From 513757cd6e12ee919bfce32df90e83c35966068a Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sun, 31 Mar 2024 22:36:03 +0800 Subject: [PATCH 01/14] Update pyproject.toml --- pyproject.toml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5c8e85c0a6..16390a8054 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ in-project = true # https://python-poetry.org/docs/pyproject/#dependencies-and-dependency-groups [tool.poetry.dependencies] -python = "^3.8" +python = "^3.10" # Run `poetry install` in the project root opencv-python = "^4.8.0.76" @@ -49,7 +49,7 @@ regex = "*" pydantic = "^2.5.1" tenacity = "^8.2.3" # The following dependencies will not be installed -# when you just run `poetry install` or `pip install hcaptcha-challenger` +# when you just run `poetry install --all-extras` or `pip install hcaptcha-challenger` undetected-chromedriver = { version = "^3.5.3", optional = true } webdriver-manager = { version = "^4.0.1", optional = true } selenium = { version = "*", optional = true } @@ -82,7 +82,7 @@ filterwarnings = "ignore::DeprecationWarning" [tool.black] line-length = 100 -target-version = ["py38", "py39", "py310", "py311"] +target-version = ["py310", "py311", "py312"] skip-magic-trailing-comma = true [tool.poetry.extras] From aa77c3cf1eb95da4535546369b4537c6af0cadf8 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sun, 31 Mar 2024 22:54:45 +0800 Subject: [PATCH 02/14] 1 --- examples/lvm_challenge/vision_minor.ipynb | 48 +++++++++++++++++++++++ pyproject.toml | 3 ++ 2 files changed, 51 insertions(+) create mode 100644 examples/lvm_challenge/vision_minor.ipynb diff --git a/examples/lvm_challenge/vision_minor.ipynb b/examples/lvm_challenge/vision_minor.ipynb new file mode 100644 index 0000000000..86a060e7d9 --- /dev/null +++ b/examples/lvm_challenge/vision_minor.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true, + "is_executing": true + }, + "outputs": [], + "source": [ + "# open ../../../static/img/brand/wordmark.png as base64 str\n", + "import base64\n", + "from pathlib import Path\n", + "\n", + "from IPython.display import HTML\n", + "\n", + "img_path = Path(\"../../../static/img/brand/wordmark.png\")\n", + "img_base64 = base64.b64encode(img_path.read_bytes()).decode(\"utf-8\")\n", + "\n", + "# display b64 image in notebook\n", + "HTML(f'')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 16390a8054..0d2c50abd9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,6 +48,9 @@ ftfy = "*" regex = "*" pydantic = "^2.5.1" tenacity = "^8.2.3" +langchain = "^0.1.13" +langchain-anthropic = "^0.1.4" + # The following dependencies will not be installed # when you just run `poetry install --all-extras` or `pip install hcaptcha-challenger` undetected-chromedriver = { version = "^3.5.3", optional = true } From 30026bbfe4b7d31c754b83f8e11a68acae9748c5 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sun, 7 Apr 2024 22:29:39 +0800 Subject: [PATCH 03/14] Update .gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 61ee239a97..de2ae8749e 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,5 @@ assets/image_label_binary/off_road_vehicle/20** profile_pluggable_model.md ./*.png tests/record_json +docs/lvm_challenge/*.jpeg +docs/lvm_challenge/*.png \ No newline at end of file From 42006459fb48f8bd78fcbaaa2b1fb775ba3ae8e8 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sun, 14 Apr 2024 13:16:13 +0800 Subject: [PATCH 04/14] new-schema --- .gitignore | 5 +- automation/collector.py | 2 +- automation/sentinel.py | 5 +- backend/main.py | 24 +- backend/routers/__init__.py | 9 + backend/routers/challenge.py | 78 ++++ backend/routers/datalake.py | 18 + examples/demo_classifier_self_supervised.py | 26 +- examples/demo_find_unique_object.py | 2 +- examples/faker_client.py | 63 +++ examples/lvm_challenge/vision_minor.ipynb | 48 -- hcaptcha_challenger/__init__.py | 37 +- hcaptcha_challenger/agents/__init__.py | 4 +- .../agents/pipline/__init__.py | 5 - .../agents/playwright/control.py | 31 +- .../agents/playwright/dragon.py | 432 ++++++++++++++++++ .../{components/middleware.py => models.py} | 68 ++- .../{agents/pipline/control.py => pipline.py} | 16 +- .../{components => tools}/__init__.py | 0 .../{components => tools}/common.py | 4 +- .../cv_toolkit/__init__.py | 0 .../cv_toolkit/appears_only_once.py | 0 .../cv_toolkit/largest_animal.py | 0 .../{components => tools}/image_downloader.py | 0 .../image_label_area_select.py | 2 +- .../image_label_binary.py} | 10 +- .../{components => tools}/prompt_handler.py | 10 +- .../zero_shot_image_classifier.py | 2 +- pyproject.toml | 6 +- tests/__init__.py | 0 tests/test_downloader.py | 2 +- tests/test_normal_playwright.py | 2 +- tests/test_prompt_handler.py | 2 +- 33 files changed, 772 insertions(+), 141 deletions(-) create mode 100644 backend/routers/__init__.py create mode 100644 backend/routers/challenge.py create mode 100644 backend/routers/datalake.py create mode 100644 examples/faker_client.py delete mode 100644 examples/lvm_challenge/vision_minor.ipynb delete mode 100644 hcaptcha_challenger/agents/pipline/__init__.py create mode 100644 hcaptcha_challenger/agents/playwright/dragon.py rename hcaptcha_challenger/{components/middleware.py => models.py} (62%) rename hcaptcha_challenger/{agents/pipline/control.py => pipline.py} (97%) rename hcaptcha_challenger/{components => tools}/__init__.py (100%) rename hcaptcha_challenger/{components => tools}/common.py (97%) rename hcaptcha_challenger/{components => tools}/cv_toolkit/__init__.py (100%) rename hcaptcha_challenger/{components => tools}/cv_toolkit/appears_only_once.py (100%) rename hcaptcha_challenger/{components => tools}/cv_toolkit/largest_animal.py (100%) rename hcaptcha_challenger/{components => tools}/image_downloader.py (100%) rename hcaptcha_challenger/{components => tools}/image_label_area_select.py (97%) rename hcaptcha_challenger/{components/image_classifier.py => tools/image_label_binary.py} (96%) rename hcaptcha_challenger/{components => tools}/prompt_handler.py (84%) rename hcaptcha_challenger/{components => tools}/zero_shot_image_classifier.py (98%) create mode 100644 tests/__init__.py diff --git a/.gitignore b/.gitignore index de2ae8749e..abb605fa13 100644 --- a/.gitignore +++ b/.gitignore @@ -156,4 +156,7 @@ profile_pluggable_model.md ./*.png tests/record_json docs/lvm_challenge/*.jpeg -docs/lvm_challenge/*.png \ No newline at end of file +docs/lvm_challenge/*.png +**/logs/** +examples/*.md +docs/logs/ \ No newline at end of file diff --git a/automation/collector.py b/automation/collector.py index 8cdd7f1c25..1ca3d6967a 100644 --- a/automation/collector.py +++ b/automation/collector.py @@ -245,7 +245,7 @@ async def _collete_datasets(self, context: ASyncContext, sitelink: str): await page.goto(sitelink) - await agent.handle_checkbox() + await agent.click_checkbox() for pth in range(1, self.per_times + 1): with suppress(Exception): diff --git a/automation/sentinel.py b/automation/sentinel.py index cbe12f1b29..e911122d1c 100644 --- a/automation/sentinel.py +++ b/automation/sentinel.py @@ -23,7 +23,8 @@ import hcaptcha_challenger as solver from hcaptcha_challenger import label_cleaning, split_prompt_message -from hcaptcha_challenger.agents import AgentT, QuestionResp, Malenia +from hcaptcha_challenger.agents import AgentT, Malenia +from hcaptcha_challenger.models import QuestionResp from hcaptcha_challenger.onnx.yolo import is_matched_ash_of_war from hcaptcha_challenger.utils import SiteKey @@ -228,7 +229,7 @@ async def collete_datasets(self, context: ASyncContext, sitekey: str, batch: int sitelink = SiteKey.as_sitelink(sitekey) await page.goto(sitelink) - await agent.handle_checkbox() + await agent.click_checkbox() for pth in range(1, batch + 1): try: diff --git a/backend/main.py b/backend/main.py index de488d5d9f..209977ff45 100644 --- a/backend/main.py +++ b/backend/main.py @@ -1,15 +1,25 @@ -from typing import Union - from fastapi import FastAPI +from fastapi.responses import RedirectResponse + +from routers import challenge_router, datalake_router app = FastAPI() +app.include_router(challenge_router, prefix="/challenge") +app.include_router(datalake_router, prefix="/datalake") + @app.get("/") -async def read_root(): - return {"Hello": "World"} +async def home(): + return RedirectResponse(url="https://github.com/QIN2DIM/hcaptcha-challenger") + + +@app.get("/ping", response_model=str) +async def ping(): + return "pong" + +if __name__ == "__main__": + import uvicorn -@app.get("/items/{item_id}") -async def read_item(item_id: int, q: Union[str, None] = None): - return {"item_id": item_id, "q": q} + uvicorn.run("main:app", host="0.0.0.0", port=33777) diff --git a/backend/routers/__init__.py b/backend/routers/__init__.py new file mode 100644 index 0000000000..bc7c2d9ba0 --- /dev/null +++ b/backend/routers/__init__.py @@ -0,0 +1,9 @@ +# -*- coding: utf-8 -*- +# Time : 2024/4/14 12:24 +# Author : QIN2DIM +# GitHub : https://github.com/QIN2DIM +# Description: +from .challenge import router as challenge_router +from .datalake import router as datalake_router + +__all__ = ["challenge_router", "datalake_router"] diff --git a/backend/routers/challenge.py b/backend/routers/challenge.py new file mode 100644 index 0000000000..6eea3d42c7 --- /dev/null +++ b/backend/routers/challenge.py @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- +# Time : 2024/4/14 12:25 +# Author : QIN2DIM +# GitHub : https://github.com/QIN2DIM +# Description: +from fastapi import APIRouter + + +from typing import List + +from pydantic import BaseModel, Field, Base64Str + +router = APIRouter() + + +class SelfSupervisedPayload(BaseModel): + """hCaptcha payload of the image_label_binary challenge""" + + prompt: str = Field(..., description="challenge prompt") + challenge_images: List[Base64Str] = Field(default_factory=list) + positive_labels: List[str] | None = Field(default_factory=list) + negative_labels: List[str] | None = Field(default_factory=list) + + +class SelfSupervisedResponse(BaseModel): + """The binary classification result of the image, in the same order as the challenge_images.""" + + results: List[bool] = Field(default_factory=list) + + +import os +from pathlib import Path +from typing import List + +import hcaptcha_challenger as solver +from hcaptcha_challenger import handle, ModelHub, DataLake, register_pipline +import pandas as pd + +# Init local-side of the ModelHub +solver.install(upgrade=True, clip=True) + +images_dir = Path("tmp_dir/image_label_binary/streetlamp") + +prompt = "streetlamp" + +# Patch datalake maps not updated in time +datalake_post = { + # => prompt: sedan car + handle(prompt): {"positive_labels": ["streetlamp"], "negative_labels": ["duck", "shark"]} +} + + +def prelude_self_supervised_config(): + modelhub = ModelHub.from_github_repo() + modelhub.parse_objects() + for prompt_, serialized_binary in datalake_post.items(): + modelhub.datalake[prompt_] = DataLake.from_serialized(serialized_binary) + clip_model = register_pipline(modelhub, fmt="onnx") + + return modelhub, clip_model + + +def demo(image_paths: List[Path]): + modelhub, clip_model = prelude_self_supervised_config() + + classifier = solver.BinaryClassifier(modelhub=modelhub, clip_model=clip_model) + if results := classifier.execute(prompt, image_paths, self_supervised=True): + output = [ + {"image": f"![]({image_path})", "result": result} + for image_path, result in zip(image_paths, results) + ] + mdk = pd.DataFrame.from_records(output).to_markdown() + Path(f"result_{prompt}.md").write_text(mdk, encoding="utf8") + + +@router.post("/image_label_binary/clip", response_model=SelfSupervisedResponse) +async def read_item(payload: SelfSupervisedPayload): + return SelfSupervisedResponse() diff --git a/backend/routers/datalake.py b/backend/routers/datalake.py new file mode 100644 index 0000000000..ae419ccbe5 --- /dev/null +++ b/backend/routers/datalake.py @@ -0,0 +1,18 @@ +# -*- coding: utf-8 -*- +# Time : 2024/4/14 12:25 +# Author : QIN2DIM +# GitHub : https://github.com/QIN2DIM +# Description: +from fastapi import APIRouter + +router = APIRouter() + + +@router.get("/sync", response_model=str) +async def datalake_sync_from_github_repo(): + pass + + +@router.get("/list", response_model=list) +async def datalake_list(): + pass diff --git a/examples/demo_classifier_self_supervised.py b/examples/demo_classifier_self_supervised.py index be8bee2470..122e4a83ae 100644 --- a/examples/demo_classifier_self_supervised.py +++ b/examples/demo_classifier_self_supervised.py @@ -10,11 +10,12 @@ import hcaptcha_challenger as solver from hcaptcha_challenger import handle, ModelHub, DataLake, register_pipline + # Init local-side of the ModelHub solver.install(upgrade=True, clip=True) assets_dir = Path(__file__).parent.parent.joinpath("assets") -images_dir = assets_dir.joinpath("image_label_binary", "off_road_vehicle") +images_dir = assets_dir.joinpath("image_label_binary/off_road_vehicle") prompt = "Please click each image containing a sedan car" @@ -33,7 +34,7 @@ def prelude_self_supervised_config(): modelhub.parse_objects() for prompt_, serialized_binary in datalake_post.items(): modelhub.datalake[prompt_] = DataLake.from_serialized(serialized_binary) - clip_model = register_pipline(modelhub) + clip_model = register_pipline(modelhub, fmt="onnx") return modelhub, clip_model @@ -49,13 +50,30 @@ def get_test_images() -> List[Path]: def demo(): + def output_markdown_preview(): + """# pip install pandas tabulate""" + try: + import pandas as pd + import tabulate + except ImportError: + for image_path, result in zip(image_paths, results): + print(image_path, f"{result=}") + else: + output = [ + {"image": f"![]({image_path})", "result": result} + for image_path, result in zip(image_paths, results) + ] + mdk = pd.DataFrame.from_records(output).to_markdown() + mdk = f"- prompt: `{prompt}`\n\n{mdk}" + Path("result.md").write_text(mdk, encoding="utf8") + print(mdk) + modelhub, clip_model = prelude_self_supervised_config() image_paths = get_test_images() classifier = solver.BinaryClassifier(modelhub=modelhub, clip_model=clip_model) if results := classifier.execute(prompt, image_paths, self_supervised=True): - for image_path, result in zip(image_paths, results): - print(f"{image_path.name=} - {result=} {classifier.model_name=}") + output_markdown_preview() if __name__ == "__main__": diff --git a/examples/demo_find_unique_object.py b/examples/demo_find_unique_object.py index 0d1c776b29..8f863cba1b 100644 --- a/examples/demo_find_unique_object.py +++ b/examples/demo_find_unique_object.py @@ -7,7 +7,7 @@ from tqdm import tqdm import hcaptcha_challenger as solver -from hcaptcha_challenger.components.cv_toolkit.appears_only_once import ( +from hcaptcha_challenger.tools.cv_toolkit.appears_only_once import ( limited_radius, annotate_objects, find_unique_object, diff --git a/examples/faker_client.py b/examples/faker_client.py new file mode 100644 index 0000000000..75ede49800 --- /dev/null +++ b/examples/faker_client.py @@ -0,0 +1,63 @@ +# -*- coding: utf-8 -*- +# Time : 2024/4/1 21:10 +# Author : QIN2DIM +# GitHub : https://github.com/QIN2DIM +# Description: +from __future__ import annotations + +import asyncio +from pathlib import Path + +from playwright.async_api import async_playwright, BrowserContext + +from hcaptcha_challenger.agents import AgentV +from hcaptcha_challenger.agents import Malenia +from hcaptcha_challenger.onnx.modelhub import ModelHub +from hcaptcha_challenger.utils import SiteKey + + +# 1. You need to deploy sub-thread tasks and actively run `install(upgrade=True)` every 20 minutes +# 2. You need to make sure to run `install(upgrade=True, clip=True)` before each instantiation +# install(upgrade=True, clip=True) + + +async def main(headless: bool = False): + async with async_playwright() as p: + browser = await p.chromium.launch(headless=headless) + context = await browser.new_context(locale="en-US") + await Malenia.apply_stealth(context) + await mime(context) + await context.close() + + +async def mime(context: BrowserContext): + page = await context.new_page() + + modelhub = ModelHub.from_github_repo() + modelhub.parse_objects() + + agent = AgentV.into_solver( + # page, the control handle of the Playwright Page + page=page, + # modelhub, Register modelhub externally, and the agent can patch custom configurations + modelhub=modelhub, + # tmp_dir, Mount the cache directory to the current working folder + tmp_dir=Path("tmp_dir"), + ) + + sitekey = SiteKey.user_easy + + if EXECUTION == "challenge": + sitelink = SiteKey.as_sitelink(sitekey) + await page.goto(sitelink) + await agent.ms.click_checkbox() + await agent.wait_for_challenge() + elif EXECUTION == "collect": + await agent.wait_for_collect(sitekey, batch=25) + + +if __name__ == "__main__": + EXECUTION = "collect" + # EXECUTION = "challenge" + + encrypted_resp = asyncio.run(main(headless=False)) diff --git a/examples/lvm_challenge/vision_minor.ipynb b/examples/lvm_challenge/vision_minor.ipynb deleted file mode 100644 index 86a060e7d9..0000000000 --- a/examples/lvm_challenge/vision_minor.ipynb +++ /dev/null @@ -1,48 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "initial_id", - "metadata": { - "collapsed": true, - "is_executing": true - }, - "outputs": [], - "source": [ - "# open ../../../static/img/brand/wordmark.png as base64 str\n", - "import base64\n", - "from pathlib import Path\n", - "\n", - "from IPython.display import HTML\n", - "\n", - "img_path = Path(\"../../../static/img/brand/wordmark.png\")\n", - "img_base64 = base64.b64encode(img_path.read_bytes()).decode(\"utf-8\")\n", - "\n", - "# display b64 image in