基于Opencv和Opengl的三维图像还原技术源码解读

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资源摘要信息:"recon3d.rar_三维还原" 该压缩包文件recon3d.rar中的内容涉及了三维重建技术,特别是通过使用OpenCV(Open Source Computer Vision Library)和OpenGL(Open Graphics Library)这两个重要的编程库来实现三维模型的还原与展示。OpenCV是一个开源的计算机视觉和机器学习软件库,包含了大量的计算机视觉和图像处理功能;而OpenGL是一个跨语言、跨平台的应用程序编程接口(API),用于渲染2D和3D矢量图形。具体来说,该资源包包含了以下知识点: 1. **OpenCV基础应用:** OpenCV库中包含了大量用于图像处理和计算机视觉的函数,文件列表中的cvFuncs.cpp、cvFuncs2.cpp、cvFuncs3.cpp、main.cpp、main-algo3bkup.cpp文件表明了源代码中可能使用了OpenCV的不同模块来处理图像,比如图像读取、转换、滤波、边缘检测、特征提取、三维重建算法等。 2. **OpenGL基础应用:** OpenGL提供了绘制三维图形的接口,glFuncs.cpp文件和glut.h头文件暗示了程序中使用了OpenGL和GLUT(OpenGL Utility Toolkit)来进行三维场景的渲染。GLUT简化了OpenGL编程模型,提供了创建窗口、处理输入输出等基础功能。 3. **三维重建算法实现:** 三维重建通常需要从二维图像中获取足够的信息来构建三维模型,这需要复杂的算法。从文件名main-algo3bkup.cpp推测,该程序可能包含算法备份或者是多个算法的实现。算法可能包括立体匹配、深度图生成、三维点云构建、网格生成等。 4. **图像处理研究:** 该资源对于图像处理的研究非常有帮助。图像处理研究者可以通过对OpenCV源代码的学习,掌握图像预处理、特征提取、模式识别等领域的知识。同时,对于OpenGL的学习可以帮助理解如何从三维数据生成逼真的图像。 5. **编程实践:** 该资源包还涉及到编程实践知识。开发者需要理解如何组织代码、如何将OpenCV和OpenGL的功能集成到一个应用程序中,以及如何处理常见的图像处理和三维图形渲染问题。 6. **环境配置与开发:** 使用这个资源包进行开发需要配置相应的开发环境。这通常包括安装OpenCV库、OpenGL库、以及可能需要的编译器和集成开发环境(IDE)。开发者还需要熟悉相应的API调用方式和程序的结构布局。 7. **三维视觉与渲染:** 三维视觉涉及到图像采集、处理以及三维场景的重建和渲染。OpenGL通常用于这一阶段的图形渲染,它能够让开发者直观地在屏幕上绘制出三维模型。 综合来看,这个资源包是三维视觉和计算机图形学研究者和开发者的宝贵资料,它包含了从基础的图像处理到复杂的三维重建算法的完整实现过程。通过对这个资源包的学习和使用,开发者可以掌握从二维图像数据提取三维信息、三维数据处理以及通过计算机图形学技术渲染出三维场景的关键技能。

运行代码: import scipy.io import mne from mne.bem import make_watershed_bem import random import string # Load .mat files inner_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.inner_skull.mat') outer_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.outer_skull.mat') scalp = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.scalp.mat') print(inner_skull.keys()) # Assuming these .mat files contain triangulated surfaces, we will extract vertices and triangles # This might need adjustment based on the actual structure of your .mat files inner_skull_vertices = inner_skull['Vertices'] inner_skull_triangles = inner_skull['Faces'] outer_skull_vertices = outer_skull['Vertices'] outer_skull_triangles = outer_skull['Faces'] scalp_vertices = scalp['Vertices'] scalp_triangles = scalp['Faces'] subjects_dir = 'E:\MATLABproject\data\MRI\Visit1_040318' subject = ''.join(random.choices(string.ascii_uppercase + string.ascii_lowercase, k=8)) # Prepare surfaces for MNE # Prepare surfaces for MNE surfs = [ mne.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=[0.01], subjects_dir=subjects_dir), # brain mne.make_bem_model(outer_skull_vertices, outer_skull_triangles, conductivity=[0.016], subjects_dir=subjects_dir), # skull mne.make_bem_model(scalp_vertices, scalp_triangles, conductivity=[0.33], subjects_dir=subjects_dir), # skin ] # Create BEM solution model = make_watershed_bem(surfs) solution = mne.make_bem_solution(model) 时报错: Traceback (most recent call last): File "E:\pythonProject\MEG\头模型.py", line 30, in <module> mne.make_bem_model(inner_skull_vertices, inner_skull_triangles, conductivity=[0.01], subjects_dir=subjects_dir), # brain File "<decorator-gen-68>", line 12, in make_bem_model File "E:\anaconda\envs\pythonProject\lib\site-packages\mne\bem.py", line 712, in make_bem_model subject_dir = op.join(subjects_dir, subject) File "E:\anaconda\envs\pythonProject\lib\ntpath.py", line 117, in join genericpath._check_arg_types('join', path, *paths) File "E:\anaconda\envs\pythonProject\lib\genericpath.py", line 152, in _check_arg_types raise TypeError(f'{funcname}() argument must be str, bytes, or ' TypeError: join() argument must be str, bytes, or os.PathLike object, not 'ndarray' 进程已结束,退出代码1

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import scipy.io import mne from mne.bem import make_watershed_bem # Load .mat files inner_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.inner_skull.mat') outer_skull = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.outer_skull.mat') scalp = scipy.io.loadmat('E:\MATLABproject\data\MRI\Visit1_040318\\tess_mri_COR_MPRAGE_RECON-mocoMEMPRAGE_FOV_220-298665.scalp.mat') print(inner_skull.keys()) # Assuming these .mat files contain triangulated surfaces, we will extract vertices and triangles # This might need adjustment based on the actual structure of your .mat files inner_skull_vertices = inner_skull['Vertices'] inner_skull_triangles = inner_skull['Faces'] outer_skull_vertices = outer_skull['Vertices'] outer_skull_triangles = outer_skull['Faces'] scalp_vertices = scalp['Vertices'] scalp_triangles = scalp['Faces'] # Prepare surfaces for MNE surfs = [ mne.bem.BEMSurface(inner_skull_vertices, inner_skull_triangles, sigma=0.01, id=4), # brain mne.bem.BEMSurface(outer_skull_vertices, outer_skull_triangles, sigma=0.016, id=3), # skull mne.bem.BEMSurface(scalp_vertices, scalp_triangles, sigma=0.33, id=5), # skin ] # Create BEM model model = mne.bem.BEM(surfs, conductivity=[0.3, 0.006, 0.3], is_sphere=False) model.plot(show=False) # Create BEM solution solution = mne.make_bem_solution(model) 运行代码时报错; Traceback (most recent call last): File "E:\pythonProject\MEG\头模型.py", line 24, in <module> mne.bem.BEMSurface(inner_skull_vertices, inner_skull_triangles, sigma=0.01, id=4), # brain AttributeError: module 'mne.bem' has no attribute 'BEMSurface'

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