├── .github
    └── workflows
    │   ├── black.yml
    │   ├── isort.yml
    │   └── publish-image.yml
├── .gitignore
├── Dockerfile
├── LICENSE
├── R
    ├── normalize_image_intensity.R
    └── prep_gcps.R
├── README.md
├── automate-metashape.Rproj
├── calibration
    ├── RP02-1618153-SC.csv
    ├── RP02-1622152-SC.csv
    ├── RP04-1806002-SC.csv
    └── RP04-1923118-OB.csv
├── config
    └── config-base.yml
├── prior-versions
    └── metashape_v1.6-1.8
    │   ├── R
    │       ├── prep_configs.R
    │       └── prep_gcps.R
    │   ├── config
    │       ├── base.yml
    │       ├── derived.yml
    │       └── example.yml
    │   └── python
    │       ├── metashape_workflow.py
    │       ├── metashape_workflow_functions.py
    │       └── read_yaml.py
├── python
    ├── metashape_workflow.py
    └── metashape_workflow_functions.py
└── settings.json


/.github/workflows/black.yml:
--------------------------------------------------------------------------------
 1 | name: Black Linter
 2 | 
 3 | # This allows Black be called from the isort workflow
 4 | on:
 5 |   workflow_call:
 6 | 
 7 | jobs:
 8 |   black-linter:
 9 |     name: Run Black
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Determine Branch Name
14 |         run: |
15 |           if [ "${{ github.event_name }}" = "pull_request" ]; then
16 |             echo "BRANCH_NAME=${{ github.head_ref }}" >> $GITHUB_ENV
17 |           else
18 |             BRANCH_NAME=$(echo ${GITHUB_REF#refs/heads/})
19 |             echo "BRANCH_NAME=$BRANCH_NAME" >> $GITHUB_ENV
20 |           fi
21 | 
22 |       - name: Checkout code
23 |         uses: actions/checkout@v3
24 |         with:
25 |           ref: ${{ env.BRANCH_NAME }}
26 | 
27 |       - name: Pull latest changes
28 |         run: git pull origin ${{ env.BRANCH_NAME }}
29 | 
30 |       - name: Run Black formatter
31 |         uses: psf/black@stable
32 |         with:
33 |           options: "--verbose"
34 |           src: "."
35 |           jupyter: true
36 | 
37 |       - name: Push changes
38 |         uses: stefanzweifel/git-auto-commit-action@v4
39 |         with:
40 |           commit_message: Apply black formatting changes


--------------------------------------------------------------------------------
/.github/workflows/isort.yml:
--------------------------------------------------------------------------------
 1 | name: Code Formatter
 2 | 
 3 | # Add 'pull_request:' beneath 'on:' to apply on active PRs
 4 | on:
 5 |   push:
 6 |     branches:
 7 |       - main
 8 | 
 9 | jobs:
10 |   isort:
11 |     name: Run Isort
12 |     runs-on: ubuntu-latest
13 | 
14 |     # Redundant to determine branch name since workflow only runs on main, but will be useful if we also want the workflow to run PRs
15 |     steps:
16 |       - name: Determine Branch Name
17 |         run: |
18 |           if [ "${{ github.event_name }}" = "pull_request" ]; then
19 |             echo "BRANCH_NAME=${{ github.head_ref }}" >> $GITHUB_ENV
20 |           else
21 |             BRANCH_NAME=$(echo ${GITHUB_REF#refs/heads/})
22 |             echo "BRANCH_NAME=$BRANCH_NAME" >> $GITHUB_ENV
23 |           fi
24 | 
25 |       - name: Checkout code
26 |         uses: actions/checkout@v3
27 |         with:
28 |           ref: ${{ env.BRANCH_NAME }}
29 | 
30 |       - name: Set up Python
31 |         uses: actions/setup-python@v2
32 |         with:
33 |           python-version: 3.9
34 | 
35 |       - name: Run Isort
36 |         run: pip install isort==5.13.2 && isort .
37 | 
38 |       - name: Push changes
39 |         uses: stefanzweifel/git-auto-commit-action@v5
40 |         with:
41 |           commit_message: Apply isort formatting changes
42 | 
43 |   # Ensure Black runs after Isort has completed
44 |   black:
45 |     needs: isort
46 |     uses: ./.github/workflows/black.yml
47 | 
48 |   # Ensure publish-image runs after Black and Isort have completed
49 |   publish-image:
50 |     needs: [isort, black]
51 |     uses: ./.github/workflows/publish-image.yml


--------------------------------------------------------------------------------
/.github/workflows/publish-image.yml:
--------------------------------------------------------------------------------
 1 | name: Publish image to Github Packages
 2 | 
 3 | on:
 4 |   push:
 5 |     branches-ignore:
 6 |       # Do not run on pushes to main, because this is separately triggred on pushes to main by the
 7 |       # isort workflow, and we want to make sure it runs after isort, and not twice
 8 |       - main 
 9 |   workflow_call:
10 | 
11 | env:
12 |   REGISTRY: ghcr.io
13 |   IMAGE_NAME: ${{ github.repository }}
14 | 
15 | jobs:
16 |   build-and-push-image:
17 |     runs-on: ubuntu-latest
18 | 
19 |     permissions:
20 |       contents: read
21 |       packages: write
22 |       attestations: write
23 |       id-token: write
24 | 
25 |     steps:
26 |       - name: Checkout repository
27 |         uses: actions/checkout@v4
28 | 
29 |       - name: Log in to the Container registry
30 |         uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
31 |         with:
32 |           registry: ${{ env.REGISTRY }}
33 |           username: ${{ github.actor }}
34 |           password: ${{ secrets.GITHUB_TOKEN }}
35 | 
36 |       - name: Extract metadata (tags, labels) for Docker
37 |         id: meta
38 |         uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
39 |         with:
40 |           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
41 |           tags: |
42 |             # The first 4 lines are default; have to include them in order to specify the
43 |             # 5th one (apply latest tag to main branch)
44 |             type=schedule
45 |             type=ref,event=branch
46 |             type=ref,event=tag
47 |             type=ref,event=pr
48 |             type=raw,value=latest,enable={{is_default_branch}}
49 | 
50 |       - name: Build and push Docker image
51 |         id: push
52 |         uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
53 |         with:
54 |           context: .
