mirror of
https://github.com/RapidAI/RapidOCR.git
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chore: seperate the ocr_web project
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SWHL committed
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@@ -1,38 +0,0 @@
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name: Push rapidocr_web to pypi
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on:
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push:
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branches: [ main ]
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paths:
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- 'ocrweb/rapidocr_web/**'
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- '!ocrweb/rapidocr_web/ocr_web.spec'
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- 'docs/doc_whl_rapidocr_web.md'
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- 'ocrweb/setup.py'
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- '.github/workflows/gen_whl_to_pypi_rapidocr_web.yml'
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jobs:
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GenerateWHL_PushPyPi:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python 3.10
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uses: actions/setup-python@v4
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with:
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python-version: '3.10'
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architecture: 'x64'
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- name: Run setup.py
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run: |
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cd ocrweb
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pip install -r requirements.txt
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python -m pip install --upgrade pip
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python setup.py bdist_wheel "${{ github.event.head_commit.message }}"
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- name: Publish distribution 📦 to PyPI
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uses: pypa/gh-action-pypi-publish@v1.5.0
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with:
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password: ${{ secrets.RAPIDOCR_OPENVINO }}
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packages_dir: ocrweb/dist/
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@@ -1,158 +0,0 @@
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name: Package ocrweb
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on:
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push:
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branches: main
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paths:
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- 'ocrweb/rapidocr_web/ocrweb.py'
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- 'ocrweb/rapidocr_web/task.py'
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- 'ocrweb/rapidocr_web/static/**'
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- 'ocrweb/rapidocr_web/templates/**'
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env:
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package_name: RapidOCRWeb
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rapidocr_web_dir: ocrweb/rapidocr_web/
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jobs:
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create_release:
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runs-on: ubuntu-latest
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outputs:
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upload_url: ${{ steps.create_release.outputs.upload_url }}
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steps:
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- name: get_cur_time
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uses: srfrnk/current-time@master
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id: get_cur_time
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with:
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format: YYYY-MM-DD-HH-mm-ss
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- name: create_release
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id: create_release
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uses: marvinpinto/action-automatic-releases@latest
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with:
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repo_token: ${{ secrets.GITHUB_TOKEN }}
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automatic_release_tag: Draft-${{ steps.get_cur_time.outputs.formattedTime }}
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prerelease: false
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draft: true
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build_windows:
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name: build_windows
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runs-on: ${{ matrix.os }}
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needs: create_release
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strategy:
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matrix:
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os: ['windows-2022', 'windows-2019']
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steps:
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- name: Pull latest code
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uses: actions/checkout@v4
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- name: Set up Python 3.7
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uses: actions/setup-python@v4
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with:
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python-version: '3.7'
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architecture: 'x64'
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- name: Display Python version
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run: python -c "import sys; print(sys.version)"
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- name: Install environments
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run: |
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pip3 install Pillow pyinstaller Flask>=2.1.0 rapidocr_onnxruntime>=1.2.8 get_pypi_latest_version
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- name: Run pyinstaller
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id: run_pyinstaller
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shell: bash
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run: |
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cd ${{ env.rapidocr_web_dir }}
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pyinstaller ocrweb.spec
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cp -r static templates dist/${{ env.package_name }}
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rm dist/${{ env.package_name }}/cv2/opencv_videoio_ffmpe*.dll || echo "未找到opencv_videoio_ffmpeg"
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echo "VERSION=$(get_pypi_latest_version rapid_videocr)" >> $GITHUB_OUTPUT
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- name: Concat file path
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id: concat_file_path
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shell: bash
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env:
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ZIP_WIN_NAME: ${{ env.package_name }}-${{ steps.run_pyinstaller.outputs.VERSION }}-${{ matrix.os }}-${{ runner.arch }}-no-installer.zip
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Win_DIST_DIR: ${{ github.workspace }}\ocrweb\rapidocr_web\dist
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run: |
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echo "ZIP_NAME=$ZIP_WIN_NAME" >> $GITHUB_OUTPUT
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echo "Win_PACKAGE_DIR=$Win_DIST_DIR\${{ env.package_name }}" >> $GITHUB_OUTPUT
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echo "Win_ZIP_FULL_PATH=$Win_DIST_DIR\\$ZIP_WIN_NAME" >> $GITHUB_OUTPUT
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- name: Zip package
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run: |
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powershell Compress-Archive ${{ steps.concat_file_path.outputs.Win_PACKAGE_DIR }} ${{ steps.concat_file_path.outputs.Win_ZIP_FULL_PATH }}
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- name: Upload release asset
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id: upload-release-asset-windows
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uses: actions/upload-release-asset@v1
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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with:
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upload_url: ${{ needs.create_release.outputs.upload_url }}
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asset_path: ${{ steps.concat_file_path.outputs.Win_ZIP_FULL_PATH }}
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asset_name: ${{ steps.concat_file_path.outputs.ZIP_NAME }}
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asset_content_type: application/zip
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build_linux:
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name: build_linux
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runs-on: ${{ matrix.os }}
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needs: create_release
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strategy:
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matrix:
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os: ['ubuntu-20.04', 'ubuntu-22.04', 'macos-13', 'macos-12', 'macos-11']
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steps:
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- name: Pull latest code
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uses: actions/checkout@v4
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- name: Set up Python 3.7
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uses: actions/setup-python@v4
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with:
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python-version: '3.7'
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architecture: 'x64'
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- name: Display Python version
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run: python -c "import sys; print(sys.version)"
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- name: Install environments
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run: |
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pip3 install Pillow pyinstaller Flask>=2.1.0 rapidocr_onnxruntime>=1.2.8 get_pypi_latest_version
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- name: Run pyinstaller
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id: run_pyinstaller
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shell: bash
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run: |
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cd ocrweb/rapidocr_web/
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pyinstaller ocrweb.spec
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cp -r static templates dist/${{ env.package_name }}
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rm dist/${{ env.package_name }}/cv2/opencv_videoio_ffmpe*.dll || echo "未找到opencv_videoio_ffmpeg"
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echo "VERSION=$(get_pypi_latest_version rapid_videocr)" >> $GITHUB_OUTPUT
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- name: Concat file path
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id: concat_file_path
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env:
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ZIP_NAME: ${{ env.package_name }}-${{ steps.run_pyinstaller.outputs.VERSION }}-${{ matrix.os }}-${{ runner.arch }}-no-installer.zip
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Linux_DIST_DIR: ${{ github.workspace }}/ocrweb/rapidocr_web/dist
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run: |
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echo "ZIP_NAME=$ZIP_NAME" >> $GITHUB_OUTPUT
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echo "Linux_PACKAGE_DIR=$Linux_DIST_DIR/${{ env.package_name }}" >> $GITHUB_OUTPUT
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echo "Linux_ZIP_FULL_PATH=$Linux_DIST_DIR/$ZIP_NAME" >> $GITHUB_OUTPUT
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echo "Linux_DIST_DIR=$Linux_DIST_DIR" >> $GITHUB_OUTPUT
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- name: Zip package
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run: |
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cd ${{ steps.concat_file_path.outputs.Linux_DIST_DIR }}
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zip -r ${{ steps.concat_file_path.outputs.ZIP_NAME }} ${{ env.package_name }}
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- name: Upload release asset
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id: upload-release-asset-linux-macOS
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uses: actions/upload-release-asset@v1
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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with:
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upload_url: ${{ needs.create_release.outputs.upload_url }}
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asset_path: ${{ steps.concat_file_path.outputs.Linux_ZIP_FULL_PATH }}
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asset_name: ${{ steps.concat_file_path.outputs.ZIP_NAME }}
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asset_content_type: application/zip
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@@ -1 +0,0 @@
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### See [Documentation](https://rapidai.github.io/RapidOCRDocs/install_usage/rapidocr_web/rapidocr_web/)
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+1
-1
@@ -1 +1 @@
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### See [Documentation](https://rapidai.github.io/RapidOCRDocs/main/install_usage/rapidocr_web/usage/)
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See [RapidOCRWeb](https://github.com/RapidAI/RapidOCRWeb) for details.
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@@ -1,3 +0,0 @@
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# -*- encoding: utf-8 -*-
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# @Author: SWHL
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# @Contact: liekkaskono@163.com
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@@ -1,47 +0,0 @@
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# -*- encoding: utf-8 -*-
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# @Author: SWHL
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# @Contact: liekkaskono@163.com
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import argparse
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from pathlib import Path
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from wsgiref.simple_server import make_server
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from flask import Flask, render_template, request
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try:
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from rapidocr_web.task import OCRWebUtils
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except:
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from task import OCRWebUtils
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root_dir = Path(__file__).resolve().parent
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app = Flask(__name__, template_folder="templates")
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app.config["MAX_CONTENT_LENGTH"] = 3 * 1024 * 1024
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processor = OCRWebUtils()
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@app.route("/")
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def index():
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return render_template("index.html")
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@app.route("/ocr", methods=["POST"])
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def ocr():
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if request.method == "POST":
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img_str = request.get_json().get("file", None)
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ocr_res = processor(img_str)
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return ocr_res
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def main():
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parser = argparse.ArgumentParser("rapidocr_web")
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parser.add_argument("-ip", "--ip", type=str, default="0.0.0.0", help="IP Address")
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parser.add_argument("-p", "--port", type=int, default=9003, help="IP port")
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args = parser.parse_args()
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print(f"Successfully launched and visit http://{args.ip}:{args.port} to view.")
