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Image loading for Android and Compose Multiplatform.

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Quick Overview

Coil is a powerful and efficient image loading library for Android, designed to simplify the process of loading and caching images in Android applications. It provides a simple and intuitive API, and is optimized for performance and memory usage.

Pros

  • Performance: Coil is designed to be highly performant, with features like automatic image resizing, progressive loading, and efficient memory management.
  • Flexibility: Coil supports a wide range of image sources, including local files, network URLs, and resources, and can be easily integrated into any Android project.
  • Customization: Coil provides a high degree of customization, allowing developers to configure various aspects of the image loading process, such as caching, transformations, and error handling.
  • Coroutines Support: Coil is built on top of Kotlin coroutines, which makes it easy to integrate with modern Android development practices.

Cons

  • Learning Curve: While Coil is relatively easy to use, it may have a steeper learning curve compared to some other image loading libraries, especially for developers who are not familiar with Kotlin and coroutines.
  • Dependency on Kotlin: Coil is written in Kotlin and requires Kotlin to be used in the project, which may be a drawback for developers who prefer to use Java.
  • Limited Support for Legacy Android Versions: Coil requires a minimum Android API level of 21, which may limit its use in projects that need to support older Android versions.
  • Potential Performance Issues with Large Images: While Coil is generally performant, it may struggle with loading and caching very large images, which could lead to performance issues in some cases.

Code Examples

Here are a few examples of how to use Coil in your Android project:

Loading an Image from a URL:

Coil.load("https://example.com/image.jpg") {
    crossfade(true)
    placeholder(R.drawable.placeholder)
    error(R.drawable.error)
}

This code loads an image from a URL and applies a crossfade animation, a placeholder image, and an error image.

Transforming an Image:

Coil.load("https://example.com/image.jpg") {
    transformations(CircleCropTransformation())
}

This code loads an image from a URL and applies a circular crop transformation to the image.

Caching an Image:

val imageLoader = ImageLoader.Builder(context)
    .diskCache {
        DiskCache.Builder()
            .directory(context.cacheDir.resolve("image_cache"))
            .maxSizeBytes(100L * 1024 * 1024) // 100MB
            .build()
    }
    .build()

Coil.load("https://example.com/image.jpg", imageLoader)

This code creates a custom ImageLoader instance with a disk cache, and then uses that instance to load an image from a URL.

Getting Started

To get started with Coil, you'll need to add the library to your project's dependencies. You can do this by adding the following to your app-level build.gradle file:

dependencies {
    implementation("io.coil-kt:coil:2.2.2")
}

Once you've added the dependency, you can start using Coil in your Android project. Here's a simple example of how to load an image into an ImageView:

val imageView: ImageView = findViewById(R.id.image_view)
Coil.load("https://example.com/image.jpg") {
    target(imageView)
}

This code loads an image from a URL and sets it as the source of the ImageView. You can customize the image loading process by using the various configuration options provided by Coil, such as setting placeholders, transformations, and error handling.

Competitor Comparisons

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Pros of Glide

  • Glide has a larger and more active community, with more contributors and a longer history of development.
  • Glide provides a wider range of features, including support for animated GIFs, video playback, and advanced caching mechanisms.
  • Glide has better documentation and a more extensive set of examples, making it easier for new users to get started.

Cons of Glide

  • Glide has a larger codebase and can be more complex to understand and customize for specific use cases.
  • Glide may have a higher memory footprint and performance overhead compared to Coil, especially for simple image loading tasks.
  • Glide's dependency on the Android Support Library can make it less compatible with newer Android versions and libraries.

Code Comparison

Glide:

Glide.with(context)
     .load(imageUrl)
     .into(imageView)

Coil:

imageView.load(imageUrl)
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Pros of Picasso

  • Picasso is a mature and well-established library, with a large community and extensive documentation.
  • It provides a simple and straightforward API for loading and caching images.
  • Picasso has a wide range of features, including support for transformations, placeholders, and error handling.

Cons of Picasso

  • Picasso is a synchronous library, which can lead to performance issues on the main thread.
  • It may not be as efficient as newer, asynchronous image loading libraries like Coil.
  • Picasso's caching mechanism may not be as advanced as some of the newer libraries.

Code Comparison

Picasso:

Picasso.get()
    .load("https://example.com/image.jpg")
    .placeholder(R.drawable.placeholder)
    .error(R.drawable.error)
    .into(imageView)

Coil:

imageView.load("https://example.com/image.jpg") {
    placeholder(R.drawable.placeholder)
    error(R.drawable.error)
}

As you can see, Coil's API is more concise and expressive, while Picasso's API is more verbose and requires more boilerplate code.

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An Android library for managing images and the memory they use.

Pros of Fresco

  • Fresco provides a comprehensive set of features, including support for various image formats, caching, and automatic memory management.
  • Fresco has a large and active community, with extensive documentation and a wide range of sample code available.
  • Fresco is highly customizable, allowing developers to tailor its behavior to their specific needs.

Cons of Fresco

  • Fresco has a larger codebase and a more complex API compared to Coil, which may make it more challenging for some developers to get started.
  • Fresco's dependency on the Facebook ecosystem may be a concern for developers who prefer a more independent solution.

