AB Links Solver

// ==UserScript==

// @name AB Links Solver

// @namespace ABLinks Solver(Solves Ablinks images)

// @version 3.0

// @description Solves AbLink images

// @author engageub

// @match *://*/*

// @noframes

// @connect https://unpkg.com

// @require https://unpkg.com/opencv.js@1.2.1/opencv.js

// @require https://unpkg.com/jimp@0.5.2/browser/lib/jimp.min.js

// @require https://unpkg.com/tesseract.js@2.1.5/dist/tesseract.min.js

// @grant GM_xmlhttpRequest

// @antifeature referral-link

// ==/UserScript==

// This script solves Ablink images with words and having 4 different options

// Number identification logic for comparing words and numbers will be implemented in the next versions

// Accuracy can be improved by adding more filters for different types of images and fonts

// This script does not have a global matcher, you will need to add the websites in the matcher section manually, till

// all the solutions are implemented

// Your account will be locked for 24 hours, if 3 incorrect solutions are provided consecutively in 10 minutes. (This is the default but depends on website)

// To avoid this add a rotator to change the website whenever an incorrect solution is provided.

// TODO: Refactor Code

(function() {

'use strict';

var questions = [];

var questionImages = [];

var questionImage = "";

var questionImageSource = "";

var numericWordArray = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten"];

async function waitForImage(imgElement) {

return await new Promise(res => {

if (imgElement.complete) {

return res();

}

imgElement.onload = () => res();

imgElement.onerror = () => res();

});

}

async function toDataURL(c){

return await new Promise(function(resolve){

const dataURI = c.toDataURL('image/png');

return resolve(dataURI);

})

}

async function removeNoiseUsingImageData(imgdata,width,height,threshold){

return await new Promise(function(resolve){

var noiseCount =0;

var noiseRowStart = 0;

for (let column = 0; column < width; column++) {

let count = 0;

for (let row = 0; row < height; row++) {

let position = row * width + column;

let pixelAtPosition = imgdata[position];

//Remove noise from first row and last row

if(row == 0 || row == height-1){

imgdata[position] = 0xFFFFFFFF;

}

if (pixelAtPosition == 0xFF000000){

if(noiseCount == 0){

noiseRowStart = row;

}

noiseCount++;

}else{

//Define the number of consecutive pixels to be considered as noise

if(noiseCount > 0 && noiseCount <= threshold){

//Start from noiseRow till current row and remove noise

while(noiseRowStart < row){

let noisePosition = noiseRowStart * width + column;

imgdata[noisePosition] = 0xFFFFFFFF;

noiseRowStart++;

}

}

noiseCount =0;

}

}

}

return resolve(imgdata);

})

}

async function imageUsingOCRAntibotQuestion(image) {

if (!image || !image.src) {

console.log("No images found");

return;

}

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = image.src

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img.width;

c.height = img.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

// console.log(data);

await ctx.putImageData(imageData, 0, 0);

let src = await cv.imread(c);

let dst = new cv.Mat();

let ksize = new cv.Size(3, 3);

// You can try more different parameters

await cv.GaussianBlur(src, dst, ksize, 0, 0, cv.BORDER_DEFAULT);

await cv.imshow(c, dst);

src.delete();

dst.delete();

//console.log( c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

async function imageUsingOCRAntibotLowValues(image) {

if (!image || !image.src) {

console.log("No images found");

return;

}

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = image.src;

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img.width;

c.height = img.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

//console.log(await c.toDataURL());

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

//Make the image visible

for (let i = 0; i < data.length; i += 4) {

if ((data[i] < 100 || data[i + 1] < 100 || data[i + 2] < 100) && data[i+3]>0) {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

} else {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

}

data[i + 3] = 255;

}

//Remove Noise from Image

var imgdata = await new Uint32Array(data.buffer);

imgdata = await removeNoiseUsingImageData(imgdata,c.width,c.height,1);

await ctx.putImageData(imageData, 0, 0);