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server = make_server(args.ip, args.port, app)
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server.serve_forever()
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if __name__ == "__main__":
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main()
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@@ -1,72 +0,0 @@
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# -*- mode: python ; coding: utf-8 -*-
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from pathlib import Path
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import rapidocr_onnxruntime
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block_cipher = None
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package_name = 'rapidocr_onnxruntime'
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install_dir = Path(rapidocr_onnxruntime.__file__).resolve().parent
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onnx_paths = list(install_dir.rglob('*.onnx'))
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yaml_paths = list(install_dir.rglob('*.yaml'))
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onnx_add_data = [(str(v.parent), f'{package_name}/{v.parent.name}')
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for v in onnx_paths]
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yaml_add_data = []
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for v in yaml_paths:
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if package_name == v.parent.name:
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yaml_add_data.append((str(v.parent / '*.yaml'), package_name))
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else:
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yaml_add_data.append(
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(str(v.parent / '*.yaml'), f'{package_name}/{v.parent.name}'))
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add_data = list(set(yaml_add_data + onnx_add_data))
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a = Analysis(
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['ocrweb.py'],
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pathex=[],
|
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binaries=[],
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datas=add_data,
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hiddenimports=[],
|
||||
hookspath=[],
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hooksconfig={},
|
||||
runtime_hooks=[],
|
||||
excludes=[],
|
||||
win_no_prefer_redirects=False,
|
||||
win_private_assemblies=False,
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cipher=block_cipher,
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noarchive=False,
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||||
)
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pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
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exe = EXE(
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pyz,
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a.scripts,
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[],
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exclude_binaries=True,
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name='RapidOCRWeb',
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debug=False,
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bootloader_ignore_signals=False,
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||||
strip=False,
|
||||
upx=True,
|
||||
console=True,
|
||||
disable_windowed_traceback=False,
|
||||
argv_emulation=False,
|
||||
target_arch=None,
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||||
codesign_identity=None,
|
||||
entitlements_file=None,
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||||
icon=['./static/css/favicon.ico'],
|
||||
)
|
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coll = COLLECT(
|
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exe,
|
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a.binaries,
|
||||
a.zipfiles,
|
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a.datas,
|
||||
strip=False,
|
||||
upx=True,
|
||||
upx_exclude=[],
|
||||
name='RapidOCRWeb',
|
||||
)
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 17 KiB |
@@ -1,99 +0,0 @@
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body{
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||||
background-color:#ebedef;
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||||
min-height: 100%;
|
||||
margin: 0;
|
||||
}
|
||||
.btn-gen {
|
||||
/* position: absolute;top:540px; left: 500px; */
|
||||
background-color: #00a1d6;
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||||
text-align: center;
|
||||
border-radius: 18px;
|
||||
margin-top: 5px;
|
||||
font-size: 15px;
|
||||
padding: 5px 10px;
|
||||
height: 20px;
|
||||
width: 120px;
|
||||
text-transform: uppercase;
|
||||
color: #fff;
|
||||
border:none;
|
||||
}
|
||||
.btn-gen:hover,
|
||||
.btn-gen:focus {
|
||||
border-color: #23AAEE;
|
||||
background-color: #23AAEE;
|
||||
color: white;
|
||||
cursor: pointer;
|
||||
}
|
||||
.area{
|
||||
text-align: center;
|
||||
height: auto;
|
||||
margin: auto;
|
||||
}
|
||||
.leftarea{
|
||||
float: left;
|
||||
width: 50%;
|
||||
height: auto;
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.rightarea{
|
||||
float: left;
|
||||
width: 50%;
|
||||
height: auto;
|
||||
}
|
||||
.table{
|
||||
width: auto;
|
||||
height: auto;
|
||||
margin: 0 auto;
|
||||
|
||||
}
|
||||
.span_title{
|
||||
width: 98%;
|
||||
height: 36px;
|
||||
margin-top: 4px;
|
||||
line-height: 32px;
|
||||
background-color: #00a1d6;
|
||||
border: 1px solid #00a1d6;
|
||||
border-radius: 20px;
|
||||
color: #fff;
|
||||
display: inline-block;
|
||||
text-align: center;
|
||||
font-size: 22px;
|
||||
transition: .3s;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
.uplodNote{
|
||||
font-size: 10px;
|
||||
color:#A1A1A1;
|
||||
}
|
||||
|
||||
a{
|
||||
text-decoration:none;
|
||||
}
|
||||
|
||||
/* wrapper */
|
||||
.leftarea>#wrapper {
|
||||
position: absolute; top: 45px; left:0px;
|
||||
width: 98%;
|
||||
height: 100%;
|
||||
background:
|
||||
linear-gradient(#1a98ca, #1a98ca),
|
||||
linear-gradient(90deg, #ffffff33 1px,transparent 0,transparent 19px),
|
||||
linear-gradient( #ffffff33 1px,transparent 0,transparent 19px),
|
||||
linear-gradient(transparent, #1a98ca);
|
||||
background-size:100% 1.5%, 10% 100%,100% 8%, 100% 100%;
|
||||
background-repeat:no-repeat, repeat, repeat, no-repeat;
|
||||
background-position: 0% 100%, 0 0, 0 0, 0 0;
|
||||
/* 初始位置 */
|
||||
clip-path: polygon(0% 0%, 100% 0%, 100% 1.5%, 0% 1.5%);
|
||||
/* 添加动画效果 */
|
||||
animation: move 1s infinite linear;
|
||||
}
|
||||
|
||||
@keyframes move{
|
||||
to{
|
||||
background-position: 0 100%,0 0, 0 0, 0 0;
|
||||
/* 终止位置 */
|
||||
clip-path: polygon(0% 0%, 100% 0%, 100% 100%, 0% 100%);
|
||||
}
|
||||
}
|
||||
@@ -1,4 +0,0 @@
|
||||
/*! jQuery v3.0.0 | (c) jQuery Foundation | jquery.org/license */
|
||||
!function(a,b){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){"use strict";var c=[],d=a.document,e=Object.getPrototypeOf,f=c.slice,g=c.concat,h=c.push,i=c.indexOf,j={},k=j.toString,l=j.hasOwnProperty,m=l.toString,n=m.call(Object),o={};function p(a,b){b=b||d;var c=b.createElement("script");c.text=a,b.head.appendChild(c).parentNode.removeChild(c)}var q="3.0.0",r=function(a,b){return new r.fn.init(a,b)},s=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,t=/^-ms-/,u=/-([a-z])/g,v=function(a,b){return b.toUpperCase()};r.fn=r.prototype={jquery:q,constructor:r,length:0,toArray:function(){return f.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:f.call(this)},pushStack:function(a){var b=r.merge(this.constructor(),a);return b.prevObject=this,b},each:function(a){return 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g,h,i,j,k,m,n,o=[e||d],p=l.call(b,"type")?b.type:b,q=l.call(b,"namespace")?b.namespace.split("."):[];if(h=i=e=e||d,3!==e.nodeType&&8!==e.nodeType&&!pb.test(p+r.event.triggered)&&(p.indexOf(".")>-1&&(q=p.split("."),p=q.shift(),q.sort()),k=p.indexOf(":")<0&&"on"+p,b=b[r.expando]?b:new r.Event(p,"object"==typeof b&&b),b.isTrigger=f?2:3,b.namespace=q.join("."),b.rnamespace=b.namespace?new RegExp("(^|\\.)"+q.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,b.result=void 0,b.target||(b.target=e),c=null==c?Line truncated
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||||
@@ -1,90 +0,0 @@
|
||||
# -*- encoding: utf-8 -*-
|
||||
# @Author: SWHL
|
||||
# @Contact: liekkaskono@163.com
|
||||
import base64
|
||||
import copy
|
||||
import json
|
||||
from collections import namedtuple
|
||||
from functools import reduce
|
||||
from typing import List, Tuple, Union
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from rapidocr_onnxruntime import RapidOCR
|
||||
|
||||
|
||||
class OCRWebUtils:
|
||||
def __init__(self) -> None:
|
||||
self.ocr = RapidOCR()
|
||||
self.WebReturn = namedtuple(
|
||||
"WebReturn",
|
||||
["image", "total_elapse", "elapse_part", "rec_res", "det_boxes"],
|
||||
)
|
||||
|
||||
def __call__(self, img_content: str) -> namedtuple:
|
||||
if img_content is None:
|
||||
raise ValueError("img is None")
|
||||
img = self.prepare_img(img_content)
|
||||
ocr_res, elapse = self.ocr(img)
|
||||
return self.get_web_result(img, ocr_res, elapse)
|
||||
|
||||
def prepare_img(self, img_str: str) -> np.ndarray:
|
||||
img_str = img_str.split(",")[1]
|
||||
image = base64.b64decode(img_str + "=" * (-len(img_str) % 4))
|
||||
nparr = np.frombuffer(image, np.uint8)
|
||||
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||||
if image.ndim == 2:
|
||||
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
|
||||
return image
|
||||
|
||||
def get_web_result(
|
||||
self, img: np.ndarray, ocr_res: List, elapse: List
|
||||
) -> Tuple[Union[str, List, str, str]]:
|
||||
if ocr_res is None:
|
||||
total_elapse, elapse_part = 0, ""
|
||||
img_str = self.img_to_base64(img)
|
||||
rec_res = json.dumps([], indent=2, ensure_ascii=False)
|
||||
boxes = ""
|
||||
else:
|
||||
boxes, txts, scores = list(zip(*ocr_res))
|
||||
scores = [f"{v:.4f}" for v in scores]
|
||||
rec_res = list(zip(range(len(txts)), txts, scores))
|
||||
rec_res = json.dumps(rec_res, indent=2, ensure_ascii=False)
|
||||
|
||||
det_im = self.draw_text_det_res(np.array(boxes), img)
|
||||
img_str = self.img_to_base64(det_im)
|
||||
|
||||
total_elapse = reduce(lambda x, y: float(x) + float(y), elapse)
|
||||
elapse_part = ",".join([f"{x:.4f}" for x in elapse])
|
||||
|
||||
web_return = self.WebReturn(
|
||||
image=img_str,
|
||||
total_elapse=f"{total_elapse:.4f}",
|
||||
elapse_part=elapse_part,
|
||||
rec_res=rec_res,
|
||||
det_boxes=boxes,
|
||||
)
|
||||
return json.dumps(web_return._asdict())
|
||||
|
||||
@staticmethod
|
||||
def img_to_base64(img) -> str:
|
||||
img = cv2.imencode(".png", img)[1]
|
||||
img_str = str(base64.b64encode(img))[2:-1]
|
||||
return img_str
|
||||
|
||||
@staticmethod
|
||||
def draw_text_det_res(dt_boxes: np.ndarray, raw_im: np.ndarray) -> np.ndarray:
|
||||
src_im = copy.deepcopy(raw_im)
|
||||
for i, box in enumerate(dt_boxes):
|
||||
box = np.array(box).astype(np.int32).reshape(-1, 2)
|
||||
cv2.polylines(src_im, [box], True, color=(0, 0, 255), thickness=1)
|
||||
cv2.putText(
|
||||
src_im,
|
||||
str(i),
|
||||
(int(box[0][0]), int(box[0][1])),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(0, 0, 0),
|
||||
2,
|
||||
)
|
||||
return src_im
|
||||
@@ -1,198 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>RapidOCR Demo</title>
|
||||
<link rel=stylesheet type="text/css" href="/static/css/main.css">
