Retake 📸

Website · All Hoverfly models · Maven Central

By Hoverfly. On-device photo quality checker for Kotlin Multiplatform: Android, iOS, macOS, JVM desktop, JavaScript and WebAssembly. It looks at a photo and says whether it's good or should be retaken, and why: blurry, too dark, too bright, glare, noisy, or a finger over the lens.

import io.github.rajumark.hoverfly.retake.Retake

Retake().use { retake ->
    val check = retake.check(pixels, width, height)   // ARGB pixels: a Bitmap, a camera frame, an ImageBitmap
    if (!check.isGood) showHint(check.issues.first().hint)
    // a shaky photo: PhotoCheck(retake: BLUR, blur=0.991, dark=0.012, bright=0.002, glare=0.0, noise=0.0, finger=0.0)
}

• Six problems, one call. A score from 0 to 1 for blur, dark, bright, glare, noise and finger, plus the verdict and a short hint for each problem.
• Screenshots are not photos. Screenshots, documents, chats and graphics count as good, so a gallery app doesn't flag them.
• Tiny. 0.5 MB of int8 weights, 0.48M parameters.
• No dependencies. Inference is plain Kotlin. There is no ONNX Runtime, TFLite, ML Kit or native code.
• Private and offline. The model ships inside the library on every platform. There is no network, no permission and no telemetry.
• Every platform, same results. Android, iOS, macOS, JVM desktop, JavaScript and WebAssembly, tested against the reference model on each.

// build.gradle.kts: commonMain, or any platform source set
dependencies {
    implementation("io.github.rajumark:retake:1.0.0")
}

It's on Maven Central, so no extra repository is needed. Gradle picks the right artifact for each platform:

The sample app with the example photos. Every check runs on the device.

Blurry	Glare	Finger	Screenshot
retake: blur 99%	retake: glare	retake: finger 100%	good (not a camera photo)

The KMP sample on each platform:

Android	iOS	Desktop	Web (Wasm)

val retake = Retake()          // loads the model: do it off the main thread, keep one instance

val check = retake.check(argb, width, height)        // ARGB ints (0xAARRGGBB), row by row
val check2 = retake.checkRgb(rgb, width, height)     // or RGB bytes, 3 per pixel

check.isGood                   // true when no issue reached the line
check.issues                   // the problems found, most certain first: [BLUR, DARK]
check.score(Issue.GLARE)       // 0..1
check.scores                   // every issue's score
check.strongest                // (issue, score) with the highest score, even when below the line
Issue.BLUR.hint                // "Blurry: hold still and tap to focus"

retake.check(argb, width, height, threshold = 0.7f)  // stricter line: only clear problems (default 0.5)
retake.close()                 // frees the model's memory

Any size works: photos larger than 1024 px on the long side are shrunk first (area averaging, like the model was trained), smaller ones are used as they are. check() is thread-safe.

On Android there are two shortcuts:

retake.check(bitmap)                 // an upright Bitmap (camera preview, decoded photo)
retake.check(context, uri)           // a photo file or content:// Uri: decoded at reduced size + EXIF-rotated (blocking)

check(context, uri) decodes large camera photos at 1/2, 1/4 or 1/8 size, which makes it several times faster than decoding the full photo first.

From Java:

try (Retake retake = new Retake()) {
    PhotoCheck check = retake.check(argb, width, height);
}

Real photos. On 240 random photos from the LIVE In the Wild Challenge database (real phone photos with real defects), hand-labelled before the model was tested on them and scored once: Retake's verdict is right for 86.6% of them. It keeps 87.9% of the good photos and catches 73.7% of the bad ones.

The OpenCV rules and CLIP had their lines tuned on the validation set, to be fair to them. The OpenCV rules can't detect a finger at all, and with tuned lines they still flag about half of all good photos; CLIP flags almost every photo for something.

Speed. One check of a 1040 × 780 photo (shrink, views and model) in the pure-Kotlin library takes about 90 ms on a laptop JVM and about 235 ms in a browser (JS or Wasm). In the release sample app on an Android emulator, an 800 px photo takes 110–150 ms. It's made for checking a photo after it's taken, not every preview frame.

