.flake8

@@ -0,0 +1,2 @@
+[flake8]
+max-line-length = 120

.github/workflows/build.yml

@@ -0,0 +1,43 @@
+name: Build
+
+on:
+  push:
+    branches: ["master"]
+  pull_request:
+
+jobs:
+  linting:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.x"
+
+      - name: Apply pre-commit
+        uses: pre-commit/action@v3.0.0
+        with:
+          extra_args: --all-files
+
+  test-production:
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.x"
+
+      - name: Run tests
+        run: |
+          pip install . -U
+          pip install -r requirements-dev.txt
+          python setup.py sdist
+          pytest -vv --cov --profile

.github/workflows/pypi.yml

@@ -0,0 +1,31 @@
+on:
+  release:
+    types:
+      - created
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.x"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install wheel
+
+      - name: Build
+        run: |
+          python setup.py sdist bdist_wheel
+
+      - name: Publish to PyPI
+        if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
+        uses: pypa/gh-action-pypi-publish@release/v1.5
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -0,0 +1,92 @@
+# Compiled source #
+###################
+*.com
+*.class
+*.dll
+*.exe
+*.o
+*.so
+*.pyc
+.ipynb_checkpoints
+*~
+*#
+build*
+*egg-info
+dist/*
+
+# Packages #
+###################
+# it's better to unpack these files and commit the raw source
+# git has its own built in compression methods
+*.7z
+*.dmg
+*.gz
+*.iso
+*.jar
+*.rar
+*.tar
+*.zip
+
+# Logs and databases #
+######################
+*.log
+*.sql
+*.sqlite
+
+# OS generated files #
+######################
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Images
+######################
+*.jpg
+*.gif
+*.png
+*.svg
+*.ico
+
+# Video
+######################
+*.wmv
+*.mpg
+*.mpeg
+*.mp4
+*.mov
+*.flv
+*.avi
+*.ogv
+*.ogg
+*.webm
+
+# Audio
+######################
+*.wav
+*.mp3
+*.wma
+
+# Fonts
+######################
+Fonts
+*.eot
+*.ttf
+*.woff
+
+# Format
+######################
+CPPLINT.cfg
+.clang-format
+
+# Gtags
+######################
+GPATH
+GRTAGS
+GSYMS
+GTAGS
+
+*.pth

.pre-commit-config.yaml

@@ -0,0 +1,45 @@
+exclude: |
+  (?x)(
+      ^superpoint_superglue_deployment/superpoint.py |
+      ^superpoint_superglue_deployment/superglue.py
+  )
+
+repos:
+  - repo: https://github.com/pycqa/flake8
+    rev: 4.0.1
+    hooks:
+      - id: flake8
+        additional_dependencies: [pep8-naming]
+
+  - repo: https://github.com/psf/black
+    rev: 22.6.0
+    hooks:
+      - id: black
+        language_version: python3
+
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v0.961
+    hooks:
+      - id: mypy
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v2.7.1
+    hooks:
+      - id: prettier
+        types_or: [json, markdown, yaml]
+
+  - repo: https://github.com/lovesegfault/beautysh
+    rev: v6.2.1
+    hooks:
+      - id: beautysh

LICENSE

@@ -1,8 +0,0 @@
-MIT License
-Copyright (c) <year> <copyright holders>
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

MANIFEST.in

@@ -0,0 +1,2 @@
+include README.md
+include requirements.txt

README.md

@@ -1,3 +1,114 @@
-# Welding-Parts-Anomaly-Detection
+<p align="center">
+<a href="https://github.com/xmba15/superpoint_superglue_deployment/actions/workflows/build.yml" target="_blank">
+  <img src="https://github.com/xmba15/superpoint_superglue_deployment/actions/workflows/build.yml/badge.svg" alt="Build Status">
+</a>
+</p>
 
