Batch-Bild-CAPTCHA-Lösung: Verarbeitung von mehr als 1000 Bildern

Wenn Sie Hunderte oder Tausende von Bild-CAPTCHAs lösen müssen, ist die sequentielle Verarbeitung zu langsam. In dieser Anleitung wird gezeigt, wie Sie mithilfe von CaptchaAI eine Stapelverarbeitungspipeline erstellen, die Ergebnisse für mehr als 1.000 Bilder gleichzeitig übermittelt, abfragt und sammelt.

Architektur

[Image Queue] → [Submit Workers] → [Poll Workers] → [Results Store]
     ↓                ↓                  ↓                ↓
  1000 images    20 concurrent      Adaptive poll     CSV/JSON output
                   submits           intervals

Python: Asynchroner Batch-Prozessor

import asyncio
import aiohttp
import base64
import json
import time
import csv
from pathlib import Path

API_KEY = "YOUR_API_KEY"
SUBMIT_URL = "https://ocr.captchaai.com/in.php"
RESULT_URL = "https://ocr.captchaai.com/res.php"
MAX_CONCURRENT_SUBMITS = 20
MAX_CONCURRENT_POLLS = 30
POLL_INTERVAL = 5


async def submit_image(session, sem, image_path):
    """Submit a single image CAPTCHA."""
    async with sem:
        with open(image_path, "rb") as f:
            img_b64 = base64.b64encode(f.read()).decode()

        data = {
            "key": API_KEY,
            "method": "base64",
            "body": img_b64,
            "json": "1",
        }

        async with session.post(SUBMIT_URL, data=data) as resp:
            result = await resp.json()

        if result["status"] != 1:
            return {"file": str(image_path), "error": result["request"]}

        return {
            "file": str(image_path),
            "task_id": result["request"],
            "submitted_at": time.time(),
        }


async def poll_result(session, sem, task):
    """Poll for a single task result."""
    async with sem:
        for attempt in range(24):
            await asyncio.sleep(POLL_INTERVAL)

            params = {
                "key": API_KEY,
                "action": "get",
                "id": task["task_id"],
                "json": "1",
            }

            async with session.get(RESULT_URL, params=params) as resp:
                result = await resp.json()

            if result["status"] == 1:
                return {
                    "file": task["file"],
                    "task_id": task["task_id"],
                    "answer": result["request"],
                    "solve_time": time.time() - task["submitted_at"],
                }
            if result["request"] != "CAPCHA_NOT_READY":
                return {
                    "file": task["file"],
                    "task_id": task["task_id"],
                    "error": result["request"],
                }

        return {
            "file": task["file"],
            "task_id": task["task_id"],
            "error": "TIMEOUT",
        }


async def process_batch(image_dir, output_file="results.csv"):
    """Process all images in a directory."""
    image_paths = sorted(Path(image_dir).glob("*.png")) + \
                  sorted(Path(image_dir).glob("*.jpg"))

    print(f"Found {len(image_paths)} images")

    submit_sem = asyncio.Semaphore(MAX_CONCURRENT_SUBMITS)
    poll_sem = asyncio.Semaphore(MAX_CONCURRENT_POLLS)

    async with aiohttp.ClientSession() as session:
        # Phase 1: Submit all images
        print("Submitting...")
        submit_tasks = [
            submit_image(session, submit_sem, path)
            for path in image_paths
        ]
        submissions = await asyncio.gather(*submit_tasks)

        # Separate successes and errors
        pending = [s for s in submissions if "task_id" in s]
        errors = [s for s in submissions if "error" in s]
        print(f"Submitted: {len(pending)}, Errors: {len(errors)}")

        # Phase 2: Poll all pending tasks
        print("Polling for results...")
        poll_tasks = [
            poll_result(session, poll_sem, task)
            for task in pending
        ]
        results = await asyncio.gather(*poll_tasks)

    # Combine results
    all_results = results + errors

    # Write to CSV
    with open(output_file, "w", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=[
            "file", "task_id", "answer", "solve_time", "error"
        ])
        writer.writeheader()
        for r in all_results:
            writer.writerow({
                "file": r.get("file", ""),
                "task_id": r.get("task_id", ""),
                "answer": r.get("answer", ""),
                "solve_time": round(r.get("solve_time", 0), 2),
                "error": r.get("error", ""),
            })

    solved = sum(1 for r in results if "answer" in r)
    failed = sum(1 for r in results if "error" in r)
    print(f"Done: {solved} solved, {failed} failed, {len(errors)} submit errors")
    print(f"Results saved to {output_file}")


# Run
asyncio.run(process_batch("./captcha_images"))

Erwartete Ausgabe:

Found 1000 images
Submitting...
Submitted: 997, Errors: 3
Polling for results...
Done: 985 solved, 12 failed, 3 submit errors
Results saved to results.csv