//console.log( c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

async function imageUsingOCRAntibotHighValues(image) {

if (!image || !image.src) {

console.log("No images found");

return;

}

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = image.src;

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img.width;

c.height = img.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

//console.log(await c.toDataURL());

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

//Make the image visible

for (let i = 0; i < data.length; i += 4) {

if ((data[i] > 100 || data[i + 1] > 100 || data[i + 2] > 100) && data[i + 3] > 0) {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

} else {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

}

data[i + 3] = 255;

}

//Remove Noise from Image

var imgdata = await new Uint32Array(data.buffer);

imgdata = await removeNoiseUsingImageData(imgdata,c.width,c.height,1);

await ctx.putImageData(imageData, 0, 0);

//console.log( c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

async function splitImageUsingOCRAntibotLowValues(questionImageSource) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = questionImageSource;

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img.width;

c.height = img.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

//console.log(await c.toDataURL());

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

//Make the image visible

for (let i = 0; i < data.length; i += 4) {

if ((data[i] < 100 || data[i + 1] < 100 || data[i + 2] < 100) && data[i+3]>0) {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

} else {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

}

data[i + 3] = 255;

}

await ctx.putImageData(imageData, 0, 0);

//console.log(c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (splitImage(imageDataURI));

}

async function splitImageUsingDefaultValues(questionImageSource) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = questionImageSource;

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img.width;

c.height = img.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

//console.log(await c.toDataURL());

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

//Make the image visible

for (let i = 0; i < data.length; i += 4) {

if (data[i] > 0 && data[i + 1] > 0 && data[i + 2] > 100 && data[i+3]>0) {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

} else {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

}

data[i + 3] = 255;

}

var imgdata = await new Uint32Array(data.buffer);

//Remove Noise from Image

imgdata = await removeNoiseUsingImageData(imgdata,c.width,c.height,1);

await ctx.putImageData(imageData, 0, 0);

//console.log(c.toDataURL());

let imageDataURI = await toDataURL(c);

return await splitImage(imageDataURI);

}

async function splitImageUsingOCRAntibotHighValues(questionImageSource) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = questionImageSource;

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img.width;

c.height = img.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

//console.log(await c.toDataURL());

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

//Make the image visible

for (let i = 0; i < data.length; i += 4) {

if ((data[i] > 100 || data[i + 1] > 100 || data[i + 2] > 100) && data[i + 3] > 0) {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

} else {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

}

data[i + 3] = 255;

}

var imgdata = await new Uint32Array(data.buffer);

//Remove Noise from Image

imgdata = await removeNoiseUsingImageData(imgdata,c.width,c.height,1);

await ctx.putImageData(imageData, 0, 0);

let imageDataURI = await toDataURL(c);

return await splitImage(imageDataURI);

}

async function splitImage(imgSource) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = imgSource

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img.width;

c.height = img.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

var imgdata = await new Uint32Array(data.buffer);

//Scan from left to right

//Get the weight of white spaces

//Ignore first white space and last white space

var sequenceLength = 0;

var prevColumn = 0;

var hashMap = new Map();

var first = 0;

var second = 0;

var third = 0;

var firstMaxColumn = 0;

var secondMaxColumn = 0;

var thirdMaxColumn = 0;

//Remove Noise from Image

imgdata = await removeNoiseUsingImageData(imgdata,c.width,c.height,1);

//await ctx.putImageData(imageData, 0, 0);

//console.log(await c.toDataURL());

for (let column = Math.floor(0.1 * c.width); column < c.width; column++) {

var count = 0;

for (let row = 0; row < c.height; row++) {

var position = row * c.width + column;

var pixelAtPosition = imgdata[position];

if (pixelAtPosition == 0xFFFFFFFF) {

count++;

}

}

//Get the blank spaces based on weight of the column

if (count > Math.floor(0.88 * c.height) && column != 0) {

if (column - prevColumn == 1) {

sequenceLength = sequenceLength + 1;

