Project：Image2Model Assistant

[lfonsocapone]

简介：一个摄影测量数据辅助工具，旨在优化用户拍摄的图像集，使其更适合用于 3D 重建任务（例如在 Meshroom 中使用）。该工具通过分析图像的角度、分布和质量，给出改进建议并生成优化后的图片数据。

项目目标
1. 提高 3D 模型重建的精度：通过分析用户拍摄的图像，标记出问题区域（如拍摄不足、角度不佳等）。
2. 优化用户体验：为摄影测量初学者提供简单的工具，指导拍摄过程。
3. 增强 Meshroom 或其他工具的功能：输出优化的图像集，并兼容现有摄影测量工作流。

主要功能
1. 图像质量分析：
• 评估模糊度、曝光度、噪点等指标。
• 生成每张图像的质量分数。
2. 图像分布检测：
• 分析相机位置和角度是否覆盖目标物体的各个角度。
• 可视化图像分布（使用 3D 渲染）。
3. 改进建议生成：
• 提示未覆盖区域。
• 建议补拍的视角和距离。
4. 优化数据输出：
• 根据分析结果过滤低质量图片。
• 生成优化后的图像集。

技术实现

1. 编程语言
   • Python（主语言，便于与 Meshroom 等工具集成）。
   • OpenCV（图像处理）。
   • Matplotlib 或 Plotly（数据可视化）。

2. 核心算法
   • 图像质量分析：
   • 使用 OpenCV 的图像质量检测函数（如 Laplacian 模糊检测）。
   • 使用直方图分析曝光情况。
   • 图像分布检测：
   • 通过 EXIF 数据提取拍摄角度和位置。
   • 使用 shapely 或自定义算法生成覆盖区域。
   • 3D 可视化：
   • 使用 Matplotlib 的 3D 图形或 PyVista 绘制相机位置。

代码实现

以下是一个简化的代码框架：

图像质量分析模块

import cv2
import os

def analyze_image_quality(image_path):
"""
分析图像质量，返回模糊度和曝光度分数。
"""
image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
# 模糊度检测
blur_score = cv2.Laplacian(image, cv2.CV_64F).var()
# 曝光检测
hist = cv2.calcHist([image], [0], None, [256], [0, 256])
exposure_score = hist[200:].sum() / hist.sum() # 简单曝光度分析
return blur_score, exposure_score

def batch_analyze(folder_path):
"""
批量分析文件夹内的图片质量。
"""
results = []
for filename in os.listdir(folder_path):
if filename.lower().endswith(('.jpg', '.png')):
image_path = os.path.join(folder_path, filename)
blur, exposure = analyze_image_quality(image_path)
results.append((filename, blur, exposure))
return results

图像分布检测模块

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def visualize_image_distribution(camera_positions):
"""
可视化相机分布，camera_positions 是 [(x, y, z), ...] 格式的列表。
"""
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for pos in camera_positions:
ax.scatter(pos[0], pos[1], pos[2], c='r', marker='o')
ax.set_title("Camera Position Distribution")
plt.show()

改进建议生成模块

def suggest_improvements(camera_positions, target_coverage):
"""
根据现有相机位置，建议新的拍摄角度。
"""
suggestions = []
for angle in target_coverage:
if angle not in camera_positions:
suggestions.append(angle)
return suggestions

项目架构

Image2ModelAssistant/
│
├── analyzer/
│ ├── quality.py # 图像质量分析模块
│ ├── distribution.py # 图像分布检测模块
│ └── suggestion.py # 改进建议模块
│
├── visualizer/
│ ├── plot.py # 3D 可视化模块
│
├── main.py # 主程序入口
├── README.md # 项目说明文档
└── requirements.txt # 依赖列表

项目投稿流程
1. 完善项目文档
• 在 README.md 中描述项目目标、安装步骤、使用方法和示例。
• 编写 CONTRIBUTING.md，说明如何参与开发。
2. 测试功能
• 测试各模块的功能，确保代码质量和可靠性。
• 提供测试数据集和结果。
3. 提交到 GitHub
• 创建一个公开仓库，上传代码和文档。
• 在开源社区（例如 Meshroom 的 GitHub Issues）中介绍你的项目，说明如何使用它辅助 Meshroom。
4. 后续改进
• 根据社区反馈调整功能。
• 持续优化代码和性能。

