update: dev

.clang-format

@@ -1,40 +0,0 @@
-BasedOnStyle: Chromium
-IncludeBlocks: Preserve
-IncludeCategories:
-  - Regex: '^<.*>'
-    Priority: 1
-  - Regex: '^".*"'
-    Priority: 2
-SortIncludes: true
-Language: Cpp
-AccessModifierOffset: 2
-AlignAfterOpenBracket: true
-AlignConsecutiveAssignments: false
-AlignConsecutiveDeclarations: false
-AlignEscapedNewlines: Right
-AlignOperands: true
-AlignTrailingComments: false
-AllowAllParametersOfDeclarationOnNextLine: true
-AllowShortBlocksOnASingleLine: false
-AllowShortCaseLabelsOnASingleLine: true
-AllowShortFunctionsOnASingleLine: None
-AllowShortIfStatementsOnASingleLine: true
-AllowShortLoopsOnASingleLine: true
-AlwaysBreakAfterReturnType: None
-AlwaysBreakBeforeMultilineStrings: true
-AlwaysBreakTemplateDeclarations: false
-BinPackArguments: false
-BinPackParameters: false
-BreakBeforeBraces: Attach
-BreakBeforeInheritanceComma: false
-BreakBeforeTernaryOperators: true
-BreakStringLiterals: false
-ColumnLimit: 88
-CompactNamespaces: false
-ConstructorInitializerAllOnOneLineOrOnePerLine: true
-ConstructorInitializerIndentWidth: 4
-ContinuationIndentWidth: 4
-IndentCaseLabels: true
-IndentWidth: 4
-TabWidth: 4
-UseTab: Never

.flake8

@@ -0,0 +1,4 @@
+[flake8]
+max-line-length = 80
+extend-ignore = E203,E501,E402
+exclude = .git,__pycache__,build,.venv/,third_party

.github/workflows/ci.yml

@@ -11,7 +11,6 @@ on:
 jobs:
   build:
     runs-on: ubuntu-latest
-    # runs-on: self-hosted
 
     steps:
     - name: Checkout code
@@ -29,9 +28,5 @@ jobs:
         pip install -r requirements.txt
         sudo apt-get update && sudo apt-get install ffmpeg libsm6 libxext6 -y
 
-    - name: Build and install
-      run: pip install .
-
     - name: Run tests
-      # run: python -m pytest
-      run: python tests/test_basic.py
+      run: python test_app_cli.py

.github/workflows/format.yml

@@ -1,23 +1,24 @@
-# This is a format job. Pre-commit has a first-party GitHub action, so we use
-# that: https://github.com/pre-commit/action
-
-name: Format
-
+name: Format and Lint Checks
 on:
-  workflow_dispatch:
-  pull_request:
   push:
     branches:
-    - main
-
+      - main
+    paths:
+      - '*.py'
+  pull_request:
+    types: [ assigned, opened, synchronize, reopened ]
 jobs:
-  pre-commit:
-    name: Format
+  check:
+    name: Format and Lint Checks
     runs-on: ubuntu-latest
-    # runs-on: self-hosted
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-python@v5
-      with:
-        python-version: "3.x"
-    - uses: pre-commit/[email protected]
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+          cache: 'pip'
+      - run: python -m pip install --upgrade pip
+      - run: python -m pip install .[dev]
+      - run: python -m flake8 ui/*.py hloc/*.py hloc/matchers/*.py hloc/extractors/*.py 
+      - run: python -m isort ui/*.py hloc/*.py hloc/matchers/*.py hloc/extractors/*.py --check-only --diff
+      - run: python -m black ui/*.py hloc/*.py hloc/matchers/*.py hloc/extractors/*.py --check --diff

.github/workflows/pip.yml

@@ -1,62 +0,0 @@
-name: Pip
-on:
-  workflow_dispatch:
-  pull_request:
-  push:
-    branches:
-      - main
-
-jobs:
-  build:
-    strategy:
-      fail-fast: false
-      matrix:
-        platform: [ubuntu-latest]
-        python-version: ["3.9", "3.10"]
-
-    runs-on: ${{ matrix.platform }}
-    # runs-on: self-hosted
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        submodules: recursive
-
-    - uses: actions/setup-python@v5
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Upgrade setuptools and wheel
-      run: |
-        pip install --upgrade setuptools wheel
-
-    - name: Install dependencies on Ubuntu
-      if: runner.os == 'Linux'
-      run: |
-        sudo apt-get update
-        sudo apt-get install libopencv-dev -y
-
-    - name: Install dependencies on macOS
-      if: runner.os == 'macOS'
-      run: |
-        brew update
-        brew install opencv
-
-    - name: Install dependencies on Windows
-      if: runner.os == 'Windows'
-      run: |
-        choco install opencv -y
-
-    - name: Add requirements
-      run: python -m pip install --upgrade wheel setuptools
-
-    - name: Install Python dependencies
-      run: |
-        pip install pytest
-        pip install -r requirements.txt
-        sudo apt-get update && sudo apt-get install ffmpeg libsm6 libxext6 -y
-
-    - name: Build and install
-      run: pip install .
-
-    - name: Test
-      run: python -m pytest

.github/workflows/release-drafter.yml

@@ -0,0 +1,16 @@
+name: Release Drafter
+
+on:
+  push:
+    # branches to consider in the event; optional, defaults to all
+    branches:
+      - master
+
+jobs:
+  update_release_draft:
+    runs-on: ubuntu-latest
+    steps:
+      # Drafts your next Release notes as Pull Requests are merged into "master"
+      - uses: release-drafter/[email protected]
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/release.yml

@@ -1,95 +0,0 @@
-name: PyPI Release
-on:
-  release:
-    types: [published]
-
-jobs:
-  build:
-    strategy:
-      fail-fast: false
-      matrix:
-        platform: [ubuntu-latest]
-        python-version: ["3.9", "3.10", "3.11"]
-
-    runs-on: ${{ matrix.platform }}
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        submodules: recursive
-
-    - uses: actions/setup-python@v5
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Upgrade setuptools and wheel
-      run: |
-        pip install --upgrade setuptools wheel
-
-    - name: Install dependencies on Ubuntu
-      if: runner.os == 'Linux'
-      run: |
-        sudo apt-get update
-        sudo apt-get install libopencv-dev -y
-
-    - name: Install dependencies on macOS
-      if: runner.os == 'macOS'
-      run: |
-        brew update
-        brew install opencv
-
-    - name: Install dependencies on Windows
-      if: runner.os == 'Windows'
-      run: |
-        choco install opencv -y
-
-    - name: Add requirements
-      run: python -m pip install --upgrade setuptools wheel build
-
-    - name: Install Python dependencies
-      run: |
-        pip install pytest
-        pip install -r requirements.txt
-        sudo apt-get update && sudo apt-get install ffmpeg libsm6 libxext6 -y
-
-    - name: Build source distribution
-      run: |
-        python -m build --outdir dist/
-        ls -lh dist/
-
-    - name: Upload to GitHub Release
-      if: matrix.python-version == '3.10' && github.event_name == 'release'
-      uses: softprops/action-gh-release@v2
-      with:
-        files: dist/*.whl
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-    - name: Archive wheels
-      if: matrix.python-version == '3.10' && github.event_name == 'release'
-      uses: actions/upload-artifact@v4
-      with:
-        name: dist
-        path: dist/*.whl
-
-
-  pypi-publish:
-    name: upload release to PyPI
-    needs: build
-    runs-on: ubuntu-latest
-    environment: pypi
-    permissions:
-      # IMPORTANT: this permission is mandatory for Trusted Publishing
-      id-token: write
-    steps:
-      # retrieve your distributions here
-      - name: Download artifacts
-        uses: actions/download-artifact@v4
-        with:
-          name: dist
-          path: dist
-
-      - name: List dist directory
-        run: ls -lh dist/
-
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1

.gitignore

@@ -1,4 +1,5 @@
 build/
+# lib
 bin/
 cmake_modules/
 cmake-build-debug/
@@ -25,9 +26,3 @@ gen_example.py
 datasets/lines/terrace0.JPG
 datasets/lines/terrace1.JPG
 datasets/South-Building*
-*.pkl
-oryx-build-commands.txt
-.ruff_cache*
-dist
-tmp
-backup*

.pre-commit-config.yaml

@@ -1,88 +0,0 @@
-# To use:
-#
-#     pre-commit run -a
-#
-# Or:
-#
-#     pre-commit run --all-files
-#
-# Or:
-#
-#     pre-commit install  # (runs every time you commit in git)
-#
-# To update this file:
-#
-#     pre-commit autoupdate
-#
-# See https://github.com/pre-commit/pre-commit
-
-ci:
-  autoupdate_commit_msg: "chore: update pre-commit hooks"
-  autofix_commit_msg: "style: pre-commit fixes"
-
-repos:
-# Standard hooks
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v5.0.0
-  hooks:
-  - id: check-added-large-files
-    exclude: ^imcui/third_party/
-  - id: check-case-conflict
-    exclude: ^imcui/third_party/
-  - id: check-merge-conflict
-    exclude: ^imcui/third_party/
-  - id: check-symlinks
-    exclude: ^imcui/third_party/
-  - id: check-yaml
-    exclude: ^imcui/third_party/
-  - id: debug-statements
-    exclude: ^imcui/third_party/
-  - id: end-of-file-fixer
-    exclude: ^imcui/third_party/
-  - id: mixed-line-ending
-    exclude: ^imcui/third_party/
-  - id: requirements-txt-fixer
-    exclude: ^imcui/third_party/
-  - id: trailing-whitespace
-    exclude: ^imcui/third_party/
-
-- repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: "v0.8.4"
-  hooks:
-    - id: ruff
-      args: ["--fix", "--show-fixes", "--extend-ignore=E402"]
-    - id: ruff-format
-      exclude: ^(docs|imcui/third_party/)
-
-# Checking static types
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: "v1.14.0"
-  hooks:
-    - id: mypy
-      files: "setup.py"
-      args: []
-      additional_dependencies: [types-setuptools]
-      exclude: ^imcui/third_party/
-# Changes tabs to spaces
-- repo: https://github.com/Lucas-C/pre-commit-hooks
-  rev: v1.5.5
-  hooks:
-  - id: remove-tabs
-    exclude: ^(docs|imcui/third_party/)
-
-# CMake formatting
-- repo: https://github.com/cheshirekow/cmake-format-precommit
-  rev: v0.6.13
-  hooks:
-  - id: cmake-format
-    additional_dependencies: [pyyaml]
-    types: [file]
-    files: (\.cmake|CMakeLists.txt)(.in)?$
-    exclude: ^imcui/third_party/
-
-# Suggested hook if you add a .clang-format file
-- repo: https://github.com/pre-commit/mirrors-clang-format
-  rev: v13.0.0
-  hooks:
-  - id: clang-format
-    exclude: ^imcui/third_party/

CODE_OF_CONDUCT.md

@@ -1,128 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the
-  overall community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or
-  advances of any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email
-  address, without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-[email protected].
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series
-of actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or
-permanent ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior,  harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within
-the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
-
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.

Dockerfile

@@ -11,7 +11,7 @@ RUN apt-get update && apt-get install -y git-lfs
 RUN git lfs install
 
 # Clone the Git repository
-RUN git clone --recursive https://github.com/Vincentqyw/image-matching-webui.git /code
+RUN git clone https://huggingface.co/spaces/Realcat/image-matching-webui /code
 
 RUN conda create -n imw python=${PYTHON_VERSION}
 RUN echo "source activate imw" > ~/.bashrc

MANIFEST.in

@@ -1,12 +0,0 @@
-# logo
-include imcui/assets/logo.webp
-
-recursive-include imcui/ui *.yaml
-recursive-include imcui/api *.yaml
-recursive-include imcui/third_party *.yaml *.cfg *.yml
-
-# ui examples
-# recursive-include imcui/datasets *.JPG *.jpg *.png
-
-# model
-recursive-include imcui/third_party/SuperGluePretrainedNetwork *.pth

README.md

@@ -9,94 +9,81 @@ app_file: app.py
 pinned: true
 license: apache-2.0
 ---
+
 [![Contributors][contributors-shield]][contributors-url]
 [![Forks][forks-shield]][forks-url]
 [![Stargazers][stars-shield]][stars-url]
 [![Issues][issues-shield]][issues-url]
 
 <p align="center">
-  <h1 align="center"><br><ins>Image Matching WebUI</ins>
-  <br>Matching Keypoints between two images</h1>
+  <h1 align="center"><br><ins>Image Matching WebUI</ins><br>Identify matching points between two images</h1> 
 </p>
-<div align="center">
-  <a target="_blank" href="https://github.com/Vincentqyw/image-matching-webui/actions/workflows/release.yml"><img src="https://github.com/Vincentqyw/image-matching-webui/actions/workflows/release.yml/badge.svg" alt="PyPI Release"></a>
-  <a target="_blank" href='https://huggingface.co/spaces/Realcat/image-matching-webui'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
-  <a target="_blank" href="https://pypi.org/project/imcui"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/imcui?style=flat&logo=pypi&label=imcui&link=https%3A%2F%2Fpypi.org%2Fproject%2Fimcui"></a>
-  <a target="_blank" href="https://hub.docker.com/r/vincentqin/image-matching-webui"><img alt="Docker Image Version" src="https://img.shields.io/docker/v/vincentqin/image-matching-webui?sort=date&arch=amd64&logo=docker&label=imcui&link=https%3A%2F%2Fhub.docker.com%2Fr%2Fvincentqin%2Fimage-matching-webui"></a>
-   <a target="_blank" href="https://pepy.tech/projects/imcui"><img src="https://static.pepy.tech/badge/imcui" alt="PyPI Downloads"></a>
-
-</div>
 
 ## Description
 
-`Image Matching WebUI (IMCUI)` efficiently matches image pairs using multiple famous image matching algorithms. The tool features a Graphical User Interface (GUI) designed using [gradio](https://gradio.app/). You can effortlessly select two images and a matching algorithm and obtain a precise matching result.
+This simple tool efficiently matches image pairs using multiple famous image matching algorithms. The tool features a Graphical User Interface (GUI) designed using [gradio](https://gradio.app/). You can effortlessly select two images and a matching algorithm and obtain a precise matching result.
 **Note**: the images source can be either local images or webcam images.
 