}

} else {

if ((column - sequenceLength != 1) && (column != 0 || sequenceLength != 0 || column != c.width - 1)) {

// If current element is

// greater than first

if (sequenceLength > first) {

third = second;

thirdMaxColumn = secondMaxColumn;

second = first;

secondMaxColumn = firstMaxColumn;

first = sequenceLength;

firstMaxColumn = column - 1;

} else if (sequenceLength > second) {

third = second;

thirdMaxColumn = secondMaxColumn;

second = sequenceLength;

secondMaxColumn = column - 1;

} else if (sequenceLength > third) {

third = sequenceLength;

thirdMaxColumn = column - 1;

}

}

sequenceLength = 0;

}

prevColumn = column;

}

firstMaxColumn = firstMaxColumn - Math.floor(first / 2)

secondMaxColumn = secondMaxColumn - Math.floor(second / 2)

thirdMaxColumn = thirdMaxColumn - Math.floor(third / 2)

var columnArray = [firstMaxColumn, secondMaxColumn, thirdMaxColumn];

columnArray = await columnArray.sort(function(a, b) {

return a - b;

});

await ctx.putImageData(imageData, 0, 0);

let url = await questionImage.src.replace(/^data:image\/\w+;base64,/, "");

let buffer = await new Buffer(url, 'base64');

//Check if overlaps are detected and split the images

var len = [];

len[0] = columnArray[0] - 0;

len[1] = columnArray[1] - columnArray[0];

len[2] = columnArray[2] - columnArray[1];

len[3] = c.width - columnArray[2];

for (let i = 0; i < len.length; i++) {

if (len[i] < Math.floor(0.1 * c.width)) {

console.log("Overlap detected");

return;

break;

}

}

await new Promise((resolve, reject) => {

Jimp.read(buffer).then(async function(data) {

await data.crop(0, 0, columnArray[0], questionImage.height)

.getBase64(Jimp.AUTO, async function(err, src) {

let img = new Image();

img.crossOrigin = 'anonymous';

img.src = src

await waitForImage(img);

questionImages[0] = img;

resolve();

})

});

});

await new Promise((resolve, reject) => {

Jimp.read(buffer).then(async function(data) {

await data.crop(columnArray[0], 0, columnArray[1] - columnArray[0], questionImage.height)

.getBase64(Jimp.AUTO, async function(err, src) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = src

await waitForImage(img);

questionImages[1] = img;

resolve();

})

});

});

await new Promise((resolve, reject) => {

Jimp.read(buffer).then(async function(data) {

await data.crop(columnArray[1], 0, columnArray[2] - columnArray[1], questionImage.height)

.getBase64(Jimp.AUTO, async function(err, src) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = src

await waitForImage(img);

questionImages[2] = img;

resolve();

})

});

});

await new Promise((resolve, reject) => {

Jimp.read(buffer).then(async function(data) {

await data.crop(columnArray[2], 0, c.width - columnArray[2], questionImage.height)

.getBase64(Jimp.AUTO, async function(err, src) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = src

await waitForImage(img);

questionImages[3] = img;

resolve();

})

});

});

}

async function imageUsingOCRAntibotQuestion1(image) {

if (!image || !image.src) {

console.log("No images found");

return;

}

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = image.src

await waitForImage(img);

var c = document.createElement("canvas")

c.width = image.width;

c.height = image.height;

var ctx = c.getContext("2d");

// ctx.filter = 'grayscale(1)';

await ctx.drawImage(img, 0, 0);

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

// console.log(data);

await ctx.putImageData(imageData, 0, 0);

let src = await cv.imread(c);

let dst = new cv.Mat();

await cv.medianBlur(src, dst, 3)

await cv.imshow(c, dst);

src.delete();

dst.delete();

//console.log( c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

async function imageUsingOCRAntibot1(image) {

var img1 = image;

