diff --git a/.dockerignore b/.dockerignore deleted file mode 100644 index a78fefd..0000000 --- a/.dockerignore +++ /dev/null @@ -1,4 +0,0 @@ -.git -venv -.venv - diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..526c8a3 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.sh text eol=lf \ No newline at end of file diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index e9db305..ca3e514 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -4,9 +4,10 @@ on: - main - staging - expt - pull_request: - + branches: + - main + - staging # Trigger the workflow on release activity release: # Only use the types keyword to narrow down the activity types that will trigger your workflow. @@ -21,21 +22,22 @@ env: DOCKERHUB_ORG: classtranscribe jobs: - docker: + build-server: + name: Build server Docker image runs-on: ubuntu-latest - strategy: - fail-fast: true - matrix: - name: - - latextranscribe - include: - - name: latextranscribe - FOLDER: . - IMAGE: latextranscribe - + env: + FOLDER: ./server + IMAGE: latextranscribe + DOCKERFILE: Dockerfile.cpu steps: - - uses: actions/checkout@v2 - + - uses: actions/checkout@v4 + - name: Install the latest version of uv + uses: astral-sh/setup-uv@v5 + - name: Download model weights using huggingface_hub + run: | + cd server + HF_HUB_ENABLE_HF_TRANSFER=1 uv run download_models.py + du -hs models/ # calculate some variables that are used later - name: github branch run: | @@ -45,7 +47,7 @@ jobs: BRANCH=${GITHUB_REF##*/} fi echo "GITHUB_BRANCH=${BRANCH}" >> $GITHUB_ENV - + # Commit was for main/release branch, build a new version if [ "$BRANCH" == "master" -o "$BRANCH" == "main" ]; then version="$(cat gui/package.json | jq -r .version)" @@ -65,7 +67,7 @@ jobs: # build the docker image, this will always run to make sure # the Dockerfile still works. - - name: Build image + - name: Build Docker image uses: elgohr/Publish-Docker-Github-Action@2.22 env: BRANCH: ${{ env.GITHUB_BRANCH }} @@ -74,11 +76,12 @@ jobs: GITSHA1: ${{ github.sha }} with: registry: docker.pkg.github.com - name: ${{ github.repository_owner }}/${{ github.event.repository.name }}/${{ matrix.IMAGE }} + name: ${{ github.repository_owner }}/${{ github.event.repository.name }}/${{ env.IMAGE }} username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - context: ${{ matrix.FOLDER }} + workdir: ${{ env.FOLDER }} tags: "${{ env.TAGS }}" + dockerfile: ${{ env.DOCKERFILE }} buildargs: BRANCH,VERSION,BUILDNUMBER,GITSHA1 no_push: true @@ -111,9 +114,102 @@ jobs: BUILDNUMBER: ${{ github.run_number }} GITSHA1: ${{ github.sha }} with: - name: ${{ env.DOCKERHUB_ORG }}/${{ matrix.IMAGE }} + name: ${{ env.DOCKERHUB_ORG }}/${{ env.IMAGE }} username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - context: ${{ matrix.FOLDER }} + workdir: ${{ env.FOLDER }} tags: "${{ env.TAGS }}" + dockerfile: ${{ env.DOCKERFILE }} buildargs: BRANCH,VERSION,BUILDNUMBER,GITSHA1 + build-frontend: + name: Build frontend Docker image + runs-on: ubuntu-latest + env: + FOLDER: ./frontend + IMAGE: latextranscribe-frontend + DOCKERFILE: Dockerfile + steps: + - uses: actions/checkout@v4 + # calculate some variables that are used later + - name: github branch + run: | + if [ "${{ github.event.release.target_commitish }}" != "" ]; then + BRANCH="${{ github.event.release.target_commitish }}" + else + BRANCH=${GITHUB_REF##*/} + fi + echo "GITHUB_BRANCH=${BRANCH}" >> $GITHUB_ENV + + # Commit was for main/release branch, build a new version + if [ "$BRANCH" == "master" -o "$BRANCH" == "main" ]; then + version="$(cat gui/package.json | jq -r .version)" + echo "VERSION=$(version)" >> $GITHUB_ENV + tags="latest" + oldversion="" + while [ "${oldversion}" != "${version}" ]; do + oldversion="${version}" + tags="${tags},${version}" + version=${version%.*} + done + echo "TAGS=${tags}" >> $GITHUB_ENV + else + echo "VERSION=$BRANCH" >> $GITHUB_ENV + echo "TAGS=$BRANCH" >> $GITHUB_ENV + fi + + # build the docker image, this will always run to make sure + # the Dockerfile still works. + - name: Build Docker image + uses: elgohr/Publish-Docker-Github-Action@2.22 + env: + BRANCH: ${{ env.GITHUB_BRANCH }} + VERSION: ${{ env.VERSION }} + BUILDNUMBER: ${{ github.run_number }} + GITSHA1: ${{ github.sha }} + with: + registry: docker.pkg.github.com + name: ${{ github.repository_owner }}/${{ github.event.repository.name }}/${{ env.IMAGE }} + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + workdir: ${{ env.FOLDER }} + tags: "${{ env.TAGS }}" + dockerfile: ${{ env.DOCKERFILE }} + buildargs: BRANCH,VERSION,BUILDNUMBER,GITSHA1 + no_push: true + + # TODO: need publish permissions for ghcr.io + # this will publish to github container registry + #- name: Publish to GitHub + # if: github.event_name != 'pull_request' && github.repository == env.MAIN_REPO + # uses: elgohr/Publish-Docker-Github-Action@2.22 + # env: + # BRANCH: ${{ env.GITHUB_BRANCH }} + # VERSION: ${{ env.VERSION }} + # BUILDNUMBER: ${{ github.run_number }} + # GITSHA1: ${{ github.sha }} + # with: + # registry: ghcr.io + # name: ${{ github.repository_owner }}/${{ matrix.IMAGE }} + # username: ${{ secrets.GHCR_USERNAME }} + # password: ${{ secrets.GHCR_PASSWORD }} + # context: ${{ matrix.FOLDER }} + # tags: "${{ env.TAGS }}" + # buildargs: BRANCH,VERSION,BUILDNUMBER,GITSHA1 + + # this will publish to dockerhub + - name: Publish to Docker Hub + if: github.event_name != 'pull_request' && github.repository == env.MAIN_REPO + uses: elgohr/Publish-Docker-Github-Action@2.22 + env: + BRANCH: ${{ env.GITHUB_BRANCH }} + VERSION: ${{ env.VERSION }} + BUILDNUMBER: ${{ github.run_number }} + GITSHA1: ${{ github.sha }} + with: + name: ${{ env.DOCKERHUB_ORG }}/${{ env.IMAGE }} + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + workdir: ${{ env.FOLDER }} + tags: "${{ env.TAGS }}" + dockerfile: ${{ env.DOCKERFILE }} + buildargs: BRANCH,VERSION,BUILDNUMBER,GITSHA1 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 5dd2470..3d39d8e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,11 @@ # Custom -server/models/Layout/* -server/models/MFD/* -server/models/MFR/* -server/models/TabRec/* +server/models/Layout/ +server/models/MFD/ +server/models/MFR/ +server/models/TabRec/ + +server/inputs/ +server/outputs/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index b8ddf2a..0000000 --- a/Dockerfile +++ /dev/null @@ -1,46 +0,0 @@ -FROM python:3.11-slim-bookworm - -# Install OS dependencies vim-tiny netcat-openbsd -# git and clang for detectron2 -RUN apt-get -qq update && \ - apt-get -qq install --no-install-recommends git clang && \ - apt-get -qq clean autoclean && \ - apt-get -qq autoremove && \ - rm -rf /var/lib/apt/lists/* && \ - pip install --upgrade pip - -# Install CUDA (and other setup tools); This next line is based on Dockerfile in https://github.com/jim60105/docker-whisperX/ -# docker-whisperX needs numpy<2 do we? (see # https://github.com/jim60105/docker-whisperX/issues/40) -# # torch==2.2.2 torchaudio==2.2.2 \ -# pyannote.audio==3.1.1 \ -# "numpy<2.0" -# RUN pip --force-reinstall pip setuptools wheel -# pip install -U --extra-index-url https://download.pytorch.org/whl/cu126 - -# detectron needs PyTorch ≥ 1.8 and companion torchvision -# RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 -RUN python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu - -# https://detectron2.readthedocs.io/en/latest/tutorials/install.html -# It takes 37s to build detectron on my M1 mac -RUN git clone https://github.com/facebookresearch/detectron2.git -RUN CC=clang CXX=clang++ python -m pip install -e detectron2 - -RUN pip install fastapi uvicorn - -COPY . . - -CMD ["uvicorn" , "helloworld:app" , "--host", "0.0.0.0"] - - - - -#COPY requirements-linux-gpudocker-build.txt . -#RUN pip install --no-cache-dir -r requirements-linux-gpudocker-build.txt - -#COPY . . - -# CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"] - -# Making a note for later, For M1 support docker-whisperX also includes -# apt-get update && apt-get install -y --no-install-recommends libgomp1=12.2.0-14 libsndfile1=1.2.0-1; diff --git a/README.md b/README.md index e36fe99..206c759 100644 --- a/README.md +++ b/README.md @@ -1,39 +1,101 @@ # Latex Transcribe -## Running the official DockerHub image - -(This is sketch and these instructions are untested - assume there are typos, errors, guesses (e.g. Port number), and ommissions that need to be fixed) - -docker pull classtranscribe/latextranscribe:latest - -docker run -i -p 8080:8080 -t latextranscribe - -## Building Locally - -### Local build with Docker +## Quick Start +### Backend +- To develop locally, [download the ML models](#downloading-the-ml-models-for-pipeline), then [setup the backend/frontend