-Try it on
-<a href='https://huggingface.co/spaces/Realcat/image-matching-webui'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
-<a target="_blank" href="https://lightning.ai/realcat/studios/image-matching-webui"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a>
+Try it on <a href='https://huggingface.co/spaces/Realcat/image-matching-webui'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> 
+<a target="_blank" href="https://lightning.ai/realcat/studios/image-matching-webui">
+  <img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/>
+</a>
 
 Here is a demo of the tool:
 
-https://github.com/Vincentqyw/image-matching-webui/assets/18531182/263534692-c3484d1b-cc00-4fdc-9b31-e5b7af07ecd9
+![demo](assets/demo.gif)
 
 The tool currently supports various popular image matching algorithms, namely:
-
-| Algorithm        | Supported | Conference/Journal | Year | GitHub Link |
-|------------------|-----------|--------------------|------|-------------|
-| DaD            | ✅ | ARXIV   | 2025 | [Link](https://github.com/Parskatt/dad) |
-| MINIMA         | ✅ | ARXIV   | 2024 | [Link](https://github.com/LSXI7/MINIMA) |
-| XoFTR          | ✅ | CVPR    | 2024 | [Link](https://github.com/OnderT/XoFTR) |
-| EfficientLoFTR | ✅ | CVPR    | 2024 | [Link](https://github.com/zju3dv/EfficientLoFTR) |
-| MASt3R         | ✅ | CVPR    | 2024 | [Link](https://github.com/naver/mast3r) |
-| DUSt3R         | ✅ | CVPR    | 2024 | [Link](https://github.com/naver/dust3r) |
-| OmniGlue       | ✅ | CVPR    | 2024 | [Link](https://github.com/Vincentqyw/omniglue-onnx) |
-| XFeat          | ✅ | CVPR    | 2024 | [Link](https://github.com/verlab/accelerated_features) |
-| RoMa           | ✅ | CVPR    | 2024 | [Link](https://github.com/Vincentqyw/RoMa) |
-| DeDoDe         | ✅ | 3DV     | 2024 | [Link](https://github.com/Parskatt/DeDoDe) |
-| Mickey         | ❌ | CVPR    | 2024 | [Link](https://github.com/nianticlabs/mickey) |
-| GIM            | ✅ | ICLR    | 2024 | [Link](https://github.com/xuelunshen/gim) |
-| ALIKED         | ✅ | ICCV    | 2023 | [Link](https://github.com/Shiaoming/ALIKED) |
-| LightGlue      | ✅ | ICCV    | 2023 | [Link](https://github.com/cvg/LightGlue) |
-| DarkFeat       | ✅ | AAAI    | 2023 | [Link](https://github.com/THU-LYJ-Lab/DarkFeat) |
-| SFD2           | ✅ | CVPR    | 2023 | [Link](https://github.com/feixue94/sfd2) |
-| IMP            | ✅ | CVPR    | 2023 | [Link](https://github.com/feixue94/imp-release) |
-| ASTR           | ❌ | CVPR    | 2023 | [Link](https://github.com/ASTR2023/ASTR) |
-| SEM            | ❌ | CVPR    | 2023 | [Link](https://github.com/SEM2023/SEM) |
-| DeepLSD        | ❌ | CVPR    | 2023 | [Link](https://github.com/cvg/DeepLSD) |
-| GlueStick      | ✅ | ICCV    | 2023 | [Link](https://github.com/cvg/GlueStick) |
-| ConvMatch      | ❌ | AAAI    | 2023 | [Link](https://github.com/SuhZhang/ConvMatch) |
-| LoFTR          | ✅ | CVPR    | 2021 | [Link](https://github.com/zju3dv/LoFTR) |
-| SOLD2          | ✅ | CVPR    | 2021 | [Link](https://github.com/cvg/SOLD2) |
-| LineTR         | ❌ | RA-L    | 2021 | [Link](https://github.com/yosungho/LineTR) |
-| DKM            | ✅ | CVPR    | 2023 | [Link](https://github.com/Parskatt/DKM) |
-| NCMNet         | ❌ | CVPR    | 2023 | [Link](https://github.com/xinliu29/NCMNet) |
-| TopicFM        | ✅ | AAAI    | 2023 | [Link](https://github.com/Vincentqyw/TopicFM) |
-| AspanFormer    | ✅ | ECCV    | 2022 | [Link](https://github.com/Vincentqyw/ml-aspanformer) |
-| LANet          | ✅ | ACCV    | 2022 | [Link](https://github.com/wangch-g/lanet) |
-| LISRD          | ❌ | ECCV    | 2022 | [Link](https://github.com/rpautrat/LISRD) |
-| REKD           | ❌ | CVPR    | 2022 | [Link](https://github.com/bluedream1121/REKD) |
-| CoTR           | ✅ | ICCV    | 2021 | [Link](https://github.com/ubc-vision/COTR) |
-| ALIKE          | ✅ | TMM     | 2022 | [Link](https://github.com/Shiaoming/ALIKE) |
-| RoRD           | ✅ | IROS    | 2021 | [Link](https://github.com/UditSinghParihar/RoRD) |
-| SGMNet         | ✅ | ICCV    | 2021 | [Link](https://github.com/vdvchen/SGMNet) |
-| SuperPoint     | ✅ | CVPRW   | 2018 | [Link](https://github.com/magicleap/SuperPointPretrainedNetwork) |
-| SuperGlue      | ✅ | CVPR    | 2020 | [Link](https://github.com/magicleap/SuperGluePretrainedNetwork) |
-| D2Net          | ✅ | CVPR    | 2019 | [Link](https://github.com/Vincentqyw/d2-net) |
-| R2D2           | ✅ | NeurIPS | 2019 | [Link](https://github.com/naver/r2d2) |
-| DISK           | ✅ | NeurIPS | 2020 | [Link](https://github.com/cvlab-epfl/disk) |
-| Key.Net        | ❌ | ICCV    | 2019 | [Link](https://github.com/axelBarroso/Key.Net) |
-| OANet          | ❌ | ICCV    | 2019 | [Link](https://github.com/zjhthu/OANet) |
-| SOSNet         | ✅ | CVPR    | 2019 | [Link](https://github.com/scape-research/SOSNet) |
-| HardNet        | ✅ | NeurIPS | 2017 | [Link](https://github.com/DagnyT/hardnet) |
-| SIFT           | ✅ | IJCV    | 2004 | [Link](https://docs.opencv.org/4.x/da/df5/tutorial_py_sift_intro.html) |
-
+- [x] [XoFTR](https://github.com/OnderT/XoFTR), CVPR 2024
+- [x] [EfficientLoFTR](https://github.com/zju3dv/EfficientLoFTR), CVPR 2024
+- [x] [MASt3R](https://github.com/naver/mast3r), CVPR 2024
+- [x] [DUSt3R](https://github.com/naver/dust3r), CVPR 2024
+- [x] [OmniGlue](https://github.com/Vincentqyw/omniglue-onnx), CVPR 2024
+- [x] [XFeat](https://github.com/verlab/accelerated_features), CVPR 2024
+- [x] [RoMa](https://github.com/Vincentqyw/RoMa), CVPR 2024
+- [x] [DeDoDe](https://github.com/Parskatt/DeDoDe), 3DV 2024
+- [ ] [Mickey](https://github.com/nianticlabs/mickey), CVPR 2024
+- [x] [GIM](https://github.com/xuelunshen/gim), ICLR 2024
+- [ ] [DUSt3R](https://github.com/naver/dust3r), arXiv 2023
+- [x] [LightGlue](https://github.com/cvg/LightGlue), ICCV 2023
+- [x] [DarkFeat](https://github.com/THU-LYJ-Lab/DarkFeat), AAAI 2023
+- [x] [SFD2](https://github.com/feixue94/sfd2), CVPR 2023
+- [x] [IMP](https://github.com/feixue94/imp-release), CVPR 2023
+- [ ] [ASTR](https://github.com/ASTR2023/ASTR), CVPR 2023
+- [ ] [SEM](https://github.com/SEM2023/SEM), CVPR 2023
+- [ ] [DeepLSD](https://github.com/cvg/DeepLSD), CVPR 2023
+- [x] [GlueStick](https://github.com/cvg/GlueStick), ICCV 2023
+- [ ] [ConvMatch](https://github.com/SuhZhang/ConvMatch), AAAI 2023
+- [x] [LoFTR](https://github.com/zju3dv/LoFTR), CVPR 2021
+- [x] [SOLD2](https://github.com/cvg/SOLD2), CVPR 2021
+- [ ] [LineTR](https://github.com/yosungho/LineTR), RA-L 2021
+- [x] [DKM](https://github.com/Parskatt/DKM), CVPR 2023
+- [ ] [NCMNet](https://github.com/xinliu29/NCMNet), CVPR 2023
+- [x] [TopicFM](https://github.com/Vincentqyw/TopicFM), AAAI 2023
+- [x] [AspanFormer](https://github.com/Vincentqyw/ml-aspanformer), ECCV 2022
+- [x] [LANet](https://github.com/wangch-g/lanet), ACCV 2022
+- [ ] [LISRD](https://github.com/rpautrat/LISRD), ECCV 2022
+- [ ] [REKD](https://github.com/bluedream1121/REKD), CVPR 2022
+- [x] [CoTR](https://github.com/ubc-vision/COTR), ICCV 2021
+- [x] [ALIKE](https://github.com/Shiaoming/ALIKE), TMM 2022
+- [x] [RoRD](https://github.com/UditSinghParihar/RoRD), IROS 2021
+- [x] [SGMNet](https://github.com/vdvchen/SGMNet), ICCV 2021
+- [x] [SuperPoint](https://github.com/magicleap/SuperPointPretrainedNetwork), CVPRW 2018
+- [x] [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork), CVPR 2020
+- [x] [D2Net](https://github.com/Vincentqyw/d2-net), CVPR 2019
+- [x] [R2D2](https://github.com/naver/r2d2), NeurIPS 2019
+- [x] [DISK](https://github.com/cvlab-epfl/disk), NeurIPS 2020
+- [ ] [Key.Net](https://github.com/axelBarroso/Key.Net), ICCV 2019
+- [ ] [OANet](https://github.com/zjhthu/OANet), ICCV 2019
+- [x] [SOSNet](https://github.com/scape-research/SOSNet), CVPR 2019
+- [x] [HardNet](https://github.com/DagnyT/hardnet), NeurIPS 2017
+- [x] [SIFT](https://docs.opencv.org/4.x/da/df5/tutorial_py_sift_intro.html), IJCV 2004
 
 ## How to use
 
 ### HuggingFace / Lightning AI
 
-Just try it on <a href='https://huggingface.co/spaces/Realcat/image-matching-webui'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
+Just try it on <a href='https://huggingface.co/spaces/Realcat/image-matching-webui'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> 
 <a target="_blank" href="https://lightning.ai/realcat/studios/image-matching-webui">
   <img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/>
 </a>
@@ -104,25 +91,11 @@ Just try it on <a href='https://huggingface.co/spaces/Realcat/image-matching-web
 or deploy it locally following the instructions below.
 
