BLS CAPTCHA: Befehlscodes verstehen und lösen

BLS CAPTCHAs präsentieren ein 3×3-Raster von Bildern mit einem numerischen Anweisungscode. Der Benutzer muss Zellen auswählen, die der Anweisung entsprechen. CaptchaAI löst BLS CAPTCHAs mit 100 %iger Genauigkeit – in dieser Anleitung erfahren Sie, wie Sie das Raster extrahieren, den Anweisungscode lesen und alles an die API übermitteln.

So funktionieren BLS CAPTCHAs

Ein BLS CAPTCHA zeigt Folgendes an:

1. Ein 3×3-Raster mit 9 Bildzellen
2. Ein numerischer Anweisungscode (z. B. 664, 123, 546), der angibt, welche Zellen ausgewählt werden sollen
3. Die Zellen werden von links nach rechts und von oben nach unten nummeriert:

1  2  3
4  5  6
7  8  9

Der Anweisungscode teilt dem Löser mit, nach welchem Muster er suchen soll. Die Antwort ist ein Array übereinstimmender Zellindizes (1–9).

Schritt 1: Extrahieren Sie Rasterbilder und Anweisungscode

Python (Selenium)

import base64
from selenium import webdriver
from selenium.webdriver.common.by import By

driver = webdriver.Chrome()
driver.get("https://example.com/bls-protected-page")

# Find the grid container
grid_cells = driver.find_elements(By.CSS_SELECTOR, ".captcha-grid img")

images = []
for cell in grid_cells:
    src = cell.get_attribute("src")
    if src.startswith("data:image"):
        images.append(src)
    else:
        # Download and convert to base64
        import requests
        img_data = requests.get(src).content
        b64 = base64.b64encode(img_data).decode()
        images.append(f"data:image/png;base64,{b64}")

# Extract the instruction code
instruction_el = driver.find_element(By.CSS_SELECTOR, ".captcha-instruction")
instruction_code = instruction_el.text.strip()
# e.g., "664" or parsed from "Select all boxes with number 664"

import re
code_match = re.search(r'(\d{3,})', instruction_code)
instruction = code_match.group(1) if code_match else instruction_code

print(f"Instruction: {instruction}")
print(f"Images extracted: {len(images)}")

JavaScript (Puppeteer)

const puppeteer = require('puppeteer');

const browser = await puppeteer.launch();
const page = await browser.newPage();
await page.goto('https://example.com/bls-protected-page');

// Extract grid images as base64
const images = await page.evaluate(() => {
  const cells = document.querySelectorAll('.captcha-grid img');
  return Array.from(cells).map(img => {
    const canvas = document.createElement('canvas');
    canvas.width = img.naturalWidth;
    canvas.height = img.naturalHeight;
    canvas.getContext('2d').drawImage(img, 0, 0);
    return canvas.toDataURL('image/png');
  });
});

// Extract instruction code
const instruction = await page.evaluate(() => {
  const el = document.querySelector('.captcha-instruction');
  const match = el.textContent.match(/(\d{3,})/);
  return match ? match[1] : el.textContent.trim();
});

console.log(`Instruction: ${instruction}, Images: ${images.length}`);

Schritt 2: An CaptchaAI senden

Der BLS-Solver erfordert method=bls, den instructions-Code und alle 9 Bilder als image_base64_1 bis image_base64_9.

Python

import requests
import time
import json

API_KEY = "YOUR_API_KEY"

# Prepare submission data
data = {
    "key": API_KEY,
    "method": "bls",
    "instructions": instruction,
    "json": "1",
}

# Add all 9 images
files = {}
for i, img in enumerate(images):
    files[f"image_base64_{i+1}"] = (None, img)

# Submit
resp = requests.post(
    "https://ocr.captchaai.com/in.php",
    data=data,
    files=files
).json()

if resp["status"] != 1:
    raise Exception(f"Submit error: {resp['request']}")

task_id = resp["request"]
print(f"Task ID: {task_id}")

# Poll for result
for _ in range(20):
    time.sleep(5)
    result = requests.get("https://ocr.captchaai.com/res.php", params={
        "key": API_KEY, "action": "get", "id": task_id, "json": "1"
    }).json()

    if result["status"] == 1:
        solution = json.loads(result["request"])
        print(f"Selected cells: {solution}")  # e.g., [1, 4, 7, 8]
        break
    if result["request"] != "CAPCHA_NOT_READY":
        raise Exception(f"Error: {result['request']}")