notebook\n", - "HTML(f'')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/hcaptcha_challenger/__init__.py b/hcaptcha_challenger/__init__.py index 08b933f64d..0ab9676513 100644 --- a/hcaptcha_challenger/__init__.py +++ b/hcaptcha_challenger/__init__.py @@ -5,31 +5,30 @@ # Description: from __future__ import annotations -from dataclasses import dataclass from pathlib import Path from typing import Iterable from urllib.parse import urlparse -from hcaptcha_challenger.components.image_classifier import Classifier as BinaryClassifier -from hcaptcha_challenger.components.image_classifier import LocalBinaryClassifier -from hcaptcha_challenger.components.image_label_area_select import AreaSelector -from hcaptcha_challenger.components.middleware import QuestionResp, ChallengeResp, Answers, Status -from hcaptcha_challenger.components.prompt_handler import ( +from hcaptcha_challenger.models import QuestionResp, Answers, Status, ChallengeResp +from hcaptcha_challenger.onnx.modelhub import ModelHub +from hcaptcha_challenger.onnx.resnet import ResNetControl +from hcaptcha_challenger.onnx.yolo import YOLOv8 +from hcaptcha_challenger.onnx.yolo import YOLOv8Seg +from hcaptcha_challenger.tools.image_label_area_select import AreaSelector +from hcaptcha_challenger.tools.image_label_binary import Classifier as BinaryClassifier +from hcaptcha_challenger.tools.image_label_binary import LocalBinaryClassifier +from hcaptcha_challenger.tools.prompt_handler import ( label_cleaning, diagnose_task, split_prompt_message, prompt2task, handle, ) -from hcaptcha_challenger.components.zero_shot_image_classifier import ( +from hcaptcha_challenger.tools.zero_shot_image_classifier import ( ZeroShotImageClassifier, DataLake, register_pipline, ) -from hcaptcha_challenger.onnx.modelhub import ModelHub -from hcaptcha_challenger.onnx.resnet import ResNetControl -from hcaptcha_challenger.onnx.yolo import YOLOv8 -from hcaptcha_challenger.onnx.yolo import YOLOv8Seg from hcaptcha_challenger.utils import init_log __all__ = [ @@ -56,18 +55,10 @@ ] -@dataclass -class Project: - at_dir = Path(__file__).parent - logs = at_dir.joinpath("logs") - - -project = Project() - init_log( - runtime=project.logs.joinpath("runtime.log"), - error=project.logs.joinpath("error.log"), - serialize=project.logs.joinpath("serialize.log"), + runtime=Path("logs/runtime.log"), + error=Path("logs/error.log"), + serialize=Path("logs/serialize.log"), ) @@ -90,7 +81,7 @@ def install( modelhub.assets.flush_runtime_assets(upgrade=upgrade) if clip is True: - from hcaptcha_challenger.components.zero_shot_image_classifier import register_pipline + from hcaptcha_challenger.tools.zero_shot_image_classifier import register_pipline register_pipline(modelhub, install_only=True) diff --git a/hcaptcha_challenger/agents/__init__.py b/hcaptcha_challenger/agents/__init__.py index 773027ecd7..287c38a9d0 100644 --- a/hcaptcha_challenger/agents/__init__.py +++ b/hcaptcha_challenger/agents/__init__.py @@ -3,8 +3,8 @@ # Author : QIN2DIM # GitHub : https://github.com/QIN2DIM # Description: -from hcaptcha_challenger.agents.pipline.control import AgentR from hcaptcha_challenger.agents.playwright.control import AgentT +from hcaptcha_challenger.agents.playwright.dragon import AgentV from hcaptcha_challenger.agents.playwright.tarnished import Malenia, Tarnished -__all__ = ["AgentT", "Malenia", "Tarnished", "AgentR"] +__all__ = ["AgentT", "Malenia", "Tarnished", "AgentV"] diff --git a/hcaptcha_challenger/agents/pipline/__init__.py b/hcaptcha_challenger/agents/pipline/__init__.py deleted file mode 100644 index 5399e03a1a..0000000000 --- a/hcaptcha_challenger/agents/pipline/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# -*- coding: utf-8 -*- -# Time : 2023/11/17 18:54 -# Author : QIN2DIM -# GitHub : https://github.com/QIN2DIM -# Description: diff --git a/hcaptcha_challenger/agents/playwright/control.py b/hcaptcha_challenger/agents/playwright/control.py index fe0235eda4..b642d96984 100644 --- a/hcaptcha_challenger/agents/playwright/control.py +++ b/hcaptcha_challenger/agents/playwright/control.py @@ -20,36 +20,31 @@ from playwright.async_api import TimeoutError from tenacity import * -from hcaptcha_challenger.components.common import ( +from hcaptcha_challenger.models import Status, QuestionResp, ChallengeResp, RequestType +from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake +from hcaptcha_challenger.onnx.resnet import ResNetControl +from hcaptcha_challenger.onnx.yolo import ( + YOLOv8, + YOLOv8Seg, + is_matched_ash_of_war, + finetune_keypoint, +) +from hcaptcha_challenger.tools.common import ( match_model, match_datalake, rank_models, download_challenge_images, ) -from hcaptcha_challenger.components.cv_toolkit import ( +from hcaptcha_challenger.tools.cv_toolkit import ( find_unique_object, annotate_objects, find_unique_color, ) -from hcaptcha_challenger.components.middleware import ( - Status, - QuestionResp, - ChallengeResp, - RequestType, -) -from hcaptcha_challenger.components.prompt_handler import handle -from hcaptcha_challenger.components.zero_shot_image_classifier import ( +from hcaptcha_challenger.tools.prompt_handler import handle +from hcaptcha_challenger.tools.zero_shot_image_classifier import ( ZeroShotImageClassifier, register_pipline, ) -from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake -from hcaptcha_challenger.onnx.resnet import ResNetControl -from hcaptcha_challenger.onnx.yolo import ( - YOLOv8, - YOLOv8Seg, - is_matched_ash_of_war, - finetune_keypoint, -) @dataclass diff --git a/hcaptcha_challenger/agents/playwright/dragon.py b/hcaptcha_challenger/agents/playwright/dragon.py new file mode 100644 index 0000000000..ef8f31efd2 --- /dev/null +++ b/hcaptcha_challenger/agents/playwright/dragon.py @@ -0,0 +1,432 @@ +# -*- coding: utf-8 -*- +# Time : 2024/4/7 11:43 +# Author : QIN2DIM +# GitHub : https://github.com/QIN2DIM +# Description: +import abc +import asyncio +import hashlib +import re +import shutil +from abc import ABC +from asyncio import Queue +from contextlib import suppress +from dataclasses import dataclass +from dataclasses import field +from pathlib import Path +from typing import List + +from loguru import logger +from playwright.async_api import Page, Response, TimeoutError, expect + +from hcaptcha_challenger.models import ( + ChallengeResp, + QuestionResp, + RequestType, + Status, + ChallengeImage, + ToolExecution, + CollectibleType, + Collectible, +) +from hcaptcha_challenger.onnx.modelhub import ModelHub +from hcaptcha_challenger.tools.prompt_handler import handle + +HOOK_PURCHASE = "//div[@id='webPurchaseContainer']//iframe" +HOOK_CHECKBOX = "//iframe[contains(@title, 'checkbox for hCaptcha')]" +HOOK_CHALLENGE = "//iframe[contains(@title, 'hCaptcha challenge')]" + + +@dataclass +class MechanicalSkeleton: + page: Page + + async def click_checkbox(self): + try: + checkbox = self.page.frame_locator("//iframe[contains(@title,'checkbox')]") + await checkbox.locator("#checkbox").click() + except TimeoutError as err: + logger.warning("Failed to click checkbox", reason=err) + + async def refresh_challenge(self) -> bool | None: + try: + fl = self.page.frame_locator(HOOK_CHALLENGE) + await fl.locator("//div[@class='refresh button']").click() + return True + except TimeoutError as err: + logger.warning("Failed to click refresh button", reason=err) + + def switch_to_challenge_frame(self, window: str = "login"): + if window == "login": + frame_challenge = self.page.frame_locator(HOOK_CHALLENGE) + else: + frame_purchase = self.page.frame_locator(HOOK_PURCHASE) + frame_challenge = frame_purchase.frame_locator(HOOK_CHALLENGE) + + return frame_challenge + + +@dataclass +class OminousLand(ABC): + """不祥之地：base""" + + page: Page + tmp_dir: Path + ms: MechanicalSkeleton + image_queue: Queue + + typed_dir: Path = field(default_factory=Path) + canvas_screenshot_dir: Path = field(default_factory=Path) + + label: str = "" + prompt: str = "" + + tasklist: List[ChallengeImage] = field(default_factory=list) + examples: List[ChallengeImage] = field(default_factory=list) + + qr_data: dict | None = field(default_factory=dict) + qr: QuestionResp | None = field(default_factory=QuestionResp) + + encrypted_bytes: bytes | None = b"" + + @classmethod + def draws_from( + cls, page: Page, inputs: dict | bytes, tmp_dir: Path, image_queue: Queue, **kwargs + ): + # Cache images + if not isinstance(tmp_dir, Path): + tmp_dir = Path("tmp_dir") + + typed_dir = tmp_dir / "typed_dir" + canvas_screenshot_dir = tmp_dir / "canvas_screenshot" + + monster = cls( + page=page, + ms=MechanicalSkeleton(page), + tmp_dir=tmp_dir, + image_queue=image_queue, + typed_dir=typed_dir, + canvas_screenshot_dir=canvas_screenshot_dir, + ) + + if isinstance(inputs, dict): + monster.qr_data = inputs + elif isinstance(inputs, bytes): + monster.encrypted_bytes = inputs + + return monster + + def _init_imgdb(self, label: str, prompt: str): + """run after _get_captcha""" + self.tasklist.clear() + self.examples.clear() + + inv = {"\\", "/", ":", "*", "?", "<", ">", "|", "\n"} + for c in inv: + label = label.replace(c, "") + prompt = prompt.replace(c, "") + label = label.strip() + + self.typed_dir = self.tmp_dir.joinpath(self.qr.request_type, label) + self.typed_dir.mkdir(parents=True, exist_ok=True) + + if self.qr.request_type != RequestType.ImageLabelBinary: + self.canvas_screenshot_dir = self.tmp_dir.joinpath(f"canvas_screenshot/{prompt}") + self.canvas_screenshot_dir.mkdir(parents=True, exist_ok=True) + + async def _recall_tasklist(self): + """run after _init_imgdb""" + frame_challenge = self.ms.switch_to_challenge_frame() + + if self.qr.request_type == RequestType.ImageLabelBinary: + images = frame_challenge.locator("//div[@class='task-grid']//div[@class='image']") + count = await images.count() + + background_urls = [] + challenge_images = {} + for i in range(count): + image = images.nth(i) + await expect(image).to_have_attribute( + "style", re.compile(r"url\(.+\)"), timeout=5000 + ) + style = await image.get_attribute("style") + datapoint_uri = style.split('"')[1] + background_urls.append(datapoint_uri) + + while not self.image_queue.empty(): + challenge_image: ChallengeImage = self.image_queue.get_nowait() + challenge_images[challenge_image.datapoint_uri] = challenge_image + + for url in background_urls: + challenge_image = challenge_images.get(url) + if challenge_image: + self.tasklist.append(challenge_image) + if not self.typed_dir.joinpath(challenge_image.filename).exists(): + shutil.move(src=challenge_image.runtime_fp, dst=self.typed_dir) + + elif self.qr.request_type == RequestType.ImageLabelAreaSelect: + # For the object detection task, tasklist is only used to collect datasets. + # The challenge in progress uses a canvas screenshot, not the challenge-image + canvas_bgk = frame_challenge.locator("//div[class='bounding-box-example']") + await expect(canvas_bgk).not_to_be_attached() + + # Expect only 1 image in the image_queue + while not self.image_queue.empty(): + challenge_image: ChallengeImage = self.image_queue.get_nowait() + if not self.typed_dir.joinpath(challenge_image.filename).exists(): + shutil.move(src=challenge_image.runtime_fp, dst=self.typed_dir) + # Cache image sequences for subsequent browser operations + self.tasklist.append(challenge_image) + + @abc.abstractmethod + async def _get_captcha(self, **kwargs): + raise NotImplementedError + + async def _solve_captcha(self, **kwargs): + frame_challenge = self.ms.switch_to_challenge_frame() + + match self.qr.request_type: + case RequestType.ImageLabelBinary: + pass + case RequestType.ImageLabelAreaSelect: + # Cache canvas to prepare for subsequent model processing + # canvas = frame_challenge.locator("//canvas") + # fp = self.canvas_screenshot_dir / f"{challenge_image.filename}.png" + # await canvas.screenshot(type="png", path=fp, scale="css") + pass + case RequestType.ImageLabelMultipleChoice: + pass + case _: + logger.warning("[INTERRUPT]", reason="Unknown type of challenge") + + async def _collect(self): + await self._get_captcha() + + logger.debug( + "task", + label=self.label, + type=self.qr.request_type, + requester_question=self.qr.requester_question, + trigger=self.__class__.__name__, + ) + + self._init_imgdb(self.label, self.prompt) + await self._recall_tasklist() + + async def _challenge(self): + await self._collect() + await self._solve_captcha() + + async def invoke(self, execution: ToolExecution = ToolExecution.CHALLENGE): + match execution: + case ToolExecution.COLLECT: + await self._collect() + case ToolExecution.CHALLENGE: + await self._challenge() + + +@dataclass +class ScarletWhisker(OminousLand): + """赤髯 (Chì Rán) ->> json""" + + async def _get_captcha(self, **kwargs): + self.qr = QuestionResp(**self.qr_data) + + self.prompt = self.qr.requester_question.get("en") + self.label = handle(self.prompt) + + +@dataclass +class DemonLordOfHuiYue(OminousLand): + """晦月魔君 ->> bytes""" + + async def _get_captcha(self, **kwargs): + self.qr = QuestionResp() + + # IMPORTANT + await self.page.wait_for_timeout(2000) + + frame_challenge = self.ms.switch_to_challenge_frame() + + # requester_question + prompt_element = frame_challenge.locator("//h2[@class='prompt-text']") + self.prompt = await prompt_element.text_content() + self.label = handle(self.prompt) + lang = await frame_challenge.locator("//html").get_attribute("lang") + self.qr.requester_question[lang] = self.prompt + + # request_type + if await frame_challenge.locator("//div[@class='task-grid']").count(): + self.qr.request_type = RequestType.ImageLabelBinary.value + has_exp = await frame_challenge.locator("//div[@class='challenge-example']").count() + elif await frame_challenge.locator("//div[contains(@class, 'bounding-box')]").count(): + self.qr.request_type = RequestType.ImageLabelAreaSelect.value + else: + # todo image_label_multiple_choice + self.qr.request_type = RequestType.ImageLabelMultipleChoice.value + + +@dataclass +class AgentV: + page: Page + + modelhub: ModelHub + + ms: MechanicalSkeleton = field(default_factory=MechanicalSkeleton) + + cr: ChallengeResp = field(default_factory=ChallengeResp) + + task_queue: Queue[Response] | None = None + cr_queue: Queue[ChallengeResp] = field(default_factory=Queue) + + tmp_dir: Path = field(default_factory=Path) + + image_queue: Queue = field(default_factory=Queue) + + _tool_type: ToolExecution | None = None + + def __post_init__(self): + # Control models + self.modelhub = self.modelhub or ModelHub.from_github_repo() + if not self.modelhub.label_alias: + self.modelhub.parse_objects() + + self.tmp_dir = self.tmp_dir or Path("tmp_dir") + self._cache_dir = self.tmp_dir / ".cache" + self._cache_dir.mkdir(parents=True, exist_ok=True) + + self._enable_evnet_listener(self.page) + + self.task_queue = Queue(maxsize=1) + + @classmethod + def into_solver(cls, page: Page, tmp_dir=None, modelhub: ModelHub | None = None, **kwargs): + return cls( + page=page, ms=MechanicalSkeleton(page), tmp_dir=tmp_dir, modelhub=modelhub, **kwargs + ) + + @property + def status(self): + return Status + + def _enable_evnet_listener(self, page: Page): + page.on("response", self._task_handler) + + @logger.catch + async def _task_handler(self, response: Response): + if "/getcaptcha/" in response.url: + # reset state + while not self.image_queue.empty(): + self.image_queue.get_nowait() + if self.task_queue.full(): + self.task_queue.get_nowait() + + # drop task + self.task_queue.put_nowait(response) + + # /cr 在 Submit Event 之后，cr 截至目前是明文数据 + elif "/checkcaptcha/" in response.url: + try: + metadata = await response.json() + self.cr_queue.put_nowait(ChallengeResp(**metadata)) + except Exception as err: + logger.exception(err) + + # Image GET Event 发生在 /GetCaptcha 之后，此时假设 prompt 和 label 已被正确初始化 + # _image_handler 和 _task_cr_handler 以协程方式运行，但在业务逻辑上，_task_cr_handler 先发生。 + elif response.url.startswith("https://imgs3.hcaptcha.com/tip/"): + image_bytes = await response.body() + mime_type = await response.header_value("content-type") + image_url = response.url + + suffix = ".jpeg" + if isinstance(mime_type, str): + _suffix = mime_type.split("/")[-1] + if _suffix in ["jpg", "jpeg", "png", "webp"]: + suffix = f".