 ### Requirements
-
-- [Python 3.9+](https://www.python.org/downloads/)
-
-#### Install from pip [NEW]
-
-Update: now support install from [pip](https://pypi.org/project/imcui), just run:
-
-```bash
-pip install imcui
-```
-
-#### Install from source
-
 ``` bash
 git clone --recursive https://github.com/Vincentqyw/image-matching-webui.git
 cd image-matching-webui
 conda env create -f environment.yaml
 conda activate imw
-pip install -e .
 ```
 
 or using [docker](https://hub.docker.com/r/vincentqin/image-matching-webui):
@@ -131,18 +104,10 @@ or using [docker](https://hub.docker.com/r/vincentqin/image-matching-webui):
 docker pull vincentqin/image-matching-webui:latest
 docker run -it -p 7860:7860 vincentqin/image-matching-webui:latest python app.py --server_name "0.0.0.0" --server_port=7860
 ```
-
-### Deploy to Railway
-
-Deploy to [Railway](https://railway.app/), setting up a `Custom Start Command` in `Deploy` section:
-
-``` bash
-python -m imcui.api.server
-```
-
+ 
 ### Run demo
 ``` bash
-python app.py --config ./config/config.yaml
+python3 ./app.py
 ```
 then open http://localhost:7860 in your browser.
 
@@ -150,54 +115,28 @@ then open http://localhost:7860 in your browser.
 
 ### Add your own feature / matcher
 
-I provide an example to add local feature in [imcui/hloc/extractors/example.py](imcui/hloc/extractors/example.py). Then add feature settings in `confs` in file [imcui/hloc/extract_features.py](imcui/hloc/extract_features.py). Last step is adding some settings to `matcher_zoo` in file [imcui/ui/config.yaml](imcui/ui/config.yaml).
-
-### Upload models
-
-IMCUI hosts all models on [Huggingface](https://huggingface.co/Realcat/imcui_checkpoints).  You can upload your model to Huggingface and add it to the [Realcat/imcui_checkpoints](https://huggingface.co/Realcat/imcui_checkpoints) repository.
-
+I provide an example to add local feature in [hloc/extractors/example.py](hloc/extractors/example.py). Then add feature settings in `confs` in file [hloc/extract_features.py](hloc/extract_features.py). Last step is adding some settings to `model_zoo` in file [ui/config.yaml](ui/config.yaml).
 
 ## Contributions welcome!
 
-External contributions are very much welcome. Please follow the [PEP8 style guidelines](https://www.python.org/dev/peps/pep-0008/) using a linter like flake8. This is a non-exhaustive list of features that might be valuable additions:
+External contributions are very much welcome. Please follow the [PEP8 style guidelines](https://www.python.org/dev/peps/pep-0008/) using a linter like flake8 (reformat using command `python -m black .`). This is a non-exhaustive list of features that might be valuable additions:
 
-- [x] support pip install command
-- [x] add [CPU CI](.github/workflows/ci.yml)
 - [x] add webcam support
 - [x] add [line feature matching](https://github.com/Vincentqyw/LineSegmentsDetection) algorithms
 - [x] example to add a new feature extractor / matcher
 - [x] ransac to filter outliers
-- [ ] add [rotation images](https://github.com/pidahbus/deep-image-orientation-angle-detection) options before matching
+- [ ] add [rotation images](https://github.com/pidahbus/deep-image-orientation-angle-detection) options before matching 
 - [ ] support export matches to colmap ([#issue 6](https://github.com/Vincentqyw/image-matching-webui/issues/6))
-- [x] add config file to set default parameters
-- [x] dynamically load models and reduce GPU overload
-
-Adding local features / matchers as submodules is very easy. For example, to add the [GlueStick](https://github.com/cvg/GlueStick):
-
-``` bash
-git submodule add https://github.com/cvg/GlueStick.git imcui/third_party/GlueStick
-```
+- [ ] add config file to set default parameters
+- [ ] dynamically load models and reduce GPU overload
 
-If remote submodule repositories are updated, don't forget to pull submodules with:
+Adding local features / matchers as submodules is very easy. For example, to add the [GlueStick](https://github.com/cvg/GlueStick): 
 