|
||||
<link rel="shortcut icon" href="/static/css/favicon.ico" type="image/x-icon"/>
|
||||
</head>
|
||||
<body>
|
||||
<h1 align="center">
|
||||
<b><i>RapidOCR Demo</i></b>
|
||||
<a href="https://github.com/RapidAI/RapidOCR" style="font-size: 14px;">欢迎Star</a> 
|
||||
<a href="https://github.com/RapidAI/RapidOCR/tree/main/ocrweb#readme" style="font-size:14px">了解Demo</a>
|
||||
</h1>
|
||||
<div class='area'>
|
||||
<span class="btn-gen" onclick="javascript:$('#rapid_ocr').click();">试试你的图片?（可拖拽、粘贴、点击）</span>
|
||||
<input type="file" style="display: none;" name="pic" id="rapid_ocr"/>
|
||||
<span class="uplodNote">支持PNG、JPG、JPEG、BMP、WEBP，图片大小不超过3M。</span>
|
||||
</div>
|
||||
<br/>
|
||||
<div class="area">
|
||||
<div class="leftarea" id="detectDiv">
|
||||
<span class="span_title">文 本 检 测 结 果</span><br/><br/>
|
||||
<div class="wrapper" id="wrapper" hidden></div>
|
||||
<img id="detect_img" src=""/>
|
||||
</div>
|
||||
|
||||
<div class="rightarea" id="recogDiv">
|
||||
<span id="rec_res" class="span_title">文 本 识 别 结 果</span>
|
||||
<br/><br/>
|
||||
<table id="locTable" class="table" cellspacing="0" cellpadding="5"></table>
|
||||
</div>
|
||||
</div>
|
||||
<script src="{{url_for('static', filename='js/jquery-3.0.0.min.js')}}"></script>
|
||||
<script type="text/javascript">
|
||||
// 页面加载时，执行
|
||||
var targetFile = null;
|
||||
window.onload = function () {
|
||||
$('#detect_img').attr("width", "");
|
||||
$('#detect_img').attr("height", "");
|
||||
}
|
||||
|
||||
$("#rapid_ocr").on('change', function (){
|
||||
targetFile = document.getElementById("rapid_ocr").files[0];
|
||||
requestOCR();
|
||||
});
|
||||
|
||||
$("html").on("dragover", function(event) {
|
||||
event.preventDefault();
|
||||
event.stopPropagation();
|
||||
$(this).addClass('dragging');
|
||||
});
|
||||
|
||||
$("html").on("dragleave", function(event) {
|
||||
event.preventDefault();
|
||||
event.stopPropagation();
|
||||
$(this).removeClass('dragging');
|
||||
});
|
||||
|
||||
$("html").on("drop", function(event) {
|
||||
event.preventDefault();
|
||||
event.stopPropagation();
|
||||
let trans = event.originalEvent.dataTransfer;
|
||||
handleData(trans);
|
||||
});
|
||||
|
||||
$('html').on('paste', function (e) {
|
||||
let trans = e.originalEvent.clipboardData;
|
||||
handleData(trans);
|
||||
});
|
||||
|
||||
function handleData(trans) {
|
||||
if (trans.items) {
|
||||
for (let i = 0; i < trans.items.length; i++) {
|
||||
// If dropped items aren't files, reject them
|
||||
if (trans.items[i].kind === 'file') {
|
||||
targetFile = trans.items[i].getAsFile();
|
||||
console.log(`handleData: items[${i}].name = ${targetFile.name}`);
|
||||
requestOCR();
|
||||
return;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Use DataTransfer interface to access the file(s)
|
||||
for (let i = 0; i < trans.files.length; i++) {
|
||||
targetFile = trans.files[i];
|
||||
console.log(`handleData: files[${i}].name = ${targetFile.name}`);
|
||||
requestOCR();
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function requestOCR(){
|
||||
if (!targetFile){
|
||||
return;
|
||||
}
|
||||
|
||||
// 判断图像格式是否匹配
|
||||
let imageName = targetFile.name;
|
||||
let index = imageName.lastIndexOf('.');
|
||||
let extName = imageName.substr(index + 1);
|
||||
let imgArr = ['webp', 'jpg', 'bmp', 'png', 'jpeg'];
|
||||
if (!(imgArr.includes(extName.toLocaleLowerCase()))){
|
||||
alert("图像文件格式不支持！");
|
||||
return;
|
||||
}
|
||||
|
||||
// 判断图像大小是否符合
|
||||
let imageSize = targetFile.size / 1024 / 1024;
|
||||
if (imageSize >= 3) {
|
||||
alert("图像大小超过3M！");
|
||||
return;
|
||||
}
|
||||
|
||||
var reader = new FileReader();
|
||||
reader.onload = function(e){
|
||||
var upload_data = {"file": reader.result};
|
||||
$.ajax(
|
||||
{
|
||||
url:"/ocr",
|
||||
type:"POST",
|
||||
data: JSON.stringify(upload_data),
|
||||
contentType: 'application/json; charset=UTF-8',
|
||||
dataType: 'json',
|
||||
|
||||
beforeSend:function(){
|
||||
$("#detect_img").attr('src', reader.result);
|
||||
$("#detect_img").attr("width", "95%");
|
||||
$("#detect_img").attr("height", "100%");
|
||||
|
||||
$("#wrapper").show();
|
||||
$("#locTable").hide();
|
||||
$("#rec_res").html("文本识别结果");
|
||||
},
|
||||
|
||||
success:function (data) {
|
||||
$("#wrapper").hide();
|
||||
if (data){
|
||||
if (data['image']){
|
||||
$("#detect_img").attr('src', 'data:image/jpeg;base64,'+ data['image']);
|
||||
$('#detect_img').show();
|
||||
}
|
||||
|
||||
if (data["total_elapse"]){
|
||||
document.getElementById("rec_res").textContent = "文 本 识 别 结 果(Total:" + String(data["total_elapse"]) + "s)"
|
||||
$("#rec_res").show();
|
||||
}
|
||||
|
||||
if (data["rec_res"]){
|
||||
var rec_res = JSON.parse(data["rec_res"]);
|
||||
|
||||
// console.log(data['elapse_part']);
|
||||
if (data['elapse_part']){
|
||||
var elapse_list = data['elapse_part'].split(',');
|
||||
}else{
|
||||
var elapse_list = [0, 0, 0];
|
||||
}
|
||||
var data = "";
|
||||
data += "<tr>" +
|
||||
'<th align="right">Det: ' + elapse_list[0] + 's</th>' +
|
||||
'<th align="center">Cls: ' + elapse_list[1] + 's</th>' +
|
||||
'<th align="left">Rec: ' + elapse_list[2] + 's</th>' +
|
||||
"</tr>"
|
||||
data += "<tr>" +
|
||||
'<th bgcolor="#C0C0C0">序号</th>' +
|
||||
'<th bgcolor="#C0C0C0">识别结果</th>' +
|
||||
'<th bgcolor="#C0C0C0">置信度</th>' +
|
||||
"</tr>"
|
||||
for (let i = 0; i < rec_res.length; i++) {
|
||||
const element = rec_res[i];
|
||||
let num = element[0];
|
||||
|
||||
let rec_result = element[1];
|
||||
|
||||
let score = Number(element[2]);
|
||||
score = score.toFixed(4);
|
||||
|
||||
data += "<tr>"
|
||||
data += "<td>" + num + "</td>";
|
||||
data += "<td align='left'>" + rec_result + "</td>";
|
||||
data += "<td>" + score + "</td>";
|
||||
data += "</tr>"
|
||||
}
|
||||
document.getElementById("locTable").innerHTML = data;
|
||||
$("#locTable").show();
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
);
|
||||
}
|
||||
reader.readAsDataURL(targetFile)
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,6 +0,0 @@
|
||||
Pillow<=10.0.0
|
||||
requests
|
||||
Flask>=2.1.0, <=3.0.0
|
||||
rapidocr_onnxruntime>=1.3.0,<=2.0.0
|
||||
get_pypi_latest_version
|
||||
wheel
|
||||
@@ -1,73 +0,0 @@
|
||||
# -*- encoding: utf-8 -*-
|
||||
# @Author: SWHL
|
||||
# @Contact: liekkaskono@163.com
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import setuptools
|
||||
from get_pypi_latest_version import GetPyPiLatestVersion
|
||||
|
||||
|
||||
def get_readme():
|
||||
root_dir = Path(__file__).resolve().parent.parent
|
||||
readme_path = str(root_dir / "docs" / "doc_whl_rapidocr_web.md")
|
||||
with open(readme_path, "r", encoding="utf-8") as f:
|
||||
readme = f.read()
|
||||
return readme
|
||||
|
||||
|
||||
MODULE_NAME = "rapidocr_web"
|
||||
|
||||
obtainer = GetPyPiLatestVersion()
|
||||
latest_version = obtainer(MODULE_NAME)
|
||||
VERSION_NUM = obtainer.version_add_one(latest_version, add_patch=True)
|
||||
|
||||
# 优先提取commit message中的语义化版本号，如无，则自动加1
|
||||
if len(sys.argv) > 2:
|
||||
match_str = " ".join(sys.argv[2:])
|
||||
matched_versions = obtainer.extract_version(match_str)
|
||||
if matched_versions:
|
||||
VERSION_NUM = matched_versions
|
||||
sys.argv = sys.argv[:2]
|
||||
|
||||
setuptools.setup(
|
||||
name=MODULE_NAME,
|
||||
version=VERSION_NUM,
|
||||
platforms="Any",
|
||||
description="A cross platform OCR Library based on OnnxRuntime.",
|
||||
long_description=get_readme(),
|
||||
long_description_content_type="text/markdown",
|
||||
author="SWHL",
|
||||
author_email="liekkaskono@163.com",
|
||||
url="https://github.com/RapidAI/RapidOCR",
|
||||
download_url="https://github.com/RapidAI/RapidOCR.git",
|
||||
license="Apache-2.0",
|
||||
include_package_data=True,
|
||||
install_requires=["requests", "Flask>=2.1.0", "rapidocr_onnxruntime"],
|
||||
packages=[
|
||||
MODULE_NAME,
|
||||
f"{MODULE_NAME}.static.css",
|
||||
f"{MODULE_NAME}.static.js",
|
||||
f"{MODULE_NAME}.templates",
|
||||
],
|
||||
package_data={"": ["*.ico", "*.css", "*.js", "*.html"]},
|
||||
keywords=[
|
||||
"ocr,text_detection,text_recognition,db,onnxruntime,paddleocr,openvino,rapidocr"
|
||||
],
|
||||
classifiers=[
|
||||
"Programming Language :: Python :: 3.6",
|
||||
"Programming Language :: Python :: 3.7",
|
||||
"Programming Language :: Python :: 3.8",
|
||||
"Programming Language :: Python :: 3.9",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Programming Language :: Python :: 3.11",
|
||||
"Programming Language :: Python :: 3.12",
|
||||
"Programming Language :: Python :: 3.13",
|
||||
],
|
||||
python_requires=">=3.6",
|
||||
entry_points={
|
||||
"console_scripts": [
|
||||
f"{MODULE_NAME}={MODULE_NAME}.ocrweb:main",
|
||||
],
|
||||
},
|
||||
)
|
||||
@@ -1 +0,0 @@
|
||||
### See [Documentation](https://rapidai.github.io/RapidOCRDocs/main/install_usage/rapidocr_web/ocrweb_multi/)
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 38 KiB |
@@ -1,21 +0,0 @@
|
||||
import os
|
||||
import shutil
|
||||
|
||||
print("Compile ocrweb")
|
||||
os.system("pyinstaller -y main.spec")
|
||||
|
||||
print("Compile wrapper")
|
||||
os.system("windres .\wrapper.rc -O coff -o wrapper.res")
|
||||
os.system("gcc .\wrapper.c wrapper.res -o dist/ocrweb.exe")
|
||||
|
||||
print("Copy config.yaml")
|
||||
shutil.copy2("config.yaml", "dist/config.yaml")
|
||||
|
||||
print("Copy models")
|
||||
shutil.copytree("models", "dist/models", dirs_exist_ok=True)
|
||||
os.remove("dist/models/.gitkeep")
|
||||
|
||||
print("Pack to ocrweb.zip")
|
||||
shutil.make_archive("ocrweb", "zip", "dist")
|
||||
|
||||
print("Done")
|
||||
@@ -1,104 +0,0 @@
|
||||
server:
|
||||
host: 127.0.0.1
|
||||
port: 8001
|
||||
# OCR接口Token, 为null时将跳过Token验证
|
||||
token: null
|
||||
|
||||
global:
|
||||
use_cuda: false
|
||||
verbose: false
|
||||
cuda_provider:
|
||||
device_id: 0
|
||||
arena_extend_strategy: kNextPowerOfTwo
|
||||
cudnn_conv_algo_search: EXHAUSTIVE
|
||||
do_copy_in_default_stream: true
|
||||
|
||||
# 模型配置
|
||||
models:
|
||||
# 位置检测模型
|
||||
detect:
|
||||
det_en:
|
||||
path: models/en_PP-OCRv3_det_infer.onnx
|
||||
config: &detectConfig
|
||||
pre_process:
|
||||
- class: DetResizeForTest
|
||||
limit_side_len: 736
|
||||
limit_type: min
|
||||
- class: NormalizeImage
|
||||
std: [0.229, 0.224, 0.225]
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
# 1 / 255
|
||||
scale: 0.00392156862745098
|
||||
order: hwc
|
||||
- class: ToCHWImage
|
||||
- class: KeepKeys
|
||||
keep_keys: ["image", "shape"]
|
||||
post_process:
|
||||
thresh: 0.3
|
||||
box_thresh: 0.5
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.6
|
||||
use_dilation: true
|
||||
det_ch:
|
||||
path: models/ch_PP-OCRv3_det_infer.onnx
|
||||
config: *detectConfig
|
||||
det_ml:
|
||||
path: models/ch_PP-OCRv3_det_infer.onnx
|
||||
config: *detectConfig
|
||||
# 方向检测模型
|
||||
classify:
|
||||
cls_ml:
|
||||
path: models/ch_ppocr_mobile_v2.0_cls_infer.meta.onnx
|
||||
config:
|
||||
batch_size: 8
|
||||
score_thresh: 0.9
|
||||
# 文字识别模型
|
||||
recognize:
|
||||
rec_ch:
|
||||
path: models/ch_PP-OCRv3_rec_infer.meta.onnx
|
||||
config: &recognizeConfig
|
||||
batch_size: 8
|
||||
rec_cht:
|
||||
path: models/chinese_cht_PP-OCRv3_rec_infer.meta.onnx
|
||||
config: *recognizeConfig
|
||||
rec_en:
|
||||
path: models/en_PP-OCRv3_rec_infer.meta.onnx
|
||||
config: *recognizeConfig
|
||||
rec_ja:
|
||||
path: models/japan_PP-OCRv3_rec_infer.meta.onnx
|
||||
config: *recognizeConfig
|
||||
|
||||
# 多语言配置
|
||||
languages:
|
||||
ch:
|
||||
name: 中文
|
||||
models:
|
||||
detect: det_ch
|
||||
classify: cls_ml
|
||||
recognize: rec_ch
|
||||
config: &languageConfig
|
||||
text_score: 0.5
|
||||