55 |           push: true
56 |           tags: ${{ steps.meta.outputs.tags }}
57 |           labels: ${{ steps.meta.outputs.labels }}
58 | 
59 |       # Diabling attestation for now because it is creating two images in the registry for every
60 |       # push and making it look messy
61 |       
62 |       # - name: Generate artifact attestation
63 |       #   uses: actions/attest-build-provenance@v1
64 |       #   with:
65 |       #     subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
66 |       #     subject-digest: ${{ steps.push.outputs.digest }}
67 |       #     push-to-registry: true
68 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | .Rproj.user
  2 | .Rhistory
  3 | .RData
  4 | .Ruserdata
  5 | metashape.lic
  6 | 
  7 | 
  8 | # Created by https://www.gitignore.io/api/python
  9 | # Edit at https://www.gitignore.io/?templates=python
 10 | 
 11 | ### Python ###
 12 | # Byte-compiled / optimized / DLL files
 13 | __pycache__/
 14 | *.py[cod]
 15 | *$py.class
 16 | 
 17 | # C extensions
 18 | *.so
 19 | 
 20 | # Distribution / packaging
 21 | .Python
 22 | build/
 23 | develop-eggs/
 24 | dist/
 25 | downloads/
 26 | eggs/
 27 | .eggs/
 28 | lib/
 29 | lib64/
 30 | parts/
 31 | sdist/
 32 | var/
 33 | wheels/
 34 | pip-wheel-metadata/
 35 | share/python-wheels/
 36 | *.egg-info/
 37 | .installed.cfg
 38 | *.egg
 39 | MANIFEST
 40 | 
 41 | # PyInstaller
 42 | #  Usually these files are written by a python script from a template
 43 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 44 | *.manifest
 45 | *.spec
 46 | 
 47 | # Installer logs
 48 | pip-log.txt
 49 | pip-delete-this-directory.txt
 50 | 
 51 | # Unit test / coverage reports
 52 | htmlcov/
 53 | .tox/
 54 | .nox/
 55 | .coverage
 56 | .coverage.*
 57 | .cache
 58 | nosetests.xml
 59 | coverage.xml
 60 | *.cover
 61 | .hypothesis/
 62 | .pytest_cache/
 63 | 
 64 | # Translations
 65 | *.mo
 66 | *.pot
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | target/
 76 | 
 77 | # pyenv
 78 | .python-version
 79 | 
 80 | # pipenv
 81 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 82 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 83 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 84 | #   install all needed dependencies.
 85 | #Pipfile.lock
 86 | 
 87 | # celery beat schedule file
 88 | celerybeat-schedule
 89 | 
 90 | # SageMath parsed files
 91 | *.sage.py
 92 | 
 93 | # Spyder project settings
 94 | .spyderproject
 95 | .spyproject
 96 | 
 97 | # Rope project settings
 98 | .ropeproject
 99 | 
100 | # Mr Developer
101 | .mr.developer.cfg
102 | .project
103 | .pydevproject
104 | 
105 | # mkdocs documentation
106 | /site
107 | 
108 | # mypy
109 | .mypy_cache/
110 | .dmypy.json
111 | dmypy.json
112 | 
113 | # Pyre type checker
114 | .pyre/
115 | 
116 | # End of https://www.gitignore.io/api/python
117 | 
118 | 
119 | ## apparently added by pycharm
120 | .idea/
121 | 
122 | ## dev folder for temp configs etc
123 | dev/
124 | 
125 | ## Mac system file
126 | .DS_Store


--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
 1 | # Use a GPU-enabled base image for Ubuntu 24.04
 2 | FROM nvcr.io/nvidia/cuda:12.8.1-runtime-ubuntu24.04
 3 | 
 4 | USER root
 5 | 
 6 | # Adapted from https://github.com/jeffgillan/agisoft_metashape/blob/main/Dockerfile
 7 | LABEL authors="David Russell"
 8 | LABEL maintainer="djrussell@ucdavis"
 9 | 
10 | # Create user account with password-less sudo abilities
11 | RUN useradd -s /bin/bash -g 100 -G sudo -m user
12 | RUN /usr/bin/printf '%s\n%s\n' 'password' 'password'| passwd user
13 | RUN echo "user ALL=(ALL) NOPASSWD:ALL" >> /etc/sudoers
14 | 
15 | ENV DEBIAN_FRONTEND=noninteractive
16 | 
17 | # Install basic dependencies
18 | RUN apt-get update &&            \
19 |     apt-get install -y libglib2.0-dev libglib2.0-0 libgl1 libglu1-mesa libcurl4 wget python3-venv python3-full libgomp1 && \
20 |     rm -rf /var/lib/apt/lists/*
21 | 
22 | # Download the Metashape .whl file
23 | RUN cd /opt && wget https://download.agisoft.com/Metashape-2.2.0-cp37.cp38.cp39.cp310.cp311-abi3-linux_x86_64.whl
24 | 
25 | # Create a virtual environment for Metashape
26 | RUN python3 -m venv /opt/venv_metashape
27 | 
28 | # Activate the virtual environment and install Metashape and PyYAML
29 | RUN /opt/venv_metashape/bin/pip install --upgrade pip
30 | RUN /opt/venv_metashape/bin/pip install /opt/Metashape-2.2.0-cp37.cp38.cp39.cp310.cp311-abi3-linux_x86_64.whl
31 | RUN /opt/venv_metashape/bin/pip install PyYAML
32 | 
33 | # Remove the downloaded wheel file
34 | RUN rm /opt/*.whl
35 | 
36 | # Set the container workdir
37 | WORKDIR /app
38 | # Copy files from current directory into /app
39 | COPY . /app
40 | 
41 | # Set the default command and default arguments
42 | ENV PATH="/opt/venv_metashape/bin:${PATH}"
43 | ENTRYPOINT ["python3", "/app/python/metashape_workflow.py"]
44 | CMD ["/data/config.yml"]
45 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | BSD 3-Clause License
 2 | 
 3 | Copyright (c) 2022, UC Davis
 4 | Authors: Derek Young, Alex Mandel, Mallika Nocco, Robert Hijmans, and contributors
 5 | All rights reserved.