 ``` bash
-git submodule update --init --recursive  # init and download
-git submodule update --remote  # update
-```
-
-if you only want to update one submodule, use `git submodule update --remote imcui/third_party/GlueStick`.
-
-To format code before committing, run:
-
-```bash
-pre-commit run -a  # Auto-checks and fixes
+git submodule add https://github.com/cvg/GlueStick.git third_party/GlueStick
 ```
 
-## Contributors
-
-<a href="https://github.com/Vincentqyw/image-matching-webui/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=Vincentqyw/image-matching-webui" />
-</a>
+If remote submodule repositories are updated, don't forget to pull submodules with `git submodule update --remote`, if you only want to update one submodule, use `git submodule update --remote third_party/GlueStick`.
 
 ## Resources
 - [Image Matching: Local Features & Beyond](https://image-matching-workshop.github.io)
@@ -214,4 +153,4 @@ This code is built based on [Hierarchical-Localization](https://github.com/cvg/H
 [stars-shield]: https://img.shields.io/github/stars/Vincentqyw/image-matching-webui.svg?style=for-the-badge
 [stars-url]: https://github.com/Vincentqyw/image-matching-webui/stargazers
 [issues-shield]: https://img.shields.io/github/issues/Vincentqyw/image-matching-webui.svg?style=for-the-badge
-[issues-url]: https://github.com/Vincentqyw/image-matching-webui/issues
+[issues-url]: https://github.com/Vincentqyw/image-matching-webui/issues

{imcui/api → api}/client.py

@@ -1,232 +1,225 @@
-import argparse
-import base64
-import os
-import pickle
-import time
-from typing import Dict, List
-
-import cv2
-import numpy as np
-import requests
-
-ENDPOINT = "http://127.0.0.1:8001"
-if "REMOTE_URL_RAILWAY" in os.environ:
-    ENDPOINT = os.environ["REMOTE_URL_RAILWAY"]
-
-print(f"API ENDPOINT: {ENDPOINT}")
-
-API_VERSION = f"{ENDPOINT}/version"
-API_URL_MATCH = f"{ENDPOINT}/v1/match"
-API_URL_EXTRACT = f"{ENDPOINT}/v1/extract"
-
-
-def read_image(path: str) -> str:
-    """
-    Read an image from a file, encode it as a JPEG and then as a base64 string.
-
-    Args:
-        path (str): The path to the image to read.
-
-    Returns:
-        str: The base64 encoded image.
-    """
-    # Read the image from the file
-    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
-
-    # Encode the image as a png, NO COMPRESSION!!!
-    retval, buffer = cv2.imencode(".png", img)
-
-    # Encode the JPEG as a base64 string
-    b64img = base64.b64encode(buffer).decode("utf-8")
-
-    return b64img
-
-
-def do_api_requests(url=API_URL_EXTRACT, **kwargs):
-    """
-    Helper function to send an API request to the image matching service.
-
-    Args:
-        url (str): The URL of the API endpoint to use. Defaults to the
-            feature extraction endpoint.
-        **kwargs: Additional keyword arguments to pass to the API.
-
-    Returns:
-        List[Dict[str, np.ndarray]]: A list of dictionaries containing the
-            extracted features. The keys are "keypoints", "descriptors", and
-            "scores", and the values are ndarrays of shape (N, 2), (N, ?),
-            and (N,), respectively.
-    """
-    # Set up the request body
-    reqbody = {
-        # List of image data base64 encoded
-        "data": [],
-        # List of maximum number of keypoints to extract from each image
-        "max_keypoints": [100, 100],
-        # List of timestamps for each image (not used?)
-        "timestamps": ["0", "1"],
-        # Whether to convert the images to grayscale
-        "grayscale": 0,
-        # List of image height and width
-        "image_hw": [[640, 480], [320, 240]],
-        # Type of feature to extract
-        "feature_type": 0,
-        # List of rotation angles for each image
-        "rotates": [0.0, 0.0],
-        # List of scale factors for each image
-        "scales": [1.0, 1.0],
-        # List of reference points for each image (not used)
-        "reference_points": [[640, 480], [320, 240]],
-        # Whether to binarize the descriptors
-        "binarize": True,
-    }
-    # Update the request body with the additional keyword arguments
-    reqbody.update(kwargs)
-    try:
-        # Send the request
-        r = requests.post(url, json=reqbody)
-        if r.status_code == 200:
-            # Return the response
-            return r.json()
-        else:
-            # Print an error message if the response code is not 200
-            print(f"Error: Response code {r.status_code} - {r.text}")
-    except Exception as e:
-        # Print an error message if an exception occurs
-        print(f"An error occurred: {e}")
-
-
-def send_request_match(path0: str, path1: str) -> Dict[str, np.ndarray]:
-    """
-    Send a request to the API to generate a match between two images.
-
-    Args:
-        path0 (str): The path to the first image.
-        path1 (str): The path to the second image.
-
-    Returns:
-        Dict[str, np.ndarray]: A dictionary containing the generated matches.
-            The keys are "keypoints0", "keypoints1", "matches0", and "matches1",
-            and the values are ndarrays of shape (N, 2), (N, 2), (N, 2), and
-            (N, 2), respectively.
-    """
-    files = {"image0": open(path0, "rb"), "image1": open(path1, "rb")}
-    try:
-        # TODO: replace files with post json
-        response = requests.post(API_URL_MATCH, files=files)
-        pred = {}
-        if response.status_code == 200:
-            pred = response.json()
-            for key in list(pred.keys()):
-                pred[key] = np.array(pred[key])
-        else:
-            print(f"Error: Response code {response.status_code} - {response.text}")
-    finally:
-        files["image0"].close()
-        files["image1"].close()
-    return pred
-
-
-def send_request_extract(
-    input_images: str, viz: bool = False
-) -> List[Dict[str, np.ndarray]]:
-    """
-    Send a request to the API to extract features from an image.
-
-    Args:
-        input_images (str): The path to the image.
-
-    Returns:
-        List[Dict[str, np.ndarray]]: A list of dictionaries containing the
-            extracted features. The keys are "keypoints", "descriptors", and
-            "scores", and the values are ndarrays of shape (N, 2), (N, 128),
-            and (N,), respectively.
-    """
-    image_data = read_image(input_images)
-    inputs = {
-        "data": [image_data],
-    }
-    response = do_api_requests(
-        url=API_URL_EXTRACT,
-        **inputs,
-    )
-    # breakpoint()
-    # print("Keypoints detected: {}".format(len(response[0]["keypoints"])))
-
-    # draw matching, debug only
-    if viz:
-        from hloc.utils.viz import plot_keypoints
-        from ui.viz import fig2im, plot_images
-
-        kpts = np.array(response[0]["keypoints_orig"])
-        if "image_orig" in response[0].keys():
-            img_orig = np.array(["image_orig"])
-
-            output_keypoints = plot_images([img_orig], titles="titles", dpi=300)
-            plot_keypoints([kpts])
-            output_keypoints = fig2im(output_keypoints)
-            cv2.imwrite(
-                "demo_match.jpg",
-                output_keypoints[:, :, ::-1].copy(),  # RGB -> BGR
-            )
-    return response
-
-
-def get_api_version():
-    try:
-        response = requests.get(API_VERSION).json()
-        print("API VERSION: {}".format(response["version"]))
-    except Exception as e:
-        print(f"An error occurred: {e}")
-
-
-if __name__ == "__main__":
-    from pathlib import Path
-
-    parser = argparse.ArgumentParser(
-        description="Send text to stable audio server and receive generated audio."
-    )
-    parser.add_argument(
-        "--image0",
-        required=False,
-        help="Path for the file's melody",
-        default=str(
-            Path(__file__).parents[1]
-            / "datasets/sacre_coeur/mapping_rot/02928139_3448003521_rot45.jpg"
-        ),
-    )
-    parser.add_argument(
-        "--image1",
-        required=False,
-        help="Path for the file's melody",
-        default=str(
-            Path(__file__).parents[1]
-            / "datasets/sacre_coeur/mapping_rot/02928139_3448003521_rot90.jpg"
-        ),
-    )
-    args = parser.parse_args()
-
-    # get api version
-    get_api_version()
-
-    # request match
-    # for i in range(10):
-    #     t1 = time.time()
-    #     preds = send_request_match(args.image0, args.image1)
-    #     t2 = time.time()
-    #     print(
-    #         "Time cost1: {} seconds, matched: {}".format(
-    #             (t2 - t1), len(preds["mmkeypoints0_orig"])
-    #         )
-    #     )
-
-    # request extract
-    for i in range(1000):
-        t1 = time.time()
-        preds = send_request_extract(args.image0)
-        t2 = time.time()
-        print(f"Time cost2: {(t2 - t1)} seconds")
-
-    # dump preds
-    with open("preds.pkl", "wb") as f:
-        pickle.dump(preds, f)
+import argparse
+import base64
+import os
+import pickle
+import time
+from typing import Dict, List
+
+import cv2
+import numpy as np
+import requests
+
+ENDPOINT = "http://127.0.0.1:8001"
+if "REMOTE_URL_RAILWAY" in os.environ:
+    ENDPOINT = os.environ["REMOTE_URL_RAILWAY"]
+
+print(f"API ENDPOINT: {ENDPOINT}")
+
+API_VERSION = f"{ENDPOINT}/version"
+API_URL_MATCH = f"{ENDPOINT}/v1/match"
+API_URL_EXTRACT = f"{ENDPOINT}/v1/extract"
+
+
+def read_image(path: str) -> str:
+    """
+    Read an image from a file, encode it as a JPEG and then as a base64 string.
+
+    Args:
+        path (str): The path to the image to read.
+
+    Returns:
+        str: The base64 encoded image.
+    """
+    # Read the image from the file
+    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+
+    # Encode the image as a png, NO COMPRESSION!!!
+    retval, buffer = cv2.imencode(".png", img)
+
+    # Encode the JPEG as a base64 string
+    b64img = base64.b64encode(buffer).decode("utf-8")
+
+    return b64img
+
+
+def do_api_requests(url=API_URL_EXTRACT, **kwargs):
+    """
+    Helper function to send an API request to the image matching service.
+
+    Args:
+        url (str): The URL of the API endpoint to use. Defaults to the
+            feature extraction endpoint.
+        **kwargs: Additional keyword arguments to pass to the API.
+
+    Returns:
+        List[Dict[str, np.ndarray]]: A list of dictionaries containing the
+            extracted features. The keys are "keypoints", "descriptors", and
+            "scores", and the values are ndarrays of shape (N, 2), (N, ?),
+            and (N,), respectively.
+    """
+    # Set up the request body
+    reqbody = {
+        # List of image data base64 encoded
+        "data": [],
+        # List of maximum number of keypoints to extract from each image
+        "max_keypoints": [100, 100],
+        # List of timestamps for each image (not used?)
+        "timestamps": ["0", "1"],
+        # Whether to convert the images to grayscale
+        "grayscale": 0,
+        # List of image height and width
+        "image_hw": [[640, 480], [320, 240]],
+        # Type of feature to extract
+        "feature_type": 0,
+        # List of rotation angles for each image
+        "rotates": [0.0, 0.0],
+        # List of scale factors for each image
+        "scales": [1.0, 1.0],
+        # List of reference points for each image (not used)
+        "reference_points": [[640, 480], [320, 240]],
+        # Whether to binarize the descriptors
+        "binarize": True,
+    }
+    # Update the request body with the additional keyword arguments
+    reqbody.update(kwargs)
+    try:
+        # Send the request
+        r = requests.post(url, json=reqbody)
+        if r.status_code == 200:
+            # Return the response
+            return r.json()
+        else:
+            # Print an error message if the response code is not 200
+            print(f"Error: Response code {r.status_code} - {r.text}")
+    except Exception as e:
+        # Print an error message if an exception occurs
+        print(f"An error occurred: {e}")
+
+
+def send_request_match(path0: str, path1: str) -> Dict[str, np.ndarray]:
+    """
+    Send a request to the API to generate a match between two images.
+
+    Args:
+        path0 (str): The path to the first image.
+        path1 (str): The path to the second image.
+
+    Returns:
+        Dict[str, np.ndarray]: A dictionary containing the generated matches.
+            The keys are "keypoints0", "keypoints1", "matches0", and "matches1",
+            and the values are ndarrays of shape (N, 2), (N, 2), (N, 2), and
+            (N, 2), respectively.
+    """
+    files = {"image0": open(path0, "rb"), "image1": open(path1, "rb")}
+    try:
+        # TODO: replace files with post json
+        response = requests.post(API_URL_MATCH, files=files)
+        pred = {}
+        if response.status_code == 200:
+            pred = response.json()
+            for key in list(pred.keys()):
+                pred[key] = np.array(pred[key])
+        else:
+            print(
+                f"Error: Response code {response.status_code} - {response.text}"
+            )
+    finally:
+        files["image0"].close()
+        files["image1"].close()
+    return pred
+
+
+def send_request_extract(
+    input_images: str, viz: bool = False
+) -> List[Dict[str, np.ndarray]]:
+    """
+    Send a request to the API to extract features from an image.
+
+    Args:
+        input_images (str): The path to the image.
+
+    Returns:
+        List[Dict[str, np.ndarray]]: A list of dictionaries containing the
+            extracted features. The keys are "keypoints", "descriptors", and
+            "scores", and the values are ndarrays of shape (N, 2), (N, 128),
+            and (N,), respectively.
+    """
+    image_data = read_image(input_images)
+    inputs = {
+        "data": [image_data],
+    }
+    response = do_api_requests(
+        url=API_URL_EXTRACT,
+        **inputs,
+    )
+    print("Keypoints detected: {}".format(len(response[0]["keypoints"])))
+
+    # draw matching, debug only
+    if viz:
+        from hloc.utils.viz import plot_keypoints
+        from ui.viz import fig2im, plot_images
+
+        kpts = np.array(response[0]["keypoints_orig"])
+        if "image_orig" in response[0].keys():
+            img_orig = np.array(["image_orig"])
+
+            output_keypoints = plot_images([img_orig], titles="titles", dpi=300)
+            plot_keypoints([kpts])
+            output_keypoints = fig2im(output_keypoints)
+            cv2.imwrite(
+                "demo_match.jpg",
+                output_keypoints[:, :, ::-1].copy(),  # RGB -> BGR
+            )
+    return response
+
+
+def get_api_version():
+    try:
+        response = requests.get(API_VERSION).json()
+        print("API VERSION: {}".format(response["version"]))
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Send text to stable audio server and receive generated audio."
+    )
+    parser.add_argument(
+        "--image0",
+        required=False,
+        help="Path for the file's melody",
+        default="datasets/sacre_coeur/mapping_rot/02928139_3448003521_rot45.jpg",
+    )
+    parser.add_argument(
+        "--image1",
+        required=False,
+        help="Path for the file's melody",
+        default="datasets/sacre_coeur/mapping_rot/02928139_3448003521_rot90.jpg",
+    )
+    args = parser.parse_args()
+
+    # get api version
+    get_api_version()
+
+    # request match
+    # for i in range(10):
+    #     t1 = time.time()
+    #     preds = send_request_match(args.image0, args.image1)
+    #     t2 = time.time()
+    #     print(
+    #         "Time cost1: {} seconds, matched: {}".format(
+    #             (t2 - t1), len(preds["mmkeypoints0_orig"])
+    #         )
+    #     )
+
+    # request extract
+    for i in range(10):
+        t1 = time.time()
+        preds = send_request_extract(args.image0)
+        t2 = time.time()
+        print(f"Time cost2: {(t2 - t1)} seconds")
+
+    # dump preds
+    with open("preds.pkl", "wb") as f:
+        pickle.dump(preds, f)

imcui/api/core.py → api/server.py

@@ -1,308 +1,499 @@
-# api.py
-import warnings
-from pathlib import Path
-from typing import Any, Dict, Optional
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from ..hloc import extract_features, logger, match_dense, match_features
