215 lines
7.6 KiB
Plaintext
215 lines
7.6 KiB
Plaintext
---
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id: frame-processors-plugins-overview
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title: Creating Frame Processor Plugins
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sidebar_label: Overview
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---
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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## Overview
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Frame Processor Plugins are **native functions** which can be directly called from a JS Frame Processor. (See ["Frame Processors"](frame-processors))
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They **receive a frame from the Camera** as an input and can return any kind of output. For example, a `scanQRCodes` function returns an array of detected QR code strings in the frame:
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```tsx {4-5}
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function App() {
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const frameProcessor = useFrameProcessor((frame) => {
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'worklet'
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const qrCodes = scanQRCodes(frame)
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console.log(`QR Codes in Frame: ${qrCodes}`)
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}, [])
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return (
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<Camera frameProcessor={frameProcessor} {...cameraProps} />
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)
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}
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```
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To achieve **maximum performance**, the `scanQRCodes` function is written in a native language (e.g. Objective-C), but it will be directly called from the VisionCamera Frame Processor JavaScript-Runtime.
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### Types
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Similar to a TurboModule, the Frame Processor Plugin Registry API automatically manages type conversion from JS <-> native. They are converted into the most efficient data-structures, as seen here:
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| JS Type | Objective-C/Swift Type | Java/Kotlin Type |
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|----------------------|-------------------------------|----------------------------|
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| `number` | `NSNumber*` (double) | `Double` |
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| `boolean` | `NSNumber*` (boolean) | `Boolean` |
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| `string` | `NSString*` | `String` |
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| `[]` | `NSArray*` | `ReadableNativeArray` |
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| `{}` | `NSDictionary*` | `ReadableNativeMap` |
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| `undefined` / `null` | `nil` | `null` |
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| `(any, any) => void` | [`RCTResponseSenderBlock`][4] | `(Object, Object) -> void` |
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| [`Frame`][1] | [`Frame*`][2] | [`ImageProxy`][3] |
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### Return values
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Return values will automatically be converted to JS values, assuming they are representable in the ["Types" table](#types). So the following Java Frame Processor Plugin:
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```java
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@Override
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public Object callback(ImageProxy image, Object[] params) {
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return "cat";
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}
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```
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Returns a `string` in JS:
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```js
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export function detectObject(frame: Frame): string {
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'worklet'
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const result = __detectObject(frame)
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console.log(result) // <-- "cat"
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}
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```
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You can also manipulate the buffer and return it (or a copy of it) by returning a [`Frame`][2]/[`ImageProxy`][3] instance:
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```java
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@Override
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public Object callback(ImageProxy image, Object[] params) {
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ImageProxy resizedImage = new ImageProxy(/* ... */);
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return resizedImage;
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}
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```
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Which returns a [`Frame`](https://github.com/mrousavy/react-native-vision-camera/blob/main/src/Frame.ts) in JS:
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```js
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const frameProcessor = useFrameProcessor((frame) => {
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'worklet';
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// creates a new `Frame` that's 720x480
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const resizedFrame = resize(frame, 720, 480)
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// by downscaling the frame, the `detectObjects` function runs faster.
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const objects = detectObjects(resizedFrame)
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console.log(objects)
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}, [])
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```
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### Parameters
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Frame Processors can also accept parameters, following the same type convention as [return values](#return-values):
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```ts
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const frameProcessor = useFrameProcessor((frame) => {
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'worklet'
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const codes = scanCodes(frame, ['qr', 'barcode'])
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}, [])
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```
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Or with multiple ("variadic") parameters:
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```ts
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const frameProcessor = useFrameProcessor((frame) => {
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'worklet'
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const codes = scanCodes(frame, true, 'hello-world', 42)
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}, [])
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```
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### Exceptions
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To let the user know that something went wrong you can use Exceptions:
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```java
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@Override
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public Object callback(ImageProxy image, Object[] params) {
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if (params[0] instanceof String) {
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// ...
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} else {
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throw new Exception("First argument has to be a string!");
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}
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}
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```
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Which will throw a JS-error:
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```ts
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const frameProcessor = useFrameProcessor((frame) => {
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'worklet'
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try {
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const codes = scanCodes(frame, true)
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} catch (e) {
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console.log(`Error: ${e.message}`)
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}
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}, [])
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```
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## What's possible?
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You can run any native code you want in a Frame Processor Plugin. Just like in the native iOS and Android Camera APIs, you will receive a frame (`CMSampleBuffer` on iOS, `ImageProxy` on Android) which you can use however you want. In other words; **everything is possible**.
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## Implementations
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### Long-running Frame Processors
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If your Frame Processor takes longer than a single frame interval to execute, or runs asynchronously, you can create a **copy of the frame** and dispatch the actual frame processing to a **separate thread**.
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For example, a realtime video chat application might use WebRTC to send the frames to the server. I/O operations (networking) are asynchronous, and we don't _need_ to wait for the upload to succeed before pushing the next frame, so we copy the frame and perform the upload on another Thread.
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```java
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@Override
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public Object callback(ImageProxy image, Object[] params) {
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String serverURL = (String)params[0];
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ImageProxy imageCopy = new ImageProxy(/* ... */);
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uploaderQueue.runAsync(() -> {
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WebRTC.uploadImage(imageCopy, serverURL);
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imageCopy.close();
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});
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return null;
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}
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```
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### Async Frame Processors with Event Emitters
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You might also run some very complex AI algorithms which are not fast enough to smoothly run at **30 FPS** (**33ms**). To not drop any frames you can create a custom "frame queue" which processes the copied frames and calls back into JS via a React event emitter. For this you'll have to create a Native Module that handles the asynchronous native -> JS communication, see ["Sending events to JavaScript" (Android)](https://reactnative.dev/docs/native-modules-android#sending-events-to-javascript) and ["Sending events to JavaScript" (iOS)](https://reactnative.dev/docs/native-modules-ios#sending-events-to-javascript).
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This might look like this for the user:
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```tsx
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function App() {
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const frameProcessor = useFrameProcessor((frame) => {
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'worklet'
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SomeAI.process(frame) // does not block frame processor, runs async
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}, [])
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useEffect(() => {
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SomeAI.addListener((results) => {
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// gets called asynchronously, goes through the React Event Emitter system
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console.log(`AI results: ${results}`)
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})
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}, [])
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return (
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<Camera frameProcessor={frameProcessor} {...cameraProps} />
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)
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}
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```
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This way you can handle queueing up the frames yourself and asynchronously call back into JS at some later point in time using event emitters.
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### Benchmarking Frame Processor Plugins
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Your Frame Processor Plugins have to be fast. VisionCamera automatically detects slow Frame Processors and outputs relevant information in the native console (Xcode: **Debug Area**, Android Studio: **Logcat**):
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<div align="center">
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<img src={useBaseUrl("img/slow-log.png")} width="80%" />
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</div>
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<div align="center">
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<img src={useBaseUrl("img/slow-log-2.png")} width="80%" />
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</div>
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<br />
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#### 🚀 Create your first Frame Processor Plugin for [iOS](frame-processors-plugins-ios) or [Android](frame-processors-plugins-android)!
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[1]: https://github.com/mrousavy/react-native-vision-camera/blob/main/src/Frame.ts
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[2]: https://github.com/mrousavy/react-native-vision-camera/blob/main/ios/Frame%20Processor/Frame.h
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[3]: https://developer.android.com/reference/androidx/camera/core/ImageProxy
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[4]: https://github.com/facebook/react-native/blob/9a43eac7a32a6ba3164a048960101022a92fcd5a/React/Base/RCTBridgeModule.h#L20-L24
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