use_angle_cls: true
|
||||
verbose: false
|
||||
min_height: 30
|
||||
cht:
|
||||
name: 繁体中文
|
||||
models:
|
||||
detect: det_ch
|
||||
classify: cls_ml
|
||||
recognize: rec_cht
|
||||
config: *languageConfig
|
||||
ja:
|
||||
name: 日文
|
||||
models:
|
||||
detect: det_ch
|
||||
classify: cls_ml
|
||||
recognize: rec_ja
|
||||
config: *languageConfig
|
||||
en:
|
||||
name: 英文
|
||||
models:
|
||||
detect: det_en
|
||||
classify: cls_ml
|
||||
recognize: rec_en
|
||||
config: *languageConfig
|
||||
@@ -1,79 +0,0 @@
|
||||
# -*- encoding: utf-8 -*-
|
||||
# @Author: SWHL
|
||||
# @Contact: liekkaskono@163.com
|
||||
import logging
|
||||
import cv2
|
||||
import numpy as np
|
||||
from flask import Flask, send_file, request, make_response
|
||||
from waitress import serve
|
||||
|
||||
|
||||
from rapidocr.main import detect_recognize
|
||||
from utils.config import conf
|
||||
from utils.utils import tojson, parse_bool
|
||||
|
||||
app = Flask(__name__)
|
||||
log = logging.getLogger("app")
|
||||
# 设置上传文件大小
|
||||
app.config["MAX_CONTENT_LENGTH"] = 3 * 1024 * 1024
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
return send_file("static/index.html")
|
||||
|
||||
|
||||
def json_response(data, status=200):
|
||||
return make_response(tojson(data), status, {"content-type": "application/json"})
|
||||
|
||||
|
||||
@app.route("/lang")
|
||||
def get_languages():
|
||||
"""返回可用语言列表"""
|
||||
data = [
|
||||
{"code": key, "name": val["name"]} for key, val in conf["languages"].items()
|
||||
]
|
||||
result = {"msg": "OK", "data": data}
|
||||
log.info("Send langs: %s", data)
|
||||
return json_response(result)
|
||||
|
||||
|
||||
@app.route("/ocr", methods=["POST", "GET"])
|
||||
def ocr():
|
||||
"""执行文字识别"""
|
||||
if conf["server"].get("token"):
|
||||
if request.values.get("token") != conf["server"]["token"]:
|
||||
return json_response({"msg": "invalid token"}, status=403)
|
||||
|
||||
lang = request.values.get("lang") or "ch"
|
||||
detect = parse_bool(request.values.get("detect") or "true")
|
||||
classify = parse_bool(request.values.get("classify") or "true")
|
||||
|
||||
image_file = request.files.get("image")
|
||||
if not image_file:
|
||||
return json_response({"msg": "no image"}, 400)
|
||||
nparr = np.frombuffer(image_file.stream.read(), np.uint8)
|
||||
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||||
log.info(
|
||||
"Input: image %s, lang=%s, detect=%s, classify=%s",
|
||||
image.shape,
|
||||
lang,
|
||||
detect,
|
||||
classify,
|
||||
)
|
||||
if image.ndim == 2:
|
||||
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
|
||||
result = detect_recognize(image, lang=lang, detect=detect, classify=classify)
|
||||
log.info("OCR Done %s %s", result["ts"], len(result["results"]))
|
||||
return json_response({"msg": "OK", "data": result})
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logging.basicConfig(level="INFO")
|
||||
logging.getLogger("waitress").setLevel(logging.INFO)
|
||||
if parse_bool(conf.get("debug", "0")):
|
||||
# Debug
|
||||
app.run(host=conf["server"]["host"], port=conf["server"]["port"], debug=True)
|
||||
else:
|
||||
# Deploy with waitress
|
||||
serve(app, host=conf["server"]["host"], port=conf["server"]["port"])
|
||||
@@ -1,52 +0,0 @@
|
||||
# -*- mode: python ; coding: utf-8 -*-
|
||||
|
||||
|
||||
block_cipher = None
|
||||
|
||||
|
||||
a = Analysis(
|
||||
['main.py'],
|
||||
pathex=[],
|
||||
binaries=[],
|
||||
datas=[
|
||||
('static', 'static'),
|
||||
],
|
||||
hiddenimports=[],
|
||||
hookspath=[],
|
||||
hooksconfig={},
|
||||
runtime_hooks=[],
|
||||
excludes=[],
|
||||
win_no_prefer_redirects=False,
|
||||
win_private_assemblies=False,
|
||||
cipher=block_cipher,
|
||||
noarchive=False,
|
||||
)
|
||||
pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
|
||||
|
||||
exe = EXE(
|
||||
pyz,
|
||||
a.scripts,
|
||||
[],
|
||||
exclude_binaries=True,
|
||||
name='main',
|
||||
debug=False,
|
||||
bootloader_ignore_signals=False,
|
||||
strip=False,
|
||||
upx=True,
|
||||
console=True,
|
||||
disable_windowed_traceback=False,
|
||||
argv_emulation=False,
|
||||
target_arch=None,
|
||||
codesign_identity=None,
|
||||
entitlements_file=None,
|
||||
)
|
||||
coll = COLLECT(
|
||||
exe,
|
||||
a.binaries,
|
||||
a.zipfiles,
|
||||
a.datas,
|
||||
strip=False,
|
||||
upx=True,
|
||||
upx_exclude=[],
|
||||
name='ocrweb',
|
||||
)
|
||||
Whitespace-only changes.
Whitespace-only changes.
@@ -1,122 +0,0 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import copy
|
||||
import math
|
||||
import json
|
||||
from typing import List
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from utils.utils import OrtInferSession
|
||||
|
||||
|
||||
class ClsPostProcess:
|
||||
"""Convert between text-label and text-index"""
|
||||
|
||||
def __init__(self, label_list):
|
||||
super(ClsPostProcess, self).__init__()
|
||||
self.label_list = label_list
|
||||
|
||||
def __call__(self, preds, label=None):
|
||||
pred_idxs = preds.argmax(axis=1)
|
||||
decode_out = [
|
||||
(self.label_list[idx], preds[i, idx]) for i, idx in enumerate(pred_idxs)
|
||||
]
|
||||
if label is None:
|
||||
return decode_out
|
||||
|
||||
label = [(self.label_list[idx], 1.0) for idx in label]
|
||||
return decode_out, label
|
||||
|
||||
|
||||
class TextClassifier:
|
||||
def __init__(self, path, config):
|
||||
self.cls_batch_num = config["batch_size"]
|
||||
self.cls_thresh = config["score_thresh"]
|
||||
|
||||
session_instance = OrtInferSession(path)
|
||||
self.session = session_instance.session
|
||||
metamap = self.session.get_modelmeta().custom_metadata_map
|
||||
|
||||
self.cls_image_shape = json.loads(metamap["shape"])
|
||||
|
||||
labels = json.loads(metamap["labels"])
|
||||
self.postprocess_op = ClsPostProcess(labels)
|
||||
self.input_name = session_instance.get_input_name()
|
||||
|
||||
def resize_norm_img(self, img):
|
||||
img_c, img_h, img_w = self.cls_image_shape
|
||||
h, w = img.shape[:2]
|
||||
ratio = w / float(h)
|
||||
if math.ceil(img_h * ratio) > img_w:
|
||||
resized_w = img_w
|
||||
else:
|
||||
resized_w = int(math.ceil(img_h * ratio))
|
||||
|
||||
resized_image = cv2.resize(img, (resized_w, img_h))
|
||||
resized_image = resized_image.astype("float32")
|
||||
if img_c == 1:
|
||||
resized_image = resized_image / 255
|
||||
resized_image = resized_image[np.newaxis, :]
|
||||
else:
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
padding_im = np.zeros((img_c, img_h, img_w), dtype=np.float32)
|
||||
padding_im[:, :, :resized_w] = resized_image
|
||||
return padding_im
|
||||
|
||||
def __call__(self, img_list: List[np.ndarray]):
|
||||
if isinstance(img_list, np.ndarray):
|
||||
img_list = [img_list]
|
||||
|
||||
img_list = copy.deepcopy(img_list)
|
||||
|
||||
# Calculate the aspect ratio of all text bars
|
||||
width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
|
||||
|
||||
# Sorting can speed up the cls process
|
||||
indices = np.argsort(np.array(width_list))
|
||||
|
||||
img_num = len(img_list)
|
||||
cls_res = [["", 0.0]] * img_num
|
||||
batch_num = self.cls_batch_num
|
||||
for beg_img_no in range(0, img_num, batch_num):
|
||||
end_img_no = min(img_num, beg_img_no + batch_num)
|
||||
max_wh_ratio = 0
|
||||
for ino in range(beg_img_no, end_img_no):
|
||||
h, w = img_list[indices[ino]].shape[0:2]
|
||||
wh_ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||||
|
||||
norm_img_batch = []
|
||||
for ino in range(beg_img_no, end_img_no):
|
||||
norm_img = self.resize_norm_img(img_list[indices[ino]])
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32)
|
||||
|
||||
onnx_inputs = {self.input_name: norm_img_batch}
|
||||
prob_out = self.session.run(None, onnx_inputs)[0]
|
||||
cls_result = self.postprocess_op(prob_out)
|
||||
|
||||
for rno in range(len(cls_result)):
|
||||
label, score = cls_result[rno]
|
||||
cls_res[indices[beg_img_no + rno]] = [label, score]
|
||||
if label == "180" and score > self.cls_thresh:
|
||||
img_list[indices[beg_img_no + rno]] = cv2.rotate(
|
||||
img_list[indices[beg_img_no + rno]], 1
|
||||
)
|
||||
return img_list, cls_res
|
||||
@@ -1,99 +0,0 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# -*- encoding: utf-8 -*-
|
||||
# @Author: SWHL
|
||||
# @Contact: liekkaskono@163.com
|
||||
|
||||
import numpy as np
|
||||
|
||||
from utils.utils import OrtInferSession
|
||||
from .detect_process import DBPostProcess, create_operators, transform
|
||||
|
||||
|
||||
class TextDetector:
|
||||
def __init__(self, path, config):
|
||||
self.preprocess_op = create_operators(config["pre_process"])
|
||||
self.postprocess_op = DBPostProcess(**config["post_process"])
|
||||
|
||||
session_instance = OrtInferSession(path)
|
||||
self.session = session_instance.session
|
||||
self.input_name = session_instance.get_input_name()
|
||||
|
||||
def __call__(self, img):
|
||||
if img is None:
|
||||
raise ValueError("img is None")
|
||||
|
||||
ori_im_shape = img.shape[:2]
|
||||
|
||||
data = {"image": img}
|
||||
data = transform(data, self.preprocess_op)
|
||||
img, shape_list = data
|
||||
if img is None:
|
||||
return None, 0
|
||||
|
||||
img = np.expand_dims(img, axis=0).astype(np.float32)
|
||||
shape_list = np.expand_dims(shape_list, axis=0)
|
||||
|
||||
preds = self.session.run(None, {self.input_name: img})
|
||||
|
||||
post_result = self.postprocess_op(preds[0], shape_list)
|
||||
|
||||
dt_boxes = post_result[0]["points"]
|
||||
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im_shape)
|
||||
return dt_boxes
|
||||
|
||||
def order_points_clockwise(self, pts):
|
||||
"""
|
||||
reference from:
|
||||
https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
|
||||
sort the points based on their x-coordinates
|
||||
"""
|
||||
xSorted = pts[np.argsort(pts[:, 0]), :]
|
||||
|
||||
# grab the left-most and right-most points from the sorted
|
||||
# x-roodinate points
|
||||
leftMost = xSorted[:2, :]
|
||||
rightMost = xSorted[2:, :]
|
||||
|
||||
# now, sort the left-most coordinates according to their
|
||||
# y-coordinates so we can grab the top-left and bottom-left
|
||||
# points, respectively
|
||||
leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
|
||||
(tl, bl) = leftMost
|
||||
|
||||
rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
|
||||
(tr, br) = rightMost
|
||||
|
||||
rect = np.array([tl, tr, br, bl], dtype="float32")
|
||||
return rect
|
||||
|
||||
def clip_det_res(self, points, img_height, img_width):
|
||||
for pno in range(points.shape[0]):
|
||||
points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
|
||||
points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
|
||||
return points
|
||||
|
||||
def filter_tag_det_res(self, dt_boxes, image_shape):
|
||||
"""对检测结果进行过滤"""
|
||||
img_height, img_width = image_shape[:2]
|
||||
dt_boxes_new = []
|
||||
for box in dt_boxes:
|
||||
box = self.order_points_clockwise(box)
|
||||
box = self.clip_det_res(box, img_height, img_width)
|
||||
rect_width = int(np.linalg.norm(box[0] - box[1]))
|
||||
rect_height = int(np.linalg.norm(box[0] - box[3]))
|
||||
if rect_width <= 3 or rect_height <= 3:
|
||||
continue
|
||||
dt_boxes_new.append(box)
|
||||
return dt_boxes_new
|
||||
@@ -1,379 +0,0 @@
|
||||
"""
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
# -*- encoding: utf-8 -*-
|
||||
# @Author: SWHL
|
||||
# @Contact: liekkaskono@163.com
|
||||
from copy import deepcopy
|
||||
import sys
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pyclipper
|
||||
import six
|
||||
from shapely.geometry import Polygon
|
||||
|
||||
|
||||
class DecodeImage:
|
||||
"""decode image"""
|
||||
|
||||
def __init__(self, img_mode="RGB", channel_first=False):
|
||||
self.img_mode = img_mode
|
||||