{_suffix}" + + fn = f"{hashlib.md5(image_bytes).hexdigest()}{suffix}" + fp = self._cache_dir / fn + fp.write_bytes(image_bytes) + + # waiting for lock + element = ChallengeImage( + datapoint_uri=image_url, filename=fn, body=image_bytes, runtime_fp=fp + ) + self.image_queue.put_nowait(element) + + @logger.catch + async def _tool_execution(self): + qr_data = await self.task_queue.get() + + driver_conf = {"page": self.page, "tmp_dir": self.tmp_dir, "image_queue": self.image_queue} + runnable: OminousLand | None = None + + match content_type := qr_data.headers.get("content-type"): + case "application/octet-stream": + driver_conf["inputs"] = await qr_data.body() + runnable = DemonLordOfHuiYue.draws_from(**driver_conf) + case "application/json": + data = await qr_data.json() + if data.get("pass"): + self.cr_queue.put_nowait(ChallengeResp(**data)) + else: + driver_conf["inputs"] = data + runnable = ScarletWhisker.draws_from(**driver_conf) + case _: + raise ValueError(f"Unknown Challenge Response Protocol - {content_type=}") + + if isinstance(runnable, OminousLand): + await runnable.invoke(execution=self._tool_type) + + async def wait_for_challenge( + self, execution_timeout: float = 150.0, response_timeout: float = 30.0 + ) -> Status: + self._tool_type = ToolExecution.CHALLENGE + + # CoroutineTask: Assigning human-computer challenge tasks to the main thread coroutine. + # Wait for the task to finish executing + try: + await asyncio.wait_for(self._tool_execution(), timeout=execution_timeout) + except asyncio.TimeoutError: + logger.error("Challenge execution timed out", timeout=execution_timeout) + return self.status.CHALLENGE_EXECUTION_TIMEOUT + + # CoroutineTask: Waiting for hCAPTCHA response processing result + # After the completion of the human-machine challenge workflow, + # it is expected to obtain a signal indicating whether the challenge was successful in the cr_queue. + self.cr = ChallengeResp() + try: + self.cr = await self.cr_queue.get() + except asyncio.TimeoutError: + logger.error("Timeout waiting for challenge response", timeout=response_timeout) + return self.status.CHALLENGE_RESPONSE_TIMEOUT + else: + logger.debug("[DONE]", **self.cr.model_dump(by_alias=True)) + + # Match: Timeout / Loss + if not self.cr or not self.cr.is_pass: + return self.status.CHALLENGE_RETRY + if self.cr.is_pass: + return self.status.CHALLENGE_SUCCESS + + async def wait_for_collect( + self, point: CollectibleType | None = None, *, batch: int = 20, timeout: float = 30.0 + ): + self._tool_type = ToolExecution.COLLECT + + sitelink = Collectible(point=point).fixed_sitelink + + await self.page.goto(sitelink) + await self.ms.click_checkbox() + + logger.debug("run collector", url=self.page.url) + + if batch >= 1: + for i in range(1, batch + 1): + with suppress(asyncio.TimeoutError): + await asyncio.wait_for(self._tool_execution(), timeout=timeout) + if not await self.ms.refresh_challenge(): + return await self.wait_for_collect(point=point, batch=batch - i) + + logger.success("The dataset collection is complete.", sitelink=sitelink) diff --git a/hcaptcha_challenger/components/middleware.py b/hcaptcha_challenger/models.py similarity index 62% rename from hcaptcha_challenger/components/middleware.py rename to hcaptcha_challenger/models.py index 3936435f15..a699bf6345 100644 --- a/hcaptcha_challenger/components/middleware.py +++ b/hcaptcha_challenger/models.py @@ -5,13 +5,15 @@ # Description: from __future__ import annotations +import base64 from enum import Enum from pathlib import Path -from typing import List, Dict, Any +from typing import List, Dict, Any, Union +from uuid import UUID -from pydantic import BaseModel, Field, field_validator +from pydantic import BaseModel, Field, field_validator, UUID4, AnyHttpUrl -from hcaptcha_challenger.components.prompt_handler import label_cleaning +from hcaptcha_challenger.tools.prompt_handler import label_cleaning class Status(str, Enum): @@ -21,6 +23,46 @@ class Status(str, Enum): CHALLENGE_RETRY = "retry" # (New Challenge) Types of challenges not yet scheduled CHALLENGE_BACKCALL = "backcall" + # Failed to pass the challenge within the specified time frame + CHALLENGE_EXECUTION_TIMEOUT = "challenge_execution_timeout" + CHALLENGE_RESPONSE_TIMEOUT = "challenge_response_timeout" + + +class Collectible(BaseModel): + point: UUID4 | AnyHttpUrl = Field(..., description="sitelink or sitekey") + + @field_validator("point") + def validate_point(cls, v: str): + def is_valid_uuid4(string): + try: + uuid_obj = UUID(string) + except ValueError: + return False + return uuid_obj.version == 4 + + _sitekey = "c86d730b-300a-444c-a8c5-5312e7a93628" + _sitelink = "https://accounts.hcaptcha.com/demo" + + if not isinstance(v, str): + v = f"{_sitelink}?sitekey={_sitekey}" + elif is_valid_uuid4(v): + v = f"{_sitelink}?sitekey={v}" + elif not v.startswith(_sitelink): + v = f"{_sitelink}?sitekey={_sitekey}" + + return v + + @property + def fixed_sitelink(self) -> str: + return self.point + + +CollectibleType = Union[UUID4, AnyHttpUrl, str] + + +class ToolExecution(str, Enum): + CHALLENGE = "challenge" + COLLECT = "collect" class ImageTask(BaseModel): @@ -142,3 +184,23 @@ class Answers(BaseModel): motionData: str = "" n: str = "" c: str = "" + + +class ChallengeImage(BaseModel): + datapoint_uri: str = Field(default="", description="图片的临时访问链接") + + filename: str = Field(default="challenge-image.jpeg", description="HASH 后的文件名，带有后缀") + + body: bytes = Field(default=b"", description="图片缓存字节") + + runtime_fp: Path = Field( + default_factory=Path, description="图片的临时缓存路径，fp = typed_dir / filename" + ) + + def save(self, typed_dir: Path) -> Path: + fp = typed_dir / self.filename + fp.write_bytes(self.body) + return fp + + def convert_body_to_base64(self) -> str: + return base64.b64encode(self.body).decode("utf8") diff --git a/hcaptcha_challenger/agents/pipline/control.py b/hcaptcha_challenger/pipline.py similarity index 97% rename from hcaptcha_challenger/agents/pipline/control.py rename to hcaptcha_challenger/pipline.py index 64ad5bd699..a7117e2cdc 100644 --- a/hcaptcha_challenger/agents/pipline/control.py +++ b/hcaptcha_challenger/pipline.py @@ -14,26 +14,26 @@ from PIL import Image from loguru import logger -from hcaptcha_challenger.components.common import ( +from hcaptcha_challenger.models import Status, QuestionResp, RequestType, Answers +from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake +from hcaptcha_challenger.onnx.resnet import ResNetControl +from hcaptcha_challenger.onnx.yolo import YOLOv8, is_matched_ash_of_war, YOLOv8Seg +from hcaptcha_challenger.tools.common import ( match_model, download_challenge_images, rank_models, match_datalake, ) -from hcaptcha_challenger.components.cv_toolkit import ( +from hcaptcha_challenger.tools.cv_toolkit import ( annotate_objects, find_unique_object, find_unique_color, ) -from hcaptcha_challenger.components.middleware import Status, QuestionResp, RequestType, Answers -from hcaptcha_challenger.components.prompt_handler import handle -from hcaptcha_challenger.components.zero_shot_image_classifier import ( +from hcaptcha_challenger.tools.prompt_handler import handle +from hcaptcha_challenger.tools.zero_shot_image_classifier import ( ZeroShotImageClassifier, register_pipline, ) -from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake -from hcaptcha_challenger.onnx.resnet import ResNetControl -from hcaptcha_challenger.onnx.yolo import YOLOv8, is_matched_ash_of_war, YOLOv8Seg @dataclass diff --git a/hcaptcha_challenger/components/__init__.py b/hcaptcha_challenger/tools/__init__.py similarity index 100% rename from hcaptcha_challenger/components/__init__.py rename to hcaptcha_challenger/tools/__init__.py diff --git a/hcaptcha_challenger/components/common.py b/hcaptcha_challenger/tools/common.py similarity index 97% rename from hcaptcha_challenger/components/common.py rename to hcaptcha_challenger/tools/common.py index 59859c47ae..ce6adafeee 100644 --- a/hcaptcha_challenger/components/common.py +++ b/hcaptcha_challenger/tools/common.py @@ -13,11 +13,11 @@ from pathlib import Path from typing import Literal, List, Tuple -from hcaptcha_challenger.components.image_downloader import Cirilla -from hcaptcha_challenger.components.middleware import QuestionResp +from hcaptcha_challenger.models import QuestionResp from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake from hcaptcha_challenger.onnx.resnet import ResNetControl from hcaptcha_challenger.onnx.yolo import YOLOv8 +from hcaptcha_challenger.tools.image_downloader import Cirilla def rank_models( diff --git a/hcaptcha_challenger/components/cv_toolkit/__init__.py b/hcaptcha_challenger/tools/cv_toolkit/__init__.py similarity index 100% rename from hcaptcha_challenger/components/cv_toolkit/__init__.py rename to hcaptcha_challenger/tools/cv_toolkit/__init__.py diff --git a/hcaptcha_challenger/components/cv_toolkit/appears_only_once.py b/hcaptcha_challenger/tools/cv_toolkit/appears_only_once.py similarity index 100% rename from hcaptcha_challenger/components/cv_toolkit/appears_only_once.py rename to hcaptcha_challenger/tools/cv_toolkit/appears_only_once.py diff --git a/hcaptcha_challenger/components/cv_toolkit/largest_animal.py b/hcaptcha_challenger/tools/cv_toolkit/largest_animal.py similarity index 100% rename from hcaptcha_challenger/components/cv_toolkit/largest_animal.py rename to hcaptcha_challenger/tools/cv_toolkit/largest_animal.py diff --git a/hcaptcha_challenger/components/image_downloader.py b/hcaptcha_challenger/tools/image_downloader.py similarity index 100% rename from hcaptcha_challenger/components/image_downloader.py rename to hcaptcha_challenger/tools/image_downloader.py diff --git a/hcaptcha_challenger/components/image_label_area_select.py b/hcaptcha_challenger/tools/image_label_area_select.py similarity index 97% rename from hcaptcha_challenger/components/image_label_area_select.py rename to hcaptcha_challenger/tools/image_label_area_select.py index fc9ec1b08d..c0e4c44bc9 100644 --- a/hcaptcha_challenger/components/image_label_area_select.py +++ b/hcaptcha_challenger/tools/image_label_area_select.py @@ -10,9 +10,9 @@ from loguru import logger -from hcaptcha_challenger.components.prompt_handler import handle from hcaptcha_challenger.onnx.modelhub import ModelHub from hcaptcha_challenger.onnx.yolo import YOLOv8 +from hcaptcha_challenger.tools.prompt_handler import handle class AreaSelector: diff --git a/hcaptcha_challenger/components/image_classifier.py b/hcaptcha_challenger/tools/image_label_binary.py similarity index 96% rename from hcaptcha_challenger/components/image_classifier.py rename to hcaptcha_challenger/tools/image_label_binary.py index 08b51e6218..c4a07bbe26 100644 --- a/hcaptcha_challenger/components/image_classifier.py +++ b/hcaptcha_challenger/tools/image_label_binary.py @@ -13,14 +13,14 @@ from PIL import Image from loguru import logger -from hcaptcha_challenger.components.common import rank_models -from hcaptcha_challenger.components.prompt_handler import handle -from hcaptcha_challenger.components.zero_shot_image_classifier import ( +from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake +from hcaptcha_challenger.onnx.resnet import ResNetControl +from hcaptcha_challenger.tools.common import rank_models +from hcaptcha_challenger.tools.prompt_handler import handle +from hcaptcha_challenger.tools.zero_shot_image_classifier import ( ZeroShotImageClassifier, register_pipline, ) -from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake -from hcaptcha_challenger.onnx.resnet import ResNetControl class Classifier: diff --git a/hcaptcha_challenger/components/prompt_handler.py b/hcaptcha_challenger/tools/prompt_handler.py similarity index 84% rename from hcaptcha_challenger/components/prompt_handler.py rename to hcaptcha_challenger/tools/prompt_handler.py index 153d11e537..46e9c032f7 100644 --- a/hcaptcha_challenger/components/prompt_handler.py +++ b/hcaptcha_challenger/tools/prompt_handler.py @@ -51,10 +51,16 @@ def split_prompt_message(prompt_message: str, lang: str) -> str: if "containing" in prompt_message: th = re.split(r"containing", prompt_message)[-1][1:].strip() return th[2:].strip() if th.startswith("a") else th + if prompt_message.startswith("please select all"): + prompt_message = prompt_message.replace("please select all ", "").strip() + return prompt_message + if prompt_message.startswith("please click on the"): + prompt_message = prompt_message.replace("please click on ", "").strip() + return prompt_message if prompt_message.startswith("select all") and "images" not in prompt_message: return prompt_message.split("select all")[-1].strip() - if "select all" in prompt_message: - return re.split(r"all (.*) images", prompt_message)[1].strip() + if "select all images of" in prompt_message: + return prompt_message.split("select all images of")[-1].strip() return prompt_message diff --git a/hcaptcha_challenger/components/zero_shot_image_classifier.py b/hcaptcha_challenger/tools/zero_shot_image_classifier.py similarity index 98% rename from hcaptcha_challenger/components/zero_shot_image_classifier.py rename to hcaptcha_challenger/tools/zero_shot_image_classifier.py index a7f05068c1..75f4332973 100644 --- a/hcaptcha_challenger/components/zero_shot_image_classifier.py +++ b/hcaptcha_challenger/tools/zero_shot_image_classifier.py @@ -13,10 +13,10 @@ import onnxruntime from PIL.Image import Image -from hcaptcha_challenger.components.prompt_handler import handle from hcaptcha_challenger.onnx.clip import MossCLIP from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake from hcaptcha_challenger.onnx.utils import is_cuda_pipline_available +from hcaptcha_challenger.tools.prompt_handler import handle def register_pipline( diff --git a/pyproject.toml b/pyproject.toml index 0d2c50abd9..5175370e57 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,7 @@ classifiers = [ in-project = true # https://python-poetry.org/docs/pyproject/#dependencies-and-dependency-groups +# pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 [tool.poetry.dependencies] python = "^3.10" @@ -48,8 +49,6 @@ ftfy = "*" regex = "*" pydantic = "^2.5.1" tenacity = "^8.2.3" -langchain = "^0.1.13" -langchain-anthropic = "^0.1.4" # The following dependencies will not be installed # when you just run `poetry install --all-extras` or `pip install hcaptcha-challenger` @@ -59,8 +58,7 @@ selenium = { version = "*", optional = true } playwright = { version = "*", optional = true } PyGithub = { version = "^1.59.1", optional = true } istockphoto = { version = "0.1.2", optional = true } -fastapi = { version = "*", optional = true } -uvicorn = { version = "*", extras = ["standard"], optional = true } +fastapi = { version = "*", extras=["all"], optional = true} [tool.poetry.group.test.dependencies] # https://docs.pytest.org/en/stable/reference/plugin_list.html#plugin-list diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_downloader.py b/tests/test_downloader.py index 63424a9379..1261182e0d 100644 --- a/tests/test_downloader.py +++ b/tests/test_downloader.py @@ -27,7 +27,7 @@ async def _downloader(): sitelink = SiteKey.as_sitelink(SiteKey.user_easy) await page.goto(sitelink) - await agent.handle_checkbox() + await agent.click_checkbox() await agent.collect() diff --git a/tests/test_normal_playwright.py b/tests/test_normal_playwright.py index f6c6f9a610..c7f7e36049 100644 --- a/tests/test_normal_playwright.py +++ b/tests/test_normal_playwright.py @@ -27,7 +27,7 @@ async def _normal_instance(): sitelink = SiteKey.as_sitelink(SiteKey.user_easy) await page.goto(sitelink) - await agent.handle_checkbox() + await agent.click_checkbox() await agent._reset_state() if not agent.qr.requester_question.keys(): diff --git a/tests/test_prompt_handler.py b/tests/test_prompt_handler.py index 16a971ad7e..abc2060050 100644 --- a/tests/test_prompt_handler.py +++ b/tests/test_prompt_handler.py @@ -11,7 +11,7 @@ import pytest from hcaptcha_challenger import split_prompt_message, label_cleaning, handle -from hcaptcha_challenger.components.prompt_handler import BAD_CODE +from hcaptcha_challenger.tools.prompt_handler import BAD_CODE pattern = re.compile(r"[^\x00-\x7F]") From 78e8c0090b1805db2c2858a0b3eba565ea58f9df Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sun, 14 Apr 2024 19:50:37 +0800 Subject: [PATCH 05/14] feat(backend): examples, payload design --- .gitignore | 3 +- .../0a250855340d6919d7d40e027e14ddb7.jpeg | Bin 0 -> 4341 bytes .../0dc5bf222566c789b1400017681e6634.jpeg | Bin 0 -> 4336 bytes .../1c4b5b2a29bd01c9e190203b13191de1.jpeg | Bin 0 -> 4175 bytes .../1f625f42bdc6a648b2311b2b8ac9a573.jpeg | Bin 0 -> 4365 bytes .../23006438f204b8032730e54d9c048f03.jpeg | Bin 0 -> 4402 bytes .../2b588e6d099ae24de6bd002373237016.jpeg | Bin 0 -> 4301 bytes .../2b86207da94d4a29f01325b01794aff1.jpeg | Bin 0 -> 4476 bytes .../2bef67821261920c3d1197c971d5db2c.jpeg | Bin 0 -> 3954 bytes .../31506b75a59a3ffb9c01038d9757aaf2.jpeg | Bin 0 -> 4250 bytes 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+solver.install(upgrade=True, clip=True) -router = APIRouter() +modelhub = ModelHub.from_github_repo() +modelhub.parse_objects() + +clip_model = register_pipline(modelhub, fmt="onnx") +logger.success( + "register clip_model", tool=clip_model.__class__.__name__, modelhub=modelhub.__class__.