JavaScript

const axios = require('axios');
const FormData = require('form-data');

const form = new FormData();
form.append('key', 'YOUR_API_KEY');
form.append('method', 'bls');
form.append('instructions', instruction);
form.append('json', '1');

images.forEach((img, i) => {
  form.append(`image_base64_${i + 1}`, img);
});

const submit = await axios.post('https://ocr.captchaai.com/in.php', form, {
  headers: form.getHeaders(),
});
const taskId = submit.data.request;

// Poll
let solution = null;
for (let i = 0; i < 20; i++) {
  await new Promise(r => setTimeout(r, 5000));
  const poll = await axios.get('https://ocr.captchaai.com/res.php', {
    params: { key: 'YOUR_API_KEY', action: 'get', id: taskId, json: 1 }
  });
  if (poll.data.status === 1) {
    solution = JSON.parse(poll.data.request);
    break;
  }
}
console.log('Selected cells:', solution); // e.g., [2, 4, 7]

Schritt 3: Klicken Sie auf die gelösten Zellen

# Selenium — click the cells returned by CaptchaAI
grid_cells = driver.find_elements(By.CSS_SELECTOR, ".captcha-grid .cell")

for cell_index in solution:
    # cell_index is 1-based
    grid_cells[cell_index - 1].click()

# Submit the form
submit_btn = driver.find_element(By.CSS_SELECTOR, ".captcha-submit")
submit_btn.click()

// Puppeteer
const cells = await page.$$('.captcha-grid .cell');
for (const idx of solution) {
  await cells[idx - 1].click();
}
await page.click('.captcha-submit');

Vollständiger Arbeitsablauf

def solve_bls_captcha(driver, api_key):
    """Extract, solve, and submit a BLS CAPTCHA."""
    import base64, requests, time, json, re

    # 1. Extract images
    grid_cells = driver.find_elements(By.CSS_SELECTOR, ".captcha-grid img")
    images = []
    for cell in grid_cells:
        src = cell.get_attribute("src")
        if src.startswith("data:image"):
            images.append(src)
        else:
            img_data = requests.get(src).content
            b64 = base64.b64encode(img_data).decode()
            images.append(f"data:image/png;base64,{b64}")

    # 2. Extract instruction
    el = driver.find_element(By.CSS_SELECTOR, ".captcha-instruction")
    match = re.search(r'(\d{3,})', el.text)
    instruction = match.group(1)

    # 3. Submit to CaptchaAI
    data = {"key": api_key, "method": "bls", "instructions": instruction, "json": "1"}
    files = {f"image_base64_{i+1}": (None, img) for i, img in enumerate(images)}
    resp = requests.post("https://ocr.captchaai.com/in.php", data=data, files=files).json()
    task_id = resp["request"]

    # 4. Poll
    for _ in range(20):
        time.sleep(5)
        result = requests.get("https://ocr.captchaai.com/res.php", params={
            "key": api_key, "action": "get", "id": task_id, "json": "1"
        }).json()
        if result["status"] == 1:
            solution = json.loads(result["request"])
            break

    # 5. Click cells
    clickable = driver.find_elements(By.CSS_SELECTOR, ".captcha-grid .cell")
    for idx in solution:
        clickable[idx - 1].click()

    return solution

Fehlerbehebung

FAQ

Was bedeutet der BLS-Anweisungscode?

Der Anweisungscode (z. B. „664“) teilt dem CAPTCHA mit, welche Zellen passenden Inhalt enthalten. CaptchaAI analysiert es – Sie müssen den Code nicht selbst interpretieren.

Wie genau ist CaptchaAI für BLS?

CaptchaAI meldet 100 % Genauigkeit für BLS CAPTCHAs.

Lösen Sie BLS CAPTCHAs mit CaptchaAI – 100 % Genauigkeit

Holen Sie sich Ihren API-Schlüssel unter captchaai.com.

Verwandte Leitfäden

• Rasterbildkoordinatenzuordnung
• BLS CAPTCHA Parameter Deep Dive
• CaptchaAI API-Antwortformate