-from ..hloc.utils.viz import add_text, plot_keypoints
-from ..ui.utils import filter_matches, get_feature_model, get_model
-from ..ui.viz import display_matches, fig2im, plot_images
-
-warnings.simplefilter("ignore")
-
-
-class ImageMatchingAPI(torch.nn.Module):
-    default_conf = {
-        "ransac": {
-            "enable": True,
-            "estimator": "poselib",
-            "geometry": "homography",
-            "method": "RANSAC",
-            "reproj_threshold": 3,
-            "confidence": 0.9999,
-            "max_iter": 10000,
-        },
-    }
-
-    def __init__(
-        self,
-        conf: dict = {},
-        device: str = "cpu",
-        detect_threshold: float = 0.015,
-        max_keypoints: int = 1024,
-        match_threshold: float = 0.2,
-    ) -> None:
-        """
-        Initializes an instance of the ImageMatchingAPI class.
-
-        Args:
-            conf (dict): A dictionary containing the configuration parameters.
-            device (str, optional): The device to use for computation. Defaults to "cpu".
-            detect_threshold (float, optional): The threshold for detecting keypoints. Defaults to 0.015.
-            max_keypoints (int, optional): The maximum number of keypoints to extract. Defaults to 1024.
-            match_threshold (float, optional): The threshold for matching keypoints. Defaults to 0.2.
-
-        Returns:
-            None
-        """
-        super().__init__()
-        self.device = device
-        self.conf = {**self.default_conf, **conf}
-        self._updata_config(detect_threshold, max_keypoints, match_threshold)
-        self._init_models()
-        if device == "cuda":
-            memory_allocated = torch.cuda.memory_allocated(device)
-            memory_reserved = torch.cuda.memory_reserved(device)
-            logger.info(f"GPU memory allocated: {memory_allocated / 1024**2:.3f} MB")
-            logger.info(f"GPU memory reserved: {memory_reserved / 1024**2:.3f} MB")
-        self.pred = None
-
-    def parse_match_config(self, conf):
-        if conf["dense"]:
-            return {
-                **conf,
-                "matcher": match_dense.confs.get(conf["matcher"]["model"]["name"]),
-                "dense": True,
-            }
-        else:
-            return {
-                **conf,
-                "feature": extract_features.confs.get(conf["feature"]["model"]["name"]),
-                "matcher": match_features.confs.get(conf["matcher"]["model"]["name"]),
-                "dense": False,
-            }
-
-    def _updata_config(
-        self,
-        detect_threshold: float = 0.015,
-        max_keypoints: int = 1024,
-        match_threshold: float = 0.2,
-    ):
-        self.dense = self.conf["dense"]
-        if self.conf["dense"]:
-            try:
-                self.conf["matcher"]["model"]["match_threshold"] = match_threshold
-            except TypeError as e:
-                logger.error(e)
-        else:
-            self.conf["feature"]["model"]["max_keypoints"] = max_keypoints
-            self.conf["feature"]["model"]["keypoint_threshold"] = detect_threshold
-            self.extract_conf = self.conf["feature"]
-
-        self.match_conf = self.conf["matcher"]
-
-    def _init_models(self):
-        # initialize matcher
-        self.matcher = get_model(self.match_conf)
-        # initialize extractor
-        if self.dense:
-            self.extractor = None
-        else:
-            self.extractor = get_feature_model(self.conf["feature"])
-
-    def _forward(self, img0, img1):
-        if self.dense:
-            pred = match_dense.match_images(
-                self.matcher,
-                img0,
-                img1,
-                self.match_conf["preprocessing"],
-                device=self.device,
-            )
-            last_fixed = "{}".format(  # noqa: F841
-                self.match_conf["model"]["name"]
-            )
-        else:
-            pred0 = extract_features.extract(
-                self.extractor, img0, self.extract_conf["preprocessing"]
-            )
-            pred1 = extract_features.extract(
-                self.extractor, img1, self.extract_conf["preprocessing"]
-            )
-            pred = match_features.match_images(self.matcher, pred0, pred1)
-        return pred
-
-    def _convert_pred(self, pred):
-        ret = {
-            k: v.cpu().detach()[0].numpy() if isinstance(v, torch.Tensor) else v
-            for k, v in pred.items()
-        }
-        ret = {
-            k: v[0].cpu().detach().numpy() if isinstance(v, list) else v
-            for k, v in ret.items()
-        }
-        return ret
-
-    @torch.inference_mode()
-    def extract(self, img0: np.ndarray, **kwargs) -> Dict[str, np.ndarray]:
-        """Extract features from a single image.
-
-        Args:
-            img0 (np.ndarray): image
-
-        Returns:
-            Dict[str, np.ndarray]: feature dict
-        """
-
-        # setting prams
-        self.extractor.conf["max_keypoints"] = kwargs.get("max_keypoints", 512)
-        self.extractor.conf["keypoint_threshold"] = kwargs.get(
-            "keypoint_threshold", 0.0
-        )
-
-        pred = extract_features.extract(
-            self.extractor, img0, self.extract_conf["preprocessing"]
-        )
-        pred = self._convert_pred(pred)
-        # back to origin scale
-        s0 = pred["original_size"] / pred["size"]
-        pred["keypoints_orig"] = (
-            match_features.scale_keypoints(pred["keypoints"] + 0.5, s0) - 0.5
-        )
-        # TODO: rotate back
-        binarize = kwargs.get("binarize", False)
-        if binarize:
-            assert "descriptors" in pred
-            pred["descriptors"] = (pred["descriptors"] > 0).astype(np.uint8)
-            pred["descriptors"] = pred["descriptors"].T  # N x DIM
-        return pred
-
-    @torch.inference_mode()
-    def forward(
-        self,
-        img0: np.ndarray,
-        img1: np.ndarray,
-    ) -> Dict[str, np.ndarray]:
-        """
-        Forward pass of the image matching API.
-
-        Args:
-            img0: A 3D NumPy array of shape (H, W, C) representing the first image.
-                  Values are in the range [0, 1] and are in RGB mode.
-            img1: A 3D NumPy array of shape (H, W, C) representing the second image.
-                  Values are in the range [0, 1] and are in RGB mode.
-
-        Returns:
-            A dictionary containing the following keys:
-            - image0_orig: The original image 0.
-            - image1_orig: The original image 1.
-            - keypoints0_orig: The keypoints detected in image 0.
-            - keypoints1_orig: The keypoints detected in image 1.
-            - mkeypoints0_orig: The raw matches between image 0 and image 1.
-            - mkeypoints1_orig: The raw matches between image 1 and image 0.
-            - mmkeypoints0_orig: The RANSAC inliers in image 0.
-            - mmkeypoints1_orig: The RANSAC inliers in image 1.
-            - mconf: The confidence scores for the raw matches.
-            - mmconf: The confidence scores for the RANSAC inliers.
-        """
-        # Take as input a pair of images (not a batch)
-        assert isinstance(img0, np.ndarray)
-        assert isinstance(img1, np.ndarray)
-        self.pred = self._forward(img0, img1)
-        if self.conf["ransac"]["enable"]:
-            self.pred = self._geometry_check(self.pred)
-        return self.pred
-
-    def _geometry_check(
-        self,
-        pred: Dict[str, Any],
-    ) -> Dict[str, Any]:
-        """
-        Filter matches using RANSAC. If keypoints are available, filter by keypoints.
-        If lines are available, filter by lines. If both keypoints and lines are
-        available, filter by keypoints.
-
-        Args:
-            pred (Dict[str, Any]): dict of matches, including original keypoints.
-                                  See :func:`filter_matches` for the expected keys.
-
-        Returns:
-            Dict[str, Any]: filtered matches
-        """
-        pred = filter_matches(
-            pred,
-            ransac_method=self.conf["ransac"]["method"],
-            ransac_reproj_threshold=self.conf["ransac"]["reproj_threshold"],
-            ransac_confidence=self.conf["ransac"]["confidence"],
-            ransac_max_iter=self.conf["ransac"]["max_iter"],
-        )
-        return pred
-
-    def visualize(
-        self,
-        log_path: Optional[Path] = None,
-    ) -> None:
-        """
-        Visualize the matches.
-
-        Args:
-            log_path (Path, optional): The directory to save the images. Defaults to None.
-
-        Returns:
-            None
-        """
-        if self.conf["dense"]:
-            postfix = str(self.conf["matcher"]["model"]["name"])
-        else:
-            postfix = "{}_{}".format(
-                str(self.conf["feature"]["model"]["name"]),
-                str(self.conf["matcher"]["model"]["name"]),
-            )
-        titles = [
-            "Image 0 - Keypoints",
-            "Image 1 - Keypoints",
-        ]
-        pred: Dict[str, Any] = self.pred
-        image0: np.ndarray = pred["image0_orig"]
-        image1: np.ndarray = pred["image1_orig"]
-        output_keypoints: np.ndarray = plot_images(
-            [image0, image1], titles=titles, dpi=300
-        )
-        if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys():
-            plot_keypoints([pred["keypoints0_orig"], pred["keypoints1_orig"]])
-            text: str = (
-                f"# keypoints0: {len(pred['keypoints0_orig'])} \n"
-                + f"# keypoints1: {len(pred['keypoints1_orig'])}"
-            )
-            add_text(0, text, fs=15)
-        output_keypoints = fig2im(output_keypoints)
-        # plot images with raw matches
-        titles = [
-            "Image 0 - Raw matched keypoints",
-            "Image 1 - Raw matched keypoints",
-        ]
-        output_matches_raw, num_matches_raw = display_matches(
-            pred, titles=titles, tag="KPTS_RAW"
-        )
-        # plot images with ransac matches
-        titles = [
-            "Image 0 - Ransac matched keypoints",
-            "Image 1 - Ransac matched keypoints",
-        ]
-        output_matches_ransac, num_matches_ransac = display_matches(
-            pred, titles=titles, tag="KPTS_RANSAC"
-        )
-        if log_path is not None:
-            img_keypoints_path: Path = log_path / f"img_keypoints_{postfix}.png"
-            img_matches_raw_path: Path = log_path / f"img_matches_raw_{postfix}.png"
-            img_matches_ransac_path: Path = (
-                log_path / f"img_matches_ransac_{postfix}.png"
-            )
-            cv2.imwrite(
-                str(img_keypoints_path),
-                output_keypoints[:, :, ::-1].copy(),  # RGB -> BGR
-            )
-            cv2.imwrite(
-                str(img_matches_raw_path),
-                output_matches_raw[:, :, ::-1].copy(),  # RGB -> BGR
-            )
-            cv2.imwrite(
-                str(img_matches_ransac_path),
-                output_matches_ransac[:, :, ::-1].copy(),  # RGB -> BGR
-            )
-            plt.close("all")
+# server.py
+import base64
+import io
+import sys
+import warnings
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import uvicorn
+from fastapi import FastAPI, File, UploadFile
+from fastapi.exceptions import HTTPException
+from fastapi.responses import JSONResponse
+from PIL import Image
+
+sys.path.append(str(Path(__file__).parents[1]))
+
+from api.types import ImagesInput
+from hloc import DEVICE, extract_features, logger, match_dense, match_features
+from hloc.utils.viz import add_text, plot_keypoints
+from ui import get_version
+from ui.utils import filter_matches, get_feature_model, get_model
+from ui.viz import display_matches, fig2im, plot_images
+
+warnings.simplefilter("ignore")
+
+
+def decode_base64_to_image(encoding):
+    if encoding.startswith("data:image/"):
+        encoding = encoding.split(";")[1].split(",")[1]
+    try:
+        image = Image.open(io.BytesIO(base64.b64decode(encoding)))
+        return image
+    except Exception as e:
+        logger.warning(f"API cannot decode image: {e}")
+        raise HTTPException(
+            status_code=500, detail="Invalid encoded image"
+        ) from e
+
+
+def to_base64_nparray(encoding: str) -> np.ndarray:
+    return np.array(decode_base64_to_image(encoding)).astype("uint8")
+
+
+class ImageMatchingAPI(torch.nn.Module):
+    default_conf = {
+        "ransac": {
+            "enable": True,
+            "estimator": "poselib",
+            "geometry": "homography",
+            "method": "RANSAC",
+            "reproj_threshold": 3,
+            "confidence": 0.9999,
+            "max_iter": 10000,
+        },
+    }
+
+    def __init__(
+        self,
+        conf: dict = {},
+        device: str = "cpu",
+        detect_threshold: float = 0.015,
+        max_keypoints: int = 1024,
+        match_threshold: float = 0.2,
+    ) -> None:
+        """
+        Initializes an instance of the ImageMatchingAPI class.
+
+        Args:
+            conf (dict): A dictionary containing the configuration parameters.
+            device (str, optional): The device to use for computation. Defaults to "cpu".
+            detect_threshold (float, optional): The threshold for detecting keypoints. Defaults to 0.015.
+            max_keypoints (int, optional): The maximum number of keypoints to extract. Defaults to 1024.
+            match_threshold (float, optional): The threshold for matching keypoints. Defaults to 0.2.
+
+        Returns:
+            None
+        """
+        super().__init__()
+        self.device = device
+        self.conf = {**self.default_conf, **conf}
+        self._updata_config(detect_threshold, max_keypoints, match_threshold)
+        self._init_models()
+        if device == "cuda":
+            memory_allocated = torch.cuda.memory_allocated(device)
+            memory_reserved = torch.cuda.memory_reserved(device)
+            logger.info(
+                f"GPU memory allocated: {memory_allocated / 1024**2:.3f} MB"
+            )
+            logger.info(
+                f"GPU memory reserved: {memory_reserved / 1024**2:.3f} MB"
+            )
+        self.pred = None
+
+    def parse_match_config(self, conf):
+        if conf["dense"]:
+            return {
+                **conf,
+                "matcher": match_dense.confs.get(
+                    conf["matcher"]["model"]["name"]
+                ),
+                "dense": True,
+            }
+        else:
+            return {
+                **conf,
+                "feature": extract_features.confs.get(
+                    conf["feature"]["model"]["name"]
+                ),
+                "matcher": match_features.confs.get(
+                    conf["matcher"]["model"]["name"]
+                ),
+                "dense": False,
+            }
+
+    def _updata_config(
+        self,
+        detect_threshold: float = 0.015,
+        max_keypoints: int = 1024,
+        match_threshold: float = 0.2,
+    ):
+        self.dense = self.conf["dense"]
+        if self.conf["dense"]:
+            try:
+                self.conf["matcher"]["model"][
+                    "match_threshold"
+                ] = match_threshold
+            except TypeError as e:
+                logger.error(e)
+        else:
+            self.conf["feature"]["model"]["max_keypoints"] = max_keypoints
+            self.conf["feature"]["model"][
+                "keypoint_threshold"
+            ] = detect_threshold
+            self.extract_conf = self.conf["feature"]
+
+        self.match_conf = self.conf["matcher"]
+
+    def _init_models(self):
+        # initialize matcher
+        self.matcher = get_model(self.match_conf)
+        # initialize extractor
+        if self.dense:
+            self.extractor = None
+        else:
+            self.extractor = get_feature_model(self.conf["feature"])
+
+    def _forward(self, img0, img1):
+        if self.dense:
+            pred = match_dense.match_images(
+                self.matcher,
+                img0,
+                img1,
+                self.match_conf["preprocessing"],
