#!/usr/bin/python#! -*- encoding: utf-8 -*-# openmvg使用示例# usage : python tutorial_demo.py

import osimport subprocessimport sys

# openmvg编译bin目录(可cp -p到/usr/local/bin/)OPENMVG_SFM_BIN = "/home/work/tools/openMVG_Build/Linux-x86_64-RELEASE"# pmvs编译bin目录(可cp -p到/usr/local/bin/)PMVS_BIN = "/home/work/tools/CMVS-PMVS/build/main"# openmvg相机参数目录CAMERA_SENSOR_WIDTH_DIRECTORY = "/home/work/tools/openMVG/src/openMVG/exif/sensor_width_database"

# 0. 下载测试照片os.chdir(os.path.dirname(os.path.abspath(__file__)))data_dir = os.path.abspath("./book")#data_dir = os.path.abspath("./ImageDataset_SceauxCastle")'''if not os.path.exists(data_dir): pImageDataCheckout = subprocess.Popen([ "git", "clone", "https://github.com/openMVG/ImageDataset_SceauxCastle.git" ]) pImageDataCheckout.wait()'''input_dir = os.path.join(data_dir, "images")output_dir = data_dirprint ("Using input dir : ", input_dir)print (" output_dir : ", output_dir)matches_dir = os.path.join(output_dir, "matches")camera_file_params = os.path.join(CAMERA_SENSOR_WIDTH_DIRECTORY, "sensor_width_camera_database.txt") #相机参数if not os.path.exists(matches_dir): os.mkdir(matches_dir)

# 1. 从图片数据集中生成场景描述文件sfm_data.jsonprint ("----------1. Intrinsics analysis----------")pIntrisics = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_SfMInit_ImageListing"), "-i", input_dir, "-o", matches_dir, "-d", camera_file_params, "-c", "3"] )#*注：如果产出的sfm_data.json里intrinsics内容为空，通常是在图片没有exif信息导致获取不到相机焦距、ccd尺寸等参数，用带exif的原图即可。pIntrisics.wait()

# 2. 计算图像特征print ("----------2. Compute features----------")pFeatures = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeFeatures"), "-i", matches_dir+"/sfm_data.json", "-o", matches_dir, "-m", "SIFT", "-f" , "1"] )pFeatures.wait()

# 3. 计算几何匹配print ("----------3. Compute matches----------")pMatches = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeMatches"), "-i", matches_dir+"/sfm_data.json", "-o", matches_dir, "-f", "1", "-n", "ANNL2"] )pMatches.wait()

# 4. 执行增量三维重建reconstruction_dir = os.path.join(output_dir,"reconstruction_sequential")print ("----------4. Do Incremental/Sequential reconstruction----------") #set manually the initial pair to avoid the prompt questionpRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_IncrementalSfM"), "-i", matches_dir+"/sfm_data.json", "-m", matches_dir, "-o", reconstruction_dir] )pRecons.wait()

# 5. 计算场景结构颜色print ("----------5. Colorize Structure----------")pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeSfM_DataColor"), "-i", reconstruction_dir+"/sfm_data.bin", "-o", os.path.join(reconstruction_dir,"colorized.ply")] )pRecons.wait()

# 6. 测量稳健三角print ("----------6. Structure from Known Poses (robust triangulation)----------")pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeStructureFromKnownPoses"), "-i", reconstruction_dir+"/sfm_data.bin", "-m", matches_dir, "-o", os.path.join(reconstruction_dir,"robust.ply")] )pRecons.wait()

'''# 使用全局SfM管道重建Reconstruction for the global SfM pipeline# 3.1 全局sfm管道几何匹配print ("----------3.1. Compute matches (for the global SfM Pipeline)----------")pMatches = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeMatches"), "-i", matches_dir+"/sfm_data.json", "-o", matches_dir, "-r", "0.8", "-g", "e"] )pMatches.wait()

# 4.1 执行全局三维重建reconstruction_dir = os.path.join(output_dir,"reconstruction_global")print ("----------4.1. Do Global reconstruction----------")pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_GlobalSfM"), "-i", matches_dir+"/sfm_data.json", "-m", matches_dir, "-o", reconstruction_dir] )pRecons.wait()

# 5.1 计算场景结构颜色print ("----------5.1. Colorize Structure----------")pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeSfM_DataColor"), "-i", reconstruction_dir+"/sfm_data.bin", "-o", os.path.join(reconstruction_dir,"colorized.ply")] )pRecons.wait()

# 6.1 测量稳健三角print ("----------6.1. Structure from Known Poses (robust triangulation)----------")pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_ComputeStructureFromKnownPoses"), "-i", reconstruction_dir+"/sfm_data.bin", "-m", matches_dir, "-o", os.path.join(reconstruction_dir,"robust.ply")] )pRecons.wait()'''

# 7. 把openMVG生成的SfM_Data转为适用于PMVS输入格式的文件print ("----------7. Export to PMVS/CMVS----------")pRecons = subprocess.Popen( [os.path.join(OPENMVG_SFM_BIN, "openMVG_main_openMVG2PMVS"), "-i", reconstruction_dir+"/sfm_data.bin", "-o", reconstruction_dir] )pRecons.wait()#*注：执行后会在-o路径下生成一个PMVS目录，包含 models, txt, visualize 三个子目录：models为空；txt包含对应图像的txt文档，每个里面都是一个3x4的矩阵，大概是相机位姿；visualize包含11张图像，不确定是原图像还是校正过的图像

# 8. 使用PMVS重建稠密点云、表面、纹理print ("----------8. pmvs2----------")pRecons = subprocess.Popen( [os.path.join(PMVS_BIN, "pmvs2"), reconstruction_dir+"/PMVS/", "pmvs_options.txt"] ) # 注：不要修改pmvs_options.txt文件名pRecons.wait()#*注：执行后会在./PMVS/models文件夹中生成一个pmvs_options.txt.ply点云文件，用meshlab打开即可看到重建出来的彩色稠密点云。

#执行三维重建测试python 3dr_test.py