yolo-helpers/dist/yolo-segment/node.js

Helper functions to use models converted from YOLO in browser and Node.js

"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
exports.detectSegment = detectSegment;
exports.detectSegmentSync = detectSegmentSync;
const tf = __importStar(require("@tensorflow/tfjs-node"));
const common_1 = require("./common");
const promises_1 = require("fs/promises");
const fs_1 = require("fs");
const common_2 = require("../tensorflow/common");
__exportStar(require("./common"), exports);
/**
 * boxes features:
 *   - x, y, width, height
 *   - highest confidence, class_index
 *   - mask coefficients for each channel
 *
 * mask features:
 * - [height, width, channel]: 0 for background, 1 for object
 *
 * The x, y, width, height are in pixel unit, NOT normalized in the range of [0, 1].
 * The the pixel units are scaled to the input_shape.
 *
 * The confidence are already normalized between 0 to 1.
 */
async function detectSegment(args) {
    let { model } = args;
    let input_shape = args.input_shape || (0, common_2.getModelInputShape)(model);
    let buffer = 'file' in args ? await (0, promises_1.readFile)(args.file) : null;
    let result = tf.tidy(() => {
        let input = 'tensor' in args ? args.tensor : tf.node.decodeImage(buffer);
        input = (0, common_2.preprocessInput)(input, input_shape);
        return model.predict(input, {});
    });
    let output_boxes = result[0].array().then(data => {
        result[0].dispose();
        return data;
    });
    let output_masks = result[1].array().then(data => {
        result[1].dispose();
        return data;
    });
    return await (0, common_1.decodeSegment)({
        ...args,
        input_shape,
        output_boxes: await output_boxes,
        output_masks: await output_masks,
    });
}
/**
 * Sync version of `detectSegment`.
 */
function detectSegmentSync(args) {
    let { model } = args;
    let input_shape = args.input_shape || (0, common_2.getModelInputShape)(model);
    let buffer = 'file' in args ? (0, fs_1.readFileSync)(args.file) : null;
    let output = tf.tidy(() => {
        let input = 'tensor' in args ? args.tensor : tf.node.decodeImage(buffer);
        input = (0, common_2.preprocessInput)(input, input_shape);
        let result = model.predict(input, {});
        let output_boxes = result[0].arraySync();
        let output_masks = result[1].arraySync();
        return {
            output_boxes,
            output_masks,
        };
    });
    return (0, common_1.decodeSegmentSync)({
        ...args,
        input_shape,
        ...output,
    });
}