-永生机械斗臂焊接件异常情况检测算法开发
+# 📝 simple library to make life easy when deploying superpoint, superglue models
+
+---
+
+## :gear: Installation
+
+---
+
+```bash
+pip install superpoint_superglue_deployment
+```
+
+## :tada: TODO
+
+---
+
+- [x] interface to deploy superpoint, superglue
+- [x] testing on real data
+
+## :running: How to Run
+
+---
+
+### Basic usage
+
+```python
+import cv2
+import numpy as np
+from loguru import logger
+
+from superpoint_superglue_deployment import Matcher
+
+
+def main():
+    query_image = cv2.imread("./data/images/one_pillar_pagoda_1.jpg")
+    ref_image = cv2.imread("./data/images/one_pillar_pagoda_2.jpg")
+
+    query_gray = cv2.imread("./data/images/one_pillar_pagoda_1.jpg", 0)
+    ref_gray = cv2.imread("./data/images/one_pillar_pagoda_2.jpg", 0)
+
+    superglue_matcher = Matcher(
+        {
+            "superpoint": {
+                "input_shape": (-1, -1),
+                "keypoint_threshold": 0.003,
+            },
+            "superglue": {
+                "match_threshold": 0.5,
+            },
+            "use_gpu": True,
+        }
+    )
+    query_kpts, ref_kpts, _, _, matches = superglue_matcher.match(query_gray, ref_gray)
+    M, mask = cv2.findHomography(
+        np.float64([query_kpts[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2),
+        np.float64([ref_kpts[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2),
+        method=cv2.USAC_MAGSAC,
+        ransacReprojThreshold=5.0,
+        maxIters=10000,
+        confidence=0.95,
+    )
+    logger.info(f"number of inliers: {mask.sum()}")
+    matches = np.array(matches)[np.all(mask > 0, axis=1)]
+    matches = sorted(matches, key=lambda match: match.distance)
+    matched_image = cv2.drawMatches(
+        query_image,
+        query_kpts,
+        ref_image,
+        ref_kpts,
+        matches[:50],
+        None,
+        flags=2,
+    )
+    cv2.imwrite("matched_image.jpg", matched_image)
+
+
+if __name__ == "__main__":
+    main()
+```
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xmba15/superpoint_superglue_deployment/master/docs/images/matched_image.jpg" alt="matched image sample">
+</p>
+
+- [Notebook with detailed sample code for SuperPoint](notebooks/demo_superpoint.ipynb)
+- [Notebook with detailed sample code for SuperGlue](notebooks/demo_superglue.ipynb)
+
+- Command line to test matching two images after installing the library
+
+```bash
+match_two_images --query_path [path/to/query/image] --ref_path [path/to/reference/image] --use_gpu
+```
+
+## 🎛 Development environment
+
+---
+
+```bash
+mamba env create --file environment.yml
+mamba activate superpoint_superglue_deployment
+```
+
+## :gem: References
+
+---
+
+- [SuperPoint: Self-Supervised Interest Point Detection and Description.](https://github.com/rpautrat/SuperPoint)
+- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://github.com/magicleap/SuperGluePretrainedNetwork)