__name__ +) class SelfSupervisedPayload(BaseModel): """hCaptcha payload of the image_label_binary challenge""" prompt: str = Field(..., description="challenge prompt") - challenge_images: List[Base64Str] = Field(default_factory=list) + challenge_images: List[Base64Bytes] = Field(default_factory=list) positive_labels: List[str] | None = Field(default_factory=list) negative_labels: List[str] | None = Field(default_factory=list) @@ -28,51 +49,33 @@ class SelfSupervisedResponse(BaseModel): results: List[bool] = Field(default_factory=list) -import os -from pathlib import Path -from typing import List - -import hcaptcha_challenger as solver -from hcaptcha_challenger import handle, ModelHub, DataLake, register_pipline -import pandas as pd - -# Init local-side of the ModelHub -solver.install(upgrade=True, clip=True) - -images_dir = Path("tmp_dir/image_label_binary/streetlamp") - -prompt = "streetlamp" - -# Patch datalake maps not updated in time -datalake_post = { - # => prompt: sedan car - handle(prompt): {"positive_labels": ["streetlamp"], "negative_labels": ["duck", "shark"]} -} - +def invoke_clip_tool(payload: SelfSupervisedPayload) -> List[bool]: + label = handle(payload.prompt) -def prelude_self_supervised_config(): - modelhub = ModelHub.from_github_repo() - modelhub.parse_objects() - for prompt_, serialized_binary in datalake_post.items(): - modelhub.datalake[prompt_] = DataLake.from_serialized(serialized_binary) - clip_model = register_pipline(modelhub, fmt="onnx") + if any(payload.positive_labels) and any(payload.negative_labels): + serialized = { + "positive_labels": payload.positive_labels, + "negative_labels": payload.negative_labels, + } + modelhub.datalake[label] = DataLake.from_serialized(serialized) - return modelhub, clip_model + if not (dl := modelhub.datalake.get(label)): + dl = DataLake.from_challenge_prompt(label) + tool = ZeroShotImageClassifier.from_datalake(dl) + # Default to `RESNET.OPENAI` perf_counter 1.794s + model = clip_model or register_pipline(modelhub) -def demo(image_paths: List[Path]): - modelhub, clip_model = prelude_self_supervised_config() + response: List[bool] = [] + for image_data in payload.challenge_images: + results = tool(model, image=Image.open(BytesIO(image_data))) + trusted = results[0]["label"] in tool.positive_labels + response.append(trusted) - classifier = solver.BinaryClassifier(modelhub=modelhub, clip_model=clip_model) - if results := classifier.execute(prompt, image_paths, self_supervised=True): - output = [ - {"image": f"![]({image_path})", "result": result} - for image_path, result in zip(image_paths, results) - ] - mdk = pd.DataFrame.from_records(output).to_markdown() - Path(f"result_{prompt}.md").write_text(mdk, encoding="utf8") + return response -@router.post("/image_label_binary/clip", response_model=SelfSupervisedResponse) -async def read_item(payload: SelfSupervisedPayload): - return SelfSupervisedResponse() +@router.post("/image_label_binary", response_model=SelfSupervisedResponse) +async def challenge_image_label_binary(payload: SelfSupervisedPayload): + results = invoke_clip_tool(payload) + return SelfSupervisedResponse(results=results) diff --git a/examples/demo_classifier_self_supervised.py b/examples/demo_classifier_self_supervised.py index 122e4a83ae..1819d9105b 100644 --- a/examples/demo_classifier_self_supervised.py +++ b/examples/demo_classifier_self_supervised.py @@ -10,7 +10,6 @@ import hcaptcha_challenger as solver from hcaptcha_challenger import handle, ModelHub, DataLake, register_pipline - # Init local-side of the ModelHub solver.install(upgrade=True, clip=True) diff --git a/examples/demo_find_unique_object.py b/examples/demo_find_unique_object.py index 8f863cba1b..0853f225d6 100644 --- a/examples/demo_find_unique_object.py +++ b/examples/demo_find_unique_object.py @@ -7,14 +7,14 @@ from tqdm import tqdm import hcaptcha_challenger as solver +from hcaptcha_challenger.onnx.modelhub import ModelHub +from hcaptcha_challenger.onnx.yolo import YOLOv8Seg from hcaptcha_challenger.tools.cv_toolkit.appears_only_once import ( limited_radius, annotate_objects, find_unique_object, find_unique_color, ) -from hcaptcha_challenger.onnx.modelhub import ModelHub -from hcaptcha_challenger.onnx.yolo import YOLOv8Seg solver.install(upgrade=True) diff --git a/examples/invoke_remote_solver.py b/examples/invoke_remote_solver.py new file mode 100644 index 0000000000..a2abb00dfe --- /dev/null +++ b/examples/invoke_remote_solver.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- +# Time : 2022/9/23 17:28 +# Author : QIN2DIM +# Github : https://github.com/QIN2DIM +# Description: +import base64 +import random +from pathlib import Path +from typing import List + +# pip install pandas tabulate +import pandas as pd +from httpx import Client + +BASE_URL = "http://localhost:33777" +client = Client(base_url=BASE_URL, timeout=30) + + +def invoke_remove_tool(self_supervised_payload: dict): + response = client.post("/challenge/image_label_binary", json=self_supervised_payload) + response.raise_for_status() + results = response.json()["results"] + + return results + + +def show_and_cache(image_paths: List[Path], results: List[str], prompt: str): + output = [ + {"image": f"![]({image_path})", "result": result} + for image_path, result in zip(image_paths, results) + ] + mdk = pd.DataFrame.from_records(output).to_markdown() + mdk = f"- prompt: `{prompt}`\n\n{mdk}" + print(mdk) + + fp = Path(f"results {prompt}.md") + fp.write_text(mdk, encoding="utf8") + print(f"\nsaved ->> {fp.resolve()}") + + +def run(): + images_dir = Path(__file__).parent.parent.joinpath("assets/image_label_binary/streetlamp") + image_paths = list(images_dir.glob("*.jpeg")) + if not image_paths: + return + random.shuffle(image_paths) + image_paths = image_paths[:5] + + prompt = "streetlamp" + challenge_images = [base64.b64encode(fp.read_bytes()).decode() for fp in image_paths] + self_supervised_payload = { + "prompt": prompt, + "challenge_images": challenge_images, + "positive_labels": ["streetlamp", "light"], + "negative_labels": ["duck", "shark", "swan"], + } + + results = invoke_remove_tool(self_supervised_payload) + + show_and_cache(image_paths, results, prompt) + + +if __name__ == "__main__": + run() From 2df91aac2e5820443105a513832c5b9955aba888 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sun, 14 Apr 2024 21:02:59 +0800 Subject: [PATCH 06/14] Update .gitignore --- .gitignore | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 79a7771cfb..e818d8578a 100644 --- a/.gitignore +++ b/.gitignore @@ -157,7 +157,9 @@ profile_pluggable_model.md tests/record_json docs/lvm_challenge/*.jpeg docs/lvm_challenge/*.png -**/logs/** +logs examples/*.md docs/logs/ docs/*.md +node_modules +pnpm-lock.yaml \ No newline at end of file From e8f202b8fc528e46d7320ca325934684046a2141 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Mon, 15 Apr 2024 08:17:44 +0800 Subject: [PATCH 07/14] x1 --- backend/readme.md | 5 +- backend/routers/challenge.py | 57 +----- examples/faker_client.py | 17 +- .../agents/playwright/dragon.py | 179 ++++++++++++++++-- hcaptcha_challenger/models.py | 21 +- .../tools/zero_shot_image_classifier.py | 36 +++- pyproject.toml | 2 + 7 files changed, 222 insertions(+), 95 deletions(-) diff --git a/backend/readme.md b/backend/readme.md index 2d03adce3c..ce960fc378 100644 --- a/backend/readme.md +++ b/backend/readme.md @@ -1,10 +1,9 @@ ```bash -pip install fastapi -pip install uvicorn[standard] +pip install fastapi[all] ``` ```bash -uvicorn main:app --reload +python main.py ``` ```markdown diff --git a/backend/routers/challenge.py b/backend/routers/challenge.py index 5c5938fbbe..4819155297 100644 --- a/backend/routers/challenge.py +++ b/backend/routers/challenge.py @@ -3,22 +3,14 @@ # Author : QIN2DIM # GitHub : https://github.com/QIN2DIM # Description: -from io import BytesIO -from typing import List -from PIL import Image from fastapi import APIRouter from loguru import logger -from pydantic import BaseModel, Field, Base64Bytes import hcaptcha_challenger as solver -from hcaptcha_challenger import ( - handle, - ModelHub, - DataLake, - register_pipline, - ZeroShotImageClassifier, -) +from hcaptcha_challenger import ModelHub, register_pipline +from hcaptcha_challenger.models import SelfSupervisedResponse, SelfSupervisedPayload +from hcaptcha_challenger.tools.zero_shot_image_classifier import invoke_clip_tool router = APIRouter() @@ -34,48 +26,7 @@ ) -class SelfSupervisedPayload(BaseModel): - """hCaptcha payload of the image_label_binary challenge""" - - prompt: str = Field(..., description="challenge prompt") - challenge_images: List[Base64Bytes] = Field(default_factory=list) - positive_labels: List[str] | None = Field(default_factory=list) - negative_labels: List[str] | None = Field(default_factory=list) - - -class SelfSupervisedResponse(BaseModel): - """The binary classification result of the image, in the same order as the challenge_images.""" - - results: List[bool] = Field(default_factory=list) - - -def invoke_clip_tool(payload: SelfSupervisedPayload) -> List[bool]: - label = handle(payload.prompt) - - if any(payload.positive_labels) and any(payload.negative_labels): - serialized = { - "positive_labels": payload.positive_labels, - "negative_labels": payload.negative_labels, - } - modelhub.datalake[label] = DataLake.from_serialized(serialized) - - if not (dl := modelhub.datalake.get(label)): - dl = DataLake.from_challenge_prompt(label) - tool = ZeroShotImageClassifier.from_datalake(dl) - - # Default to `RESNET.OPENAI` perf_counter 1.794s - model = clip_model or register_pipline(modelhub) - - response: List[bool] = [] - for image_data in payload.challenge_images: - results = tool(model, image=Image.open(BytesIO(image_data))) - trusted = results[0]["label"] in tool.positive_labels - response.append(trusted) - - return response - - @router.post("/image_label_binary", response_model=SelfSupervisedResponse) async def challenge_image_label_binary(payload: SelfSupervisedPayload): - results = invoke_clip_tool(payload) + results = invoke_clip_tool(modelhub, payload, clip_model) return SelfSupervisedResponse(results=results) diff --git a/examples/faker_client.py b/examples/faker_client.py index 75ede49800..2d282a7b1d 100644 --- a/examples/faker_client.py +++ b/examples/faker_client.py @@ -12,7 +12,6 @@ from hcaptcha_challenger.agents import AgentV from hcaptcha_challenger.agents import Malenia -from hcaptcha_challenger.onnx.modelhub import ModelHub from hcaptcha_challenger.utils import SiteKey @@ -33,17 +32,7 @@ async def main(headless: bool = False): async def mime(context: BrowserContext): page = await context.new_page() - modelhub = ModelHub.from_github_repo() - modelhub.parse_objects() - - agent = AgentV.into_solver( - # page, the control handle of the Playwright Page - page=page, - # modelhub, Register modelhub externally, and the agent can patch custom configurations - modelhub=modelhub, - # tmp_dir, Mount the cache directory to the current working folder - tmp_dir=Path("tmp_dir"), - ) + agent = AgentV.into_solver(page=page, tmp_dir=Path("tmp_dir")) sitekey = SiteKey.user_easy @@ -57,7 +46,7 @@ async def mime(context: BrowserContext): if __name__ == "__main__": - EXECUTION = "collect" - # EXECUTION = "challenge" + # EXECUTION = "collect" + EXECUTION = "challenge" encrypted_resp = asyncio.run(main(headless=False)) diff --git a/hcaptcha_challenger/agents/playwright/dragon.py b/hcaptcha_challenger/agents/playwright/dragon.py index ef8f31efd2..b73f18fc16 100644 --- a/hcaptcha_challenger/agents/playwright/dragon.py +++ b/hcaptcha_challenger/agents/playwright/dragon.py @@ -6,6 +6,7 @@ import abc import asyncio import hashlib +import os import re import shutil from abc import ABC @@ -16,6 +17,8 @@ from pathlib import Path from typing import List +import dotenv +import httpx from loguru import logger from playwright.async_api import Page, Response, TimeoutError, expect @@ -28,19 +31,96 @@ ToolExecution, CollectibleType, Collectible, + SelfSupervisedPayload, ) +from hcaptcha_challenger.onnx.clip import MossCLIP from hcaptcha_challenger.onnx.modelhub import ModelHub from hcaptcha_challenger.tools.prompt_handler import handle +from hcaptcha_challenger.tools.zero_shot_image_classifier import invoke_clip_tool, register_pipline +from cachetools import TTLCache +from asyncache import cached + +dotenv.load_dotenv() HOOK_PURCHASE = "//div[@id='webPurchaseContainer']//iframe" HOOK_CHECKBOX = "//iframe[contains(@title, 'checkbox for hCaptcha')]" HOOK_CHALLENGE = "//iframe[contains(@title, 'hCaptcha challenge')]" +datalake_post = { + "animals possessing wings": { + "positive_labels": ["bird"], + "negative_labels": ["lion", "elephant", "bear"], + }, + "something for drinking": { + "positive_labels": ["cup", "something for drinking"], + "negative_labels": ["streetlamp", "animal"], + }, + "something used for transportation": { + "positive_labels": ["tractor"], + "negative_labels": ["cat", "clock", "eagle"], + }, + "streetlamp": {"positive_labels": ["streetlamp"], "negative_labels": ["shark", "duck", "swan"]}, +} + +_cached_ping_result = TTLCache(maxsize=10, ttl=60) + + +@cached(_cached_ping_result) +async def is_solver_edge_worker_available() -> bool: + solver_base_url = os.getenv("SOLVER_BASE_URL") + if not solver_base_url: + return False + + try: + client = httpx.AsyncClient(base_url=solver_base_url, timeout=1) + response = await client.get("/ping") + response.raise_for_status() + return True + except (httpx.HTTPStatusError, httpx.ReadTimeout) as err: + logger.warning("Failed to connect SolverEdgeWorker", base_url=solver_base_url, err=err) + return False + + +@dataclass +class SolverEdgeWorker: + solver_base_url: str | None = os.getenv("SOLVER_BASE_URL") + """ + Default to http://localhost:33777 + """ + + def __init__(self): + self.client = None + if self.solver_base_url: + self.client = httpx.AsyncClient(base_url=self.solver_base_url) + + async def invoke_clip_tool(self, payload: dict) -> List[bool]: + _service_point = "/challenge/image_label_binary" + + response = await self.client.post(_service_point, json=payload) + response.raise_for_status() + results = response.json()["results"] + + return results + + @dataclass class MechanicalSkeleton: page: Page + sew: SolverEdgeWorker = field(default_factory=SolverEdgeWorker) + + modelhub: ModelHub | None = None + + clip_model: MossCLIP | None = None + + def __post_init__(self): + self.sew = SolverEdgeWorker() + + if not self.modelhub: + self.modelhub = ModelHub.from_github_repo() + self.modelhub.parse_objects() + async def click_checkbox(self): try: checkbox = self.page.frame_locator("//iframe[contains(@title,'checkbox')]") @@ -65,6 +145,45 @@ def switch_to_challenge_frame(self, window: str = "login"): return frame_challenge + async def challenge_image_label_binary( + self, label: str, challenge_images: List[ChallengeImage] + ): + frame_challenge = self.switch_to_challenge_frame() + + challenge_images = [i.into_base64bytes() for i in challenge_images] + patched_model_prompt = datalake_post.get(label) + self_supervised_payload = { + "prompt": label, + "challenge_images": challenge_images, + **patched_model_prompt, + } + + # {{< IMAGE CLASSIFICATION >}} + if await is_solver_edge_worker_available(): + results: List[bool] = await self.sew.invoke_clip_tool(self_supervised_payload) + else: + payload = SelfSupervisedPayload(**self_supervised_payload) + results: List[bool] = invoke_clip_tool(self.modelhub, payload, self.clip_model) + + # {{< DRIVE THE BROWSER TO TAKE ON THE CHALLENGE >}} + samples = frame_challenge.locator("//div[@class='task-image']") + count = await samples.count() + positive_cases = 0 + for i in range(count): + sample = samples.nth(i) + if results[i]: + positive_cases += 1 + with suppress(TimeoutError): + await sample.click(delay=200) + elif positive_cases == 0 and i == count - 1: + await sample.click(delay=200) + + # {{< Verify >}} + with suppress(TimeoutError): + await self.page.pause() + fl = frame_challenge.locator("//div[@class='button-submit button']") + await fl.click() + @dataclass class OminousLand(ABC): @@ -75,6 +194,8 @@ class OminousLand(ABC): ms: MechanicalSkeleton image_queue: Queue + crumb_count = 1 + typed_dir: Path = field(default_factory=Path) canvas_screenshot_dir: Path = field(default_factory=Path) @@ -91,7 +212,12 @@ class OminousLand(ABC): @classmethod def draws_from( - cls, page: Page, inputs: dict | bytes, tmp_dir: Path, image_queue: Queue, **kwargs + cls, + page: Page, + inputs: dict | bytes, + tmp_dir: Path, + image_queue: Queue, + ms: MechanicalSkeleton | None = None, ): # Cache images if not isinstance(tmp_dir, Path): @@ -100,9 +226,10 @@ def draws_from( typed_dir = tmp_dir / "typed_dir" canvas_screenshot_dir = tmp_dir / "canvas_screenshot" + ms = ms or MechanicalSkeleton(page=page) monster = cls( page=page, - ms=MechanicalSkeleton(page), + ms=ms, tmp_dir=tmp_dir, image_queue=image_queue, typed_dir=typed_dir, @@ -134,6 +261,14 @@ def _init_imgdb(self, label: str, prompt: str): self.canvas_screenshot_dir = self.tmp_dir.joinpath(f"canvas_screenshot/{prompt}") self.canvas_screenshot_dir.mkdir(parents=True, exist_ok=True) + async def _recall_crumb(self): + frame_challenge = self.ms.switch_to_challenge_frame() + crumbs = frame_challenge.locator("//div[@class='Crumb']") + if await crumbs.first.is_visible(): + self.crumb_count = 2 + else: + self.crumb_count = 1 + async def _recall_tasklist(self): """run after _init_imgdb""" frame_challenge = self.ms.switch_to_challenge_frame() @@ -182,20 +317,20 @@ async def _recall_tasklist(self): async def _get_captcha(self, **kwargs): raise NotImplementedError - async def _solve_captcha(self, **kwargs): - frame_challenge = self.ms.switch_to_challenge_frame() - + async def _solve_captcha(self): match self.qr.request_type: case RequestType.ImageLabelBinary: - pass + await self.ms.challenge_image_label_binary( + label=self.label, challenge_images=self.tasklist + ) case RequestType.ImageLabelAreaSelect: # Cache canvas to prepare for subsequent model processing # canvas = frame_challenge.locator("//canvas") # fp = self.canvas_screenshot_dir / f"{challenge_image.filename}.png" # await canvas.screenshot(type="png", path=fp, scale="css") - pass + await self.ms.refresh_challenge() case RequestType.ImageLabelMultipleChoice: - pass + await self.ms.refresh_challenge() case _: logger.warning("[INTERRUPT]", reason="Unknown type of challenge") @@ -215,6 +350,7 @@ async def _collect(self): async def _challenge(self): await self._collect() + await self._recall_crumb() await self._solve_captcha() async def invoke(self, execution: ToolExecution = ToolExecution.CHALLENGE): @@ -258,7 +394,7 @@ async def _get_captcha(self, **kwargs): # request_type if await frame_challenge.locator("//div[@class='task-grid']").count(): self.qr.request_type = RequestType.ImageLabelBinary.value - has_exp = await frame_challenge.locator("//div[@class='challenge-example']").count() + # has_exp = await frame_challenge.locator("//div[@class='challenge-example']").count() elif await frame_challenge.locator("//div[contains(@class, 'bounding-box')]").count(): self.qr.request_type = RequestType.ImageLabelAreaSelect.value else: @@ -270,8 +406,6 @@ async def _get_captcha(self, **kwargs): class AgentV: page: Page - modelhub: ModelHub - ms: MechanicalSkeleton = field(default_factory=MechanicalSkeleton) cr: ChallengeResp = field(default_factory=ChallengeResp) @@ -286,11 +420,6 @@ class AgentV: _tool_type: ToolExecution | None = None def __post_init__(self): - # Control models - self.modelhub = self.modelhub or ModelHub.from_github_repo() - if not self.modelhub.label_alias: - self.modelhub.parse_objects() - self.tmp_dir = self.tmp_dir or Path("tmp_dir") self._cache_dir = self.tmp_dir / ".cache" self._cache_dir.mkdir(parents=True, exist_ok=True) @@ -300,10 +429,9 @@ def __post_init__(self): self.task_queue = Queue(maxsize=1) @classmethod - def into_solver(cls, page: Page, tmp_dir=None, modelhub: ModelHub | None = None, **kwargs): - return cls( - page=page, ms=MechanicalSkeleton(page), tmp_dir=tmp_dir, modelhub=modelhub, **kwargs - ) + def into_solver(cls, page: Page, tmp_dir=None, clip_model: MossCLIP | None = None, **kwargs): + ms = MechanicalSkeleton(page=page, clip_model=clip_model) + return cls(page=page, ms=ms, tmp_dir=tmp_dir, **kwargs) @property def status(self): @@ -359,7 +487,12 @@ async def _task_handler(self, response: Response): async def _tool_execution(self): qr_data = await self.task_queue.get() - driver_conf = {"page": self.page, "tmp_dir": self.tmp_dir, "image_queue": self.image_queue} + driver_conf = { + "page": self.page, + "tmp_dir": self.tmp_dir, + "image_queue": self.image_queue, + "ms": self.ms, + } runnable: OminousLand | None = None match content_type := qr_data.headers.get("content-type"): @@ -384,6 +517,10 @@ async def wait_for_challenge( ) -> Status: self._tool_type = ToolExecution.CHALLENGE + if not self.ms.clip_model: + modelhub = ModelHub.from_github_repo() + self.ms.clip_model = register_pipline(modelhub, fmt="onnx") + # CoroutineTask: Assigning human-computer challenge tasks to the main thread coroutine. # Wait for the task to finish executing try: diff --git a/hcaptcha_challenger/models.py b/hcaptcha_challenger/models.py index a699bf6345..f67a2fd032 100644 --- a/hcaptcha_challenger/models.py +++ b/hcaptcha_challenger/models.py @@ -11,7 +11,7 @@ from typing import List, Dict, Any, Union from uuid import UUID -from pydantic import BaseModel, Field, field_validator, UUID4, AnyHttpUrl +from pydantic import BaseModel, Field, field_validator, UUID4, AnyHttpUrl, Base64Bytes from hcaptcha_challenger.tools.prompt_handler import label_cleaning @@ -202,5 +202,20 @@ def save(self, typed_dir: Path) -> Path: fp.write_bytes(self.body) return fp - def convert_body_to_base64(self) -> str: - return base64.b64encode(self.body).decode("utf8") + def into_base64bytes(self) -> str: + return base64.b64encode(self.body).decode() + + +class SelfSupervisedPayload(BaseModel): + """hCaptcha payload of the image_label_binary challenge""" + + prompt: str = Field(..., description="challenge prompt") + challenge_images: List[Base64Bytes] = Field(default_factory=list) + positive_labels: List[str] | None = Field(default_factory=list) + negative_labels: List[str] | None = Field(default_factory=list) + + +class SelfSupervisedResponse(BaseModel): + """The binary classification result of the image, in the same order as the challenge_images.""" + + results: List[bool] = Field(default_factory=list) diff --git a/hcaptcha_challenger/tools/zero_shot_image_classifier.py b/hcaptcha_challenger/tools/zero_shot_image_classifier.py index 75f4332973..46559bc1db 100644 --- a/hcaptcha_challenger/tools/zero_shot_image_classifier.py +++ b/hcaptcha_challenger/tools/zero_shot_image_classifier.py @@ -7,16 +7,18 @@ from dataclasses import dataclass from dataclasses import field +from io import BytesIO from pathlib import Path from typing import List, Literal, Iterable, Tuple import onnxruntime -from PIL.Image import Image +from PIL import Image from hcaptcha_challenger.onnx.clip import MossCLIP from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake from hcaptcha_challenger.onnx.utils import is_cuda_pipline_available from hcaptcha_challenger.tools.prompt_handler import handle +from hcaptcha_challenger.models import SelfSupervisedPayload def register_pipline( @@ -39,6 +41,10 @@ def register_pipline( """ if fmt in ["transformers", None]: fmt = "transformers" if is_cuda_pipline_available else "onnx" + try: + import huggingface_hub # type:ignore + except ImportError: + fmt = "onnx" if fmt in ["onnx"]: v_net, t_net = None, None @@ -138,3 +144,31 @@ def __call__(self, detector: MossCLIP, image: Image, *args, **kwargs): image = [image] predictions = detector(image, candidate_labels=self.candidate_labels) return predictions + + +def invoke_clip_tool( + modelhub: ModelHub, payload: SelfSupervisedPayload, clip_model: MossCLIP | None = None +) -> List[bool]: + label = handle(payload.prompt) + + if any(payload.positive_labels) and any(payload.negative_labels): + serialized = { + "positive_labels": payload.positive_labels, + "negative_labels": payload.negative_labels, + } + modelhub.datalake[label] = DataLake.from_serialized(serialized) + + if not (dl := modelhub.datalake.get(label)): + dl = DataLake.from_challenge_prompt(label) + tool = ZeroShotImageClassifier.from_datalake(dl) + + # Default to `RESNET.OPENAI` perf_counter 1.794s + model = clip_model or register_pipline(modelhub) + + response: List[bool] = [] + for image_data in payload.challenge_images: + results = tool(model, image=Image.open(BytesIO(image_data))) + trusted = results[0]["label"] in tool.positive_labels + response.append(trusted) + + return response diff --git a/pyproject.toml b/pyproject.toml index 5175370e57..fe0283d5e1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,6 +59,8 @@ playwright = { version = "*", optional = true } PyGithub = { version = "^1.59.1", optional = true } istockphoto = { version = "0.1.2", optional = true } fastapi = { version = "*", extras=["all"], optional = true} +cachetools = "^5.3.3" +asyncache = "^0.3.1" [tool.poetry.group.test.dependencies] # https://docs.pytest.org/en/stable/reference/plugin_list.html#plugin-list From 29447f5d086420260d757c8f22b0330c55b4bd1f Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Mon, 15 Apr 2024 22:30:59 +0800 Subject: [PATCH 08/14] update: wait_for_challenge --- .../agents/playwright/dragon.py | 43 ++++++++++++++++--- hcaptcha_challenger/tools/prompt_handler.py | 3 ++ 2 files changed, 40 insertions(+), 6 deletions(-) diff --git a/hcaptcha_challenger/agents/playwright/dragon.py b/hcaptcha_challenger/agents/playwright/dragon.py index b73f18fc16..21026072f0 100644 --- a/hcaptcha_challenger/agents/playwright/dragon.py +++ b/hcaptcha_challenger/agents/playwright/dragon.py @@ -61,6 +61,18 @@ "negative_labels": ["cat", "clock", "eagle"], }, "streetlamp": {"positive_labels": ["streetlamp"], "negative_labels": ["shark", "duck", "swan"]}, + "similar to the following silhouette": { + "positive_labels": ["duck"], + "negative_labels": ["cat", "dog", "frog"], + }, + "please click on objects or entities related to work": { + "positive_labels": ["glass"], + "negative_labels": ["excavator", "tree", "nature"], + }, + "similar to the following pattern": { + "positive_labels": ["raccoon"], + "negative_labels": ["duck", "apple"], + }, } _cached_ping_result = TTLCache(maxsize=10, ttl=60) @@ -180,7 +192,6 @@ async def challenge_image_label_binary( # {{< Verify >}} with suppress(TimeoutError): - await self.page.pause() fl = frame_challenge.locator("//div[@class='button-submit button']") await fl.click() @@ -338,7 +349,7 @@ async def _collect(self): await self._get_captcha() logger.debug( - "task", + "Invoke task", label=self.label, type=self.qr.request_type, requester_question=self.qr.requester_question, @@ -351,7 +362,11 @@ async def _collect(self): async def _challenge(self): await self._collect() await self._recall_crumb() - await self._solve_captcha() + + for i in range(self.crumb_count): + if i != 0: + await self._recall_tasklist() + await self._solve_captcha() async def invoke(self, execution: ToolExecution = ToolExecution.CHALLENGE): match execution: @@ -513,10 +528,14 @@ async def _tool_execution(self): await runnable.invoke(execution=self._tool_type) async def wait_for_challenge( - self, execution_timeout: float = 150.0, response_timeout: float = 30.0 + self, + execution_timeout: float = 90, + response_timeout: float = 30.0, + retry_on_failure: bool = True, ) -> Status: self._tool_type = ToolExecution.CHALLENGE + # Initialize CLIP model if not self.ms.clip_model: modelhub = ModelHub.from_github_repo() self.ms.clip_model = register_pipline(modelhub, fmt="onnx") @@ -528,6 +547,14 @@ async def wait_for_challenge( except asyncio.TimeoutError: logger.error("Challenge execution timed out", timeout=execution_timeout) return self.status.CHALLENGE_EXECUTION_TIMEOUT + logger.debug("Invoke done", _tool_type=self._tool_type) + + # CoroutineTask: Assigned a new task + # The possible reason is that the challenge was **manually** refreshed during the task. + while self.cr_queue.empty(): + if not self.task_queue.empty(): + return await self.wait_for_challenge(execution_timeout, response_timeout) + await asyncio.sleep(0.01) # CoroutineTask: Waiting for hCAPTCHA response processing result # After the completion of the human-machine challenge workflow, @@ -539,12 +566,16 @@ async def wait_for_challenge( logger.error("Timeout waiting for challenge response", timeout=response_timeout) return self.status.CHALLENGE_RESPONSE_TIMEOUT else: - logger.debug("[DONE]", **self.cr.model_dump(by_alias=True)) - # Match: Timeout / Loss if not self.cr or not self.cr.is_pass: + if retry_on_failure: + logger.error("Invoke verification", **self.cr.model_dump(by_alias=True)) + return await self.wait_for_challenge( + execution_timeout, response_timeout, retry_on_failure=retry_on_failure + ) return self.status.CHALLENGE_RETRY if self.cr.is_pass: + logger.success("Invoke verification", **self.cr.model_dump(by_alias=True)) return self.status.CHALLENGE_SUCCESS async def wait_for_collect( diff --git a/hcaptcha_challenger/tools/prompt_handler.py b/hcaptcha_challenger/tools/prompt_handler.py index 46e9c032f7..a53f41cead 100644 --- a/hcaptcha_challenger/tools/prompt_handler.py +++ b/hcaptcha_challenger/tools/prompt_handler.py @@ -57,6 +57,9 @@ def split_prompt_message(prompt_message: str, lang: str) -> str: if prompt_message.startswith("please click on the"): prompt_message = prompt_message.replace("please click on ", "").strip() return prompt_message + if prompt_message.startswith("please click on all entities similar"): + prompt_message = prompt_message.replace("please click on all entities ", "").strip() + return prompt_message if prompt_message.startswith("select all") and "images" not in prompt_message: return prompt_message.split("select all")[-1].strip() if "select all images of" in prompt_message: From 401695d0f0efd5db40cd670d4a0f2267f9036cf1 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Tue, 16 Apr 2024 00:05:32 +0800 Subject: [PATCH 09/14] fix(solve_captcha): runtime error --- hcaptcha_challenger/agents/playwright/dragon.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/hcaptcha_challenger/agents/playwright/dragon.py b/hcaptcha_challenger/agents/playwright/dragon.py index 21026072f0..b4354c7556 100644 --- a/hcaptcha_challenger/agents/playwright/dragon.py +++ b/hcaptcha_challenger/agents/playwright/dragon.py @@ -47,6 +47,7 @@ HOOK_CHALLENGE = "//iframe[contains(@title, 'hCaptcha challenge')]" +# todo move to datalake.json datalake_post = { "animals possessing wings": { "positive_labels": ["bird"], @@ -331,9 +332,13 @@ async def _get_captcha(self, **kwargs): async def _solve_captcha(self): match self.qr.request_type: case RequestType.ImageLabelBinary: - await self.ms.challenge_image_label_binary( - label=self.label, challenge_images=self.tasklist - ) + try: + await self.ms.challenge_image_label_binary( + label=self.label, challenge_images=self.tasklist + ) + except Exception as err: + logger.error(f"An error occurred while processing the challenge task", err=err) + await self.ms.refresh_challenge() case RequestType.ImageLabelAreaSelect: # Cache canvas to prepare for subsequent model processing # canvas = frame_challenge.locator("//canvas") @@ -569,7 +574,7 @@ async def wait_for_challenge( # Match: Timeout / Loss if not self.cr or not self.cr.is_pass: if retry_on_failure: - logger.error("Invoke verification", **self.cr.model_dump(by_alias=True)) + logger.error("Invoke verification", is_pass=self.cr.is_pass) return await self.wait_for_challenge( execution_timeout, response_timeout, retry_on_failure=retry_on_failure ) From 34a6d47d9cbe999931d15d214eac7e30b4f782fe Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Thu, 18 Apr 2024 00:37:40 +0800 Subject: [PATCH 10/14] f1 --- examples/faker_client.py | 4 ++-- hcaptcha_challenger/agents/playwright/dragon.py | 6 +++--- hcaptcha_challenger/tools/prompt_handler.py | 3 +++ 3 files changed, 8 insertions(+), 5 deletions(-) diff --git a/examples/faker_client.py b/examples/faker_client.py index 2d282a7b1d..ee4800f0c3 100644 --- a/examples/faker_client.py +++ b/examples/faker_client.py @@ -46,7 +46,7 @@ async def mime(context: BrowserContext): if __name__ == "__main__": - # EXECUTION = "collect" - EXECUTION = "challenge" + EXECUTION = "collect" + # EXECUTION = "challenge" encrypted_resp = asyncio.run(main(headless=False)) diff --git a/hcaptcha_challenger/agents/playwright/dragon.py b/hcaptcha_challenger/agents/playwright/dragon.py index b4354c7556..9632dd46ee 100644 --- a/hcaptcha_challenger/agents/playwright/dragon.py +++ b/hcaptcha_challenger/agents/playwright/dragon.py @@ -66,9 +66,9 @@ "positive_labels": ["duck"], "negative_labels": ["cat", "dog", "frog"], }, - "please click on objects or entities related to work": { - "positive_labels": ["glass"], - "negative_labels": ["excavator", "tree", "nature"], + "related to work": { + "positive_labels": ["excavator"], + "negative_labels": ["glass", "tree", "nature"], }, "similar to the following pattern": { "positive_labels": ["raccoon"], diff --git a/hcaptcha_challenger/tools/prompt_handler.py b/hcaptcha_challenger/tools/prompt_handler.py index a53f41cead..3b63a98e78 100644 --- a/hcaptcha_challenger/tools/prompt_handler.py +++ b/hcaptcha_challenger/tools/prompt_handler.py @@ -60,6 +60,9 @@ def split_prompt_message(prompt_message: str, lang: str) -> str: if prompt_message.startswith("please click on all entities similar"): prompt_message = prompt_message.replace("please click on all entities ", "").strip() return prompt_message + if prompt_message.startswith("please click on objects or entities"): + prompt_message = prompt_message.replace("please click on objects or entities", "") + return prompt_message.strip() if prompt_message.startswith("select all") and "images" not in prompt_message: return prompt_message.split("select all")[-1].strip() if "select all images of" in prompt_message: From 59ec3f4d1c01a13c916ce5d51663fb817234548b Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Fri, 19 Apr 2024 02:44:19 +0800 Subject: [PATCH 11/14] f2 --- {backend => api}/main.py | 0 {backend => api}/readme.md | 0 {backend => api}/routers/__init__.py | 0 {backend => api}/routers/challenge.py | 0 {backend => api}/routers/datalake.py | 0 docker/Dockerfile | 0 docker/docker-compose.yaml | 0 examples/clip_datalake.json | 0 examples/faker_client.py | 2 + hcaptcha_challenger/{ => agents}/pipline.py | 14 +- .../agents/playwright/control.py | 14 +- .../agents/playwright/dragon.py | 7 +- hcaptcha_challenger/constant.py | 35 +++++ hcaptcha_challenger/models.py | 10 +- hcaptcha_challenger/tools/__init__.py | 26 ++++ hcaptcha_challenger/tools/common.py | 142 ------------------ hcaptcha_challenger/tools/image_downloader.py | 62 ++++++++ .../tools/image_label_binary.py | 46 +++++- hcaptcha_challenger/tools/match_model.py | 34 +++++ hcaptcha_challenger/tools/prompt_handler.py | 32 +--- src/clip_datalake.json | 0 tests/test_prompt_handler.py | 2 +- 22 files changed, 225 insertions(+), 201 deletions(-) rename {backend => api}/main.py (100%) rename {backend => api}/readme.md (100%) rename {backend => api}/routers/__init__.py (100%) rename {backend => api}/routers/challenge.py (100%) rename {backend => api}/routers/datalake.py (100%) create mode 100644 docker/Dockerfile create mode 100644 docker/docker-compose.yaml create mode 100644 examples/clip_datalake.json rename hcaptcha_challenger/{ => agents}/pipline.py (98%) create mode 100644 hcaptcha_challenger/constant.py delete mode 100644 hcaptcha_challenger/tools/common.py create mode 100644 hcaptcha_challenger/tools/match_model.py create mode 100644 src/clip_datalake.json diff --git a/backend/main.py b/api/main.py similarity index 100% rename from backend/main.py rename to api/main.py diff --git a/backend/readme.md b/api/readme.md similarity index 100% rename from backend/readme.md rename to api/readme.md diff --git a/backend/routers/__init__.py b/api/routers/__init__.py similarity index 100% rename from backend/routers/__init__.py rename to api/routers/__init__.py diff --git a/backend/routers/challenge.py b/api/routers/challenge.py similarity index 100% rename from backend/routers/challenge.py rename to api/routers/challenge.py diff --git a/backend/routers/datalake.py b/api/routers/datalake.py similarity index 100% rename from backend/routers/datalake.py rename to api/routers/datalake.py diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000000..e69de29bb2 diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/clip_datalake.json b/examples/clip_datalake.json new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/faker_client.py b/examples/faker_client.py index ee4800f0c3..8126841da2 100644 --- a/examples/faker_client.py +++ b/examples/faker_client.py @@ -8,12 +8,14 @@ import asyncio from pathlib import Path +import dotenv from playwright.async_api import async_playwright, BrowserContext from hcaptcha_challenger.agents import AgentV from