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3a04f21 d272ff0 3a04f21 d272ff0 3a04f21 d272ff0 3a04f21 d272ff0 3a04f21 d272ff0 3a04f21 d272ff0 3a04f21 d272ff0 3a04f21 d272ff0 3a04f21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 | """Image classification solvers for various captcha types.
Supports HCaptchaClassification, ReCaptchaV2Classification,
FunCaptchaClassification, and AwsClassification task types.
All classification tasks send images + question text to an OpenAI-compatible
vision model for analysis and return structured coordinate/index results.
"""
from __future__ import annotations
import base64
import io
import json
import logging
import re
from typing import Any
from openai import AsyncOpenAI
from PIL import Image
from ..core.config import Config
log = logging.getLogger(__name__)
HCAPTCHA_SYSTEM_PROMPT = """\
You are an image classification assistant for HCaptcha challenges.
You may receive:
1. optional sample/example images that show the target object, and
2. one or more candidate captcha images that must be classified.
Determine which candidate images match the question or the sample images.
Return STRICT JSON only. No markdown, no extra text.
For single-image questions (is this image X?):
{"answer": true} or {"answer": false}
For multi-image selection questions:
{"answer": [0, 2, 5]}
where numbers are 0-indexed positions of matching candidate images.
Rules:
- Return ONLY the JSON object, nothing else.
- Use example images only as references; do not include them in the returned indices.
- Be precise with your classification.
"""
RECAPTCHA_V2_SYSTEM_PROMPT = """\
You are an image classification assistant for reCAPTCHA v2 challenges.
Given a question and a grid image (3x3 or 4x4), identify which cells match the question.
The image cells are numbered 0-8 (3x3) or 0-15 (4x4), left-to-right, top-to-bottom.
Return STRICT JSON only:
{"objects": [0, 3, 6]}
where numbers are 0-indexed positions of matching cells.
Rules:
- Return ONLY the JSON object, nothing else.
- If no cells match, return {"objects": []}.
"""
FUNCAPTCHA_SYSTEM_PROMPT = """\
You are an image classification assistant for FunCaptcha challenges.
Given a question and a grid image (typically 2x3 = 6 cells), identify which cell
is the correct answer.
Cells are numbered 0-5, left-to-right, top-to-bottom.
Return STRICT JSON only:
{"objects": [3]}
where the number is the 0-indexed position of the correct cell.
Rules:
- Return ONLY the JSON object, nothing else.
- Usually only one cell is correct.
"""
AWS_SYSTEM_PROMPT = """\
You are an image classification assistant for AWS CAPTCHA challenges.
Given a question and one or more images, identify the correct answer.
Return STRICT JSON only:
{"objects": [1]}
where the number is the 0-indexed position of the matching image.
Rules:
- Return ONLY the JSON object, nothing else.
"""
class ClassificationSolver:
"""Solves image classification captchas using a vision model."""
def __init__(self, config: Config) -> None:
self._config = config
self._client = AsyncOpenAI(
base_url=config.local_base_url,
api_key=config.local_api_key,
)
async def solve(self, params: dict[str, Any]) -> dict[str, Any]:
task_type = params.get("type", "")
system_prompt = self._get_system_prompt(task_type)
question = params.get("question", "") or params.get("queries", "")
# Handle different image field names across task types
images = self._extract_images(params)
if not images:
raise ValueError("No image data provided")
examples = self._extract_examples(params)
log.info(
"Classification request: task_type=%s model=%s images=%d examples=%d question=%r",
task_type or "unknown",
self._config.captcha_multimodal_model,
len(images),
len(examples),
question[:120] if isinstance(question, str) else question,
)
result = await self._classify(system_prompt, question, images, examples=examples)
log.info("Classification parsed result: %s", result)
return result
@staticmethod
def _get_system_prompt(task_type: str) -> str:
prompts = {
"HCaptchaClassification": HCAPTCHA_SYSTEM_PROMPT,
"ReCaptchaV2Classification": RECAPTCHA_V2_SYSTEM_PROMPT,
"FunCaptchaClassification": FUNCAPTCHA_SYSTEM_PROMPT,
"AwsClassification": AWS_SYSTEM_PROMPT,
}
return prompts.get(task_type, RECAPTCHA_V2_SYSTEM_PROMPT)
@staticmethod
def _extract_images(params: dict[str, Any]) -> list[str]:
"""Extract base64 image(s) from various param formats."""
images: list[str] = []
if "image" in params:
images.append(params["image"])
if "images" in params:
imgs = params["images"]
if isinstance(imgs, list):
images.extend(imgs)
elif isinstance(imgs, str):
images.append(imgs)
if "body" in params and not images:
images.append(params["body"])
# HCaptcha queries format: list of base64 strings
if "queries" in params and isinstance(params["queries"], list):
images.extend(params["queries"])
return images
@staticmethod
def _extract_examples(params: dict[str, Any]) -> list[str]:
examples = params.get("examples")
if isinstance(examples, list):
return [item for item in examples if isinstance(item, str)]
if isinstance(examples, str):
return [examples]
return []
@staticmethod
def _prepare_image(b64_data: str) -> str:
"""Ensure image is properly formatted as a data URL."""
if b64_data.startswith("data:image"):
return b64_data
try:
img_bytes = base64.b64decode(b64_data)
img = Image.open(io.BytesIO(img_bytes))
fmt = img.format or "PNG"
mime = f"image/{fmt.lower()}"
return f"data:{mime};base64,{b64_data}"
except Exception:
return f"data:image/png;base64,{b64_data}"
async def _classify(
self,
system_prompt: str,
question: str,
images: list[str],
*,
examples: list[str] | None = None,
) -> dict[str, Any]:
content: list[dict[str, Any]] = []
prepared_examples = examples or []
if prepared_examples:
content.append(
{
"type": "text",
"text": (
"Sample images showing the target object. "
"Do not classify these; use them only as references."
),
}
)
for example_b64 in prepared_examples:
data_url = self._prepare_image(example_b64)
content.append(
{
"type": "image_url",
"image_url": {"url": data_url, "detail": "high"},
}
)
if len(images) > 1:
content.append(
{
"type": "text",
"text": (
"Candidate images to classify. "
"Indices are 0-based in display order."
),
}
)
for img_b64 in images:
data_url = self._prepare_image(img_b64)
content.append(
{
"type": "image_url",
"image_url": {"url": data_url, "detail": "high"},
}
)
user_text = question if question else "Classify this captcha image."
content.append({"type": "text", "text": user_text})
last_error: Exception | None = None
for attempt in range(self._config.captcha_retries):
try:
response = await self._client.chat.completions.create(
model=self._config.captcha_multimodal_model,
temperature=0.05,
max_tokens=512,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": content},
],
)
raw = response.choices[0].message.content or ""
log.info("Classification raw response: %s", raw[:300])
return self._parse_json(raw)
except Exception as exc:
last_error = exc
log.warning("Classification attempt %d failed: %s", attempt + 1, exc)
raise RuntimeError(
f"Classification failed after {self._config.captcha_retries} attempts: {last_error}"
)
@staticmethod
def _parse_json(text: str) -> dict[str, Any]:
match = re.search(r"```(?:json)?\s*(.*?)\s*```", text, re.DOTALL)
cleaned = match.group(1) if match else text.strip()
data = json.loads(cleaned)
if not isinstance(data, dict):
raise ValueError(f"Expected JSON object, got {type(data).__name__}")
return data
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