How do you download and save an image from a given url in Android? Improve this question. Lance Roberts Droidman Droidman Add a comment. Active Oldest Votes. Edit as of Just displaying an image using Picasso is as simple as: Picasso. It uses its own ImageLoader that once initialized has a global instance which can be used to download images in a single line of code: ImageLoader.
I have included examples for progressive JPEG's and animated images into the sample project. Conclusion - "I have learned about the great stuff, what should I use now? If your app saves images or other files as a result of a user or an automated action and you don't need the images to be displayed often, use the Android DownloadManager. And here's the BasicImageDownloader. Bitmap; import android. BitmapFactory; import android. AsyncTask; import android.
NonNull; import android. Log; import java. BufferedInputStream; import java. ByteArrayOutputStream; import java. File; import java. FileOutputStream; import java. IOException; import java. InputStream; import java. URL; import java. URLConnection; import java. HashSet; import java. The URL is probably not pointing to a file". Improve this answer.
What about the onPictureTaken callback which gives the picture as byte[], can one get a URL to that picture, straight from the camera?
Or is basic old outputStream the only way in Android to save a picture which was taken by a camera without using the built in intent? That seems strange, because the natural thing to do after onPictureTaken is of course to save it.
Is there no particular support for doing that? Tombola Hi! This post is about downloading a picture from the web. This private class need to be placed in your Activity class as a subclass. After the image is downloaded, in the onPostExecute method, it calls the saveImage method defined above to save the image.
The AsyncTask for downloading the image is defined, but we need to execute it in order to run that AsyncTask. Related Articles. Table of Contents. Save Article. Improve Article. Like Article. Last Updated : 17 Sep, RequiresApi Build. Recommended Articles. Article Contributed By :. Save my name, email, and site URL in my browser for next time I post a comment. Delivered by FeedBurner.
In Figure 3, the green dots represent input colors, and the red dots are the center points that replace the input colors. Each cell is bounded by blue lines.
The result of applying VQ to an image reduces the number of unique colors, replacing each group of colors with a single color that's "pretty close" in visual quality. This technique also allows you to define the maximum number of unique colors in your image. For example, Figure 4 shows the a parrot head in Immediately, you can see that there's a loss of quality; most of the gradient colors have been replaced, imparting a banding effect to the image.
This image needs more than 16 unique colors. Setting up a VQ step in your pipeline can help you get a better sense of the true number of unique colors that your image uses, and can help you reduce them significantly.
There are a number of readily available tools that you can use to help you implement this technique. If you are using JPG images, there are several small changes you can make that potentially provide significant file-size savings. These include:. When choosing tools, remember that photo exporting tools can insert unnecessary metadata, such as GPS information, into your images.
At a minimum, try to leverage existing tools to help strip out this information from your files. WebP is a newer image format supported from Android 4. This format provides superior lossless and lossy compression for images on the web. Using WebP, developers can create smaller, richer images.
For more information about WebP, visit the WebP site. Different image formats are suitable for different types of images. Figure 5 shows two images that come out quite differently depending on which compression scheme the developer applies. The image on the left has many small details, and thus compresses more efficiently with JPG.
The image on the right, with runs of the same color, compresses more efficiently with PNG. The only thing to keep in mind is that it only has native support on devices running Android 4.
Fortunately, the large majority of devices satisfy that requirement. There are several techniques you can use to achieve the right balance between compression and image quality. The other technique takes advantage of the Butteraugli library, and is usable for all image formats. The power of JPG and WebP comes from the fact that you can use a scalar value to balance quality against file size.
The trick is finding out what the correct quality value is for your image. Too low a quality level produces a small file at the cost of image quality. Too high a quality level increases file size without providing a noticeable benefit to the user.
The most straightforward solution is to pick some non-maximum value, and use that value. However, be aware that the quality value affects every image differently. You should make sure to test your chosen maximum value against a representative sample of images. Also, make sure to perform all of your tests against the original images, and not on compressed versions.
For large media applications that upload and re-send millions of JPGs a day, hand-tuning for each asset is impractical. You might address this challenge by specifying several different quality levels, according to image category.
The Butteraugli project is a library to test an image's Psychovisual Error Threshold: the point at which a viewer starts to notice image degradation. In other words, this project attempts to quantify how distorted your compressed image is. You can then choose the image that is the best balance of file size and Butteraugli level. Butteraugli allows you to proceed based on either output or input.
That is, you can look for the lowest quality setting before a user perceives noticeable distortion in the resulting image, or you can iteratively set image-distortion levels to learn their associated quality levels. It is tempting to keep only a single resolution of an image on a server.
When a device accesses the image, the server serves it at that one resolution and leaves downscaling to the device. This solution is convenient for the developer, but potentially painful for the user, because the solution forces the user to download much more data than they need.
You should instead store multiple sizes of images, and serve the size that is most appropriate for a particular use case. For example, for a thumbnail, serving an actual thumbnail image instead of serving and downscaling a full-size version consumes much less network bandwidth. This approach is good for download speed, and is less costly for users who may be using limited or metered data plans.
Proceeding like this also results in the image's taking less space on the device and in main memory. In the case of large images, such as 4K ones, this approach also saves the device from having to resize images before loading them.
Implementing this approach requires that you have a backend image service to provide images at various resolutions with proper caching. There are existing services that can provide help with this task. For example, App Engine comes with image resizing functionality already installed. Content and code samples on this page are subject to the licenses described in the Content License. App Basics. Build your first app.
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