interests or even match them with other users that share similar interests.

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Automatic visual image enhancement for web apps

Various factors can have an effect on the visual quality of photos captured by a wide variety of digital cameras. Technical limitations of cameras, coupled with changing conditions in which users take photos, results in a wide range of visual quality. Camera-related limitations arise from a combination of poor optics, noisy sensors, and the modest capabilities of mobile camera phones that are used to take photos in conditions that range from bright daylight to indoor scenes with incandescent light or even dark night scenes.

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