 6 | 
 7 | Redistribution and use in source and binary forms, with or without
 8 | modification, are permitted provided that the following conditions are met:
 9 | 
10 | 1. Redistributions of source code must retain the above copyright notice, this
11 |    list of conditions and the following disclaimer.
12 | 
13 | 2. Redistributions in binary form must reproduce the above copyright notice,
14 |    this list of conditions and the following disclaimer in the documentation
15 |    and/or other materials provided with the distribution.
16 | 
17 | 3. Neither the name of the copyright holder nor the names of its
18 |    contributors may be used to endorse or promote products derived from
19 |    this software without specific prior written permission.
20 | 
21 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
22 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
23 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
24 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
25 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
26 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
27 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
28 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
29 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
30 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
31 | 


--------------------------------------------------------------------------------
/R/normalize_image_intensity.R:
--------------------------------------------------------------------------------
 1 | # Function to take a directory of images and normalize their intensities, saving the normalized images in a new directory named {input_dir}_normalized
 2 | 
 3 | library(magick)
 4 | library(stringr)
 5 | library(furrr)
 6 | 
 7 | # Function for a single image, to parallelize across within a directory of images
 8 | normalize_one_img = function(img_file, img_dir, out_dir) {
 9 |   
10 |   # get the relative path below the specified folder (for saving in same folder structure within the "..._normalized" folder)
11 |   rel_path = str_replace(img_file, pattern = fixed(img_dir), replacement = "")
12 |   
13 |   out_file = file.path(out_dir, rel_path)
14 |   
15 |   # if already computed for this image, skip
16 |   if(file.exists(out_file)) return(FALSE)
17 |   
18 |   img = image_read(img_file)
19 |   
20 |   img_norm = image_normalize(img)
21 | 
22 |   # create dir if doesn't exist
23 |   out_dir_img = dirname(out_file)
24 |   if(!dir.exists(out_dir_img)) dir.create(out_dir_img, recursive = TRUE)
25 |   
26 |   image_write(img_norm, out_file)
27 |   
28 |   gc()
29 |   
30 | }
31 | 
32 | 
33 | # Function to parallelize across images in a directory
34 | normalize_images_in_dir = function(img_dir) {
35 | 
36 |   # What string to append to the input directory name to store the normalized images
37 |   out_dir = paste0(img_dir, "_normalized")
38 |   if(!dir.exists(out_dir)) dir.create(out_dir, recursive=TRUE)
39 |   
40 |   img_files = list.files(img_dir, pattern = "(JPG|jpg|tif|TIF)$", recursive = TRUE, full.names = TRUE)
41 |   
42 |   gc()
43 |   
44 |   plan(multisession)
45 |   
46 |   future_walk(img_files, normalize_one_img, img_dir = img_dir, out_dir = out_dir)
47 | 
48 | }
49 | 


--------------------------------------------------------------------------------
/R/prep_gcps.R:
--------------------------------------------------------------------------------
  1 | ### Author: Derek Young, UC Davis
  2 | 
  3 | ### This script does the following:
  4 | ### - Loads a user-created data file of the image files (and coordinates in each image) where GCPs are located
  5 | ### - Loads a geospatial file containing the locations of the GCPs
  6 | ### - Loads a 10 m DEM and extracts elevation values at each GCP
  7 | ### - Compiles all of the above into a file needed by Metashape for its GCP workflow
  8 | ### - Produces a PDF that shows each GCP image and the location of the GCP for QAQC
  9 | 
 10 | 
 11 | ### This script requires the following folders/files in the main mission imagery directory (the one containing 100MEDIA etc):
 12 | ### - gcps/raw/gcps.gpkg : geospatial data file with each gcp id in the column "gcp_id". Must be in the same projection as the whole Metashape project, and must be in meters xy (usually a UTM zone).
 13 | ### - gcps/raw/gcp_imagecoords.csv : data file listing the images and coordinates in each where a GCP is visible (created manually by technician via imagery inspection)
 14 | ### - dem_usgs/dem_usgs.tif : 10 m USGS dem extending well beyond the project flight area
 15 | 
 16 | ### This script assumes all images to be linked to GCPs have standard DJI naming and directory structure ("100MEDIA/DJI_xxxx.JPG").
