@bigin/ns-firebase/mlkit/objectdetection/index.ios.js

Fire. Base. Firebase!

import { ImageSource, Utils } from '@nativescript/core';
import { MLKitObjectDetection as MLKitObjectDetectionBase, ObjectDetectionCategory } from './objectdetection-common';
export class MLKitObjectDetection extends MLKitObjectDetectionBase {
	createDetector() {
		return getDetector(true, this.classify, this.multiple);
	}
	createSuccessListener() {
		return (objects, error) => {
			if (error !== null) {
				console.log(error.localizedDescription);
			} else if (objects !== null && objects.count > 0) {
				const result = {
					objects: [],
				};
				for (let i = 0, l = objects.count; i < l; i++) {
					const obj = objects.objectAtIndex(i);
					result.objects.push(getMLKitObjectDetectionResultItem(obj, this.lastVisionImage));
				}
				this.notify({
					eventName: MLKitObjectDetection.scanResultEvent,
					object: this,
					value: result,
				});
			}
		};
	}
	rotateRecording() {
		return true;
	}
}
function getDetector(stream, classify, multiple) {
	const firVision = FIRVision.vision();
	const fIRVisionObjectDetectorOptions = FIRVisionObjectDetectorOptions.new();
	fIRVisionObjectDetectorOptions.detectorMode = stream ? 1 : 0;
	fIRVisionObjectDetectorOptions.shouldEnableClassification = classify || false;
	fIRVisionObjectDetectorOptions.shouldEnableMultipleObjects = multiple || false;
	return firVision.objectDetectorWithOptions(fIRVisionObjectDetectorOptions);
}
export function detectObjects(options) {
	return new Promise((resolve, reject) => {
		try {
			const detector = getDetector(false, options.classify, options.multiple);
			detector.processImageCompletion(getImage(options), (objects, error) => {
				if (error !== null) {
					reject(error.localizedDescription);
				} else if (objects !== null) {
					const result = {
						objects: [],
					};
					const image = options.image instanceof ImageSource ? options.image.ios : options.image.imageSource.ios;
					for (let i = 0, l = objects.count; i < l; i++) {
						const obj = objects.objectAtIndex(i);
						result.objects.push(getMLKitObjectDetectionResultItem(obj, image));
					}
					resolve(result);
				}
			});
		} catch (ex) {
			console.log('Error in firebase.mlkit.detectObjects: ' + ex);
			reject(ex);
		}
	});
}
function getMLKitObjectDetectionResultItem(obj, image) {
	console.log('>> getMLKitObjectDetectionResultItem, image: ' + image);
	let imageWidth;
	let imageHeight;
	let { x, y } = obj.frame.origin;
	let { width, height } = obj.frame.size;
	if (image) {
		imageWidth = image.size.width;
		imageHeight = image.size.height;
		const origX = x;
		const origWidth = width;
		const origImageWidth = imageWidth;
		if (Utils.ios.isLandscape()) {
			if (UIDevice.currentDevice.orientation === 4) {
				x = image.size.width - (width + x);
				y = image.size.height - (height + y);
			}
		} else {
			x = image.size.height - (height + y);
			y = origX;
			width = height;
			height = origWidth;
			imageWidth = imageHeight;
			imageHeight = origImageWidth;
		}
	}
	return {
		id: obj.trackingID,
		category: ObjectDetectionCategory[obj.classificationCategory],
		confidence: obj.confidence,
		ios: obj,
		bounds: {
			origin: {
				x,
				y,
			},
			size: {
				width,
				height,
			},
		},
		image: {
			width: imageWidth,
			height: imageHeight,
		},
	};
}
function getImage(options) {
	const image = options.image instanceof ImageSource ? options.image.ios : options.image.imageSource.ios;
	return FIRVisionImage.alloc().initWithImage(image);
}
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