self.channel_first = channel_first
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
if six.PY2:
|
||||
assert (
|
||||
type(img) is str and len(img) > 0
|
||||
), "invalid input 'img' in DecodeImage"
|
||||
else:
|
||||
assert (
|
||||
type(img) is bytes and len(img) > 0
|
||||
), "invalid input 'img' in DecodeImage"
|
||||
|
||||
img = np.frombuffer(img, dtype="uint8")
|
||||
img = cv2.imdecode(img, 1)
|
||||
if img is None:
|
||||
return None
|
||||
|
||||
if self.img_mode == "GRAY":
|
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
||||
elif self.img_mode == "RGB":
|
||||
assert img.shape[2] == 3, f"invalid shape of image[{img.shape}]"
|
||||
img = img[:, :, ::-1]
|
||||
|
||||
if self.channel_first:
|
||||
img = img.transpose((2, 0, 1))
|
||||
data["image"] = img
|
||||
return data
|
||||
|
||||
|
||||
class NormalizeImage:
|
||||
"""normalize image such as substract mean, divide std"""
|
||||
|
||||
def __init__(self, scale=None, mean=None, std=None, order="chw"):
|
||||
self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
|
||||
mean = mean if mean is not None else [0.485, 0.456, 0.406]
|
||||
std = std if std is not None else [0.229, 0.224, 0.225]
|
||||
shape = (3, 1, 1) if order == "chw" else (1, 1, 3)
|
||||
self.mean = np.array(mean).reshape(shape).astype("float32")
|
||||
self.std = np.array(std).reshape(shape).astype("float32")
|
||||
|
||||
def __call__(self, data):
|
||||
img = np.array(data["image"]).astype(np.float32)
|
||||
data["image"] = (img * self.scale - self.mean) / self.std
|
||||
return data
|
||||
|
||||
|
||||
class ToCHWImage:
|
||||
"""convert hwc image to chw image"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
data["image"] = img.transpose((2, 0, 1))
|
||||
return data
|
||||
|
||||
|
||||
class KeepKeys:
|
||||
def __init__(self, keep_keys):
|
||||
self.keep_keys = keep_keys
|
||||
|
||||
def __call__(self, data):
|
||||
data_list = []
|
||||
for key in self.keep_keys:
|
||||
data_list.append(data[key])
|
||||
return data_list
|
||||
|
||||
|
||||
class DetResizeForTest:
|
||||
def __init__(self, **kwargs):
|
||||
super(DetResizeForTest, self).__init__()
|
||||
self.resize_type = 0
|
||||
if "image_shape" in kwargs:
|
||||
self.image_shape = kwargs["image_shape"]
|
||||
self.resize_type = 1
|
||||
elif "limit_side_len" in kwargs:
|
||||
self.limit_side_len = kwargs["limit_side_len"]
|
||||
self.limit_type = kwargs.get("limit_type", "min")
|
||||
elif "resize_long" in kwargs:
|
||||
self.resize_type = 2
|
||||
self.resize_long = kwargs.get("resize_long", 960)
|
||||
else:
|
||||
self.limit_side_len = 736
|
||||
self.limit_type = "min"
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
src_h, src_w, _ = img.shape
|
||||
|
||||
if self.resize_type == 0:
|
||||
# img, shape = self.resize_image_type0(img)
|
||||
img, [ratio_h, ratio_w] = self.resize_image_type0(img)
|
||||
elif self.resize_type == 2:
|
||||
img, [ratio_h, ratio_w] = self.resize_image_type2(img)
|
||||
else:
|
||||
# img, shape = self.resize_image_type1(img)
|
||||
img, [ratio_h, ratio_w] = self.resize_image_type1(img)
|
||||
data["image"] = img
|
||||
data["shape"] = np.array([src_h, src_w, ratio_h, ratio_w])
|
||||
return data
|
||||
|
||||
def resize_image_type1(self, img):
|
||||
resize_h, resize_w = self.image_shape
|
||||
ori_h, ori_w = img.shape[:2] # (h, w, c)
|
||||
ratio_h = float(resize_h) / ori_h
|
||||
ratio_w = float(resize_w) / ori_w
|
||||
img = cv2.resize(img, (int(resize_w), int(resize_h)))
|
||||
# return img, np.array([ori_h, ori_w])
|
||||
return img, [ratio_h, ratio_w]
|
||||
|
||||
def resize_image_type0(self, img):
|
||||
"""
|
||||
resize image to a size multiple of 32 which is required by the network
|
||||
args:
|
||||
img(array): array with shape [h, w, c]
|
||||
return(tuple):
|
||||
img, (ratio_h, ratio_w)
|
||||
"""
|
||||
limit_side_len = self.limit_side_len
|
||||
h, w, _ = img.shape
|
||||
|
||||
# limit the max side
|
||||
if self.limit_type == "max":
|
||||
if max(h, w) > limit_side_len:
|
||||
if h > w:
|
||||
ratio = float(limit_side_len) / h
|
||||
else:
|
||||
ratio = float(limit_side_len) / w
|
||||
else:
|
||||
ratio = 1.0
|
||||
else:
|
||||
if min(h, w) < limit_side_len:
|
||||
if h < w:
|
||||
ratio = float(limit_side_len) / h
|
||||
else:
|
||||
ratio = float(limit_side_len) / w
|
||||
else:
|
||||
ratio = 1.0
|
||||
resize_h = int(h * ratio)
|
||||
resize_w = int(w * ratio)
|
||||
|
||||
resize_h = int(round(resize_h / 32) * 32)
|
||||
resize_w = int(round(resize_w / 32) * 32)
|
||||
|
||||
try:
|
||||
if int(resize_w) <= 0 or int(resize_h) <= 0:
|
||||
return None, (None, None)
|
||||
img = cv2.resize(img, (int(resize_w), int(resize_h)))
|
||||
except:
|
||||
print(img.shape, resize_w, resize_h)
|
||||
sys.exit(0)
|
||||
ratio_h = resize_h / float(h)
|
||||
ratio_w = resize_w / float(w)
|
||||
return img, [ratio_h, ratio_w]
|
||||
|
||||
def resize_image_type2(self, img):
|
||||
h, w, _ = img.shape
|
||||
|
||||
resize_w = w
|
||||
resize_h = h
|
||||
|
||||
# Fix the longer side
|
||||
if resize_h > resize_w:
|
||||
ratio = float(self.resize_long) / resize_h
|
||||
else:
|
||||
ratio = float(self.resize_long) / resize_w
|
||||
|
||||
resize_h = int(resize_h * ratio)
|
||||
resize_w = int(resize_w * ratio)
|
||||
|
||||
max_stride = 128
|
||||
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
|
||||
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
|
||||
img = cv2.resize(img, (int(resize_w), int(resize_h)))
|
||||
ratio_h = resize_h / float(h)
|
||||
ratio_w = resize_w / float(w)
|
||||
|
||||
return img, [ratio_h, ratio_w]
|
||||
|
||||
|
||||
def transform(data, ops=None):
|
||||
"""transform"""
|
||||
if ops is None:
|
||||
ops = []
|
||||
|
||||
for op in ops:
|
||||
data = op(data)
|
||||
if data is None:
|
||||
return None
|
||||
return data
|
||||
|
||||
|
||||
def create_operators(op_param_list):
|
||||
"""
|
||||
create operators based on the config
|
||||
"""
|
||||
op_map = globals()
|
||||
ops = []
|
||||
for args in op_param_list:
|
||||
args = deepcopy(args)
|
||||
op_class = op_map[args.pop("class")]
|
||||
ops.append(op_class(**args))
|
||||
return ops
|
||||
|
||||
|
||||
def draw_text_det_res(dt_boxes, img_path):
|
||||
src_im = cv2.imread(img_path)
|
||||
for box in dt_boxes:
|
||||
box = np.array(box).astype(np.int32).reshape(-1, 2)
|
||||
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
|
||||
return src_im
|
||||
|
||||
|
||||
class DBPostProcess:
|
||||
"""The post process for Differentiable Binarization (DB)."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
thresh=0.3,
|
||||
box_thresh=0.7,
|
||||
max_candidates=1000,
|
||||
unclip_ratio=2.0,
|
||||
use_dilation=False,
|
||||
):
|
||||
self.thresh = thresh
|
||||
self.box_thresh = box_thresh
|
||||
self.max_candidates = max_candidates
|
||||
self.unclip_ratio = unclip_ratio
|
||||
self.min_size = 3
|
||||
|
||||
if use_dilation:
|
||||
self.dilation_kernel = np.array([[1, 1], [1, 1]])
|
||||
else:
|
||||
self.dilation_kernel = None
|
||||
|
||||
def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
|
||||
"""
|
||||
_bitmap: single map with shape (1, H, W),
|
||||
whose values are binarized as {0, 1}
|
||||
"""
|
||||
|
||||
bitmap = _bitmap
|
||||
height, width = bitmap.shape
|
||||
|
||||
outs = cv2.findContours(
|
||||
(bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
|
||||
)
|
||||
if len(outs) == 3:
|
||||
img, contours, _ = outs[0], outs[1], outs[2]
|
||||
elif len(outs) == 2:
|
||||
contours, _ = outs[0], outs[1]
|
||||
|
||||
num_contours = min(len(contours), self.max_candidates)
|
||||
|
||||
boxes = []
|
||||
scores = []
|
||||
for index in range(num_contours):
|
||||
contour = contours[index]
|
||||
points, sside = self.get_mini_boxes(contour)
|
||||
if sside < self.min_size:
|
||||
continue
|
||||
points = np.array(points)
|
||||
score = self.box_score_fast(pred, points.reshape(-1, 2))
|
||||
if self.box_thresh > score:
|
||||
continue
|
||||
|
||||
box = self.unclip(points).reshape(-1, 1, 2)
|
||||
box, sside = self.get_mini_boxes(box)
|
||||
if sside < self.min_size + 2:
|
||||
continue
|
||||
box = np.array(box)
|
||||
|
||||
box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
|
||||
box[:, 1] = np.clip(
|
||||
np.round(box[:, 1] / height * dest_height), 0, dest_height
|
||||
)
|
||||
boxes.append(box.astype(np.int16))
|
||||
scores.append(score)
|
||||
return np.array(boxes, dtype=np.int16), scores
|
||||
|
||||
def unclip(self, box):
|
||||
unclip_ratio = self.unclip_ratio
|
||||
poly = Polygon(box)
|
||||
distance = poly.area * unclip_ratio / poly.length
|
||||
offset = pyclipper.PyclipperOffset()
|
||||
offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
|
||||
expanded = np.array(offset.Execute(distance))
|
||||
return expanded
|
||||
|
||||
def get_mini_boxes(self, contour):
|
||||
bounding_box = cv2.minAreaRect(contour)
|
||||
points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
|
||||
|
||||
index_1, index_2, index_3, index_4 = 0, 1, 2, 3
|
||||
if points[1][1] > points[0][1]:
|
||||
index_1 = 0
|
||||
index_4 = 1
|
||||
else:
|
||||
index_1 = 1
|
||||
index_4 = 0
|
||||
if points[3][1] > points[2][1]:
|
||||
index_2 = 2
|
||||
index_3 = 3
|
||||
else:
|
||||
index_2 = 3
|
||||
index_3 = 2
|
||||
|
||||
box = [points[index_1], points[index_2], points[index_3], points[index_4]]
|
||||
return box, min(bounding_box[1])
|
||||
|
||||
def box_score_fast(self, bitmap, _box):
|
||||
h, w = bitmap.shape[:2]
|
||||
box = _box.copy()
|
||||
xmin = np.clip(np.floor(box[:, 0].min()).astype(int), 0, w - 1)
|
||||
xmax = np.clip(np.ceil(box[:, 0].max()).astype(int), 0, w - 1)
|
||||
ymin = np.clip(np.floor(box[:, 1].min()).astype(int), 0, h - 1)
|
||||
ymax = np.clip(np.ceil(box[:, 1].max()).astype(int), 0, h - 1)
|
||||
|
||||
mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
|
||||
box[:, 0] = box[:, 0] - xmin
|
||||
box[:, 1] = box[:, 1] - ymin
|
||||
cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
|
||||
return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0]
|
||||
|
||||
def __call__(self, pred, shape_list):
|
||||
pred = pred[:, 0, :, :]
|
||||
segmentation = pred > self.thresh
|
||||
|
||||
boxes_batch = []
|
||||
for batch_index in range(pred.shape[0]):
|
||||
src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
|
||||
if self.dilation_kernel is not None:
|
||||
mask = cv2.dilate(
|
||||
np.array(segmentation[batch_index]).astype(np.uint8),
|
||||
self.dilation_kernel,
|
||||
)
|
||||
else:
|
||||
mask = segmentation[batch_index]
|
||||
boxes, scores = self.boxes_from_bitmap(
|
||||
pred[batch_index], mask, src_w, src_h
|
||||
)
|
||||
|
||||
boxes_batch.append({"points": boxes})
|
||||
return boxes_batch
|
||||
@@ -1,57 +0,0 @@
|
||||
# -*- encoding: utf-8 -*-
|
||||
# @Author: SWHL
|
||||
# @Contact: liekkaskono@163.com
|
||||
import copy
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
from utils.config import conf
|
||||
from rapidocr.rapid_ocr_api import RapidOCR
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def load_language_model(lang="ch"):
|
||||
models = conf["languages"][lang]
|
||||
print("model", models)
|
||||
return RapidOCR(models)
|
||||
|
||||
|
||||
def detect_recognize(image, lang="ch", detect=True, classify=True):
|
||||
model = load_language_model(lang)
|
||||
results, ts = model(image, detect=detect, classify=classify)
|
||||
ts["total"] = sum(ts.values())
|
||||
return {"ts": ts, "results": results}
|
||||
|
||||
|
||||
def check_and_read_gif(img_path):
|
||||
if Path(img_path).suffix.lower() == "gif":
|
||||
gif = cv2.VideoCapture(img_path)
|
||||
ret, frame = gif.read()
|
||||
if not ret:
|
||||
print("Cannot read {}. This gif image maybe corrupted.")