+                device=self.device,
+            )
+            last_fixed = "{}".format(  # noqa: F841
+                self.match_conf["model"]["name"]
+            )
+        else:
+            pred0 = extract_features.extract(
+                self.extractor, img0, self.extract_conf["preprocessing"]
+            )
+            pred1 = extract_features.extract(
+                self.extractor, img1, self.extract_conf["preprocessing"]
+            )
+            pred = match_features.match_images(self.matcher, pred0, pred1)
+        return pred
+
+    @torch.inference_mode()
+    def extract(self, img0: np.ndarray, **kwargs) -> Dict[str, np.ndarray]:
+        """Extract features from a single image.
+
+        Args:
+            img0 (np.ndarray): image
+
+        Returns:
+            Dict[str, np.ndarray]: feature dict
+        """
+
+        # setting prams
+        self.extractor.conf["max_keypoints"] = kwargs.get("max_keypoints", 512)
+        self.extractor.conf["keypoint_threshold"] = kwargs.get(
+            "keypoint_threshold", 0.0
+        )
+
+        pred = extract_features.extract(
+            self.extractor, img0, self.extract_conf["preprocessing"]
+        )
+        pred = {
+            k: v.cpu().detach()[0].numpy() if isinstance(v, torch.Tensor) else v
+            for k, v in pred.items()
+        }
+        # back to origin scale
+        s0 = pred["original_size"] / pred["size"]
+        pred["keypoints_orig"] = (
+            match_features.scale_keypoints(pred["keypoints"] + 0.5, s0) - 0.5
+        )
+        # TODO: rotate back
+
+        binarize = kwargs.get("binarize", False)
+        if binarize:
+            assert "descriptors" in pred
+            pred["descriptors"] = (pred["descriptors"] > 0).astype(np.uint8)
+            pred["descriptors"] = pred["descriptors"].T  # N x DIM
+        return pred
+
+    @torch.inference_mode()
+    def forward(
+        self,
+        img0: np.ndarray,
+        img1: np.ndarray,
+    ) -> Dict[str, np.ndarray]:
+        """
+        Forward pass of the image matching API.
+
+        Args:
+            img0: A 3D NumPy array of shape (H, W, C) representing the first image.
+                  Values are in the range [0, 1] and are in RGB mode.
+            img1: A 3D NumPy array of shape (H, W, C) representing the second image.
+                  Values are in the range [0, 1] and are in RGB mode.
+
+        Returns:
+            A dictionary containing the following keys:
+            - image0_orig: The original image 0.
+            - image1_orig: The original image 1.
+            - keypoints0_orig: The keypoints detected in image 0.
+            - keypoints1_orig: The keypoints detected in image 1.
+            - mkeypoints0_orig: The raw matches between image 0 and image 1.
+            - mkeypoints1_orig: The raw matches between image 1 and image 0.
+            - mmkeypoints0_orig: The RANSAC inliers in image 0.
+            - mmkeypoints1_orig: The RANSAC inliers in image 1.
+            - mconf: The confidence scores for the raw matches.
+            - mmconf: The confidence scores for the RANSAC inliers.
+        """
+        # Take as input a pair of images (not a batch)
+        assert isinstance(img0, np.ndarray)
+        assert isinstance(img1, np.ndarray)
+        self.pred = self._forward(img0, img1)
+        if self.conf["ransac"]["enable"]:
+            self.pred = self._geometry_check(self.pred)
+        return self.pred
+
+    def _geometry_check(
+        self,
+        pred: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """
+        Filter matches using RANSAC. If keypoints are available, filter by keypoints.
+        If lines are available, filter by lines. If both keypoints and lines are
+        available, filter by keypoints.
+
+        Args:
+            pred (Dict[str, Any]): dict of matches, including original keypoints.
+                                  See :func:`filter_matches` for the expected keys.
+
+        Returns:
+            Dict[str, Any]: filtered matches
+        """
+        pred = filter_matches(
+            pred,
+            ransac_method=self.conf["ransac"]["method"],
+            ransac_reproj_threshold=self.conf["ransac"]["reproj_threshold"],
+            ransac_confidence=self.conf["ransac"]["confidence"],
+            ransac_max_iter=self.conf["ransac"]["max_iter"],
+        )
+        return pred
+
+    def visualize(
+        self,
+        log_path: Optional[Path] = None,
+    ) -> None:
+        """
+        Visualize the matches.
+
+        Args:
+            log_path (Path, optional): The directory to save the images. Defaults to None.
+
+        Returns:
+            None
+        """
+        if self.conf["dense"]:
+            postfix = str(self.conf["matcher"]["model"]["name"])
+        else:
+            postfix = "{}_{}".format(
+                str(self.conf["feature"]["model"]["name"]),
+                str(self.conf["matcher"]["model"]["name"]),
+            )
+        titles = [
+            "Image 0 - Keypoints",
+            "Image 1 - Keypoints",
+        ]
+        pred: Dict[str, Any] = self.pred
+        image0: np.ndarray = pred["image0_orig"]
+        image1: np.ndarray = pred["image1_orig"]
+        output_keypoints: np.ndarray = plot_images(
+            [image0, image1], titles=titles, dpi=300
+        )
+        if (
+            "keypoints0_orig" in pred.keys()
+            and "keypoints1_orig" in pred.keys()
+        ):
+            plot_keypoints([pred["keypoints0_orig"], pred["keypoints1_orig"]])
+            text: str = (
+                f"# keypoints0: {len(pred['keypoints0_orig'])} \n"
+                + f"# keypoints1: {len(pred['keypoints1_orig'])}"
+            )
+            add_text(0, text, fs=15)
+        output_keypoints = fig2im(output_keypoints)
+        # plot images with raw matches
+        titles = [
+            "Image 0 - Raw matched keypoints",
+            "Image 1 - Raw matched keypoints",
+        ]
+        output_matches_raw, num_matches_raw = display_matches(
+            pred, titles=titles, tag="KPTS_RAW"
+        )
+        # plot images with ransac matches
+        titles = [
+            "Image 0 - Ransac matched keypoints",
+            "Image 1 - Ransac matched keypoints",
+        ]
+        output_matches_ransac, num_matches_ransac = display_matches(
+            pred, titles=titles, tag="KPTS_RANSAC"
+        )
+        if log_path is not None:
+            img_keypoints_path: Path = log_path / f"img_keypoints_{postfix}.png"
+            img_matches_raw_path: Path = (
+                log_path / f"img_matches_raw_{postfix}.png"
+            )
+            img_matches_ransac_path: Path = (
+                log_path / f"img_matches_ransac_{postfix}.png"
+            )
+            cv2.imwrite(
+                str(img_keypoints_path),
+                output_keypoints[:, :, ::-1].copy(),  # RGB -> BGR
+            )
+            cv2.imwrite(
+                str(img_matches_raw_path),
+                output_matches_raw[:, :, ::-1].copy(),  # RGB -> BGR
+            )
+            cv2.imwrite(
+                str(img_matches_ransac_path),
+                output_matches_ransac[:, :, ::-1].copy(),  # RGB -> BGR
+            )
+            plt.close("all")
+
+
+class ImageMatchingService:
+    def __init__(self, conf: dict, device: str):
+        self.conf = conf
+        self.api = ImageMatchingAPI(conf=conf, device=device)
+        self.app = FastAPI()
+        self.register_routes()
+
+    def register_routes(self):
+
+        @self.app.get("/version")
+        async def version():
+            return {"version": get_version()}
+
+        @self.app.post("/v1/match")
+        async def match(
+            image0: UploadFile = File(...), image1: UploadFile = File(...)
+        ):
+            """
+            Handle the image matching request and return the processed result.
+
+            Args:
+                image0 (UploadFile): The first image file for matching.
+                image1 (UploadFile): The second image file for matching.
+
+            Returns:
+                JSONResponse: A JSON response containing the filtered match results
+                              or an error message in case of failure.
+            """
+            try:
+                # Load the images from the uploaded files
+                image0_array = self.load_image(image0)
+                image1_array = self.load_image(image1)
+
+                # Perform image matching using the API
+                output = self.api(image0_array, image1_array)
+
+                # Keys to skip in the output
+                skip_keys = ["image0_orig", "image1_orig"]
+
+                # Postprocess the output to filter unwanted data
+                pred = self.postprocess(output, skip_keys)
+
+                # Return the filtered prediction as a JSON response
+                return JSONResponse(content=pred)
+            except Exception as e:
+                # Return an error message with status code 500 in case of exception
+                return JSONResponse(content={"error": str(e)}, status_code=500)
+
+        @self.app.post("/v1/extract")
+        async def extract(input_info: ImagesInput):
+            """
+            Extract keypoints and descriptors from images.
+
+            Args:
+                input_info: An object containing the image data and options.
+
+            Returns:
+                A list of dictionaries containing the keypoints and descriptors.
+            """
+            try:
+                preds = []
+                for i, input_image in enumerate(input_info.data):
+                    # Load the image from the input data
+                    image_array = to_base64_nparray(input_image)
+                    # Extract keypoints and descriptors
+                    output = self.api.extract(
+                        image_array,
+                        max_keypoints=input_info.max_keypoints[i],
+                        binarize=input_info.binarize,
+                    )
+                    # Do not return the original image and image_orig
+                    # skip_keys = ["image", "image_orig"]
+                    skip_keys = []
+
+                    # Postprocess the output
+                    pred = self.postprocess(output, skip_keys)
+                    preds.append(pred)
+                # Return the list of extracted features
+                return JSONResponse(content=preds)
+            except Exception as e:
+                # Return an error message if an exception occurs
+                return JSONResponse(content={"error": str(e)}, status_code=500)
+
+    def load_image(self, file_path: Union[str, UploadFile]) -> np.ndarray:
+        """
+        Reads an image from a file path or an UploadFile object.
+
+        Args:
+            file_path: A file path or an UploadFile object.
+
+        Returns:
+            A numpy array representing the image.
+        """
+        if isinstance(file_path, str):
+            file_path = Path(file_path).resolve(strict=False)
+        else:
+            file_path = file_path.file
+        with Image.open(file_path) as img:
+            image_array = np.array(img)
+        return image_array
+
+    def postprocess(
+        self, output: dict, skip_keys: list, binarize: bool = True
+    ) -> dict:
+        pred = {}
+        for key, value in output.items():
+            if key in skip_keys:
+                continue
+            if isinstance(value, np.ndarray):
+                pred[key] = value.tolist()
+        return pred
+
+    def run(self, host: str = "0.0.0.0", port: int = 8001):
+        uvicorn.run(self.app, host=host, port=port)
+
+
+if __name__ == "__main__":
+    conf = {
+        "feature": {
+            "output": "feats-superpoint-n4096-rmax1600",
+            "model": {
+                "name": "superpoint",
+                "nms_radius": 3,
+                "max_keypoints": 4096,
+                "keypoint_threshold": 0.005,
+            },
+            "preprocessing": {
+                "grayscale": True,
+                "force_resize": True,
+                "resize_max": 1600,
+                "width": 640,
+                "height": 480,
+                "dfactor": 8,
+            },
+        },
+        "matcher": {
+            "output": "matches-NN-mutual",
+            "model": {
+                "name": "nearest_neighbor",
+                "do_mutual_check": True,
+                "match_threshold": 0.2,
+            },
+        },
+        "dense": False,
+    }
+
+    service = ImageMatchingService(conf=conf, device=DEVICE)
+    service.run()

{imcui/api → api}/test/CMakeLists.txt

@@ -6,12 +6,11 @@ find_package(OpenCV REQUIRED)
 
 find_package(Boost REQUIRED COMPONENTS system)
 if(Boost_FOUND)
-  include_directories(${Boost_INCLUDE_DIRS})
+    include_directories(${Boost_INCLUDE_DIRS})
 endif()
 
 add_executable(client client.cpp)
 
-target_include_directories(client PRIVATE ${Boost_LIBRARIES}
-                                          ${OpenCV_INCLUDE_DIRS})
+target_include_directories(client PRIVATE ${Boost_LIBRARIES} ${OpenCV_INCLUDE_DIRS})
 
 target_link_libraries(client PRIVATE curl jsoncpp b64 ${OpenCV_LIBS})

{imcui/api → api}/test/build_and_run.sh

@@ -1,16 +1,16 @@
-# g++ main.cpp -I/usr/include/opencv4 -lcurl -ljsoncpp -lb64 -lopencv_core -lopencv_imgcodecs -o main
-# sudo apt-get update
-# sudo apt-get install libboost-all-dev -y
-# sudo apt-get install libcurl4-openssl-dev libjsoncpp-dev libb64-dev libopencv-dev -y
-
-cd build
-cmake ..
-make -j12
-
-echo " ======== RUN DEMO ========"
-
-./client
-
-echo " ======== END DEMO ========"
-
-cd ..
+# g++ main.cpp -I/usr/include/opencv4 -lcurl -ljsoncpp -lb64 -lopencv_core -lopencv_imgcodecs -o main
+# sudo apt-get update
+# sudo apt-get install libboost-all-dev -y
+# sudo apt-get install libcurl4-openssl-dev libjsoncpp-dev libb64-dev libopencv-dev -y
+
+cd build
+cmake ..
+make -j12
+
+echo " ======== RUN DEMO ========"
+
+./client
+
+echo " ======== END DEMO ========"
+
+cd ..

{imcui/api → api}/test/client.cpp

@@ -1,81 +1,84 @@
-#include <curl/curl.h>
-#include <opencv2/opencv.hpp>
-#include "helper.h"
-
-int main() {
-    std::string img_path =
-        "../../../datasets/sacre_coeur/mapping_rot/02928139_3448003521_rot45.jpg";
-    cv::Mat original_img = cv::imread(img_path, cv::IMREAD_GRAYSCALE);
-
-    if (original_img.empty()) {
-        throw std::runtime_error("Failed to decode image");
-    }
-
-    // Convert the image to Base64
-    std::string base64_img = image_to_base64(original_img);
-
-    // Convert the Base64 back to an image
-    cv::Mat decoded_img = base64_to_image(base64_img);
-    cv::imwrite("decoded_image.jpg", decoded_img);
-    cv::imwrite("original_img.jpg", original_img);
-
-    // The images should be identical
-    if (cv::countNonZero(original_img != decoded_img) != 0) {
-        std::cerr << "The images are not identical" << std::endl;
-        return -1;
-    } else {
-        std::cout << "The images are identical!" << std::endl;
-    }
-
-    // construct params
-    APIParams params{.data = {base64_img},
-                     .max_keypoints = {100, 100},
-                     .timestamps = {"0", "1"},
-                     .grayscale = {0},
-                     .image_hw = {{480, 640}, {240, 320}},
-                     .feature_type = 0,
-                     .rotates = {0.0f, 0.0f},
-                     .scales = {1.0f, 1.0f},
-                     .reference_points = {{1.23e+2f, 1.2e+1f},
-                                          {5.0e-1f, 3.0e-1f},
-                                          {2.3e+2f, 2.2e+1f},
-                                          {6.0e-1f, 4.0e-1f}},
-                     .binarize = {1}};
-
-    KeyPointResults kpts_results;
-
-    // Convert the parameters to JSON
-    Json::Value jsonData = paramsToJson(params);
-    std::string url = "http://127.0.0.1:8001/v1/extract";
-    Json::StreamWriterBuilder writer;
-    std::string output = Json::writeString(writer, jsonData);
-
-    CURL* curl;
-    CURLcode res;
-    std::string readBuffer;
-
-    curl_global_init(CURL_GLOBAL_DEFAULT);
-    curl = curl_easy_init();
-    if (curl) {