count_env.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2024/6/13 0013 下午 12:03
+# @Author  : liudan
+# @File    : count_env.py
+# @Software: pycharm
+import cv2
+import os
+from PIL import Image, ImageDraw
+import numpy as np
+
+from skimage.metrics import structural_similarity as compare_ssim
+import json
+import random
+import yaml
+import demo_env
+from demo_env import registration_demo
+
+
+def compare_boxes_similarity(image1_path, image2_path, json_file_path, similarity_threshold=0.4):
+    try:
+        if not os.path.exists(image1_path):
+            raise FileNotFoundError(f"Image file {image1_path} not found")
+
+
+        image1 = Image.open(image1_path)# 原图尺寸，未resize
+        image2 = wrap_image
+
+        draw1 = ImageDraw.Draw(image1)
+
+        # 存储相似度结果和是否相同的判断
+        similarity_results = []
+        same_content_boxes = []
+
+
+        with open(json_file_path, 'r') as f:
+            data = json.load(f)
+
+
+        for shape in data['shapes']:
+            if 'points' in shape:
+                shape['points'] = [[int(round(x)), int(round(y))] for x, y in shape['points']]
+                x1, y1 = shape['points'][0]
+                x2, y2 = shape['points'][1]
+
+            # 从两幅图像中截取对应区域
+            region1 = image1.crop((x1, y1, x2, y2))
+            # region1 = image1.crop((x1, y1, x2, y2)).convert('L')
+            draw1.rectangle([x1, y1, x2, y2], outline='red', width=2)
+            image1.save(os.path.join(params['save_dir'], f'save_annotated1_{i}.jpg'))
+            region1.save(os.path.join(params['save_dir'], f'111111{i}.jpg'))
+
+
+            # region2 = image2.crop((left-80, top, right-80, bottom))
+            region2 = image2[y1:y2, x1:x2]
+            # region2 = cv2.cvtColor(region2, cv2.COLOR_BGR2GRAY)
+            # region2= region2.transpose(Image.FLIP_TOP_BOTTOM) #旋转180°针对pillowImage对象
+            # region2 = cv2.rotate(region2, cv2.ROTATE_180)
+
+            filename = f'json_image_{shape["label"]}_{i}.jpg'
+            cv2.imwrite(os.path.join(params['save_dir'], filename), region2)
+            cv2.rectangle(image2, (x1, y1), (x2, y2),(0,255,0), 2)
+            cv2.imwrite(os.path.join(params['save_dir'], f'save_annotated2_{i}.jpg'), image2)
+
+            # 将PIL图像转换为numpy数组，以便进行计算
+            arr1 = np.array(region1)
+            arr2 = region2 # region2一直是numpy数组，所以上述image1和image2处理方式不同
+
+            # 确保两个数组的形状是相同的
+            assert arr1.shape == arr2.shape, "Images do not have the same size for the given box"
+
+            # 使用SSIM计算相似度（范围在-1到1之间，1表示完全相似）
+            # ssim = compare_ssim(arr1, arr2, multichannel=False) # 这是旧版，可以计算灰度图相似度，对于计算彩色图像即使设置multichannel=True也错
+            ssim = compare_ssim(arr1, arr2, channel_axis=2)
+
+
+            similarity_results.append(ssim)
+
+            if ssim > similarity_threshold:
+                same_content_boxes.append(shape)
+                cv2.rectangle(image2, (x1, y1), (x2, y2),(0,255,0), 2)
+
+                text = "Similarity: " + str(round(ssim, 3))
+                text_pos = (x1, y1 - 5)
+                # 参数：图像, 文本, 文本位置, 字体类型, 字体大小, 字体颜色, 字体粗细
+                cv2.putText(image2, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 2)
+                cv2.imwrite(os.path.join(params['visualization_dir'],f'{wrap_images_name[:-8]}_{i}.jpg'), image2)
+
+            else:
+                cv2.rectangle(image2, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                text = "score: " + str(round(ssim, 3))
+                text_pos = (x1, y1 - 5)
+                # 参数：图像, 文本, 文本位置, 字体类型, 字体大小, 字体颜色, 字体粗细
+                cv2.putText(image2, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 2)
+                cv2.imwrite(os.path.join(params['visualization_dir'], f'{wrap_images_name[:-8]}_{i}.jpg'), image2)
+
+        return similarity_results, same_content_boxes
+
+    except FileNotFoundError as e:
+        print(f"An error occurred: {e}")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+    return None, None
+
+
+def read_params_from_yml(yml_file_path):
+    with open(yml_file_path, 'r') as file:
+        params = yaml.safe_load(file)
+    return params
+
+
+
+
+
+if __name__ == "__main__":
+    yml_file_path = 'params.yml'
+    params = read_params_from_yml(yml_file_path)
+
+
+
+    wrap_images_all = registration_demo(params['image_dir'],params['demo_image_path'], params['json_ref_path'], params['ref_image_path'])
+    for i, item in enumerate(wrap_images_all):
+
+        wrap_image,wrap_images_name = item
+        similarity_results, same_content_boxes = compare_boxes_similarity(params['path_to_image1'], wrap_image, params['json_file_path'],
+                                                                          params['similarity_threshold'])
+        # 打印所有坐标框的相似度结果
+        print(f"{wrap_images_name}\n")
+        for idx, score in enumerate(similarity_results, 1):
+            print(f"Similarity Score for Box {idx}: {score}")
+
+        # 打印被认为是相同内容的坐标框
+        print("Boxes with the same content:")
+        for shape in same_content_boxes:
+            print(shape['label'] + ' object is same as template')