|
||||
return None, False
|
||||
if len(frame.shape) == 2 or frame.shape[-1] == 1:
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
|
||||
imgvalue = frame[:, :, ::-1]
|
||||
return imgvalue, True
|
||||
return None, False
|
||||
|
||||
|
||||
def draw_text_det_res(dt_boxes, raw_im):
|
||||
src_im = copy.deepcopy(raw_im)
|
||||
for i, box in enumerate(dt_boxes):
|
||||
box = np.array(box).astype(np.int32).reshape(-1, 2)
|
||||
cv2.polylines(src_im, [box], True, color=(0, 0, 255), thickness=1)
|
||||
cv2.putText(
|
||||
src_im,
|
||||
str(i),
|
||||
(int(box[0][0]), int(box[0][1])),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(0, 0, 0),
|
||||
2,
|
||||
)
|
||||
return src_im
|
||||
@@ -1,160 +0,0 @@
|
||||
# !/usr/bin/env python
|
||||
# -*- encoding: utf-8 -*-
|
||||
import copy
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from functools import lru_cache
|
||||
from utils.config import conf
|
||||
from utils.utils import Ticker
|
||||
from .classify import TextClassifier
|
||||
from .detect import TextDetector
|
||||
from .recognize import TextRecognizer
|
||||
|
||||
|
||||
def get_rotate_crop_image(img, points):
|
||||
"""根据box定义, 从图像中截取相应的部分, 通过透视变换转换为标准长方形图像"""
|
||||
img_crop_width = int(
|
||||
max(
|
||||
np.linalg.norm(points[0] - points[1]),
|
||||
np.linalg.norm(points[2] - points[3]),
|
||||
)
|
||||
)
|
||||
img_crop_height = int(
|
||||
max(
|
||||
np.linalg.norm(points[0] - points[3]),
|
||||
np.linalg.norm(points[1] - points[2]),
|
||||
)
|
||||
)
|
||||
pts_std = np.float32(
|
||||
[
|
||||
[0, 0],
|
||||
[img_crop_width, 0],
|
||||
[img_crop_width, img_crop_height],
|
||||
[0, img_crop_height],
|
||||
]
|
||||
)
|
||||
transform = cv2.getPerspectiveTransform(points, pts_std)
|
||||
dst_img = cv2.warpPerspective(
|
||||
img,
|
||||
transform,
|
||||
(img_crop_width, img_crop_height),
|
||||
borderMode=cv2.BORDER_REPLICATE,
|
||||
flags=cv2.INTER_CUBIC,
|
||||
)
|
||||
dst_img_height, dst_img_width = dst_img.shape[:2]
|
||||
# 将竖向的文字方向转为横向, 仅当 高>1.5*宽 时进行转换
|
||||
if dst_img_height * 1.0 / dst_img_width >= 1.5:
|
||||
dst_img = np.rot90(dst_img)
|
||||
return dst_img
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def load_onnx_model(step, name):
|
||||
model_config = conf["models"][step][name]
|
||||
model_class = {
|
||||
"detect": TextDetector,
|
||||
"classify": TextClassifier,
|
||||
"recognize": TextRecognizer,
|
||||
}[step]
|
||||
return model_class(model_config["path"], model_config.get("config"))
|
||||
|
||||
|
||||
class RapidOCR:
|
||||
def __init__(self, config):
|
||||
super(RapidOCR).__init__()
|
||||
self.config = config
|
||||
self.text_score = config["config"]["text_score"]
|
||||
self.min_height = config["config"]["min_height"]
|
||||
|
||||
models = config["models"]
|
||||
self.text_detector = load_onnx_model("detect", models["detect"])
|
||||
self.text_recognizer = load_onnx_model("recognize", models["recognize"])
|
||||
self.text_cls = load_onnx_model("classify", models["classify"])
|
||||
|
||||
def __call__(self, img: np.ndarray, detect=True, classify=True):
|
||||
ticker = Ticker()
|
||||
h, w = img.shape[:2]
|
||||
if not detect or h < self.min_height:
|
||||
dt_boxes, img_crop_list = self.get_boxes_img_without_det(img, h, w)
|
||||
ticker.tick("detect")
|
||||
else:
|
||||
dt_boxes = self.text_detector(img)
|
||||
ticker.tick("detect")
|
||||
|
||||
if dt_boxes is None or len(dt_boxes) < 1:
|
||||
return [], ticker.maps
|
||||
if conf["global"]["verbose"]:
|
||||
print(f"boxes num: {len(dt_boxes)}")
|
||||
|
||||
dt_boxes = self.sorted_boxes(dt_boxes)
|
||||
|
||||
img_crop_list = self.get_crop_img_list(img, dt_boxes)
|
||||
ticker.tick("post-detect")
|
||||
|
||||
if classify:
|
||||
# 进行子图像角度修正
|
||||
img_crop_list, _ = self.text_cls(img_crop_list)
|
||||
ticker.tick("classify")
|
||||
if conf["global"]["verbose"]:
|
||||
print(f"cls num: {len(img_crop_list)}")
|
||||
|
||||
recog_result = self.text_recognizer(img_crop_list)
|
||||
ticker.tick("recognize")
|
||||
if conf["global"]["verbose"]:
|
||||
print(f"rec_res num: {len(recog_result)}")
|
||||
|
||||
results = self.filter_boxes_rec_by_score(dt_boxes, recog_result)
|
||||
ticker.tick("post-recognize")
|
||||
return results, ticker.maps
|
||||
|
||||
def get_boxes_img_without_det(self, img, h, w):
|
||||
x0, y0, x1, y1 = 0, 0, w, h
|
||||
dt_boxes = np.array([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
|
||||
dt_boxes = dt_boxes[np.newaxis, ...]
|
||||
img_crop_list = [img]
|
||||
return dt_boxes, img_crop_list
|
||||
|
||||
def get_crop_img_list(self, img, dt_boxes):
|
||||
img_crop_list = []
|
||||
for box in dt_boxes:
|
||||
tmp_box = copy.deepcopy(box)
|
||||
img_crop = get_rotate_crop_image(img, tmp_box)
|
||||
img_crop_list.append(img_crop)
|
||||
return img_crop_list
|
||||
|
||||
@staticmethod
|
||||
def sorted_boxes(dt_boxes):
|
||||
"""对文本框检测结果进行排序, 调整为从上到下、从左到右
|
||||
|
||||
args:
|
||||
dt_boxes(array): detected text boxes with shape [4, 2]
|
||||
return:
|
||||
sorted boxes(array) with shape [4, 2]
|
||||
"""
|
||||
|
||||
class AlignBox:
|
||||
def __init__(self, data) -> None:
|
||||
self.data = data
|
||||
self.x = data[0][0]
|
||||
self.y = data[0][1]
|
||||
|
||||
def __lt__(self, other: "AlignBox"):
|
||||
dy = self.y - other.y
|
||||
# y差距小于10, 视为相等, 根据x排序
|
||||
if abs(dy) < 10:
|
||||
return self.x < other.x
|
||||
# 否则根据y排序
|
||||
return dy < 0
|
||||
|
||||
align_boxes = sorted([AlignBox(b) for b in dt_boxes])
|
||||
return [b.data for b in align_boxes]
|
||||
|
||||
def filter_boxes_rec_by_score(self, dt_boxes, rec_res):
|
||||
results = []
|
||||
for box, rec_reuslt in zip(dt_boxes, rec_res):
|
||||
text, score = rec_reuslt
|
||||
if score >= self.text_score:
|
||||
results.append({"box": box, "text": text, "score": score})
|
||||
return results
|
||||
@@ -1,158 +0,0 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import json
|
||||
import math
|
||||
from typing import List
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from utils.utils import OrtInferSession
|
||||
|
||||
|
||||
class CTCLabelDecode:
|
||||
"""Convert between text-label and text-index"""
|
||||
|
||||
def __init__(self, characters: List[str]):
|
||||
super(CTCLabelDecode, self).__init__()
|
||||
|
||||
self.characters = characters
|
||||
self.characters.append(" ")
|
||||
|
||||
dict_character = self.add_special_char(self.characters)
|
||||
self.character = dict_character
|
||||
|
||||
self.dict = {}
|
||||
for i, char in enumerate(dict_character):
|
||||
self.dict[char] = i
|
||||
|
||||
def __call__(self, preds, label=None):
|
||||
preds_idx = preds.argmax(axis=2)
|
||||
preds_prob = preds.max(axis=2)
|
||||
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
|
||||
if label is None:
|
||||
return text
|
||||
label = self.decode(label)
|
||||
return text, label
|
||||
|
||||
def add_special_char(self, dict_character):
|
||||
dict_character = ["blank"] + dict_character
|
||||
return dict_character
|
||||
|
||||
def get_ignored_tokens(self):
|
||||
return [0] # for ctc blank
|
||||
|
||||
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
|
||||
"""convert text-index into text-label."""