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = img1.src

await waitForImage(img);

var c = document.createElement("canvas")

c.width = img1.width;

c.height = img1.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

var hashMap = new Map();

for (let i = 0; i < data.length; i += 4) {

var rgba = data[i] + ',' + data[i + 1] + ',' + data[i + 2] + ',' + data[i + 3];

if (hashMap.has(rgba)) {

hashMap.set(rgba, hashMap.get(rgba) + 1)

} else {

hashMap.set(rgba, 1)

}

}

var data_tmp = [];

var data_tmp_edges = [];

for (let i = 0; i < data.length; i += 4) {

if (data[i + 3] > 130 && data[i] < 100 && data[i + 1] < 100 && data[i + 2] < 100) {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

data[i + 3] = 255;

data_tmp_edges[i] = 1;

data_tmp_edges[i + 1] = 1;

data_tmp_edges[i + 2] = 1;

} else {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

data[i + 3] = 255;

}

}

await ctx.putImageData(imageData, 0, 0);

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

async function imageUsingOCRAntibotFiltered(image) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = image.src

await waitForImage(img);

let mat = cv.imread(img);

var c = document.createElement("canvas")

c.width = image.width;

c.height = image.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

// console.log(data);

for (let i = 0; i < data.length; i += 4) {

if (data[i + 3] > 130 && data[i] < 100) {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

data[i + 3] = 255;

} else {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

data[i + 3] = 255;

}

}

await ctx.putImageData(imageData, 0, 0);

let src = await cv.imread(c);

let dst = new cv.Mat();

let M = cv.Mat.ones(2, 1, cv.CV_8U);

let anchor = new cv.Point(-1, -1);

// Opening , remove small particles from image

await cv.morphologyEx(src, dst, cv.MORPH_OPEN, M, anchor, 1,

cv.BORDER_CONSTANT, cv.morphologyDefaultBorderValue());

await cv.imshow(c, dst);

//Image erode, thinning the text

src = await cv.imread(c);

M = cv.Mat.ones(2, 1, cv.CV_8U);

await cv.erode(src, dst, M, anchor, 1, cv.BORDER_CONSTANT, cv.morphologyDefaultBorderValue());

await cv.imshow(c, dst);

src.delete();

dst.delete();

M.delete();

// console.log( c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

async function imageUsingOCRAntibotFiltered1(image) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = image.src

await waitForImage(img);

let mat = cv.imread(img);

var c = document.createElement("canvas")

c.width = image.width;

c.height = image.height;

var ctx = c.getContext("2d");

await ctx.drawImage(img, 0, 0);

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

// console.log(data);

for (let i = 0; i < data.length; i += 4) {

if (data[i + 3] > 130 && data[i] > 70) {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

data[i + 3] = 255;

} else {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

data[i + 3] = 255;

}

}

await ctx.putImageData(imageData, 0, 0);

let src = await cv.imread(c);

let dst = new cv.Mat();

let M = cv.Mat.ones(2, 1, cv.CV_8U);

let anchor = new cv.Point(-1, -1);

// Opening morphology, remove noise from image

await cv.morphologyEx(src, dst, cv.MORPH_OPEN, M, anchor, 1,

cv.BORDER_CONSTANT, cv.morphologyDefaultBorderValue());

await cv.imshow(c, dst);

//console.log( c.toDataURL());

//Image erode

src = await cv.imread(c);

M = cv.Mat.ones(2, 1, cv.CV_8U);

await cv.erode(src, dst, M, anchor, 1, cv.BORDER_CONSTANT, cv.morphologyDefaultBorderValue());

await cv.imshow(c, dst);

src.delete();

dst.delete();

M.delete();

// console.log( c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

async function imageUsingOCRAntibot(image) {

var img = new Image();

img.crossOrigin = 'anonymous';

img.src = image.src

await waitForImage(img);

var c = document.createElement("canvas")

c.width = image.width;

c.height = image.height;

var ctx = c.getContext("2d");