environment without Docker](#local-build-without-docker). +- To build a full Docker image, [download the ML models](#downloading-the-ml-models-for-pipeline), then [build the backend (CPU / GPU) and frontend images](#building-docker-image-locally). +## Running the official DockerHub image +- Only up-to-date with the main branch. ```sh -docker build -t latextranscribe . - -docker run -p 127.0.0.1:8000:8000 latextranscribe +docker pull classtranscribe/latextranscribe:latest +docker run -i -p 8080:80 -t latextranscribe ``` -Then open http://127.0.0.1:8000 in your favorite browser - -### Local build without docker - -(This is sketch and these instructions are untested - assume there are typos, errors, guesses, and ommissions that need to be fixed) - - -Assuming you have python3.11 installed. Note these instructions essentialy mirror the steps in Dockerfile. - -````sh -python3.11 -m venv venv -source ./venv/bin/activate - -pip install --upgrade pip -pip install -r requirements.txt -Hmm todo - -```` - +## Downloading the ML models for Pipeline +- Recommended: Install the [uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) package manager. +- First, download the ML models (warning: large download). + - With `uv` (no dependencies required thanks to [inline script dependencies](https://docs.astral.sh/uv/guides/scripts/#declaring-script-dependencies)): + ```sh + cd server && uv run download_models.py + ``` + - Or with `pip`: + ```sh + pip install huggingface-hub + cd server && python download_models.py + ``` +## Building Docker Image Locally +### Backend +- Build and run the backend Docker image: + - CPU: + ```sh + docker build -t latextranscribe -f server/Dockerfile.cpu ./server/ + docker run --name latextranscribe --rm -i -p 8081:80 -t latextranscribe + ``` + - GPU (CUDA >= 12.4): + ```sh + docker build -t latextranscribe -f server/Dockerfile.gpu ./server/ + docker run --name latextranscribe --rm --gpus '"device=0"' -i -p 8081:80 -t latextranscribe + ``` +- Go to `http://localhost:8081` and you should now see `The server is running!` +### Frontend +- Build and run the frontend Docker image: + ```sh + docker build -t latextranscribe-frontend -f frontend/Dockerfile ./frontend/ + docker run --name latextranscribe-frontend --rm -i -p 8080:80 -t latextranscribe-frontend + ``` +- Go to `http://localhost:8080` and you should now see the frontend. + +## Developing Locally +- There are two options to develop locally. + ### Docker Compose Watch + - Run in the project root: + ```sh + docker compose up --build --watch + ``` + - Go to `http://localhost:8080` and you should now see the frontend. + ### Local build without Docker + #### Backend + - Switch into the `server` directory: + ```sh + cd server/ + ``` + - Create a virtual environment and install the dependencies: + - CPU: + ```sh + uv venv --seed # creates the virtual environment + uv sync --extra cpu --no-install-package detectron2 --no-install-package struct-eqtable # installs dependencies other than the two source packages + uv sync --extra cpu # installs the two source packages + ``` + - GPU (CUDA >= 12.4): + ```sh + uv venv --seed # creates the virtual environment + uv sync --extra cu124 --no-install-package detectron2 --no-install-package struct-eqtable # installs dependencies other than the two source packages + uv sync --extra cu124 # installs the two source packages + ``` + - Run the server (`uv run` runs `python` in the virtual environment): + ```sh + uv run uvicorn app:app --host 0.0.0.0 --port 8081 + ``` + - TESTING – Or run the ML pipeline: + ```sh + uv run main.py + ``` + #### Frontend + - Switch into the `frontend` directory: + ```sh + cd frontend/ + ``` + - Install the dependencies: + ```sh + npm install + ``` + - Run the development server: + ```sh + npm run dev + ``` + - Go to `http://localhost:5173` and you should now see the frontend. + +## Others +- To add a project dependency: [`uv add`](https://docs.astral.sh/uv/reference/cli/#uv-add) +- To sync the project dependencies again: `uv sync --extra cpu` or `uv sync --extra cu124` \ No newline at end of file diff --git a/docker-compose.yaml b/docker-compose.yaml new file mode 100644 index 0000000..77f3243 --- /dev/null +++ b/docker-compose.yaml @@ -0,0 +1,30 @@ +services: + backend: + build: + context: ./server + dockerfile: Dockerfile.cpu + image: latextranscribe + ports: + - 8081:80 + command: ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "80", "--reload"] + develop: + watch: + - action: sync + path: ./server + target: /app + ignore: + - .venv/ + frontend: + build: + context: ./frontend + # https://docs.docker.com/reference/compose-file/build/#target + target: dev + image: latextranscribe-frontend + ports: + - 8080:5173 + command: ["npm", "run", "dev"] + develop: + watch: + - action: sync + path: ./frontend/src + target: /app/src diff --git a/frontend/.dockerignore b/frontend/.dockerignore new file mode 100644 index 0000000..b512c09 --- /dev/null +++ b/frontend/.dockerignore @@ -0,0 +1 @@ +node_modules \ No newline at end of file diff --git a/frontend/.env.development b/frontend/.env.development new file mode 100644 index 0000000..8ad3903 --- /dev/null +++ b/frontend/.env.development @@ -0,0 +1 @@ +VITE_ROOT_API="http://localhost:8081" \ No newline at end of file diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..8ee54e8 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,30 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +.DS_Store +dist +dist-ssr +coverage +*.local + +/cypress/videos/ +/cypress/screenshots/ + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + +*.tsbuildinfo diff --git a/frontend/.vscode/extensions.json b/frontend/.vscode/extensions.json new file mode 100644 index 0000000..a7cea0b --- /dev/null +++ b/frontend/.vscode/extensions.json @@ -0,0 +1,3 @@ +{ + "recommendations": ["Vue.volar"] +} diff --git a/frontend/Dockerfile b/frontend/Dockerfile new file mode 100644 index 0000000..ecf0e08 --- /dev/null +++ b/frontend/Dockerfile @@ -0,0 +1,17 @@ +FROM node:lts-alpine AS base +WORKDIR /app +COPY package.json package-lock.json ./ +RUN npm install +COPY . . + +FROM base AS dev +EXPOSE 80 +CMD ["npm", "run", "dev"] + +FROM base AS build +RUN npm run build + +FROM joseluisq/static-web-server:latest AS prod +COPY --from=build /app/dist /app +EXPOSE 80 +CMD ["--port", "80", "--root", "/app"] \ No newline at end of file diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000..e9d7061 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,19 @@ +# Frontend + +## Project Setup + +```sh +npm install +``` + +### Compile and Hot-Reload for Development + +```sh +npm run dev +``` + +### Compile and Minify for Production + +```sh +npm run build +``` diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..30a9752 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + LatexTranscribe + + +
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"latextranscribe-frontend", + "version": "1.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview" + }, + "dependencies": { + "axios": "^1.7.9", + "vue": "^3.5.13", + "vue-router": "^4.5.0" + }, + "devDependencies": { + "@vitejs/plugin-vue": "^5.2.1", + "vite": "^6.0.11", + "vite-plugin-vue-devtools": "^7.7.0" + } +} \ No newline at end of file diff --git a/frontend/public/favicon.ico b/frontend/public/favicon.ico new file mode 100644 index 0000000..df36fcf Binary files /dev/null and b/frontend/public/favicon.ico differ diff --git a/frontend/src/App.vue b/frontend/src/App.vue new file mode 100644 index 0000000..b3d26c0 --- /dev/null +++ b/frontend/src/App.vue @@ -0,0 +1,205 @@ + + + + + diff --git a/frontend/src/assets/base.css b/frontend/src/assets/base.css new file mode 100644 index 0000000..8816868 --- /dev/null +++ b/frontend/src/assets/base.css @@ -0,0 +1,86 @@ +/* color palette from */ +:root { + --vt-c-white: #ffffff; + --vt-c-white-soft: #f8f8f8; + --vt-c-white-mute: #f2f2f2; + + --vt-c-black: #181818; + --vt-c-black-soft: #222222; + --vt-c-black-mute: #282828; + + --vt-c-indigo: #2c3e50; + + --vt-c-divider-light-1: rgba(60, 60, 60, 0.29); + --vt-c-divider-light-2: rgba(60, 60, 60, 0.12); + --vt-c-divider-dark-1: rgba(84, 84, 84, 0.65); + --vt-c-divider-dark-2: rgba(84, 84, 84, 0.48); + + --vt-c-text-light-1: var(--vt-c-indigo); + --vt-c-text-light-2: rgba(60, 60, 60, 0.66); + --vt-c-text-dark-1: var(--vt-c-white); + --vt-c-text-dark-2: rgba(235, 235, 235, 0.64); +} + +/* semantic color variables for this project */ +:root { + --color-background: var(--vt-c-white); + --color-background-soft: var(--vt-c-white-soft); + --color-background-mute: var(--vt-c-white-mute); + + --color-border: var(--vt-c-divider-light-2); + --color-border-hover: var(--vt-c-divider-light-1); + + --color-heading: var(--vt-c-text-light-1); + --color-text: var(--vt-c-text-light-1); + + --section-gap: 160px; +} + +@media (prefers-color-scheme: dark) { + :root { + --color-background: var(--vt-c-black); + --color-background-soft: var(--vt-c-black-soft); + --color-background-mute: var(--vt-c-black-mute); + + --color-border: var(--vt-c-divider-dark-2); + --color-border-hover: var(--vt-c-divider-dark-1); + + --color-heading: var(--vt-c-text-dark-1); + --color-text: var(--vt-c-text-dark-2); + } +} + +*, +*::before, +*::after { + box-sizing: border-box; + margin: 0; + font-weight: normal; +} + +body { + min-height: 100vh; + color: var(--color-text); + background: var(--color-background); + transition: + color 0.5s, + background-color 0.5s; + line-height: 1.6; + font-family: + Inter, + -apple-system, + BlinkMacSystemFont, + 'Segoe UI', + Roboto, + Oxygen, + Ubuntu, + Cantarell, + 'Fira Sans', + 'Droid Sans', + 'Helvetica Neue', + sans-serif; + font-size: 15px; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} diff --git a/frontend/src/assets/logo.svg b/frontend/src/assets/logo.svg new file mode 100644 index 0000000..7565660 --- /dev/null +++ b/frontend/src/assets/logo.svg @@ -0,0 +1 @@ + diff --git a/frontend/src/assets/main.css b/frontend/src/assets/main.css new file mode 100644 index 0000000..36fb845 --- /dev/null +++ b/frontend/src/assets/main.css @@ -0,0 +1,35 @@ +@import './base.css'; + +#app { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + font-weight: normal; +} + +a, +.green { + text-decoration: none; + color: hsla(160, 100%, 37%, 1); + transition: 0.4s; + padding: 3px; +} + +@media (hover: hover) { + a:hover { + background-color: hsla(160, 100%, 37%, 0.2); + } +} + +@media (min-width: 1024px) { + body { + display: flex; + place-items: center; + } + + #app { + display: grid; + grid-template-columns: 1fr 1fr; + padding: 0 2rem; + } +} diff --git a/frontend/src/images/sample.png b/frontend/src/images/sample.png new file mode 100644 index 0000000..3caf3d5 Binary files /dev/null and b/frontend/src/images/sample.png differ diff --git a/frontend/src/images/udlmock1result.png b/frontend/src/images/udlmock1result.png new file mode 100644 index 0000000..62ef472 Binary files /dev/null and b/frontend/src/images/udlmock1result.png differ diff --git a/frontend/src/main.js b/frontend/src/main.js new file mode 100644 index 0000000..dbbcf11 --- /dev/null +++ b/frontend/src/main.js @@ -0,0 +1,7 @@ + +import { createApp } from 'vue' +import App from './App.vue' + +const app = createApp(App) + +app.mount('#app') diff --git a/frontend/vite.config.js b/frontend/vite.config.js new file mode 100644 index 0000000..ac65271 --- /dev/null +++ b/frontend/vite.config.js @@ -0,0 +1,21 @@ +import { fileURLToPath, URL } from 'node:url' + +import { defineConfig } from 'vite' +import vue from '@vitejs/plugin-vue' +import vueDevTools from 'vite-plugin-vue-devtools' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [ + vue(), + vueDevTools(), + ], + resolve: { + alias: { + '@': fileURLToPath(new URL('./src', import.meta.url)) + }, + }, + server: { + host: true, + } +}) diff --git a/helloworld.py b/helloworld.py deleted file mode 100644 index 4f65480..0000000 --- a/helloworld.py +++ /dev/null @@ -1,18 +0,0 @@ -# Example use: (see https://pypi.org/project/uvicorn/) -# uvicorn helloworld:app - - -async def app(scope, receive, send): - assert scope['type'] == 'http' - - await send({ - 'type': 'http.response.start', - 'status': 200, - 'headers': [ - (b'content-type', b'text/plain'), - ], - }) - await send({ - 'type': 'http.response.body', - 'body': b'Hello, world!', - }) diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index e3ac794..0000000 --- a/requirements.txt +++ /dev/null @@ -1,250 +0,0 @@ -absl-py==2.1.0 -accelerate==1.1.1 -addict==2.4.0 -aiofiles==23.2.1 -aiohappyeyeballs==2.4.3 -aiohttp==3.10.10 -aiosignal==1.3.1 -albucore==0.0.20 -albumentations==1.4.21 -annotated-types==0.7.0 -antlr4-python3-runtime==4.9.3 -anyio==3.7.1 -astor==0.8.1 -async-timeout==4.0.3 -attrdict==2.0.1 -attrs==24.2.0 -azure-core==1.32.0 -azure-identity==1.19.0 -babel==2.16.0 -bce-python-sdk==0.9.23 -beautifulsoup4==4.12.3 -black==25.1.0 -blinker==1.8.2 -braceexpand==0.1.7 -cachetools==5.5.0 -certifi==2024.8.30 -cffi==1.17.1 -charset-normalizer==3.4.0 -click==8.1.7 -cloudpickle==3.1.0 -contourpy==1.3.0 -cryptography==43.0.3 -cssselect==1.2.0 -cssutils==2.11.1 -cycler==0.12.1 -Cython==3.0.11 -datasets==3.1.0 -decorator==5.1.1 -defusedxml==0.7.1 -# TODO detectron2 -dill==0.3.8 -diskcache==5.6.3 -distro==1.9.0 -doclayout_yolo==0.0.2 -easyocr==1.7.2 -einops==0.8.0 -et_xmlfile==2.0.0 -eva-decord==0.6.1 -eval_type_backport==0.2.0 -evaluate==0.4.3 -exceptiongroup==1.2.2 -fairscale==0.4.13 -fastapi==0.115.4 -ffmpy==0.4.0 -filelock==3.16.1 -fire==0.7.0 -Flask==3.0.3 -flask-babel==4.0.0 -fonttools==4.54.1 -frozenlist==1.5.0 -fsspec==2024.9.0 -ftfy==6.3.1 -future==1.0.0 -fvcore==0.1.5.post20221221 -gradio==5.6.0 -gradio_client==1.4.3 -grpcio==1.70.0 -h11==0.14.0 -httpcore==1.0.6 -httpx==0.27.2 -huggingface-hub==0.26.2 -hydra-core==1.3.2 -idna==3.10 -imageio==2.36.0 -imgaug==0.4.0 -importlib_metadata==8.5.0 -interegular==0.3.3 -iopath==0.1.9 -itsdangerous==2.2.0 -Jinja2==3.1.4 -jiter==0.7.0 -joblib==1.4.2 -jsonschema==4.23.0 -jsonschema-specifications==2024.10.1 -kiwisolver==1.4.7 -lark==1.2.2 -lazy_loader==0.4 -Levenshtein==0.26.1 -lightning==2.4.0 -lightning-utilities==0.11.8 -llvmlite==0.43.0 -lmdb==1.5.1 -#TODO lmdeploy==0.6.2.post1 -lxml==5.3.0 -Markdown==3.7 -markdown-it-py==3.0.0 -MarkupSafe==2.1.5 -matplotlib==3.9.2 -mdurl==0.1.2 -mmengine-lite==0.10.5 -more-itertools==10.5.0 -mpmath==1.3.0 -msal==1.31.1 -msal-extensions==1.2.0 -multidict==6.1.0 -multiprocess==0.70.16 -munch==4.0.0 -mypy-extensions==1.0.0 -nest-asyncio==1.6.0 -networkx==3.4.2 -ninja==1.11.1.1 -nltk==3.9.1 -numba==0.60.0 -numpy==1.26.4 -# TODO -# nvidia-cublas-cu12==12.1.3.1 -# nvidia-cuda-cupti-cu12==12.1.105 -# nvidia-cuda-nvrtc-cu12==12.1.105 -# nvidia-cuda-runtime-cu12==12.1.105 -# nvidia-cudnn-cu12==8.9.2.26 -# nvidia-cufft-cu12==11.0.2.54 -# nvidia-curand-cu12==10.3.2.106 -# nvidia-cusolver-cu12==11.4.5.107 -# nvidia-cusparse-cu12==12.1.0.106 -# nvidia-nccl-cu12==2.20.5 -# nvidia-nvjitlink-cu12==12.6.77 -# nvidia-nvtx-cu12==12.1.105 -omegaconf==2.3.0 -openai==1.3.5 -opencv-contrib-python==4.6.0.66 -opencv-python==4.6.0.66 -opencv-python-headless==4.10.0.84 -openpyxl==3.1.5 -opt-einsum==3.3.0 -ordered-set==4.1.0 -orjson==3.10.10 -outlines==0.0.46 -packaging==24.1 -paddleocr==2.7.3 -paddlepaddle==2.6.2 -# TODO paddlepaddle-gpu==2.6.2 -pandas==2.2.3 -pathspec==0.12.1 -pdf2docx==0.5.8 -peft==0.11.1 -pillow==11.0.0 -platformdirs==4.3.6 -portalocker==2.10.1 -premailer==3.10.0 -propcache==0.2.0 -protobuf==5.28.3 -psutil==6.1.0 -py-cpuinfo==9.0.0 -pyairports==2.1.1 -pyarrow==18.0.0 -pyclipper==1.3.0.post6 -pycocotools==2.0.8 -pycountry==24.6.1 -pycparser==2.22 -pycryptodome==3.21.0 -pydantic==2.9.2 -pydantic_core==2.23.4 -pydub==0.25.1 -Pygments==2.18.0 -PyJWT==2.10.0 -PyLaTeX==1.4.2 -PyMuPDF==1.24.13 -pynvml==11.5.3 -pyparsing==3.2.0 -pypdf==5.1.0 -pypdfium2==4.30.0 -python-bidi==0.6.3 -python-dateutil==2.9.0.post0 -python-docx==1.1.2 -python-Levenshtein==0.26.1 -python-multipart==0.0.12 -pytorch-lightning==2.4.0 -pytz==2024.2 -PyYAML==6.0.2 -RapidFuzz==3.10.1 -rarfile==4.2 -referencing==0.35.1 -regex==2024.9.11 -requests==2.32.3 -rich==13.9.4 -rpds-py==0.21.0 -ruamel.yaml==0.18.6 -ruamel.yaml.clib==0.2.12 -ruff==0.8.0 -safehttpx==0.1.1 -safetensors==0.4.5 -scikit-image==0.24.0 -scipy==1.14.1 -sconf==0.2.5 -seaborn==0.13.2 -semantic-version==2.10.0 -sentencepiece==0.2.0 -shapely==2.0.6 -shellingham==1.5.4 -shortuuid==1.0.13 -simsimd==5.9.9 -six==1.16.0 -sniffio==1.3.1 -soupsieve==2.6 -starlette==0.41.2 -stringzilla==3.10.7 -struct-eqtable==0.3.2 -supervision==0.18.0 -sympy==1.13.3 -tabulate==0.9.0 -tensorboard==2.18.0 -tensorboard-data-server==0.7.2 -termcolor==2.5.0 -thop==0.1.1.post2209072238 -tifffile==2024.9.20 -tiktoken==0.8.0 -timm==0.9.16 -tokenizers==0.19.1 -tomli==2.0.2 -tomlkit==0.12.0 -#OSX Nope torch==2.3.1 -torch==2.2.2 -torchmetrics==1.5.1 -#OSX nope torchtext==0.18.0 -torchtext==0.17.2 -#OSX Nope torchvision==0.18.1 -torchvision==0.17.2 -tqdm==4.66.6 -transformers==4.42.4 -#OSX Nope triton==2.3.1 - -typer==0.13.1 -typing_extensions==4.12.2 -tzdata==2024.2 -ultralytics==8.1.24 -ultralytics-thop==2.0.10 -unimernet==0.2.1 -urllib3==2.2.3 -uvicorn==0.32.0 -visualdl==2.5.3 -Wand==0.6.13 -wcwidth==0.2.13 -webdataset==0.2.100 -websockets==12.0 -Werkzeug==3.1.0 -xxhash==3.5.0 -yacs==0.1.8 -yapf==0.40.2 -yarl==1.17.1 -zipp==3.20.2 diff --git a/server/.dockerignore b/server/.dockerignore new file mode 100644 index 0000000..e1e0463 --- /dev/null +++ b/server/.dockerignore @@ -0,0 +1,4 @@ +.venv +.cache +inputs +outputs \ No newline at end of file diff --git a/server/.python-version b/server/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/server/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/server/Dockerfile.cpu b/server/Dockerfile.cpu new file mode 100644 index 0000000..542e0b4 --- /dev/null +++ b/server/Dockerfile.cpu @@ -0,0 +1,106 @@ +# syntax=docker/dockerfile:1 +# Reference: https://hynek.me/articles/docker-uv/ +FROM python:3.11-slim-bookworm AS build +SHELL ["sh", "-exc"] + +### Start build prep. +### This should be a separate build container for better reuse. +RUN apt-get update -qy && \ + apt-get install -qyy \ + -o APT::Install-Recommends=false \ + -o APT::Install-Suggests=false \ + git \ + clang \ + ca-certificates + +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +# - Silence uv complaining about not being able to use hard links, +# - tell uv to byte-compile packages for faster application startups, +# - prevent uv from accidentally downloading isolated Python builds, +# - pick a Python, +# - and finally declare `/app` as the target for `uv sync`. +ENV UV_LINK_MODE=copy \ + UV_COMPILE_BYTECODE=1 \ + UV_PYTHON_DOWNLOADS=never \ + UV_PYTHON=python3.11 \ + UV_PROJECT_ENVIRONMENT=/app/.venv + +# - Use clang as the default C compiler +# - Use clang++ as the default C++ compiler +ENV CC=clang \ + CXX=clang++ + +### End build prep -- this is where your app Dockerfile should start. + +# Synchronize DEPENDENCIES without the application itself. +# This layer is cached until uv.lock or pyproject.toml change, which are +# only temporarily mounted into the build container since we don't need +# them in the production one. +RUN uv venv --seed +RUN --mount=type=cache,target=/root/.cache \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + uv sync \ + --extra cpu \ + --locked \ + --no-dev \ + --no-install-project \ + --no-install-package detectron2 \ + --no-install-package struct-eqtable + +# Install the problematic source packages separately +RUN --mount=type=cache,target=/root/.cache \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + uv sync \ + --extra cpu \ + --locked \ + --no-dev \ + --no-install-project + +########################################################################## + +FROM python:3.11-slim-bookworm +SHELL ["sh", "-exc"] + +# Optional: add the application virtualenv to search path. +ENV PATH=/app/.venv/bin:$PATH + +# Don't run your app as root. +RUN groupadd -r app && useradd -r -d /app -g app -N app + +ENTRYPOINT ["/docker-entrypoint.sh"] +# See . +STOPSIGNAL SIGINT + +# Note how the runtime dependencies differ from build-time ones. +# Notably, there is no uv either! +RUN apt-get update -qy && apt-get install -qyy \ + -o APT::Install-Recommends=false \ + -o APT::Install-Suggests=false \ + libpcre3 \ + libxml2 \ + libgl1 \ + libglib2.0-0 \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* + +COPY docker-entrypoint.sh / +RUN chmod +x /docker-entrypoint.sh + +# Copy the pre-built `/app` directory to the runtime container +# and change the ownership to user app and group app in one step. +COPY --from=build --chown=app:app /app /app + +# Copy files needed to run the Python application +COPY models/ /app/models/ + +COPY configs/ /app/configs/ +COPY src/ /app/src/ +COPY app.py example-server.py main.py /app/ + +USER app +WORKDIR /app + +CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "80"] \ No newline at end of file diff --git a/server/Dockerfile.gpu b/server/Dockerfile.gpu new file mode 100644 index 0000000..bad337b --- /dev/null +++ b/server/Dockerfile.gpu @@ -0,0 +1,106 @@ +# syntax=docker/dockerfile:1 +# Reference: https://hynek.me/articles/docker-uv/ +FROM python:3.11-slim-bookworm AS build +SHELL ["sh", "-exc"] + +### Start build prep. +### This should be a separate build container for better reuse. +RUN apt-get update -qy && \ + apt-get install -qyy \ + -o APT::Install-Recommends=false \ + -o APT::Install-Suggests=false \ + git \ + clang \ + ca-certificates + +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +# - Silence uv complaining about not being able to use hard links, +# - tell uv to byte-compile packages for faster application startups, +# - prevent uv from accidentally downloading isolated Python builds, +# - pick a Python, +# - and finally declare `/app` as the target for `uv sync`. +ENV UV_LINK_MODE=copy \ + UV_COMPILE_BYTECODE=1 \ + UV_PYTHON_DOWNLOADS=never \ + UV_PYTHON=python3.11 \ + UV_PROJECT_ENVIRONMENT=/app/.venv + +# - Use clang as the default C compiler +# - Use clang++ as the default C++ compiler +ENV CC=clang \ + CXX=clang++ + +### End build prep -- this is where your app Dockerfile should start. + +# Synchronize DEPENDENCIES without the application itself. +# This layer is cached until uv.lock or pyproject.toml change, which are +# only temporarily mounted into the build container since we don't need +# them in the production one. +RUN uv venv --seed +RUN --mount=type=cache,target=/root/.cache \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + uv sync \ + --extra cu124 \ + --locked \ + --no-dev \ + --no-install-project \ + --no-install-package detectron2 \ + --no-install-package struct-eqtable + +# Install the problematic source packages separately +RUN --mount=type=cache,target=/root/.cache \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + uv sync \ + --extra cu124 \ + --locked \ + --no-dev \ + --no-install-project + +########################################################################## + +FROM nvidia/cuda:12.6.3-base-ubuntu22.04 +SHELL ["sh", "-exc"] + +# Optional: add the application virtualenv to search path. +ENV PATH=/app/.venv/bin:$PATH + +# Don't run your app as root. +RUN groupadd -r app && useradd -r -d /app -g app -N app + +ENTRYPOINT ["/docker-entrypoint.sh"] +# See . +STOPSIGNAL SIGINT + +# Note how the runtime dependencies differ from build-time ones. +# Notably, there is no uv either! +RUN apt-get update -qy && apt-get install -qyy \ + -o APT::Install-Recommends=false \ + -o APT::Install-Suggests=false \ + libpcre3 \ + libxml2 \ + libgl1 \ + libglib2.0-0 \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* + +COPY docker-entrypoint.sh / +RUN chmod +x /docker-entrypoint.sh + +# Copy the pre-built `/app` directory to the runtime container +# and change the ownership to user app and group app in one step. +COPY --from=build --chown=app:app /app /app + +# Copy files needed to run the Python application +COPY models/ /app/models/ + +COPY configs/ /app/configs/ +COPY src/ /app/src/ +COPY app.py example-server.py main.py /app/ + +USER app +WORKDIR /app + +CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "80"] \ No newline at end of file diff --git a/server/app.py b/server/app.py index 07e66db..084c44e 100644 --- a/server/app.py +++ b/server/app.py @@ -1,5 +1,6 @@ from fastapi import FastAPI, File, UploadFile from fastapi.responses import Response +from fastapi.middleware.cors import CORSMiddleware # Import CORS middleware from PIL import Image import io @@ -8,21 +9,40 @@ app = FastAPI() +# Allow CORS only from frontend running on localhost:5173 +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Allow frontend only from localhost:5173 + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + config_path = "./configs/config.yaml" -pipeline = Pipeline(load_config(config_path)) +# TODO: Fix image paths +# pipeline = Pipeline(load_config(config_path), "", "") + def process_image(image: Image.Image) -> Image.Image: """Example image processing function (grayscale conversion).""" return image.convert("L") + +@app.get("/") +async def home(): + return "The server is running!" + + @app.post("/upload/") async def upload_image(file: UploadFile = File(...)): try: # Read image bytes from the uploaded file image_bytes = await file.read() + print(f"File size: {len(image_bytes)} bytes") # Convert the bytes to a PIL Image image = Image.open(io.BytesIO(image_bytes)) + print("Image verified successfully!") # Process the image processed_image = process_image(image) @@ -34,6 +54,6 @@ async def upload_image(file: UploadFile = File(...)): img_bytes.seek(0) return Response(content=img_bytes.read(), media_type="image/png") - + except Exception as e: return Response(status=500) diff --git a/server/configs/app_config.yaml b/server/configs/app_config.yaml index 2278678..41dc019 100644 --- a/server/configs/app_config.yaml +++ b/server/configs/app_config.yaml @@ -1,5 +1,3 @@ -input_path: null -output_path: null models: formula_detection: model_name: formula_detection_yolo diff --git a/server/configs/config.yaml b/server/configs/config.yaml index 39ccaad..41dc019 100644 --- a/server/configs/config.yaml +++ b/server/configs/config.yaml @@ -1,5 +1,3 @@ -input_path: /work/hdd/bdsh/dzhao3/data/sample -output_path: /work/hdd/bdsh/dzhao3/output models: formula_detection: model_name: formula_detection_yolo diff --git a/server/configs/test.yaml b/server/configs/test.yaml index 44de430..a9ff72a 100644 --- a/server/configs/test.yaml +++ b/server/configs/test.yaml @@ -1,5 +1,3 @@ -input_path: /work/hdd/bdsh/dzhao3/data/sample -output_path: /work/hdd/bdsh/dzhao3/output models: formula_detection: model_name: formula_detection_yolo diff --git a/server/configs/unimernet.yaml b/server/configs/unimernet.yaml index a2cda58..e39cfed 100644 --- a/server/configs/unimernet.yaml +++ b/server/configs/unimernet.yaml @@ -2,13 +2,13 @@ model: arch: unimernet model_type: unimernet model_config: - model_name: ./models/unimernet_tiny + model_name: ./models/unimernet_base max_seq_len: 1536 load_pretrained: True - pretrained: './models/unimernet_tiny/pytorch_model.pth' + pretrained: './models/unimernet_base/unimernet_base.pth' tokenizer_config: - path: ./models/unimernet_tiny + path: ./models/unimernet_base datasets: formula_rec_eval: @@ -18,7 +18,6 @@ datasets: image_size: - 192 - 672 - run: runner: runner_iter task: unimernet_train diff --git a/server/docker-entrypoint.sh b/server/docker-entrypoint.sh new file mode 100755 index 0000000..5257476 --- /dev/null +++ b/server/docker-entrypoint.sh @@ -0,0 +1,3 @@ +#!/bin/sh +# TODO: setup +exec "$@" diff --git a/server/download_models.py b/server/download_models.py index db7dc53..45b5774 100644 --- a/server/download_models.py +++ b/server/download_models.py @@ -1,6 +1,20 @@ +# /// script +# requires-python = ">=3.11" +# dependencies = [ +# "huggingface-hub[hf-transfer]", +# ] +# /// from huggingface_hub import snapshot_download -snapshot_download(repo_id='opendatalab/pdf-extract-kit-1.0', local_dir='./', allow_patterns='models/Layout/*') -snapshot_download(repo_id='opendatalab/pdf-extract-kit-1.0', local_dir='./', allow_patterns='models/MFD/*') -snapshot_download(repo_id='opendatalab/pdf-extract-kit-1.0', local_dir='./', allow_patterns='models/MFR/*') -snapshot_download(repo_id='opendatalab/pdf-extract-kit-1.0', local_dir='./', allow_patterns='models/TabRec/*') \ No newline at end of file +print("Downloading models...") +snapshot_download( + repo_id="opendatalab/pdf-extract-kit-1.0", + local_dir="./", + allow_patterns=[ + "models/Layout/*", + "models/MFD/*", + "models/MFR/unimernet_base/*", + "models/TabRec/*", + ], +) +print("Download complete.") diff --git a/server/example-server.py b/server/example-server.py new file mode 100644 index 0000000..507246c --- /dev/null +++ b/server/example-server.py @@ -0,0 +1,8 @@ +from fastapi import FastAPI + +app = FastAPI() + + +@app.get("/") +def read_root(): + return "Hello, World!" diff --git a/server/main.py b/server/main.py index 65b657b..3addf98 100644 --- a/server/main.py +++ b/server/main.py @@ -1,28 +1,56 @@ import argparse +import os import yaml import warnings + from src.pipeline import Pipeline + def parse_args(): - parser = argparse.ArgumentParser(description="Run a task with a given configuration file.") - parser.add_argument('--config', type=str, default="./configs/config.yaml", help='Path to the configuration files.') - parser.add_argument('--output_dir', type=str, default="outputs/pipeline", help='Path to the configuration files.') + def dir_or_file_path(path): + if os.path.exists(path): + return path + else: + raise argparse.ArgumentTypeError(f"{path} is not a valid path") + + parser = argparse.ArgumentParser( + description="Run a task with a given configuration file." + ) + parser.add_argument( + "--config", + type=str, + default="./configs/config.yaml", + help="Path to the main configuration file.", + ) + parser.add_argument( + "--input_path", + type=dir_or_file_path, + default="inputs/", + help="Path to the input directory / file.", + ) + parser.add_argument( + "--output_path", + type=dir_or_file_path, + default="outputs/", + help="Path to the output directory.", + ) return parser.parse_args() + def load_config(config_path): if config_path is None: warnings.warn( - ("Configuration path is None. Please provide a valid configuration file path. ") + "Configuration path is None. Please provide a valid configuration file path. " ) return None - - with open(config_path, 'r') as file: + + with open(config_path, "r") as file: config = yaml.safe_load(file) return config + if __name__ == "__main__": args = parse_args() config = load_config(args.config) - pipeline = Pipeline(config) - pipeline.predict() - + pipeline = Pipeline(config, args.input_path, args.output_path) + pipeline.predict(save=True) diff --git a/server/pyproject.toml b/server/pyproject.toml new file mode 100644 index 0000000..a150fb9 --- /dev/null +++ b/server/pyproject.toml @@ -0,0 +1,82 @@ +[project] +name = "latextranscribe" +version = "0.1.0" +description = "Latex Transcribe" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "detectron2", + "fastapi[standard]>=0.115.8", + "flask>=3.1.0", + "huggingface-hub>=0.28.1", + "matplotlib>=3.10.0", + "opencv-python-headless>=4.11.0.86", + "paddleocr>=2.9.1", + "paddlepaddle>=2.6.2", + "struct-eqtable", + "supervision>=0.24.0", + "ultralytics>=8.3.72", + "unimernet>=0.2.3", +] + +# CPU install: uv sync --extra cpu +# GPU install: uv sync --extra cu124 +# https://docs.astral.sh/uv/guides/integration/pytorch/#configuring-accelerators-with-optional-dependencies +[project.optional-dependencies] +cpu = ["torch~=2.6.0", "torchvision~=0.21.0"] +cu124 = ["torch~=2.6.0", "torchvision~=0.21.0"] + +[tool.uv] +conflicts = [[{ extra = "cpu" }, { extra = "cu124" }]] +no-build-isolation-package = ["detectron2", "struct-eqtable"] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cpu", extra = "cpu" }, + { index = "pytorch-cu124", extra = "cu124" }, +] +torchvision = [ + { index = "pytorch-cpu", extra = "cpu" }, + { index = "pytorch-cu124", extra = "cu124" }, +] +detectron2 = { git = "https://github.com/facebookresearch/detectron2.git" } +struct-eqtable = { git = "https://github.com/UniModal4Reasoning/StructEqTable-Deploy.git" } + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cu124" +url = "https://download.pytorch.org/whl/cu124" +explicit = true + +[[tool.uv.dependency-metadata]] +name = "detectron2" +version = "0.6" +# https://github.com/facebookresearch/detectron2/blob/9604f5995cc628619f0e4fd913453b4d7d61db3f/setup.py#L162 +requires-dist = [ + "torch", + "Pillow>=7.1", + "matplotlib", + "pycocotools>=2.0.2", + "termcolor>=1.1", + "yacs>=0.1.8", + "tabulate", + "cloudpickle", + "tqdm>4.29.0", + "tensorboard", + "fvcore>=0.1.5,<0.1.6", + "iopath>=0.1.7,<0.1.10", + "dataclasses; python_version<'3.7'", + "omegaconf>=2.1,<2.4", + "hydra-core>=1.1", + "black", + "packaging", +] + +[[tool.uv.dependency-metadata]] +name = "struct-eqtable" +version = "0.3.3" +requires-dist = ["torch", "transformers<=4.47", "accelerate", "einops"] diff --git a/requirements-Delu-firstpass.txt b/server/requirements-Delu-firstpass.txt similarity index 100% rename from requirements-Delu-firstpass.txt rename to server/requirements-Delu-firstpass.txt diff --git a/requirements-angrave-m1-extra-gpu-stuff.txt b/server/requirements-angrave-m1-extra-gpu-stuff.txt similarity index 100% rename from requirements-angrave-m1-extra-gpu-stuff.txt rename to server/requirements-angrave-m1-extra-gpu-stuff.txt diff --git a/requirements-angrave-osx-m1-play.txt b/server/requirements-angrave-osx-m1-play.txt similarity index 100% rename from requirements-angrave-osx-m1-play.txt rename to server/requirements-angrave-osx-m1-play.txt diff --git a/requirements-linux-gpudocker-build.txt b/server/requirements-linux-gpudocker-build.txt similarity index 100% rename from requirements-linux-gpudocker-build.txt rename to server/requirements-linux-gpudocker-build.txt diff --git a/server/src/image_object.py b/server/src/image_object.py index deb23b4..efff444 100644 --- a/server/src/image_object.py +++ b/server/src/image_object.py @@ -5,10 +5,12 @@ import json import torch + class PrettyFloat(float): def __repr__(self): return str(round(int(self * 100) / 100.0, 2)) - + + def pretty_floats(obj): if isinstance(obj, torch.Tensor): return list(map(pretty_floats, obj.tolist())) @@ -22,6 +24,7 @@ def pretty_floats(obj): PrettyFloat(obj) return obj + class ImageObject: def __init__(self, image_path=None, image=None, image_name=None): if image_path is not None: @@ -31,7 +34,7 @@ def __init__(self, image_path=None, image=None, image_name=None): else: self.image = image self.image_name = image_name - + self.image_filtered = cv2.cvtColor(np.array(self.image), cv2.COLOR_RGB2BGR) self.image_np = cv2.cvtColor(np.array(self.image), cv2.COLOR_RGB2BGR) self.results = [] @@ -40,29 +43,29 @@ def __init__(self, image_path=None, image=None, image_name=None): def get_orig_image(self): return self.image - + def get_curr_image(self): return Image.fromarray(self.image_filtered) - + def get_candidates(self): return self.candidates - + def filter_candidate(self, task, cls, score): # priority candidates since models are unique - if cls == 'table': + if cls == "table": return False - - if cls == 'formula' and task == 'formula_detection': + + if cls == "formula" and task == "formula_detection": return False - + return True - + def apply_bbox(self, box): x_min, y_min, x_max, y_max = map(int, box) crop = self.image_np[y_min:y_max, x_min:x_max] self.image_filtered[y_min:y_max, x_min:x_max] = np.asarray([0, 0, 0]) return Image.fromarray(crop) - + def create_candidates(self, task, boxes, classes, scores=None, filter_func=None): for i in range(len(classes)): box = boxes[i] @@ -81,7 +84,6 @@ def create_candidates(self, task, boxes, classes, scores=None, filter_func=None) continue self.candidates.append((box, cls, self.apply_bbox(box))) - def add_visualization(self, task, vis): self.visualizations.append((task, vis)) @@ -89,10 +91,14 @@ def add_visualization(self, task, vis): def save_visualizations(self, output_path): for i, (task, vis) in enumerate(self.visualizations): try: - cv2.imwrite(os.path.join(output_path, f"{self.image_name}_{task}_{i}.png"), vis) + cv2.imwrite( + os.path.join(output_path, f"{self.image_name}_{task}_{i}.png"), vis + ) except: try: - vis.save(os.path.join(output_path, f"{self.image_name}_{task}_{i}.png")) + vis.save( + os.path.join(output_path, f"{self.image_name}_{task}_{i}.png") + ) except: pass @@ -101,20 +107,29 @@ def add_results(self, task, result, cls=None, bbox=None): print(result) if type(result) == str: bbox = [str(round(int(x * 100) / 100.0, 2)) for x in bbox] - self.results.append(pretty_floats({'task': task, 'cls': cls, - 'bbox': bbox, 'text': result})) - + self.results.append( + pretty_floats({"task": task, "cls": cls, "bbox": bbox, "text": result}) + ) + elif type(result) == list: for i in range(len(result)): - bbox = [str(round(int(x * 100) / 100.0, 2)) for x in result[i]['bbox']] - self.results.append(pretty_floats({'task': task, 'cls': result[i]['cls'], - 'bbox': bbox, 'text': result[i]['text']})) + bbox = [str(round(int(x * 100) / 100.0, 2)) for x in result[i]["bbox"]] + self.results.append( + pretty_floats( + { + "task": task, + "cls": result[i]["cls"], + "bbox": bbox, + "text": result[i]["text"], + } + ) + ) def save_results(self, output_path): try: - with open(os.path.join(output_path, f"{self.image_name}_results.json"), 'w') as f: + with open( + os.path.join(output_path, f"{self.image_name}_results.json"), "w" + ) as f: json.dump(self.results, f) except Exception as e: print("Failed with", e) - - diff --git a/server/src/pipeline.py b/server/src/pipeline.py index 75952ed..23494de 100644 --- a/server/src/pipeline.py +++ b/server/src/pipeline.py @@ -1,28 +1,31 @@ -import cv2 -import os - from src.image_object import ImageObject from src.utils import get_image_paths from src.registry import MODEL_REGISTRY + # for registry purposes -from src.tasks import formula_detection, formula_recognition, layout_detection, omniparser, table_recognition +from src.tasks import ( + formula_detection, + formula_recognition, + layout_detection, + table_recognition, +) + class Pipeline: - def __init__(self, config): + def __init__(self, config, input_path: str, outut_path: str): self.models = {} - for task in config['models']: - ModelClass = MODEL_REGISTRY.get(config['models'][task]['model_name']) - self.models[task] = ModelClass(config['models'][task]) - + for task in config["models"]: + ModelClass = MODEL_REGISTRY.get(config["models"][task]["model_name"]) + self.models[task] = ModelClass(config["models"][task]) + self.images = {} - if config['input_path'] is not None: - image_paths = get_image_paths(config['input_path']) - for image_path in image_paths: - image_name = image_path.split("/")[-1].split(".")[0] - self.images[image_name] = ImageObject(image_path) + image_paths = get_image_paths(input_path) + for image_path in image_paths: + image_name = image_path.split("/")[-1].split(".")[0] + self.images[image_name] = ImageObject(image_path) - self.output_path = config['output_path'] + self.output_path = outut_path def add_image(self, name, image): self.images[name] = ImageObject(image=image, image_name=name) @@ -32,42 +35,32 @@ def detect_candidates(self, task): out = self.models[task].predict(self.images[image_name].get_curr_image()) print(out["results"]) self.images[image_name].add_visualization(task, out["vis"]) - self.images[image_name].create_candidates(task, out["results"]["boxes"], - out["results"]["classes"], - out["results"]["scores"]) + self.images[image_name].create_candidates( + task, + out["results"]["boxes"], + out["results"]["classes"], + out["results"]["scores"], + ) def transcribe_image(self): for image_name in self.images: image = self.images[image_name] candidates = image.get_candidates() - for (box, cls, crop) in candidates: - + for box, cls, crop in candidates: # should all be one of these two for now if cls in ["formula", "table"]: task = f"{cls}_recognition" else: - # task = "base_recognition" continue out = self.models[task].predict(crop) if out["vis"] is not None: image.add_visualization(task, out["vis"]) - image.add_results(task, out["results"], cls, box) - - try: - out = self.models["base_recognition"].predict(image.get_curr_image()) - if out["vis"] is not None: - image.add_visualization("base_recognition", out["vis"]) - image.add_results("base_recognition", out["results"]) - except: - pass - - + def predict(self, save=False): self.detect_candidates("layout_detection") self.detect_candidates("formula_detection") - # self.detect_candidates("layout_detection") self.transcribe_image() if save: @@ -79,4 +72,3 @@ def predict(self, save=False): self.images[image_name].save_results(self.output_path) else: return self.images - diff --git a/server/src/tasks/formula_detection.py b/server/src/tasks/formula_detection.py index b9fbd52..1eddcda 100644 --- a/server/src/tasks/formula_detection.py +++ b/server/src/tasks/formula_detection.py @@ -1,16 +1,10 @@ -import os -import cv2 import torch from ultralytics import YOLO from src.registry import MODEL_REGISTRY from src.utils import visualize_bbox -import numpy as np -from PIL import Image -from collections import defaultdict - -@MODEL_REGISTRY.register('formula_detection_yolo') +@MODEL_REGISTRY.register("formula_detection_yolo") class FormulaDetectionYOLO: def __init__(self, config): """ @@ -20,22 +14,21 @@ def __init__(self, config): config (dict): Configuration dictionary containing model parameters. """ # Mapping from class IDs to class names - self.id_to_names = { - 0: 'inline', - 1: 'isolated' - } + self.id_to_names = {0: "inline", 1: "isolated"} # Load the YOLO model from the specified path - self.model = YOLO(config['model_path']) + self.model = YOLO(config["model_path"]) # Set model parameters - self.img_size = config.get('img_size', 1280) - self.pdf_dpi = config.get('pdf_dpi', 200) - self.conf_thres = config.get('conf_thres', 0.25) - self.iou_thres = config.get('iou_thres', 0.45) - self.visualize = config.get('visualize', True) - self.device = config.get('device', 'cuda' if torch.cuda.is_available() else 'cpu') - self.batch_size = config.get('batch_size', 1) + self.img_size = config.get("img_size", 1280) + self.pdf_dpi = config.get("pdf_dpi", 200) + self.conf_thres = config.get("conf_thres", 0.25) + self.iou_thres = config.get("iou_thres", 0.45) + self.visualize = config.get("visualize", True) + self.device = config.get( + "device", "cuda" if torch.cuda.is_available() else "cpu" + ) + self.batch_size = config.get("batch_size", 1) def predict(self, image): """ @@ -49,19 +42,25 @@ def predict(self, image): Returns: list: List of prediction results. """ - result = self.model.predict(image, imgsz=self.img_size, conf=self.conf_thres, iou=self.iou_thres, verbose=False)[0] + result = self.model.predict( + image, + imgsz=self.img_size, + conf=self.conf_thres, + iou=self.iou_thres, + verbose=False, + )[0] if self.visualize: - boxes = result.__dict__['boxes'].xyxy - classes = result.__dict__['boxes'].cls - scores = result.__dict__['boxes'].conf - + boxes = result.__dict__["boxes"].xyxy + classes = result.__dict__["boxes"].cls + scores = result.