Node.js: Worker-Pool-Batchprozessor

const axios = require('axios');
const fs = require('fs');
const path = require('path');
const { createObjectCsvWriter } = require('csv-writer');

const API_KEY = 'YOUR_API_KEY';
const SUBMIT_URL = 'https://ocr.captchaai.com/in.php';
const RESULT_URL = 'https://ocr.captchaai.com/res.php';
const MAX_CONCURRENT = 20;
const POLL_INTERVAL_MS = 5000;

class BatchProcessor {
  constructor(concurrency = MAX_CONCURRENT) {
    this.concurrency = concurrency;
    this.results = [];
    this.processed = 0;
    this.total = 0;
  }

  async submitImage(imagePath) {
    const imgBase64 = fs.readFileSync(imagePath, { encoding: 'base64' });
    const resp = await axios.post(SUBMIT_URL, null, {
      params: {
        key: API_KEY,
        method: 'base64',
        body: imgBase64,
        json: 1,
      },
    });

    if (resp.data.status !== 1) {
      throw new Error(resp.data.request);
    }
    return resp.data.request;
  }

  async pollResult(taskId) {
    for (let i = 0; i < 24; i++) {
      await new Promise(r => setTimeout(r, POLL_INTERVAL_MS));
      const resp = await axios.get(RESULT_URL, {
        params: { key: API_KEY, action: 'get', id: taskId, json: 1 },
      });

      if (resp.data.status === 1) return resp.data.request;
      if (resp.data.request !== 'CAPCHA_NOT_READY') {
        throw new Error(resp.data.request);
      }
    }
    throw new Error('TIMEOUT');
  }

  async processOne(imagePath) {
    const startTime = Date.now();
    try {
      const taskId = await this.submitImage(imagePath);
      const answer = await this.pollResult(taskId);
      this.processed++;
      const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
      console.log(`[${this.processed}/${this.total}] ${path.basename(imagePath)}: ${answer} (${elapsed}s)`);
      return { file: imagePath, answer, solveTime: elapsed, error: '' };
    } catch (err) {
      this.processed++;
      return { file: imagePath, answer: '', solveTime: 0, error: err.message };
    }
  }

  async run(imageDir, outputFile = 'results.csv') {
    const files = fs.readdirSync(imageDir)
      .filter(f => /\.(png|jpg|jpeg|gif)$/i.test(f))
      .map(f => path.join(imageDir, f));

    this.total = files.length;
    console.log(`Processing ${this.total} images with ${this.concurrency} workers`);

    // Process in chunks
    for (let i = 0; i < files.length; i += this.concurrency) {
      const chunk = files.slice(i, i + this.concurrency);
      const chunkResults = await Promise.all(
        chunk.map(f => this.processOne(f))
      );
      this.results.push(...chunkResults);
    }

    // Write CSV
    const csvWriter = createObjectCsvWriter({
      path: outputFile,
      header: [
        { id: 'file', title: 'File' },
        { id: 'answer', title: 'Answer' },
        { id: 'solveTime', title: 'Solve Time (s)' },
        { id: 'error', title: 'Error' },
      ],
    });
    await csvWriter.writeRecords(this.results);

    const solved = this.results.filter(r => r.answer).length;
    console.log(`Done: ${solved}/${this.total} solved. Results: ${outputFile}`);
  }
}

const processor = new BatchProcessor(20);
processor.run('./captcha_images');

Mengenabhängige Batchverarbeitung

Vermeiden Sie 429-Fehler, indem Sie Ihre Einsendungsrate verfolgen:

class RateLimiter:
    def __init__(self, max_per_second=10):
        self.max_per_second = max_per_second
        self.timestamps = []

    async def acquire(self):
        now = time.time()
        self.timestamps = [t for t in self.timestamps if now - t < 1.0]

        if len(self.timestamps) >= self.max_per_second:
            wait = 1.0 - (now - self.timestamps[0])
            if wait > 0:
                await asyncio.sleep(wait)

        self.timestamps.append(time.time())

# Use in submit loop
rate_limiter = RateLimiter(max_per_second=10)

async def submit_with_rate_limit(session, image_path):
    await rate_limiter.acquire()
    # ... submit as before

Fortschrittsverfolgung

import sys

class ProgressTracker:
    def __init__(self, total):
        self.total = total
        self.completed = 0
        self.solved = 0
        self.failed = 0
        self.start_time = time.time()

    def update(self, success=True):
        self.completed += 1
        if success:
            self.solved += 1
        else:
            self.failed += 1

        elapsed = time.time() - self.start_time
        rate = self.completed / elapsed if elapsed > 0 else 0
        eta = (self.total - self.completed) / rate if rate > 0 else 0

        sys.stdout.write(
            f"\r[{self.completed}/{self.total}] "
            f"Solved: {self.solved} | Failed: {self.failed} | "
            f"Rate: {rate:.1f}/s | ETA: {eta:.0f}s"
        )
        sys.stdout.flush()

Fehlerbehebung

FAQ

Wie viele Bilder kann ich gleichzeitig einreichen?

20–30 gleichzeitige Einreichungen funktionieren gut. Darüber hinaus besteht die Gefahr, dass Sie auf Tariflimits stoßen. Verwenden Sie ein Semaphor, um die Parallelität zu begrenzen.

Wie viel kosten 1000 Bilder?

Überprüfen Sie Ihren aktuellen Tarif unter captchaai.com. Image/OCR CAPTCHAs gehören zu den günstigsten Lösungstypen.

Verarbeiten Sie Tausende von CAPTCHAs mit CaptchaAI

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

Verwandte Leitfäden

• Asynchrones CAPTCHA-Lösen mit Python asyncio
• Parallele vs. sequenzielle CAPTCHA-Performance
• Ratenbegrenzungen und Drosselung