From b9d3cb715bbcfee613bf22147d91ed44a6d09812 Mon Sep 17 00:00:00 2001 From: yadonglu Date: Sat, 4 Jan 2025 20:06:33 -0800 Subject: [PATCH] docker demo, migration, speedup inference using cv2 --- .gitignore | 16 +- Dockerfile | 203 +++++ LICENSE | 790 +++++++++--------- README.md | 122 +-- SECURITY.md | 82 +- __pycache__/utils.cpython-312.pyc | Bin 27847 -> 27830 bytes demo.ipynb | 796 +++++++++--------- demo/__init__.py | 0 demo/app.py | 479 +++++++++++ demo/executor/anthropic_executor.py | 136 ++++ demo/gui_agent/anthropic_agent.py | 207 +++++ demo/gui_agent/llm_utils/llm_utils.py | 109 +++ demo/gui_agent/llm_utils/oai.py | 117 +++ demo/gui_agent/llm_utils/qwen.py | 107 +++ demo/gui_agent/llm_utils/run_llm.py | 44 + demo/loop.py | 236 ++++++ demo/omniparser_agent/vlm_agent.py | 417 ++++++++++ demo/remote_request.py | 154 ++-- demo/tmp/outputs/screenshot.png | Bin 0 -> 1436362 bytes demo/tools/__init__.py | 16 + demo/tools/base.py | 69 ++ demo/tools/bash.py | 136 ++++ demo/tools/collection.py | 34 + demo/tools/colorful_text.py | 27 + demo/tools/computer.py | 519 ++++++++++++ demo/tools/edit.py | 290 +++++++ demo/tools/run.py | 42 + demo/tools/screen_capture.py | 185 +++++ gradio_demo.py | 208 ++--- omniparser.py | 120 +-- requirements.txt | 49 +- util/action_matching.py | 848 +++++++++---------- util/action_type.py | 88 +- util/box_annotator.py | 524 ++++++------ utils.py | 1076 ++++++++++++------------- weights/convert_safetensor_to_pt.py | 52 +- 36 files changed, 5842 insertions(+), 2456 deletions(-) create mode 100644 Dockerfile create mode 100644 demo/__init__.py create mode 100644 demo/app.py create mode 100644 demo/executor/anthropic_executor.py create mode 100644 demo/gui_agent/anthropic_agent.py create mode 100644 demo/gui_agent/llm_utils/llm_utils.py create mode 100644 demo/gui_agent/llm_utils/oai.py create mode 100644 demo/gui_agent/llm_utils/qwen.py create mode 100644 demo/gui_agent/llm_utils/run_llm.py create mode 100644 demo/loop.py create mode 100644 demo/omniparser_agent/vlm_agent.py create mode 100644 demo/tmp/outputs/screenshot.png create mode 100644 demo/tools/__init__.py create mode 100644 demo/tools/base.py create mode 100644 demo/tools/bash.py create mode 100644 demo/tools/collection.py create mode 100644 demo/tools/colorful_text.py create mode 100644 demo/tools/computer.py create mode 100644 demo/tools/edit.py create mode 100644 demo/tools/run.py create mode 100644 demo/tools/screen_capture.py diff --git a/.gitignore b/.gitignore index bb58b8d..2ad14a7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,8 @@ -weights/icon_caption_blip2 -weights/icon_caption_florence -weights/icon_detect/ -weights/icon_detect_v1_5/ -weights/icon_detect_v1_5_2/ -.gradio -__pycache__/ -debug.ipynb +weights/icon_caption_blip2 +weights/icon_caption_florence +weights/icon_detect/ +weights/icon_detect_v1_5/ +weights/icon_detect_v1_5_2/ +.gradio +__pycache__/ +debug.ipynb diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..b0e7fdd --- /dev/null +++ b/Dockerfile @@ -0,0 +1,203 @@ +# Dockerfile for OmniParser with GPU support and OpenGL libraries +# +# This Dockerfile is intended to create an environment with NVIDIA CUDA +# support and the necessary dependencies to run the OmniParser project. +# The configuration is designed to support applications that rely on +# Python 3.12, OpenCV, Hugging Face transformers, and Gradio. Additionally, +# it includes steps to pull large files from Git LFS and a script to +# convert model weights from .safetensor to .pt format. The container +# runs a Gradio server by default, exposed on port 7861. +# +# Base image: nvidia/cuda:12.3.1-devel-ubuntu22.04 +# +# Key features: +# - System dependencies for OpenGL to support graphical libraries. +# - Miniconda for Python 3.12, allowing for environment management. +# - Git Large File Storage (LFS) setup for handling large model files. +# - Requirement file installation, including specific versions of +# OpenCV and Hugging Face Hub. +# - Entrypoint script execution with Gradio server configuration for +# external access. + +# If it is gpu enviroment, use nvidia/cuda:12.3.1-devel-ubuntu22.04, otherwise use ubuntu:22.04 +# FROM nvidia/cuda:12.3.1-devel-ubuntu22.04 +FROM docker.io/ubuntu:22.04 + +# Install system dependencies with explicit OpenGL libraries +RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y \ + git \ + git-lfs \ + wget \ + libgl1 \ + libglib2.0-0 \ + libsm6 \ + libxext6 \ + libxrender1 \ + libglu1-mesa \ + libglib2.0-0 \ + libsm6 \ + libxrender1 \ + libxext6 \ + python3-opencv \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* \ + && git lfs install + +# Install Miniconda for Python 3.12 +RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && \ + bash miniconda.sh -b -p /opt/conda && \ + rm miniconda.sh +ENV PATH="/opt/conda/bin:$PATH" + +# Create and activate Conda environment with Python 3.12, and set it as the default +RUN conda create -n omni python=3.12 && \ + echo "source activate omni" > ~/.bashrc +ENV CONDA_DEFAULT_ENV=omni +ENV PATH="/opt/conda/envs/omni/bin:$PATH" + +# Set the working directory in the container +WORKDIR /usr/src/app + +# Copy project files and requirements +COPY . . +COPY requirements.txt /usr/src/app/requirements.txt + +# Initialize Git LFS and pull LFS files +RUN git lfs install && \ + git lfs pull + +# Install dependencies from requirements.txt with specific opencv-python-headless version +RUN . /opt/conda/etc/profile.d/conda.sh && conda activate omni && \ + # pip uninstall -y opencv-python opencv-python-headless && \ + # pip install --no-cache-dir opencv-python-headless==4.8.1.78 && \ + pip install -r requirements.txt && \ + pip install huggingface_hub + +# Run download.py to fetch model weights and convert safetensors to .pt format +# RUN . /opt/conda/etc/profile.d/conda.sh && conda activate omni && \ +# python download.py && \ +# echo "Contents of weights directory:" && \ +# ls -lR weights && \ +# python weights/convert_safetensor_to_pt.py + +# Expose the default Gradio port +EXPOSE 7861 + +# Configure Gradio to be accessible externally +ENV GRADIO_SERVER_NAME="0.0.0.0" + +# Copy and set permissions for entrypoint script +# COPY entrypoint.sh /usr/src/app/entrypoint.sh +# RUN chmod +x /usr/src/app/entrypoint.sh + +# To debug, keep the container running +# CMD ["tail", "-f", "/dev/null"] + +################################################################################################ +# virtual display related setup --> from anthropic-quickstarts/computer-use-demo/Dockerfile + +ENV DEBIAN_FRONTEND=noninteractive +ENV DEBIAN_PRIORITY=high + +RUN apt-get update && \ + apt-get -y upgrade && \ + apt-get -y install \ + # UI Requirements + xvfb \ + xterm \ + xdotool \ + scrot \ + imagemagick \ + sudo \ + mutter \ + x11vnc \ + # Python/pyenv reqs + build-essential \ + libssl-dev \ + zlib1g-dev \ + libbz2-dev \ + libreadline-dev \ + libsqlite3-dev \ + curl \ + git \ + libncursesw5-dev \ + xz-utils \ + tk-dev \ + libxml2-dev \ + libxmlsec1-dev \ + libffi-dev \ + liblzma-dev \ + # Network tools + net-tools \ + netcat \ + # PPA req + software-properties-common && \ + # Userland apps + sudo add-apt-repository ppa:mozillateam/ppa && \ + sudo apt-get install -y --no-install-recommends \ + libreoffice \ + firefox-esr \ + x11-apps \ + xpdf \ + gedit \ + xpaint \ + tint2 \ + galculator \ + pcmanfm \ + unzip && \ + apt-get clean + +# Install noVNC +RUN git clone --branch v1.5.0 https://github.com/novnc/noVNC.git /opt/noVNC && \ + git clone --branch v0.12.0 https://github.com/novnc/websockify /opt/noVNC/utils/websockify && \ + ln -s /opt/noVNC/vnc.html /opt/noVNC/index.html + +# setup user +ENV USERNAME=computeruse +ENV HOME=/home/$USERNAME +RUN useradd -m -s /bin/bash -d $HOME $USERNAME +RUN echo "${USERNAME} ALL=(ALL) NOPASSWD: ALL" >> /etc/sudoers +USER computeruse +WORKDIR $HOME + +# setup python +RUN git clone https://github.com/pyenv/pyenv.git ~/.pyenv && \ + cd ~/.pyenv && src/configure && make -C src && cd .. && \ + echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc && \ + echo 'command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc && \ + echo 'eval "$(pyenv init -)"' >> ~/.bashrc +ENV PYENV_ROOT="$HOME/.pyenv" +ENV PATH="$PYENV_ROOT/bin:$PATH" +ENV PYENV_VERSION_MAJOR=3 +ENV PYENV_VERSION_MINOR=11 +ENV PYENV_VERSION_PATCH=6 +ENV PYENV_VERSION=$PYENV_VERSION_MAJOR.$PYENV_VERSION_MINOR.$PYENV_VERSION_PATCH +RUN eval "$(pyenv init -)" && \ + pyenv install $PYENV_VERSION && \ + pyenv global $PYENV_VERSION && \ + pyenv rehash + +ENV PATH="$HOME/.pyenv/shims:$HOME/.pyenv/bin:$PATH" + +RUN python -m pip install --upgrade pip==23.1.2 setuptools==58.0.4 wheel==0.40.0 && \ + python -m pip config set global.disable-pip-version-check true + +# only reinstall if requirements.txt changes +# COPY --chown=$USERNAME:$USERNAME computer_use_demo/requirements.txt $HOME/computer_use_demo/requirements.txt +# RUN python -m pip install -r $HOME/computer_use_demo/requirements.txt + +# setup desktop env & app +# COPY --chown=$USERNAME:$USERNAME image/ $HOME +# COPY --chown=$USERNAME:$USERNAME computer_use_demo/ $HOME/computer_use_demo/ + +ARG DISPLAY_NUM=1 +ARG HEIGHT=768 +ARG WIDTH=1024 +ENV DISPLAY_NUM=$DISPLAY_NUM +ENV HEIGHT=$HEIGHT +ENV WIDTH=$WIDTH + +# Set the entrypoint +# ENTRYPOINT ["/usr/src/app/entrypoint.sh"] + +# sudo docker build . -t omniparser-x-demo:local # 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- Logo -

- -[![arXiv](https://img.shields.io/badge/Paper-green)](https://arxiv.org/abs/2408.00203) -[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) - -📢 [[Project Page](https://microsoft.github.io/OmniParser/)] [[Blog Post](https://www.microsoft.com/en-us/research/articles/omniparser-for-pure-vision-based-gui-agent/)] [[Models](https://huggingface.co/microsoft/OmniParser)] [huggingface space](https://huggingface.co/spaces/microsoft/OmniParser) - -**OmniParser** is a comprehensive method for parsing user interface screenshots into structured and easy-to-understand elements, which significantly enhances the ability of GPT-4V to generate actions that can be accurately grounded in the corresponding regions of the interface. - -## News -- [2024/11/26] We release an updated version, OmniParser V1.5 which features 1) more fine grained/small icon detection, 2) prediction of whether each screen element is interactable or not. Examples in the demo.ipynb. -- [2024/10] OmniParser was the #1 trending model on huggingface model hub (starting 10/29/2024). -- [2024/10] Feel free to checkout our demo on [huggingface space](https://huggingface.co/spaces/microsoft/OmniParser)! (stay tuned for OmniParser + Claude Computer Use) -- [2024/10] Both Interactive Region Detection Model and Icon functional description model are released! [Hugginface models](https://huggingface.co/microsoft/OmniParser) -- [2024/09] OmniParser achieves the best performance on [Windows Agent Arena](https://microsoft.github.io/WindowsAgentArena/)! - -## Install -Install environment: -```python -conda create -n "omni" python==3.12 -conda activate omni -pip install -r requirements.txt -``` - -Then download the model ckpts files in: https://huggingface.co/microsoft/OmniParser, and put them under weights/, default folder structure is: weights/icon_detect, weights/icon_caption_florence, weights/icon_caption_blip2. - -Finally, convert the safetensor to .pt file. -```python -python weights/convert_safetensor_to_pt.py -``` - -## Examples: -We put together a few simple examples in the demo.ipynb. - -## Gradio Demo -To run gradio demo, simply run: -```python -python gradio_demo.py -``` - -## Model Weights License -For the model checkpoints on huggingface model hub, please note that icon_detect model is under AGPL license since it is a license inherited from the original yolo model. And icon_caption_blip2 & icon_caption_florence is under MIT license. Please refer to the LICENSE file in the folder of each model: https://huggingface.co/microsoft/OmniParser. - -## 📚 Citation -Our technical report can be found [here](https://arxiv.org/abs/2408.00203). -If you find our work useful, please consider citing our work: -``` -@misc{lu2024omniparserpurevisionbased, - title={OmniParser for Pure Vision Based GUI Agent}, - author={Yadong Lu and Jianwei Yang and Yelong Shen and Ahmed Awadallah}, - year={2024}, - eprint={2408.00203}, - archivePrefix={arXiv}, - primaryClass={cs.CV}, - url={https://arxiv.org/abs/2408.00203}, -} -``` +# OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent + +

+ Logo +

+ +[![arXiv](https://img.shields.io/badge/Paper-green)](https://arxiv.org/abs/2408.00203) +[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + +📢 [[Project Page](https://microsoft.github.io/OmniParser/)] [[Blog Post](https://www.microsoft.com/en-us/research/articles/omniparser-for-pure-vision-based-gui-agent/)] [[Models](https://huggingface.co/microsoft/OmniParser)] [huggingface space](https://huggingface.co/spaces/microsoft/OmniParser) + +**OmniParser** is a comprehensive method for parsing user interface screenshots into structured and easy-to-understand elements, which significantly enhances the ability of GPT-4V to generate actions that can be accurately grounded in the corresponding regions of the interface. + +## News +- [2024/11/26] We release an updated version, OmniParser V1.5 which features 1) more fine grained/small icon detection, 2) prediction of whether each screen element is interactable or not. Examples in the demo.ipynb. +- [2024/10] OmniParser was the #1 trending model on huggingface model hub (starting 10/29/2024). +- [2024/10] Feel free to checkout our demo on [huggingface space](https://huggingface.co/spaces/microsoft/OmniParser)! (stay tuned for OmniParser + Claude Computer Use) +- [2024/10] Both Interactive Region Detection Model and Icon functional description model are released! [Hugginface models](https://huggingface.co/microsoft/OmniParser) +- [2024/09] OmniParser achieves the best performance on [Windows Agent Arena](https://microsoft.github.io/WindowsAgentArena/)! + +## Install +Install environment: +```python +conda create -n "omni" python==3.12 +conda activate omni +pip install -r requirements.txt +``` + +Then download the model ckpts files in: https://huggingface.co/microsoft/OmniParser, and put them under weights/, default folder structure is: weights/icon_detect, weights/icon_caption_florence, weights/icon_caption_blip2. + +Finally, convert the safetensor to .pt file. +```python +python weights/convert_safetensor_to_pt.py +``` + +## Examples: +We put together a few simple examples in the demo.ipynb. + +## Gradio Demo +To run gradio demo, simply run: +```python +python gradio_demo.py +``` + +## Model Weights License +For the model checkpoints on huggingface model hub, please note that icon_detect model is under AGPL license since it is a license inherited from the original yolo model. And icon_caption_blip2 & icon_caption_florence is under MIT license. Please refer to the LICENSE file in the folder of each model: https://huggingface.co/microsoft/OmniParser. + +## 📚 Citation +Our technical report can be found [here](https://arxiv.org/abs/2408.00203). +If you find our work useful, please consider citing our work: +``` +@misc{lu2024omniparserpurevisionbased, + title={OmniParser for Pure Vision Based GUI Agent}, + author={Yadong Lu and Jianwei Yang and Yelong Shen and Ahmed Awadallah}, + year={2024}, + eprint={2408.00203}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2408.00203}, +} +``` diff --git a/SECURITY.md b/SECURITY.md index b3c89ef..6b906d4 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -1,41 +1,41 @@ - - -## Security - -Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin). - -If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below. - -## Reporting Security Issues - -**Please do not report security vulnerabilities through public GitHub issues.** - -Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report). - -If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). 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Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs. + +## Preferred Languages + +We prefer all communications to be in English. + +## Policy + +Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd). + + diff --git a/__pycache__/utils.cpython-312.pyc b/__pycache__/utils.cpython-312.pyc index a1cfe483e04c8c41c5fe26779a0cd752014c5ce2..6216b7f24229c6d13fb631b54e1d3a080d1304d5 100644 GIT binary patch delta 1318 zcmah`YfMvj6u-Au;PPmp^tQLO(1#UhDW&#-rHr>)gHV|(K7h^!Ep5j@tq3AACE4p@ zG+y%G;x*CivL#DN_MxoXq9!J|naH+e`Csm}P&Er~elU9^=t$<5CA;c}x$MKvhx1E* z=bYck`JLMt^s5Xi`(7%|L&WHs2}Z-O&&!_VsLSoKBf|ssLq~>>jvgO4Haaq3KQesy zjiV_g{x}ys2XcySSemE?jfT%KL}>%R76-6pXv(4*BEM>Aj2GOtUR7}H0VOO`9v zq>hrol9m;mT*h@Au47K8@tQR)BJ-wbVl*Ee0NDonzXX3X4Cq8b3SE&KStHxZ4$gK$ zxr55i1bTR~mkacOxKSw)fihaKw5kGJGrs3V$jbEsM4?J-Nm`ezO}wg^vo^8)bo}Cp znG;MLOh9WBr)uVlTd17-L>Uud`#7B=O*p~$#x*6PbggA0KC~0P9lRO5bwE&d-#h>& zO(x000r0WuElv$CDVn^I}-+7%Y=m^EO!WNa-71U;-wqM)3|xf^X$ew zo0f6(F0k30MPAY8r;m`QQwY>da z;RY|v2O~9&s6BZITeojl8spGdD63T1Dg2Z$fh;DB8MD zBX~OmbtmWTm}^h?lC6x7sbl*u>7chGb$V+yhJFX$^|ckB*Jp~!89P%kW2bWd#E`ra z@S|@eOGHYa>e-=}ptIm)`#2%!H}f(Fr{ByN&yCQI^J7rIIVIV7C0BHn4b;k|qH|No z)%ZfmwaLZfTyp?C3VdlYi0lii)dxAtj!R=qoHZ@F7EgZL#C7yvmWy2KM)!3L{Tht# z|5RM!x@Wd*)$EU&Zj0Lz!{Rc#?4SGk@U=Srz{NlIXYEXMCQEk9vY2d3_Wn4Z z^X|Fld~@$P_p68a{fD^XC%IgPp-(vM4ejvWR^*E->g?g;(}(S6{DI@gLPuxpyQYu5 zxjRV_Po?-}AS<^KyrwCSZIHM?XZh#i0v{X5OX@Cymv{Vz$tCurTV0D=vktdV*%LQ0 zrjIO3mTOj6*%MPBlO@OuQNs~6%sGK*&glyY3!0e{{H;p{`+wCw>{>6ZmsnzWR%K+3 zY%e=?V;HI&v7)SJOd!Woeny(19316H7?=AY<#<31LUJ6OHChN>+mOd3QaAX__}&)8 zXQh~08*fioIcv9|b+djta^=+GDJBBu(pooP-V-Zc)l@S<*2~p6cuh+l6G``qK(H>S zDLAEAnizt@X&4YusO(KK^+db+z3j7hp zvL$L*qQ5#9H-FK;QoDuQ0K&~%4ZPZv$CQ$$^Tjbd*1AUOW2LL4fvw=lM${;&7JS>> zsz=Pruu|%=l6*qy1l{~fI0E0EHxrV#Tesi3e{Wu&E9qa}}NRrP@CI43S}siR$tGLz|! zi&C5flYR3l-n>Y4W3rl=-Q*6HnkGnZWOx{S+4wgK6MhhJy8t&;Q7)}jn@C_1~s`f z%S2dH%9%RzV>dVCzpCP>$>jCXZ^U>Kj2-wC3A6|b=PLmxP>%(N`S81_brroL&KH2b z3Fr?n!MJC+9(wli75j7Rq3Fodb!I0HWhSP$i79^Ks8BWy4|th|xcW{Y72s7KST+q( zWA=@_W%~*>2?If19f}UGR_Lzy7JaaDM5x=DaI#Kz_T$&mz&Ji%!*}hsb*-8lb_szK%=Zz=6_ha~7O6 zgBez$f1Ci};aXrYZy?K9>mLmd`5U zh~y)mU52NCEYxOQtG5beL(FLWWTN-xsYK-F+?}1geFv?f{eXmJLoo?5*+!e790AiI zy%-`cgiMX=c}>jd-y53>8=JBlHwj8B(@$@p_kd!kv;raIkY+WNg*UMS2eReM6rPf?)nTL1t6 diff --git a/demo.ipynb b/demo.ipynb index 835c69a..0ebe9b4 100644 --- a/demo.ipynb +++ b/demo.ipynb @@ -1,398 +1,398 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model to cuda\n" - ] - } - ], - "source": [ - "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", - "import torch\n", - "from ultralytics import YOLO\n", - "from PIL import Image\n", - "device = 'cuda'\n", - "model_path='weights/icon_detect/best.pt'\n", - "model_path='weights/icon_detect_v1_5/model_v1_5.pt'\n", - "\n", - "som_model = get_yolo_model(model_path)\n", - "\n", - "som_model.to(device)\n", - "print('model to {}'.format(device))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Florence2LanguageForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n", - " - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n", - " - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n", - " - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n" - ] - } - ], - "source": [ - "# two choices for caption model: fine-tuned blip2 or florence2\n", - "import importlib\n", - "import utils\n", - "importlib.reload(utils)\n", - "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", - "# caption_model_processor = get_caption_model_processor(model_name=\"blip2\", model_name_or_path=\"weights/icon_caption_blip2\", device=device)\n", - "caption_model_processor = get_caption_model_processor(model_name=\"florence2\", model_name_or_path=\"weights/icon_caption_florence\", device=device)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(device(type='cuda', index=0), ultralytics.models.yolo.model.YOLO)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "som_model.device, type(som_model) " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "image size: (1919, 1079)\n", - "\n", - "image 1/1 /home/yadonglu/OmniParser/imgs/word.png: 736x1280 115 icons, 51.7ms\n", - "Speed: 5.0ms preprocess, 51.7ms inference, 1.6ms postprocess per image at shape (1, 3, 736, 1280)\n" - ] - } - ], - "source": [ - "# reload utils\n", - "import importlib\n", - "import utils\n", - "importlib.reload(utils)\n", - "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", - "\n", - "image_path = 'imgs/google_page.png'\n", - "image_path = 'imgs/windows_home.png'\n", - "# image_path = 'imgs/windows_multitab.png'\n", - "# image_path = 'imgs/omni3.jpg'\n", - "# image_path = 'imgs/ios.png'\n", - "image_path = 'imgs/word.png'\n", - "# image_path = 'imgs/excel2.png'\n", - "# image_path = 'imgs/mobile.png'\n", - "\n", - "image = Image.open(image_path)\n", - "image_rgb = image.convert('RGB')\n", - "print('image size:', image.size)\n", - "\n", - "box_overlay_ratio = max(image.size) / 3200\n", - "draw_bbox_config = {\n", - " 'text_scale': 0.8 * box_overlay_ratio,\n", - " 'text_thickness': max(int(2 * box_overlay_ratio), 1),\n", - " 'text_padding': max(int(3 * box_overlay_ratio), 1),\n", - " 'thickness': max(int(3 * box_overlay_ratio), 1),\n", - "}\n", - "BOX_TRESHOLD = 0.05\n", - "\n", - "import time\n", - "start = time.time()\n", - "ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.5}, use_paddleocr=True)\n", - "text, ocr_bbox = ocr_bbox_rslt\n", - "cur_time_ocr = time.time() \n", - "\n", - "dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128)\n", - "cur_time_caption = time.time()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot dino_labled_img it is in base64\n", - "import base64\n", - "import matplotlib.pyplot as plt\n", - "import io\n", - "plt.figure(figsize=(15,15))\n", - "\n", - "image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))\n", - "plt.axis('off')\n", - "\n", - "plt.imshow(image)\n", - "# print(len(parsed_content_list))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typebboxinteractivitycontentID
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True \n", - "\n", - " content ID \n", - "0 AutoSave 0 \n", - "1 F 9~U 1 \n", - "2 Document 10.docx General* Last Modified: Jus... 2 \n", - "3 O Search 3 \n", - "4 File 4 \n", - ".. ... ... \n", - "146 Redo 146 \n", - "147 Notifications. 147 \n", - "148 minimizing a window. 148 \n", - "149 Redo 149 \n", - "150 M0,0L9,0 4.5,5z 150 \n", - "\n", - "[151 rows x 5 columns]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "df = pd.DataFrame(parsed_content_list)\n", - "df['ID'] = range(len(df))\n", - "\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'icon',\n", - " 'bbox': [0.7569739818572998,\n", - " 0.128729909658432,\n", - " 0.7768653035163879,\n", - " 0.14445137977600098],\n", - " 'interactivity': True,\n", - " 'content': 'Select option.'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parsed_content_list[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pilot", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model to cuda\n" + ] + } + ], + "source": [ + "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", + "import torch\n", + "from ultralytics import YOLO\n", + "from PIL import Image\n", + "device = 'cuda'\n", + "model_path='weights/icon_detect/best.pt'\n", + "model_path='weights/icon_detect_v1_5/model_v1_5.pt'\n", + "\n", + "som_model = get_yolo_model(model_path)\n", + "\n", + "som_model.to(device)\n", + "print('model to {}'.format(device))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Florence2LanguageForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n", + " - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n", + " - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n", + " - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n" + ] + } + ], + "source": [ + "# two choices for caption model: fine-tuned blip2 or florence2\n", + "import importlib\n", + "import utils\n", + "importlib.reload(utils)\n", + "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", + "# caption_model_processor = get_caption_model_processor(model_name=\"blip2\", model_name_or_path=\"weights/icon_caption_blip2\", device=device)\n", + "caption_model_processor = get_caption_model_processor(model_name=\"florence2\", model_name_or_path=\"weights/icon_caption_florence\", device=device)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(device(type='cuda', index=0), ultralytics.models.yolo.model.YOLO)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "som_model.device, type(som_model) " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image size: (1919, 1079)\n", + "\n", + "image 1/1 /home/yadonglu/OmniParser/imgs/word.png: 736x1280 115 icons, 51.7ms\n", + "Speed: 5.0ms preprocess, 51.7ms inference, 1.6ms postprocess per image at shape (1, 3, 736, 1280)\n" + ] + } + ], + "source": [ + "# reload utils\n", + "import importlib\n", + "import utils\n", + "importlib.reload(utils)\n", + "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", + "\n", + "image_path = 'imgs/google_page.png'\n", + "image_path = 'imgs/windows_home.png'\n", + "# image_path = 'imgs/windows_multitab.png'\n", + "# image_path = 'imgs/omni3.jpg'\n", + "# image_path = 'imgs/ios.png'\n", + "image_path = 'imgs/word.png'\n", + "# image_path = 'imgs/excel2.png'\n", + "# image_path = 'imgs/mobile.png'\n", + "\n", + "image = Image.open(image_path)\n", + "image_rgb = image.convert('RGB')\n", + "print('image size:', image.size)\n", + "\n", + "box_overlay_ratio = max(image.size) / 3200\n", + "draw_bbox_config = {\n", + " 'text_scale': 0.8 * box_overlay_ratio,\n", + " 'text_thickness': max(int(2 * box_overlay_ratio), 1),\n", + " 'text_padding': max(int(3 * box_overlay_ratio), 1),\n", + " 'thickness': max(int(3 * box_overlay_ratio), 1),\n", + "}\n", + "BOX_TRESHOLD = 0.05\n", + "\n", + "import time\n", + "start = time.time()\n", + "ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.5}, use_paddleocr=True)\n", + "text, ocr_bbox = ocr_bbox_rslt\n", + "cur_time_ocr = time.time() \n", + "\n", + "dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128)\n", + "cur_time_caption = time.time()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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151 rows × 5 columns