 17 | ### In input data file gcp_imagecoords.csv, images are specified without "DJI_", leading zeros, and ".JPG". The image directory is specified without "MEDIA"
 18 | 
 19 | 
 20 | #### Load packages ####
 21 | 
 22 | library(sf)
 23 | library(raster)
 24 | library(dplyr)
 25 | library(stringr)
 26 | library(magick)
 27 | library(ggplot2)
 28 | 
 29 | #### User-defined vars (only used when running interactivesly) ####
 30 | 
 31 | dir_manual = "/home/derek/Downloads/crater_gcps"
 32 | 
 33 | 
 34 | 
 35 | #### Load data ####
 36 | 
 37 | ### All relevant GCP data should be in the top-level mission imagery folder
 38 | ### Load folder from the command line argument
 39 | 
 40 | 
 41 | dir = commandArgs(trailingOnly=TRUE)
 42 | 
 43 | if(length(dir) == 0) {
 44 |   dir = dir_manual
 45 | }
 46 | 
 47 | gcps = read_sf(paste0(dir,"/gcps/raw/gcps.geojson"))
 48 | imagecoords = read.csv(paste0(dir,"/gcps/raw/gcp_imagecoords.csv"),header=TRUE,stringsAsFactors=FALSE)
 49 | dem_usgs = raster(paste0(dir,"/dem_usgs/dem_usgs.tif"))
 50 | 
 51 | # remove blank lines from image coords file
 52 | imagecoords = imagecoords %>%
 53 |   filter(!is.na(x))
 54 | 
 55 | 
 56 | #### Make prepared data directory if it doesn't ecist ####
 57 | dir.create(paste0(dir,"/gcps/prepared"),showWarnings=FALSE)
 58 | 
 59 | 
 60 | 
 61 | #### Create GCP table in the format required by metashape_control and metashape_functions ####
 62 | 
 63 | # Extract elev
 64 | gcp_table = gcps
 65 | gcp_table$elev = suppressWarnings(extract(dem_usgs,gcp_table,method="bilinear"))
 66 | 
 67 | # Extract coords
 68 | coords = st_coordinates(gcp_table)
 69 | gcp_table = cbind(gcp_table,coords)
 70 | 
 71 | # Remove geospatial info
 72 | st_geometry(gcp_table) = NULL
 73 | 
 74 | # Reorder columns, add "point" prefix to GCP names
 75 | gcp_table = gcp_table %>%
 76 |   dplyr::select(gcp_id,x=X,y=Y,elev) %>%
 77 |   dplyr::mutate(gcp_id = paste0("point",gcp_id))
 78 |   
 79 | write.table(gcp_table,paste0(dir,"/gcps/prepared/gcp_table.csv"),row.names=FALSE,col.names=FALSE,sep=",")
 80 | 
 81 | 
 82 | #### Create image coordinate-to-gcp table in the format required by metashape_control and metashape_functions ####
 83 | 
 84 | imagecoords_table = imagecoords %>%
 85 |   mutate(gcp_id = paste0("point",gcp)) %>%
 86 |   mutate(image_text = paste0("DJI_",str_pad(image_file,4,pad="0"),".JPG")) %>%
 87 |   mutate(part_text = paste0("PART_",str_pad(part_folder,2,pad="0"))) %>%
 88 |   mutate(folder_text = paste0(media_folder,"MEDIA")) %>%
 89 |   mutate(image_path = paste0(part_text,"/",folder_text,"/",image_text)) %>%
 90 |   dplyr::select(gcp_id,image_path,x,y) %>%
 91 |   arrange(gcp_id,image_path)
 92 | 
 93 | # remove blank lines from image coords file
 94 | imagecoords = imagecoords %>%
 95 |   filter(!is.na(gcp))
 96 | 
 97 | 
 98 | write.table(imagecoords_table,paste0(dir,"/gcps/prepared/gcp_imagecoords_table.csv"),row.names=FALSE,col.names=FALSE,sep=",")
 99 | 
100 | 
101 | #### Export a PDF of images with the GCP circled on each ####
102 | 
103 | 
104 | pdf(paste0(dir,"/gcps/prepared/gcp_qaqc.pdf"))
105 | 
106 | for(i in 1:nrow(imagecoords_table)) {
107 |   
108 |   imagecoords_row = imagecoords_table[i,]
109 |   
110 |   img = image_read(paste0(dir,"/",imagecoords_row$image_path))
111 |   img = image_scale(img,"10%")
112 |   img = image_flip(img)
113 |   
114 |   img_x = imagecoords_row$x/10
115 |   img_y = imagecoords_row$y/10
116 |   img_gcp = imagecoords_row$gcp_id
117 |   img_path = imagecoords_row$image_path
118 |   
119 |   map = image_ggplot(img) +
120 |     geom_point(x=img_x,y=img_y,size=20,pch=1,fill=NA,color="red",stroke=1) +
121 |     geom_point(x=img_x,y=img_y,size=20,pch=3,fill=NA,color="red",stroke=0.5) +
122 |     labs(title=paste0(img_gcp,"\n",img_path))
123 | 
124 |   print(map)
125 |   
126 |   cat("Completed GCP ", i, " of ",nrow(imagecoords_table),"\r")
127 |   
128 | }
129 | 
130 | garbage = dev.off()
131 | 
132 | 
133 | 
134 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # Easy, reproducible Metashape workflows
  2 | 
  3 | A simple command line tool to automate end-to-end photogrammetry workflows using [Agisoft Metashape](https://www.agisoft.com/).  Metashape is proprietary structure-from-motion photogrammetry software that creates 3D point clouds, digital elevation models, mesh models, and orthomosaics from overlapping aerial imagery. Our no-code automation increases the speed of image product creation and makes your workflows fully reproducible and documented. Run parameters are controlled through a single, intuitive configuration file. We offer a [native installation workflow](#native-installation-workflow) as well as a [docker workflow](#docker-workflow). You need to provide **1.** a Metashape license, **2.** your aerial images, and optionally **3.** [ground control points](#preparing-ground-control-points-gcps).
  4 | 
  5 | 
  6 | <br/>
  7 | <br/>
  8 | 
  9 | 
 10 | ## Native Installation Workflow
 11 | 
 12 | ### Download and Install Software
 13 | **Python:** You need Python 3.7-3.11. We recommend the [Anaconda distribution](https://www.anaconda.com/distribution/) because it includes all the required libraries. When installing, if asked whether the installer should initialize Anaconda3, say "yes". Anaconda must be initialized upon install such that `python` can be called from the command line. A way to check is to simply enter `python` at your command prompt and see if the resulting header info includes Anaconda and Python 3. If it doesn't, you may still need to initialize your Conda install. **Alternative option:** If you want a minimal python installation (such as if you're installing on a computing cluster), you can install [miniconda](https://docs.conda.io/en/latest/miniconda.html) instead. After intalling miniconda, you will need to install additional packages required by our scripts (currently only `PyYAML`) using `pip install {package_name}`.