__dict__["boxes"].conf + vis_result = visualize_bbox(image, boxes, classes, scores, self.id_to_names) return { - 'vis': vis_result, - 'results': { + "vis": vis_result, + "results": { "boxes": boxes.tolist(), "scores": scores, "classes": ["formula" for i in range(len(classes))], - } - } \ No newline at end of file + }, + } diff --git a/server/src/tasks/formula_recognition.py b/server/src/tasks/formula_recognition.py index d5d79d8..cd3dd36 100644 --- a/server/src/tasks/formula_recognition.py +++ b/server/src/tasks/formula_recognition.py @@ -2,19 +2,15 @@ import logging import argparse -import cv2 import torch -import numpy as np -from PIL import Image import unimernet.tasks as tasks from unimernet.common.config import Config from unimernet.processors import load_processor -import os from src.registry import MODEL_REGISTRY -@MODEL_REGISTRY.register('formula_recognition_unimernet') +@MODEL_REGISTRY.register("formula_recognition_unimernet") class FormulaRecognitionUniMERNet: def __init__(self, config): """ @@ -24,9 +20,9 @@ def __init__(self, config): config (dict): Configuration dictionary containing model parameters. """ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.model_dir = config['model_path'] - self.cfg_path = config.get('cfg_path', "pdf_extract_kit/configs/unimernet.yaml") - self.batch_size = config.get('batch_size', 1) + self.model_dir = config["model_path"] + self.cfg_path = config.get("cfg_path", "pdf_extract_kit/configs/unimernet.yaml") + self.batch_size = config.get("batch_size", 1) # Load the UniMERNet model self.model, self.vis_processor = self.load_model_and_processor() @@ -35,22 +31,27 @@ def load_model_and_processor(self): try: args = argparse.Namespace(cfg_path=self.cfg_path, options=None) cfg = Config(args) - cfg.config.model.pretrained = os.path.join(self.model_dir, "pytorch_model.pth") + cfg.config.model.pretrained = os.path.join( + self.model_dir, "pytorch_model.pth" + ) cfg.config.model.model_config.model_name = self.model_dir cfg.config.model.tokenizer_config.path = self.model_dir task = tasks.setup_task(cfg) model = task.build_model(cfg).to(self.device) - vis_processor = load_processor('formula_image_eval', cfg.config.datasets.formula_rec_eval.vis_processor.eval) + vis_processor = load_processor( + "formula_image_eval", + cfg.config.datasets.formula_rec_eval.vis_processor.eval, + ) return model, vis_processor except Exception as e: logging.error(f"Error loading model and processor: {e}") raise - + def predict(self, image): # Process the image using the visual processor and prepare it for the model image = self.vis_processor(image).unsqueeze(0).to(self.device) # Generate the prediction using the model output = self.model.generate({"image": image}) pred = output["pred_str"][0] - - return {'vis': None, 'results': pred} \ No newline at end of file + + return {"vis": None, "results": pred} diff --git a/server/src/tasks/layout_detection.py b/server/src/tasks/layout_detection.py index adc569b..c40ba81 100644 --- a/server/src/tasks/layout_detection.py +++ b/server/src/tasks/layout_detection.py @@ -1,12 +1,10 @@ -import os -import cv2 import numpy as np -from PIL import Image from src.registry import MODEL_REGISTRY from .layoutlmv3_util.model_init import Layoutlmv3_Predictor from src.utils import visualize_bbox + @MODEL_REGISTRY.register("layout_detection_layoutlmv3") class LayoutDetectionLayoutlmv3: def __init__(self, config): @@ -18,19 +16,19 @@ def __init__(self, config): """ # Mapping from class IDs to class names self.id_to_names = { - 0: 'title', - 1: 'text', - 2: 'abandon', - 3: 'figure', - 4: 'figure_caption', - 5: 'table', - 6: 'table_caption', - 7: 'table_footnote', - 8: 'formula', - 9: 'formula_caption' + 0: "title", + 1: "text", + 2: "abandon", + 3: "figure", + 4: "figure_caption", + 5: "table", + 6: "table_caption", + 7: "table_footnote", + 8: "formula", + 9: "formula_caption", } - self.model = Layoutlmv3_Predictor(config.get('model_path', None)) - self.visualize = config.get('visualize', False) + self.model = Layoutlmv3_Predictor(config.get("model_path", None)) + self.visualize = config.get("visualize", False) def predict(self, image): """ @@ -46,12 +44,11 @@ def predict(self, image): """ layout_res = self.model(np.array(image), ignore_catids=[]) poly = np.array([det["poly"] for det in layout_res["layout_dets"]]) - boxes = poly[:, [0,1,4,5]] + boxes = poly[:, [0, 1, 4, 5]] scores = np.array([det["score"] for det in layout_res["layout_dets"]]) classes = np.array([det["category_id"] for det in layout_res["layout_dets"]]) - - vis_result = visualize_bbox(image, boxes, classes, scores, self.id_to_names) + vis_result = visualize_bbox(image, boxes, classes, scores, self.id_to_names) return { "vis": vis_result, @@ -59,5 +56,5 @@ def predict(self, image): "boxes": boxes.tolist(), "scores": scores, "classes": [self.id_to_names[classes[i]] for i in range(len(classes))], - } + }, } diff --git a/server/src/tasks/layoutlmv3_util/layoutlmv3_base_inference.yaml b/server/src/tasks/layoutlmv3_util/layoutlmv3_base_inference.yaml index 3e0fd30..a0a92a2 100644 --- a/server/src/tasks/layoutlmv3_util/layoutlmv3_base_inference.yaml +++ b/server/src/tasks/layoutlmv3_util/layoutlmv3_base_inference.yaml @@ -1,6 +1,6 @@ AUG: DETR: true -CACHE_DIR: ~/cache/huggingface +CACHE_DIR: .cache/huggingface CUDNN_BENCHMARK: false DATALOADER: ASPECT_RATIO_GROUPING: true @@ -69,7 +69,7 @@ MODEL: FREEZE_AT: 2 NAME: build_vit_fpn_backbone CONFIG_PATH: '' - DEVICE: cuda + DEVICE: cpu FPN: FUSE_TYPE: sum IN_FEATURES: diff --git a/server/src/tasks/omniparser.py b/server/src/tasks/omniparser.py index d9ac505..fb95e62 100644 --- a/server/src/tasks/omniparser.py +++ b/server/src/tasks/omniparser.py @@ -8,37 +8,66 @@ import json from src.registry import MODEL_REGISTRY + @MODEL_REGISTRY.register("omniparser") class Omniparser(object): def __init__(self, config): self.config = config - - self.som_model = get_yolo_model(model_path=config['model_path']) + + self.som_model = get_yolo_model(model_path=config["model_path"]) def predict(self, image): - ocr_bbox_rslt, is_goal_filtered = check_ocr_box_image(image, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}) + ocr_bbox_rslt, is_goal_filtered = check_ocr_box_image( + image, + output_bb_format="xyxy", + goal_filtering=None, + easyocr_args={"paragraph": False, "text_threshold": 0.9}, + ) text, ocr_bbox = ocr_bbox_rslt if ocr_bbox is None: - return { - 'vis': None, - 'results': None - } + return {"vis": None, "results": None} - draw_bbox_config = self.config['draw_bbox_config'] - BOX_TRESHOLD = self.config['box_threshold'] - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, 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) + draw_bbox_config = self.config["draw_bbox_config"] + BOX_TRESHOLD = self.config["box_threshold"] + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img( + image, + 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, + ) dino_image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) # formating output - return_list = [{'bbox': [coord[0], coord[1], coord[0] + coord[2], coord[1] + coord[3]], - 'text': parsed_content_list[i].split(': ')[1], 'cls':'text'} for i, (k, coord) in enumerate(label_coordinates.items()) if i < len(parsed_content_list)] + return_list = [ + { + "bbox": [coord[0], coord[1], coord[0] + coord[2], coord[1] + coord[3]], + "text": parsed_content_list[i].split(": ")[1], + "cls": "text", + } + for i, (k, coord) in enumerate(label_coordinates.items()) + if i < len(parsed_content_list) + ] return_list.extend( - [{'bbox': [coord[0], coord[1], coord[0] + coord[2], coord[1] + coord[3]], - 'text': 'None', 'cls':'icon'} for i, (k, coord) in enumerate(label_coordinates.items()) if i >= len(parsed_content_list)] - ) - - return { - 'vis': dino_image, - 'results': return_list - } + [ + { + "bbox": [ + coord[0], + coord[1], + coord[0] + coord[2], + coord[1] + coord[3], + ], + "text": "None", + "cls": "icon", + } + for i, (k, coord) in enumerate(label_coordinates.items()) + if i >= len(parsed_content_list) + ] + ) + + return {"vis": dino_image, "results": return_list} diff --git a/server/src/tasks/table_recognition.py b/server/src/tasks/table_recognition.py index 31aab7c..c178ec6 100644 --- a/server/src/tasks/table_recognition.py +++ b/server/src/tasks/table_recognition.py @@ -1,6 +1,5 @@ import torch -from PIL import Image from struct_eqtable import build_model from src.registry import MODEL_REGISTRY @@ -14,35 +13,30 @@ def __init__(self, config): Args: config (dict): Configuration dictionary containing model parameters. """ - assert torch.cuda.is_available(), "CUDA must be available for StructEqTable model." + # assert torch.cuda.is_available(), ( + # "CUDA must