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X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. Type a message to play with your beloved assistant. +''' + +class Sender(StrEnum): + USER = "user" + BOT = "assistant" + TOOL = "tool" + + +def setup_state(state): + + if "messages" not in state: + state["messages"] = [] + if "model" not in state: + # state["model"] = "gpt-4o + ShowUI" + state["model"] = "omniparser + gpt-4o" + # _reset_model(state) + if "provider" not in state: + if state["model"] == "qwen2vl + ShowUI": + state["provider"] = "DashScopeAPI" + elif state["model"] == "gpt-4o + ShowUI": + state["provider"] = "openai" + else: + state["provider"] = os.getenv("API_PROVIDER", "anthropic") or "anthropic" + + if "provider_radio" not in state: + state["provider_radio"] = state["provider"] + + if "openai_api_key" not in state: # Fetch API keys from environment variables + state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "") + if "anthropic_api_key" not in state: + state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "") + if "qwen_api_key" not in state: + state["qwen_api_key"] = os.getenv("QWEN_API_KEY", "") + + # Set the initial api_key based on the provider + if "api_key" not in state: + if state["provider"] == "openai": + state["api_key"] = state["openai_api_key"] + elif state["provider"] == "anthropic": + state["api_key"] = state["anthropic_api_key"] + elif state["provider"] == "qwen": + state["api_key"] = state["qwen_api_key"] + else: + state["api_key"] = "" + # print(f"state['api_key']: {state['api_key']}") + if not state["api_key"]: + print("API key not found. Please set it in the environment or paste in textbox.") + + if "selected_screen" not in state: + state['selected_screen'] = SELECTED_SCREEN_INDEX if SCREEN_NAMES else 0 + + if "auth_validated" not in state: + state["auth_validated"] = False + if "responses" not in state: + state["responses"] = {} + if "tools" not in state: + state["tools"] = {} + if "only_n_most_recent_images" not in state: + state["only_n_most_recent_images"] = 10 # 10 + if "custom_system_prompt" not in state: + state["custom_system_prompt"] = load_from_storage("system_prompt") or "" + # remove if want to use default system prompt + device_os_name = "Windows" if platform.system() == "Windows" else "Mac" if platform.system() == "Darwin" else "Linux" + state["custom_system_prompt"] += f"\n\nNOTE: you are operating a {device_os_name} machine" + if "hide_images" not in state: + state["hide_images"] = False + if 'chatbot_messages' not in state: + state['chatbot_messages'] = [] + + + +def _reset_model(state): + state["model"] = PROVIDER_TO_DEFAULT_MODEL_NAME[cast(APIProvider, state["provider"])] + + +async def main(state): + """Render loop for Gradio""" + setup_state(state) + return "Setup completed" + + +def validate_auth(provider: APIProvider, api_key: str | None): + if provider == APIProvider.ANTHROPIC: + if not api_key: + return "Enter your Anthropic API key to continue." + if provider == APIProvider.BEDROCK: + import boto3 + + if not boto3.Session().get_credentials(): + return "You must have AWS credentials set up to use the Bedrock API." + if provider == APIProvider.VERTEX: + import google.auth + from google.auth.exceptions import DefaultCredentialsError + + if not os.environ.get("CLOUD_ML_REGION"): + return "Set the CLOUD_ML_REGION environment variable to use the Vertex API." + try: + google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) + except DefaultCredentialsError: + return "Your google cloud credentials are not set up correctly." + + +def load_from_storage(filename: str) -> str | None: + """Load data from a file in the storage directory.""" + try: + file_path = CONFIG_DIR / filename + if file_path.exists(): + data = file_path.read_text().strip() + if data: + return data + except Exception as e: + print(f"Debug: Error loading {filename}: {e}") + return None + + +def save_to_storage(filename: str, data: str) -> None: + """Save data to a file in the storage directory.""" + try: + CONFIG_DIR.mkdir(parents=True, exist_ok=True) + file_path = CONFIG_DIR / filename + file_path.write_text(data) + # Ensure only user can read/write the file + file_path.chmod(0o600) + except Exception as e: + print(f"Debug: Error saving {filename}: {e}") + + +def _api_response_callback(response: APIResponse[BetaMessage], response_state: dict): + response_id = datetime.now().isoformat() + response_state[response_id] = response + + +def _tool_output_callback(tool_output: ToolResult, tool_id: str, tool_state: dict): + tool_state[tool_id] = tool_output + + +def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="bot"): + + def _render_message(message: str | BetaTextBlock | BetaToolUseBlock | ToolResult, hide_images=False): + + print(f"_render_message: {str(message)[:100]}") + + if isinstance(message, str): + return message + + is_tool_result = not isinstance(message, str) and ( + isinstance(message, ToolResult) + or message.__class__.__name__ == "ToolResult" + or message.__class__.__name__ == "CLIResult" + ) + if not message or ( + is_tool_result + and hide_images + and not hasattr(message, "error") + and not hasattr(message, "output") + ): # return None if hide_images is True + return + # render tool result + if is_tool_result: + message = cast(ToolResult, message) + if message.output: + return message.output + if message.error: + return f"Error: {message.error}" + if message.base64_image and not hide_images: + # somehow can't display via gr.Image + # image_data = base64.b64decode(message.base64_image) + # return gr.Image(value=Image.open(io.BytesIO(image_data))) + return f'' + + elif isinstance(message, BetaTextBlock) or isinstance(message, TextBlock): + return f"Analysis: {message.text}" + elif isinstance(message, BetaToolUseBlock) or isinstance(message, ToolUseBlock): + # return f"Tool Use: {message.name}\nInput: {message.input}" + return f"Next I will perform the following action: {message.input}" + else: + return message + + def _truncate_string(s, max_length=500): + """Truncate long strings for concise printing.""" + if isinstance(s, str) and len(s) > max_length: + return s[:max_length] + "..." + return s + # processing Anthropic messages + message = _render_message(message, hide_images) + + if sender == "bot": + chatbot_state.append((None, message)) + else: + chatbot_state.append((message, None)) + + # Create a concise version of the chatbot state for printing + concise_state = [(_truncate_string(user_msg), _truncate_string(bot_msg)) + for user_msg, bot_msg in chatbot_state] + # print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)") + +def process_input(user_input, state): + + setup_state(state) + + # Append the user message to state["messages"] + if state["model"] == "gpt-4o + ShowUI" or state["model"] == "qwen2vl + ShowUI": + state["messages"].append( + { + "role": "user", + "content": [TextBlock(type="text", text=user_input)], + } + ) + elif state["model"] == "claude-3-5-sonnet-20241022": + state["messages"].append( + { + "role": Sender.USER, + "content": [TextBlock(type="text", text=user_input)], + } + ) + elif state["model"] == "omniparser + gpt-4o" or state["model"] == "omniparser + phi35v": + state["messages"].append( + { + "role": "user", + "content": [TextBlock(type="text", text=user_input)], + } + ) + + # Append the user's message to chatbot_messages with None for the assistant's reply + state['chatbot_messages'].append((user_input, None)) + yield state['chatbot_messages'] # Yield to update the chatbot UI with the user's message + + # Run sampling_loop_sync with the chatbot_output_callback + for loop_msg in sampling_loop_sync( + system_prompt_suffix=state["custom_system_prompt"], + model=state["model"], + provider=state["provider"], + messages=state["messages"], + output_callback=partial(chatbot_output_callback, chatbot_state=state['chatbot_messages'], hide_images=state["hide_images"]), + tool_output_callback=partial(_tool_output_callback, tool_state=state["tools"]), + api_response_callback=partial(_api_response_callback, response_state=state["responses"]), + api_key=state["api_key"], + only_n_most_recent_images=state["only_n_most_recent_images"], + selected_screen=state['selected_screen'] + ): + if loop_msg is None: + yield state['chatbot_messages'] + print("End of task. Close the loop.") + break + + yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI + + +# with gr.Blocks(theme=gr.themes.Default()) as demo: +with gr.Blocks(theme='YTheme/Minecraft') as demo: + state = gr.State({}) # Use Gradio's state management + + setup_state(state.value) # Initialize the state + + # Retrieve screen details + gr.Markdown("# OmniParser + ✖️ Demo") + + if not os.getenv("HIDE_WARNING", False): + gr.Markdown(INTRO_TEXT) + + with gr.Accordion("Settings", open=True): + with gr.Row(): + with gr.Column(): + model = gr.Dropdown( + label="Model", + choices=["omniparser + gpt-4o", "omniparser + phi35v", "claude-3-5-sonnet-20241022"], + value="omniparser + gpt-4o", # Set to one of the choices + interactive=True, + ) + with gr.Column(): + provider = gr.Dropdown( + label="API Provider", + choices=[option.value for option in APIProvider], + value="openai", + interactive=False, + ) + with gr.Column(): + api_key = gr.Textbox( + label="API Key", + type="password", + value=state.value.get("api_key", ""), + placeholder="Paste your API key here", + interactive=True, + ) + with gr.Column(): + custom_prompt = gr.Textbox( + label="System Prompt Suffix", + value="", + interactive=True, + ) + with gr.Column(): + screen_options, primary_index = get_screen_details() + SCREEN_NAMES = screen_options + SELECTED_SCREEN_INDEX = primary_index + screen_selector = gr.Dropdown( + label="Select Screen", + choices=screen_options, + value=screen_options[primary_index] if screen_options else None, + interactive=True, + ) + with gr.Column(): + only_n_images = gr.Slider( + label="N most recent screenshots", + minimum=0, + maximum=10, + step=1, + value=2, + interactive=True, + ) + # hide_images = gr.Checkbox(label="Hide screenshots", value=False) + + # Define the merged dictionary with task mappings + # merged_dict = json.load(open("examples/ootb_examples.json", "r")) + merged_dict = {} + + def update_only_n_images(only_n_images_value, state): + state["only_n_most_recent_images"] = only_n_images_value + + # Callback to update the second dropdown based on the first selection + def update_second_menu(selected_category): + return gr.update(choices=list(merged_dict.get(selected_category, {}).keys())) + + # Callback to update the third dropdown based on the second selection + def update_third_menu(selected_category, selected_option): + return gr.update(choices=list(merged_dict.get(selected_category, {}).get(selected_option, {}).keys())) + + # Callback to update the textbox based on the third selection + def update_textbox(selected_category, selected_option, selected_task): + task_data = merged_dict.get(selected_category, {}).get(selected_option, {}).get(selected_task, {}) + prompt = task_data.get("prompt", "") + preview_image = task_data.get("initial_state", "") + task_hint = "Task Hint: " + task_data.get("hint", "") + return prompt, preview_image, task_hint + + # Function to update the global variable when the dropdown changes + def update_selected_screen(selected_screen_name, state): + global SCREEN_NAMES + global SELECTED_SCREEN_INDEX + SELECTED_SCREEN_INDEX = SCREEN_NAMES.index(selected_screen_name) + print(f"Selected screen updated to: {SELECTED_SCREEN_INDEX}") + state['selected_screen'] = SELECTED_SCREEN_INDEX + + def update_model(model_selection, state): + state["model"] = model_selection + print(f"Model updated to: {state['model']}") + + if model_selection == "claude-3-5-sonnet-20241022": + # Provider can be any of the current choices except 'openai' + provider_choices = [option.value for option in APIProvider if option.value != "openai"] + provider_value = "anthropic" # Set default to 'anthropic' + provider_interactive = True + api_key_placeholder = "claude API key" + elif model_selection == "omniparser + gpt-4o" or model_selection == "omniparser + phi35v": + # Provider can be any of the current choices except 'openai' + provider_choices = ["openai"] + provider_value = "openai" + provider_interactive = False + api_key_placeholder = "openai API key" + else: + # Default case + provider_choices = [option.value for option in APIProvider] + provider_value = state.get("provider", provider_choices[0]) + provider_interactive = True + api_key_placeholder = "" + + # Update the provider in state + state["provider"] = provider_value + + # Update api_key in state based on the provider + if provider_value == "openai": + state["api_key"] = state.get("openai_api_key", "") + elif provider_value == "anthropic": + state["api_key"] = state.get("anthropic_api_key", "") + elif provider_value == "qwen": + state["api_key"] = state.get("qwen_api_key", "") + else: + state["api_key"] = "" + + # Use gr.update() instead of gr.Dropdown.update() + provider_update = gr.update( + choices=provider_choices, + value=provider_value, + interactive=provider_interactive + ) + + # Update the API Key textbox + api_key_update = gr.update( + placeholder=api_key_placeholder, + value=state["api_key"] + ) + + return provider_update, api_key_update + + def update_api_key_placeholder(provider_value, model_selection): + if model_selection == "claude-3-5-sonnet-20241022": + if provider_value == "anthropic": + return gr.update(placeholder="anthropic API key") + elif provider_value == "bedrock": + return gr.update(placeholder="bedrock API key") + elif provider_value == "vertex": + return gr.update(placeholder="vertex API key") + else: + return gr.update(placeholder="") + elif model_selection == "gpt-4o + ShowUI": + return gr.update(placeholder="openai API key") + else: + return gr.update(placeholder="") + + def update_system_prompt_suffix(system_prompt_suffix, state): + state["custom_system_prompt"] = system_prompt_suffix + + + api_key.change(fn=lambda key: save_to_storage(API_KEY_FILE, key), inputs=api_key) + + with gr.Row(): + # submit_button = gr.Button("Submit") # Add submit button + with gr.Column(scale=8): + chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Computer Use OOTB...", container=False) + with gr.Column(scale=1, min_width=50): + submit_button = gr.Button(value="Send", variant="primary") + + chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) + + model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) + provider.change(fn=update_api_key_placeholder, inputs=[provider, model], outputs=api_key) + screen_selector.change(fn=update_selected_screen, inputs=[screen_selector, state], outputs=None) + only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) + + + # chat_input.submit(process_input, [chat_input, state], chatbot) + submit_button.click(process_input, [chat_input, state], chatbot) + +demo.launch(share=True, server_port=7861, server_name='0.0.0.0') # TODO: allowed_paths diff --git a/demo/executor/anthropic_executor.py b/demo/executor/anthropic_executor.py new file mode 100644 index 0000000..df61810 --- /dev/null +++ b/demo/executor/anthropic_executor.py @@ -0,0 +1,136 @@ +import asyncio +from typing import Any, Dict, cast +from collections.abc import Callable +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock +from tools import BashTool, ComputerTool, EditTool, ToolCollection, ToolResult + + +class AnthropicExecutor: + def __init__( + self, + output_callback: Callable[[BetaContentBlockParam], None], + tool_output_callback: Callable[[Any, str], None], + selected_screen: int = 0 + ): + self.tool_collection = ToolCollection( + ComputerTool(selected_screen=selected_screen), + BashTool(), + EditTool(), + ) + self.output_callback = output_callback + self.tool_output_callback = tool_output_callback + + def __call__(self, response: BetaMessage, messages: list[BetaMessageParam]): + new_message = { + "role": "assistant", + "content": cast(list[BetaContentBlockParam], response.content), + } + if new_message not in messages: + messages.append(new_message) + else: + print("new_message already in messages, there are duplicates.") + + tool_result_content: list[BetaToolResultBlockParam] = [] + for content_block in cast(list[BetaContentBlock], response.content): + + self.output_callback(content_block, sender="bot") + # Execute the tool + if content_block.type == "tool_use": + # Run the asynchronous tool execution in a synchronous context + result = asyncio.run(self.tool_collection.run( + name=content_block.name, + tool_input=cast(dict[str, Any], content_block.input), + )) + + self.output_callback(result, sender="bot") + + tool_result_content.append( + _make_api_tool_result(result, content_block.id) + ) + self.tool_output_callback(result, content_block.id) + + # Craft messages based on the content_block + # Note: to display the messages in the gradio, you should organize the messages in the following way (user message, bot message) + + display_messages = _message_display_callback(messages) + # display_messages = [] + + # Send the messages to the gradio + for user_msg, bot_msg in display_messages: + # yield [user_msg, bot_msg], tool_result_content + yield [None, None], tool_result_content + + if not tool_result_content: + return messages + + return tool_result_content + +def _message_display_callback(messages): + display_messages = [] + for msg in messages: + try: + if isinstance(msg["content"][0], TextBlock): + display_messages.append((msg["content"][0].text, None)) # User message + elif isinstance(msg["content"][0], BetaTextBlock): + display_messages.append((None, msg["content"][0].text)) # Bot message + elif isinstance(msg["content"][0], BetaToolUseBlock): + display_messages.append((None, f"Tool Use: {msg['content'][0].name}\nInput: {msg['content'][0].input}")) # Bot message + elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image": + display_messages.append((None, f'')) # Bot message + else: + print(msg["content"][0]) + except Exception as e: + print("error", e) + pass + return display_messages + +def _make_api_tool_result( + result: ToolResult, tool_use_id: str +) -> BetaToolResultBlockParam: + """Convert an agent ToolResult to an API ToolResultBlockParam.""" + tool_result_content: list[BetaTextBlockParam | BetaImageBlockParam] | str = [] + is_error = False + if result.error: + is_error = True + tool_result_content = _maybe_prepend_system_tool_result(result, result.error) + else: + if result.output: + tool_result_content.append( + { + "type": "text", + "text": _maybe_prepend_system_tool_result(result, result.output), + } + ) + if result.base64_image: + tool_result_content.append( + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": result.base64_image, + }, + } + ) + return { + "type": "tool_result", + "content": tool_result_content, + "tool_use_id": tool_use_id, + "is_error": is_error, + } + + +def _maybe_prepend_system_tool_result(result: ToolResult, result_text: str): + if result.system: + result_text = f"{result.system}\n{result_text}" + return result_text \ No newline at end of file diff --git a/demo/gui_agent/anthropic_agent.py b/demo/gui_agent/anthropic_agent.py new file mode 100644 index 0000000..09d857e --- /dev/null +++ b/demo/gui_agent/anthropic_agent.py @@ -0,0 +1,207 @@ +""" +Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. +""" +import asyncio +import platform +from collections.abc import Callable +from datetime import datetime +from enum import StrEnum +from typing import Any, cast + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic.types import ( + ToolResultBlockParam, +) +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock + +from tools import BashTool, ComputerTool, EditTool, ToolCollection, ToolResult + +from PIL import Image +from io import BytesIO +import gradio as gr +from typing import Dict + + +BETA_FLAG = "computer-use-2024-10-22" + + +class APIProvider(StrEnum): + ANTHROPIC = "anthropic" + BEDROCK = "bedrock" + VERTEX = "vertex" + + +PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { + APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", + APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", + APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", +} + + +# Check OS + +SYSTEM_PROMPT = f""" +* You are utilizing a Windows system with internet access. +* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. + +""" + + +class AnthropicActor: + def __init__( + self, + model: str, + provider: APIProvider, + system_prompt_suffix: str, + api_key: str, + api_response_callback: Callable[[APIResponse[BetaMessage]], None], + max_tokens: int = 4096, + only_n_most_recent_images: int | None = None, + selected_screen: int = 0, + print_usage: bool = True, + ): + self.model = model + self.provider = provider + self.system_prompt_suffix = system_prompt_suffix + self.api_key = api_key + self.api_response_callback = api_response_callback + self.max_tokens = max_tokens + self.only_n_most_recent_images = only_n_most_recent_images + self.selected_screen = selected_screen + + self.tool_collection = ToolCollection( + ComputerTool(selected_screen=selected_screen), + BashTool(), + EditTool(), + ) + + self.system = ( + f"{SYSTEM_PROMPT}{' ' + system_prompt_suffix if system_prompt_suffix else ''}" + ) + + self.total_token_usage = 0 + self.total_cost = 0 + self.print_usage = print_usage + + # Instantiate the appropriate API client based on the provider + if provider == APIProvider.ANTHROPIC: + self.client = Anthropic(api_key=api_key) + elif provider == APIProvider.VERTEX: + self.client = AnthropicVertex() + elif provider == APIProvider.BEDROCK: + self.client = AnthropicBedrock() + + def __call__( + self, + *, + messages: list[BetaMessageParam] + ): + """ + Generate a response given history messages. + """ + if self.only_n_most_recent_images: + _maybe_filter_to_n_most_recent_images(messages, self.only_n_most_recent_images) + + # Call the API synchronously + raw_response = self.client.beta.messages.with_raw_response.create( + max_tokens=self.max_tokens, + messages=messages, + model=self.model, + system=self.system, + tools=self.tool_collection.to_params(), + betas=["computer-use-2024-10-22"], + ) + + self.api_response_callback(cast(APIResponse[BetaMessage], raw_response)) + + response = raw_response.parse() + print(f"AnthropicActor response: {response}") + + self.total_token_usage += response.usage.input_tokens + response.usage.output_tokens + self.total_cost += (response.usage.input_tokens * 3 / 1000000 + response.usage.output_tokens * 15 / 1000000) + + if self.print_usage: + print(f"Claude total token usage so far: {self.total_token_usage}, total cost so far: $USD{self.total_cost}") + + return response + + +def _maybe_filter_to_n_most_recent_images( + messages: list[BetaMessageParam], + images_to_keep: int, + min_removal_threshold: int = 10, +): + """ + With the assumption that images are screenshots that are of diminishing value as + the conversation progresses, remove all but the final `images_to_keep` tool_result + images in place, with a chunk of min_removal_threshold to reduce the amount we + break the implicit prompt cache. + """ + if images_to_keep is None: + return messages + + tool_result_blocks = cast( + list[ToolResultBlockParam], + [ + item + for message in messages + for item in ( + message["content"] if isinstance(message["content"], list) else [] + ) + if isinstance(item, dict) and item.get("type") == "tool_result" + ], + ) + + total_images = sum( + 1 + for tool_result in tool_result_blocks + for content in tool_result.get("content", []) + if isinstance(content, dict) and content.get("type") == "image" + ) + + images_to_remove = total_images - images_to_keep + # for better cache behavior, we want to remove in chunks + images_to_remove -= images_to_remove % min_removal_threshold + + for tool_result in tool_result_blocks: + if isinstance(tool_result.get("content"), list): + new_content = [] + for content in tool_result.get("content", []): + if isinstance(content, dict) and content.get("type") == "image": + if images_to_remove > 0: + images_to_remove -= 1 + continue + new_content.append(content) + tool_result["content"] = new_content + + + +if __name__ == "__main__": + pass + # client = Anthropic(api_key="") + # response = client.beta.messages.with_raw_response.create( + # max_tokens=4096, + # model="claude-3-5-sonnet-20241022", + # system=SYSTEM_PROMPT, + # # tools=ToolCollection( + # # ComputerTool(selected_screen=0), + # # BashTool(), + # # EditTool(), + # # ).to_params(), + # betas=["computer-use-2024-10-22"], + # messages=[ + # {"role": "user", "content": "click on (199, 199)."} + # ], + # ) + + # print(f"AnthropicActor response: {response.parse().usage.input_tokens+response.parse().usage.output_tokens}") \ No newline at end of file diff --git a/demo/gui_agent/llm_utils/llm_utils.py b/demo/gui_agent/llm_utils/llm_utils.py new file mode 100644 index 0000000..fbc4a42 --- /dev/null +++ b/demo/gui_agent/llm_utils/llm_utils.py @@ -0,0 +1,109 @@ +import os +import re +import ast +import base64 + + +def is_image_path(text): + # Checking if the input text ends with typical image file extensions + image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") + if text.endswith(image_extensions): + return True + else: + return False + + +def encode_image(image_path): + """Encode image file to base64.""" + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + + +def is_url_or_filepath(input_string): + # Check if input_string is a URL + url_pattern = re.compile( + r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+" + ) + if url_pattern.match(input_string): + return "URL" + + # Check if input_string is a file path + file_path = os.path.abspath(input_string) + if os.path.exists(file_path): + return "File path" + + return "Invalid" + + +def extract_data(input_string, data_type): + # Regular expression to extract content starting from '```python' until the end if there are no closing backticks + pattern = f"```{data_type}" + r"(.*?)(```|$)" + # Extract content + # re.DOTALL allows '.' to match newlines as well + matches = re.findall(pattern, input_string, re.DOTALL) + # Return the first match if exists, trimming whitespace and ignoring potential closing backticks + return matches[0][0].strip() if matches else input_string + + +def parse_input(code): + """Use AST to parse the input string and extract the function name, arguments, and keyword arguments.""" + + def get_target_names(target): + """Recursively get all variable names from the assignment target.""" + if isinstance(target, ast.Name): + return [target.id] + elif isinstance(target, ast.Tuple): + names = [] + for elt in target.elts: + names.extend(get_target_names(elt)) + return names + return [] + + def extract_value(node): + """提取 AST 节点的实际值""" + if isinstance(node, ast.Constant): + return node.value + elif isinstance(node, ast.Name): + # TODO: a better way to handle variables + raise ValueError( + f"Arguments should be a Constant, got a variable {node.id} instead." + ) + # 添加其他需要处理的 AST 节点类型 + return None + + try: + tree = ast.parse(code) + for node in ast.walk(tree): + if isinstance(node, ast.Assign): + targets = [] + for t in node.targets: + targets.extend(get_target_names(t)) + if isinstance(node.value, ast.Call): + func_name = node.value.func.id + args = [ast.dump(arg) for arg in node.value.args] + kwargs = { + kw.arg: extract_value(kw.value) for kw in node.value.keywords + } + print(f"Input: {code.strip()}") + print(f"Output Variables: {targets}") + print(f"Function Name: {func_name}") + print(f"Arguments: {args}") + print(f"Keyword Arguments: {kwargs}") + elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call): + targets = [] + func_name = extract_value(node.value.func) + args = [extract_value(arg) for arg in node.value.args] + kwargs = {kw.arg: extract_value(kw.value) for kw in node.value.keywords} + + except SyntaxError: + print(f"Input: {code.strip()}") + print("No match found") + + return targets, func_name, args, kwargs + + +if __name__ == "__main__": + import json + s='{"Reasoning": "The Docker icon has been successfully clicked, and the Docker application should now be opening. No further actions are required.", "Next Action": None}' + json_str = json.loads(s) + print(json_str) \ No newline at end of file diff --git a/demo/gui_agent/llm_utils/oai.py b/demo/gui_agent/llm_utils/oai.py new file mode 100644 index 0000000..ad1d54e --- /dev/null +++ b/demo/gui_agent/llm_utils/oai.py @@ -0,0 +1,117 @@ + +import os +import logging +import base64 +import requests + +# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image + +def is_image_path(text): + # Checking if the input text ends with typical image file extensions + image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") + if text.endswith(image_extensions): + return True + else: + return False + +def encode_image(image_path): + """Encode image file to base64.""" + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + + + +# from openai import OpenAI +# client = OpenAI( +# api_key=os.environ.get("OPENAI_API_KEY") +# ) + + +def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): + + api_key = api_key or os.environ.get("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY is not set") + + headers = {"Content-Type": "application/json", + "Authorization": f"Bearer {api_key}"} + + final_messages = [{"role": "system", "content": system}] + + # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + if type(messages) == list: + for item in messages: + contents = [] + if isinstance(item, dict): + for cnt in item["content"]: + if isinstance(cnt, str): + if is_image_path(cnt): + base64_image = encode_image(cnt) + content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}} + else: + content = {"type": "text", "text": cnt} + else: + # in this case it is a text block from anthropic + content = {"type": "text", "text": str(cnt)} + + contents.append(content) + + message = {"role": 'user', "content": contents} + else: # str + contents.append({"type": "text", "text": item}) + message = {"role": "user", "content": contents} + + final_messages.append(message) + + + elif isinstance(messages, str): + final_messages = [{"role": "user", "content": messages}] + # import pdb; pdb.set_trace() + + print("[oai] sending messages:", {"role": "user", "content": messages}) + + payload = { + "model": llm, + "messages": final_messages, + "max_tokens": max_tokens, + "temperature": temperature, + # "stop": stop, + } + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + + response = requests.post( + "https://api.openai.com/v1/chat/completions", headers=headers, json=payload + ) + + try: + text = response.json()['choices'][0]['message']['content'] + token_usage = int(response.json()['usage']['total_tokens']) + return text, token_usage + + # return error message if the response is not successful + except Exception as e: + print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") + return response.json() + + +if __name__ == "__main__": + + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY is not set") + + text, token_usage = run_oai_interleaved( + messages= [{"content": [ + "What is in the screenshot?", + "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"], + "role": "user" + }], + llm="gpt-4o-mini", + system="You are a helpful assistant", + api_key=api_key, + max_tokens=256, + temperature=0) + + print(text, token_usage) + # There is an introduction describing the Calyx... 