kopia lustrzana https://github.com/OpenDroneMap/ODM
Merge branch 'master' of https://github.com/OpenDroneMap/OpenDroneMap
commit
98018b8b8b
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@ -150,7 +150,7 @@ If you want to build your own Docker image from sources, type:
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docker run -it --rm -v $(pwd)/images:/code/images -v $(pwd)/odm_orthophoto:/code/odm_orthophoto -v $(pwd)/odm_texturing:/code/odm_texturing my_odm_image
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Using this method, the containerized ODM will process the images in the OpenDroneMap/images directory and output results
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to the OpenDroneMap/odm_orthophoto and OpenDroneMap/odm_texturing directories as described in the **Viewing Results** section.
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to the OpenDroneMap/odm_orthophoto and OpenDroneMap/odm_texturing directories as described in the [Viewing Results](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Output-and-Results) section.
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If you want to view other results outside the Docker image simply add which directories you're interested in to the run command in the same pattern
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established above. For example, if you're interested in the dense cloud results generated by PMVS and in the orthophoto,
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simply use the following `docker run` command after building the image:
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@ -381,8 +381,13 @@ void OdmOrthoPhoto::createOrthoPhoto()
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}
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// Init ortho photo
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photo_ = cv::Mat::zeros(rowRes, colRes, CV_8UC4) + cv::Scalar(255, 255, 255, 0);
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depth_ = cv::Mat::zeros(rowRes, colRes, CV_32F) - std::numeric_limits<float>::infinity();
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try{
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photo_ = cv::Mat::zeros(rowRes, colRes, CV_8UC4) + cv::Scalar(255, 255, 255, 0);
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depth_ = cv::Mat::zeros(rowRes, colRes, CV_32F) - std::numeric_limits<float>::infinity();
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}catch(const cv::Exception &e){
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std::cerr << "Couldn't allocate enough memory to render the orthophoto (" << colRes << "x" << rowRes << " cells = " << ((long long)colRes * (long long)rowRes * 4) << " bytes). Try to reduce the -resolution parameter or add more RAM.\n";
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exit(1);
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}
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// Contains the vertices of the mesh.
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pcl::PointCloud<pcl::PointXYZ>::Ptr meshCloud (new pcl::PointCloud<pcl::PointXYZ>);
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@ -153,7 +153,7 @@ def config():
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parser.add_argument('--opensfm-processes',
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metavar='<positive integer>',
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default=context.num_cores,
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default=1,
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type=int,
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help=('The maximum number of processes to use in dense '
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'reconstruction. Default: %(default)s'))
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