|
||||
|
||||
result_list = []
|
||||
ignored_tokens = self.get_ignored_tokens()
|
||||
batch_size = len(text_index)
|
||||
for batch_idx in range(batch_size):
|
||||
char_list = []
|
||||
conf_list = []
|
||||
for idx in range(len(text_index[batch_idx])):
|
||||
if text_index[batch_idx][idx] in ignored_tokens:
|
||||
continue
|
||||
if is_remove_duplicate:
|
||||
# only for predict
|
||||
if (
|
||||
idx > 0
|
||||
and text_index[batch_idx][idx - 1] == text_index[batch_idx][idx]
|
||||
):
|
||||
continue
|
||||
char_list.append(self.character[int(text_index[batch_idx][idx])])
|
||||
if text_prob is not None:
|
||||
conf_list.append(text_prob[batch_idx][idx])
|
||||
else:
|
||||
conf_list.append(1)
|
||||
# avoid `Mean of empty slice.` warning
|
||||
score = np.mean(conf_list) if conf_list else 0
|
||||
text = "".join(char_list)
|
||||
result_list.append((text, score))
|
||||
return result_list
|
||||
|
||||
|
||||
class TextRecognizer:
|
||||
def __init__(self, path, config):
|
||||
self.rec_batch_num = config.get("rec_batch_num", 6)
|
||||
|
||||
session_instance = OrtInferSession(path)
|
||||
self.session = session_instance.session
|
||||
|
||||
metamap = session_instance.session.get_modelmeta().custom_metadata_map
|
||||
chars = metamap["dictionary"].splitlines()
|
||||
self.postprocess_op = CTCLabelDecode(chars)
|
||||
self.rec_image_shape = json.loads(metamap["shape"])
|
||||
self.input_name = session_instance.get_input_name()
|
||||
|
||||
def resize_norm_img(self, img, max_wh_ratio):
|
||||
img_channel, img_height, img_width = self.rec_image_shape
|
||||
assert img_channel == img.shape[2]
|
||||
|
||||
img_width = int((32 * max_wh_ratio))
|
||||
max_wh_ratio = 1
|
||||
|
||||
h, w = img.shape[:2]
|
||||
ratio = w / float(h)
|
||||
if math.ceil(img_height * ratio) > img_width:
|
||||
resized_w = img_width
|
||||
else:
|
||||
resized_w = int(math.ceil(img_height * ratio))
|
||||
|
||||
resized_image = cv2.resize(img, (resized_w, img_height))
|
||||
resized_image = resized_image.astype("float32")
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
|
||||
padding_im = np.zeros((img_channel, img_height, img_width), dtype=np.float32)
|
||||
padding_im[:, :, 0:resized_w] = resized_image
|
||||
return padding_im
|
||||
|
||||
def __call__(self, img_list: List[np.ndarray]):
|
||||
if isinstance(img_list, np.ndarray):
|
||||
img_list = [img_list]
|
||||
|
||||
# Calculate the aspect ratio of all text bars
|
||||
width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
|
||||
|
||||
# Sorting can speed up the recognition process
|
||||
indices = np.argsort(np.array(width_list))
|
||||
|
||||
img_num = len(img_list)
|
||||
rec_res = [["", 0.0]] * img_num
|
||||
|
||||
batch_num = self.rec_batch_num
|
||||
for beg_img_no in range(0, img_num, batch_num):
|
||||
end_img_no = min(img_num, beg_img_no + batch_num)
|
||||
max_wh_ratio = 0
|
||||
for ino in range(beg_img_no, end_img_no):
|
||||
h, w = img_list[indices[ino]].shape[0:2]
|
||||
wh_ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||||
|
||||
norm_img_batch = []
|
||||
for ino in range(beg_img_no, end_img_no):
|
||||
norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
|
||||
norm_img_batch.append(norm_img[np.newaxis, :])
|
||||
norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32)
|
||||
|
||||
onnx_inputs = {self.input_name: norm_img_batch}
|
||||
preds = self.session.run(None, onnx_inputs)[0]
|
||||
rec_result = self.postprocess_op(preds)
|
||||
|
||||
for rno in range(len(rec_result)):
|
||||
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
|
||||
return rec_res
|
||||
@@ -1,9 +0,0 @@
|
||||
onnxruntime>=1.7.0
|
||||
opencv-python-headless==4.5.4.60
|
||||
six>=1.15.0
|
||||
pyclipper>=1.2.1
|
||||
numpy>=1.19.1
|
||||
Shapely>=1.7.1
|
||||
Flask>=2.1.2
|
||||
PyYAML
|
||||
waitress
|
||||
@@ -1,143 +0,0 @@
|
||||
html {
|
||||
height: 100%;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
body {
|
||||
background-color: #ebedef;
|
||||
min-height: 100%;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.btn-gen {
|
||||
background-color: #00a1d6;
|
||||
text-align: center;
|
||||
border-radius: 18px;
|
||||
margin: 0 5px 0 5px;
|
||||
font-size: 15px;
|
||||
padding: 5px 10px;
|
||||
height: 20px;
|
||||
min-width: 120px;
|
||||
text-transform: uppercase;
|
||||
color: #fff;
|
||||
border: none;
|
||||
}
|
||||
|
||||
.btn-gen:hover,
|
||||
.btn-gen:focus {
|
||||
border-color: #23AAEE;
|
||||
background-color: #23AAEE;
|
||||
color: white;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.row {
|
||||
margin: 15px;
|
||||
}
|
||||
|
||||
.small {
|
||||
font-size: 0.8em;
|
||||
}
|
||||
|
||||
.verysmall {
|
||||
font-size: 0.5em;
|
||||
}
|
||||
|
||||
.area {
|
||||
text-align: center;
|
||||
height: auto;
|
||||
margin: auto;
|
||||
}
|
||||
|
||||
.leftarea {
|
||||
float: left;
|
||||
width: 50%;
|
||||
height: auto;
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.rightarea {
|
||||
float: left;
|
||||
width: 50%;
|
||||
height: auto;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.table {
|
||||
width: auto;
|
||||
height: auto;
|
||||
margin: 0 auto;
|
||||
|
||||
}
|
||||
|
||||
.span_title {
|
||||
width: 98%;
|
||||
height: 36px;
|
||||
margin-top: 4px;
|
||||
line-height: 32px;
|
||||
background-color: #00a1d6;
|
||||
border: 1px solid #00a1d6;
|
||||
border-radius: 20px;
|
||||
color: #fff;
|
||||
display: inline-block;
|
||||
text-align: center;
|
||||
font-size: 22px;
|
||||
transition: .3s;
|
||||
box-sizing: border-box;
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.uplodNote {
|
||||
font-size: 10px;
|
||||
color: #A1A1A1;
|
||||
}
|
||||
|
||||
a {
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
#input-hint {
|
||||
margin: auto;
|
||||
cursor: pointer;
|
||||
|
||||
}
|
||||
|
||||
#result_view {
|
||||
position: relative;
|
||||
width: 95%;
|
||||
margin: auto;
|
||||
}
|
||||
|
||||
#result_view canvas {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
}
|
||||
|
||||
/* wrapper */
|
||||
#wrapper {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
background:
|
||||
linear-gradient(#1a98ca, #1a98ca),
|
||||
linear-gradient(90deg, #ffffff33 1px, transparent 0, transparent 19px),
|
||||
linear-gradient(#ffffff33 1px, transparent 0, transparent 19px),
|
||||
linear-gradient(transparent, #1a98ca);
|
||||
background-size: 100% 1.5%, 10% 100%, 100% 8%, 100% 100%;
|
||||
background-repeat: no-repeat, repeat, repeat, no-repeat;
|
||||
background-position: 0% 100%, 0 0, 0 0, 0 0;
|
||||
/* 初始位置 */
|
||||
clip-path: polygon(0% 0%, 100% 0%, 100% 1.5%, 0% 1.5%);
|
||||
/* 添加动画效果 */
|
||||
animation: move 1s infinite linear;
|
||||
}
|
||||
|
||||
@keyframes move {
|
||||
to {
|
||||
background-position: 0 100%, 0 0, 0 0, 0 0;
|
||||
/* 终止位置 */
|
||||
clip-path: polygon(0% 0%, 100% 0%, 100% 100%, 0% 100%);
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 17 KiB |
@@ -1,4 +0,0 @@
|
||||
<svg viewBox="0 0 400 200"
|
||||
xmlns="http://www.w3.org/2000/svg">
|
||||
<text x="4" y="20" class="large">未选择图片</text>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 129 B |
@@ -1,233 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="zh">
|
||||
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>RapidOCR Demo</title>
|
||||
<link rel=stylesheet type="text/css" href="/static/css/main.css">
|
||||
<link rel="shortcut icon" href="/static/favicon.ico" type="image/x-icon" />
|
||||
<style>
|
||||
h1 {
|
||||
text-align: center;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.header {
|
||||
padding: 10px;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="header">
|
||||
<h1>
|
||||
Rapid🗲OCR Demo
|
||||
 
|
||||
<a href="https://github.com/RapidAI/RapidOCR" class="verysmall">欢迎Star</a> 
|
||||
<a href="https://github.com/RapidAI/RapidOCR/tree/main/ocrweb#readme" class="verysmall">了解Demo</a>
|
||||
</h1>
|
||||
</div>
|
||||
<div class="area">
|
||||
<input type="file" style="display: none;" name="pic" id="target_image" />
|
||||
<div class="row">
|
||||
<span id="input-hint" class="btn-gen">选择图片 <span class="small">支持拖拽/粘贴/点击; 支持常见图片格式; 大小不超过3M</span></span>
|
||||
</div>
|
||||
<div class="row">
|
||||
<span>选择语言:</span>
|
||||
<span id="langs"></span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="area">
|
||||
<div class="leftarea" id="detectDiv">
|
||||
<span class="span_title">文 本 检 测 结 果</span><br /><br />
|
||||
<div id="result_view">
|
||||
<img src="/static/hint.svg" alt="目标图片" style="display: none">
|
||||
<canvas id="overlay"></canvas>
|
||||
<div id="wrapper" style="display: none"></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="rightarea" id="recogDiv">
|
||||
<span id="rec_res" class="span_title">文 本 识 别 结 果</span>
|
||||
<br /><br />
|
||||
<table id="locTable" class="table" cellspacing="0" cellpadding="5"></table>
|
||||
</div>
|
||||
</div>
|
||||
<script src="/static/js/jquery-3.0.0.min.js"></script>
|
||||
<script type="text/javascript">
|
||||
const cvs = $('canvas')[0];
|
||||
const ctx = cvs.getContext('2d');
|
||||
var targetFile = null;
|
||||
$(function () {
|
||||
// onload
|
||||
$.ajax({
|
||||
url: '/lang',
|
||||
type: 'GET',
|
||||
success: (data) => {
|
||||
console.log(data);
|
||||
let langs = $('#langs');
|
||||
langs.html('');
|
||||
let checked = ' checked';
|
||||
for (let item of data.data) {
|
||||
langs.append(`<label class="btn-gen"><input name="lang" type="radio" value="${item.code}"${checked}>${item.name}</label>`);
|
||||
checked = '';
|
||||
}
|
||||
$('input[name=lang]').on('change', requestOCR);
|
||||
}
|
||||
})
|
||||
})
|
||||
|
||||
$('#input-hint').on('click', function (e) {
|
||||
$('#target_image').click();
|
||||
});
|
||||
$('body').on('dragover', function (e) {
|
||||
e.preventDefault();
|
||||
});
|
||||
function handleData(trans) {
|
||||
if (trans.items) {
|
||||
for (let i = 0; i < trans.items.length; i++) {
|
||||
// If dropped items aren't files, reject them
|
||||
if (trans.items[i].kind === 'file') {
|
||||
targetFile = trans.items[i].getAsFile();
|
||||
console.log(`handleData: items[${i}].name = ${targetFile.name}`);
|
||||
requestOCR();
|
||||
return;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Use DataTransfer interface to access the file(s)
|
||||
for (let i = 0; i < trans.files.length; i++) {
|
||||
targetFile = trans.files[i];
|
||||
console.log(`handleData: files[${i}].name = ${targetFile.name}`);
|
||||
requestOCR();
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
$('body').on('paste', function (e) {
|
||||
let trans = e.originalEvent.clipboardData;
|
||||
handleData(trans);
|
||||
})
|
||||
$('body').on('drop', function (e) {
|
||||
e.preventDefault();
|
||||
let trans = e.originalEvent.dataTransfer;
|
||||
handleData(trans);
|
||||
});
|
||||
$("#target_image").on('change', function () {
|
||||
targetFile = $("#target_image")[0].files[0];
|
||||
console.log('Use file', targetFile)
|
||||
requestOCR();
|
||||
});
|
||||
$('#result_view > img').on('load', function (e) {
|
||||
let img = $('#result_view > img')[0];