 14 | 
 15 | **Reproducible workflow scripts (python):** Simply clone this repository to your machine! `git clone https://github.com/open-forest-observatory/automate-metashape.git`
 16 | 
 17 | **Metashape:** You must install the Metashape Python 3 module (Metashape version 2.0). Download the [current .whl file](https://www.agisoft.com/downloads/installer/) and install it following [these instructions](https://agisoft.freshdesk.com/support/solutions/articles/31000148930-how-to-install-metashape-stand-alone-python-module) (using the name of the .whl file that you downloaded). NOTE: If you wish to use an older version of Metashape (v1.6-1.8), the primary scripts here (for v2.0) are not backwards-compatible, but scripts for older versions (with somewhat more limited configuration options) are archived in `prior-versions/`. For the Metashape v1.6-1.8-compatible scripts, you need Python 3.5-3.7.
 18 | 
 19 | **Metashape license:** You need a license (and associated license file) for Metashape. The easiest way to get the license file (assuming you own a license) is by installing the [Metashape Professional Edition GUI software](https://www.agisoft.com/downloads/installer/) (distinct from the Python module) and registering it following the prompts in the software (note you need to purchase a license first). UC Davis users, inquire over the geospatial listserv or the #spatial Slack channel for information on joining a floating license pool. Once you have a license file (whether a node-locked or floating license), you need to set the `agisoft_LICENSE` environment variable (search onilne for instructions for your OS; look for how to *permanently* set it) to the path to the folder containing the license file (`metashape.lic`). On many Linux systems, assuming the Metashape GUI is installed in `/opt/metashape-pro/`, you can set the environment variable with `export agisoft_LICENSE=/opt/metashape-pro/`, though if run directly in a bash terminal it will only be effective during that bash session.
 20 | 
 21 | <br/>
 22 | 
 23 | ** Internal OFO developers only: Python and the Metashape python module are pre-installed and ready for use on 'ofo-dev' instances launched from Exosphere and as well as 'Open-Forest-Observatory' template launched from CACAO. The software is installed in a conda environment. `conda activate meta`
 24 | 
 25 | <br/>
 26 | 
 27 | ### Organizing raw imagery (and associated files) for processing
 28 | 
 29 | Images should be organized such that there is one root level that contains all the photos from the flight mission to be processed (these photos may optionally be organized within sub-folders), and no other missions. If your workflow is to include the **optional** inputs of _spectral calibration_, [ground control points (GCPs)](#preparing-ground-control-points-gcps), and/or a _USGS DEM_, this root-level folder *must* also contain a corresponding folder for each. For example:
 30 | 
 31 | ```
 32 | mission001_photos
 33 | ├───100MEDIA
 34 | |       DJI_0001.JPG
 35 | |       DJI_0002.JPG
 36 | |       ...
 37 | ├───101MEDIA
 38 | |       DJI_0001.JPG
 39 | |       DJI_0002.JPG
 40 | |       ...
 41 | ├───102MEDIA
 42 | |       DJI_0001.JPG
 43 | |       DJI_0002.JPG
 44 | |       ...
 45 | ├───gcps
 46 | |       ...
 47 | ├───dem_usgs
 48 | |       dem_usgs.tif
 49 | └───calibration
 50 |         RP04-1923118-OB.csv
 51 | ```
 52 | 
 53 | The namings for the ancillary data folders (`gcps`, `dem_usgs`, and `calibration`) must exactly match these if they are to be a part of the workflow.
 54 | 
 55 | A **sample RGB photo dataset** (which includes GCPs and a USGS DEM) may be [downloaded here](https://ucdavis.box.com/s/hv8m8fibe164pjj0mssdx1mj8qb996k8) (1.5 GB). Note this dataset has sparse photos (low overlap), so photogrammetry results are unimpressive.
 56 | 
 57 | <br/>
 58 | 
 59 | ### Workflow configuration
 60 | 
 61 | All of the parameters defining the Metashape workflow are specified in the configuration file (a [YAML-format](https://docs.ansible.com/ansible/latest/reference_appendices/YAMLSyntax.html) file). This includes directories of input and output files, workflow steps to include, quality settings, and many other parameters.
 62 | 
 63 | An example configuration file is provided in this repo at [`config/config-base.yml`](/config/config-base.yml). Edit the parameter values to meet your specifications. The file contains comments explaining the purpose of each customizable parameter.  You can directly edit the `config-base.yml` or save a new copy somewhere on the your local computer. You will specify the path of this config.yml in the [python run command](#running-the-workflow).
 64 | 
 65 | Note: Please do not remove or add parameters to the configuration file; adding will have no effect unless the Python code is changed along with the addition, and removing will produce errors.
 66 | 
 67 | <br/>
 68 | 
 69 | ### Running the Workflow
 70 | 
 71 | The general command line call to run the workflow has two required components:
 72 | 1. Call to Python
 73 | 2. Path to metashape workflow Python script (`metashape_workflow.py`)
 74 | 
 75 | For example:
 76 | 
 77 | `python {repo_path}/python/metashape_workflow.py`
 78 | 
 79 | <br/>
 80 | 
 81 | With this minimalist run command, the script assumes your config.yml file is located in the repo at `{repo_path}/config/config-base.yml`
 82 | 
 83 | <br/>
 84 | 
 85 | If your config file is located in a different directory, use the optional flag `--config_file`   
 86 | 
 87 | For example: 
 88 | 
 89 | `python {repo_path}/python/metashape_workflow.py --config_file {config_path}/{config_file}.yml`
 90 | 
 91 | 
 92 | <br/>
 93 | 
 94 | **Additional run command flags**. Using these flags will override parameters specified in the config.yml file. 
 95 | 
 96 | `--photo-path`    Path to the directory that contains the aerial images (usually jpegs) to be processed
 97 | 
 98 | `--photo-path-secondary`   Path to the directory that contains aerial images which are aligned only after all other processing is done. Not commonly used. 
