tensorflow-crop-distribution.js

const cv = require('opencv4nodejs')
const tf = require('@tensorflow/tfjs-node');
const h = require('hasard')
const mat = cv.imread('./tmp/lenna.png')

const rect = {
	x: 0,
	y: 0,
	w: 400,
	h: 512
}

const getHistAxis = channel => ([
  {
    channel,
    bins: 256,
    ranges: [0, 256]
  }
]);

const getDiff = function(rect){
	const size = {w: mat.cols, h: mat.rows}

	const intTensor = tf.tensor4d(mat.getData(), [1, size.h, size.w, 3])

	const tensorValues = intTensor.dataSync();

	const floatTensor = intTensor.div(255);

	const cropped = tf.image.cropAndResize(floatTensor, [[rect.y/size.h, rect.x/size.w, (rect.y+rect.h)/size.h, (rect.x+rect.w)/size.w]], [0], [rect.h, rect.w]);

	const croppedValues = cropped.mul(255).toInt().dataSync();
	const d = new Uint8Array(croppedValues);
	const mat2 = new cv.Mat(d, rect.h, rect.w, cv.CV_8UC3);
	const mat3 = new cv.Mat(rect.h, rect.w, cv.CV_8UC3);
	mat.getRegion(new cv.Rect(rect.x, rect.y, rect.w, rect.h)).copyTo(mat3)
	const diff = mat3.absdiff(mat2)
	const hist = cv.calcHist(diff, getHistAxis(0)).add(cv.calcHist(diff, getHistAxis(1))).add(cv.calcHist(diff, getHistAxis(2)));
	const all = rect.w*rect.h*3;
	return {hist, all}
	
}

const xRef = h.reference(h.integer([0, 511]))
const yRef = h.reference(h.integer([0, 511]))
const min = h.fn(Math.min.bind(Math))

const hasardRect = h.object({
		x: xRef,
		y: yRef,
		w: min(h.substract(512, xRef), h.integer([1, 10])),
		h: min(h.substract(512, yRef), h.integer([1, 10]))
})

const rects = hasardRect.run(1000);

let max = 0;
let rectMax;
const hists = []
let count = 0
rects.map(r => {
	const {hist, all } = getDiff(r)
	hists.push(hist);
	count += all;
})
const sum = hists.reduce((a,b) => a.add(b));
console.log(sum.getDataAsArray().map(a => a[0]).join(','))
console.log('count', count)