|
||||
cvs.width = e.target.naturalWidth;
|
||||
cvs.height = e.target.naturalHeight;
|
||||
ctx.clearRect(0, 0, cvs.width, cvs.height);
|
||||
ctx.drawImage(img, 0, 0);
|
||||
});
|
||||
function getTs(ts, keys) {
|
||||
let t = 0;
|
||||
for (let key of keys) {
|
||||
t += (ts[key] || 0);
|
||||
}
|
||||
return t.toFixed(4);
|
||||
}
|
||||
function drawPolyLines(points) {
|
||||
ctx.moveTo(points[0][0], points[0][1]);
|
||||
for (let pt of points) {
|
||||
ctx.lineTo(pt[0], pt[1]);
|
||||
}
|
||||
ctx.lineTo(points[0][0], points[0][1]);
|
||||
ctx.stroke();
|
||||
}
|
||||
function requestOCR() {
|
||||
if (!targetFile) {
|
||||
return;
|
||||
}
|
||||
// 判断图像格式是否匹配
|
||||
let imageName = targetFile.name;
|
||||
let index = imageName.lastIndexOf('.');
|
||||
let extName = imageName.substr(index + 1);
|
||||
let imgArr = ['webp', 'jpg', 'bmp', 'png', 'jpeg'];
|
||||
if (!(imgArr.includes(extName.toLocaleLowerCase()))) {
|
||||
alert("图像文件格式不支持！");
|
||||
return;
|
||||
}
|
||||
|
||||
// 判断图像大小是否符合
|
||||
let imageSize = targetFile.size / 1024 / 1024;
|
||||
if (imageSize >= 3) {
|
||||
alert("图像大小超过3M!");
|
||||
return;
|
||||
}
|
||||
var reader = new FileReader();
|
||||
reader.onload = function (e) {
|
||||
$('#result_view > img').attr('src', e.target.result);
|
||||
};
|
||||
reader.readAsDataURL(targetFile);
|
||||
|
||||
var formdata = new FormData();
|
||||
var token = location.hash.slice(1);
|
||||
formdata.set('token', token);
|
||||
formdata.set('image', targetFile);
|
||||
formdata.set('lang', $('input[name=lang]:checked').val());
|
||||
|
||||
$.ajax({
|
||||
url: "/ocr",
|
||||
type: "POST",
|
||||
data: formdata,
|
||||
processData: false,
|
||||
contentType: false,
|
||||
beforeSend: function () {
|
||||
$("#wrapper").show();
|
||||
$("#locTable").hide();
|
||||
$("#rec_res").html("文本识别结果");
|
||||
},
|
||||
error: function (evt) {
|
||||
console.log(evt);
|
||||
alert('检测失败: ' + evt.status + ', ' + evt.statusText);
|
||||
},
|
||||
success: function (resp) {
|
||||
$("#wrapper").hide();
|
||||
if (resp.msg !== 'OK') {
|
||||
console.log(resp);
|
||||
alert('检测失败: ', resp.msg);
|
||||
return;
|
||||
}
|
||||
let data = resp.data;
|
||||
$("#rec_res").html("文 本 检 测 结 果(Total:" + data.ts.total.toFixed(3) + "s)")
|
||||
$("#rec_res").show();
|
||||
let html = [];
|
||||
html.push('<tr>' +
|
||||
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|
||||
'<th align="center">Cls: ' + getTs(data.ts, ['classify', 'post-classify']) + 's</th>' +
|
||||
'<th align="left">Rec: ' + getTs(data.ts, ['recognize', 'post-recognize']) + 's</th>' +
|
||||
"</tr>")
|
||||
html.push("<tr>" +
|
||||
'<th bgcolor="#C0C0C0">序号</th>' +
|
||||
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|
||||
'<th bgcolor="#C0C0C0">置信度</th>' +
|
||||
"</tr>")
|
||||
ctx.strokeStyle = '#ff0000'
|
||||
ctx.font = '16px sans-serif'
|
||||
for (let idx = 0; idx < data.results.length; idx++) {
|
||||
let result = data.results[idx];
|
||||
drawPolyLines(result.box);
|
||||
ctx.fillText(idx, result.box[0][0], result.box[0][1]);
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||||
ctx.strokeText(idx, result.box[0][0], result.box[0][1]);
|
||||
html.push('<tr><td>');
|
||||
html.push(idx);
|
||||
html.push('</td><td>');
|
||||
html.push(result.text);
|
||||
html.push('</td><td>');
|
||||
html.push(result.score.toFixed(4));
|
||||
html.push('</td></tr>');
|
||||
}
|
||||
$("#locTable").html(html.join(''));
|
||||
$("#locTable").show();
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||||
}
|
||||
});
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||||
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|
||||
</script>
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||||
</body>
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||||
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||||
</html>
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||||
-4
@@ -1,4 +0,0 @@
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b,c,d=0;b=" "+a+" ";while(c=this[d++])if(1===c.nodeType&&(" "+mb(c)+" ").replace(lb," ").indexOf(b)>-1)return!0;return!1}});var nb=/\r/g,ob=/[\x20\t\r\n\f]+/g;r.fn.extend({val:function(a){var b,c,d,e=this[0];{if(arguments.length)return d=r.isFunction(a),this.each(function(c){var e;1===this.nodeType&&(e=d?a.call(this,c,r(this).val()):a,null==e?e="":"number"==typeof e?e+="":r.isArray(e)&&(e=r.map(e,function(a){return null==a?"":a+""})),b=r.valHooks[this.type]||r.valHooks[this.nodeName.toLowerCase()],b&&"set"in b&&void 0!==b.set(this,e,"value")||(this.value=e))});if(e)return b=r.valHooks[e.type]||r.valHooks[e.nodeName.toLowerCase()],b&&"get"in b&&void 0!==(c=b.get(e,"value"))?c:(c=e.value,"string"==typeof c?c.replace(nb,""):null==c?"":c)}}}),r.extend({valHooks:{option:{get:function(a){var b=r.find.attr(a,"value");return null!=b?b:r.trim(r.text(a)).replace(ob," ")}},select:{get:function(a){for(var b,c,d=a.options,e=a.selectedIndex,f="select-one"===a.type,g=f?null:[],h=f?e+1:d.length,i=0>e?h:f?e:0;h>i;i++)if(c=d[i],(c.selected||i===e)&&!c.disabled&&(!c.parentNode.disabled||!r.nodeName(c.parentNode,"optgroup"))){if(b=r(c).val(),f)return b;g.push(b)}return g},set:function(a,b){var c,d,e=a.options,f=r.makeArray(b),g=e.length;while(g--)d=e[g],(d.selected=r.inArray(r.valHooks.option.get(d),f)>-1)&&(c=!0);return c||(a.selectedIndex=-1),f}}}}),r.each(["radio","checkbox"],function(){r.valHooks[this]={set:function(a,b){return r.isArray(b)?a.checked=r.inArray(r(a).val(),b)>-1:void 0}},o.checkOn||(r.valHooks[this].get=function(a){return null===a.getAttribute("value")?"on":a.value})});var pb=/^(?:focusinfocus|focusoutblur)$/;r.extend(r.event,{trigger:function(b,c,e,f){var g,h,i,j,k,m,n,o=[e||d],p=l.call(b,"type")?b.type:b,q=l.call(b,"namespace")?b.namespace.split("."):[];if(h=i=e=e||d,3!==e.nodeType&&8!==e.nodeType&&!pb.test(p+r.event.triggered)&&(p.indexOf(".")>-1&&(q=p.split("."),p=q.shift(),q.sort()),k=p.indexOf(":")<0&&"on"+p,b=b[r.expando]?b:new r.Event(p,"object"==typeof b&&b),b.isTrigger=f?2:3,b.namespace=q.join("."),b.rnamespace=b.namespace?new RegExp("(^|\\.)"+q.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,b.result=void 0,b.target||(b.target=e),c=null==c?Line truncated
|
||||
@@ -1,27 +0,0 @@
|
||||
# -*- encoding: utf-8 -*-
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import yaml
|
||||
|
||||
root_dir = Path(__file__).parent.parent
|
||||
|
||||
|
||||
def get_resource_path(name: str):
|
||||
"""依次检查资源文件的多个可能路径, 返回首个存在的路径"""
|
||||
for path in [
|
||||
# wrapper.exe 所在目录
|
||||
Path(root_dir.parent, name),
|
||||
# main.exe 所在目录 / main.py 所在目录
|
||||
Path(root_dir, name),
|
||||
# main.exe 所在目录
|
||||
Path(sys.argv[0]).parent / name,
|
||||
# 工作目录
|
||||
Path(name),
|
||||
]:
|
||||
if path.exists():
|
||||
print("Loaded:", path)
|
||||
return path
|
||||
raise FileNotFoundError(name)
|
||||
|
||||
|
||||
conf = yaml.safe_load(get_resource_path("config.yaml").read_text(encoding="utf-8"))
|
||||
@@ -1,86 +0,0 @@
|
||||
import json
|
||||
import time
|
||||
import warnings
|
||||
|
||||
from onnxruntime import (
|
||||
get_available_providers,
|
||||
get_device,
|
||||
SessionOptions,
|
||||
InferenceSession,
|
||||
)
|
||||
from utils.config import conf, get_resource_path
|
||||
|
||||
|
||||
def parse_bool(val):
|
||||
if not isinstance(val, str):
|
||||
return bool(val)
|
||||
return val.lower() in ("1", "true", "yes")
|
||||
|
||||
|
||||
def default(obj):
|
||||
if hasattr(obj, "tolist"):
|
||||
return obj.tolist()
|
||||
return obj
|
||||
|
||||
|
||||
def tojson(obj, **kws):
|
||||
return json.dumps(obj, default=default, ensure_ascii=False, **kws) + "\n"
|
||||
|
||||
|
||||
class OrtInferSession:
|
||||
def __init__(self, model_path):
|
||||
ort_conf = conf["global"]
|
||||
sess_opt = SessionOptions()
|
||||
sess_opt.log_severity_level = 4
|
||||
sess_opt.enable_cpu_mem_arena = False
|
||||
|
||||
cuda_ep = "CUDAExecutionProvider"
|
||||
cpu_ep = "CPUExecutionProvider"
|
||||
|
||||
providers = []
|
||||
if (
|
||||
ort_conf["use_cuda"]
|
||||
and get_device() == "GPU"
|
||||
and cuda_ep in get_available_providers()
|
||||
):
|
||||
providers = [(cuda_ep, ort_conf[cuda_ep])]
|
||||
|
||||
providers.append(cpu_ep)
|
||||
|
||||
self.session = InferenceSession(
|
||||
str(get_resource_path(model_path)),
|
||||
sess_options=sess_opt,
|
||||
providers=providers,
|
||||
)
|
||||
|
||||
if ort_conf["use_cuda"] and cuda_ep not in self.session.get_providers():
|
||||
warnings.warn(
|
||||
f"{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be executed under {cpu_ep}.\n"
|
||||
"Please ensure the installed onnxruntime-gpu version matches your cuda and cudnn version, "
|
||||
"you can check their relations from the offical web site: "
|
||||
"https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html",
|
||||
RuntimeWarning,
|
||||
)
|
||||
|
||||
def get_input_name(self, input_idx=0):
|
||||
return self.session.get_inputs()[input_idx].name
|
||||
|
||||
def get_output_name(self, output_idx=0):
|
||||
return self.session.get_outputs()[output_idx].name
|
||||
|
||||
|
||||
class Ticker:
|
||||
def __init__(self, reset=True) -> None:
|
||||
self.ts = time.perf_counter()
|
||||
self.reset = reset
|
||||
self.maps = {}
|
||||
|
||||
def tick(self, name, reset=None):
|
||||
ts = time.perf_counter()
|
||||
if reset is None:
|
||||
reset = self.reset
|
||||
dt = ts - self.ts
|
||||
if reset:
|
||||
self.ts = ts
|
||||
self.maps[name] = dt
|
||||
return dt
|
||||
@@ -1,55 +0,0 @@
|
||||
/*
|
||||
针对Pyinstaller目录下文件过多的问题, 使用外部exe+system调用的方式实现资源文件/依赖库分离
|
||||
*/
|
||||
#include <windows.h>
|
||||
#include <stdio.h>
|
||||
|
||||
void combine(char *destination, const char *path1, const char *path2)
|
||||
{
|
||||
if (path1 == NULL && path2 == NULL)
|
||||
{
|
||||
strcpy(destination, "");
|
||||
}
|
||||
else if (path2 == NULL || strlen(path2) == 0)
|
||||
{
|
||||
strcpy(destination, path1);
|
||||
}
|
||||
else if (path1 == NULL || strlen(path1) == 0)
|
||||
{
|
||||
strcpy(destination, path2);
|
||||
}
|
||||
else
|
||||
{
|
||||
strcpy(destination, path1);
|
||||
|
||||
size_t idx = 0, sepIdx = 0;
|
||||
size_t size1 = strlen(path1);
|
||||
while (idx < size1)
|
||||
{
|
||||
idx++;
|
||||
if (destination[idx] == '\\' || destination[idx] == '/')
|
||||
{
|
||||
sepIdx = idx;
|
||||
}
|
||||
}
|
||||
// Trim destination: delete from last separator to end.
|
||||
destination[sepIdx + 1] = '\0';
|
||||
strcat(destination, path2);
|
||||
}
|
||||
}
|
||||
|
||||
void main()
|
||||
{
|
||||
// Set title
|
||||
system("title Rapid OCR Server");
|
||||
// Get wrapper exe path
|
||||
TCHAR path[MAX_PATH];
|
||||
GetModuleFileName(NULL, path, MAX_PATH);
|
||||
|
||||
TCHAR exe_path[MAX_PATH];
|
||||
// Get real exe path from wrapper exe path
|
||||
combine(exe_path, path, "ocrweb\\main.exe");
|
||||
printf("Run real exe: %s\n", exe_path);
|
||||
// Run real exe
|
||||
system(exe_path);
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
id ICON "static/favicon.ico"
|
||||
Reference in new issue
Block a user