hcaptcha_challenger.agents import Malenia from hcaptcha_challenger.utils import SiteKey +dotenv.load_dotenv() # 1. You need to deploy sub-thread tasks and actively run `install(upgrade=True)` every 20 minutes # 2. You need to make sure to run `install(upgrade=True, clip=True)` before each instantiation diff --git a/hcaptcha_challenger/pipline.py b/hcaptcha_challenger/agents/pipline.py similarity index 98% rename from hcaptcha_challenger/pipline.py rename to hcaptcha_challenger/agents/pipline.py index a7117e2cdc..c05e810193 100644 --- a/hcaptcha_challenger/pipline.py +++ b/hcaptcha_challenger/agents/pipline.py @@ -18,19 +18,15 @@ from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake from hcaptcha_challenger.onnx.resnet import ResNetControl from hcaptcha_challenger.onnx.yolo import YOLOv8, is_matched_ash_of_war, YOLOv8Seg -from hcaptcha_challenger.tools.common import ( +from hcaptcha_challenger.tools import ( + match_datalake, match_model, - download_challenge_images, rank_models, - match_datalake, -) -from hcaptcha_challenger.tools.cv_toolkit import ( + download_challenge_images, + find_unique_color, annotate_objects, find_unique_object, - find_unique_color, -) -from hcaptcha_challenger.tools.prompt_handler import handle -from hcaptcha_challenger.tools.zero_shot_image_classifier import ( + handle, ZeroShotImageClassifier, register_pipline, ) diff --git a/hcaptcha_challenger/agents/playwright/control.py b/hcaptcha_challenger/agents/playwright/control.py index b642d96984..a12937aa0c 100644 --- a/hcaptcha_challenger/agents/playwright/control.py +++ b/hcaptcha_challenger/agents/playwright/control.py @@ -29,19 +29,15 @@ is_matched_ash_of_war, finetune_keypoint, ) -from hcaptcha_challenger.tools.common import ( - match_model, +from hcaptcha_challenger.tools import ( match_datalake, + match_model, rank_models, download_challenge_images, -) -from hcaptcha_challenger.tools.cv_toolkit import ( - find_unique_object, - annotate_objects, find_unique_color, -) -from hcaptcha_challenger.tools.prompt_handler import handle -from hcaptcha_challenger.tools.zero_shot_image_classifier import ( + annotate_objects, + find_unique_object, + handle, ZeroShotImageClassifier, register_pipline, ) diff --git a/hcaptcha_challenger/agents/playwright/dragon.py b/hcaptcha_challenger/agents/playwright/dragon.py index 9632dd46ee..e3d468d3b8 100644 --- a/hcaptcha_challenger/agents/playwright/dragon.py +++ b/hcaptcha_challenger/agents/playwright/dragon.py @@ -19,6 +19,8 @@ import dotenv import httpx +from asyncache import cached +from cachetools import TTLCache from loguru import logger from playwright.async_api import Page, Response, TimeoutError, expect @@ -35,10 +37,7 @@ ) from hcaptcha_challenger.onnx.clip import MossCLIP from hcaptcha_challenger.onnx.modelhub import ModelHub -from hcaptcha_challenger.tools.prompt_handler import handle -from hcaptcha_challenger.tools.zero_shot_image_classifier import invoke_clip_tool, register_pipline -from cachetools import TTLCache -from asyncache import cached +from hcaptcha_challenger.tools import handle, register_pipline, invoke_clip_tool dotenv.load_dotenv() diff --git a/hcaptcha_challenger/constant.py b/hcaptcha_challenger/constant.py new file mode 100644 index 0000000000..0baa4bd85b --- /dev/null +++ b/hcaptcha_challenger/constant.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +# Time : 2024/4/19 2:29 +# Author : QIN2DIM +# GitHub : https://github.com/QIN2DIM +# Description: +BAD_CODE = { + "а": "a", + "е": "e", + "e": "e", + "i": "i", + "і": "i", + "ο": "o", + "с": "c", + "ԁ": "d", + "ѕ": "s", + "һ": "h", + "у": "y", + "р": "p", + "ϳ": "j", + "х": "x", + "\u0405": "S", + "\u0042": "B", + "\u0052": "R", + "\u0049": "I", + "\u0043": "C", + "\u004b": "K", + "\u039a": "K", + "\u0053": "S", + "\u0421": "C", + "\u006c": "l", + "\u0399": "I", + "\u0392": "B", + "ー": "一", + "土": "士", +} diff --git a/hcaptcha_challenger/models.py b/hcaptcha_challenger/models.py index f67a2fd032..bb2ef7df1b 100644 --- a/hcaptcha_challenger/models.py +++ b/hcaptcha_challenger/models.py @@ -13,7 +13,7 @@ from pydantic import BaseModel, Field, field_validator, UUID4, AnyHttpUrl, Base64Bytes -from hcaptcha_challenger.tools.prompt_handler import label_cleaning +from hcaptcha_challenger.constant import BAD_CODE class Status(str, Enum): @@ -131,12 +131,16 @@ def check_requester_question_example(cls, v: str | List[str]): def cache(self, tmp_dir: Path): shape_type = self.request_config.get("shape_type", "") - requester_question = label_cleaning(self.requester_question.get("en", "")) + # label cleaning + requester_question = self.requester_question.get("en", "") + for c in BAD_CODE: + requester_question = requester_question.replace(c, BAD_CODE[c]) + answer_keys = list(self.requester_restricted_answer_set.keys()) ak = f".{answer_keys[0]}" if len(answer_keys) > 0 else "" fn = f"{self.request_type}.{shape_type}.{requester_question}{ak}.json" - inv = {"\\", "/", ":", "*", "?", "<", ">", "|"} + inv = {"\\", "/", ":", "*", "?", "<", ">", "|", "\n"} for c in inv: fn = fn.replace(c, "") diff --git a/hcaptcha_challenger/tools/__init__.py b/hcaptcha_challenger/tools/__init__.py index de089d3940..6df50f747d 100644 --- a/hcaptcha_challenger/tools/__init__.py +++ b/hcaptcha_challenger/tools/__init__.py @@ -3,3 +3,29 @@ # Author : QIN2DIM # GitHub : https://github.com/QIN2DIM # Description: +from .cv_toolkit import ( + annotate_objects, + find_unique_object, + find_similar_objects, + find_unique_color, +) +from .image_downloader import download_challenge_images +from .image_label_binary import rank_models, match_datalake +from .match_model import match_model +from .prompt_handler import handle +from .zero_shot_image_classifier import ZeroShotImageClassifier, register_pipline, invoke_clip_tool + +__all__ = [ + "download_challenge_images", + "rank_models", + "match_datalake", + "annotate_objects", + "find_unique_object", + "find_similar_objects", + "handle", + "ZeroShotImageClassifier", + "register_pipline", + "match_model", + "find_unique_color", + "invoke_clip_tool", +] diff --git a/hcaptcha_challenger/tools/common.py b/hcaptcha_challenger/tools/common.py deleted file mode 100644 index ce6adafeee..0000000000 --- a/hcaptcha_challenger/tools/common.py +++ /dev/null @@ -1,142 +0,0 @@ -# -*- coding: utf-8 -*- -# Time : 2023/11/18 22:38 -# Author : QIN2DIM -# GitHub : https://github.com/QIN2DIM -# Description: -from __future__ import annotations - -import asyncio -import hashlib -import shutil -import time -from contextlib import suppress -from pathlib import Path -from typing import Literal, List, Tuple - -from hcaptcha_challenger.models import QuestionResp -from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake -from hcaptcha_challenger.onnx.resnet import ResNetControl -from hcaptcha_challenger.onnx.yolo import YOLOv8 -from hcaptcha_challenger.tools.image_downloader import Cirilla - - -def rank_models( - nested_models: List[str], example_paths: List[Path], modelhub: ModelHub -) -> Tuple[ResNetControl, str] | None: - # {{< Rank ResNet Models >}} - rank_ladder = [] - - for example_path in example_paths: - img_stream = example_path.read_bytes() - for model_name in reversed(nested_models): - if (net := modelhub.match_net(focus_name=model_name)) is None: - return - control = ResNetControl.from_pluggable_model(net) - result_, proba = control.execute(img_stream, proba=True) - if result_ and proba[0] > 0.68: - rank_ladder.append([control, model_name, proba]) - if proba[0] > 0.87: - break - - # {{< Catch-all Rules >}} - if rank_ladder: - alts = sorted(rank_ladder, key=lambda x: x[-1][0], reverse=True) - best_model, model_name = alts[0][0], alts[0][1] - return best_model, model_name - - -def match_datalake(modelhub: ModelHub, label: str) -> DataLake: - # prelude datalake - if dl := modelhub.datalake.get(label): - return dl - - # prelude clip_candidates - for ket in reversed(modelhub.clip_candidates.keys()): - if ket in label: - candidates = modelhub.clip_candidates[ket] - if candidates and len(candidates) > 2: - dl = DataLake.from_binary_labels(candidates[:1], candidates[1:]) - return dl - - # catch-all - dl = DataLake.from_challenge_prompt(raw_prompt=label) - return dl - - -def match_model( - label: str, ash: str, modelhub: ModelHub, select: Literal["yolo", "resnet"] = None -) -> ResNetControl | YOLOv8: - """match solution after `tactical_retreat`""" - focus_label = modelhub.label_alias.get(label, "") - - # Match YOLOv8 model - if not focus_label or select == "yolo": - focus_name, classes = modelhub.apply_ash_of_war(ash=ash) - session = modelhub.match_net(focus_name=focus_name) - detector = YOLOv8.from_pluggable_model(session, classes) - return detector - - # Match ResNet model - focus_name = focus_label - if not focus_name.endswith(".onnx"): - focus_name = f"{focus_name}.onnx" - net = modelhub.match_net(focus_name=focus_name) - control = ResNetControl.from_pluggable_model(net) - return control - - -async def download_challenge_images( - qr: QuestionResp, label: str, tmp_dir: Path, ignore_examples: bool = False -): - request_type = qr.request_type - ks = list(qr.requester_restricted_answer_set.keys()) - - inv = {"\\", "/", ":", "*", "?", "<", ">", "|"} - for c in inv: - label = label.replace(c, "") - label = label.strip() - - if len(ks) > 0: - typed_dir = tmp_dir.joinpath(request_type, label, ks[0]) - else: - typed_dir = tmp_dir.joinpath(request_type, label) - typed_dir.mkdir(parents=True, exist_ok=True) - - ciri = Cirilla() - container = [] - tasks = [] - for i, tk in enumerate(qr.tasklist): - challenge_img_path = typed_dir.joinpath(f"{time.time()}.{i}.png") - context = (challenge_img_path, tk.datapoint_uri) - container.append(context) - tasks.append(asyncio.create_task(ciri.elder_blood(context))) - - examples = [] - if not ignore_examples: - with suppress(Exception): - for i, uri in enumerate(qr.requester_question_example): - example_img_path = typed_dir.joinpath(f"{time.time()}.exp.{i}.png") - context = (example_img_path, uri) - examples.append(context) - tasks.append(asyncio.create_task(ciri.elder_blood(context))) - - await asyncio.gather(*tasks) - - # Optional deduplication - _img_paths = [] - for src, _ in container: - cache = src.read_bytes() - dst = typed_dir.joinpath(f"{hashlib.md5(cache).hexdigest()}.png") - shutil.move(src, dst) - _img_paths.append(dst) - - # Optional deduplication - _example_paths = [] - if examples: - for src, _ in examples: - cache = src.read_bytes() - dst = typed_dir.joinpath(f"{hashlib.md5(cache).hexdigest()}.png") - shutil.move(src, dst) - _example_paths.append(dst) - - return _img_paths, _example_paths diff --git a/hcaptcha_challenger/tools/image_downloader.py b/hcaptcha_challenger/tools/image_downloader.py index 082221a4b2..0eaac3aa73 100644 --- a/hcaptcha_challenger/tools/image_downloader.py +++ b/hcaptcha_challenger/tools/image_downloader.py @@ -6,14 +6,19 @@ from __future__ import annotations import asyncio +import hashlib +import shutil import sys +import time from abc import ABC, abstractmethod +from contextlib import suppress from pathlib import Path from typing import Any, Tuple, List import httpx from httpx import AsyncClient from tenacity import * +from hcaptcha_challenger.models import QuestionResp DownloadList = List[Tuple[Path, str]] @@ -99,3 +104,60 @@ def common_download(container: DownloadList): for img_path, url in container: resp = httpx.get(url) img_path.write_bytes(resp.content) + + +async def download_challenge_images( + qr: QuestionResp, label: str, tmp_dir: Path, ignore_examples: bool = False +): + request_type = qr.request_type + ks = list(qr.requester_restricted_answer_set.keys()) + + inv = {"\\", "/", ":", "*", "?", "<", ">", "|", "\n"} + for c in inv: + label = label.replace(c, "") + label = label.strip() + + if len(ks) > 0: + typed_dir = tmp_dir.joinpath(request_type, label, ks[0]) + else: + typed_dir = tmp_dir.joinpath(request_type, label) + typed_dir.mkdir(parents=True, exist_ok=True) + + ciri = Cirilla() + container = [] + tasks = [] + for i, tk in enumerate(qr.tasklist): + challenge_img_path = typed_dir.joinpath(f"{time.time()}.{i}.png") + context = (challenge_img_path, tk.datapoint_uri) + container.append(context) + tasks.append(asyncio.create_task(ciri.elder_blood(context))) + + examples = [] + if not ignore_examples: + with suppress(Exception): + for i, uri in enumerate(qr.requester_question_example): + example_img_path = typed_dir.joinpath(f"{time.time()}.exp.{i}.png") + context = (example_img_path, uri) + examples.append(context) + tasks.append(asyncio.create_task(ciri.elder_blood(context))) + + await asyncio.gather(*tasks) + + # Optional deduplication + _img_paths = [] + for src, _ in container: + cache = src.read_bytes() + dst = typed_dir.joinpath(f"{hashlib.md5(cache).hexdigest()}.png") + shutil.move(src, dst) + _img_paths.append(dst) + + # Optional deduplication + _example_paths = [] + if examples: + for src, _ in examples: + cache = src.read_bytes() + dst = typed_dir.joinpath(f"{hashlib.md5(cache).hexdigest()}.png") + shutil.move(src, dst) + _example_paths.append(dst) + + return _img_paths, _example_paths diff --git a/hcaptcha_challenger/tools/image_label_binary.py b/hcaptcha_challenger/tools/image_label_binary.py index c4a07bbe26..6eec557a5a 100644 --- a/hcaptcha_challenger/tools/image_label_binary.py +++ b/hcaptcha_challenger/tools/image_label_binary.py @@ -7,7 +7,7 @@ from contextlib import suppress from pathlib import Path -from typing import List, Dict +from typing import List, Dict, Tuple import cv2 from PIL import Image @@ -15,7 +15,6 @@ from hcaptcha_challenger.onnx.modelhub import ModelHub, DataLake from hcaptcha_challenger.onnx.resnet import ResNetControl -from hcaptcha_challenger.tools.common import rank_models from hcaptcha_challenger.tools.prompt_handler import handle from hcaptcha_challenger.tools.zero_shot_image_classifier import ( ZeroShotImageClassifier, @@ -154,3 +153,46 @@ def __init__(self, model_path: Path): def parse_once(self, image: bytes) -> bool | None: with suppress(Exception): return self.model.execute(image) + + +def rank_models( + nested_models: List[str], example_paths: List[Path], modelhub: ModelHub +) -> Tuple[ResNetControl, str] | None: + # {{< Rank ResNet Models >}} + rank_ladder = [] + + for example_path in example_paths: + img_stream = example_path.read_bytes() + for model_name in reversed(nested_models): + if (net := modelhub.match_net(focus_name=model_name)) is None: + return + control = ResNetControl.from_pluggable_model(net) + result_, proba = control.execute(img_stream, proba=True) + if result_ and proba[0] > 0.68: + rank_ladder.append([control, model_name, proba]) + if proba[0] > 0.87: + break + + # {{< Catch-all Rules >}} + if rank_ladder: + alts = sorted(rank_ladder, key=lambda x: x[-1][0], reverse=True) + best_model, model_name = alts[0][0], alts[0][1] + return best_model, model_name + + +def match_datalake(modelhub: ModelHub, label: str) -> DataLake: + # prelude datalake + if dl := modelhub.datalake.get(label): + return dl + + # prelude clip_candidates + for ket in reversed(modelhub.clip_candidates.keys()): + if ket in label: + candidates = modelhub.clip_candidates[ket] + if candidates and len(candidates) > 2: + dl = DataLake.from_binary_labels(candidates[:1], candidates[1:]) + return dl + + # catch-all + dl = DataLake.from_challenge_prompt(raw_prompt=label) + return dl diff --git a/hcaptcha_challenger/tools/match_model.py b/hcaptcha_challenger/tools/match_model.py new file mode 100644 index 0000000000..37c4bc6b8b --- /dev/null +++ b/hcaptcha_challenger/tools/match_model.py @@ -0,0 +1,34 @@ +# -*- coding: utf-8 -*- +# Time : 2023/11/18 22:38 +# Author : QIN2DIM +# GitHub : https://github.com/QIN2DIM +# Description: +from __future__ import annotations + +from typing import Literal + +from hcaptcha_challenger.onnx.modelhub import ModelHub +from hcaptcha_challenger.onnx.resnet import ResNetControl +from hcaptcha_challenger.onnx.yolo import YOLOv8 + + +def match_model( + label: str, ash: str, modelhub: ModelHub, select: Literal["yolo", "resnet"] = None +) -> ResNetControl | YOLOv8: + """match solution after `tactical_retreat`""" + focus_label = modelhub.label_alias.get(label, "") + + # Match YOLOv8 model + if not focus_label or select == "yolo": + focus_name, classes = modelhub.apply_ash_of_war(ash=ash) + session = modelhub.match_net(focus_name=focus_name) + detector = YOLOv8.from_pluggable_model(session, classes) + return detector + + # Match ResNet model + focus_name = focus_label + if not focus_name.endswith(".onnx"): + focus_name = f"{focus_name}.onnx" + net = modelhub.match_net(focus_name=focus_name) + control = ResNetControl.from_pluggable_model(net) + return control diff --git a/hcaptcha_challenger/tools/prompt_handler.py b/hcaptcha_challenger/tools/prompt_handler.py index 3b63a98e78..3e232a6f9f 100644 --- a/hcaptcha_challenger/tools/prompt_handler.py +++ b/hcaptcha_challenger/tools/prompt_handler.py @@ -4,37 +4,7 @@ # GitHub : https://github.com/QIN2DIM # Description: import re - -BAD_CODE = { - "а": "a", - "е": "e", - "e": "e", - "i": "i", - "і": "i", - "ο": "o", - "с": "c", - "ԁ": "d", - "ѕ": "s", - "һ": "h", - "у": "y", - "р": "p", - "ϳ": "j", - "х": "x", - "\u0405": "S", - "\u0042": "B", - "\u0052": "R", - "\u0049": "I", - "\u0043": "C", - "\u004b": "K", - "\u039a": "K", - "\u0053": "S", - "\u0421": "C", - "\u006c": "l", - "\u0399": "I", - "\u0392": "B", - "ー": "一", - "土": "士", -} +from hcaptcha_challenger.constant import BAD_CODE def split_prompt_message(prompt_message: str, lang: str) -> str: diff --git a/src/clip_datalake.json b/src/clip_datalake.json