-        struct curl_slist* hs = NULL;
-        hs = curl_slist_append(hs, "Content-Type: application/json");
-        curl_easy_setopt(curl, CURLOPT_HTTPHEADER, hs);
-        curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
-        curl_easy_setopt(curl, CURLOPT_POSTFIELDS, output.c_str());
-        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
-        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);
-        res = curl_easy_perform(curl);
-
-        if (res != CURLE_OK)
-            fprintf(
-                stderr, "curl_easy_perform() failed: %s\n", curl_easy_strerror(res));
-        else {
-            // std::cout << "Response from server: " << readBuffer << std::endl;
-            kpts_results = decode_response(readBuffer);
-        }
-        curl_easy_cleanup(curl);
-    }
-    curl_global_cleanup();
-
-    return 0;
-}
+#include <curl/curl.h>
+#include <opencv2/opencv.hpp>
+#include "helper.h"
+
+int main() {
+    std::string img_path = "../../../datasets/sacre_coeur/mapping_rot/02928139_3448003521_rot45.jpg";
+    cv::Mat original_img = cv::imread(img_path, cv::IMREAD_GRAYSCALE);
+
+    if (original_img.empty()) {
+        throw std::runtime_error("Failed to decode image");
+    }
+
+    // Convert the image to Base64
+    std::string base64_img = image_to_base64(original_img);
+
+    // Convert the Base64 back to an image
+    cv::Mat decoded_img = base64_to_image(base64_img);
+    cv::imwrite("decoded_image.jpg", decoded_img);
+    cv::imwrite("original_img.jpg", original_img);
+
+    // The images should be identical
+    if (cv::countNonZero(original_img != decoded_img) != 0) {
+        std::cerr << "The images are not identical" << std::endl;
+        return -1;
+    } else {
+        std::cout << "The images are identical!" << std::endl;
+    }
+
+    // construct params
+    APIParams params{
+        .data = {base64_img},
+        .max_keypoints = {100, 100},
+        .timestamps = {"0", "1"},
+        .grayscale = {0},
+        .image_hw = {{480, 640}, {240, 320}},
+        .feature_type = 0,
+        .rotates = {0.0f, 0.0f},
+        .scales = {1.0f, 1.0f},
+        .reference_points = {
+            {1.23e+2f, 1.2e+1f},
+            {5.0e-1f, 3.0e-1f},
+            {2.3e+2f, 2.2e+1f},
+            {6.0e-1f, 4.0e-1f}
+        },
+        .binarize = {1}
+    };
+
+    KeyPointResults kpts_results;
+
+    // Convert the parameters to JSON
+    Json::Value jsonData = paramsToJson(params);
+    std::string url = "http://127.0.0.1:8001/v1/extract";
+    Json::StreamWriterBuilder writer;
+    std::string output = Json::writeString(writer, jsonData);
+
+    CURL* curl;
+    CURLcode res;
+    std::string readBuffer;
+
+    curl_global_init(CURL_GLOBAL_DEFAULT);
+    curl = curl_easy_init();
+    if (curl) {
+        struct curl_slist* hs = NULL;
+        hs = curl_slist_append(hs, "Content-Type: application/json");
+        curl_easy_setopt(curl, CURLOPT_HTTPHEADER, hs);
+        curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
+        curl_easy_setopt(curl, CURLOPT_POSTFIELDS, output.c_str());
+        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
+        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);
+        res = curl_easy_perform(curl);
+
+        if (res != CURLE_OK)
+            fprintf(stderr, "curl_easy_perform() failed: %s\n",
+                    curl_easy_strerror(res));
+        else {
+            // std::cout << "Response from server: " << readBuffer << std::endl;
+           kpts_results =  decode_response(readBuffer);
+        }
+        curl_easy_cleanup(curl);
+    }
+    curl_global_cleanup();
+
+    return 0;
+}

{imcui/api → api}/test/helper.h

@@ -1,405 +1,410 @@
-
-#include <b64/encode.h>
-#include <fstream>
-#include <jsoncpp/json/json.h>
-#include <opencv2/opencv.hpp>
-#include <sstream>
-#include <vector>
-
-// base64 to image
-#include <boost/archive/iterators/base64_from_binary.hpp>
-#include <boost/archive/iterators/binary_from_base64.hpp>
-#include <boost/archive/iterators/transform_width.hpp>
-
-/// Parameters used in the API
-struct APIParams {
-    /// A list of images, base64 encoded
-    std::vector<std::string> data;
-
-    /// The maximum number of keypoints to detect for each image
-    std::vector<int> max_keypoints;
-
-    /// The timestamps of the images
-    std::vector<std::string> timestamps;
-
-    /// Whether to convert the images to grayscale
-    bool grayscale;
-
-    /// The height and width of each image
-    std::vector<std::vector<int>> image_hw;
-
-    /// The type of feature detector to use
-    int feature_type;
-
-    /// The rotations of the images
-    std::vector<double> rotates;
-
-    /// The scales of the images
-    std::vector<double> scales;
-
-    /// The reference points of the images
-    std::vector<std::vector<float>> reference_points;
-
-    /// Whether to binarize the descriptors
-    bool binarize;
-};
-
-/**
- * @brief Contains the results of a keypoint detector.
- *
- * @details Stores the keypoints and descriptors for each image.
- */
-class KeyPointResults {
-      public:
-    KeyPointResults() {
-    }
-
-    /**
-     * @brief Constructor.
-     *
-     * @param kp The keypoints for each image.
-     */
-    KeyPointResults(const std::vector<std::vector<cv::KeyPoint>>& kp,
-                    const std::vector<cv::Mat>& desc)
-        : keypoints(kp), descriptors(desc) {
-    }
-
-    /**
-     * @brief Append keypoints to the result.
-     *
-     * @param kpts The keypoints to append.
-     */
-    inline void append_keypoints(std::vector<cv::KeyPoint>& kpts) {
-        keypoints.emplace_back(kpts);
-    }
-
-    /**
-     * @brief Append descriptors to the result.
-     *
-     * @param desc The descriptors to append.
-     */
-    inline void append_descriptors(cv::Mat& desc) {
-        descriptors.emplace_back(desc);
-    }
-
-    /**
-     * @brief Get the keypoints.
-     *
-     * @return The keypoints.
-     */
-    inline std::vector<std::vector<cv::KeyPoint>> get_keypoints() {
-        return keypoints;
-    }
-
-    /**
-     * @brief Get the descriptors.
-     *
-     * @return The descriptors.
-     */
-    inline std::vector<cv::Mat> get_descriptors() {
-        return descriptors;
-    }
-
-      private:
-    std::vector<std::vector<cv::KeyPoint>> keypoints;
-    std::vector<cv::Mat> descriptors;
-    std::vector<std::vector<float>> scores;
-};
-
-/**
- * @brief Decodes a base64 encoded string.
- *
- * @param base64 The base64 encoded string to decode.
- * @return The decoded string.
- */
-std::string base64_decode(const std::string& base64) {
-    using namespace boost::archive::iterators;
-    using It = transform_width<binary_from_base64<std::string::const_iterator>, 8, 6>;
-
-    // Find the position of the last non-whitespace character
-    auto end = base64.find_last_not_of(" \t\n\r");
-    if (end != std::string::npos) {
-        // Move one past the last non-whitespace character
-        end += 1;
-    }
-
-    // Decode the base64 string and return the result
-    return std::string(It(base64.begin()), It(base64.begin() + end));
-}
-
-/**
- * @brief Decodes a base64 string into an OpenCV image
- *
- * @param base64 The base64 encoded string
- * @return The decoded OpenCV image
- */
-cv::Mat base64_to_image(const std::string& base64) {
-    // Decode the base64 string
-    std::string decodedStr = base64_decode(base64);
-
-    // Decode the image
-    std::vector<uchar> data(decodedStr.begin(), decodedStr.end());
-    cv::Mat img = cv::imdecode(data, cv::IMREAD_GRAYSCALE);
-
-    // Check for errors
-    if (img.empty()) {
-        throw std::runtime_error("Failed to decode image");
-    }
-
-    return img;
-}
-
-/**
- * @brief Encodes an OpenCV image into a base64 string
- *
- * This function takes an OpenCV image and encodes it into a base64 string.
- * The image is first encoded as a PNG image, and then the resulting
- * bytes are encoded as a base64 string.
- *
- * @param img The OpenCV image
- * @return The base64 encoded string
- *
- * @throws std::runtime_error if the image is empty or encoding fails
- */
-std::string image_to_base64(cv::Mat& img) {
-    if (img.empty()) {
-        throw std::runtime_error("Failed to read image");
-    }
-
-    // Encode the image as a PNG
-    std::vector<uchar> buf;
-    if (!cv::imencode(".png", img, buf)) {
-        throw std::runtime_error("Failed to encode image");
-    }
-
-    // Encode the bytes as a base64 string
-    using namespace boost::archive::iterators;
-    using It =
-        base64_from_binary<transform_width<std::vector<uchar>::const_iterator, 6, 8>>;
-    std::string base64(It(buf.begin()), It(buf.end()));
-
-    // Pad the string with '=' characters to a multiple of 4 bytes
-    base64.append((3 - buf.size() % 3) % 3, '=');
-
-    return base64;
-}
-
-/**
- * @brief Callback function for libcurl to write data to a string
- *
- * This function is used as a callback for libcurl to write data to a string.
- * It takes the contents, size, and nmemb as parameters, and writes the data to
- * the string.
- *
- * @param contents The data to write
- * @param size The size of the data
- * @param nmemb The number of members in the data
- * @param s The string to write the data to
- * @return The number of bytes written
- */
-size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* s) {
-    size_t newLength = size * nmemb;
-    try {
-        // Resize the string to fit the new data
-        s->resize(s->size() + newLength);
-    } catch (std::bad_alloc& e) {
-        // If there's an error allocating memory, return 0
-        return 0;
-    }
-
-    // Copy the data to the string
-    std::copy(static_cast<const char*>(contents),
-              static_cast<const char*>(contents) + newLength,
-              s->begin() + s->size() - newLength);
-    return newLength;
-}
-
-// Helper functions
-
-/**
- * @brief Helper function to convert a type to a Json::Value
- *
- * This function takes a value of type T and converts it to a Json::Value.
- * It is used to simplify the process of converting a type to a Json::Value.
- *
- * @param val The value to convert
- * @return The converted Json::Value
- */
-template <typename T> Json::Value toJson(const T& val) {
-    return Json::Value(val);
-}
-
-/**
- * @brief Converts a vector to a Json::Value
- *
- * This function takes a vector of type T and converts it to a Json::Value.
- * Each element in the vector is appended to the Json::Value array.
- *
- * @param vec The vector to convert to Json::Value
- * @return The Json::Value representing the vector
- */
-template <typename T> Json::Value vectorToJson(const std::vector<T>& vec) {
-    Json::Value json(Json::arrayValue);
-    for (const auto& item : vec) {
-        json.append(item);
-    }
-    return json;
-}
-
-/**
- * @brief Converts a nested vector to a Json::Value
- *
- * This function takes a nested vector of type T and converts it to a
- * Json::Value. Each sub-vector is converted to a Json::Value array and appended
- * to the main Json::Value array.
- *
- * @param vec The nested vector to convert to Json::Value
- * @return The Json::Value representing the nested vector
- */
-template <typename T>
-Json::Value nestedVectorToJson(const std::vector<std::vector<T>>& vec) {
-    Json::Value json(Json::arrayValue);
-    for (const auto& subVec : vec) {
-        json.append(vectorToJson(subVec));
-    }
-    return json;
-}
-
-/**
- * @brief Converts the APIParams struct to a Json::Value
- *
- * This function takes an APIParams struct and converts it to a Json::Value.
- * The Json::Value is a JSON object with the following fields:
- * - data: a JSON array of base64 encoded images
- * - max_keypoints: a JSON array of integers, max number of keypoints for each
- * image
- * - timestamps: a JSON array of timestamps, one for each image
- * - grayscale: a JSON boolean, whether to convert images to grayscale
- * - image_hw: a nested JSON array, each sub-array contains the height and width
- * of an image
- * - feature_type: a JSON integer, the type of feature detector to use
- * - rotates: a JSON array of doubles, the rotation of each image
- * - scales: a JSON array of doubles, the scale of each image
- * - reference_points: a nested JSON array, each sub-array contains the
- * reference points of an image
- * - binarize: a JSON boolean, whether to binarize the descriptors
- *
- * @param params The APIParams struct to convert
- * @return The Json::Value representing the APIParams struct
- */
-Json::Value paramsToJson(const APIParams& params) {
-    Json::Value json;
-    json["data"] = vectorToJson(params.data);
-    json["max_keypoints"] = vectorToJson(params.max_keypoints);
-    json["timestamps"] = vectorToJson(params.timestamps);
-    json["grayscale"] = toJson(params.grayscale);
-    json["image_hw"] = nestedVectorToJson(params.image_hw);
-    json["feature_type"] = toJson(params.feature_type);
-    json["rotates"] = vectorToJson(params.rotates);
-    json["scales"] = vectorToJson(params.scales);
-    json["reference_points"] = nestedVectorToJson(params.reference_points);
-    json["binarize"] = toJson(params.binarize);
-    return json;
-}
-
-template <typename T> cv::Mat jsonToMat(Json::Value json) {
-    int rows = json.size();
-    int cols = json[0].size();
-
-    // Create a single array to hold all the data.
-    std::vector<T> data;
-    data.reserve(rows * cols);
-
-    for (int i = 0; i < rows; i++) {
-        for (int j = 0; j < cols; j++) {
-            data.push_back(static_cast<T>(json[i][j].asInt()));
-        }
-    }
-
-    // Create a cv::Mat object that points to the data.
-    cv::Mat mat(rows, cols, CV_8UC1,
-                data.data());  // Change the type if necessary.
-    // cv::Mat mat(cols, rows,CV_8UC1, data.data());  // Change the type if
-    // necessary.
-
-    return mat;
-}
-
-/**
- * @brief Decodes the response of the server and prints the keypoints
- *
- * This function takes the response of the server, a JSON string, and decodes
- * it. It then prints the keypoints and draws them on the original image.
- *
- * @param response The response of the server
- * @return The keypoints and descriptors
- */
-KeyPointResults decode_response(const std::string& response, bool viz = true) {
-    Json::CharReaderBuilder builder;
-    Json::CharReader* reader = builder.newCharReader();
-
-    Json::Value jsonData;
-    std::string errors;
-
-    // Parse the JSON response
-    bool parsingSuccessful = reader->parse(
-        response.c_str(), response.c_str() + response.size(), &jsonData, &errors);
-    delete reader;
-
-    if (!parsingSuccessful) {
-        // Handle error
-        std::cout << "Failed to parse the JSON, errors:" << std::endl;
-        std::cout << errors << std::endl;
-        return KeyPointResults();
-    }
-
-    KeyPointResults kpts_results;
-
-    // Iterate over the images
-    for (const auto& jsonItem : jsonData) {
-        auto jkeypoints = jsonItem["keypoints"];
-        auto jkeypoints_orig = jsonItem["keypoints_orig"];
-        auto jdescriptors = jsonItem["descriptors"];
-        auto jscores = jsonItem["scores"];
-        auto jimageSize = jsonItem["image_size"];
-        auto joriginalSize = jsonItem["original_size"];
-        auto jsize = jsonItem["size"];
-
-        std::vector<cv::KeyPoint> vkeypoints;
-        std::vector<float> vscores;
-
-        // Iterate over the keypoints
-        int counter = 0;
-        for (const auto& keypoint : jkeypoints_orig) {
-            if (counter < 10) {
-                // Print the first 10 keypoints
-                std::cout << keypoint[0].asFloat() << ", " << keypoint[1].asFloat()
-                          << std::endl;
-            }
-            counter++;
-            // Convert the Json::Value to a cv::KeyPoint
-            vkeypoints.emplace_back(
-                cv::KeyPoint(keypoint[0].asFloat(), keypoint[1].asFloat(), 0.0));
-        }
-
-        if (viz && jsonItem.isMember("image_orig")) {
-            auto jimg_orig = jsonItem["image_orig"];
-            cv::Mat img = jsonToMat<uchar>(jimg_orig);
-            cv::imwrite("viz_image_orig.jpg", img);
-
-            // Draw keypoints on the image
-            cv::Mat imgWithKeypoints;