36986 diff --git a/demo/gui_agent/llm_utils/qwen.py b/demo/gui_agent/llm_utils/qwen.py new file mode 100644 index 0000000..2d3e6e7 --- /dev/null +++ b/demo/gui_agent/llm_utils/qwen.py @@ -0,0 +1,107 @@ + +import os +import logging +import base64 +import requests + +import dashscope +# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image + +def is_image_path(text): + return False + +def encode_image(image_path): + return "" + + +def run_qwen(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): + + api_key = api_key or os.environ.get("QWEN_API_KEY") + if not api_key: + raise ValueError("QWEN_API_KEY is not set") + + dashscope.api_key = api_key + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + + final_messages = [{"role": "system", "content": [{"text": system}]}] + # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + if type(messages) == list: + for item in messages: + contents = [] + if isinstance(item, dict): + for cnt in item["content"]: + if isinstance(cnt, str): + if is_image_path(cnt): + # base64_image = encode_image(cnt) + content = [{"image": cnt}] + # content = {"type": "image_url", "image_url": {"url": image_url}} + else: + content = {"text": cnt} + contents.append(content) + + message = {"role": item["role"], "content": contents} + else: # str + contents.append({"text": item}) + message = {"role": "user", "content": contents} + + final_messages.append(message) + + print("[qwen-vl] sending messages:", final_messages) + + response = dashscope.MultiModalConversation.call( + model='qwen-vl-max-0809', + messages=final_messages + ) + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + + try: + text = response.output.choices[0].message.content[0]['text'] + usage = response.usage + + if "total_tokens" not in usage: + token_usage = int(usage["input_tokens"] + usage["output_tokens"]) + else: + token_usage = int(usage["total_tokens"]) + + return text, token_usage + # return response.json()['choices'][0]['message']['content'] + # return error message if the response is not successful + except Exception as e: + print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") + return response.json() + + + +if __name__ == "__main__": + api_key = os.environ.get("QWEN_API_KEY") + if not api_key: + raise ValueError("QWEN_API_KEY is not set") + + dashscope.api_key = api_key + + final_messages = [{"role": "user", + "content": [ + {"text": "What is in the screenshot?"}, + {"image": "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"} + ] + } + ] + response = dashscope.MultiModalConversation.call(model='qwen-vl-max-0809', messages=final_messages) + + print(response) + + text = response.output.choices[0].message.content[0]['text'] + usage = response.usage + + if "total_tokens" not in usage: + if "image_tokens" in usage: + token_usage = usage["input_tokens"] + usage["output_tokens"] + usage["image_tokens"] + else: + token_usage = usage["input_tokens"] + usage["output_tokens"] + else: + token_usage = usage["total_tokens"] + + print(text, token_usage) + # The screenshot is from a video game... 1387 \ No newline at end of file diff --git a/demo/gui_agent/llm_utils/run_llm.py b/demo/gui_agent/llm_utils/run_llm.py new file mode 100644 index 0000000..bc207dd --- /dev/null +++ b/demo/gui_agent/llm_utils/run_llm.py @@ -0,0 +1,44 @@ +import base64 +import logging +from .oai import run_oai_interleaved +from .gemini import run_gemini_interleaved + +def run_llm(prompt, llm="gpt-4o-mini", max_tokens=256, temperature=0, stop=None): + log_prompt(prompt) + + # turn string prompt into list + if isinstance(prompt, str): + prompt = [prompt] + elif isinstance(prompt, list): + pass + else: + raise ValueError(f"Invalid prompt type: {type(prompt)}") + + if llm.startswith("gpt"): # gpt series + out = run_oai_interleaved( + prompt, + llm, + max_tokens, + temperature, + stop + ) + elif llm.startswith("gemini"): # gemini series + out = run_gemini_interleaved( + prompt, + llm, + max_tokens, + temperature, + stop + ) + else: + raise ValueError(f"Invalid llm: {llm}") + logging.info( + f"========Output for {llm}=======\n{out}\n============================") + return out + +def log_prompt(prompt): + prompt_display = [prompt] if isinstance(prompt, str) else prompt + prompt_display = "\n\n".join(prompt_display) + logging.info( + f"========Prompt=======\n{prompt_display}\n============================") + \ No newline at end of file diff --git a/demo/loop.py b/demo/loop.py new file mode 100644 index 0000000..3d506c2 --- /dev/null +++ b/demo/loop.py @@ -0,0 +1,236 @@ +""" +Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. +""" +import time +import json +import asyncio +import platform +from collections.abc import Callable +from datetime import datetime +from enum import StrEnum +from typing import Any, cast, Dict + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic.types import ( + ToolResultBlockParam, + TextBlock, +) +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from tools import BashTool, ComputerTool, EditTool, ToolCollection, ToolResult + +import torch + +from gui_agent.anthropic_agent import AnthropicActor +from executor.anthropic_executor import AnthropicExecutor +from omniparser_agent.vlm_agent import OmniParser, VLMAgent +from tools.colorful_text import colorful_text_showui, colorful_text_vlm +from tools.screen_capture import get_screenshot +from gui_agent.llm_utils.oai import encode_image + + +BETA_FLAG = "computer-use-2024-10-22" + + +class APIProvider(StrEnum): + ANTHROPIC = "anthropic" + BEDROCK = "bedrock" + VERTEX = "vertex" + OPENAI = "openai" + QWEN = "qwen" + + +PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { + APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", + APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", + APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", + # APIProvider.OPENAI: "gpt-4o", + # APIProvider.QWEN: "qwen2vl", +} + + +# This system prompt is optimized for the Docker environment in this repository and +# specific tool combinations enabled. +# We encourage modifying this system prompt to ensure the model has context for the +# environment it is running in, and to provide any additional information that may be +# helpful for the task at hand. +SYSTEM_PROMPT = f""" +* You are utilizing a Windows system with internet access. +* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. + +""" + +import base64 +from PIL import Image +from io import BytesIO + +def sampling_loop_sync( + *, + model: str, + provider: APIProvider | None, + system_prompt_suffix: str, + messages: list[BetaMessageParam], + output_callback: Callable[[BetaContentBlock], None], + tool_output_callback: Callable[[ToolResult, str], None], + api_response_callback: Callable[[APIResponse[BetaMessage]], None], + api_key: str, + only_n_most_recent_images: int | None = 2, + max_tokens: int = 4096, + selected_screen: int = 0 +): + """ + Synchronous agentic sampling loop for the assistant/tool interaction of computer use. + """ + print('in sampling_loop_sync, model:', model) + if model == "claude-3-5-sonnet-20241022": + omniparser = OmniParser(url="http://127.0.0.1:8000/send_text/", + selected_screen=selected_screen,) + + # Register Actor and Executor + actor = AnthropicActor( + model=model, + provider=provider, + system_prompt_suffix=system_prompt_suffix, + api_key=api_key, + api_response_callback=api_response_callback, + max_tokens=max_tokens, + only_n_most_recent_images=only_n_most_recent_images, + selected_screen=selected_screen + ) + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + executor = AnthropicExecutor( + output_callback=output_callback, + tool_output_callback=tool_output_callback, + selected_screen=selected_screen + ) + + elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + omniparser = OmniParser(url="http://127.0.0.1:8000/send_text/", + selected_screen=selected_screen,) + + actor = VLMAgent( + model=model, + provider=provider, + system_prompt_suffix=system_prompt_suffix, + api_key=api_key, + api_response_callback=api_response_callback, + selected_screen=selected_screen, + output_callback=output_callback, + ) + + executor = AnthropicExecutor( + output_callback=output_callback, + tool_output_callback=tool_output_callback, + selected_screen=selected_screen + ) + + # elif model == "gpt-4o + ShowUI" or model == "qwen2vl + ShowUI": + # planner = VLMPlanner( + # model=model, + # provider=provider, + # system_prompt_suffix=system_prompt_suffix, + # api_key=api_key, + # api_response_callback=api_response_callback, + # selected_screen=selected_screen, + # output_callback=output_callback, + # ) + + # if torch.cuda.is_available(): device = torch.device("cuda") + # elif torch.backends.mps.is_available(): device = torch.device("mps") + # else: device = torch.device("cpu") # support: 'cpu', 'mps', 'cuda' + # print(f"showUI-2B inited on device: {device}.") + + # actor = ShowUIActor( + # model_path="./showui-2b/", + # # Replace with your local path, e.g., "C:\\code\\ShowUI-2B", "/Users/your_username/ShowUI-2B/". + # device=device, + # split='web', # 'web' or 'phone' + # selected_screen=selected_screen, + # output_callback=output_callback, + # ) + + # executor = ShowUIExecutor( + # output_callback=output_callback, + # tool_output_callback=tool_output_callback, + # selected_screen=selected_screen + # ) + + else: + raise ValueError(f"Model {model} not supported") + print(f"Model Inited: {model}, Provider: {provider}") + + tool_result_content = None + + print(f"Start the message loop. User messages: {messages}") + + if model == "claude-3-5-sonnet-20241022": # Anthropic loop + while True: + parsed_screen = omniparser() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} + import pdb; pdb.set_trace() + screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text') + # # messages[-1]['content'].append(screen_info_block) + screen_info_dict = {"role": "user", "content": [screen_info_block]} + messages.append(screen_info_dict) + response = actor(messages=messages) + + for message, tool_result_content in executor(response, messages): + yield message + + if not tool_result_content: + return messages + + messages.append({"content": tool_result_content, "role": "user"}) + + elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + while True: + parsed_screen = omniparser() + response, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) + + for message, tool_result_content in executor(response, messages): + yield message + + if not tool_result_content: + return messages + + # import pdb; pdb.set_trace() + # messages.append({"role": "user", + # "content": ["History plan:\n" + str(vlm_response_json['Reasoning'])]}) + + # messages.append({"content": tool_result_content, "role": "user"}) + + elif model == "gpt-4o + ShowUI" or model == "qwen2vl + ShowUI": # ShowUI loop + while True: + vlm_response = planner(messages=messages) + + next_action = json.loads(vlm_response).get("Next Action") + yield next_action + + if next_action == None or next_action == "" or next_action == "None": + final_sc, final_sc_path = get_screenshot(selected_screen=selected_screen) + output_callback(f'No more actions from {colorful_text_vlm}. End of task. Final State:\n', + sender="bot") + yield None + + output_callback(f"{colorful_text_vlm} sending action to {colorful_text_showui}:\n{next_action}", sender="bot") + + actor_response = actor(messages=next_action) + yield actor_response + + for message, tool_result_content in executor(actor_response, messages): + time.sleep(1) + yield message + + # since showui executor has no feedback for now, we use "actor_response" to represent its response + # update messages for the next loop + messages.append({"role": "user", + "content": ["History plan:\n" + str(json.loads(vlm_response)) + + "\nHistory actions:\n" + str(actor_response["content"])]}) + print(f"End of loop. Messages: {str(messages)[:100000]}. Total cost: $USD{planner.total_cost:.5f}") \ No newline at end of file diff --git a/demo/omniparser_agent/vlm_agent.py b/demo/omniparser_agent/vlm_agent.py new file mode 100644 index 0000000..53ba46a --- /dev/null +++ b/demo/omniparser_agent/vlm_agent.py @@ -0,0 +1,417 @@ +import json +import asyncio +import platform +from collections.abc import Callable +from datetime import datetime +from enum import StrEnum +from typing import Any, cast, Dict, Callable +import uuid +import requests +from PIL import Image, ImageDraw +import base64 +from io import BytesIO + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic.types import TextBlock, ToolResultBlockParam +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage + +from tools.screen_capture import get_screenshot +from gui_agent.llm_utils.oai import run_oai_interleaved, encode_image +from gui_agent.llm_utils.qwen import run_qwen +from gui_agent.llm_utils.llm_utils import extract_data +from tools.colorful_text import colorful_text_showui, colorful_text_vlm + + +SYSTEM_PROMPT = f""" +* You are utilizing a Windows system with internet access. +* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. + +""" + + +class OmniParser: + def __init__(self, + url: str, + selected_screen: int = 0) -> None: + self.url = url + self.selected_screen = selected_screen + + def __call__(self,): + screenshot, screenshot_path = get_screenshot(selected_screen=self.selected_screen) + screenshot_path = str(screenshot_path) + image_base64 = encode_image(screenshot_path) + + # response = requests.post(self.url, json={"base64_image": image_base64, 'prompt': 'omniparser process'}) + # response_json = response.json() + # example response_json: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "latency": 0.1} + # Debug + response_json = {"som_image_base64": image_base64, "parsed_content_list": ['debug1', 'debug2'], "latency": 0.1} + print('omniparser latency:', response_json['latency']) + response_json = self.reformat_messages(response_json) + return response_json + + def reformat_messages(self, response_json: dict): + parsed_content_list = response_json["parsed_content_list"] + screen_info = "" + # Debug + # for idx, element in enumerate(parsed_content_list): + # element['idx'] = idx + # if element['type'] == 'text': + # # screen_info += f'''

\n''' + # screen_info += f'ID: {idx}, Text: {element["content"]}\n' + # elif element['type'] == 'icon': + # # screen_info += f'''{element['content']} \n''' + # screen_info += f'ID: {idx}, Icon: {element["content"]}\n' + response_json['screen_info'] = screen_info + return response_json + + + +class VLMAgent: + def __init__( + self, + model: str, + provider: str, + system_prompt_suffix: str, + api_key: str, + output_callback: Callable, + api_response_callback: Callable, + max_tokens: int = 4096, + only_n_most_recent_images: int | None = None, + selected_screen: int = 0, + print_usage: bool = True, + ): + if model == "gpt-4o + ShowUI": + self.model = "gpt-4o-2024-11-20" + elif model == "gpt-4o-mini + ShowUI": + self.model = "gpt-4o-mini" # "gpt-4o-mini" + elif model == "qwen2vl + ShowUI": + self.model = "qwen2vl" + elif model == "omniparser + gpt-4o": + self.model = "gpt-4o-2024-11-20" + else: + raise ValueError(f"Model {model} not supported") + + self.provider = provider + self.system_prompt_suffix = system_prompt_suffix + self.api_key = api_key + self.api_response_callback = api_response_callback + self.max_tokens = max_tokens + self.only_n_most_recent_images = only_n_most_recent_images + self.selected_screen = selected_screen + self.output_callback = output_callback + + self.print_usage = print_usage + self.total_token_usage = 0 + self.total_cost = 0 + + self.system = ( + # f"{SYSTEM_PROMPT}{' ' + system_prompt_suffix if system_prompt_suffix else ''}" + f"{system_prompt_suffix}" + ) + + def __call__(self, messages: list, parsed_screen: list[str, list]): + # example parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} + screen_info = parsed_screen["screen_info"] + # drop looping actions msg, byte image etc + planner_messages = messages + # planner_messages = _message_filter_callback(messages) + + print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) + # import pdb; pdb.set_trace() + planner_messages = _keep_latest_images(planner_messages) + # if self.only_n_most_recent_images: + # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) + + system = self._get_system_prompt(screen_info) + self.system_prompt_suffix + + # Take a screenshot + screenshot, screenshot_path = get_screenshot(selected_screen=self.selected_screen) + screen_width, screen_height = screenshot.size + screenshot_path = str(screenshot_path) + image_base64 = encode_image(screenshot_path) + + som_image_data = base64.b64decode(parsed_screen['som_image_base64']) + som_screenshot_path = f"./tmp/outputs/screenshot_som_{uuid.uuid4().hex}.png" + with open(som_screenshot_path, "wb") as f: + f.write(som_image_data) + + self.output_callback(f'Screenshot for {colorful_text_vlm}:\n', + sender="bot") + self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") + + + if isinstance(planner_messages[-1], dict): + if not isinstance(planner_messages[-1]["content"], list): + planner_messages[-1]["content"] = [planner_messages[-1]["content"]] + planner_messages[-1]["content"].append(screenshot_path) + planner_messages[-1]["content"].append(som_screenshot_path) + + print(f"Sending messages to VLMPlanner : {planner_messages}") + + if "gpt" in self.model: + vlm_response, token_usage = run_oai_interleaved( + messages=planner_messages, + system=system, + llm=self.model, + api_key=self.api_key, + max_tokens=self.max_tokens, + temperature=0, + ) + print(f"oai token usage: {token_usage}") + self.total_token_usage += token_usage + self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ + + elif "qwen" in self.model: + vlm_response, token_usage = run_qwen( + messages=planner_messages, + system=system, + llm=self.model, + api_key=self.api_key, + max_tokens=self.max_tokens, + temperature=0, + ) + print(f"qwen token usage: {token_usage}") + self.total_token_usage += token_usage + self.total_cost += (token_usage * 0.02 / 7.25 / 1000) # 1USD=7.25CNY, https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api + elif "phi" in self.model: + pass # TODO + else: + raise ValueError(f"Model {self.model} not supported") + + print(f"VLMPlanner response: {vlm_response}") + + if self.print_usage: + print(f"VLMPlanner total token usage so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}") + + vlm_response_json = extract_data(vlm_response, "json") + vlm_response_json = json.loads(vlm_response_json) + + # map "box_id" to "idx" in parsed_screen, and output the xy coordinate of bbox + # TODO add try except for the case when "box_id" is not in the response + # if 'Box ID' in vlm_response_json: + try: + bbox = parsed_screen["parsed_content_list"][int(vlm_response_json["Box ID"])]["bbox"] + vlm_response_json["coordinate"] = [int((bbox[0] + bbox[2]) / 2 * screen_width), int((bbox[1] + bbox[3]) / 2 * screen_height)] + # draw a circle on the screenshot image to indicate the action + self.draw_action(vlm_response_json, image_base64) + except: + print("No Box ID in the response.") + + + # vlm_plan_str = '\n'.join([f'{key}: {value}' for key, value in json.loads(response).items()]) + vlm_plan_str = "" + for key, value in vlm_response_json.items(): + if key == "Reasoning": + vlm_plan_str += f'{value}' + else: + vlm_plan_str += f'\n{key}: {value}' + + # self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") + + # construct the response so that anthropicExcutor can execute the tool + analysis = BetaTextBlock(text=vlm_plan_str, type='text') + if 'coordinate' in vlm_response_json: + move_cursor_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': 'mouse_move', 'coordinate': vlm_response_json["coordinate"]}, + name='computer', type='tool_use') + response_content = [analysis, move_cursor_block] + else: + response_content = [analysis] + if vlm_response_json["Next Action"] == "type": + click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') + sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]}, + name='computer', type='tool_use') + response_content.extend([click_block, sim_content_block]) + elif vlm_response_json["Next Action"] == "None": + print("Task paused/completed.") + else: + sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': vlm_response_json["Next Action"]}, + name='computer', type='tool_use') + response_content.append(sim_content_block) + + response = BetaMessage(id=f'toolu_{uuid.uuid4()}', content=response_content, model='', role='assistant', type='message', stop_reason='tool_use', usage=BetaUsage(input_tokens=0, output_tokens=0)) + return response, vlm_response_json + + + def _api_response_callback(self, response: APIResponse): + self.api_response_callback(response) + + + def reformat_messages(self, messages: list): + pass + + def _get_system_prompt(self, screen_info: str = ""): + datetime_str = datetime.now().strftime("%A, %B %d, %Y") + os_name = platform.system() + return f""" +You are using an {os_name} device. +You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot. +You can only interact with the desktop GUI (no terminal or application menu access). + +You may be given some history plan and actions, this is the response from the previous loop. +You should carefully consider your plan base on the task, screenshot, and history actions. + +Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info} + +Your available "Next Action" only include: +- type: type a string of text. +- left_click: Describe the ui element to be clicked. +- enter: Press an enter key. +- escape: Press an ESCAPE key. +- hover: Describe the ui element to be hovered. +- scroll: Scroll the screen, you must specify up or down. +- press: Describe the ui element to be pressed. + +Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. + +Output format: +```json +{{ + "Reasoning": str, # describe what is in the current screen, taking into account the history, then describe your step-by-step thoughts on how to achieve the task, choose one action from available actions at a time. + "Next Action": "action_type, action description" | "None" # one action at a time, describe it in short and precisely. + 'Box ID': n, + 'value': "xxx" # if the action is type, you should provide the text to type. +}} +``` + +One Example: +```json +{{ + "Reasoning": "The current screen shows google result of amazon, in previous action I have searched amazon on google. Then I need to click on the first search results to go to amazon.com.", + "Next Action": "left_click", + 'Box ID': m, +}} +``` + +Another Example: +```json +{{ + "Reasoning": "The current screen shows the front page of amazon. There is no previous action. Therefore I need to type "Apple watch" in the search bar.", + "Next Action": "type", + 'Box ID': n, + 'value': "Apple watch" +}} +``` + +IMPORTANT NOTES: +1. You should only give a single action at a time. +2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. +3. Attach the next action prediction in the "Next Action". +4. You should not include other actions, such as keyboard shortcuts. +5. When the task is completed, you should say "Next Action": "None" in the json field. +""" + def draw_action(self, vlm_response_json, image_base64): + # draw a circle using the coordinate in parsed_screen['som_image_base64'] + image_data = base64.b64decode(image_base64) + image = Image.open(BytesIO(image_data)) + + draw = ImageDraw.Draw(image) + x, y = vlm_response_json["coordinate"] + radius = 10 + draw.ellipse((x - radius, y - radius, x + radius, y + radius), outline='red', width=3) + buffered = BytesIO() + image.save('demo.png') + image.save(buffered, format="PNG") + image_with_circle_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") + self.output_callback(f'Action performed on the Screenshot (red circle), for {colorful_text_vlm}:\n', sender="bot") + + +def _keep_latest_images(messages): + for i in range(len(messages)-1): + if isinstance(messages[i]["content"], list): + for cnt in messages[i]["content"]: + if isinstance(cnt, str): + if cnt.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")): + messages[i]["content"].remove(cnt) + return messages + + +def _maybe_filter_to_n_most_recent_images( + messages: list[BetaMessageParam], + images_to_keep: int, + min_removal_threshold: int = 10, +): + """ + With the assumption that images are screenshots that are of diminishing value as + the conversation progresses, remove all but the final `images_to_keep` tool_result + images in place, with a chunk of min_removal_threshold to reduce the amount we + break the implicit prompt cache. + """ + if images_to_keep is None: + return messages + + tool_result_blocks = cast( + list[ToolResultBlockParam], + [ + item + for message in messages + for item in ( + message["content"] if isinstance(message["content"], list) else [] + ) + if isinstance(item, dict) and item.get("type") == "tool_result" + ], + ) + + total_images = sum( + 1 + for tool_result in tool_result_blocks + for content in tool_result.get("content", []) + if isinstance(content, dict) and content.get("type") == "image" + ) + + images_to_remove = total_images - images_to_keep + # for better cache behavior, we want to remove in chunks + images_to_remove -= images_to_remove % min_removal_threshold + + for tool_result in tool_result_blocks: + if isinstance(tool_result.get("content"), list): + new_content = [] + for content in tool_result.get("content", []): + if isinstance(content, dict) and content.get("type") == "image": + if images_to_remove > 0: + images_to_remove -= 1 + continue + new_content.append(content) + tool_result["content"] = new_content + + +def _message_filter_callback(messages): + filtered_list = [] + try: + for msg in messages: + if msg.get('role') in ['user']: + if not isinstance(msg["content"], list): + msg["content"] = [msg["content"]] + if isinstance(msg["content"][0], TextBlock): + filtered_list.append(str(msg["content"][0].text)) # User message + elif isinstance(msg["content"][0], str): + filtered_list.append(msg["content"][0]) # User message + else: + print("[_message_filter_callback]: drop message", msg) + continue + + # elif msg.get('role') in ['assistant']: + # if isinstance(msg["content"][0], TextBlock): + # msg["content"][0] = str(msg["content"][0].text) + # elif isinstance(msg["content"][0], BetaTextBlock): + # msg["content"][0] = str(msg["content"][0].text) + # elif isinstance(msg["content"][0], BetaToolUseBlock): + # msg["content"][0] = str(msg['content'][0].input) + # elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image": + # msg["content"][0] = f'' + # else: + # print("[_message_filter_callback]: drop message", msg) + # continue + # filtered_list.append(msg["content"][0]) # User message + + else: + print("[_message_filter_callback]: drop message", msg) + continue + + except Exception as e: + print("[_message_filter_callback]: error", e) + + return filtered_list \ No newline at end of file diff --git a/demo/remote_request.py b/demo/remote_request.py index 22f399a..d0bc1c3 100644 --- a/demo/remote_request.py +++ b/demo/remote_request.py @@ -1,78 +1,78 @@ -# uvicorn remote_request:app --host 0.0.0.0 --port 8000 --reload - -import sys -import os -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model -import torch -from PIL import Image -from typing import Dict, Tuple, List -import base64 - - -config = { - 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', - 'device': 'cpu', - 'caption_model_name': 'florence2', - 'caption_model_path': '../weights/icon_caption_florence', - 'BOX_TRESHOLD': 0.05 -} - - -class Omniparser(object): - def __init__(self, config: Dict): - self.config = config - device = 'cuda' if torch.cuda.is_available() else 'cpu' - - self.som_model = get_yolo_model(model_path=config['som_model_path']) - self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device) - print('Omniparser initialized!!!') - - def parse(self, image_base64: str): - image_path = '../imgs/demo_image.jpg' - with open(image_path, "wb") as fh: - fh.write(base64.b64decode(image_base64)) - print('Parsing image:', image_path) - - image = Image.open(image_path) - print('image size:', image.size) - - box_overlay_ratio = max(image.size) / 3200 - draw_bbox_config = { - 'text_scale': 0.8 * box_overlay_ratio, - 'text_thickness': max(int(2 * box_overlay_ratio), 1), - 'text_padding': max(int(3 * box_overlay_ratio), 1), - 'thickness': max(int(3 * box_overlay_ratio), 1), - } - BOX_TRESHOLD = config['BOX_TRESHOLD'] - - ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.8}, use_paddleocr=True) - text, ocr_bbox = ocr_bbox_rslt - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) - with open('../imgs/demo_image_som.jpg', "wb") as fh: - fh.write(base64.b64decode(dino_labled_img)) - - return dino_labled_img, parsed_content_list - - -from fastapi import FastAPI -from pydantic import BaseModel - -app = FastAPI() - -class Item(BaseModel): - base64_image: str - prompt: str - -Omniparser = Omniparser(config) - -@app.post("/send_text/") -async def send_text(item: Item): - print('start parsing...') - import time - start = time.time() - dino_labled_img, parsed_content_list = Omniparser.parse(item.base64_image) - latency = time.time() - start - print('time:', latency) +# uvicorn remote_request:app --host 0.0.0.0 --port 8000 --reload + +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model +import torch +from PIL import Image +from typing import Dict, Tuple, List +import base64 + + +config = { + 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', + 'device': 'cpu', + 'caption_model_name': 'florence2', + 'caption_model_path': '../weights/icon_caption_florence', + 'BOX_TRESHOLD': 0.05 +} + + +class Omniparser(object): + def __init__(self, config: Dict): + self.config = config + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + self.som_model = get_yolo_model(model_path=config['som_model_path']) + self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device) + print('Omniparser initialized!!!') + + def parse(self, image_base64: str): + image_path = '../imgs/demo_image.jpg' + with open(image_path, "wb") as fh: + fh.write(base64.b64decode(image_base64)) + print('Parsing image:', image_path) + + image = Image.open(image_path) + print('image size:', image.size) + + box_overlay_ratio = max(image.size) / 3200 + draw_bbox_config = { + 'text_scale': 0.8 * box_overlay_ratio, + 'text_thickness': max(int(2 * box_overlay_ratio), 1), + 'text_padding': max(int(3 * box_overlay_ratio), 1), + 'thickness': max(int(3 * box_overlay_ratio), 1), + } + BOX_TRESHOLD = config['BOX_TRESHOLD'] + + ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.8}, use_paddleocr=True) + text, ocr_bbox = ocr_bbox_rslt + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) + with open('../imgs/demo_image_som.jpg', "wb") as fh: + fh.write(base64.b64decode(dino_labled_img)) + + return dino_labled_img, parsed_content_list + + +from fastapi import FastAPI +from pydantic import BaseModel + +app = FastAPI() + +class Item(BaseModel): + base64_image: str + prompt: str + +Omniparser = Omniparser(config) + +@app.post("/send_text/") +async def send_text(item: Item): + print('start parsing...') + import time + start = time.time() + dino_labled_img, parsed_content_list = Omniparser.parse(item.base64_image) + latency = time.time() - start + print('time:', latency) return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency} \ No newline at end of file diff --git a/demo/tmp/outputs/screenshot.png b/demo/tmp/outputs/screenshot.png new file mode 100644 index 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zGzTnG2C#L0d)H<7h8yVM!j!