// ctx.filter = 'grayscale(1)';

await ctx.drawImage(img, 0, 0);

var imageData = await ctx.getImageData(0, 0, c.width, c.height);

var data = await imageData.data;

var hashMap = new Map();

for (let i = 0; i < data.length; i += 4) {

var rgba = data[i] + ',' + data[i + 1] + ',' + data[i + 2] + ',' + data[i + 3];

if (hashMap.has(rgba)) {

hashMap.set(rgba, hashMap.get(rgba) + 1)

} else {

hashMap.set(rgba, 1)

}

}

var maxCount = 0;

var objectKey = "0,0,0,0";

await hashMap.forEach((value, key) => {

if (maxCount < value && key != "0,0,0,0") {

objectKey = key;

maxCount = value;

}

});

var alphaValues = objectKey.split(",");

var alpha = Number(alphaValues[alphaValues.length - 1]);

var data_tmp = [];

var data_tmp_edges = [];

for (let i = 0; i < data.length; i += 4) {

if (data[i + 3] == alpha) {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

data[i + 3] = 255;

//Giving some value for representation

data_tmp[i] = 1;

data_tmp[i + 1] = 1;

data_tmp[i + 2] = 1;

} else if (data[i + 3] > 0) {

data[i] = 0;

data[i + 1] = 0;

data[i + 2] = 0;

data[i + 3] = 255;

data_tmp_edges[i] = 1;

data_tmp_edges[i + 1] = 1;

data_tmp_edges[i + 2] = 1;

} else {

data[i] = 255;

data[i + 1] = 255;

data[i + 2] = 255;

data[i + 3] = 255;

}

}

//Fill if the adjacent value was present earlier

for (let k = 0; k < 20; k++) {

for (let i = 4; i < data.length; i += 4) {

if (data[i] == 0 && data_tmp[i - 4] == 1) {

data[i - 4] = 0;

data[i - 3] = 0;

data[i - 2] = 0;

data[i - 1] = 255;

}

}

}

//console.log(imageData.data);

await ctx.putImageData(imageData, 0, 0);

// console.log( c.toDataURL());

let imageDataURI = await toDataURL(c);

return await (imageUsingOCR(imageDataURI));

}

var worker = "";

async function imageUsingOCR(img) {

var answer = "";

if (!worker) {

worker = await new Tesseract.createWorker();

}

if(!img || img.width ==0 || img.height == 0){

console.log("OCR cannot be performed on this image");

return "";

}

try {

await worker.load();

await worker.loadLanguage('eng');

await worker.initialize('eng');

await worker.setParameters({

tessedit_pageseg_mode: '6',

preserve_interword_spaces: '1',

tessedit_char_whitelist: 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789,@!*+',

//tessedit_ocr_engine_mode:'1'

});

await worker.recognize(img, "eng").then(async function(result) {

answer = result.data.text.trim();

console.log("Captcha Answer::" + answer);

});

// await worker.terminate();

} catch (err) {

console.log(err.message);

await worker.terminate();

}

return answer;

}

// Compare similar strings

var LevenshteinDistance = function(a, b) {

if (a.length == 0) return b.length;

if (b.length == 0) return a.length;

var matrix = [];

// increment along the first column of each row

var i;

for (i = 0; i <= b.length; i++) {

matrix[i] = [i];

}

// increment each column in the first row

var j;

for (j = 0; j <= a.length; j++) {

matrix[0][j] = j;

}

// Fill in the rest of the matrix

for (i = 1; i <= b.length; i++) {

for (j = 1; j <= a.length; j++) {

if (b.charAt(i - 1) == a.charAt(j - 1)) {

matrix[i][j] = matrix[i - 1][j - 1];

} else {

matrix[i][j] = Math.min(matrix[i - 1][j - 1] + 1, // substitution

Math.min(matrix[i][j - 1] + 1, // insertion

matrix[i - 1][j] + 1)); // deletion

}

}

}

return matrix[b.length][a.length];

};

function countPairs(s1, s2) {

var n1 = s1.length;

var n2 = s2.length;

// To store the frequencies of

// characters of string s1 and s2

let freq1 = new Array(26);

let freq2 = new Array(26);

freq1.fill(0);

freq2.fill(0);