new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_prompt_handler.py b/tests/test_prompt_handler.py index abc2060050..4d5d631989 100644 --- a/tests/test_prompt_handler.py +++ b/tests/test_prompt_handler.py @@ -11,7 +11,7 @@ import pytest from hcaptcha_challenger import split_prompt_message, label_cleaning, handle -from hcaptcha_challenger.tools.prompt_handler import BAD_CODE +from hcaptcha_challenger.constant import BAD_CODE pattern = re.compile(r"[^\x00-\x7F]") From 46aa578901456218cf9e1b27700e2d886851dcea Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Fri, 19 Apr 2024 06:09:46 +0800 Subject: [PATCH 12/14] f3 --- hcaptcha_challenger/onnx/modelhub.py | 94 +++++++++++++++++--------- src/clip_datalake.json | 0 src/{objects.yaml => objects2023.yaml} | 0 src/objects2024.yaml | 31 +++++++++ 4 files changed, 92 insertions(+), 33 deletions(-) delete mode 100644 src/clip_datalake.json rename src/{objects.yaml => objects2023.yaml} (100%) create mode 100644 src/objects2024.yaml diff --git a/hcaptcha_challenger/onnx/modelhub.py b/hcaptcha_challenger/onnx/modelhub.py index ac46954692..69a1e2e875 100644 --- a/hcaptcha_challenger/onnx/modelhub.py +++ b/hcaptcha_challenger/onnx/modelhub.py @@ -6,6 +6,7 @@ from __future__ import annotations import gc +import inspect import json import os import shutil @@ -14,7 +15,7 @@ from datetime import datetime, timedelta from json import JSONDecodeError from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, List, Tuple, Any, Literal from urllib.parse import urlparse import cv2 @@ -27,11 +28,18 @@ from tenacity import * from tqdm import tqdm -from hcaptcha_challenger.utils import from_dict_to_model - DEFAULT_KEYPOINT_MODEL = "COCO2020_yolov8m.onnx" +def from_dict_to_model(cls, data: Dict[str, Any]): + return cls( + **{ + key: (data[key] if val.default == val.empty else data.get(key, val.default)) + for key, val in inspect.signature(cls).parameters.items() + } + ) + + @logger.catch @retry( retry=retry_if_exception_type((httpx.ConnectTimeout, httpx.ConnectError)), @@ -252,6 +260,7 @@ class ModelHub: lang: str = "en" label_alias: Dict[str, str] = field(default_factory=dict) + model_slots: Dict[str, ModelSlot] = field(default_factory=dict) """ Image classification --- @@ -262,6 +271,7 @@ class ModelHub: yolo_names: List[str] = field(default_factory=list) ashes_of_war: Dict[str, List[str]] = field(default_factory=dict) + yolo_modelscope: Dict[str, YOLOModelscope] = field(default_factory=dict) """ Object Detection --- @@ -277,7 +287,7 @@ class ModelHub: "find the ｛z｝ pictures most similar to ｛y｝ in the ｛x_i｝ pictures" """ - circle_segment_model: str = field(default=str) + circle_segment_model: str = "appears_only_once_2309_yolov8s-seg.onnx" """ Image Segmentation --- @@ -327,9 +337,22 @@ def __post_init__(self): self.assets_dir.mkdir(mode=0o777, parents=True, exist_ok=True) @classmethod - def from_github_repo(cls, username: str = "QIN2DIM", lang: str = "en", **kwargs): - release_url = f"https://api.github.com/repos/{username}/hcaptcha-challenger/releases" - objects_url = f"https://raw.githubusercontent.com/{username}/hcaptcha-challenger/main/src/objects.yaml" + def from_github_repo( + cls, + username: str = "QIN2DIM", + lang: str = "en", + repo: str = "hcaptcha-challenger", + conf_: str = "objects2024.yaml", + **kwargs, + ): + release_url = ( + kwargs.get("release_url", os.getenv("RELEASE_URL")) + or f"https://api.github.com/repos/{username}/{repo}/releases" + ) + objects_url = ( + kwargs.get("objects_url", os.getenv("OBJECTS_URL")) + or f"https://raw.githubusercontent.com/{username}/{repo}/main/src/{conf_}" + ) instance = cls(release_url=release_url, objects_url=objects_url, lang=lang) instance.assets = Assets.from_release_url(release_url) @@ -356,34 +379,20 @@ def parse_objects(self): os.remove(self.objects_path) return - label_to_i18n_mapping: dict = data.get("label_alias", {}) - if label_to_i18n_mapping: - for model_name, lang_to_prompts in label_to_i18n_mapping.items(): - for lang, prompts in lang_to_prompts.items(): - if lang != self.lang: - continue - self.label_alias.update({prompt.strip(): model_name for prompt in prompts}) - - yolo2names: Dict[str, List[str]] = data.get("ashes_of_war", {}) - if yolo2names: - self.yolo_names = [cl for cc in yolo2names.values() for cl in cc] - self.ashes_of_war = yolo2names - - nested_categories = data.get("nested_categories", {}) - self.nested_categories = nested_categories or {} - - self.circle_segment_model = data.get( - "circle_seg", "appears_only_once_2309_yolov8s-seg.onnx" - ) + # Match model slots + for slot in data.get("model_slots", []): + if prompt_ := slot.get("requester_question"): + self.model_slots[prompt_] = ModelSlot(**slot) - datalake = data.get("datalake", {}) - if datalake: - for prompt, serialized_binary in datalake.items(): - datalake[prompt] = DataLake.from_serialized(serialized_binary) - self.datalake = datalake or {} + # Match YOLO models + for ym in data.get("yolo_modelscope", []): + if model_name_ := ym.get("model"): + self.yolo_modelscope[model_name_] = YOLOModelscope(**ym) - clip_candidates = data.get("clip_candidates", {}) - self.clip_candidates = clip_candidates or {} + # Match CLIP selections + for selection in data.get("clip_selections", []): + if prompt_ := selection.get("requester_question"): + self.datalake[prompt_] = DataLake.from_clip_selection(**selection) def pull_model(self, focus_name: str): """ @@ -593,3 +602,22 @@ def from_serialized(cls, fields: Dict[str, List[str]]): @classmethod def from_binary_labels(cls, positive_labels: List[str], negative_labels: List[str]): return cls(positive_labels=positive_labels, negative_labels=negative_labels) + + @classmethod + def from_clip_selection(cls, requester_question: str, positive: List[str], negative: List[str]): + return cls( + positive_labels=positive, negative_labels=negative, raw_prompt=requester_question + ) + + +@dataclass +class ModelSlot: + requester_question: str + request_type: Literal["image_label_binary", "image_label_area_select"] + related_models: List[str] = field(default_factory=list) + + +@dataclass +class YOLOModelscope: + model: str + labels: List[str] = field(default_factory=list) diff --git a/src/clip_datalake.json b/src/clip_datalake.json deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/src/objects.yaml b/src/objects2023.yaml similarity index 100% rename from src/objects.yaml rename to src/objects2023.yaml diff --git a/src/objects2024.yaml b/src/objects2024.yaml new file mode 100644 index 0000000000..61168d311d --- /dev/null +++ b/src/objects2024.yaml @@ -0,0 +1,31 @@ +$ref: + release_url: https://api.github.com/repos/QIN2DIM/hcaptcha-challenger/releases + objects_url: https://raw.githubusercontent.com/QDIN2DIM/hcaptcha-challenger/main/src/objects2024.yaml +model_slots: + - requester_question: Please click on the turtle's head + request_type: image_label_area_select + related_models: + - head_of_the_animal_turtle_2309_yolov8s.onnx + - requester_question: Please click all images contain electronic device + request_type: image_label_binary + related_models: + - electronic_device2316.onnx + - nested_electronic_device_mouse2309.onnx + - nested_electronic_device_circuit_board2309.onnx + - requester_question: Please click all images contain plant + request_type: image_label_binary + related_models: + - nested_plant2311.onnx + - plant2319.onnx +yolo_modelscope: + - model: head_of_the_animal_turtle_2309_yolov8s.onnx + labels: + - turtle-head +clip_selections: + - requester_question: Please click all images contain football stadium + positive: + - football stadium + negative: + - mouse + - natural landscape + - river From 1b0440347ff2f6290222b45ecead808cb5a2c4d1 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sat, 20 Apr 2024 15:29:55 +0800 Subject: [PATCH 13/14] f3 --- automation/collector.py | 4 +-- automation/sentinel.py | 4 +-- examples/faker_client.py | 5 +-- hcaptcha_challenger/__init__.py | 17 ++++----- hcaptcha_challenger/onnx/modelhub.py | 13 +++---- hcaptcha_challenger/tools/prompt_handler.py | 40 +++++---------------- tests/test_prompt_handler.py | 4 +-- 7 files changed, 30 insertions(+), 57 deletions(-) diff --git a/automation/collector.py b/automation/collector.py index 1ca3d6967a..7c7f2a7160 100644 --- a/automation/collector.py +++ b/automation/collector.py @@ -27,7 +27,7 @@ from loguru import logger from playwright.async_api import BrowserContext as ASyncContext, async_playwright -from hcaptcha_challenger import split_prompt_message, label_cleaning +from hcaptcha_challenger import regularize_prompt_message, label_cleaning from hcaptcha_challenger.agents import AgentT, Malenia TEMPLATE_BINARY_DATASETS = """ @@ -81,7 +81,7 @@ def __post_init__(self): self.mixed_label = self.issue.title.split(" ")[1].strip() self.parent_prompt = self.issue.title.split("@")[-1].strip() else: - self.mixed_label = split_prompt_message(self.challenge_prompt, lang="en") + self.mixed_label = regularize_prompt_message(self.challenge_prompt) self.parent_prompt = "image_label_binary" @classmethod diff --git a/automation/sentinel.py b/automation/sentinel.py index e911122d1c..6c06818528 100644 --- a/automation/sentinel.py +++ b/automation/sentinel.py @@ -22,7 +22,7 @@ from playwright.async_api import BrowserContext as ASyncContext, async_playwright, Page import hcaptcha_challenger as solver -from hcaptcha_challenger import label_cleaning, split_prompt_message +from hcaptcha_challenger import label_cleaning, regularize_prompt_message from hcaptcha_challenger.agents import AgentT, Malenia from hcaptcha_challenger.models import QuestionResp from hcaptcha_challenger.onnx.yolo import is_matched_ash_of_war @@ -169,7 +169,7 @@ def _bypass_motion(self): since=datetime.now() - timedelta(days=14), assignee=self.assignees[0], ): - mixed_label = split_prompt_message(self.issue_prompt, lang="en") + mixed_label = regularize_prompt_message(self.issue_prompt) if issue.created_at + timedelta(hours=24) > datetime.now(): issue.add_to_labels("🏹 ci: sentinel") if mixed_label in issue.title.lower(): diff --git a/examples/faker_client.py b/examples/faker_client.py index 8126841da2..0cfe12a2d2 100644 --- a/examples/faker_client.py +++ b/examples/faker_client.py @@ -14,6 +14,7 @@ from hcaptcha_challenger.agents import AgentV from hcaptcha_challenger.agents import Malenia from hcaptcha_challenger.utils import SiteKey +from hcaptcha_challenger import install dotenv.load_dotenv() @@ -48,7 +49,7 @@ async def mime(context: BrowserContext): if __name__ == "__main__": - EXECUTION = "collect" - # EXECUTION = "challenge" + # EXECUTION = "collect" + EXECUTION = "challenge" encrypted_resp = asyncio.run(main(headless=False)) diff --git a/hcaptcha_challenger/__init__.py b/hcaptcha_challenger/__init__.py index 0ab9676513..df7ef16cd3 100644 --- a/hcaptcha_challenger/__init__.py +++ b/hcaptcha_challenger/__init__.py @@ -20,7 +20,7 @@ from hcaptcha_challenger.tools.prompt_handler import ( label_cleaning, diagnose_task, - split_prompt_message, + regularize_prompt_message, prompt2task, handle, ) @@ -44,7 +44,7 @@ "ChallengeResp", "label_cleaning", "diagnose_task", - "split_prompt_message", + "regularize_prompt_message", "prompt2task", "handle", "ModelHub", @@ -56,19 +56,20 @@ init_log( - runtime=Path("logs/runtime.log"), - error=Path("logs/error.log"), - serialize=Path("logs/serialize.log"), + runtime=Path("logs/{time:YYYY-MM-DD}/runtime.log"), + error=Path("logs/{time:YYYY-MM-DD}/error.log"), + serialize=Path("logs/{time:YYYY-MM-DD}/serialize.log"), ) def install( upgrade: bool | None = False, - username: str = "QIN2DIM", - lang: str = "en", flush_yolo: bool | Iterable[str] = False, pypi: bool = False, clip: bool = False, + username: str = "QIN2DIM", + repo: str = "hcaptcha-challenger", + conf_="objects2024.yaml", **kwargs, ): if pypi is True: @@ -76,7 +77,7 @@ def install( PyPI("hcaptcha-challenger").install() - modelhub = ModelHub.from_github_repo(username=username, lang=lang) + modelhub = ModelHub.from_github_repo(username=username, repo=repo, conf_=conf_) modelhub.pull_objects(upgrade=upgrade) modelhub.assets.flush_runtime_assets(upgrade=upgrade) diff --git a/hcaptcha_challenger/onnx/modelhub.py b/hcaptcha_challenger/onnx/modelhub.py index 69a1e2e875..aafef727a0 100644 --- a/hcaptcha_challenger/onnx/modelhub.py +++ b/hcaptcha_challenger/onnx/modelhub.py @@ -257,8 +257,6 @@ class ModelHub: assets_dir = models_dir.joinpath("_assets") objects_path = models_dir.joinpath("objects.yaml") - lang: str = "en" - label_alias: Dict[str, str] = field(default_factory=dict) model_slots: Dict[str, ModelSlot] = field(default_factory=dict) """ @@ -319,7 +317,7 @@ class ModelHub: clip_candidates: Dict[str, List[str]] = field(default_factory=dict) """ - CLIP self-supervised candidates + [DEPRECATED] CLIP self-supervised candidates """ release_url: str = "" @@ -334,13 +332,13 @@ class ModelHub: """ def __post_init__(self): - self.assets_dir.mkdir(mode=0o777, parents=True, exist_ok=True) + self.assets = Assets.from_release_url(self.release_url) + self.assets_dir.mkdir(parents=True, exist_ok=True) @classmethod def from_github_repo( cls, username: str = "QIN2DIM", - lang: str = "en", repo: str = "hcaptcha-challenger", conf_: str = "objects2024.yaml", **kwargs, @@ -354,10 +352,7 @@ def from_github_repo( or f"https://raw.githubusercontent.com/{username}/{repo}/main/src/{conf_}" ) - instance = cls(release_url=release_url, objects_url=objects_url, lang=lang) - instance.assets = Assets.from_release_url(release_url) - - return instance + return cls(release_url=release_url, objects_url=objects_url) def pull_objects(self, upgrade: bool = False): """Network request""" diff --git a/hcaptcha_challenger/tools/prompt_handler.py b/hcaptcha_challenger/tools/prompt_handler.py index 3e232a6f9f..5ee4fdcb06 100644 --- a/hcaptcha_challenger/tools/prompt_handler.py +++ b/hcaptcha_challenger/tools/prompt_handler.py @@ -7,36 +7,12 @@ from hcaptcha_challenger.constant import BAD_CODE -def split_prompt_message(prompt_message: str, lang: str) -> str: +def regularize_prompt_message(prompt_message: str) -> str: """Detach label from challenge prompt""" - if lang.startswith("zh"): - if "中包含" in prompt_message or "上包含" in prompt_message: - return re.split(r"击|(的每)", prompt_message)[2] - if "的每" in prompt_message: - return re.split(r"(包含)|(的每)", prompt_message)[3] - if "包含" in prompt_message: - return re.split(r"(包含)|(的图)", prompt_message)[3] - elif lang.startswith("en"): - prompt_message = prompt_message.replace(".", "").lower() - if "containing" in prompt_message: - th = re.split(r"containing", prompt_message)[-1][1:].strip() - return th[2:].strip() if th.startswith("a") else th - if prompt_message.startswith("please select all"): - prompt_message = prompt_message.replace("please select all ", "").strip() - return prompt_message - if prompt_message.startswith("please click on the"): - prompt_message = prompt_message.replace("please click on ", "").strip() - return prompt_message - if prompt_message.startswith("please click on all entities similar"): - prompt_message = prompt_message.replace("please click on all entities ", "").strip() - return prompt_message - if prompt_message.startswith("please click on objects or entities"): - prompt_message = prompt_message.replace("please click on objects or entities", "") - return prompt_message.strip() - if prompt_message.startswith("select all") and "images" not in prompt_message: - return prompt_message.split("select all")[-1].strip() - if "select all images of" in prompt_message: - return prompt_message.split("select all images of")[-1].strip() + prompt_message = prompt_message.lower() + if prompt_message.endswith("."): + prompt_message = prompt_message[:-1] + prompt_message = prompt_message.strip() return prompt_message @@ -71,12 +47,12 @@ def diagnose_task(words: str) -> str: return words -def prompt2task(prompt: str, lang: str = "en") -> str: - prompt = split_prompt_message(prompt, lang) +def prompt2task(prompt: str) -> str: + prompt = regularize_prompt_message(prompt) prompt = label_cleaning(prompt) prompt = diagnose_task(prompt) return prompt def handle(x): - return split_prompt_message(label_cleaning(x), "en") + return regularize_prompt_message(label_cleaning(x)) diff --git a/tests/test_prompt_handler.py b/tests/test_prompt_handler.py index 4d5d631989..d09b9c7c73 100644 --- a/tests/test_prompt_handler.py +++ b/tests/test_prompt_handler.py @@ -10,7 +10,7 @@ import pytest -from hcaptcha_challenger import split_prompt_message, label_cleaning, handle +from hcaptcha_challenger import regularize_prompt_message, label_cleaning, handle from hcaptcha_challenger.constant import BAD_CODE pattern = re.compile(r"[^\x00-\x7F]") @@ -23,7 +23,7 @@ @pytest.mark.parametrize("prompt", prompts) def test_split_prompt_message(prompt: str): - result = split_prompt_message(prompt, lang="en") + result = regularize_prompt_message(prompt) assert result != prompt From cfc745458ccb4d954dd59b3bb1cd41083fc61d13 Mon Sep 17 00:00:00 2001 From: QIN2DIM <62018067+QIN2DIM@users.noreply.github.com> Date: Sat, 20 Apr 2024 19:13:52 +0800 Subject: [PATCH 14/14] f3af5 --- examples/faker_client.py | 9 +- .../agents/playwright/dragon.py | 125 +++++++----------- hcaptcha_challenger/constant.py | 2 + hcaptcha_challenger/models.py | 15 ++- hcaptcha_challenger/onnx/modelhub.py | 33 ++--- hcaptcha_challenger/tools/image_downloader.py | 4 +- hcaptcha_challenger/tools/prompt_handler.py | 6 +- src/objects2024.yaml | 20 ++- 8 files changed, 96 insertions(+), 118 deletions(-) diff --git a/examples/faker_client.py b/examples/faker_client.py index 0cfe12a2d2..53aaeeca25 100644 --- a/examples/faker_client.py +++ b/examples/faker_client.py @@ -14,12 +14,12 @@ from hcaptcha_challenger.agents import AgentV from hcaptcha_challenger.agents import Malenia from hcaptcha_challenger.utils import SiteKey -from hcaptcha_challenger import install dotenv.load_dotenv() # 1. You need to deploy sub-thread tasks and actively run `install(upgrade=True)` every 20 minutes # 2. You need to make sure to run `install(upgrade=True, clip=True)` before each instantiation +# from hcaptcha_challenger import install # install(upgrade=True, clip=True) @@ -29,6 +29,7 @@ async def main(headless: bool = False): context = await browser.new_context(locale="en-US") await Malenia.apply_stealth(context) await mime(context) + await context.close() @@ -45,11 +46,11 @@ async def mime(context: BrowserContext): await agent.ms.click_checkbox() await agent.wait_for_challenge() elif EXECUTION == "collect": - await agent.wait_for_collect(sitekey, batch=25) + await agent.wait_for_collect(sitekey, batch=2) if __name__ == "__main__": - # EXECUTION = "collect" - EXECUTION = "challenge" + EXECUTION = "collect" + # EXECUTION = "challenge" encrypted_resp = asyncio.run(main(headless=False)) diff --git a/hcaptcha_challenger/agents/playwright/dragon.py b/hcaptcha_challenger/agents/playwright/dragon.py index e3d468d3b8..5ab6063169 100644 --- a/hcaptcha_challenger/agents/playwright/dragon.py +++ b/hcaptcha_challenger/agents/playwright/dragon.py @@ -9,6 +9,7 @@ import os import re import shutil +import uuid from abc import ABC from asyncio import Queue from contextlib import suppress @@ -24,6 +25,7 @@ from loguru import logger from playwright.async_api import Page, Response, TimeoutError, expect +from hcaptcha_challenger.constant import INV from hcaptcha_challenger.models import ( ChallengeResp, QuestionResp, @@ -46,35 +48,6 @@ HOOK_CHALLENGE = "//iframe[contains(@title, 'hCaptcha challenge')]" -# todo move to datalake.json -datalake_post = { - "animals possessing wings": { - "positive_labels": ["bird"], - "negative_labels": ["lion", "elephant", "bear"], - }, - "something for drinking": { - "positive_labels": ["cup", "something for drinking"], - "negative_labels": ["streetlamp", "animal"], - }, - "something used for transportation": { - "positive_labels": ["tractor"], - "negative_labels": ["cat", "clock", "eagle"], - }, - "streetlamp": {"positive_labels": ["streetlamp"], "negative_labels": ["shark", "duck", "swan"]}, - "similar to the following silhouette": { - "positive_labels": ["duck"], - "negative_labels": ["cat", "dog", "frog"], - }, - "related to work": { - "positive_labels": ["excavator"], - "negative_labels": ["glass", "tree", "nature"], - }, - "similar to the following pattern": { - "positive_labels": ["raccoon"], - "negative_labels": ["duck", "apple"], - }, -} - _cached_ping_result = TTLCache(maxsize=10, ttl=60) @@ -121,10 +94,8 @@ class MechanicalSkeleton: page: Page sew: SolverEdgeWorker = field(default_factory=SolverEdgeWorker) - - modelhub: ModelHub | None = None - - clip_model: MossCLIP | None = None + modelhub: ModelHub = field(default_factory=ModelHub) + clip_model: MossCLIP = field(default_factory=MossCLIP) def __post_init__(self): self.sew = SolverEdgeWorker() @@ -162,12 +133,18 @@ async def challenge_image_label_binary( ): frame_challenge = self.switch_to_challenge_frame() + # {{< Reload SELF-SUPERVISED CONFIGURATION >}} + if not (model_slot := self.modelhub.model_slots.get(label)): + return + if not (clip_selection := model_slot.clip_selection): + return + challenge_images = [i.into_base64bytes() for i in challenge_images] - patched_model_prompt = datalake_post.get(label) + clip_selection = clip_selection.model_dump() self_supervised_payload = { "prompt": label, "challenge_images": challenge_images, - **patched_model_prompt, + **clip_selection, } # {{< IMAGE CLASSIFICATION >}} @@ -177,7 +154,7 @@ async def challenge_image_label_binary( payload = SelfSupervisedPayload(**self_supervised_payload) results: List[bool] = invoke_clip_tool(self.modelhub, payload, self.clip_model) - # {{< DRIVE THE BROWSER TO TAKE ON THE CHALLENGE >}} + # {{< DRIVE THE BROWSER TO WORK ON THE CHALLENGE >}} samples = frame_challenge.locator("//div[@class='task-image']") count = await samples.count() positive_cases = 0 @@ -254,23 +231,21 @@ def draws_from( return monster - def _init_imgdb(self, label: str, prompt: str): + def _init_imgdb(self, label: str): """run after _get_captcha""" self.tasklist.clear() self.examples.clear() - inv = {"\\", "/", ":", "*", "?", "<", ">", "|", "\n"} - for c in inv: + for c in INV: label = label.replace(c, "") - prompt = prompt.replace(c, "") - label = label.strip() self.typed_dir = self.tmp_dir.joinpath(self.qr.request_type, label) self.typed_dir.mkdir(parents=True, exist_ok=True) - if self.qr.request_type != RequestType.ImageLabelBinary: - self.canvas_screenshot_dir = self.tmp_dir.joinpath(f"canvas_screenshot/{prompt}") - self.canvas_screenshot_dir.mkdir(parents=True, exist_ok=True) + self.canvas_screenshot_dir = self.tmp_dir.joinpath( + f"canvas_screenshot/{self.qr.request_type}/{label}" + ) + self.canvas_screenshot_dir.mkdir(parents=True, exist_ok=True) async def _recall_crumb(self): frame_challenge = self.ms.switch_to_challenge_frame() @@ -280,7 +255,7 @@ async def _recall_crumb(self): else: self.crumb_count = 1 - async def _recall_tasklist(self): + async def _recall_tasklist(self, capture_screenshot: bool = True): """run after _init_imgdb""" frame_challenge = self.ms.switch_to_challenge_frame() @@ -299,16 +274,21 @@ async def _recall_tasklist(self): datapoint_uri = style.split('"')[1] background_urls.append(datapoint_uri) + logger.debug(f"{self.image_queue.qsize()=}") while not self.image_queue.empty(): challenge_image: ChallengeImage = self.image_queue.get_nowait() + challenge_image.move_to(self.typed_dir) challenge_images[challenge_image.datapoint_uri] = challenge_image for url in background_urls: - challenge_image = challenge_images.get(url) + challenge_image: ChallengeImage = challenge_images.get(url) if challenge_image: self.tasklist.append(challenge_image) - if not self.typed_dir.joinpath(challenge_image.filename).exists(): - shutil.move(src=challenge_image.runtime_fp, dst=self.typed_dir) + + if capture_screenshot: + canvas = frame_challenge.locator("//div[@class='challenge-container']") + fp = self.canvas_screenshot_dir / f"{uuid.uuid4()}.png" + await canvas.screenshot(type="png", path=fp, scale="css") elif self.qr.request_type == RequestType.ImageLabelAreaSelect: # For the object detection task, tasklist is only used to collect datasets. @@ -319,11 +299,14 @@ async def _recall_tasklist(self): # Expect only 1 image in the image_queue while not self.image_queue.empty(): challenge_image: ChallengeImage = self.image_queue.get_nowait() - if not self.typed_dir.joinpath(challenge_image.filename).exists(): - shutil.move(src=challenge_image.runtime_fp, dst=self.typed_dir) - # Cache image sequences for subsequent browser operations + challenge_image.move_to(self.typed_dir) self.tasklist.append(challenge_image) + if self.image_queue.qsize() == 0: + canvas = frame_challenge.locator("//canvas") + fp = self.canvas_screenshot_dir / f"{challenge_image.filename}.png" + await canvas.screenshot(type="png", path=fp, scale="css") + @abc.abstractmethod async def _get_captcha(self, **kwargs): raise NotImplementedError @@ -339,17 +322,13 @@ async def _solve_captcha(self): logger.error(f"An error occurred while processing the challenge task", err=err) await self.ms.refresh_challenge() case RequestType.ImageLabelAreaSelect: - # Cache canvas to prepare for subsequent model processing - # canvas = frame_challenge.locator("//canvas") - # fp = self.canvas_screenshot_dir / f"{challenge_image.filename}.png" - # await canvas.screenshot(type="png", path=fp, scale="css") await self.ms.refresh_challenge() case RequestType.ImageLabelMultipleChoice: await self.ms.refresh_challenge() case _: logger.warning("[INTERRUPT]", reason="Unknown type of challenge") - async def _collect(self): + async def _collect(self, capture_screenshot: bool = True): await self._get_captcha() logger.debug( @@ -360,8 +339,8 @@ async def _collect(self): trigger=self.__class__.__name__, ) - self._init_imgdb(self.label, self.prompt) - await self._recall_tasklist() + self._init_imgdb(self.label) + await self._recall_tasklist(capture_screenshot=capture_screenshot) async def _challenge(self): await self._collect() @@ -426,15 +405,12 @@ class AgentV: page: Page ms: MechanicalSkeleton = field(default_factory=MechanicalSkeleton) - cr: ChallengeResp = field(default_factory=ChallengeResp) - - task_queue: Queue[Response] | None = None - cr_queue: Queue[ChallengeResp] = field(default_factory=Queue) - tmp_dir: Path = field(default_factory=Path) - image_queue: Queue = field(default_factory=Queue) + cr_queue: Queue[ChallengeResp] = field(default_factory=Queue) + _image_queue: Queue[ChallengeImage] = field(default_factory=Queue) + _task_queue: Queue[Response] = field(default_factory=Queue) _tool_type: ToolExecution | None = None @@ -442,11 +418,10 @@ def __post_init__(self): self.tmp_dir = self.tmp_dir or Path("tmp_dir") self._cache_dir = self.tmp_dir / ".cache" self._cache_dir.mkdir(parents=True, exist_ok=True) + self._task_queue = Queue(maxsize=1) self._enable_evnet_listener(self.page) - self.task_queue = Queue(maxsize=1) - @classmethod def into_solver(cls, page: Page, tmp_dir=None, clip_model: MossCLIP | None = None, **kwargs): ms = MechanicalSkeleton(page=page, clip_model=clip_model) @@ -463,13 +438,13 @@ def _enable_evnet_listener(self, page: Page): async def _task_handler(self, response: Response): if "/getcaptcha/" in response.url: # reset state - while not self.image_queue.empty(): - self.image_queue.get_nowait() - if self.task_queue.full(): - self.task_queue.get_nowait() + while not self._image_queue.empty(): + self._image_queue.get_nowait() + if self._task_queue.full(): + self._task_queue.get_nowait() # drop task - self.task_queue.put_nowait(response) + self._task_queue.put_nowait(response) # /cr 在 Submit Event 之后，cr 截至目前是明文数据 elif "/checkcaptcha/" in response.url: @@ -500,16 +475,16 @@ async def _task_handler(self, response: Response): element = ChallengeImage( datapoint_uri=image_url, filename=fn, body=image_bytes, runtime_fp=fp ) - self.image_queue.put_nowait(element) + self._image_queue.put_nowait(element) @logger.catch async def _tool_execution(self): - qr_data = await self.task_queue.get() + qr_data = await self._task_queue.get() driver_conf = { "page": self.page, "tmp_dir": self.tmp_dir, - "image_queue": self.image_queue, + "image_queue": self._image_queue, "ms": self.ms, } runnable: OminousLand | None = None @@ -556,7 +531,7 @@ async def wait_for_challenge( # CoroutineTask: Assigned a new task # The possible reason is that the challenge was **manually** refreshed during the task. while self.cr_queue.empty(): - if not self.task_queue.empty(): + if not self._task_queue.empty(): return await self.wait_for_challenge(execution_timeout, response_timeout) await asyncio.sleep(0.01) diff --git a/hcaptcha_challenger/constant.py b/hcaptcha_challenger/constant.py index 0baa4bd85b..f99678f4e9 100644 --- a/hcaptcha_challenger/constant.py +++ b/hcaptcha_challenger/constant.py @@ -33,3 +33,5 @@ "ー": "一", "土": "士", } + +INV = {"\\", "/", ":", "*", "?", "<", ">", "|", "\n"} diff --git a/hcaptcha_challenger/models.py b/hcaptcha_challenger/models.py index bb2ef7df1b..12a01d09e3 100644 --- a/hcaptcha_challenger/models.py +++ b/hcaptcha_challenger/models.py @@ -6,6 +6,7 @@ from __future__ import annotations import base64 +import shutil from enum import Enum from pathlib import Path from typing import List, Dict, Any, Union @@ -13,7 +14,7 @@ from pydantic import BaseModel, Field, field_validator, UUID4, AnyHttpUrl, Base64Bytes -from hcaptcha_challenger.constant import BAD_CODE +from hcaptcha_challenger.constant import BAD_CODE, INV class Status(str, Enum): @@ -140,8 +141,7 @@ def cache(self, tmp_dir: Path): ak = f".{answer_keys[0]}" if len(answer_keys) > 0 else "" fn = f"{self.request_type}.{shape_type}.{requester_question}{ak}.json" - inv = {"\\", "/", ":", "*", "?", "<", ">", "|", "\n"} - for c in inv: + for c in INV: fn = fn.replace(c, "") if tmp_dir and tmp_dir.exists(): @@ -209,14 +209,19 @@ def save(self, typed_dir: Path) -> Path: def into_base64bytes(self) -> str: return base64.b64encode(self.body).decode() + def move_to(self, dst: Path): + if dst.is_dir(): + dst = dst / self.filename + return shutil.move(self.runtime_fp, dst=dst) + class SelfSupervisedPayload(BaseModel): """hCaptcha payload of the image_label_binary challenge""" prompt: str = Field(..., description="challenge prompt") challenge_images: List[Base64Bytes] = Field(default_factory=list) - positive_labels: List[str] | None = Field(default_factory=list) - negative_labels: List[str] | None = Field(default_factory=list) + positive_labels: List[str] | None = Field(default_factory=list, alias="positive") + negative_labels: List[str] | None = Field(default_factory=list, alias="negative") class SelfSupervisedResponse(BaseModel): diff --git a/hcaptcha_challenger/onnx/modelhub.py b/hcaptcha_challenger/onnx/modelhub.py index aafef727a0..f26ff1f12d 100644 --- a/hcaptcha_challenger/onnx/modelhub.py +++ b/hcaptcha_challenger/onnx/modelhub.py @@ -27,6 +27,7 @@ from onnxruntime import InferenceSession from tenacity import * from tqdm import tqdm +from pydantic import BaseModel, Field DEFAULT_KEYPOINT_MODEL = "COCO2020_yolov8m.onnx" @@ -376,19 +377,15 @@ def parse_objects(self): # Match model slots for slot in data.get("model_slots", []): - if prompt_ := slot.get("requester_question"): - self.model_slots[prompt_] = ModelSlot(**slot) + if not (requester_question := slot.get("requester_question")): + continue + self.model_slots[requester_question] = ModelSlot(**slot) # Match YOLO models for ym in data.get("yolo_modelscope", []): if model_name_ := ym.get("model"): self.yolo_modelscope[model_name_] = YOLOModelscope(**ym) - # Match CLIP selections - for selection in data.get("clip_selections", []): - if prompt_ := selection.get("requester_question"): - self.datalake[prompt_] = DataLake.from_clip_selection(**selection) - def pull_model(self, focus_name: str): """ 1. node_id: Record the insertion point @@ -605,14 +602,18 @@ def from_clip_selection(cls, requester_question: str, positive: List[str], negat ) -@dataclass -class ModelSlot: - requester_question: str - request_type: Literal["image_label_binary", "image_label_area_select"] - related_models: List[str] = field(default_factory=list) +class CLIPSelection(BaseModel): + positive: List[str] = Field(default_factory=list) + negative: List[str] = Field(default_factory=list) -@dataclass -class YOLOModelscope: - model: str - labels: List[str] = field(default_factory=list) +class ModelSlot(BaseModel): + requester_question: str = Field(...) + request_type: Literal["image_label_binary", "image_label_area_select"] = Field(...) + related_models: List[str] | None = Field(default_factory=list) + clip_selection: CLIPSelection | None = Field(None) + + +class YOLOModelscope(BaseModel): + model: str = Field(...) + labels: List[str] = Field(default_factory=list) diff --git a/hcaptcha_challenger/tools/image_downloader.py b/hcaptcha_challenger/tools/image_downloader.py index 0eaac3aa73..dc5a6865b1 100644 --- a/hcaptcha_challenger/tools/image_downloader.py +++ b/hcaptcha_challenger/tools/image_downloader.py @@ -19,6 +19,7 @@ from httpx import AsyncClient from tenacity import * from hcaptcha_challenger.models import QuestionResp +from hcaptcha_challenger.constant import INV DownloadList = List[Tuple[Path, str]] @@ -112,8 +113,7 @@ async def download_challenge_images( request_type = qr.request_type ks = list(qr.requester_restricted_answer_set.keys()) - inv = {"\\", "/", ":", "*", "?", "<", ">", "|", "\n"} - for c in inv: + for c in INV: label = label.replace(c, "") label = label.strip() diff --git a/hcaptcha_challenger/tools/prompt_handler.py b/hcaptcha_challenger/tools/prompt_handler.py index 5ee4fdcb06..2b88045ce1 100644 --- a/hcaptcha_challenger/tools/prompt_handler.py +++ b/hcaptcha_challenger/tools/prompt_handler.py @@ -3,8 +3,7 @@ # Author : QIN2DIM # GitHub : https://github.com/QIN2DIM # Description: -import re -from hcaptcha_challenger.constant import BAD_CODE +from hcaptcha_challenger.constant import BAD_CODE, INV def regularize_prompt_message(prompt_message: str) -> str: @@ -30,8 +29,7 @@ def diagnose_task(words: str) -> str: raise TypeError(f"({words})TASK should be string type data") # Filename contains illegal characters - inv = {"\\", "/", ":", "*", "?", "<", ">", "|"} - if s := set(words) & inv: + if s := set(words) & INV: raise TypeError(f"({words})TASK contains invalid characters({s})") # Normalized separator diff --git a/src/objects2024.yaml b/src/objects2024.yaml index 61168d311d..d277bebf8f 100644 --- a/src/objects2024.yaml +++ b/src/objects2024.yaml @@ -12,20 +12,16 @@ model_slots: - electronic_device2316.onnx - nested_electronic_device_mouse2309.onnx - nested_electronic_device_circuit_board2309.onnx - - requester_question: Please click all images contain plant + - requester_question: please select the objects that can be cut with a knife or scissors request_type: image_label_binary - related_models: - - nested_plant2311.onnx - - plant2319.onnx + clip_selection: + positive: + - eggplant + negative: + - glass + - truck + - drill yolo_modelscope: - model: head_of_the_animal_turtle_2309_yolov8s.onnx labels: - turtle-head -clip_selections: - - requester_question: Please click all images contain football stadium - positive: - - football stadium - negative: - - mouse - - natural landscape - - river