>bZM#p%M%#w>;0ZMgTn#zZRdJnm)Hf%T(cjPSOLqX* N&WOF+>$V-u`46QzFg^eP literal 0 HcmV?d00001 diff --git a/demo/tools/__init__.py b/demo/tools/__init__.py new file mode 100644 index 0000000..e0bf3e2 --- /dev/null +++ b/demo/tools/__init__.py @@ -0,0 +1,16 @@ +from .base import CLIResult, ToolResult +from .bash import BashTool +from .collection import ToolCollection +from .computer import ComputerTool +from .edit import EditTool +from .screen_capture import get_screenshot + +__ALL__ = [ + BashTool, + CLIResult, + ComputerTool, + EditTool, + ToolCollection, + ToolResult, + get_screenshot, +] diff --git a/demo/tools/base.py b/demo/tools/base.py new file mode 100644 index 0000000..1acf24a --- /dev/null +++ b/demo/tools/base.py @@ -0,0 +1,69 @@ +from abc import ABCMeta, abstractmethod +from dataclasses import dataclass, fields, replace +from typing import Any + +from anthropic.types.beta import BetaToolUnionParam + + +class BaseAnthropicTool(metaclass=ABCMeta): + """Abstract base class for Anthropic-defined tools.""" + + @abstractmethod + def __call__(self, **kwargs) -> Any: + """Executes the tool with the given arguments.""" + ... + + @abstractmethod + def to_params( + self, + ) -> BetaToolUnionParam: + raise NotImplementedError + + +@dataclass(kw_only=True, frozen=True) +class ToolResult: + """Represents the result of a tool execution.""" + + output: str | None = None + error: str | None = None + base64_image: str | None = None + system: str | None = None + + def __bool__(self): + return any(getattr(self, field.name) for field in fields(self)) + + def __add__(self, other: "ToolResult"): + def combine_fields( + field: str | None, other_field: str | None, concatenate: bool = True + ): + if field and other_field: + if concatenate: + return field + other_field + raise ValueError("Cannot combine tool results") + return field or other_field + + return ToolResult( + output=combine_fields(self.output, other.output), + error=combine_fields(self.error, other.error), + base64_image=combine_fields(self.base64_image, other.base64_image, False), + system=combine_fields(self.system, other.system), + ) + + def replace(self, **kwargs): + """Returns a new ToolResult with the given fields replaced.""" + return replace(self, **kwargs) + + +class CLIResult(ToolResult): + """A ToolResult that can be rendered as a CLI output.""" + + +class ToolFailure(ToolResult): + """A ToolResult that represents a failure.""" + + +class ToolError(Exception): + """Raised when a tool encounters an error.""" + + def __init__(self, message): + self.message = message diff --git a/demo/tools/bash.py b/demo/tools/bash.py new file mode 100644 index 0000000..56eb912 --- /dev/null +++ b/demo/tools/bash.py @@ -0,0 +1,136 @@ +import asyncio +import os +from typing import ClassVar, Literal + +from anthropic.types.beta import BetaToolBash20241022Param + +from .base import BaseAnthropicTool, CLIResult, ToolError, ToolResult + + +class _BashSession: + """A session of a bash shell.""" + + _started: bool + _process: asyncio.subprocess.Process + + command: str = "/bin/bash" + _output_delay: float = 0.2 # seconds + _timeout: float = 120.0 # seconds + _sentinel: str = "<>" + + def __init__(self): + self._started = False + self._timed_out = False + + async def start(self): + if self._started: + return + + self._process = await asyncio.create_subprocess_shell( + self.command, + shell=False, + stdin=asyncio.subprocess.PIPE, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + + self._started = True + + def stop(self): + """Terminate the bash shell.""" + if not self._started: + raise ToolError("Session has not started.") + if self._process.returncode is not None: + return + self._process.terminate() + + async def run(self, command: str): + """Execute a command in the bash shell.""" + if not self._started: + raise ToolError("Session has not started.") + if self._process.returncode is not None: + return ToolResult( + system="tool must be restarted", + error=f"bash has exited with returncode {self._process.returncode}", + ) + if self._timed_out: + raise ToolError( + f"timed out: bash has not returned in {self._timeout} seconds and must be restarted", + ) + + # we know these are not None because we created the process with PIPEs + assert self._process.stdin + assert self._process.stdout + assert self._process.stderr + + # send command to the process + self._process.stdin.write( + command.encode() + f"; echo '{self._sentinel}'\n".encode() + ) + await self._process.stdin.drain() + + # read output from the process, until the sentinel is found + output = "" + try: + async with asyncio.timeout(self._timeout): + while True: + await asyncio.sleep(self._output_delay) + data = await self._process.stdout.readline() + if not data: + break + line = data.decode() + output += line + if self._sentinel in line: + output = output.replace(self._sentinel, "") + break + except asyncio.TimeoutError: + self._timed_out = True + raise ToolError( + f"timed out: bash has not returned in {self._timeout} seconds and must be restarted", + ) from None + + error = await self._process.stderr.read() + error = error.decode() + + return CLIResult(output=output.strip(), error=error.strip()) + + +class BashTool(BaseAnthropicTool): + """ + A tool that allows the agent to run bash commands. + The tool parameters are defined by Anthropic and are not editable. + """ + + _session: _BashSession | None + name: ClassVar[Literal["bash"]] = "bash" + api_type: ClassVar[Literal["bash_20241022"]] = "bash_20241022" + + def __init__(self): + self._session = None + super().__init__() + + async def __call__( + self, command: str | None = None, restart: bool = False, **kwargs + ): + if restart: + if self._session: + self._session.stop() + self._session = _BashSession() + await self._session.start() + + return ToolResult(system="tool has been restarted.") + + if self._session is None: + self._session = _BashSession() + await self._session.start() + + if command is not None: + return await self._session.run(command) + + raise ToolError("no command provided.") + + def to_params(self) -> BetaToolBash20241022Param: + return { + "type": self.api_type, + "name": self.name, + } \ No newline at end of file diff --git a/demo/tools/collection.py b/demo/tools/collection.py new file mode 100644 index 0000000..693a1dd --- /dev/null +++ b/demo/tools/collection.py @@ -0,0 +1,34 @@ +"""Collection classes for managing multiple tools.""" + +from typing import Any + +from anthropic.types.beta import BetaToolUnionParam + +from .base import ( + BaseAnthropicTool, + ToolError, + ToolFailure, + ToolResult, +) + + +class ToolCollection: + """A collection of anthropic-defined tools.""" + + def __init__(self, *tools: BaseAnthropicTool): + self.tools = tools + self.tool_map = {tool.to_params()["name"]: tool for tool in tools} + + def to_params( + self, + ) -> list[BetaToolUnionParam]: + return [tool.to_params() for tool in self.tools] + + async def run(self, *, name: str, tool_input: dict[str, Any]) -> ToolResult: + tool = self.tool_map.get(name) + if not tool: + return ToolFailure(error=f"Tool {name} is invalid") + try: + return await tool(**tool_input) + except ToolError as e: + return ToolFailure(error=e.message) diff --git a/demo/tools/colorful_text.py b/demo/tools/colorful_text.py new file mode 100644 index 0000000..5804187 --- /dev/null +++ b/demo/tools/colorful_text.py @@ -0,0 +1,27 @@ +""" +Define some colorful stuffs for better visualization in the chat. +""" + +# Define the RGB colors for each letter +colors = { + 'S': 'rgb(106, 158, 210)', + 'h': 'rgb(111, 163, 82)', + 'o': 'rgb(209, 100, 94)', + 'w': 'rgb(238, 171, 106)', + 'U': 'rgb(0, 0, 0)', + 'I': 'rgb(0, 0, 0)', +} + +# Construct the colorful "ShowUI" word +colorful_text_showui = "**"+''.join( + f'{letter}' + for letter in "ShowUI" +)+"**" + + +colorful_text_vlm = "**OmniParser Agent**" + +colorful_text_user = "**User**" + +# print(f"colorful_text_showui: {colorful_text_showui}") +# **ShowUI** \ No newline at end of file diff --git a/demo/tools/computer.py b/demo/tools/computer.py new file mode 100644 index 0000000..e49f932 --- /dev/null +++ b/demo/tools/computer.py @@ -0,0 +1,519 @@ +import subprocess +import platform +import pyautogui +import asyncio +import base64 +import os +import time +if platform.system() == "Darwin": + import Quartz # uncomment this line if you are on macOS +from enum import StrEnum +from pathlib import Path +from typing import Literal, TypedDict +from uuid import uuid4 +from screeninfo import get_monitors + +from PIL import ImageGrab, Image +from functools import partial + +from anthropic.types.beta import BetaToolComputerUse20241022Param + +from .base import BaseAnthropicTool, ToolError, ToolResult +from .run import run + +OUTPUT_DIR = "./tmp/outputs" + +TYPING_DELAY_MS = 12 +TYPING_GROUP_SIZE = 50 + +Action = Literal[ + "key", + "type", + "mouse_move", + "left_click", + "left_click_drag", + "right_click", + "middle_click", + "double_click", + "screenshot", + "cursor_position", +] + + +class Resolution(TypedDict): + width: int + height: int + + +MAX_SCALING_TARGETS: dict[str, Resolution] = { + "XGA": Resolution(width=1024, height=768), # 4:3 + "WXGA": Resolution(width=1280, height=800), # 16:10 + "FWXGA": Resolution(width=1366, height=768), # ~16:9 +} + + +class ScalingSource(StrEnum): + COMPUTER = "computer" + API = "api" + + +class ComputerToolOptions(TypedDict): + display_height_px: int + display_width_px: int + display_number: int | None + + +def chunks(s: str, chunk_size: int) -> list[str]: + return [s[i : i + chunk_size] for i in range(0, len(s), chunk_size)] + + +def get_screen_details(): + screens = get_monitors() + screen_details = [] + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s.x) + + # Loop through sorted screens and assign positions + primary_index = 0 + for i, screen in enumerate(sorted_screens): + if i == 0: + layout = "Left" + elif i == len(sorted_screens) - 1: + layout = "Right" + else: + layout = "Center" + + if screen.is_primary: + position = "Primary" + primary_index = i + else: + position = "Secondary" + screen_info = f"Screen {i + 1}: {screen.width}x{screen.height}, {layout}, {position}" + screen_details.append(screen_info) + + return screen_details, primary_index + + +class ComputerTool(BaseAnthropicTool): + """ + A tool that allows the agent to interact with the screen, keyboard, and mouse of the current computer. + Adapted for Windows using 'pyautogui'. + """ + + name: Literal["computer"] = "computer" + api_type: Literal["computer_20241022"] = "computer_20241022" + width: int + height: int + display_num: int | None + + _screenshot_delay = 2.0 + _scaling_enabled = True + + @property + def options(self) -> ComputerToolOptions: + width, height = self.scale_coordinates( + ScalingSource.COMPUTER, self.width, self.height + ) + return { + "display_width_px": width, + "display_height_px": height, + "display_number": self.display_num, + } + + def to_params(self) -> BetaToolComputerUse20241022Param: + return {"name": self.name, "type": self.api_type, **self.options} + + def __init__(self, selected_screen: int = 0, is_scaling: bool = False): + super().__init__() + + # Get screen width and height using Windows command + self.display_num = None + self.offset_x = 0 + self.offset_y = 0 + self.selected_screen = selected_screen + self.is_scaling = is_scaling + self.width, self.height = self.get_screen_size() + + # Path to cliclick + self.cliclick = "cliclick" + self.key_conversion = {"Page_Down": "pagedown", + "Page_Up": "pageup", + "Super_L": "win", + "Escape": "esc"} + + system = platform.system() # Detect platform + if system == "Windows": + screens = get_monitors() + sorted_screens = sorted(screens, key=lambda s: s.x) + if self.selected_screen < 0 or self.selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + screen = sorted_screens[self.selected_screen] + bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) + + elif system == "Darwin": # macOS + max_displays = 32 # Maximum number of displays to handle + active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] + screens = [] + for display_id in active_displays: + bounds = Quartz.CGDisplayBounds(display_id) + screens.append({ + 'id': display_id, 'x': int(bounds.origin.x), 'y': int(bounds.origin.y), + 'width': int(bounds.size.width), 'height': int(bounds.size.height), + 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display + }) + sorted_screens = sorted(screens, key=lambda s: s['x']) + if self.selected_screen < 0 or self.selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + screen = sorted_screens[self.selected_screen] + bbox = (screen['x'], screen['y'], screen['x'] + screen['width'], screen['y'] + screen['height']) + else: # Linux or other OS + cmd = "xrandr | grep ' primary' | awk '{print $4}'" + try: + # output = subprocess.check_output(cmd, shell=True).decode() + # resolution = output.strip().split()[0] + # width, height = map(int, resolution.split('x')) + # bbox = (0, 0, width, height) # Assuming single primary screen for simplicity + screen = get_monitors()[0] + bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) + except subprocess.CalledProcessError: + raise RuntimeError("Failed to get screen resolution on Linux.") + + self.offset_x = screen['x'] if system == "Darwin" else screen.x + self.offset_y = screen['y'] if system == "Darwin" else screen.y + self.bbox = bbox + + + async def __call__( + self, + *, + action: Action, + text: str | None = None, + coordinate: tuple[int, int] | None = None, + **kwargs, + ): + print(f"action: {action}, text: {text}, coordinate: {coordinate}, is_scaling: {self.is_scaling}") + if action in ("mouse_move", "left_click_drag"): + if coordinate is None: + raise ToolError(f"coordinate is required for {action}") + if text is not None: + raise ToolError(f"text is not accepted for {action}") + if not isinstance(coordinate, (list, tuple)) or len(coordinate) != 2: + raise ToolError(f"{coordinate} must be a tuple of length 2") + # if not all(isinstance(i, int) and i >= 0 for i in coordinate): + if not all(isinstance(i, int) for i in coordinate): + raise ToolError(f"{coordinate} must be a tuple of non-negative ints") + + if self.is_scaling: + x, y = self.scale_coordinates( + ScalingSource.API, coordinate[0], coordinate[1] + ) + else: + x, y = coordinate + + # print(f"scaled_coordinates: {x}, {y}") + # print(f"offset: {self.offset_x}, {self.offset_y}") + + # x += self.offset_x # TODO - check if this is needed + # y += self.offset_y + + print(f"mouse move to {x}, {y}") + + if action == "mouse_move": + pyautogui.moveTo(x, y) + return ToolResult(output=f"Moved mouse to ({x}, {y})") + elif action == "left_click_drag": + current_x, current_y = pyautogui.position() + pyautogui.dragTo(x, y, duration=0.5) # Adjust duration as needed + return ToolResult(output=f"Dragged mouse from ({current_x}, {current_y}) to ({x}, {y})") + + if action in ("key", "type"): + if text is None: + raise ToolError(f"text is required for {action}") + if coordinate is not None: + raise ToolError(f"coordinate is not accepted for {action}") + if not isinstance(text, str): + raise ToolError(output=f"{text} must be a string") + + if action == "key": + # Handle key combinations + keys = text.split('+') + for key in keys: + key = self.key_conversion.get(key.strip(), key.strip()) + key = key.lower() + pyautogui.keyDown(key) # Press down each key + for key in reversed(keys): + key = self.key_conversion.get(key.strip(), key.strip()) + key = key.lower() + pyautogui.keyUp(key) # Release each key in reverse order + return ToolResult(output=f"Pressed keys: {text}") + + elif action == "type": + pyautogui.typewrite(text, interval=TYPING_DELAY_MS / 1000) # Convert ms to seconds + pyautogui.press('enter') + screenshot_base64 = (await self.screenshot()).base64_image + return ToolResult(output=text, base64_image=screenshot_base64) + + if action in ( + "left_click", + "right_click", + "double_click", + "middle_click", + "screenshot", + "cursor_position", + "left_press", + ): + if text is not None: + raise ToolError(f"text is not accepted for {action}") + if coordinate is not None: + raise ToolError(f"coordinate is not accepted for {action}") + + if action == "screenshot": + return await self.screenshot() + elif action == "cursor_position": + x, y = pyautogui.position() + x, y = self.scale_coordinates(ScalingSource.COMPUTER, x, y) + return ToolResult(output=f"X={x},Y={y}") + else: + if action == "left_click": + pyautogui.click() + elif action == "right_click": + pyautogui.rightClick() + elif action == "middle_click": + pyautogui.middleClick() + elif action == "double_click": + pyautogui.doubleClick() + elif action == "left_press": + pyautogui.mouseDown() + time.sleep(1) + pyautogui.mouseUp() + return ToolResult(output=f"Performed {action}") + + raise ToolError(f"Invalid action: {action}") + + async def screenshot(self): + + import time + time.sleep(1) + + """Take a screenshot of the current screen and return a ToolResult with the base64 encoded image.""" + output_dir = Path(OUTPUT_DIR) + output_dir.mkdir(parents=True, exist_ok=True) + path = output_dir / f"screenshot_{uuid4().hex}.png" + + ImageGrab.grab = partial(ImageGrab.grab, all_screens=True) + + # Detect platform + system = platform.system() + + if system == "Windows": + # Windows: Use screeninfo to get monitor details + screens = get_monitors() + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s.x) + + if self.selected_screen < 0 or self.selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + + screen = sorted_screens[self.selected_screen] + bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) + + elif system == "Darwin": # macOS + # macOS: Use Quartz to get monitor details + max_displays = 32 # Maximum number of displays to handle + active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] + + # Get the display bounds (resolution) for each active display + screens = [] + for display_id in active_displays: + bounds = Quartz.CGDisplayBounds(display_id) + screens.append({ + 'id': display_id, + 'x': int(bounds.origin.x), + 'y': int(bounds.origin.y), + 'width': int(bounds.size.width), + 'height': int(bounds.size.height), + 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display + }) + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s['x']) + + if self.selected_screen < 0 or self.selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + + screen = sorted_screens[self.selected_screen] + bbox = (screen['x'], screen['y'], screen['x'] + screen['width'], screen['y'] + screen['height']) + + else: # Linux or other OS + cmd = "xrandr | grep ' primary' | awk '{print $4}'" + try: + # output = subprocess.check_output(cmd, shell=True).decode() + # resolution = output.strip().split()[0] + # width, height = map(int, resolution.split('x')) + # bbox = (0, 0, width, height) # Assuming single primary screen for simplicity + screen = get_monitors()[0] + bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) + except subprocess.CalledProcessError: + raise RuntimeError("Failed to get screen resolution on Linux.") + + # Take screenshot using the bounding box + screenshot = ImageGrab.grab(bbox=bbox) + + # Set offsets (for potential future use) + self.offset_x = screen['x'] if system == "Darwin" else screen.x + self.offset_y = screen['y'] if system == "Darwin" else screen.y + + print(f"target_dimension {self.target_dimension}") + + if not hasattr(self, 'target_dimension'): + screenshot = self.padding_image(screenshot) + self.target_dimension = MAX_SCALING_TARGETS["WXGA"] + + # Resize if target_dimensions are specified + print(f"offset is {self.offset_x}, {self.offset_y}") + print(f"target_dimension is {self.target_dimension}") + screenshot = screenshot.resize((self.target_dimension["width"], self.target_dimension["height"])) + + # Save the screenshot + screenshot.save(str(path)) + + if path.exists(): + # Return a ToolResult instance instead of a dictionary + return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) + + raise ToolError(f"Failed to take screenshot: {path} does not exist.") + + def padding_image(self, screenshot): + """Pad the screenshot to 16:10 aspect ratio, when the aspect ratio is not 16:10.""" + _, height = screenshot.size + new_width = height * 16 // 10 + + padding_image = Image.new("RGB", (new_width, height), (255, 255, 255)) + # padding to top left + padding_image.paste(screenshot, (0, 0)) + return padding_image + + async def shell(self, command: str, take_screenshot=True) -> ToolResult: + """Run a shell command and return the output, error, and optionally a screenshot.""" + _, stdout, stderr = await run(command) + base64_image = None + + if take_screenshot: + # delay to let things settle before taking a screenshot + await asyncio.sleep(self._screenshot_delay) + base64_image = (await self.screenshot()).base64_image + + return ToolResult(output=stdout, error=stderr, base64_image=base64_image) + + def scale_coordinates(self, source: ScalingSource, x: int, y: int): + """Scale coordinates to a target maximum resolution.""" + if not self._scaling_enabled: + return x, y + ratio = self.width / self.height + target_dimension = None + + for target_name, dimension in MAX_SCALING_TARGETS.items(): + # allow some error in the aspect ratio - not ratios are exactly 16:9 + if abs(dimension["width"] / dimension["height"] - ratio) < 0.02: + if dimension["width"] < self.width: + target_dimension = dimension + self.target_dimension = target_dimension + # print(f"target_dimension: {target_dimension}") + break + + if target_dimension is None: + # TODO: currently we force the target to be WXGA (16:10), when it cannot find a match + target_dimension = MAX_SCALING_TARGETS["WXGA"] + self.target_dimension = MAX_SCALING_TARGETS["WXGA"] + + # should be less than 1 + x_scaling_factor = target_dimension["width"] / self.width + y_scaling_factor = target_dimension["height"] / self.height + if source == ScalingSource.API: + if x > self.width or y > self.height: + raise ToolError(f"Coordinates {x}, {y} are out of bounds") + # scale up + return round(x / x_scaling_factor), round(y / y_scaling_factor) + # scale down + return round(x * x_scaling_factor), round(y * y_scaling_factor) + + def get_screen_size(self): + if platform.system() == "Windows": + # Use screeninfo to get primary monitor on Windows + screens = get_monitors() + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s.x) + + if self.selected_screen is None: + primary_monitor = next((m for m in get_monitors() if m.is_primary), None) + return primary_monitor.width, primary_monitor.height + elif self.selected_screen < 0 or self.selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + else: + screen = sorted_screens[self.selected_screen] + return screen.width, screen.height + + elif platform.system() == "Darwin": + # macOS part using Quartz to get screen information + max_displays = 32 # Maximum number of displays to handle + active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] + + # Get the display bounds (resolution) for each active display + screens = [] + for display_id in active_displays: + bounds = Quartz.CGDisplayBounds(display_id) + screens.append({ + 'id': display_id, + 'x': int(bounds.origin.x), + 'y': int(bounds.origin.y), + 'width': int(bounds.size.width), + 'height': int(bounds.size.height), + 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display + }) + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s['x']) + + if self.selected_screen is None: + # Find the primary monitor + primary_monitor = next((screen for screen in screens if screen['is_primary']), None) + if primary_monitor: + return primary_monitor['width'], primary_monitor['height'] + else: + raise RuntimeError("No primary monitor found.") + elif self.selected_screen < 0 or self.selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + else: + # Return the resolution of the selected screen + screen = sorted_screens[self.selected_screen] + return screen['width'], screen['height'] + + else: # Linux or other OS + cmd = "xrandr | grep ' primary' | awk '{print $4}'" + try: + # output = subprocess.check_output(cmd, shell=True).decode() + # resolution = output.strip().split()[0] + # width, height = map(int, resolution.split('x')) + # return width, height + screen = get_monitors()[0] + return screen.width, screen.height + except subprocess.CalledProcessError: + raise RuntimeError("Failed to get screen resolution on Linux.") + + def get_mouse_position(self): + # TODO: enhance this func + from AppKit import NSEvent + from Quartz import CGEventSourceCreate, kCGEventSourceStateCombinedSessionState + + loc = NSEvent.mouseLocation() + # Adjust for different coordinate system + return int(loc.x), int(self.height - loc.y) + + def map_keys(self, text: str): + """Map text to cliclick key codes if necessary.""" + # For simplicity, return text as is + # Implement mapping if special keys are needed + return text \ No newline at end of file diff --git a/demo/tools/edit.py b/demo/tools/edit.py new file mode 100644 index 0000000..fd4e868 --- /dev/null +++ b/demo/tools/edit.py @@ -0,0 +1,290 @@ +from collections import defaultdict +from pathlib import Path +from typing import Literal, get_args + +from anthropic.types.beta import BetaToolTextEditor20241022Param + +from .base import BaseAnthropicTool, CLIResult, ToolError, ToolResult +from .run import maybe_truncate, run + +Command = Literal[ + "view", + "create", + "str_replace", + "insert", + "undo_edit", +] +SNIPPET_LINES: int = 4 + + +class EditTool(BaseAnthropicTool): + """ + An filesystem editor tool that allows the agent to view, create, and edit files. + The tool parameters are defined by Anthropic and are not editable. + """ + + api_type: Literal["text_editor_20241022"] = "text_editor_20241022" + name: Literal["str_replace_editor"] = "str_replace_editor" + + _file_history: dict[Path, list[str]] + + def __init__(self): + self._file_history = defaultdict(list) + super().