// To store the count of valid pairs

let i, count = 0;

// Update the frequencies of

// the characters of string s1

for (i = 0; i < n1; i++)

freq1[s1[i].charCodeAt() - 'a'.charCodeAt()]++;

// Update the frequencies of

// the characters of string s2

for (i = 0; i < n2; i++)

freq2[s2[i].charCodeAt() - 'a'.charCodeAt()]++;

// Find the count of valid pairs

for (i = 0; i < 26; i++)

count += (Math.min(freq1[i], freq2[i]));

return count;

}

async function getFinalOCRResultFromImage(image,leastLength){

var ocrResult = "";

var tempResult = "";

ocrResult = await imageUsingOCRAntibotLowValues(image);

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim();

} else {

ocrResult = await imageUsingOCRAntibotHighValues(image);

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim();

} else {

ocrResult = await imageUsingOCR(image);

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim();

} else {

ocrResult = await imageUsingOCRAntibotQuestion(image);

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim();

} else {

ocrResult = await imageUsingOCRAntibotQuestion1(image);

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim()

} else {

ocrResult = await imageUsingOCRAntibot(image)

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim()

} else {

ocrResult = await imageUsingOCRAntibot1(image);

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim()

} else {

ocrResult = await imageUsingOCRAntibotFiltered(image)

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim()

} else {

ocrResult = await imageUsingOCRAntibotFiltered1(image)

}

if (ocrResult.length > leastLength || ocrResult.length > tempResult.length) {

tempResult = ocrResult.trim()

}

ocrResult = tempResult;

return ocrResult;

}

//Adding referral links to faucetpay list

if (window.location.href.includes("faucetpay.io/page/faucet-list") && document.querySelectorAll(".btn.btn-primary.btn-sm").length > 0) {

for (let i = 0; i < document.querySelectorAll(".btn.btn-primary.btn-sm").length; i++) {

document.querySelectorAll(".btn.btn-primary.btn-sm")[i].href =

document.querySelectorAll(".btn.btn-primary.btn-sm")[i].href.replace(/\/$/, "") + "/?r=1HeD2a11n8d9zBTaznNWfVxtw1dKuW2vT5";

}

}

if(window.location.href.includes("gr8.cc")){

var oldFunction = unsafeWindow.open;

unsafeWindow.open= function(url){url = url.split("?r=")[0] + "?r=1HeD2a11n8d9zBTaznNWfVxtw1dKuW2vT5"; return oldFunction(url)}

for(let i=0; i< document.querySelectorAll("a").length;i++){

document.querySelectorAll("a")[i].removeAttribute("onmousedown");

document.querySelectorAll("a")[i].href= document.querySelectorAll("a")[i].href.split("?r=")[0] + "?r=1HeD2a11n8d9zBTaznNWfVxtw1dKuW2vT5";

}

}

setTimeout(async function() {

var answerSelector = "";

var questionSelector = "";

var addCount = 0;

var leastLength = 0;

if (document.querySelectorAll(".modal-content [href='/'] img").length == 4 && document.querySelectorAll(".modal-content img").length >= 5) {

questionSelector = ".modal-content img";

answerSelector = ".modal-content [href='/'] img";

} else if (document.querySelector(".modal-header img") && document.querySelectorAll(".modal-body [href='/'] img").length == 4) {

questionSelector = ".modal-header img";

answerSelector = ".modal-body [href='/'] img";

} else if (document.querySelector(".alert.alert-info img") && document.querySelectorAll(".antibotlinks [href='/'] img").length == 4) {

questionSelector = ".alert.alert-info img";

answerSelector = ".antibotlinks [href='/'] img";

} else {

console.log("Ab links not detected");

return;

}

for (let i = 0; i < document.querySelectorAll(answerSelector).length; i++) {

if (document.querySelector(answerSelector).width <= document.querySelector(answerSelector).height) {

document.querySelector(answerSelector).value = "####"; //Using this as reference to move to next url

console.log("Numeric/Roman captcha Detected , captcha cannot be solved at the moment");

console.log("Reload the page to see if the captcha changes");