项目亮点
• 适合摄影测量初学者和专业用户。
• 提升 Meshroom 等工具的易用性。
• 提供了可扩展的模块化设计，便于集成到现有工作流中。

[natowi]

English translation:

Introduction: A photogrammetry data assistance tool designed to optimize the set of images captured by users, making them more suitable for 3D reconstruction tasks (e.g., for use in Meshroom). This tool analyzes the angles, distribution, and quality of images, providing improvement suggestions and generating optimized image data.

Project Goals

Improve the accuracy of 3D model reconstruction: By analyzing the images captured by users, it identifies problematic areas (such as insufficient captures, poor angles, etc.).
Optimize user experience: Provide simple tools for beginners in photogrammetry to guide the shooting process.
Enhance the functionality of Meshroom or other tools: Output optimized image sets that are compatible with existing photogrammetry workflows.

Main Features

Image Quality Analysis:
    Assess metrics such as blurriness, exposure, and noise.
    Generate quality scores for each image.
Image Distribution Detection:
    Analyze whether the camera positions and angles cover all aspects of the target object.
    Visualize image distribution (using 3D rendering).
Improvement Suggestion Generation:
    Indicate uncovered areas.
    Suggest angles and distances for reshooting.
Optimized Data Output:
    Filter out low-quality images based on analysis results.
    Generate an optimized image set.

Technical Implementation

Programming Languages
    Python (main language, facilitating integration with tools like Meshroom).
    OpenCV (image processing).
    Matplotlib or Plotly (data visualization).

Core Algorithms
    Image Quality Analysis:
        Use OpenCV's image quality detection functions (e.g., Laplacian blur detection).
        Use histogram analysis for exposure assessment.
    Image Distribution Detection:
        Extract shooting angles and positions from EXIF data.
        Use Shapely or custom algorithms to generate coverage areas.
    3D Visualization:
        Use Matplotlib's 3D graphics or PyVista to plot camera positions.

Code Implementation

Here is a simplified code framework:

Image Quality Analysis Module

import cv2
import os

def analyze_image_quality(image_path):
    """
    Analyze image quality and return blur and exposure scores.
    """
    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    # Blur detection
    blur_score = cv2.Laplacian(image, cv2.CV_64F).var()
    # Exposure detection
    hist = cv2.calcHist([image], [0], None, [256], [0, 256])
    exposure_score = hist[200:].sum() / hist.sum()  # Simple exposure analysis
    return blur_score, exposure_score

def batch_analyze(folder_path):
    """
    Batch analyze the image quality in a folder.
    """
    results = []
    for filename in os.listdir(folder_path):
        if filename.lower().endswith(('.jpg', '.png')):
            image_path = os.path.join(folder_path, filename)
            blur, exposure = analyze_image_quality(image_path)
            results.append((filename, blur, exposure))
    return results

Image Distribution Detection Module

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def visualize_image_distribution(camera_positions):
    """
    Visualize camera distribution, camera_positions is a list in the format [(x, y, z), ...].
    """
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    for pos in camera_positions:
        ax.scatter(pos[0], pos[1], pos[2], c='r', marker='o')
    ax.set_title("Camera Position Distribution")
    plt.show()

Improvement Suggestion Generation Module

def suggest_improvements(camera_positions, target_coverage):
    """
    Suggest new shooting angles based on existing camera positions.
    """
    suggestions = []
    for angle in target_coverage:
        if angle not in camera_positions:
            suggestions.append(angle)
    return suggestions

Project Structure

Image2ModelAssistant/
│
├── analyzer/
│ ├── quality.py # Image quality analysis module
│ ├── distribution.py # Image distribution detection module
│ └── suggestion.py # Improvement suggestion module
│
├── visualizer/
│ ├── plot.py # 3D visualization module
│
├── main.py # Main program entry
├── README.md # Project documentation
└── requirements.txt # Dependency list