__init__() + + def to_params(self) -> BetaToolTextEditor20241022Param: + return { + "name": self.name, + "type": self.api_type, + } + + async def __call__( + self, + *, + command: Command, + path: str, + file_text: str | None = None, + view_range: list[int] | None = None, + old_str: str | None = None, + new_str: str | None = None, + insert_line: int | None = None, + **kwargs, + ): + _path = Path(path) + self.validate_path(command, _path) + if command == "view": + return await self.view(_path, view_range) + elif command == "create": + if not file_text: + raise ToolError("Parameter `file_text` is required for command: create") + self.write_file(_path, file_text) + self._file_history[_path].append(file_text) + return ToolResult(output=f"File created successfully at: {_path}") + elif command == "str_replace": + if not old_str: + raise ToolError( + "Parameter `old_str` is required for command: str_replace" + ) + return self.str_replace(_path, old_str, new_str) + elif command == "insert": + if insert_line is None: + raise ToolError( + "Parameter `insert_line` is required for command: insert" + ) + if not new_str: + raise ToolError("Parameter `new_str` is required for command: insert") + return self.insert(_path, insert_line, new_str) + elif command == "undo_edit": + return self.undo_edit(_path) + raise ToolError( + f'Unrecognized command {command}. The allowed commands for the {self.name} tool are: {", ".join(get_args(Command))}' + ) + + def validate_path(self, command: str, path: Path): + """ + Check that the path/command combination is valid. + """ + # Check if its an absolute path + if not path.is_absolute(): + suggested_path = Path("") / path + raise ToolError( + f"The path {path} is not an absolute path, it should start with `/`. Maybe you meant {suggested_path}?" + ) + # Check if path exists + if not path.exists() and command != "create": + raise ToolError( + f"The path {path} does not exist. Please provide a valid path." + ) + if path.exists() and command == "create": + raise ToolError( + f"File already exists at: {path}. Cannot overwrite files using command `create`." + ) + # Check if the path points to a directory + if path.is_dir(): + if command != "view": + raise ToolError( + f"The path {path} is a directory and only the `view` command can be used on directories" + ) + + async def view(self, path: Path, view_range: list[int] | None = None): + """Implement the view command""" + if path.is_dir(): + if view_range: + raise ToolError( + "The `view_range` parameter is not allowed when `path` points to a directory." + ) + + _, stdout, stderr = await run( + rf"find {path} -maxdepth 2 -not -path '*/\.*'" + ) + if not stderr: + stdout = f"Here's the files and directories up to 2 levels deep in {path}, excluding hidden items:\n{stdout}\n" + return CLIResult(output=stdout, error=stderr) + + file_content = self.read_file(path) + init_line = 1 + if view_range: + if len(view_range) != 2 or not all(isinstance(i, int) for i in view_range): + raise ToolError( + "Invalid `view_range`. It should be a list of two integers." + ) + file_lines = file_content.split("\n") + n_lines_file = len(file_lines) + init_line, final_line = view_range + if init_line < 1 or init_line > n_lines_file: + raise ToolError( + f"Invalid `view_range`: {view_range}. It's first element `{init_line}` should be within the range of lines of the file: {[1, n_lines_file]}" + ) + if final_line > n_lines_file: + raise ToolError( + f"Invalid `view_range`: {view_range}. It's second element `{final_line}` should be smaller than the number of lines in the file: `{n_lines_file}`" + ) + if final_line != -1 and final_line < init_line: + raise ToolError( + f"Invalid `view_range`: {view_range}. It's second element `{final_line}` should be larger or equal than its first `{init_line}`" + ) + + if final_line == -1: + file_content = "\n".join(file_lines[init_line - 1 :]) + else: + file_content = "\n".join(file_lines[init_line - 1 : final_line]) + + return CLIResult( + output=self._make_output(file_content, str(path), init_line=init_line) + ) + + def str_replace(self, path: Path, old_str: str, new_str: str | None): + """Implement the str_replace command, which replaces old_str with new_str in the file content""" + # Read the file content + file_content = self.read_file(path).expandtabs() + old_str = old_str.expandtabs() + new_str = new_str.expandtabs() if new_str is not None else "" + + # Check if old_str is unique in the file + occurrences = file_content.count(old_str) + if occurrences == 0: + raise ToolError( + f"No replacement was performed, old_str `{old_str}` did not appear verbatim in {path}." + ) + elif occurrences > 1: + file_content_lines = file_content.split("\n") + lines = [ + idx + 1 + for idx, line in enumerate(file_content_lines) + if old_str in line + ] + raise ToolError( + f"No replacement was performed. Multiple occurrences of old_str `{old_str}` in lines {lines}. Please ensure it is unique" + ) + + # Replace old_str with new_str + new_file_content = file_content.replace(old_str, new_str) + + # Write the new content to the file + self.write_file(path, new_file_content) + + # Save the content to history + self._file_history[path].append(file_content) + + # Create a snippet of the edited section + replacement_line = file_content.split(old_str)[0].count("\n") + start_line = max(0, replacement_line - SNIPPET_LINES) + end_line = replacement_line + SNIPPET_LINES + new_str.count("\n") + snippet = "\n".join(new_file_content.split("\n")[start_line : end_line + 1]) + + # Prepare the success message + success_msg = f"The file {path} has been edited. " + success_msg += self._make_output( + snippet, f"a snippet of {path}", start_line + 1 + ) + success_msg += "Review the changes and make sure they are as expected. Edit the file again if necessary." + + return CLIResult(output=success_msg) + + def insert(self, path: Path, insert_line: int, new_str: str): + """Implement the insert command, which inserts new_str at the specified line in the file content.""" + file_text = self.read_file(path).expandtabs() + new_str = new_str.expandtabs() + file_text_lines = file_text.split("\n") + n_lines_file = len(file_text_lines) + + if insert_line < 0 or insert_line > n_lines_file: + raise ToolError( + f"Invalid `insert_line` parameter: {insert_line}. It should be within the range of lines of the file: {[0, n_lines_file]}" + ) + + new_str_lines = new_str.split("\n") + new_file_text_lines = ( + file_text_lines[:insert_line] + + new_str_lines + + file_text_lines[insert_line:] + ) + snippet_lines = ( + file_text_lines[max(0, insert_line - SNIPPET_LINES) : insert_line] + + new_str_lines + + file_text_lines[insert_line : insert_line + SNIPPET_LINES] + ) + + new_file_text = "\n".join(new_file_text_lines) + snippet = "\n".join(snippet_lines) + + self.write_file(path, new_file_text) + self._file_history[path].append(file_text) + + success_msg = f"The file {path} has been edited. " + success_msg += self._make_output( + snippet, + "a snippet of the edited file", + max(1, insert_line - SNIPPET_LINES + 1), + ) + success_msg += "Review the changes and make sure they are as expected (correct indentation, no duplicate lines, etc). Edit the file again if necessary." + return CLIResult(output=success_msg) + + def undo_edit(self, path: Path): + """Implement the undo_edit command.""" + if not self._file_history[path]: + raise ToolError(f"No edit history found for {path}.") + + old_text = self._file_history[path].pop() + self.write_file(path, old_text) + + return CLIResult( + output=f"Last edit to {path} undone successfully. {self._make_output(old_text, str(path))}" + ) + + def read_file(self, path: Path): + """Read the content of a file from a given path; raise a ToolError if an error occurs.""" + try: + return path.read_text() + except Exception as e: + raise ToolError(f"Ran into {e} while trying to read {path}") from None + + def write_file(self, path: Path, file: str): + """Write the content of a file to a given path; raise a ToolError if an error occurs.""" + try: + path.write_text(file) + except Exception as e: + raise ToolError(f"Ran into {e} while trying to write to {path}") from None + + def _make_output( + self, + file_content: str, + file_descriptor: str, + init_line: int = 1, + expand_tabs: bool = True, + ): + """Generate output for the CLI based on the content of a file.""" + file_content = maybe_truncate(file_content) + if expand_tabs: + file_content = file_content.expandtabs() + file_content = "\n".join( + [ + f"{i + init_line:6}\t{line}" + for i, line in enumerate(file_content.split("\n")) + ] + ) + return ( + f"Here's the result of running `cat -n` on {file_descriptor}:\n" + + file_content + + "\n" + ) diff --git a/demo/tools/run.py b/demo/tools/run.py new file mode 100644 index 0000000..b9d3481 --- /dev/null +++ b/demo/tools/run.py @@ -0,0 +1,42 @@ +"""Utility to run shell commands asynchronously with a timeout.""" + +import asyncio + +TRUNCATED_MESSAGE: str = "To save on context only part of this file has been shown to you. You should retry this tool after you have searched inside the file with `grep -n` in order to find the line numbers of what you are looking for." +MAX_RESPONSE_LEN: int = 16000 + + +def maybe_truncate(content: str, truncate_after: int | None = MAX_RESPONSE_LEN): + """Truncate content and append a notice if content exceeds the specified length.""" + return ( + content + if not truncate_after or len(content) <= truncate_after + else content[:truncate_after] + TRUNCATED_MESSAGE + ) + + +async def run( + cmd: str, + timeout: float | None = 120.0, # seconds + truncate_after: int | None = MAX_RESPONSE_LEN, +): + """Run a shell command asynchronously with a timeout.""" + process = await asyncio.create_subprocess_shell( + cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE + ) + + try: + stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=timeout) + return ( + process.returncode or 0, + maybe_truncate(stdout.decode(), truncate_after=truncate_after), + maybe_truncate(stderr.decode(), truncate_after=truncate_after), + ) + except asyncio.TimeoutError as exc: + try: + process.kill() + except ProcessLookupError: + pass + raise TimeoutError( + f"Command '{cmd}' timed out after {timeout} seconds" + ) from exc diff --git a/demo/tools/screen_capture.py b/demo/tools/screen_capture.py new file mode 100644 index 0000000..d425141 --- /dev/null +++ b/demo/tools/screen_capture.py @@ -0,0 +1,185 @@ +import subprocess +import base64 +from pathlib import Path +from PIL import ImageGrab +from uuid import uuid4 +from screeninfo import get_monitors +import platform +if platform.system() == "Darwin": + import Quartz # uncomment this line if you are on macOS + +from PIL import ImageGrab +from functools import partial +from .base import BaseAnthropicTool, ToolError, ToolResult + + +OUTPUT_DIR = "./tmp/outputs" + +def get_screenshot(selected_screen: int = 0, resize: bool = True, target_width: int = 1920, target_height: int = 1080): + # print(f"get_screenshot selected_screen: {selected_screen}") + + # Get screen width and height using Windows command + display_num = None + offset_x = 0 + offset_y = 0 + selected_screen = selected_screen + width, height = _get_screen_size() + + """Take a screenshot of the current screen and return a ToolResult with the base64 encoded image.""" + output_dir = Path(OUTPUT_DIR) + output_dir.mkdir(parents=True, exist_ok=True) + path = output_dir / f"screenshot_{uuid4().hex}.png" + + ImageGrab.grab = partial(ImageGrab.grab, all_screens=True) + + # Detect platform + system = platform.system() + + if system == "Windows": + # Windows: Use screeninfo to get monitor details + screens = get_monitors() + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s.x) + + if selected_screen < 0 or selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + + screen = sorted_screens[selected_screen] + bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) + + elif system == "Darwin": # macOS + # macOS: Use Quartz to get monitor details + max_displays = 32 # Maximum number of displays to handle + active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] + + # Get the display bounds (resolution) for each active display + screens = [] + for display_id in active_displays: + bounds = Quartz.CGDisplayBounds(display_id) + screens.append({ + 'id': display_id, + 'x': int(bounds.origin.x), + 'y': int(bounds.origin.y), + 'width': int(bounds.size.width), + 'height': int(bounds.size.height), + 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display + }) + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s['x']) + # print(f"Darwin sorted_screens: {sorted_screens}") + + if selected_screen < 0 or selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + + screen = sorted_screens[selected_screen] + + bbox = (screen['x'], screen['y'], screen['x'] + screen['width'], screen['y'] + screen['height']) + + else: # Linux or other OS + cmd = "xrandr | grep ' primary' | awk '{print $4}'" + try: + # output = subprocess.check_output(cmd, shell=True).decode() + # resolution = output.strip().split()[0] + # width, height = map(int, resolution.split('x')) + screen = get_monitors()[0] + width, height = screen.width, screen.height + bbox = (0, 0, width, height) # Assuming single primary screen for simplicity + except subprocess.CalledProcessError: + raise RuntimeError("Failed to get screen resolution on Linux.") + + # Take screenshot using the bounding box + screenshot = ImageGrab.grab(bbox=bbox) + import os + if (display_num := os.getenv("DISPLAY_NUM")) is not None: + display_num = int(display_num) + _display_prefix = f"DISPLAY=:{display_num} " + else: + display_num = None + _display_prefix = "" + screenshot_cmd = f"{_display_prefix}scrot -p {path}" + import pdb; pdb.set_trace() + result = subprocess.run(screenshot_cmd, shell=True, capture_output=True) + + # Set offsets (for potential future use) + offset_x = screen['x'] if system == "Darwin" else screen.x + offset_y = screen['y'] if system == "Darwin" else screen.y + + # # Resize if + if resize: + screenshot = screenshot.resize((target_width, target_height)) + + # Save the screenshot + # screenshot.save(str(path)) + + if path.exists(): + # Return a ToolResult instance instead of a dictionary + return screenshot, path + + raise ToolError(f"Failed to take screenshot: {path} does not exist.") + + + + +def _get_screen_size(selected_screen: int = 0): + if platform.system() == "Windows": + # Use screeninfo to get primary monitor on Windows + screens = get_monitors() + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s.x) + if selected_screen is None: + primary_monitor = next((m for m in get_monitors() if m.is_primary), None) + return primary_monitor.width, primary_monitor.height + elif selected_screen < 0 or selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + else: + screen = sorted_screens[selected_screen] + return screen.width, screen.height + elif platform.system() == "Darwin": + # macOS part using Quartz to get screen information + max_displays = 32 # Maximum number of displays to handle + active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] + + # Get the display bounds (resolution) for each active display + screens = [] + for display_id in active_displays: + bounds = Quartz.CGDisplayBounds(display_id) + screens.append({ + 'id': display_id, + 'x': int(bounds.origin.x), + 'y': int(bounds.origin.y), + 'width': int(bounds.size.width), + 'height': int(bounds.size.height), + 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display + }) + + # Sort screens by x position to arrange from left to right + sorted_screens = sorted(screens, key=lambda s: s['x']) + + if selected_screen is None: + # Find the primary monitor + primary_monitor = next((screen for screen in screens if screen['is_primary']), None) + if primary_monitor: + return primary_monitor['width'], primary_monitor['height'] + else: + raise RuntimeError("No primary monitor found.") + elif selected_screen < 0 or selected_screen >= len(screens): + raise IndexError("Invalid screen index.") + else: + # Return the resolution of the selected screen + screen = sorted_screens[selected_screen] + return screen['width'], screen['height'] + + else: # Linux or other OS + cmd = "xrandr | grep ' primary' | awk '{print $4}'" + try: + # output = subprocess.check_output(cmd, shell=True).decode() + # resolution = output.strip().split()[0] + # width, height = map(int, resolution.split('x')) + # return width, height + screen = get_monitors()[0] + return screen.width, screen.height + except subprocess.CalledProcessError: + raise RuntimeError("Failed to get screen resolution on Linux.") diff --git a/gradio_demo.py b/gradio_demo.py index 8711c57..b09168b 100644 --- a/gradio_demo.py +++ b/gradio_demo.py @@ -1,105 +1,105 @@ -from typing import Optional - -import gradio as gr -import numpy as np -import torch -from PIL import Image -import io - - -import base64, os -from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img -import torch -from PIL import Image - -# yolo_model = get_yolo_model(model_path='weights/icon_detect/best.pt') -yolo_model = get_yolo_model(model_path='weights/icon_detect_v1_5/best.pt') -caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence") -# caption_model_processor = get_caption_model_processor(model_name="blip2", model_name_or_path="weights/icon_caption_blip2") - - - -MARKDOWN = """ -# OmniParser for Pure Vision Based General GUI Agent 🔥 -

 - -OmniParser is a screen parsing tool to convert general GUI screen to structured elements. -""" - -DEVICE = torch.device('cuda') - -# @spaces.GPU -# @torch.inference_mode() -# @torch.autocast(device_type="cuda", dtype=torch.bfloat16) -def process( - image_input, - box_threshold, - iou_threshold, - use_paddleocr, - imgsz -) -> Optional[Image.Image]: - - image_save_path = 'imgs/saved_image_demo.png' - image_input.save(image_save_path) - image = Image.open(image_save_path) - box_overlay_ratio = image.size[0] / 3200 - draw_bbox_config = { - 'text_scale': 0.8 * box_overlay_ratio, - 'text_thickness': max(int(2 * box_overlay_ratio), 1), - 'text_padding': max(int(3 * box_overlay_ratio), 1), - 'thickness': max(int(3 * box_overlay_ratio), 1), - } - # import pdb; pdb.set_trace() - - ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}, use_paddleocr=use_paddleocr) - text, ocr_bbox = ocr_bbox_rslt - # print('prompt:', prompt) - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold, imgsz=imgsz,) - image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) - print('finish processing') - parsed_content_list = '\n'.join([f'icon {i}: ' + str(v) for i,v in enumerate(parsed_content_list)]) - # parsed_content_list = str(parsed_content_list) - return image, str(parsed_content_list) - - - -with gr.Blocks() as demo: - gr.Markdown(MARKDOWN) - with gr.Row(): - with gr.Column(): - image_input_component = gr.Image( - type='pil', label='Upload image') - # set the threshold for removing the bounding boxes with low confidence, default is 0.05 - box_threshold_component = gr.Slider( - label='Box Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.05) - # set the threshold for removing the bounding boxes with large overlap, default is 0.1 - iou_threshold_component = gr.Slider( - label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1) - use_paddleocr_component = gr.Checkbox( - label='Use PaddleOCR', value=True) - imgsz_component = gr.Slider( - label='Icon Detect Image Size', minimum=640, maximum=1920, step=32, value=640) - submit_button_component = gr.Button( - value='Submit', variant='primary') - with gr.Column(): - image_output_component = gr.Image(type='pil', label='Image Output') - text_output_component = gr.Textbox(label='Parsed screen elements', placeholder='Text Output') - - submit_button_component.click( - fn=process, - inputs=[ - image_input_component, - box_threshold_component, - iou_threshold_component, - use_paddleocr_component, - imgsz_component - ], - outputs=[image_output_component, text_output_component] - ) - -# demo.launch(debug=False, show_error=True, share=True) +from typing import Optional + +import gradio as gr +import numpy as np +import torch +from PIL import Image +import io + + +import base64, os +from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img +import torch +from PIL import Image + +# yolo_model = get_yolo_model(model_path='weights/icon_detect/best.pt') +yolo_model = get_yolo_model(model_path='weights/icon_detect_v1_5/best.pt') +caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence") +# caption_model_processor = get_caption_model_processor(model_name="blip2", model_name_or_path="weights/icon_caption_blip2") + + + +MARKDOWN = """ +# OmniParser for Pure Vision Based General GUI Agent 🔥 + + +OmniParser is a screen parsing tool to convert general GUI screen to structured elements. +""" + +DEVICE = torch.device('cuda') + +# @spaces.GPU +# @torch.inference_mode() +# @torch.autocast(device_type="cuda", dtype=torch.bfloat16) +def process( + image_input, + box_threshold, + iou_threshold, + use_paddleocr, + imgsz +) -> Optional[Image.Image]: + + image_save_path = 'imgs/saved_image_demo.png' + image_input.save(image_save_path) + image = Image.open(image_save_path) + box_overlay_ratio = image.size[0] / 3200 + draw_bbox_config = { + 'text_scale': 0.8 * box_overlay_ratio, + 'text_thickness': max(int(2 * box_overlay_ratio), 1), + 'text_padding': max(int(3 * box_overlay_ratio), 1), + 'thickness': max(int(3 * box_overlay_ratio), 1), + } + # import pdb; pdb.set_trace() + + ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}, use_paddleocr=use_paddleocr) + text, ocr_bbox = ocr_bbox_rslt + # print('prompt:', prompt) + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold, imgsz=imgsz,) + image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) + print('finish processing') + parsed_content_list = '\n'.join([f'icon {i}: ' + str(v) for i,v in enumerate(parsed_content_list)]) + # parsed_content_list = str(parsed_content_list) + return image, str(parsed_content_list) + + + +with gr.Blocks() as demo: + gr.Markdown(MARKDOWN) + with gr.Row(): + with gr.Column(): + image_input_component = gr.Image( + type='pil', label='Upload image') + # set the threshold for removing the bounding boxes with low confidence, default is 0.05 + box_threshold_component = gr.Slider( + label='Box Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.05) + # set the threshold for removing the bounding boxes with large overlap, default is 0.1 + iou_threshold_component = gr.Slider( + label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1) + use_paddleocr_component = gr.Checkbox( + label='Use PaddleOCR', value=True) + imgsz_component = gr.Slider( + label='Icon Detect Image Size', minimum=640, maximum=1920, step=32, value=640) + submit_button_component = gr.Button( + value='Submit', variant='primary') + with gr.Column(): + image_output_component = gr.Image(type='pil', label='Image Output') + text_output_component = gr.Textbox(label='Parsed screen elements', placeholder='Text Output') + + submit_button_component.click( + fn=process, + inputs=[ + image_input_component, + box_threshold_component, + iou_threshold_component, + use_paddleocr_component, + imgsz_component + ], + outputs=[image_output_component, text_output_component] + ) + +# demo.launch(debug=False, show_error=True, share=True) demo.launch(share=True, server_port=7861, server_name='0.0.0.0') \ No newline at end of file diff --git a/omniparser.py b/omniparser.py index 37b7780..6ac8518 100644 --- a/omniparser.py +++ b/omniparser.py @@ -1,60 +1,60 @@ -from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_dino_model, get_yolo_model -import torch -from ultralytics import YOLO -from PIL import Image -from typing import Dict, Tuple, List -import io -import base64 - - -config = { - 'som_model_path': 'finetuned_icon_detect.pt', - 'device': 'cpu', - 'caption_model_path': 'Salesforce/blip2-opt-2.7b', - 'draw_bbox_config': { - 'text_scale': 0.8, - 'text_thickness': 2, - 'text_padding': 3, - 'thickness': 3, - }, - 'BOX_TRESHOLD': 0.05 -} - - -class Omniparser(object): - def __init__(self, config: Dict): - self.config = config - - self.som_model = get_yolo_model(model_path=config['som_model_path']) - # self.caption_model_processor = get_caption_model_processor(config['caption_model_path'], device=cofig['device']) - # self.caption_model_processor['model'].to(torch.float32) - - def parse(self, image_path: str): - print('Parsing image:', image_path) - ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}) - text, ocr_bbox = ocr_bbox_rslt - - draw_bbox_config = self.config['draw_bbox_config'] - BOX_TRESHOLD = self.config['BOX_TRESHOLD'] - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=None, ocr_text=text,use_local_semantics=False) - - image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) - # formating output - return_list = [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]}, - 'text': parsed_content_list[i].split(': ')[1], 'type':'text'} for i, (k, coord) in enumerate(label_coordinates.items()) if i < len(parsed_content_list)] - return_list.extend( - [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]}, - 'text': 'None', 'type':'icon'} for i, (k, coord) in enumerate(label_coordinates.items()) if i >= len(parsed_content_list)] - ) - - return [image, return_list] - -parser = Omniparser(config) -image_path = 'examples/pc_1.png' - -# time the parser -import time -s = time.time() -image, parsed_content_list = parser.parse(image_path) -device = config['device'] -print(f'Time taken for Omniparser on {device}:', time.time() - s) +from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_dino_model, get_yolo_model +import torch +from ultralytics import YOLO +from PIL import Image +from typing import Dict, Tuple, List +import io +import base64 + + +config = { + 'som_model_path': 'finetuned_icon_detect.pt', + 'device': 'cpu', + 'caption_model_path': 'Salesforce/blip2-opt-2.7b', + 'draw_bbox_config': { + 'text_scale': 0.8, + 'text_thickness': 2, + 'text_padding': 3, + 'thickness': 3, + }, + 'BOX_TRESHOLD': 0.05 +} + + +class Omniparser(object): + def __init__(self, config: Dict): + self.config = config + + self.som_model = get_yolo_model(model_path=config['som_model_path']) + # self.caption_model_processor = get_caption_model_processor(config['caption_model_path'], device=cofig['device']) + # self.caption_model_processor['model'].to(torch.float32) + + def parse(self, image_path: str): + print('Parsing image:', image_path) + ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}) + text, ocr_bbox = ocr_bbox_rslt + + draw_bbox_config = self.config['draw_bbox_config'] + BOX_TRESHOLD = self.config['BOX_TRESHOLD'] + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=None, ocr_text=text,use_local_semantics=False) + + image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) + # formating output + return_list = [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]}, + 'text': parsed_content_list[i].split(': ')[1], 'type':'text'} for i, (k, coord) in enumerate(label_coordinates.items()) if i < len(parsed_content_list)] + return_list.extend( + [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]}, + 'text': 'None', 'type':'icon'} for i, (k, coord) in enumerate(label_coordinates.items()) if i >= len(parsed_content_list)] + ) + + return [image, return_list] + +parser = Omniparser(config) +image_path = 'examples/pc_1.png' + +# time the parser +import time +s = time.time() +image, parsed_content_list = parser.parse(image_path) +device = config['device'] +print(f'Time taken for Omniparser on {device}:', time.time() - s) diff --git a/requirements.txt b/requirements.txt index 08bfc38..5631fb6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,18 +1,31 @@ -torch -easyocr -torchvision -supervision==0.18.0 -openai==1.3.5 -transformers -ultralytics==8.1.24 -azure-identity -numpy -opencv-python -opencv-python-headless -gradio -dill -accelerate -timm -einops==0.8.0 -paddlepaddle -paddleocr \ No newline at end of file +torch +easyocr +torchvision +supervision==0.18.0 +openai==1.3.5 +transformers +ultralytics==8.1.24 +azure-identity +numpy +opencv-python +opencv-python-headless +gradio +dill +accelerate +timm +einops==0.8.0 +paddlepaddle +paddleocr +ruff==0.6.7 +pre-commit==3.8.0 +pytest==8.3.3 +pytest-asyncio==0.23.6 +pyautogui==0.9.54 +streamlit>=1.38.0 +anthropic[bedrock,vertex]>=0.37.1 +jsonschema==4.22.0 +boto3>=1.28.57 +google-auth<3,>=2 +screeninfo +uiautomation +dashscope \ No newline at end of file diff --git a/util/action_matching.py b/util/action_matching.py index fa986b4..51c5359 100644 --- a/util/action_matching.py +++ b/util/action_matching.py @@ -1,425 +1,425 @@ -''' -Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild -''' - -import jax -import jax.numpy as jnp -import numpy as np - -# import action_type as action_type_lib -import enum - -class ActionType(enum.IntEnum): - # Placeholders for unused enum values - UNUSED_0 = 0 - UNUSED_1 = 1 - UNUSED_2 = 2 - UNUSED_8 = 8 - UNUSED_9 = 9 - - ########### Agent actions ########### - - # A type action that sends text to the emulator. Note that this simply sends - # text and does not perform any clicks for element focus or enter presses for - # submitting text. - TYPE = 3 - - # The dual point action used to represent all gestures. - DUAL_POINT = 4 - - # These actions differentiate pressing the home and back button from touches. - # They represent explicit presses of back and home performed using ADB. - PRESS_BACK = 5 - PRESS_HOME = 6 - - # An action representing that ADB command for hitting enter was performed. - PRESS_ENTER = 7 - - ########### Episode status actions ########### - - # An action used to indicate the desired task has been completed and resets - # the environment. This action should also be used in the case that the task - # has already been completed and there is nothing to do. - # e.g. The task is to turn on the Wi-Fi when it is already on - STATUS_TASK_COMPLETE = 10 - - # An action used to indicate that desired task is impossible to complete and - # resets the environment. This can be a result of many different things - # including UI changes, Android version differences, etc. - STATUS_TASK_IMPOSSIBLE = 11 - - -_TAP_DISTANCE_THRESHOLD = 0.14 # Fraction of the screen -ANNOTATION_WIDTH_AUGMENT_FRACTION = 1.4 -ANNOTATION_HEIGHT_AUGMENT_FRACTION = 1.4 - -# Interval determining if an action is a tap or a swipe. -_SWIPE_DISTANCE_THRESHOLD = 0.04 - - -def _yx_in_bounding_boxes( - yx, bounding_boxes -): - """Check if the (y,x) point is contained in each bounding box. - - Args: - yx: The (y, x) coordinate in pixels of the point. - bounding_boxes: A 2D int array of shape (num_bboxes, 4), where each row - represents a bounding box: (y_top_left, x_top_left, box_height, - box_width). Note: containment is inclusive of the bounding box edges. - - Returns: - is_inside: A 1D bool array where each element specifies if the point is - contained within the respective box. - """ - y, x = yx - - # `bounding_boxes` has shape (n_elements, 4); we extract each array along the - # last axis into shape (n_elements, 1), then squeeze unneeded dimension. - top, left, height, width = [ - jnp.squeeze(v, axis=-1) for v in jnp.split(bounding_boxes, 4, axis=-1) - ] - - # The y-axis is inverted for AndroidEnv, so bottom = top + height. - bottom, right = top + height, left + width - - return jnp.logical_and(y >= top, y <= bottom) & jnp.logical_and( - x >= left, x <= right) - - -def _resize_annotation_bounding_boxes( - annotation_positions, annotation_width_augment_fraction, - annotation_height_augment_fraction): - """Resize the bounding boxes by the given fractions. - - Args: - annotation_positions: Array of shape (N, 4), where each row represents the - (y, x, height, width) of the bounding boxes. - annotation_width_augment_fraction: The fraction to augment the box widths, - E.g., 1.4 == 240% total increase. - annotation_height_augment_fraction: Same as described for width, but for box - height. - - Returns: - Resized bounding box. - - """ - height_change = ( - annotation_height_augment_fraction * annotation_positions[:, 2]) - width_change = ( - annotation_width_augment_fraction * annotation_positions[:, 3]) - - # Limit bounding box positions to the screen. - resized_annotations = jnp.stack([ - jnp.maximum(0, annotation_positions[:, 0] - (height_change / 2)), - jnp.maximum(0, annotation_positions[:, 1] - (width_change / 2)), - jnp.minimum(1, annotation_positions[:, 2] + height_change), - jnp.minimum(1, annotation_positions[:, 3] + width_change), - ], - axis=1) - return resized_annotations - - -def is_tap_action(normalized_start_yx, - normalized_end_yx): - distance = jnp.linalg.norm( - jnp.array(normalized_start_yx) - jnp.array(normalized_end_yx)) - return distance <= _SWIPE_DISTANCE_THRESHOLD - - -def _is_non_dual_point_action(action_type): - return jnp.not_equal(action_type, ActionType.DUAL_POINT) - - -def _check_tap_actions_match( - tap_1_yx, - tap_2_yx, - annotation_positions, - matching_tap_distance_threshold_screen_percentage, - annotation_width_augment_fraction, - annotation_height_augment_fraction, -): - """Determines if two tap actions are the same.""" - resized_annotation_positions = _resize_annotation_bounding_boxes( - annotation_positions, - annotation_width_augment_fraction, - annotation_height_augment_fraction, - ) - - # Check if the ground truth tap action falls in an annotation's bounding box. - tap1_in_box = _yx_in_bounding_boxes(tap_1_yx, resized_annotation_positions) - tap2_in_box = _yx_in_bounding_boxes(tap_2_yx, resized_annotation_positions) - both_in_box = jnp.max(tap1_in_box & tap2_in_box) - - # If the ground-truth tap action falls outside any of the annotation - # bounding boxes or one of the actions is inside a bounding box and the other - # is outside bounding box or vice versa, compare the points using Euclidean - # distance. - within_threshold = ( - jnp.linalg.norm(jnp.array(tap_1_yx) - jnp.array(tap_2_yx)) - <= matching_tap_distance_threshold_screen_percentage - ) - return jnp.logical_or(both_in_box, within_threshold) - - -def _check_drag_actions_match( - drag_1_touch_yx, - drag_1_lift_yx, - drag_2_touch_yx, - drag_2_lift_yx, -): - """Determines if two drag actions are the same.""" - # Store drag deltas (the change in the y and x coordinates from touch to - # lift), magnitudes, and the index of the main axis, which is the axis with - # the greatest change in coordinate value (e.g. a drag starting at (0, 0) and - # ending at (0.3, 0.5) has a main axis index of 1). - drag_1_deltas = drag_1_lift_yx - drag_1_touch_yx - drag_1_magnitudes = jnp.abs(drag_1_deltas) - drag_1_main_axis = np.argmax(drag_1_magnitudes) - drag_2_deltas = drag_2_lift_yx - drag_2_touch_yx - drag_2_magnitudes = jnp.abs(drag_2_deltas) - drag_2_main_axis = np.argmax(drag_2_magnitudes) - - return jnp.equal(drag_1_main_axis, drag_2_main_axis) - - -def check_actions_match( - action_1_touch_yx, - action_1_lift_yx, - action_1_action_type, - action_2_touch_yx, - action_2_lift_yx, - action_2_action_type, - annotation_positions, - tap_distance_threshold = _TAP_DISTANCE_THRESHOLD, - annotation_width_augment_fraction = ANNOTATION_WIDTH_AUGMENT_FRACTION, - annotation_height_augment_fraction = ANNOTATION_HEIGHT_AUGMENT_FRACTION, -): - """Determines if two actions are considered to be the same. - - Two actions being "the same" is defined here as two actions that would result - in a similar screen state. - - Args: - action_1_touch_yx: The (y, x) coordinates of the first action's touch. - action_1_lift_yx: The (y, x) coordinates of the first action's lift. - action_1_action_type: The action type of the first action. - action_2_touch_yx: The (y, x) coordinates of the second action's touch. - action_2_lift_yx: The (y, x) coordinates of the second action's lift. - action_2_action_type: The action type of the second action. - annotation_positions: The positions of the UI annotations for the screen. It - is A 2D int array of shape (num_bboxes, 4), where each row represents a - bounding box: (y_top_left, x_top_left, box_height, box_width). Note that - containment is inclusive of the bounding box edges. - tap_distance_threshold: The threshold that determines if two taps result in - a matching screen state if they don't fall the same bounding boxes. - annotation_width_augment_fraction: The fraction to increase the width of the - bounding box by. - annotation_height_augment_fraction: The fraction to increase the height of - of the bounding box by. - - Returns: - A boolean representing whether the two given actions are the same or not. - """ - action_1_touch_yx = jnp.asarray(action_1_touch_yx) - action_1_lift_yx = jnp.asarray(action_1_lift_yx) - action_2_touch_yx = jnp.asarray(action_2_touch_yx) - action_2_lift_yx = jnp.asarray(action_2_lift_yx) - - # Checks if at least one of the actions is global (i.e. not DUAL_POINT), - # because if that is the case, only the actions' types need to be compared. - has_non_dual_point_action = jnp.logical_or( - _is_non_dual_point_action(action_1_action_type), - _is_non_dual_point_action(action_2_action_type), - ) - #print("non dual point: "+str(has_non_dual_point_action)) - - different_dual_point_types = jnp.logical_xor( - is_tap_action(action_1_touch_yx, action_1_lift_yx), - is_tap_action(action_2_touch_yx, action_2_lift_yx), - ) - #print("different dual type: "+str(different_dual_point_types)) - - is_tap = jnp.logical_and( - is_tap_action(action_1_touch_yx, action_1_lift_yx), - is_tap_action(action_2_touch_yx, action_2_lift_yx), - ) - #print("is tap: "+str(is_tap)) - - taps_match = _check_tap_actions_match( - action_1_touch_yx, - action_2_touch_yx, - annotation_positions, - tap_distance_threshold, - annotation_width_augment_fraction, - annotation_height_augment_fraction, - ) - #print("tap match: "+str(taps_match)) - - taps_match = jnp.logical_and(is_tap, taps_match) - #print("tap match: "+str(taps_match)) - - drags_match = _check_drag_actions_match( - action_1_touch_yx, action_1_lift_yx, action_2_touch_yx, action_2_lift_yx - ) - drags_match = jnp.where(is_tap, False, drags_match) - #print("drag match: "+str(drags_match)) - - return jnp.where( - has_non_dual_point_action, - jnp.equal(action_1_action_type, action_2_action_type), - jnp.where( - different_dual_point_types, - False, - jnp.logical_or(taps_match, drags_match), - ), - ) - - -def action_2_format(step_data): - # 把test数据集中的动作格式转换为计算matching score的格式 - action_type = step_data["action_type_id"] - - if action_type == 4: - if step_data["action_type_text"] == 'click': # 点击 - touch_point = step_data["touch"] - lift_point = step_data["lift"] - else: # 上下左右滑动 - if step_data["action_type_text"] == 'scroll down': - touch_point = [0.5, 0.8] - lift_point = [0.5, 0.2] - elif step_data["action_type_text"] == 'scroll up': - touch_point = [0.5, 0.2] - lift_point = [0.5, 0.8] - elif step_data["action_type_text"] == 'scroll left': - touch_point = [0.2, 0.5] - lift_point = [0.8, 0.5] - elif step_data["action_type_text"] == 'scroll right': - touch_point = [0.8, 0.5] - lift_point = [0.2, 0.5] - else: - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - - if action_type == 3: - typed_text = step_data["type_text"] - else: - typed_text = "" - - action = {"action_type": action_type, "touch_point": touch_point, "lift_point": lift_point, - "typed_text": typed_text} - - action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] - action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] - action["typed_text"] = action["typed_text"].lower() - - return action - - -def pred_2_format(step_data): - # 把模型输出的内容转换为计算action_matching的格式 - action_type = step_data["action_type"] - - if action_type == 4: # 点击 - action_type_new = 4 - touch_point = step_data["click_point"] - lift_point = step_data["click_point"] - typed_text = "" - elif action_type == 0: - action_type_new = 4 - touch_point = [0.5, 0.8] - lift_point = [0.5, 0.2] - typed_text = "" - elif action_type == 1: - action_type_new = 4 - touch_point = [0.5, 0.2] - lift_point = [0.5, 0.8] - typed_text = "" - elif action_type == 8: - action_type_new = 4 - touch_point = [0.2, 0.5] - lift_point = [0.8, 0.5] - typed_text = "" - elif action_type == 9: - action_type_new = 4 - touch_point = [0.8, 0.5] - lift_point = [0.2, 0.5] - typed_text = "" - else: - action_type_new = action_type - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - if action_type_new == 3: - typed_text = step_data["typed_text"] - - action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point, - "typed_text": typed_text} - - action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] - action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] - action["typed_text"] = action["typed_text"].lower() - - return action - - -def pred_2_format_simplified(step_data): - # 把模型输出的内容转换为计算action_matching的格式 - action_type = step_data["action_type"] - - if action_type == 'click' : # 点击 - action_type_new = 4 - touch_point = step_data["click_point"] - lift_point = step_data["click_point"] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'down': - action_type_new = 4 - touch_point = [0.5, 0.8] - lift_point = [0.5, 0.2] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'up': - action_type_new = 4 - touch_point = [0.5, 0.2] - lift_point = [0.5, 0.8] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'left': - action_type_new = 4 - touch_point = [0.2, 0.5] - lift_point = [0.8, 0.5] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'right': - action_type_new = 4 - touch_point = [0.8, 0.5] - lift_point = [0.2, 0.5] - typed_text = "" - elif action_type == 'type': - action_type_new = 3 - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = step_data["text"] - elif action_type == 'navigate_back': - action_type_new = 5 - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - elif action_type == 'navigate_home': - action_type_new = 6 - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - else: - action_type_new = action_type - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - # if action_type_new == 'type': - # typed_text = step_data["text"] - - action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point, - "typed_text": typed_text} - - action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] - action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] - action["typed_text"] = action["typed_text"].lower() - +''' +Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild +''' + +import jax +import jax.numpy as jnp +import numpy as np + +# import action_type as action_type_lib +import enum + +class ActionType(enum.IntEnum): + # Placeholders for unused enum values + UNUSED_0 = 0 + UNUSED_1 = 1 + UNUSED_2 = 2 + UNUSED_8 = 8 + UNUSED_9 = 9 + + ########### Agent actions ########### + + # A type action that sends text to the emulator. Note that this simply sends + # text and does not perform any clicks for element focus or enter presses for + # submitting text. + TYPE = 3 + + # The dual point action used to represent all gestures. + DUAL_POINT = 4 + + # These actions differentiate pressing the home and back button from touches. + # They represent explicit presses of back and home performed using ADB. + PRESS_BACK = 5 + PRESS_HOME = 6 + + # An action representing that ADB command for hitting enter was performed. + PRESS_ENTER = 7 + + ########### Episode status actions ########### + + # An action used to indicate the desired task has been completed and resets + # the environment. This action should also be used in the case that the task + # has already been completed and there is nothing to do. + # e.g. The task is to turn on the Wi-Fi when it is already on + STATUS_TASK_COMPLETE = 10 + + # An action used to indicate that desired task is impossible to complete and + # resets the environment. This can be a result of many different things + # including UI changes, Android version differences, etc. + STATUS_TASK_IMPOSSIBLE = 11 + + +_TAP_DISTANCE_THRESHOLD = 0.14 # Fraction of the screen +ANNOTATION_WIDTH_AUGMENT_FRACTION = 1.4 +ANNOTATION_HEIGHT_AUGMENT_FRACTION = 1.4 + +# Interval determining if an action is a tap or a swipe. +_SWIPE_DISTANCE_THRESHOLD = 0.04 + + +def _yx_in_bounding_boxes( + yx, bounding_boxes +): + """Check if the (y,x) point is contained in each bounding box. + + Args: + yx: The (y, x) coordinate in pixels of the point. + bounding_boxes: A 2D int array of shape (num_bboxes, 4), where each row + represents a bounding box: (y_top_left, x_top_left, box_height, + box_width). Note: containment is inclusive of the bounding box edges. + + Returns: + is_inside: A 1D bool array where each element specifies if the point is + contained within the respective box. + """ + y, x = yx + + # `bounding_boxes` has shape (n_elements, 4); we extract each array along the + # last axis into shape (n_elements, 1), then squeeze unneeded dimension. + top, left, height, width = [ + jnp.squeeze(v, axis=-1) for v in jnp.split(bounding_boxes, 4, axis=-1) + ] + + # The y-axis is inverted for AndroidEnv, so bottom = top + height. + bottom, right = top + height, left + width + + return jnp.logical_and(y >= top, y <= bottom) & jnp.logical_and( + x >= left, x <= right) + + +def _resize_annotation_bounding_boxes( + annotation_positions, annotation_width_augment_fraction, + annotation_height_augment_fraction): + """Resize the bounding boxes by the given fractions. + + Args: + annotation_positions: Array of shape (N, 4), where each row represents the + (y, x, height, width) of the bounding boxes. + annotation_width_augment_fraction: The fraction to augment the box widths, + E.g., 1.4 == 240% total increase. + annotation_height_augment_fraction: Same as described for width, but for box + height. + + Returns: + Resized bounding box. + + """ + height_change = ( + annotation_height_augment_fraction * annotation_positions[:, 2]) + width_change = ( + annotation_width_augment_fraction * annotation_positions[:, 3]) + + # Limit bounding box positions to the screen. + resized_annotations = jnp.stack([ + jnp.maximum(0, annotation_positions[:, 0] - (height_change / 2)), + jnp.maximum(0, annotation_positions[:, 1] - (width_change / 2)), + jnp.minimum(1, annotation_positions[:, 2] + height_change), + jnp.minimum(1, annotation_positions[:, 3] + width_change), + ], + axis=1) + return resized_annotations + + +def is_tap_action(normalized_start_yx, + normalized_end_yx): + distance = jnp.linalg.norm( + jnp.array(normalized_start_yx) - jnp.array(normalized_end_yx)) + return distance <= _SWIPE_DISTANCE_THRESHOLD + + +def _is_non_dual_point_action(action_type): + return jnp.not_equal(action_type, ActionType.DUAL_POINT) + + +def _check_tap_actions_match( + tap_1_yx, + tap_2_yx, + annotation_positions, + matching_tap_distance_threshold_screen_percentage, + annotation_width_augment_fraction, + annotation_height_augment_fraction, +): + """Determines if two tap actions are the same.""" + resized_annotation_positions = _resize_annotation_bounding_boxes( + annotation_positions, + annotation_width_augment_fraction, + annotation_height_augment_fraction, + ) + + # Check if the ground truth tap action falls in an annotation's bounding box. + tap1_in_box = _yx_in_bounding_boxes(tap_1_yx, resized_annotation_positions) + tap2_in_box = _yx_in_bounding_boxes(tap_2_yx, resized_annotation_positions) + both_in_box = jnp.max(tap1_in_box & tap2_in_box) + + # If the ground-truth tap action falls outside any of the annotation + # bounding boxes or one of the actions is inside a bounding box and the other + # is outside bounding box or vice versa, compare the points using Euclidean + # distance. + within_threshold = ( + jnp.linalg.norm(jnp.array(tap_1_yx) - jnp.array(tap_2_yx)) + <= matching_tap_distance_threshold_screen_percentage + ) + return jnp.logical_or(both_in_box, within_threshold) + + +def _check_drag_actions_match( + drag_1_touch_yx, + drag_1_lift_yx, + drag_2_touch_yx, + drag_2_lift_yx, +): + """Determines if two drag actions are the same.""" + # Store drag deltas (the change in the y and x coordinates from touch to + # lift), magnitudes, and the index of the main axis, which is the axis with + # the greatest change in coordinate value (e.g. a drag starting at (0, 0) and + # ending at (0.3, 0.5) has a main axis index of 1). + drag_1_deltas = drag_1_lift_yx - drag_1_touch_yx + drag_1_magnitudes = jnp.abs(drag_1_deltas) + drag_1_main_axis = np.argmax(drag_1_magnitudes) + drag_2_deltas = drag_2_lift_yx - drag_2_touch_yx + drag_2_magnitudes = jnp.abs(drag_2_deltas) + drag_2_main_axis = np.argmax(drag_2_magnitudes) + + return jnp.equal(drag_1_main_axis, drag_2_main_axis) + + +def check_actions_match( + action_1_touch_yx, + action_1_lift_yx, + action_1_action_type, + action_2_touch_yx, + action_2_lift_yx, + action_2_action_type, + annotation_positions, + tap_distance_threshold = _TAP_DISTANCE_THRESHOLD, + annotation_width_augment_fraction = ANNOTATION_WIDTH_AUGMENT_FRACTION, + annotation_height_augment_fraction = ANNOTATION_HEIGHT_AUGMENT_FRACTION, +): + """Determines if two actions are considered to be the same. + + Two actions being "the same" is defined here as two actions that would result + in a similar screen state. + + Args: + action_1_touch_yx: The (y, x) coordinates of the first action's touch. + action_1_lift_yx: The (y, x) coordinates of the first action's lift. + action_1_action_type: The action type of the first action. + action_2_touch_yx: The (y, x) coordinates of the second action's touch. + action_2_lift_yx: The (y, x) coordinates of the second action's lift. + action_2_action_type: The action type of the second action. + annotation_positions: The positions of the UI annotations for the screen. It + is A 2D int array of shape (num_bboxes, 4), where each row represents a + bounding box: (y_top_left, x_top_left, box_height, box_width). Note that + containment is inclusive of the bounding box edges. + tap_distance_threshold: The threshold that determines if two taps result in + a matching screen state if they don't fall the same bounding boxes. + annotation_width_augment_fraction: The fraction to increase the width of the + bounding box by. + annotation_height_augment_fraction: The fraction to increase the height of + of the bounding box by. + + Returns: + A boolean representing whether the two given actions are the same or not. + """ + action_1_touch_yx = jnp.asarray(action_1_touch_yx) + action_1_lift_yx = jnp.asarray(action_1_lift_yx) + action_2_touch_yx = jnp.asarray(action_2_touch_yx) + action_2_lift_yx = jnp.asarray(action_2_lift_yx) + + # Checks if at least one of the actions is global (i.e. not DUAL_POINT), + # because if that is the case, only the actions' types need to be compared. + has_non_dual_point_action = jnp.logical_or( + _is_non_dual_point_action(action_1_action_type), + _is_non_dual_point_action(action_2_action_type), + ) + #print("non dual point: "+str(has_non_dual_point_action)) + + different_dual_point_types = jnp.logical_xor( + is_tap_action(action_1_touch_yx, action_1_lift_yx), + is_tap_action(action_2_touch_yx, action_2_lift_yx), + ) + #print("different dual type: "+str(different_dual_point_types)) + + is_tap = jnp.logical_and( + is_tap_action(action_1_touch_yx, action_1_lift_yx), + is_tap_action(action_2_touch_yx, action_2_lift_yx), + ) + #print("is tap: "+str(is_tap)) + + taps_match = _check_tap_actions_match( + action_1_touch_yx, + action_2_touch_yx, + annotation_positions, + tap_distance_threshold, + annotation_width_augment_fraction, + annotation_height_augment_fraction, + ) + #print("tap match: "+str(taps_match)) + + taps_match = jnp.logical_and(is_tap, taps_match) + #print("tap match: "+str(taps_match)) + + drags_match = _check_drag_actions_match( + action_1_touch_yx, action_1_lift_yx, action_2_touch_yx, action_2_lift_yx + ) + drags_match = jnp.where(is_tap, False, drags_match) + #print("drag match: "+str(drags_match)) + + return jnp.where( + has_non_dual_point_action, + jnp.equal(action_1_action_type, action_2_action_type), + jnp.where( + different_dual_point_types, + False, + jnp.logical_or(taps_match, drags_match), + ), + ) + + +def action_2_format(step_data): + # 把test数据集中的动作格式转换为计算matching score的格式 + action_type = step_data["action_type_id"] + + if action_type == 4: + if step_data["action_type_text"] == 'click': # 点击 + touch_point = step_data["touch"] + lift_point = step_data["lift"] + else: # 上下左右滑动 + if step_data["action_type_text"] == 'scroll down': + touch_point = [0.5, 0.8] + lift_point = [0.5, 0.2] + elif step_data["action_type_text"] == 'scroll up': + touch_point = [0.5, 0.2] + lift_point = [0.5, 0.8] + elif step_data["action_type_text"] == 'scroll left': + touch_point = [0.2, 0.5] + lift_point = [0.8, 0.5] + elif step_data["action_type_text"] == 'scroll right': + touch_point = [0.8, 0.5] + lift_point = [0.2, 0.5] + else: + touch_point = [-1.0, -1.0] + lift_point = [-1.0, -1.0] + + if action_type == 3: + typed_text = step_data["type_text"] + else: + typed_text = "" + + action = {"action_type": action_type, "touch_point": touch_point, "lift_point": lift_point, + "typed_text": typed_text} + + action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] + action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] + action["typed_text"] = action["typed_text"].lower() + + return action + + +def pred_2_format(step_data): + # 把模型输出的内容转换为计算action_matching的格式 + action_type = step_data["action_type"] + + if action_type == 4: # 点击 + action_type_new = 4 + touch_point = step_data["click_point"] + lift_point = step_data["click_point"] + typed_text = "" + elif action_type == 0: + action_type_new = 4 + touch_point = [0.5, 0.8] + lift_point = [0.5, 0.2] + typed_text = "" + elif action_type == 1: + action_type_new = 4 + touch_point = [0.5, 0.2] + lift_point = [0.5, 0.8] + typed_text = "" + elif action_type == 8: + action_type_new = 4 + touch_point = [0.2, 0.5] + lift_point = [0.8, 0.5] + typed_text = "" + elif action_type == 9: + action_type_new = 4 + touch_point = [0.8, 0.5] + lift_point = [0.2, 0.5] + typed_text = "" + else: + action_type_new = action_type + touch_point = [-1.0, -1.0] + lift_point = [-1.0, -1.0] + typed_text = "" + if action_type_new == 3: + typed_text = step_data["typed_text"] + + action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point, + "typed_text": typed_text} + + action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] + action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] + action["typed_text"] = action["typed_text"].lower() + + return action + + +def pred_2_format_simplified(step_data): + # 把模型输出的内容转换为计算action_matching的格式 + action_type = step_data["action_type"] + + if action_type == 'click' : # 点击 + action_type_new = 4 + touch_point = step_data["click_point"] + lift_point = step_data["click_point"] + typed_text = "" + elif action_type == 'scroll' and step_data["direction"] == 'down': + action_type_new = 4 + touch_point = [0.5, 0.8] + lift_point = [0.5, 0.2] + typed_text = "" + elif action_type == 'scroll' and step_data["direction"] == 'up': + action_type_new = 4 + touch_point = [0.5, 0.2] + lift_point = [0.5, 0.8] + typed_text = "" + elif action_type == 'scroll' and step_data["direction"] == 'left': + action_type_new = 4 + touch_point = [0.2, 0.5] + lift_point = [0.8, 0.5] + typed_text = "" + elif action_type == 'scroll' and step_data["direction"] == 'right': + action_type_new = 4 + touch_point = [0.8, 0.5] + lift_point = [0.2, 0.5] + typed_text = "" + elif action_type == 'type': + action_type_new = 3 + touch_point = [-1.0, -1.0] + lift_point = [-1.0, -1.0] + typed_text = step_data["text"] + elif action_type == 'navigate_back': + action_type_new = 5 + touch_point = [-1.0, -1.0] + lift_point = [-1.0, -1.0] + typed_text = "" + elif action_type == 'navigate_home': + action_type_new = 6 + touch_point = [-1.0, -1.0] + lift_point = [-1.0, -1.0] + typed_text = "" + else: + action_type_new = action_type + touch_point = [-1.0, -1.0] + lift_point = [-1.0, -1.0] + typed_text = "" + # if action_type_new == 'type': + # typed_text = step_data["text"] + + action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point, + "typed_text": typed_text} + + action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] + action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] + action["typed_text"] = action["typed_text"].lower() + return action \ No newline at end of file diff --git a/util/action_type.py b/util/action_type.py index e11e5a3..2e2b06d 100644 --- a/util/action_type.py +++ b/util/action_type.py @@ -1,45 +1,45 @@ -''' -Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild -''' - -import enum - -class ActionType(enum.IntEnum): - - # Placeholders for unused enum values - UNUSED_0 = 0 - UNUSED_1 = 1 - UNUSED_2 = 2 - UNUSED_8 = 8 - UNUSED_9 = 9 - - ########### Agent actions ########### - - # A type action that sends text to the emulator. Note that this simply sends - # text and does not perform any clicks for element focus or enter presses for - # submitting text. - TYPE = 3 - - # The dual point action used to represent all gestures. - DUAL_POINT = 4 - - # These actions differentiate pressing the home and back button from touches. - # They represent explicit presses of back and home performed using ADB. - PRESS_BACK = 5 - PRESS_HOME = 6 - - # An action representing that ADB command for hitting enter was performed. - PRESS_ENTER = 7 - - ########### Episode status actions ########### - - # An action used to indicate the desired task has been completed and resets - # the environment. This action should also be used in the case that the task - # has already been completed and there is nothing to do. - # e.g. The task is to turn on the Wi-Fi when it is already on - STATUS_TASK_COMPLETE = 10 - - # An action used to indicate that desired task is impossible to complete and - # resets the environment. This can be a result of many different things - # including UI changes, Android version differences, etc. +''' +Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild +''' + +import enum + +class ActionType(enum.IntEnum): + + # Placeholders for unused enum values + UNUSED_0 = 0 + UNUSED_1 = 1 + UNUSED_2 = 2 + UNUSED_8 = 8 + UNUSED_9 = 9 + + ########### Agent actions ########### + + # A type action that sends text to the emulator. Note that this simply sends + # text and does not perform any clicks for element focus or enter presses for + # submitting text. + TYPE = 3 + + # The dual point action used to represent all gestures. + DUAL_POINT = 4 + + # These actions differentiate pressing the home and back button from touches. + # They represent explicit presses of back and home performed using ADB. + PRESS_BACK = 5 + PRESS_HOME = 6 + + # An action representing that ADB command for hitting enter was performed. + PRESS_ENTER = 7 + + ########### Episode status actions ########### + + # An action used to indicate the desired task has been completed and resets + # the environment. This action should also be used in the case that the task + # has already been completed and there is nothing to do. + # e.g. The task is to turn on the Wi-Fi when it is already on + STATUS_TASK_COMPLETE = 10 + + # An action used to indicate that desired task is impossible to complete and + # resets the environment. This can be a result of many different things + # including UI changes, Android version differences, etc. STATUS_TASK_IMPOSSIBLE = 11 \ No newline at end of file diff --git a/util/box_annotator.py b/util/box_annotator.py index 82f7116..26af49c 100644 --- a/util/box_annotator.py +++ b/util/box_annotator.py @@ -1,262 +1,262 @@ -from typing import List, Optional, Union, Tuple - -import cv2 -import numpy as np - -from supervision.detection.core import Detections -from supervision.draw.color import Color, ColorPalette - - -class BoxAnnotator: - """ - A class for drawing bounding boxes on an image using detections provided. - - Attributes: - color (Union[Color, ColorPalette]): The color to draw the bounding box, - can be a single color or a color palette - thickness (int): The thickness of the bounding box lines, default is 2 - text_color (Color): The color of the text on the bounding box, default is white - text_scale (float): The scale of the text on the bounding box, default is 0.5 - text_thickness (int): The thickness of the text on the bounding box, - default is 1 - text_padding (int): The padding around the text on the bounding box, - default is 5 - - """ - - def __init__( - self, - color: Union[Color, ColorPalette] = ColorPalette.DEFAULT, - thickness: int = 3, # 1 for seeclick 2 for mind2web and 3 for demo - text_color: Color = Color.BLACK, - text_scale: float = 0.5, # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web - text_thickness: int = 2, #1, # 2 for demo - text_padding: int = 10, - avoid_overlap: bool = True, - ): - self.color: Union[Color, ColorPalette] = color - self.thickness: int = thickness - self.text_color: Color = text_color - self.text_scale: float = text_scale - self.text_thickness: int = text_thickness - self.text_padding: int = text_padding - self.avoid_overlap: bool = avoid_overlap - - def annotate( - self, - scene: np.ndarray, - detections: Detections, - labels: Optional[List[str]] = None, - skip_label: bool = False, - image_size: Optional[Tuple[int, int]] = None, - ) -> np.ndarray: - """ - Draws bounding boxes on the frame using the detections provided. - - Args: - scene (np.ndarray): The image on which the bounding boxes will be drawn - detections (Detections): The detections for which the - bounding boxes will be drawn - labels (Optional[List[str]]): An optional list of labels - corresponding to each detection. If `labels` are not provided, - corresponding `class_id` will be used as label. - skip_label (bool): Is set to `True`, skips bounding box label annotation. - Returns: - np.ndarray: The image with the bounding boxes drawn on it - - Example: - ```python - import supervision as sv - - classes = ['person', ...] - image = ... - detections = sv.Detections(...) - - box_annotator = sv.BoxAnnotator() - labels = [ - f"{classes[class_id]} {confidence:0.2f}" - for _, _, confidence, class_id, _ in detections - ] - annotated_frame = box_annotator.annotate( - scene=image.copy(), - detections=detections, - labels=labels - ) - ``` - """ - font = cv2.FONT_HERSHEY_SIMPLEX - for i in range(len(detections)): - x1, y1, x2, y2 = detections.xyxy[i].astype(int) - class_id = ( - detections.class_id[i] if detections.class_id is not None else None - ) - idx = class_id if class_id is not None else i - color = ( - self.color.by_idx(idx) - if isinstance(self.color, ColorPalette) - else self.color - ) - cv2.rectangle( - img=scene, - pt1=(x1, y1), - pt2=(x2, y2), - color=color.as_bgr(), - thickness=self.thickness, - ) - if skip_label: - continue - - text = ( - f"{class_id}" - if (labels is None or len(detections) != len(labels)) - else labels[i] - ) - - text_width, text_height = cv2.getTextSize( - text=text, - fontFace=font, - fontScale=self.text_scale, - thickness=self.text_thickness, - )[0] - - if not self.avoid_overlap: - text_x = x1 + self.text_padding - text_y = y1 - self.text_padding - - text_background_x1 = x1 - text_background_y1 = y1 - 2 * self.text_padding - text_height - - text_background_x2 = x1 + 2 * self.text_padding + text_width - text_background_y2 = y1 - # text_x = x1 - self.text_padding - text_width - # text_y = y1 + self.text_padding + text_height - # text_background_x1 = x1 - 2 * self.text_padding - text_width - # text_background_y1 = y1 - # text_background_x2 = x1 - # text_background_y2 = y1 + 2 * self.text_padding + text_height - else: - text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 = get_optimal_label_pos(self.text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size) - - cv2.rectangle( - img=scene, - pt1=(text_background_x1, text_background_y1), - pt2=(text_background_x2, text_background_y2), - color=color.as_bgr(), - thickness=cv2.FILLED, - ) - # import pdb; pdb.set_trace() - box_color = color.as_rgb() - luminance = 0.299 * box_color[0] + 0.587 * box_color[1] + 0.114 * box_color[2] - text_color = (0,0,0) if luminance > 160 else (255,255,255) - cv2.putText( - img=scene, - text=text, - org=(text_x, text_y), - fontFace=font, - fontScale=self.text_scale, - # color=self.text_color.as_rgb(), - color=text_color, - thickness=self.text_thickness, - lineType=cv2.LINE_AA, - ) - return scene - - -def box_area(box): - return (box[2] - box[0]) * (box[3] - box[1]) - -def intersection_area(box1, box2): - x1 = max(box1[0], box2[0]) - y1 = max(box1[1], box2[1]) - x2 = min(box1[2], box2[2]) - y2 = min(box1[3], box2[3]) - return max(0, x2 - x1) * max(0, y2 - y1) - -def IoU(box1, box2, return_max=True): - intersection = intersection_area(box1, box2) - union = box_area(box1) + box_area(box2) - intersection - if box_area(box1) > 0 and box_area(box2) > 0: - ratio1 = intersection / box_area(box1) - ratio2 = intersection / box_area(box2) - else: - ratio1, ratio2 = 0, 0 - if return_max: - return max(intersection / union, ratio1, ratio2) - else: - return intersection / union - - -def get_optimal_label_pos(text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size): - """ check overlap of text and background detection box, and get_optimal_label_pos, - pos: str, position of the text, must be one of 'top left', 'top right', 'outer left', 'outer right' TODO: if all are overlapping, return the last one, i.e. outer right - Threshold: default to 0.3 - """ - - def get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size): - is_overlap = False - for i in range(len(detections)): - detection = detections.xyxy[i].astype(int) - if IoU([text_background_x1, text_background_y1, text_background_x2, text_background_y2], detection) > 0.3: - is_overlap = True - break - # check if the text is out of the image - if text_background_x1 < 0 or text_background_x2 > image_size[0] or text_background_y1 < 0 or text_background_y2 > image_size[1]: - is_overlap = True - return is_overlap - - # if pos == 'top left': - text_x = x1 + text_padding - text_y = y1 - text_padding - - text_background_x1 = x1 - text_background_y1 = y1 - 2 * text_padding - text_height - - text_background_x2 = x1 + 2 * text_padding + text_width - text_background_y2 = y1 - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - # elif pos == 'outer left': - text_x = x1 - text_padding - text_width - text_y = y1 + text_padding + text_height - - text_background_x1 = x1 - 2 * text_padding - text_width - text_background_y1 = y1 - - text_background_x2 = x1 - text_background_y2 = y1 + 2 * text_padding + text_height - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - - # elif pos == 'outer right': - text_x = x2 + text_padding - text_y = y1 + text_padding + text_height - - text_background_x1 = x2 - text_background_y1 = y1 - - text_background_x2 = x2 + 2 * text_padding + text_width - text_background_y2 = y1 + 2 * text_padding + text_height - - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - # elif pos == 'top right': - text_x = x2 - text_padding - text_width - text_y = y1 - text_padding - - text_background_x1 = x2 - 2 * text_padding - text_width - text_background_y1 = y1 - 2 * text_padding - text_height - - text_background_x2 = x2 - text_background_y2 = y1 - - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 +from typing import List, Optional, Union, Tuple + +import cv2 +import numpy as np + +from supervision.detection.core import Detections +from supervision.draw.color import Color, ColorPalette + + +class BoxAnnotator: + """ + A class for drawing bounding boxes on an image using detections provided. + + Attributes: + color (Union[Color, ColorPalette]): The color to draw the bounding box, + can be a single color or a color palette + thickness (int): The thickness of the bounding box lines, default is 2 + text_color (Color): The color of the text on the bounding box, default is white + text_scale (float): The scale of the text on the bounding box, default is 0.5 + text_thickness (int): The thickness of the text on the bounding box, + default is 1 + text_padding (int): The padding around the text on the bounding box, + default is 5 + + """ + + def __init__( + self, + color: Union[Color, ColorPalette] = ColorPalette.DEFAULT, + thickness: int = 3, # 1 for seeclick 2 for mind2web and 3 for demo + text_color: Color = Color.BLACK, + text_scale: float = 0.5, # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web + text_thickness: int = 2, #1, # 2 for demo + text_padding: int = 10, + avoid_overlap: bool = True, + ): + self.color: Union[Color, ColorPalette] = color + self.thickness: int = thickness + self.text_color: Color = text_color + self.text_scale: float = text_scale + self.text_thickness: int = text_thickness + self.text_padding: int = text_padding + self.avoid_overlap: bool = avoid_overlap + + def annotate( + self, + scene: np.ndarray, + detections: Detections, + labels: Optional[List[str]] = None, + skip_label: bool = False, + image_size: Optional[Tuple[int, int]] = None, + ) -> np.ndarray: + """ + Draws bounding boxes on the frame using the detections provided. + + Args: + scene (np.ndarray): The image on which the bounding boxes will be drawn + detections (Detections): The detections for which the + bounding boxes will be drawn + labels (Optional[List[str]]): An optional list of labels + corresponding to each detection. If `labels` are not provided, + corresponding `class_id` will be used as label. + skip_label (bool): Is set to `True`, skips bounding box label annotation. + Returns: + np.ndarray: The image with the bounding boxes drawn on it + + Example: + ```python + import supervision as sv + + classes = ['person', ...] + image = ... + detections = sv.Detections(...) + + box_annotator = sv.BoxAnnotator() + labels = [ + f"{classes[class_id]} {confidence:0.2f}" + for _, _, confidence, class_id, _ in detections + ] + annotated_frame = box_annotator.annotate( + scene=image.copy(), + detections=detections, + labels=labels + ) + ``` + """ + font = cv2.FONT_HERSHEY_SIMPLEX + for i in range(len(detections)): + x1, y1, x2, y2 = detections.xyxy[i].astype(int) + class_id = ( + detections.class_id[i] if detections.class_id is not None else None + ) + idx = class_id if class_id is not None else i + color = ( + self.color.by_idx(idx) + if isinstance(self.color, ColorPalette) + else self.color + ) + cv2.rectangle( + img=scene, + pt1=(x1, y1), + pt2=(x2, y2), + color=color.as_bgr(), + thickness=self.thickness, + ) + if skip_label: + continue + + text = ( + f"{class_id}" + if (labels is None or len(detections) != len(labels)) + else labels[i] + ) + + text_width, text_height = cv2.getTextSize( + text=text, + fontFace=font, + fontScale=self.text_scale, + thickness=self.text_thickness, + )[0] + + if not self.avoid_overlap: + text_x = x1 + self.text_padding + text_y = y1 - self.text_padding + + text_background_x1 = x1 + text_background_y1 = y1 - 2 * self.text_padding - text_height + + text_background_x2 = x1 + 2 * self.text_padding + text_width + text_background_y2 = y1 + # text_x = x1 - self.text_padding - text_width + # text_y = y1 + self.text_padding + text_height + # text_background_x1 = x1 - 2 * self.text_padding - text_width + # text_background_y1 = y1 + # text_background_x2 = x1 + # text_background_y2 = y1 + 2 * self.text_padding + text_height + else: + text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 = get_optimal_label_pos(self.text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size) + + cv2.rectangle( + img=scene, + pt1=(text_background_x1, text_background_y1), + pt2=(text_background_x2, text_background_y2), + color=color.as_bgr(), + thickness=cv2.FILLED, + ) + # import pdb; pdb.set_trace() + box_color = color.as_rgb() + luminance = 0.299 * box_color[0] + 0.587 * box_color[1] + 0.114 * box_color[2] + text_color = (0,0,0) if luminance > 160 else (255,255,255) + cv2.putText( + img=scene, + text=text, + org=(text_x, text_y), + fontFace=font, + fontScale=self.text_scale, + # color=self.text_color.as_rgb(), + color=text_color, + thickness=self.text_thickness, + lineType=cv2.LINE_AA, + ) + return scene + + +def box_area(box): + return (box[2] - box[0]) * (box[3] - box[1]) + +def intersection_area(box1, box2): + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + return max(0, x2 - x1) * max(0, y2 - y1) + +def IoU(box1, box2, return_max=True): + intersection = intersection_area(box1, box2) + union = box_area(box1) + box_area(box2) - intersection + if box_area(box1) > 0 and box_area(box2) > 0: + ratio1 = intersection / box_area(box1) + ratio2 = intersection / box_area(box2) + else: + ratio1, ratio2 = 0, 0 + if return_max: + return max(intersection / union, ratio1, ratio2) + else: + return intersection / union + + +def get_optimal_label_pos(text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size): + """ check overlap of text and background detection box, and get_optimal_label_pos, + pos: str, position of the text, must be one of 'top left', 'top right', 'outer left', 'outer right' TODO: if all are overlapping, return the last one, i.e. outer right + Threshold: default to 0.3 + """ + + def get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size): + is_overlap = False + for i in range(len(detections)): + detection = detections.xyxy[i].astype(int) + if IoU([text_background_x1, text_background_y1, text_background_x2, text_background_y2], detection) > 0.3: + is_overlap = True + break + # check if the text is out of the image + if text_background_x1 < 0 or text_background_x2 > image_size[0] or text_background_y1 < 0 or text_background_y2 > image_size[1]: + is_overlap = True + return is_overlap + + # if pos == 'top left': + text_x = x1 + text_padding + text_y = y1 - text_padding + + text_background_x1 = x1 + text_background_y1 = y1 - 2 * text_padding - text_height + + text_background_x2 = x1 + 2 * text_padding + text_width + text_background_y2 = y1 + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + # elif pos == 'outer left': + text_x = x1 - text_padding - text_width + text_y = y1 + text_padding + text_height + + text_background_x1 = x1 - 2 * text_padding - text_width + text_background_y1 = y1 + + text_background_x2 = x1 + text_background_y2 = y1 + 2 * text_padding + text_height + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + + # elif pos == 'outer right': + text_x = x2 + text_padding + text_y = y1 + text_padding + text_height + + text_background_x1 = x2 + text_background_y1 = y1 + + text_background_x2 = x2 + 2 * text_padding + text_width + text_background_y2 = y1 + 2 * text_padding + text_height + + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + # elif pos == 'top right': + text_x = x2 - text_padding - text_width + text_y = y1 - text_padding + + text_background_x1 = x2 - 2 * text_padding - text_width + text_background_y1 = y1 - 2 * text_padding - text_height + + text_background_x2 = x2 + text_background_y2 = y1 + + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 diff --git a/utils.py b/utils.py index c4865b0..5a1254d 100755 --- a/utils.py +++ b/utils.py @@ -1,538 +1,538 @@ -# from ultralytics import YOLO -import os -import io -import base64 -import time -from PIL import Image, ImageDraw, ImageFont -import json -import requests -# utility function -import os -from openai import AzureOpenAI - -import json -import sys -import os -import cv2 -import numpy as np -# %matplotlib inline -from matplotlib import pyplot as plt -import easyocr -from paddleocr import PaddleOCR -reader = easyocr.Reader(['en']) -paddle_ocr = PaddleOCR( - lang='en', # other lang also available - use_angle_cls=False, - use_gpu=False, # using cuda will conflict with pytorch in the same process - show_log=False, - max_batch_size=1024, - use_dilation=True, # improves accuracy - det_db_score_mode='slow', # improves accuracy - rec_batch_num=1024) -import time -import base64 - -import os -import ast -import torch -from typing import Tuple, List -from torchvision.ops import box_convert -import re -from torchvision.transforms import ToPILImage -import supervision as sv -import torchvision.transforms as T - - -def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None): - if not device: - device = "cuda" if torch.cuda.is_available() else "cpu" - if model_name == "blip2": - from transformers import Blip2Processor, Blip2ForConditionalGeneration - processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") - if device == 'cpu': - model = Blip2ForConditionalGeneration.from_pretrained( - model_name_or_path, device_map=None, torch_dtype=torch.float32 - ) - else: - model = Blip2ForConditionalGeneration.from_pretrained( - model_name_or_path, device_map=None, torch_dtype=torch.float16 - ).to(device) - elif model_name == "florence2": - from transformers import AutoProcessor, AutoModelForCausalLM - processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) - if device == 'cpu': - model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float32, trust_remote_code=True) - else: - model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True).to(device) - return {'model': model.to(device), 'processor': processor} - - -def get_yolo_model(model_path): - from ultralytics import YOLO - # Load the model. - model = YOLO(model_path) - return model - - -@torch.inference_mode() -def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=None): - # Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence model - - to_pil = ToPILImage() - if starting_idx: - non_ocr_boxes = filtered_boxes[starting_idx:] - else: - non_ocr_boxes = filtered_boxes - croped_pil_image = [] - for i, coord in enumerate(non_ocr_boxes): - xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) - ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) - cropped_image = image_source[ymin:ymax, xmin:xmax, :] - croped_pil_image.append(to_pil(cropped_image)) - - model, processor = caption_model_processor['model'], caption_model_processor['processor'] - if not prompt: - if 'florence' in model.config.name_or_path: - prompt = "" - else: - prompt = "The image shows" - - generated_texts = [] - device = model.device - # batch_size = 64 - for i in range(0, len(croped_pil_image), batch_size): - start = time.time() - batch = croped_pil_image[i:i+batch_size] - if model.device.type == 'cuda': - inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device, dtype=torch.float16) - else: - inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device) - if 'florence' in model.config.name_or_path: - generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=100,num_beams=3, do_sample=False) - else: - generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True, - generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) - generated_text = [gen.strip() for gen in generated_text] - generated_texts.extend(generated_text) - - return generated_texts - - - -def get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor): - to_pil = ToPILImage() - if ocr_bbox: - non_ocr_boxes = filtered_boxes[len(ocr_bbox):] - else: - non_ocr_boxes = filtered_boxes - croped_pil_image = [] - for i, coord in enumerate(non_ocr_boxes): - xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) - ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) - cropped_image = image_source[ymin:ymax, xmin:xmax, :] - croped_pil_image.append(to_pil(cropped_image)) - - model, processor = caption_model_processor['model'], caption_model_processor['processor'] - device = model.device - messages = [{"role": "user", "content": "<|image_1|>\ndescribe the icon in one sentence"}] - prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) - - batch_size = 5 # Number of samples per batch - generated_texts = [] - - for i in range(0, len(croped_pil_image), batch_size): - images = croped_pil_image[i:i+batch_size] - image_inputs = [processor.image_processor(x, return_tensors="pt") for x in images] - inputs ={'input_ids': [], 'attention_mask': [], 'pixel_values': [], 'image_sizes': []} - texts = [prompt] * len(images) - for i, txt in enumerate(texts): - input = processor._convert_images_texts_to_inputs(image_inputs[i], txt, return_tensors="pt") - inputs['input_ids'].append(input['input_ids']) - inputs['attention_mask'].append(input['attention_mask']) - inputs['pixel_values'].append(input['pixel_values']) - inputs['image_sizes'].append(input['image_sizes']) - max_len = max([x.shape[1] for x in inputs['input_ids']]) - for i, v in enumerate(inputs['input_ids']): - inputs['input_ids'][i] = torch.cat([processor.tokenizer.pad_token_id * torch.ones(1, max_len - v.shape[1], dtype=torch.long), v], dim=1) - inputs['attention_mask'][i] = torch.cat([torch.zeros(1, max_len - v.shape[1], dtype=torch.long), inputs['attention_mask'][i]], dim=1) - inputs_cat = {k: torch.concatenate(v).to(device) for k, v in inputs.items()} - - generation_args = { - "max_new_tokens": 25, - "temperature": 0.01, - "do_sample": False, - } - generate_ids = model.generate(**inputs_cat, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) - # # remove input tokens - generate_ids = generate_ids[:, inputs_cat['input_ids'].shape[1]:] - response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) - response = [res.strip('\n').strip() for res in response] - generated_texts.extend(response) - - return generated_texts - -def remove_overlap(boxes, iou_threshold, ocr_bbox=None): - assert ocr_bbox is None or isinstance(ocr_bbox, List) - - def box_area(box): - return (box[2] - box[0]) * (box[3] - box[1]) - - def intersection_area(box1, box2): - x1 = max(box1[0], box2[0]) - y1 = max(box1[1], box2[1]) - x2 = min(box1[2], box2[2]) - y2 = min(box1[3], box2[3]) - return max(0, x2 - x1) * max(0, y2 - y1) - - def IoU(box1, box2): - intersection = intersection_area(box1, box2) - union = box_area(box1) + box_area(box2) - intersection + 1e-6 - if box_area(box1) > 0 and box_area(box2) > 0: - ratio1 = intersection / box_area(box1) - ratio2 = intersection / box_area(box2) - else: - ratio1, ratio2 = 0, 0 - return max(intersection / union, ratio1, ratio2) - - def is_inside(box1, box2): - # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] - intersection = intersection_area(box1, box2) - ratio1 = intersection / box_area(box1) - return ratio1 > 0.95 - - boxes = boxes.tolist() - filtered_boxes = [] - if ocr_bbox: - filtered_boxes.extend(ocr_bbox) - # print('ocr_bbox!!!', ocr_bbox) - for i, box1 in enumerate(boxes): - # if not any(IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2) for j, box2 in enumerate(boxes) if i != j): - is_valid_box = True - for j, box2 in enumerate(boxes): - # keep the smaller box - if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): - is_valid_box = False - break - if is_valid_box: - # add the following 2 lines to include ocr bbox - if ocr_bbox: - # only add the box if it does not overlap with any ocr bbox - if not any(IoU(box1, box3) > iou_threshold and not is_inside(box1, box3) for k, box3 in enumerate(ocr_bbox)): - filtered_boxes.append(box1) - else: - filtered_boxes.append(box1) - return torch.tensor(filtered_boxes) - - -def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None): - ''' - ocr_bbox format: [{'type': 'text', 'bbox':[x,y], 'interactivity':False, 'content':str }, ...] - boxes format: [{'type': 'icon', 'bbox':[x,y], 'interactivity':True, 'content':None }, ...] - - ''' - assert ocr_bbox is None or isinstance(ocr_bbox, List) - - def box_area(box): - return (box[2] - box[0]) * (box[3] - box[1]) - - def intersection_area(box1, box2): - x1 = max(box1[0], box2[0]) - y1 = max(box1[1], box2[1]) - x2 = min(box1[2], box2[2]) - y2 = min(box1[3], box2[3]) - return max(0, x2 - x1) * max(0, y2 - y1) - - def IoU(box1, box2): - intersection = intersection_area(box1, box2) - union = box_area(box1) + box_area(box2) - intersection + 1e-6 - if box_area(box1) > 0 and box_area(box2) > 0: - ratio1 = intersection / box_area(box1) - ratio2 = intersection / box_area(box2) - else: - ratio1, ratio2 = 0, 0 - return max(intersection / union, ratio1, ratio2) - - def is_inside(box1, box2): - # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] - intersection = intersection_area(box1, box2) - ratio1 = intersection / box_area(box1) - return ratio1 > 0.80 - - # boxes = boxes.tolist() - filtered_boxes = [] - if ocr_bbox: - filtered_boxes.extend(ocr_bbox) - # print('ocr_bbox!!!', ocr_bbox) - for i, box1_elem in enumerate(boxes): - box1 = box1_elem['bbox'] - is_valid_box = True - for j, box2_elem in enumerate(boxes): - # keep the smaller box - box2 = box2_elem['bbox'] - if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): - is_valid_box = False - break - if is_valid_box: - # add the following 2 lines to include ocr bbox - if ocr_bbox: - # keep yolo boxes + prioritize ocr label - box_added = False - for box3_elem in ocr_bbox: - if not box_added: - box3 = box3_elem['bbox'] - if is_inside(box3, box1): # ocr inside icon - # box_added = True - # delete the box3_elem from ocr_bbox - try: - filtered_boxes.append({'type': 'text', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': box3_elem['content']}) - filtered_boxes.remove(box3_elem) - # print('remove ocr bbox:', box3_elem) - except: - continue - # break - elif is_inside(box1, box3): # icon inside ocr - box_added = True - # try: - # filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) - # filtered_boxes.remove(box3_elem) - # except: - # continue - break - else: - continue - if not box_added: - filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) - - else: - filtered_boxes.append(box1) - return filtered_boxes # torch.tensor(filtered_boxes) - - -def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]: - transform = T.Compose( - [ - T.RandomResize([800], max_size=1333), - T.ToTensor(), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ] - ) - image_source = Image.open(image_path).convert("RGB") - image = np.asarray(image_source) - image_transformed, _ = transform(image_source, None) - return image, image_transformed - - -def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str], text_scale: float, - text_padding=5, text_thickness=2, thickness=3) -> np.ndarray: - """ - This function annotates an image with bounding boxes and labels. - - Parameters: - image_source (np.ndarray): The source image to be annotated. - boxes (torch.Tensor): A tensor containing bounding box coordinates. in cxcywh format, pixel scale - logits (torch.Tensor): A tensor containing confidence scores for each bounding box. - phrases (List[str]): A list of labels for each bounding box. - text_scale (float): The scale of the text to be displayed. 