 99 | 
100 | `--project-path` Path where the metashape project file (.psx) will be written
101 | 
102 | `--output-path` Path where all imagery products (orthomosaic, point cloud, DEMs, mesh model, reports) will be written 
103 | 
104 | `--run-name`    The identifier for the run. Will be used in naming output files
105 | 
106 | `--project-crs` Coordinate reference system EPSG code for outputs. Eg. _EPSG:26910_
107 | 
108 | <br/>
109 | 
110 | <br/>
111 | 
112 | 
113 | #### Running workflow batches 
114 | 
115 | Running workflows in batch (i.e., multiple workflows in series) on a single computer is as simple as creating configuration file for each workflow run and calling the Python workflow script once for each. The calls can be combined into a shell script. Here is a quick workflow of how to do this:
116 | 
117 | 1. Create an empty shell script `touch metashape.sh`
118 | 2. Populate the script with run commands on different lines. Note: the only thing that changes is the name of the config file.
119 | 
120 | 
121 | ```
122 | python ~/repos/automate-metashape/python/metashape_workflow.py --config_file ~/projects/forest_structure/metashape_configs/config001.yml
123 | python ~/repos/automate-metashape/python/metashape_workflow.py --config_file ~/projects/forest_structure/metashape_configs/config002.yml
124 | python ~/repos/automate-metashape/python/metashape_workflow.py --config_file ~/projects/forest_structure/metashape_configs/config003.yml
125 | ```
126 | 3. Give the shell script file executable permissions ` chmod +x metashape.sh`
127 | 4. Make sure you are located in the directory that contains the shell script. Run the shell script `./metashape.sh`
128 | 
129 | 
130 | <br/>
131 | 
132 | ### Workflow outputs
133 | 
134 | The outputs of the workflow are the following:
135 | - **Photogrammetry outputs** (e.g., dense point cloud, orthomosaic, digital surface model, and Metashape processing report)
136 | - **A Metashape project file** (for additional future processing or for inspecting the data via the Metashape GUI)
137 | - **A processing log** (which records the processing time for each step and the full set of configuration parameters, for reproducibility)
138 | 
139 | The outputs for a given workflow run are named using the following convention: `{run_name}_abc.xyz`. For example: `set14-highQuality_ortho.tif`. The run name and output directories are specified in the configuration file.
140 | 
141 | <br/>
142 | <br/>
143 | <br/>
144 | 
145 | ---
146 | 
147 | <br/>
148 | 
149 | ## Docker Workflow
150 | 
151 | Docker, a type of software containerization, is an alternative way to run software where you don't need to install software in the traditional sense. Docker packages up the code and all its environment dependencies so the application runs reliably from one computer to another. Background information on docker and software containers can be found [here](https://foss.cyverse.org/07_reproducibility_II/).
152 | 
153 | To run a docker container on your local machine, you do need to install `docker`. You can install and run docker as a command line tool for [linux distributions](https://docs.docker.com/engine/install/) or as a graphical program (i.e, Docker Desktop) for [windows](https://docs.docker.com/desktop/setup/install/windows-install/), [macOS](https://docs.docker.com/desktop/setup/install/mac-install/), or [linux](https://docs.docker.com/desktop/setup/install/linux/). We recommend running docker commands at the terminal. If you are using Docker Desktop, you can still write commands at the terminal while Docker Desktop is open and running.
154 | 
155 | The `automate-metashape` docker image contains the python libraries needed to run the workflow, while you (the user) need to provide at minimum the **1.** aerial images; **2** a configuration file specifying your choices for processing; **3.** a license to use Metashape; and optionally **4.** [ground control points (GCPs)](#preparing-ground-control-points-gcps).   
156 | 
157 | <br/>
158 | 
159 | ### User inputs to docker workflow
160 | 
161 | To provide the input data to Metashape, you need to specify a folder from your computer to be mirrored ("mounted") within the Docker container. The files needed to run Metashape (the folder of aerial images, the configuration file, and optionally the GCPs file) must all be in the folder that you mount. The files can be located in any arbitrary directory beneath this folder. For example, if the folder you mount is `~/drone_data` on your local computer, the images could be located at `~/drone_data/images/` and the config file could be located at `~/drone_data/config.yml` The folder from your local machine is mounted into the Docker container at the path `/data/`, so for the example above, the images would be found inside the docker container at the path `/data/images/`.
162 | 
163 | <br/>
164 | 
165 | #### Image directory
166 | 
167 | The images to be processed should all be in one parent folder (and optionally organized into subfolders beneath this folder) somewhere within the data folder you will be mounting. If including GCPs, spectral calibratation, and/or the USGS DEM, follow the organization shown [here](#organizing-raw-imagery-and-associated-files-for-processing).
168 | 
169 | <br/>
170 | 
171 | #### Workflow configuration file
172 | 
173 | An example configuration file is provided in this repository at [`config/config-base.yml`](/config/config-base.yml). Please download this file to your local machine and rename it `config.yml`. By default the container expects the config YAML file describing the Metashape workflow parameters to be located at `/data/config.yaml`. So in a case where the local folder to be mounted is `~/drone_data/`, then for the config file to be mounted in the Docker container at `/data/config.yaml`, it must be located on the local computer at `~/drone_data/config.yaml`. However, the config file location can be overridden by passing a different location following an optional command line argument `--config_file` of the `docker run` command. For more information click [here](#custom-location-of-the-metashape-configuration-file).
174 | 
175 | Within the `config.yml` you will need to edit some of the project level parameters to specify where to find input images and where to put output products within the container. Within this config file, all paths will be relative to the file structure of the docker container (beginning with `/data/`). In the config.yaml, at a minimum the following entries should be updated:
176 | 
177 | * The value for 'photo_path' should be updated to `/data/{path_to_images_folder_within_mounted_folder}`
178 | * The value for 'output_path' should be updated to `/data/{path_to_desired_ouputs_folder_within_mounted_folder}` (can be any location you want; will be created if it does not exist)
179 | * The value for 'project_path' should be updated similarly as for 'output_path'.