be available for StructEqTable model." + # ) - self.model_dir = config.get('model_path', 'U4R/StructTable-InternVL2-1B') - self.max_new_tokens = config.get('max_new_tokens', 1024) - self.max_time = config.get('max_time', 30) + self.model_dir = config.get("model_path", "U4R/StructTable-InternVL2-1B") + self.max_new_tokens = config.get("max_new_tokens", 1024) + self.max_time = config.get("max_time", 30) - self.lmdeploy = config.get('lmdeploy', False) - self.flash_attn = config.get('flash_attn', True) - self.batch_size = config.get('batch_size', 1) - self.default_format = config.get('output_format', 'latex') + self.lmdeploy = config.get("lmdeploy", False) + self.flash_attn = config.get("flash_attn", True) + self.batch_size = config.get("batch_size", 1) + self.default_format = config.get("output_format", "latex") # Load the StructEqTable model - self.model = build_model( + self.model: torch.nn.Module = build_model( model_ckpt=self.model_dir, max_new_tokens=self.max_new_tokens, max_time=self.max_time, lmdeploy=self.lmdeploy, flash_attn=self.flash_attn, batch_size=self.batch_size, - ).cuda() + ).to("cpu") def predict(self, image): + results = self.model(image, output_format=self.default_format) - results = self.model( - image, output_format=self.default_format - ) - - - return { - 'vis': None, - 'results': results[0] - } + return {"vis": None, "results": results[0]} diff --git a/server/src/utils.py b/server/src/utils.py index f9c28d8..5bfe1ea 100644 --- a/server/src/utils.py +++ b/server/src/utils.py @@ -1,430 +1,45 @@ -# 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_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, ocr_bbox, image_source, caption_model_processor, prompt=None): - 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'] - if not prompt: - if 'florence' in model.config.name_or_path: - prompt = "" - else: - prompt = "The image shows" - - batch_size = 10 # Number of samples per batch - generated_texts = [] - device = model.device - - for i in range(0, len(croped_pil_image), batch_size): - 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=1024,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) - - 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): - 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: - if not any(IoU(box1, box3) > iou_threshold for k, box3 in enumerate(ocr_bbox)): - filtered_boxes.append(box1) - else: - filtered_boxes.append(box1) - return 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 models.OmniParser.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)) +from PIL import Image - 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, box_threshold, imgsz): - """ Use huggingface model to replace the original model +def get_image_paths(input_data): """ - # model = model['model'] - - result = model.predict( - source=image, - conf=box_threshold, - imgsz=imgsz - # iou=0.5, # 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 + Loads images from a single image path or a directory containing multiple images. + Args: + input_data (str): Path to a single image file or a directory containing image files. -def get_som_labeled_img(image_source, 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,imgsz=640): - """ ocr_bbox: list of xyxy format bbox + Returns: + list: List of paths to all images to be predicted. """ - w, h = image_source.size - xyxy, logits, phrases = predict_yolo(model=model, image=image_source, box_threshold=BOX_TRESHOLD, imgsz=imgsz) - 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) - - # 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, ocr_bbox, image_source, caption_model_processor, prompt=prompt) - ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] - icon_start = len(ocr_text) - parsed_content_icon_ls = [] - 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, parsed_content_merged - - -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: - result = paddle_ocr.ocr(image_path, cls=False)[0] - coord = [item[0] for item in result] - text = [item[1][0] for item in result] - 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) + images = [] + + if os.path.isdir(input_data): + # If input_data is a directory, check for nested directories + for root, dirs, files in os.walk(input_data): + if dirs: + raise ValueError( + "Input directory should not contain nested directories: {}".format( + input_data + ) + ) + for file in files: + if file.lower().endswith((".png", ".jpg", ".jpeg")): + image_path = os.path.join(root, file) + images.append(image_path) + images = sorted(images) + break # Only process the top-level directory 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 - -def check_ocr_box_image(image, output_bb_format='xywh', goal_filtering=None, easyocr_args=None): - if easyocr_args is None: - easyocr_args = {} - result = reader.readtext(image, **easyocr_args) - coord = [item[0] for item in result] - text = [item[1] for item in result] - 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] - return (text, bb), goal_filtering - -def get_image_paths(input_data): - """ - Loads images from a single image path or a directory containing multiple images. - - Args: - input_data (str): Path to a single image file or a directory containing image files. - - Returns: - list: List of paths to all images to be predicted. - """ - images = [] - - if os.path.isdir(input_data): - # If input_data is a directory, check for nested directories - for root, dirs, files in os.walk(input_data): - if dirs: - raise ValueError("Input directory should not contain nested directories: {}".format(input_data)) - for file in files: - if file.lower().endswith(('.png', '.jpg', '.jpeg')): - image_path = os.path.join(root, file) - images.append(image_path) - images = sorted(images) - break # Only process the top-level directory + # Determine the type of input data and process accordingly + if input_data.lower().endswith((".png", ".jpg", ".jpeg")): + # If input is a single image file + images = [input_data] else: - # Determine the type of input data and process accordingly - if input_data.lower().endswith(('.png', '.jpg', '.jpeg')): - # If input is a single image file - images = [input_data] - else: - raise ValueError("Unsupported input data format: {}".format(input_data)) + raise ValueError("Unsupported input data format: {}".format(input_data)) - return images + return images def colormap(N=256, normalized=False): @@ -438,6 +53,7 @@ def colormap(N=256, normalized=False): Returns: np.ndarray: Color map array of shape (N, 3). """ + def bitget(byteval, idx): """ Get the bit value at the specified index. @@ -449,7 +65,7 @@ def bitget(byteval, idx): Returns: int: The bit value (0 or 1). """ - return ((byteval & (1 << idx)) != 0) + return (byteval & (1 << idx)) != 0 cmap = np.zeros((N, 3), dtype=np.uint8) for i in range(N): @@ -461,12 +77,13 @@ def bitget(byteval, idx): b = b | (bitget(c, 2) << (7 - j)) c = c >> 3 cmap[i] = np.array([r, g, b]) - + if normalized: cmap = cmap.astype(np.float32) / 255.0 return cmap + def visualize_bbox(image_path, bboxes, classes, scores, id_to_names, alpha=0.3): """ Visualize layout detection results on an image. @@ -489,27 +106,43 @@ def visualize_bbox(image_path, bboxes, classes, scores, id_to_names, alpha=0.3): image = cv2.imread(image_path) overlay = image.copy() - + cmap = colormap(N=len(id_to_names), normalized=False) - + # Iterate over each bounding box for i, bbox in enumerate(bboxes): x_min, y_min, x_max, y_max = map(int, bbox) class_id = int(classes[i]) class_name = id_to_names[class_id] - + text = class_name + f":{scores[i]:.3f}" - + color = tuple(int(c) for c in cmap[class_id]) cv2.rectangle(overlay, (x_min, y_min), (x_max, y_max), color, -1) cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, 2) - + # Add the class name with a background rectangle - (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.9, 2) - cv2.rectangle(image, (x_min, y_min - text_height - baseline), (x_min + text_width, y_min), color, -1) - cv2.putText(image, text, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2) - + (text_width, text_height), baseline = cv2.getTextSize( + text, cv2.FONT_HERSHEY_SIMPLEX, 0.9, 2 + ) + cv2.rectangle( + image, + (x_min, y_min - text_height - baseline), + (x_min + text_width, y_min), + color, + -1, + ) + cv2.putText( + image, + text, + (x_min, y_min - 5), + cv2.FONT_HERSHEY_SIMPLEX, + 0.9, + (255, 255, 255), + 2, + ) + # Blend the overlay with the original image cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image) - - return image \ No newline at end of file + + return image diff --git a/server/uv.lock b/server/uv.lock new file mode 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