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web - - Returns: - np.ndarray: The annotated image. - """ - h, w, _ = image_source.shape - boxes = boxes * torch.Tensor([w, h, w, h]) - xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() - xywh = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xywh").numpy() - detections = sv.Detections(xyxy=xyxy) - - labels = [f"{phrase}" for phrase in range(boxes.shape[0])] - - from util.box_annotator import BoxAnnotator - box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web - annotated_frame = image_source.copy() - annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h)) - - label_coordinates = {f"{phrase}": v for phrase, v in zip(phrases, xywh)} - return annotated_frame, label_coordinates - - -def predict(model, image, caption, box_threshold, text_threshold): - """ Use huggingface model to replace the original model - """ - model, processor = model['model'], model['processor'] - device = model.device - - inputs = processor(images=image, text=caption, return_tensors="pt").to(device) - with torch.no_grad(): - outputs = model(**inputs) - - results = processor.post_process_grounded_object_detection( - outputs, - inputs.input_ids, - box_threshold=box_threshold, # 0.4, - text_threshold=text_threshold, # 0.3, - target_sizes=[image.size[::-1]] - )[0] - boxes, logits, phrases = results["boxes"], results["scores"], results["labels"] - return boxes, logits, phrases - - -def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7): - """ Use huggingface model to replace the original model - """ - # model = model['model'] - if scale_img: - result = model.predict( - source=image_path, - conf=box_threshold, - imgsz=imgsz, - iou=iou_threshold, # default 0.7 - ) - else: - result = model.predict( - source=image_path, - conf=box_threshold, - iou=iou_threshold, # default 0.7 - ) - boxes = result[0].boxes.xyxy#.tolist() # in pixel space - conf = result[0].boxes.conf - phrases = [str(i) for i in range(len(boxes))] - - return boxes, conf, phrases - - -def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): - """ ocr_bbox: list of xyxy format bbox - """ - image_source = Image.open(img_path).convert("RGB") - w, h = image_source.size - if not imgsz: - imgsz = (h, w) - # print('image size:', w, h) - xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1) - xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device) - image_source = np.asarray(image_source) - phrases = [str(i) for i in range(len(phrases))] - - # annotate the image with labels - h, w, _ = image_source.shape - if ocr_bbox: - ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h]) - ocr_bbox=ocr_bbox.tolist() - else: - print('no ocr bbox!!!') - ocr_bbox = None - # filtered_boxes = remove_overlap(boxes=xyxy, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox) - # starting_idx = len(ocr_bbox) - # print('len(filtered_boxes):', len(filtered_boxes), starting_idx) - - ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt} for box, txt in zip(ocr_bbox, ocr_text)] - xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist()] - filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem) - - # sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None - filtered_boxes_elem = sorted(filtered_boxes, key=lambda x: x['content'] is None) - # get the index of the first 'content': None - starting_idx = next((i for i, box in enumerate(filtered_boxes_elem) if box['content'] is None), -1) - filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem]) - print('len(filtered_boxes):', len(filtered_boxes), starting_idx) - - - # get parsed icon local semantics - if use_local_semantics: - caption_model = caption_model_processor['model'] - if 'phi3_v' in caption_model.config.model_type: - parsed_content_icon = get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor) - else: - parsed_content_icon = get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=prompt,batch_size=batch_size) - ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] - icon_start = len(ocr_text) - parsed_content_icon_ls = [] - # fill the filtered_boxes_elem None content with parsed_content_icon in order - for i, box in enumerate(filtered_boxes_elem): - if box['content'] is None: - box['content'] = parsed_content_icon.pop(0) - for i, txt in enumerate(parsed_content_icon): - parsed_content_icon_ls.append(f"Icon Box ID {str(i+icon_start)}: {txt}") - parsed_content_merged = ocr_text + parsed_content_icon_ls - else: - ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] - parsed_content_merged = ocr_text - - filtered_boxes = box_convert(boxes=filtered_boxes, in_fmt="xyxy", out_fmt="cxcywh") - - phrases = [i for i in range(len(filtered_boxes))] - - # draw boxes - if draw_bbox_config: - annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, **draw_bbox_config) - else: - annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, text_scale=text_scale, text_padding=text_padding) - - pil_img = Image.fromarray(annotated_frame) - buffered = io.BytesIO() - pil_img.save(buffered, format="PNG") - encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii') - if output_coord_in_ratio: - # h, w, _ = image_source.shape - label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()} - assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0] - - return encoded_image, label_coordinates, filtered_boxes_elem - - -def get_xywh(input): - x, y, w, h = input[0][0], input[0][1], input[2][0] - input[0][0], input[2][1] - input[0][1] - x, y, w, h = int(x), int(y), int(w), int(h) - return x, y, w, h - -def get_xyxy(input): - x, y, xp, yp = input[0][0], input[0][1], input[2][0], input[2][1] - x, y, xp, yp = int(x), int(y), int(xp), int(yp) - return x, y, xp, yp - -def get_xywh_yolo(input): - x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1] - x, y, w, h = int(x), int(y), int(w), int(h) - return x, y, w, h - - - -def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False): - if use_paddleocr: - if easyocr_args is None: - text_threshold = 0.5 - else: - text_threshold = easyocr_args['text_threshold'] - result = paddle_ocr.ocr(image_path, cls=False)[0] - # conf = [item[1] for item in result] - coord = [item[0] for item in result if item[1][1] > text_threshold] - text = [item[1][0] for item in result if item[1][1] > text_threshold] - else: # EasyOCR - if easyocr_args is None: - easyocr_args = {} - result = reader.readtext(image_path, **easyocr_args) - # print('goal filtering pred:', result[-5:]) - coord = [item[0] for item in result] - text = [item[1] for item in result] - # read the image using cv2 - if display_img: - opencv_img = cv2.imread(image_path) - opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR) - bb = [] - for item in coord: - x, y, a, b = get_xywh(item) - # print(x, y, a, b) - bb.append((x, y, a, b)) - cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2) - - # Display the image - plt.imshow(opencv_img) - else: - if output_bb_format == 'xywh': - bb = [get_xywh(item) for item in coord] - elif output_bb_format == 'xyxy': - bb = [get_xyxy(item) for item in coord] - # print('bounding box!!!', bb) - return (text, bb), goal_filtering - - - +# from ultralytics import YOLO +import os +import io +import base64 +import time +from PIL import Image, ImageDraw, ImageFont +import json +import requests +# utility function +import os +from openai import AzureOpenAI + +import json +import sys +import os +import cv2 +import numpy as np +# %matplotlib inline +from matplotlib import pyplot as plt +import easyocr +from paddleocr import PaddleOCR +reader = easyocr.Reader(['en']) +paddle_ocr = PaddleOCR( + lang='en', # other lang also available + use_angle_cls=False, + use_gpu=False, # using cuda will conflict with pytorch in the same process + show_log=False, + max_batch_size=1024, + use_dilation=True, # improves accuracy + det_db_score_mode='slow', # improves accuracy + rec_batch_num=1024) +import time +import base64 + +import os +import ast +import torch +from typing import Tuple, List +from torchvision.ops import box_convert +import re +from torchvision.transforms import ToPILImage +import supervision as sv +import torchvision.transforms as T + + +def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None): + if not device: + device = "cuda" if torch.cuda.is_available() else "cpu" + if model_name == "blip2": + from transformers import Blip2Processor, Blip2ForConditionalGeneration + processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + if device == 'cpu': + model = Blip2ForConditionalGeneration.from_pretrained( + model_name_or_path, device_map=None, torch_dtype=torch.float32 + ) + else: + model = Blip2ForConditionalGeneration.from_pretrained( + model_name_or_path, device_map=None, torch_dtype=torch.float16 + ).to(device) + elif model_name == "florence2": + from transformers import AutoProcessor, AutoModelForCausalLM + processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) + if device == 'cpu': + model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float32, trust_remote_code=True) + else: + model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True).to(device) + return {'model': model.to(device), 'processor': processor} + + +def get_yolo_model(model_path): + from ultralytics import YOLO + # Load the model. + model = YOLO(model_path) + return model + + +@torch.inference_mode() +def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=None): + # Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence model + + to_pil = ToPILImage() + if starting_idx: + non_ocr_boxes = filtered_boxes[starting_idx:] + else: + non_ocr_boxes = filtered_boxes + croped_pil_image = [] + for i, coord in enumerate(non_ocr_boxes): + xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) + ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) + cropped_image = image_source[ymin:ymax, xmin:xmax, :] + croped_pil_image.append(to_pil(cropped_image)) + + model, processor = caption_model_processor['model'], caption_model_processor['processor'] + if not prompt: + if 'florence' in model.config.name_or_path: + prompt = "" + else: + prompt = "The image shows" + + generated_texts = [] + device = model.device + # batch_size = 64 + for i in range(0, len(croped_pil_image), batch_size): + start = time.time() + batch = croped_pil_image[i:i+batch_size] + if model.device.type == 'cuda': + inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device, dtype=torch.float16) + else: + inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device) + if 'florence' in model.config.name_or_path: + generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=100,num_beams=3, do_sample=False) + else: + generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True, + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) + generated_text = [gen.strip() for gen in generated_text] + generated_texts.extend(generated_text) + + return generated_texts + + + +def get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor): + to_pil = ToPILImage() + if ocr_bbox: + non_ocr_boxes = filtered_boxes[len(ocr_bbox):] + else: + non_ocr_boxes = filtered_boxes + croped_pil_image = [] + for i, coord in enumerate(non_ocr_boxes): + xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) + ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) + cropped_image = image_source[ymin:ymax, xmin:xmax, :] + croped_pil_image.append(to_pil(cropped_image)) + + model, processor = caption_model_processor['model'], caption_model_processor['processor'] + device = model.device + messages = [{"role": "user", "content": "<|image_1|>\ndescribe the icon in one sentence"}] + prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + + batch_size = 5 # Number of samples per batch + generated_texts = [] + + for i in range(0, len(croped_pil_image), batch_size): + images = croped_pil_image[i:i+batch_size] + image_inputs = [processor.image_processor(x, return_tensors="pt") for x in images] + inputs ={'input_ids': [], 'attention_mask': [], 'pixel_values': [], 'image_sizes': []} + texts = [prompt] * len(images) + for i, txt in enumerate(texts): + input = processor._convert_images_texts_to_inputs(image_inputs[i], txt, return_tensors="pt") + inputs['input_ids'].append(input['input_ids']) + inputs['attention_mask'].append(input['attention_mask']) + inputs['pixel_values'].append(input['pixel_values']) + inputs['image_sizes'].append(input['image_sizes']) + max_len = max([x.shape[1] for x in inputs['input_ids']]) + for i, v in enumerate(inputs['input_ids']): + inputs['input_ids'][i] = torch.cat([processor.tokenizer.pad_token_id * torch.ones(1, max_len - v.shape[1], dtype=torch.long), v], dim=1) + inputs['attention_mask'][i] = torch.cat([torch.zeros(1, max_len - v.shape[1], dtype=torch.long), inputs['attention_mask'][i]], dim=1) + inputs_cat = {k: torch.concatenate(v).to(device) for k, v in inputs.items()} + + generation_args = { + "max_new_tokens": 25, + "temperature": 0.01, + "do_sample": False, + } + generate_ids = model.generate(**inputs_cat, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) + # # remove input tokens + generate_ids = generate_ids[:, inputs_cat['input_ids'].shape[1]:] + response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) + response = [res.strip('\n').strip() for res in response] + generated_texts.extend(response) + + return generated_texts + +def remove_overlap(boxes, iou_threshold, ocr_bbox=None): + assert ocr_bbox is None or isinstance(ocr_bbox, List) + + def box_area(box): + return (box[2] - box[0]) * (box[3] - box[1]) + + def intersection_area(box1, box2): + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + return max(0, x2 - x1) * max(0, y2 - y1) + + def IoU(box1, box2): + intersection = intersection_area(box1, box2) + union = box_area(box1) + box_area(box2) - intersection + 1e-6 + if box_area(box1) > 0 and box_area(box2) > 0: + ratio1 = intersection / box_area(box1) + ratio2 = intersection / box_area(box2) + else: + ratio1, ratio2 = 0, 0 + return max(intersection / union, ratio1, ratio2) + + def is_inside(box1, box2): + # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] + intersection = intersection_area(box1, box2) + ratio1 = intersection / box_area(box1) + return ratio1 > 0.95 + + boxes = boxes.tolist() + filtered_boxes = [] + if ocr_bbox: + filtered_boxes.extend(ocr_bbox) + # print('ocr_bbox!!!', ocr_bbox) + for i, box1 in enumerate(boxes): + # if not any(IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2) for j, box2 in enumerate(boxes) if i != j): + is_valid_box = True + for j, box2 in enumerate(boxes): + # keep the smaller box + if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): + is_valid_box = False + break + if is_valid_box: + # add the following 2 lines to include ocr bbox + if ocr_bbox: + # only add the box if it does not overlap with any ocr bbox + if not any(IoU(box1, box3) > iou_threshold and not is_inside(box1, box3) for k, box3 in enumerate(ocr_bbox)): + filtered_boxes.append(box1) + else: + filtered_boxes.append(box1) + return torch.tensor(filtered_boxes) + + +def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None): + ''' + ocr_bbox format: [{'type': 'text', 'bbox':[x,y], 'interactivity':False, 'content':str }, ...] + boxes format: [{'type': 'icon', 'bbox':[x,y], 'interactivity':True, 'content':None }, ...] + + ''' + assert ocr_bbox is None or isinstance(ocr_bbox, List) + + def box_area(box): + return (box[2] - box[0]) * (box[3] - box[1]) + + def intersection_area(box1, box2): + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + return max(0, x2 - x1) * max(0, y2 - y1) + + def IoU(box1, box2): + intersection = intersection_area(box1, box2) + union = box_area(box1) + box_area(box2) - intersection + 1e-6 + if box_area(box1) > 0 and box_area(box2) > 0: + ratio1 = intersection / box_area(box1) + ratio2 = intersection / box_area(box2) + else: + ratio1, ratio2 = 0, 0 + return max(intersection / union, ratio1, ratio2) + + def is_inside(box1, box2): + # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] + intersection = intersection_area(box1, box2) + ratio1 = intersection / box_area(box1) + return ratio1 > 0.80 + + # boxes = boxes.tolist() + filtered_boxes = [] + if ocr_bbox: + filtered_boxes.extend(ocr_bbox) + # print('ocr_bbox!!!', ocr_bbox) + for i, box1_elem in enumerate(boxes): + box1 = box1_elem['bbox'] + is_valid_box = True + for j, box2_elem in enumerate(boxes): + # keep the smaller box + box2 = box2_elem['bbox'] + if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): + is_valid_box = False + break + if is_valid_box: + # add the following 2 lines to include ocr bbox + if ocr_bbox: + # keep yolo boxes + prioritize ocr label + box_added = False + for box3_elem in ocr_bbox: + if not box_added: + box3 = box3_elem['bbox'] + if is_inside(box3, box1): # ocr inside icon + # box_added = True + # delete the box3_elem from ocr_bbox + try: + filtered_boxes.append({'type': 'text', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': box3_elem['content']}) + filtered_boxes.remove(box3_elem) + # print('remove ocr bbox:', box3_elem) + except: + continue + # break + elif is_inside(box1, box3): # icon inside ocr + box_added = True + # try: + # filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) + # filtered_boxes.remove(box3_elem) + # except: + # continue + break + else: + continue + if not box_added: + filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) + + else: + filtered_boxes.append(box1) + return filtered_boxes # torch.tensor(filtered_boxes) + + +def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]: + transform = T.Compose( + [ + T.RandomResize([800], max_size=1333), + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ] + ) + image_source = Image.open(image_path).convert("RGB") + image = np.asarray(image_source) + image_transformed, _ = transform(image_source, None) + return image, image_transformed + + +def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str], text_scale: float, + text_padding=5, text_thickness=2, thickness=3) -> np.ndarray: + """ + This function annotates an image with bounding boxes and labels. + + Parameters: + image_source (np.ndarray): The source image to be annotated. + boxes (torch.Tensor): A tensor containing bounding box coordinates. in cxcywh format, pixel scale + logits (torch.Tensor): A tensor containing confidence scores for each bounding box. + phrases (List[str]): A list of labels for each bounding box. + text_scale (float): The scale of the text to be displayed. 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web + + Returns: + np.ndarray: The annotated image. + """ + h, w, _ = image_source.shape + boxes = boxes * torch.Tensor([w, h, w, h]) + xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() + xywh = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xywh").numpy() + detections = sv.Detections(xyxy=xyxy) + + labels = [f"{phrase}" for phrase in range(boxes.shape[0])] + + from util.box_annotator import BoxAnnotator + box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web + annotated_frame = image_source.copy() + annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h)) + + label_coordinates = {f"{phrase}": v for phrase, v in zip(phrases, xywh)} + return annotated_frame, label_coordinates + + +def predict(model, image, caption, box_threshold, text_threshold): + """ Use huggingface model to replace the original model + """ + model, processor = model['model'], model['processor'] + device = model.device + + inputs = processor(images=image, text=caption, return_tensors="pt").to(device) + with torch.no_grad(): + outputs = model(**inputs) + + results = processor.post_process_grounded_object_detection( + outputs, + inputs.input_ids, + box_threshold=box_threshold, # 0.4, + text_threshold=text_threshold, # 0.3, + target_sizes=[image.size[::-1]] + )[0] + boxes, logits, phrases = results["boxes"], results["scores"], results["labels"] + return boxes, logits, phrases + + +def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7): + """ Use huggingface model to replace the original model + """ + # model = model['model'] + if scale_img: + result = model.predict( + source=image_path, + conf=box_threshold, + imgsz=imgsz, + iou=iou_threshold, # default 0.7 + ) + else: + result = model.predict( + source=image_path, + conf=box_threshold, + iou=iou_threshold, # default 0.7 + ) + boxes = result[0].boxes.xyxy#.tolist() # in pixel space + conf = result[0].boxes.conf + phrases = [str(i) for i in range(len(boxes))] + + return boxes, conf, phrases + + +def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): + """ ocr_bbox: list of xyxy format bbox + """ + image_source = Image.open(img_path).convert("RGB") + w, h = image_source.size + if not imgsz: + imgsz = (h, w) + # print('image size:', w, h) + xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1) + xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device) + image_source = np.asarray(image_source) + phrases = [str(i) for i in range(len(phrases))] + + # annotate the image with labels + h, w, _ = image_source.shape + if ocr_bbox: + ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h]) + ocr_bbox=ocr_bbox.tolist() + else: + print('no ocr bbox!!!') + ocr_bbox = None + # filtered_boxes = remove_overlap(boxes=xyxy, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox) + # starting_idx = len(ocr_bbox) + # print('len(filtered_boxes):', len(filtered_boxes), starting_idx) + + ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt} for box, txt in zip(ocr_bbox, ocr_text)] + xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist()] + filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem) + + # sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None + filtered_boxes_elem = sorted(filtered_boxes, key=lambda x: x['content'] is None) + # get the index of the first 'content': None + starting_idx = next((i for i, box in enumerate(filtered_boxes_elem) if box['content'] is None), -1) + filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem]) + print('len(filtered_boxes):', len(filtered_boxes), starting_idx) + + + # get parsed icon local semantics + if use_local_semantics: + caption_model = caption_model_processor['model'] + if 'phi3_v' in caption_model.config.model_type: + parsed_content_icon = get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor) + else: + parsed_content_icon = get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=prompt,batch_size=batch_size) + ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] + icon_start = len(ocr_text) + parsed_content_icon_ls = [] + # fill the filtered_boxes_elem None content with parsed_content_icon in order + for i, box in enumerate(filtered_boxes_elem): + if box['content'] is None: + box['content'] = parsed_content_icon.pop(0) + for i, txt in enumerate(parsed_content_icon): + parsed_content_icon_ls.append(f"Icon Box ID {str(i+icon_start)}: {txt}") + parsed_content_merged = ocr_text + parsed_content_icon_ls + else: + ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] + parsed_content_merged = ocr_text + + filtered_boxes = box_convert(boxes=filtered_boxes, in_fmt="xyxy", out_fmt="cxcywh") + + phrases = [i for i in range(len(filtered_boxes))] + + # draw boxes + if draw_bbox_config: + annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, **draw_bbox_config) + else: + annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, text_scale=text_scale, text_padding=text_padding) + + pil_img = Image.fromarray(annotated_frame) + buffered = io.BytesIO() + pil_img.save(buffered, format="PNG") + encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii') + if output_coord_in_ratio: + # h, w, _ = image_source.shape + label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()} + assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0] + + return encoded_image, label_coordinates, filtered_boxes_elem + + +def get_xywh(input): + x, y, w, h = input[0][0], input[0][1], input[2][0] - input[0][0], input[2][1] - input[0][1] + x, y, w, h = int(x), int(y), int(w), int(h) + return x, y, w, h + +def get_xyxy(input): + x, y, xp, yp = input[0][0], input[0][1], input[2][0], input[2][1] + x, y, xp, yp = int(x), int(y), int(xp), int(yp) + return x, y, xp, yp + +def get_xywh_yolo(input): + x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1] + x, y, w, h = int(x), int(y), int(w), int(h) + return x, y, w, h + + + +def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False): + if use_paddleocr: + if easyocr_args is None: + text_threshold = 0.5 + else: + text_threshold = easyocr_args['text_threshold'] + result = paddle_ocr.ocr(image_path, cls=False)[0] + # conf = [item[1] for item in result] + coord = [item[0] for item in result if item[1][1] > text_threshold] + text = [item[1][0] for item in result if item[1][1] > text_threshold] + else: # EasyOCR + if easyocr_args is None: + easyocr_args = {} + result = reader.readtext(image_path, **easyocr_args) + # print('goal filtering pred:', result[-5:]) + coord = [item[0] for item in result] + text = [item[1] for item in result] + # read the image using cv2 + if display_img: + opencv_img = cv2.imread(image_path) + opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR) + bb = [] + for item in coord: + x, y, a, b = get_xywh(item) + # print(x, y, a, b) + bb.append((x, y, a, b)) + cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2) + + # Display the image + plt.imshow(opencv_img) + else: + if output_bb_format == 'xywh': + bb = [get_xywh(item) for item in coord] + elif output_bb_format == 'xyxy': + bb = [get_xyxy(item) for item in coord] + # print('bounding box!!!', bb) + return (text, bb), goal_filtering + + + diff --git a/weights/convert_safetensor_to_pt.py b/weights/convert_safetensor_to_pt.py index 9ed0a7f..8d51a1b 100644 --- a/weights/convert_safetensor_to_pt.py +++ b/weights/convert_safetensor_to_pt.py @@ -1,26 +1,26 @@ -import torch -from ultralytics.nn.tasks import DetectionModel -from safetensors.torch import load_file -import argparse -import yaml -import os - -# accept args to specify v1 or v1_5 -parser = argparse.ArgumentParser(description='Specify version v1 or v1_5') -parser.add_argument('--weights_dir', type=str, required=True, help='Specify the path to the safetensor file', default='weights/icon_detect_v1_5') -args = parser.parse_args() - -tensor_dict = load_file(os.path.join(args.weights_dir, "model.safetensors")) -model = DetectionModel(os.path.join(args.weights_dir, "model.yaml")) -# from ultralytics import YOLO -# som_model = YOLO("yolo11m.pt") -# model = som_model.model - -model.load_state_dict(tensor_dict) -save_dict = {'model':model} - -with open(os.path.join(args.weights_dir, "train_args.yaml"), 'r') as file: - train_args = yaml.safe_load(file) -save_dict.update(train_args) -torch.save(save_dict, os.path.join(args.weights_dir, "best.pt")) - +import torch +from ultralytics.nn.tasks import DetectionModel +from safetensors.torch import load_file +import argparse +import yaml +import os + +# accept args to specify v1 or v1_5 +parser = argparse.ArgumentParser(description='Specify version v1 or v1_5') +parser.add_argument('--weights_dir', type=str, required=True, help='Specify the path to the safetensor file', default='weights/icon_detect_v1_5') +args = parser.parse_args() + +tensor_dict = load_file(os.path.join(args.weights_dir, "model.safetensors")) +model = DetectionModel(os.path.join(args.weights_dir, "model.yaml")) +# from ultralytics import YOLO +# som_model = YOLO("yolo11m.pt") +# model = som_model.model + +model.load_state_dict(tensor_dict) +save_dict = {'model':model} + +with open(os.path.join(args.weights_dir, "train_args.yaml"), 'r') as file: + train_args = yaml.safe_load(file) +save_dict.update(train_args) +torch.save(save_dict, os.path.join(args.weights_dir, "best.pt")) +

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