180 | 
181 | <br/>
182 | 
183 | ### Metashape license
184 | Users need to provide a license to use Metashape. Currently, this docker method only supports a floating license server using the format `<IP address>:<port number>`. Within a terminal, users can declare the floating license as an environment variable using the command:
185 | 
186 | `export AGISOFT_FLS=<IP_address>:<port_number>`
187 | 
188 | Keep in mind that environment variables will not persist across different terminal sessions. 
189 | 
190 | <br/>
191 | 
192 | ### Enable GPUs for accelerated processing
193 | 
194 | The use of graphical processing units (GPUs) can greatly increase the speed of photogrammetry processing. If your machine has GPU hardware, you will need extra software so docker can find and use your GPUs. Linux users simply need to install nvidia-container-toolkit via `sudo apt install nvidia-container-toolkit`. For Windows users please see [this documentation](https://docs.docker.com/desktop/features/gpu/). For macOS user, it may not be possible to use your local GPU (Apple Silicon) through Docker. 
195 | 
196 | <br/>
197 | 
198 | ### Run the docker container
199 | From a terminal, run this command: 
200 | 
201 | `docker run -v </host/data/dir>:/data -e AGISOFT_FLS=$AGISOFT_FLS --gpus all ghcr.io/open-forest-observatory/automate-metashape`
202 | 
203 | Here is a breakdown of the command:
204 | 
205 | `docker run` is the command to run a docker image
206 | 
207 | `-v </host/data/dir>:/data` is mounting a volume from your local computer into the container. We are mounting your directory that has the imagery and config file (</host/data/dir>) into the container at the path "/data".
208 | 
209 | `-e AGISOFT_FLS=$AGISOFT_FLS` is declaring your floating license to use Metashape. We set the license info as an environmental variable earlier in these instructions (i.e., `export AGISOFT_FLS=<IP_address>:<port_number>`)
210 | 
211 | `--gpus all` If the container has access to your local GPUs, use this flag to enable it.
212 | 
213 | `ghcr.io/open-forest-observatory/automate-metashape` This is the docker image that has the software to run the `automate-metashape` script. It is located in the Github container registry. When you execute the `docker run...` command, it will download the container image to your local machine and start the script to process imagery using Metashape. 
214 | 
215 | <br/>
216 | 
217 | #### Custom location of the Metashape configuration file
218 | 
219 | If your config.yaml is located anywhere other than `/data/config.yaml` (or the file is named differently), you can specify its location following one additional command line argument `--config_file` at the end of the `docker run` command. For example, if it is located in the container at `/data/configs/project_10/config.yml` (meanining, in the example above, it is located on your local computer at `~/drone_data/configs/project_10/config.yml`), just append `--config_file /data/configs/project_10/config.yml` to the `docker run` command above. So the command above would look like:
220 | 
221 | `docker run -v </host/data/dir>:/data -e AGISOFT_FLS=$AGISOFT_FLS --gpus all ghcr.io/open-forest-observatory/automate-metashape --config_file /data/configs/project_10/config.yml`
222 | 
223 | <br/>
224 | 
225 | ### Outputs
226 | 
227 | As the processing runs, the completed imagery products will be saved into the folder you specified for the `output_path` parameter in the config.yaml. In the example above, if your config.yaml specifies the `output_path` as `/data/{path_to_desired_ouputs_folder_within_mounted_folder}`, the outputs will be saved on your local computer at `~/drone_data/{path_to_desired_ouputs_folder_within_mounted_folder}`.
228 | 
229 | <br/>
230 | 
231 | ### Permissions on Linux
232 | 
233 | If running Docker on Linux without `sudo` (as in this example), your user will need to be in the `docker` group. This can be achieved with `sudo usermod -a -G docker $USER` and then logging out and in, as explained [here](https://docs.docker.com/engine/install/linux-postinstall/).
234 | 
235 | Note that the owner of the output data will be the `root` user. To set the ownership to your user account, you can run `sudo chown <username>:<username> <file name>` or `sudo chown <username>:<username> -R <folder name>`.
236 | 
237 | 
238 | <br/>
239 | <br/>
240 | 
241 | --- 
242 | 
243 | <br/>
244 | 
245 | ## Preparing ground-control points (GCPs)
246 | 
247 | Because the workflow implemented here is completely GUI-free, it is necessary to prepare GCPs in advance. The process of preparing GCPs involves recording (a) the geospatial location of the GCPs on the earth and (b) the location of the GCPs within the photos in which they appear.
248 | 
249 | Metashape requires this information in a very specific format, so this repository includes an R script to assist in producing the necessary files based on more human-readable input. The helper script is `R/prep_gcps.R`.
250 | 
251 | **GCP processing input files.** Example GCP input files are included in the [example RGB photo dataset](https://ucdavis.box.com/s/hv8m8fibe164pjj0mssdx1mj8qb996k8) under `gcps/raw/`. The files are the following:
252 | - **gcps.gpkg**: A geopackage (shapefile-like GIS format) containing the locations of each GCP on the earth. Must include an integer column called `gcp_id` that gives each GCP a unique integer ID number.
253 | - **gcp_imagecoords.csv**: A CSV table identifying the locations of the GCPs within raw drone images. Each GCP should be located in at least 5 images (ideally more). The tabls must contain the following columns:
254 |   - `gcp`: the integer ID number of the GCP (to match the ID number in `gcps.gpkg`)
255 |   - `folder`: the *integer* number of the subfolder in which the raw drone image is located. For example, if the image is in `100MEDIA`, the value that should be recorded is `100`.
256 |   - `image`: the *ingeter* number of the image in which the GCP is to be identified. For example, if the image is named `DJI_0077.JPG`, the value that should be recorded is `77`.
257 |   - `x` and `y`: the coordinates of the pixel in the image where the GCP is located. `x` and `y` are in units of pixels right and down (respectively) from the upper-left corner.
258 | 
259 | These two files must be in `gcps/raw/` at the top level of the flight mission directory (where the subfolders of images also reside). Identification of the image pixel coordinates where the GCPs are located is easy using the info tool in QGIS.
