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

Fire. Base. Firebase!

import { ImageSource, knownFolders } from '@nativescript/core';
import { getLabelsFromAppFolder, getLabelsFromFile, MLKitCustomModel as MLKitCustomModelBase } from './custommodel-common';
export class MLKitCustomModel extends MLKitCustomModelBase {
	createDetector() {
		this.modelInterpreter = getInterpreter(this.localModelFile);
		return this.modelInterpreter;
	}
	runDetector(image, onComplete) {
		const modelExpectsWidth = this.modelInputShape[1];
		const modelExpectsHeight = this.modelInputShape[2];
		const isQuantized = this.modelInputType !== 'FLOAT32';
		if (!this.inputOutputOptions) {
			this.inputOutputOptions = FIRModelInputOutputOptions.new();
			let inputType;
			const arrIn = NSMutableArray.new();
			this.modelInputShape.forEach((dim) => arrIn.addObject(dim));
			inputType = isQuantized ? 3 : 1;
			this.inputOutputOptions.setInputFormatForIndexTypeDimensionsError(0, inputType, arrIn);
			const arrOut = NSMutableArray.new();
			arrOut.addObject(1);
			arrOut.addObject(this.labels.length);
			this.inputOutputOptions.setOutputFormatForIndexTypeDimensionsError(0, inputType, arrOut);
		}
		let inputData;
		if (isQuantized) {
			inputData = TNSMLKitCameraView.scaledDataWithSizeByteCountIsQuantized(image, CGSizeMake(modelExpectsWidth, modelExpectsHeight), modelExpectsWidth * modelExpectsHeight * this.modelInputShape[3] * this.modelInputShape[0], isQuantized);
		} else {
			inputData = TNSMLKitCameraView.getInputDataWithRowsAndColumnsAndType(image, modelExpectsWidth, modelExpectsHeight, 'Float32');
		}
		const inputs = FIRModelInputs.new();
		inputs.addInputError(inputData);
		this.modelInterpreter.runWithInputsOptionsCompletion(inputs, this.inputOutputOptions, (outputs, error) => {
			if (error !== null) {
				console.log(error.localizedDescription);
			} else if (outputs !== null) {
				const probabilities = outputs.outputAtIndexError(0)[0];
				if (this.labels.length !== probabilities.count) {
					console.log(`The number of labels (${this.labels.length}) is not equal to the interpretation result (${probabilities.count})!`);
					onComplete();
				} else {
					const result = {
						result: getSortedResult(this.labels, probabilities, this.maxResults),
					};
					this.notify({
						eventName: MLKitCustomModel.scanResultEvent,
						object: this,
						value: result,
					});
				}
			}
			onComplete();
		});
	}
	createSuccessListener() {
		return (outputs, error) => {
			if (error !== null) {
				console.log(error.localizedDescription);
			} else if (outputs !== null) {
				const result = {
					result: [],
				};
				console.log('>>> outputs: ' + outputs);
				this.notify({
					eventName: MLKitCustomModel.scanResultEvent,
					object: this,
					value: result,
				});
			}
		};
	}
	rotateRecording() {
		return false;
	}
}
function getInterpreter(localModelFile) {
	if (localModelFile) {
		let localModelFilePath;
		if (localModelFile.indexOf('~/') === 0) {
			localModelFilePath = knownFolders.currentApp().path + localModelFile.substring(1);
		} else {
			localModelFilePath = NSBundle.mainBundle.pathForResourceOfType(localModelFile.substring(0, localModelFile.lastIndexOf('.')), localModelFile.substring(localModelFile.lastIndexOf('.') + 1));
		}
		const localModel = FIRCustomLocalModel.alloc().initWithModelPath(localModelFilePath);
		if (localModel) {
			return FIRModelInterpreter.modelInterpreterForLocalModel(localModel);
		} else {
			console.log('No (cloud or local) model was successfully loaded.');
		}
	}
	return null;
}
export function useCustomModel(options) {
	return new Promise((resolve, reject) => {
		try {
			const image = options.image instanceof ImageSource ? options.image.ios : options.image.imageSource.ios;
			const isQuant = options.modelInput[0].type !== 'FLOAT32';
			let inputData;
			if (isQuant) {
				inputData = TNSMLKitCameraView.scaledDataWithSizeByteCountIsQuantized(image, CGSizeMake(options.modelInput[0].shape[1], options.modelInput[0].shape[2]), options.modelInput[0].shape[1] * options.modelInput[0].shape[2] * options.modelInput[0].shape[3] * options.modelInput[0].shape[0], options.modelInput[0].type !== 'FLOAT32');
			} else {
				inputData = TNSMLKitCameraView.getInputDataWithRowsAndColumnsAndType(image, options.modelInput[0].shape[1], options.modelInput[0].shape[2], 'Float32');
			}
			const inputs = FIRModelInputs.new();
			inputs.addInputError(inputData);
			const inputOptions = FIRModelInputOutputOptions.new();
			let inputType;
			options.modelInput.forEach((dimensionAndType, i) => {
				const arrIn = NSMutableArray.new();
				dimensionAndType.shape.forEach((dim) => arrIn.addObject(dim));
				inputType = dimensionAndType.type === 'FLOAT32' ? 1 : 3;
				inputOptions.setInputFormatForIndexTypeDimensionsError(i, inputType, arrIn);
			});
			let labels;
			if (options.labelsFile.indexOf('~/') === 0) {
				labels = getLabelsFromAppFolder(options.labelsFile);
			} else {
				const labelsFile = NSBundle.mainBundle.pathForResourceOfType(options.labelsFile.substring(0, options.labelsFile.lastIndexOf('.')), options.labelsFile.substring(options.labelsFile.lastIndexOf('.') + 1));
				labels = getLabelsFromFile(labelsFile);
			}
			const arrOut = NSMutableArray.new();
			arrOut.addObject(1);
			arrOut.addObject(labels.length);
			inputOptions.setOutputFormatForIndexTypeDimensionsError(0, inputType, arrOut);
			const modelInterpreter = getInterpreter(options.localModelFile);
			modelInterpreter.runWithInputsOptionsCompletion(inputs, inputOptions, (outputs, error) => {
				if (error !== null) {
					reject(error.localizedDescription);
				} else if (outputs !== null) {
					const probabilities = outputs.outputAtIndexError(0)[0];
					if (labels.length !== probabilities.count) {
						console.log(`The number of labels in ${options.labelsFile} (${labels.length}) is not equal to the interpretation result (${probabilities.count})!`);
						return;
					}
					const result = {
						result: getSortedResult(labels, probabilities, options.maxResults),
					};
					resolve(result);
				}
			});
		} catch (ex) {
			console.log('Error in firebase.mlkit.useCustomModel: ' + ex);
			reject(ex);
		}
	});
}
function getSortedResult(labels, probabilities, maxResults = 5) {
	const result = [];
	labels.forEach((text, i) => result.push({ text, confidence: probabilities.objectAtIndex(i) }));
	result.sort((a, b) => (a.confidence < b.confidence ? 1 : a.confidence === b.confidence ? 0 : -1));
	if (result.length > maxResults) {
		result.splice(maxResults);
	}
	const softmaxScale = 1.0 / 256.0;
	result.map((r) => (r.confidence = NSNumber.numberWithFloat(softmaxScale * r.confidence)));
	return result;
}
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