-            cv::drawKeypoints(img, vkeypoints, imgWithKeypoints, cv::Scalar(0, 0, 255));
-
-            // Write the image with keypoints
-            std::string filename = "viz_image_orig_keypoints.jpg";
-            cv::imwrite(filename, imgWithKeypoints);
-        }
-
-        // Iterate over the descriptors
-        cv::Mat descriptors = jsonToMat<uchar>(jdescriptors);
-        kpts_results.append_keypoints(vkeypoints);
-        kpts_results.append_descriptors(descriptors);
-    }
-    return kpts_results;
-}
+
+#include <sstream>
+#include <fstream>
+#include <vector>
+#include <b64/encode.h>
+#include <jsoncpp/json/json.h>
+#include <opencv2/opencv.hpp>
+
+// base64 to image
+#include <boost/archive/iterators/binary_from_base64.hpp>
+#include <boost/archive/iterators/transform_width.hpp>
+#include <boost/archive/iterators/base64_from_binary.hpp>
+
+/// Parameters used in the API
+struct APIParams {
+    /// A list of images, base64 encoded
+    std::vector<std::string> data;
+
+    /// The maximum number of keypoints to detect for each image
+    std::vector<int> max_keypoints;
+
+    /// The timestamps of the images
+    std::vector<std::string> timestamps;
+
+    /// Whether to convert the images to grayscale
+    bool grayscale;
+
+    /// The height and width of each image
+    std::vector<std::vector<int>> image_hw;
+
+    /// The type of feature detector to use
+    int feature_type;
+
+    /// The rotations of the images
+    std::vector<double> rotates;
+
+    /// The scales of the images
+    std::vector<double> scales;
+
+    /// The reference points of the images
+    std::vector<std::vector<float>> reference_points;
+
+    /// Whether to binarize the descriptors
+    bool binarize;
+};
+
+/**
+ * @brief Contains the results of a keypoint detector.
+ *
+ * @details Stores the keypoints and descriptors for each image.
+ */
+class KeyPointResults {
+public:
+    KeyPointResults() {}
+
+    /**
+     * @brief Constructor.
+     *
+     * @param kp The keypoints for each image.
+     */
+    KeyPointResults(const std::vector<std::vector<cv::KeyPoint>>& kp, 
+                    const std::vector<cv::Mat>& desc)
+        : keypoints(kp), descriptors(desc) {}
+
+    /**
+     * @brief Append keypoints to the result.
+     *
+     * @param kpts The keypoints to append.
+     */
+    inline void append_keypoints(std::vector<cv::KeyPoint>& kpts) {
+        keypoints.emplace_back(kpts);
+    }
+
+    /**
+     * @brief Append descriptors to the result.
+     *
+     * @param desc The descriptors to append.
+     */
+    inline void append_descriptors(cv::Mat& desc) {
+        descriptors.emplace_back(desc);
+    }
+
+    /**
+     * @brief Get the keypoints.
+     *
+     * @return The keypoints.
+     */
+    inline std::vector<std::vector<cv::KeyPoint>> get_keypoints() {
+        return keypoints;
+    }
+
+    /**
+     * @brief Get the descriptors.
+     *
+     * @return The descriptors.
+     */
+    inline std::vector<cv::Mat> get_descriptors() {
+        return descriptors;
+    }
+
+private:
+    std::vector<std::vector<cv::KeyPoint>> keypoints;
+    std::vector<cv::Mat> descriptors;
+    std::vector<std::vector<float>> scores;
+};
+
+
+/**
+ * @brief Decodes a base64 encoded string.
+ *
+ * @param base64 The base64 encoded string to decode.
+ * @return The decoded string.
+ */
+std::string base64_decode(const std::string& base64) {
+    using namespace boost::archive::iterators;
+    using It = transform_width<binary_from_base64<std::string::const_iterator>, 8, 6>;
+
+    // Find the position of the last non-whitespace character
+    auto end = base64.find_last_not_of(" \t\n\r");
+    if (end != std::string::npos) {
+        // Move one past the last non-whitespace character
+        end += 1;
+    }
+
+    // Decode the base64 string and return the result
+    return std::string(It(base64.begin()), It(base64.begin() + end));
+}
+
+
+
+/**
+ * @brief Decodes a base64 string into an OpenCV image
+ *
+ * @param base64 The base64 encoded string
+ * @return The decoded OpenCV image
+ */
+cv::Mat base64_to_image(const std::string& base64) {
+    // Decode the base64 string
+    std::string decodedStr = base64_decode(base64);
+
+    // Decode the image
+    std::vector<uchar> data(decodedStr.begin(), decodedStr.end());
+    cv::Mat img = cv::imdecode(data, cv::IMREAD_GRAYSCALE);
+
+    // Check for errors
+    if (img.empty()) {
+        throw std::runtime_error("Failed to decode image");
+    }
+
+    return img;
+}
+
+
+/**
+ * @brief Encodes an OpenCV image into a base64 string
+ *
+ * This function takes an OpenCV image and encodes it into a base64 string.
+ * The image is first encoded as a PNG image, and then the resulting
+ * bytes are encoded as a base64 string.
+ *
+ * @param img The OpenCV image
+ * @return The base64 encoded string
+ *
+ * @throws std::runtime_error if the image is empty or encoding fails
+ */
+std::string image_to_base64(cv::Mat &img) {
+    if (img.empty()) {
+        throw std::runtime_error("Failed to read image");
+    }
+
+    // Encode the image as a PNG
+    std::vector<uchar> buf;
+    if (!cv::imencode(".png", img, buf)) {
+        throw std::runtime_error("Failed to encode image");
+    }
+
+    // Encode the bytes as a base64 string
+    using namespace boost::archive::iterators;
+    using It = base64_from_binary<transform_width<std::vector<uchar>::const_iterator, 6, 8>>;
+    std::string base64(It(buf.begin()), It(buf.end()));
+
+    // Pad the string with '=' characters to a multiple of 4 bytes
+    base64.append((3 - buf.size() % 3) % 3, '=');
+
+    return base64;
+}
+
+
+/**
+ * @brief Callback function for libcurl to write data to a string
+ *
+ * This function is used as a callback for libcurl to write data to a string.
+ * It takes the contents, size, and nmemb as parameters, and writes the data to
+ * the string.
+ *
+ * @param contents The data to write
+ * @param size The size of the data
+ * @param nmemb The number of members in the data
+ * @param s The string to write the data to
+ * @return The number of bytes written
+ */
+size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* s) {
+    size_t newLength = size * nmemb;
+    try {
+        // Resize the string to fit the new data
+        s->resize(s->size() + newLength);
+    } catch (std::bad_alloc& e) {
+        // If there's an error allocating memory, return 0
+        return 0;
+    }
+
+    // Copy the data to the string
+    std::copy(static_cast<const char*>(contents),
+              static_cast<const char*>(contents) + newLength,
+              s->begin() + s->size() - newLength);
+    return newLength;
+}
+
+// Helper functions
+
+/**
+ * @brief Helper function to convert a type to a Json::Value
+ *
+ * This function takes a value of type T and converts it to a Json::Value.
+ * It is used to simplify the process of converting a type to a Json::Value.
+ *
+ * @param val The value to convert
+ * @return The converted Json::Value
+ */
+template <typename T>
+Json::Value toJson(const T& val) {
+    return Json::Value(val);
+}
+
+/**
+ * @brief Converts a vector to a Json::Value
+ *
+ * This function takes a vector of type T and converts it to a Json::Value.
+ * Each element in the vector is appended to the Json::Value array.
+ *
+ * @param vec The vector to convert to Json::Value
+ * @return The Json::Value representing the vector
+ */
+template <typename T>
+Json::Value vectorToJson(const std::vector<T>& vec) {
+    Json::Value json(Json::arrayValue);
+    for (const auto& item : vec) {
+        json.append(item);
+    }
+    return json;
+}
+
+/**
+ * @brief Converts a nested vector to a Json::Value
+ *
+ * This function takes a nested vector of type T and converts it to a Json::Value.
+ * Each sub-vector is converted to a Json::Value array and appended to the main Json::Value array.
+ *
+ * @param vec The nested vector to convert to Json::Value
+ * @return The Json::Value representing the nested vector
+ */
+template <typename T>
+Json::Value nestedVectorToJson(const std::vector<std::vector<T>>& vec) {
+    Json::Value json(Json::arrayValue);
+    for (const auto& subVec : vec) {
+        json.append(vectorToJson(subVec));
+    }
+    return json;
+}
+
+
+
+/**
+ * @brief Converts the APIParams struct to a Json::Value
+ *
+ * This function takes an APIParams struct and converts it to a Json::Value.
+ * The Json::Value is a JSON object with the following fields:
+ * - data: a JSON array of base64 encoded images
+ * - max_keypoints: a JSON array of integers, max number of keypoints for each image
+ * - timestamps: a JSON array of timestamps, one for each image
+ * - grayscale: a JSON boolean, whether to convert images to grayscale
+ * - image_hw: a nested JSON array, each sub-array contains the height and width of an image
+ * - feature_type: a JSON integer, the type of feature detector to use
+ * - rotates: a JSON array of doubles, the rotation of each image
+ * - scales: a JSON array of doubles, the scale of each image
+ * - reference_points: a nested JSON array, each sub-array contains the reference points of an image
+ * - binarize: a JSON boolean, whether to binarize the descriptors
+ *
+ * @param params The APIParams struct to convert
+ * @return The Json::Value representing the APIParams struct
+ */
+Json::Value paramsToJson(const APIParams& params) {
+    Json::Value json;
+    json["data"] = vectorToJson(params.data);
+    json["max_keypoints"] = vectorToJson(params.max_keypoints);
+    json["timestamps"] = vectorToJson(params.timestamps);
+    json["grayscale"] = toJson(params.grayscale);
+    json["image_hw"] = nestedVectorToJson(params.image_hw);
+    json["feature_type"] = toJson(params.feature_type);
+    json["rotates"] = vectorToJson(params.rotates);
+    json["scales"] = vectorToJson(params.scales);
+    json["reference_points"] = nestedVectorToJson(params.reference_points);
+    json["binarize"] = toJson(params.binarize);
+    return json;
+}
+
+template<typename T>
+cv::Mat jsonToMat(Json::Value json) {
+    int rows = json.size();
+    int cols = json[0].size();
+
+    // Create a single array to hold all the data.
+    std::vector<T> data;
+    data.reserve(rows * cols);
+
+    for (int i = 0; i < rows; i++) {
+        for (int j = 0; j < cols; j++) {
+            data.push_back(static_cast<T>(json[i][j].asInt()));
+        }
+    }
+
+    // Create a cv::Mat object that points to the data.
+    cv::Mat mat(rows, cols, CV_8UC1, data.data());  // Change the type if necessary.
+    // cv::Mat mat(cols, rows,CV_8UC1, data.data());  // Change the type if necessary.
+
+    return mat;
+}
+
+
+
+/**
+ * @brief Decodes the response of the server and prints the keypoints
+ *
+ * This function takes the response of the server, a JSON string, and decodes
+ * it. It then prints the keypoints and draws them on the original image.
+ *
+ * @param response The response of the server
+ * @return The keypoints and descriptors
+ */
+KeyPointResults decode_response(const std::string& response, bool viz=true) {
+    Json::CharReaderBuilder builder;
+    Json::CharReader* reader = builder.newCharReader();
+
+    Json::Value jsonData;
+    std::string errors;
+
+    // Parse the JSON response
+    bool parsingSuccessful = reader->parse(response.c_str(),
+        response.c_str() + response.size(), &jsonData, &errors);
+    delete reader;
+
+    if (!parsingSuccessful) {
+        // Handle error
+        std::cout << "Failed to parse the JSON, errors:" << std::endl;
+        std::cout << errors << std::endl;
+        return KeyPointResults();
+    }
+
+    KeyPointResults kpts_results;
+
+    // Iterate over the images
+    for (const auto& jsonItem : jsonData) {
+        auto jkeypoints = jsonItem["keypoints"];
+        auto jkeypoints_orig = jsonItem["keypoints_orig"];
+        auto jdescriptors = jsonItem["descriptors"];
+        auto jscores = jsonItem["scores"];
+        auto jimageSize = jsonItem["image_size"];
+        auto joriginalSize = jsonItem["original_size"];
+        auto jsize = jsonItem["size"];
+
+        std::vector<cv::KeyPoint> vkeypoints;
+        std::vector<float> vscores;
+
+        // Iterate over the keypoints
+        int counter = 0;
+        for (const auto& keypoint : jkeypoints_orig) {
+            if (counter < 10) {
+                // Print the first 10 keypoints
+                std::cout << keypoint[0].asFloat() << ", "
+                    << keypoint[1].asFloat() << std::endl;
+            }
+            counter++;
+            // Convert the Json::Value to a cv::KeyPoint
+            vkeypoints.emplace_back(cv::KeyPoint(keypoint[0].asFloat(),
+                keypoint[1].asFloat(), 0.0));
+        }
+
+        if (viz && jsonItem.isMember("image_orig")) {
+
+            auto jimg_orig = jsonItem["image_orig"];
+            cv::Mat img = jsonToMat<uchar>(jimg_orig);
+            cv::imwrite("viz_image_orig.jpg", img);
+
+            // Draw keypoints on the image
+            cv::Mat imgWithKeypoints;
+            cv::drawKeypoints(img, vkeypoints, 
+                imgWithKeypoints, cv::Scalar(0, 0, 255));
+
+            // Write the image with keypoints
+            std::string filename = "viz_image_orig_keypoints.jpg";
+            cv::imwrite(filename, imgWithKeypoints);
+        }
+
+        // Iterate over the descriptors
+        cv::Mat descriptors = jsonToMat<uchar>(jdescriptors); 
+        kpts_results.append_keypoints(vkeypoints);
+        kpts_results.append_descriptors(descriptors);
+    }
+    return kpts_results;
+}

imcui/api/__init__.py → api/types.py

@@ -1,47 +1,16 @@
-import base64
-import io
-from typing import List
-
-import numpy as np
-from fastapi.exceptions import HTTPException
-from PIL import Image
-from pydantic import BaseModel
-
-from ..hloc import logger
-from .core import ImageMatchingAPI
-
-
-class ImagesInput(BaseModel):
-    data: List[str] = []
-    max_keypoints: List[int] = []
-    timestamps: List[str] = []
-    grayscale: bool = False
-    image_hw: List[List[int]] = [[], []]
-    feature_type: int = 0
-    rotates: List[float] = []
-    scales: List[float] = []
-    reference_points: List[List[float]] = []
-    binarize: bool = False
-
-
-def decode_base64_to_image(encoding):
-    if encoding.startswith("data:image/"):
-        encoding = encoding.split(";")[1].split(",")[1]
-    try:
-        image = Image.open(io.BytesIO(base64.b64decode(encoding)))
-        return image
-    except Exception as e:
-        logger.warning(f"API cannot decode image: {e}")
-        raise HTTPException(status_code=500, detail="Invalid encoded image") from e
-
-
-def to_base64_nparray(encoding: str) -> np.ndarray:
-    return np.array(decode_base64_to_image(encoding)).astype("uint8")
-
-
-__all__ = [
-    "ImageMatchingAPI",
-    "ImagesInput",
-    "decode_base64_to_image",
-    "to_base64_nparray",
-]
+from typing import List
+
+from pydantic import BaseModel
+
+
+class ImagesInput(BaseModel):
+    data: List[str] = []
+    max_keypoints: List[int] = []
+    timestamps: List[str] = []
+    grayscale: bool = False
+    image_hw: List[List[int]] = [[], []]
+    feature_type: int = 0
+    rotates: List[float] = []
+    scales: List[float] = []
+    reference_points: List[List[float]] = []
+    binarize: bool = False

app.py

@@ -1,6 +1,6 @@
 import argparse
 from pathlib import Path
-from imcui.ui.app_class import ImageMatchingApp
+from ui.app_class import ImageMatchingApp
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -19,13 +19,10 @@ if __name__ == "__main__":
     parser.add_argument(
         "--config",
         type=str,
-        default=Path(__file__).parent / "config/config.yaml",
+        default=Path(__file__).parent / "ui/config.yaml",
         help="config file",
     )
     args = parser.parse_args()
     ImageMatchingApp(
-        args.server_name,
-        args.server_port,
-        config=args.config,
-        example_data_root=Path("imcui/datasets"),
+        args.server_name, args.server_port, config=args.config
     ).run()

build_docker.sh

@@ -1,3 +1,3 @@
 docker build -t image-matching-webui:latest . --no-cache
 docker tag image-matching-webui:latest vincentqin/image-matching-webui:latest
-docker push vincentqin/image-matching-webui:latest
+docker push vincentqin/image-matching-webui:latest