Where it falls short: real glare (sun through a windshield, reflections on glossy paper) is caught less often than the other problems. Very dark night photos taken on purpose, or deliberate motion blur, can be flagged; raise the threshold if your app should be lenient. Finger detection was tested mostly on generated fingers, because almost no public photos show one.

sample/ is a separate Gradle build that uses the published library, never the source. It resolves io.github.rajumark only from Maven Local, or from Maven Central with -PretakeRepo=central. It has a Compose Multiplatform app for Android, desktop and iOS (8 example photos; on Android and desktop you can also check your own photo), and a web page built for both Kotlin/JS and Kotlin/Wasm.

./gradlew :retake:publishToMavenLocal
cd sample
./gradlew :androidApp:installRelease
./gradlew :desktopApp:run
./gradlew :webApp:wasmJsBrowserDevelopmentRun     # or :webApp:jsBrowserDevelopmentRun
open iosApp/iosApp.xcodeproj                       # run the iosApp scheme on a simulator

The example photos are from Unsplash (Unsplash License) with generated defects, plus one generated screenshot. Each one shows a different issue.

retake/                        the library
  src/commonMain/              public API (Retake, PhotoCheck, Issue) and the model in plain Kotlin
                               (internal/: Photo (resize + views), Network)
  src/androidMain/             Bitmap and Uri shortcuts
  src/{jvm,android,embedded}Main/   model loading
  src/modelData/               retake.bin (int8 weights)
  src/commonTest/              parity with the reference on 13 images, API, latency; runs on every target
demo/                          the library compiled to Wasm for the website's live demo
sample/                        demo apps using the published artifacts
docs/                          website (rajumark.github.io/retake)

On JVM and Android the model ships as Java resources in the jar/AAR. Kotlin/Native and the web have no resources, so the build compiles it into the library (generateEmbeddedModel).

./gradlew :retake:jvmTest
./gradlew :retake:testAndroidHostTest
./gradlew :retake:connectedAndroidDeviceTest              # on a connected device/emulator
./gradlew :retake:iosSimulatorArm64Test
./gradlew :retake:macosArm64Test
./gradlew :retake:jsNodeTest :retake:jsBrowserTest
./gradlew :retake:wasmJsNodeTest :retake:wasmJsBrowserTest

The parity tests start from raw RGB images (incl. one that has to be shrunk to 1024 px and one under 128 px). On every target they require the same 4 detail crops as the reference implementation (OpenCV + PyTorch) and every score within 0.002 of it; the largest difference on any target is 0.00023.

Blur and noise disappear when a photo is shrunk, but exposure, glare and a finger need the whole picture, so the model looks at two views. The whole photo is squeezed to 224 × 224. Four crops of 128 × 128 come from the photo at 1024 px: the three most textured parts (where blur shows) and the flattest mid-grey part (where noise shows). Two small MobileNet-style CNNs read the views, and a small head gives a score per problem. The weights are int8 with one scale per output channel; batch-norm is folded into the convolutions.

It was trained from scratch, without ImageNet weights, on about 58,000 clean photos from Open Images and Unsplash (69,000 downloaded; the ones that were already blurry, dark or blown out were left out). Each photo was damaged in code: out of focus, motion and hand shake, under and over exposure, glare, flare and reflections, sensor noise, a finger at the edge of the lens. Each defect was generated at strengths on both sides of the line where a person would retake the photo. Generated screenshots, chats, documents and posters taught it that graphics are not bad photos.

See PUBLISHING.md.

Retake is one of nine small on-device models by Hoverfly, all with the same install, the same free tier and nothing sent to a server.

See them all on the Hoverfly website.

Free for up to 10,000 monthly active devices. You don't need an API key, an account or a license file: add the dependency and ship. It works in commercial apps too, with no limit on how often each device runs it.

How devices are counted. A monthly active device is a device that runs Retake at least once in a calendar month. The limit applies separately to each product, each platform (Android, iOS, web…) and each Hoverfly model. Once a product passes it, you have 30 days to get a commercial license. The library keeps working and never checks in with a server.

Not allowed under any tier (unless agreed in writing):

• selling or redistributing Retake or its model on its own, or inside another SDK or library
• extracting, modifying, fine-tuning or retraining the model weights
• using the model or its outputs to train or distill another model
• reverse engineering the model or its file format
• offering it as a hosted API for others

Custom models. Hoverfly also designs and trains small, fast on-device models for your needs: image quality, document and ID checks, moderation, classification, language detection and more.

Contact for a commercial license or a custom model: raju348636@gmail.com or +91 63533 21951 (call or WhatsApp).

Full terms: Hoverfly Community License.

The model was trained on photos from Open Images (CC BY 2.0) and the Unsplash Lite dataset (Unsplash License).