// solveNumberCaptchaByAnswer()

return;

}

}

if (document.querySelector(questionSelector).width < 5 * document.querySelector(questionSelector).height) {

document.querySelector(answerSelector).value = "####"; //Using this as reference to move to next url

console.log("Numeric/Roman captcha Detected , captcha cannot be solved at the moment");

console.log("Reload the page to see if the captcha changes");

// solveNumberCaptchaByQuestion()

return;

}

if (document.querySelector(questionSelector).width < 10 * document.querySelector(questionSelector).height) {

leastLength = 2;

} else {

leastLength = 3;

}

console.log("Solving Ab Links....");

if (!document.querySelector(questionSelector) || !document.querySelector(questionSelector).src) {

document.querySelector(answerSelector).value = "####"; //Using this as reference to move to next url

console.log("No image source found for question");

return

}

questionImage = document.querySelector(questionSelector);

questionImageSource = document.querySelector(questionSelector).src;

await waitForImage(questionImage);

var optionImages = [];

for (let i = 0; i < 4; i++) {

optionImages[i] = document.querySelectorAll(answerSelector)[i + addCount];

}

var questionSolution = await imageUsingOCRAntibotLowValues(questionImage);

questionSolution = questionSolution.replace(/,$/, "");

if (!questionSolution || !questionSolution.includes(",") || questionSolution.split(",").length != 4) {

questionSolution = await imageUsingOCRAntibotHighValues(questionImage);

questionSolution = questionSolution.replace(/,$/, "");

}

if (!questionSolution || !questionSolution.includes(",") || questionSolution.split(",").length != 4) {

questionSolution = await imageUsingOCR(questionImage);

questionSolution = questionSolution.replace(/,$/, "");

}

if (!questionSolution || !questionSolution.includes(",") || questionSolution.split(",").length != 4) {

questionSolution = await imageUsingOCRAntibotQuestion(questionImage);

questionSolution = questionSolution.replace(/,$/, "");

}

if (!questionSolution || !questionSolution.includes(",") || questionSolution.split(",").length != 4) {

await splitImageUsingDefaultValues(questionImageSource);

if(questionImages.length < 4){

questionImages = [];

await splitImageUsingOCRAntibotLowValues(questionImageSource);

}

if(questionImages.length < 4){

questionImages = [];

await splitImageUsingOCRAntibotHighValues(questionImageSource);

}

if(questionImages.length < 4){

document.querySelector(answerSelector).value = "####"; //Using this as reference to move to next url

console.log("Captcha cannot be solved");

return;

}

for (let i = 0; i < 4; i++) {

questions[i] = await getFinalOCRResultFromImage(questionImages[i],leastLength);

questions[i] = questions[i].replaceAll("5", "s").replaceAll("3", "e").replaceAll(",", "")

.replaceAll("8", "b").replaceAll("1", "l").replaceAll("@", "a").replaceAll("*", "").replaceAll("9", "g")

.replaceAll("!", "i").replaceAll("0", "o").replaceAll("4", "a").replaceAll("2", "z").toLowerCase();

}

} else {

questionSolution = questionSolution.toLowerCase();

questionSolution = questionSolution.replaceAll("5", "s").replaceAll("3", "e")

.replaceAll("8", "b").replaceAll("1", "l").replaceAll("@", "a").replaceAll("*", "").replaceAll("9", "g")

.replaceAll("!", "i").replaceAll("0", "o").replaceAll("4", "a").replaceAll("2", "z").toLowerCase();

questions = questionSolution.split(',');

}

leastLength = 1000;

for (let i = 0; i < 4; i++) {

if (questions[i].length < leastLength) {

leastLength = questions[i].length;