Code Comparison

Coil:

val imageLoader = ImageLoader.Builder(context)
    .componentRegistry {
        add(SvgDecoder())
    }
    .build()

val request = ImageRequest.Builder(context)
    .data("https://example.com/image.jpg")
    .target(imageView)
    .build()

imageLoader.enqueue(request)

Fresco:

ImagePipeline imagePipeline = Fresco.getImagePipeline();
ImageRequestBuilder builder = ImageRequestBuilder.newBuilderWithSource(Uri.parse("https://example.com/image.jpg"));
ImageRequest request = builder.build();
DataSource<CloseableReference<CloseableImage>> dataSource = imagePipeline.fetchDecodedImage(request, context);

Powerful and flexible library for loading, caching and displaying images on Android.

Pros of Android-Universal-Image-Loader

  • Supports a wide range of image sources, including local files, web URLs, and content providers.
  • Provides a simple and easy-to-use API for loading and caching images.
  • Supports various image display options, such as placeholder and error images.

Cons of Android-Universal-Image-Loader

  • Older library, last updated in 2016, may not be actively maintained or compatible with newer Android versions.
  • Lacks some of the advanced features and performance optimizations found in more modern image loading libraries.
  • May have a larger footprint in your app due to its comprehensive feature set.

Code Comparison

Coil-kt/Coil:

val imageLoader = ImageLoader.Builder(context)
    .componentRegistry {
        add(GifDecoder())
    }
    .build()

imageLoader.load("https://example.com/image.jpg")
    .target(imageView)

Android-Universal-Image-Loader:

ImageLoader imageLoader = ImageLoader.getInstance();
ImageLoaderConfiguration config = new ImageLoaderConfiguration.Builder(context)
    .build();
imageLoader.init(config);

imageLoader.displayImage("https://example.com/image.jpg", imageView);

An Android transformation library providing a variety of image transformations for Glide.

Pros of Glide Transformations

  • Provides a wide range of image transformation options, including grayscale, blur, rounded corners, and more.
  • Supports both Glide and Picasso image loading libraries.
  • Actively maintained with regular updates and bug fixes.

Cons of Glide Transformations

  • Tied to specific image loading libraries, which may not be suitable for all projects.
  • May have performance overhead compared to more lightweight image transformation solutions.
  • Requires additional dependencies to be added to the project.

Code Comparison

Coil Transformation:

val image = coil.load("https://example.com/image.jpg") {
    transformations(CircleCropTransformation())
}

Glide Transformation:

Glide.with(context)
    .load("https://example.com/image.jpg")
    .transform(new CircleCrop())
    .into(imageView);
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Implementation of ImageView for Android that supports zooming, by various touch gestures.

Pros of PhotoView

  • Supports a wide range of image types, including GIF, SVG, and WebP.
  • Provides advanced features like pinch-to-zoom, double-tap-to-zoom, and panning.
  • Offers a customizable UI with support for various gestures and animations.

Cons of PhotoView

  • Larger codebase and dependency footprint compared to Coil.
  • May have a higher learning curve for developers unfamiliar with the library.
  • Potentially slower performance for simple image loading tasks due to the additional features.

Code Comparison

Coil:

val imageLoader = ImageLoader.Builder(context)
    .componentRegistry {
        add(GifDecoder())
    }
    .build()

val request = ImageRequest.Builder(context)
    .data("https://example.com/image.jpg")
    .build()

imageLoader.enqueue(request)

PhotoView:

PhotoView photoView = new PhotoView(context);
photoView.setImageResource(R.drawable.example);
photoView.setOnPhotoTapListener((view, x, y) -> {
    // Handle photo tap event
});
photoView.setOnMatrixChangeListener(matrix -> {
    // Handle matrix changes
});

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README

Coil

An image loading library for Android and Compose Multiplatform. Coil is:

  • Fast: Coil performs a number of optimizations including memory and disk caching, downsampling the image, automatically pausing/cancelling requests, and more.
  • Lightweight: Coil only depends on Kotlin, Coroutines, and Okio and works seamlessly with code shrinkers like Google's R8.
  • Easy to use: Coil's API leverages Kotlin's language features for simplicity and minimal boilerplate.
  • Modern: Coil is Kotlin-first and interoperates with modern libraries including Coroutines, Okio, Ktor, and OkHttp.

Coil is an acronym for: Coroutine Image Loader.

Translations: 日本語, 한국어, Русский, Svenska, Türkçe, 中文

Download

Coil is available on mavenCentral().

implementation("io.coil-kt:coil:2.7.0")

Quick Start

Compose

Import the Compose extension library:

implementation("io.coil-kt:coil-compose:2.7.0")

To load an image, use the AsyncImage composable:

AsyncImage(
    model = "https://example.com/image.jpg",
    contentDescription = null,
)

ImageViews

To load an image into an ImageView, use the load extension function:

imageView.load("https://example.com/image.jpg")

Requests can be configured with an optional trailing lambda:

imageView.load("https://example.com/image.jpg") {
    crossfade(true)
    placeholder(R.drawable.image)
}

Check out Coil's full documentation here.

License

Copyright 2024 Coil Contributors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.