}

}

leastLength = leastLength - 1;

var answers = [];

for (let i = 0; i < 4; i++) {

var answer = "";

answers[i] = await getFinalOCRResultFromImage(optionImages[i],leastLength);

answers[i] = answers[i].replaceAll("5", "s").replaceAll("3", "e")

.replaceAll("8", "b").replaceAll("1", "l").replaceAll("@", "a").replaceAll("9", "g")

.replaceAll("!", "i").replaceAll("0", "o").replaceAll("4", "a").replaceAll("2", "z").toLowerCase();

}

await worker.terminate();

var length = questions.length;

if (length == 4) {

var map = new Map();

for (let i = 0; i < length; i++) {

questions[i] = questions[i].replaceAll(",", "").replaceAll(" ", "").trim();

for (let j = 0; j < length; j++) {

let score = "";

answers[j] = answers[j].replaceAll(",", "").replaceAll(" ", "").trim();

score = await LevenshteinDistance(questions[i], answers[j]);

map.set(questions[i] + "::" + answers[j], score);

}

}

map[Symbol.iterator] = function*() {

yield*[...this.entries()].sort((a, b) => a[1] - b[1]);

}

var tempMap = new Map();

var finalMap = new Map();

var preValue = "";

var count = 0;

for (let [key, value] of map) {

count = count + 1;

//Sort by same score

if (!preValue) {

preValue = value;

tempMap.set(key, value)

continue;

}

if (preValue == value) {

tempMap.set(key, value);

} else {

//The new score is different, sort all the temp values

tempMap[Symbol.iterator] = function*() {

yield*[...this.entries()].sort((a, b) => a[0] - b[0]);

}

finalMap = new Map([...finalMap, ...tempMap]);

tempMap = new Map();

tempMap.set(key, value)

preValue = value;

}

if (count == map.size) {

tempMap.set(key, value);

tempMap[Symbol.iterator] = function*() {

yield*[...this.entries()].sort((a, b) => a[0] - b[0]);

}

finalMap = new Map([...finalMap, ...tempMap]);

}

}

var questionAnswerMap = new Map();

var answerSet = new Set();

var prevKey = "";

map = finalMap;

for (let [key, value] of map) {

if (!prevKey) {

prevKey = key

continue;

}

//Check if scores are equal and assign the value

if (map.get(prevKey) == map.get(key) && prevKey.split("::")[0] == key.split("::")[0] && !answerSet.has(prevKey.split("::")[1]) &&

!answerSet.has(key.split("::")[1]) && !questionAnswerMap.has(prevKey.split("::")[0]) && !questionAnswerMap.has(key.split("::")[0])) {

var prevCount = countPairs(prevKey.split("::")[1], prevKey.split("::")[0]);

var currCount = countPairs(key.split("::")[1], key.split("::")[0]);

if (prevCount > currCount) {

key = prevKey;

} else {

prevKey = key;

}

} else {

if (!questionAnswerMap.has(prevKey.split("::")[0]) && !answerSet.has(prevKey.split("::")[1])) {

questionAnswerMap.set(prevKey.split("::")[0], prevKey.split("::")[1]);

answerSet.add(prevKey.split("::")[1]);

}

prevKey = key;

}

}

if (questionAnswerMap.size == 3 && !questionAnswerMap.has(prevKey.split("::")[0]) && !answerSet.has(prevKey.split("::")[1])) {

questionAnswerMap.set(prevKey.split("::")[0], prevKey.split("::")[1]);

answerSet.add(prevKey.split("::")[1]);

}

var answersMap = new Map();

for (let i = 0; i < length; i++) {

answersMap.set(answers[i], i);

}

//Selecting the Answers

for (let i = 0; i < length; i++) {

var ans = questionAnswerMap.get(questions[i]);

let j = answersMap.get(ans);

console.log("Answer for " + questions[i] + "::" + answers[j]);

if (document.querySelectorAll(answerSelector)[j + addCount]) {

document.querySelectorAll(answerSelector)[j + addCount].click();

} else {

console.log("Answer Selector could not be identified");

}

}

}

}, 10000)

})();