260 | 
261 | **Running the script.** You must have R and the following packages installed: sf, raster, dplyr, stringr, magick, ggplot2. The R `bin` directory must be in your system path, or you'll need to use the full path to R. You run the script from the command line by calling `Rscript --vanilla` with the helper script and passing the location of the top-level mission imagery folder (which contains the `gcp` folder) as an argument. For example, on Windows:
262 | 
263 | ```
264 | Rscript --vanilla {path_to_repo}/R/prep_gcps.R {path_to_imagery_storage}/sample_rgb_photoset
265 | ```
266 | 
267 | **Outputs.** The script will create a `prepared` directory within the `gcps` folder containing the two files used by Metashape: `gcp_table.csv`, which contains the geospatial coordinates of the GCPs on the earth, and `gcp_imagecoords_table.csv`, which contains the pixel coordinates of the GCPs within each image. It also outputs a PDF called `gcp_qaqc.pdf`, which shows the specified location of each GCP in each image in order to quality-control the location data. If left in this folder structure (`gcps/prepared`), the Metashape workflow script will be able to find and incorporate the GCP data if GCPs are enabled in the configuration file.
268 | 
269 | <br/>
270 | 
271 | 
272 | 
273 | 


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211 | 460.0,0.6745
212 | 461.0,0.6741
213 | 462.0,0.6735
214 | 463.0,0.6734
215 | 464.0,0.6736
216 | 465.0,0.674
217 | 466.0,0.6736
218 | 467.0,0.6729
219 | 468.0,0.673
220 | 469.0,0.6733
221 | 470.0,0.673
222 | 471.0,0.6725
223 | 472.0,0.6726
224 | 473.0,0.6732
225 | 474.0,0.6737
226 | 475.0,0.6737
227 | 476.0,0.6736
228 | 477.0,0.6732
229 | 478.0,0.6727
230 | 479.0,0.6727
231 | 480.0,0.6731
232 | 481.0,0.6737
233 | 482.0,0.6743
234 | 483.0,0.675
235 | 484.0,0.6747
236 | 485.0,0.6741
237 | 486.0,0.6745
238 | 487.0,0.675
239 | 488.0,0.675
240 | 489.0,0.6751
241 | 490.0,0.6753
242 | 491.0,0.6751
243 | 492.0,0.6754
244 | 493.0,0.6765
245 | 494.0,0.6769
246 | 495.0,0.6765
247 | 496.0,0.676
248 | 497.0,0.6759
249 | 498.0,0.6764
250 | 499.0,0.6768
251 | 500.0,0.6769
252 | 501.0,0.6772
253 | 502.0,0.6779
254 | 503.0,0.6781
255 | 504.0,0.6782
256 | 505.0,0.678
257 | 506.0,0.678
258 | 507.0,0.6787
259 | 508.0,0.6791
260 | 509.0,0.6787
261 | 510.0,0.6785
262 | 511.0,0.6788
263 | 512.0,0.6792
264 | 513.0,0.6797
265 | 514.0,0.6802
266 | 515.0,0.6807
267 | 516.0,0.681
268 | 517.0,0.681
269 | 518.0,0.6808
270 | 519.0,0.6808
271 | 520.0,0.6814
272 | 521.0,0.6821
273 | 522.0,0.6824
274 | 523.0,0.6821
275 | 524.0,0.6815
276 | 525.0,0.681
277 | 526.0,0.6808
278 | 527.0,0.6816
279 | 528.0,0.6826
280 | 529.0,0.6832
281 | 530.0,0.6836
282 | 531.0,0.6839
283 | 532.0,0.6838
284 | 533.0,0.6835
285 | 534.0,0.6832
286 | 535.0,0.683
287 | 536.0,0.6834
288 | 537.0,0.6837
289 | 538.0,0.6838
290 | 539.0,0.6838
291 | 540.0,0.6839
292 | 541.0,0.6841
293 | 542.0,0.6843
294 | 543.0,0.6844
295 | 544.0,0.6844
296 | 545.0,0.6845
297 | 546.0,0.6851
298 | 547.0,0.6856
299 | 548.0,0.6857
300 | 549.0,0.6857
301 | 550.0,0.6858
302 | 551.0,0.6856
303 | 552.0,0.6858
304 | 553.0,0.6865
305 | 554.0,0.6867
306 | 555.0,0.6869
307 | 556.0,0.6872
308 | 557.0,0.6871
309 | 558.0,0.6872
310 | 559.0,0.6874
311 | 560.0,0.6876
312 | 561.0,0.6871
313 | 562.0,0.6862
314 | 563.0,0.6857
315 | 564.0,0.6857
316 | 565.0,0.6856
317 | 566.0,0.686
318 | 567.0,0.6868
319 | 568.0,0.6873
320 | 569.0,0.6874
321 | 570.0,0.6876
322 | 571.0,0.6878
323 | 572.0,0.6881
324 | 573.0,0.6886
325 | 574.0,0.6892
326 | 575.0,0.6895
327 | 576.0,0.6892
328 | 577.0,0.6883
329 | 578.0,0.6877
330 | 579.0,0.688
331 | 580.0,0.6884
332 | 581.0,0.6888
333 | 582.0,0.6891
334 | 583.0,0.6888
335 | 584.0,0.6889
336 | 585.0,0.689
337 | 586.0,0.6888
338 | 587.0,0.6888
339 | 588.0,0.6888
340 | 589.0,0.6888
341 | 590.0,0.6887
342 | 591.0,0.6886
343 | 592.0,0.6888
344 | 593.0,0.689
345 | 594.0,0.6887
346 | 595.0,0.6882
347 | 596.0,0.6884
348 | 597.0,0.689
349 | 598.0,0.6892
350 | 599.0,0.6892
351 | 600.0,0.6893
352 | 601.0,0.6893
353 | 602.0,0.6894
354 | 603.0,0.6896
355 | 604.0,0.6898
356 | 605.0,0.6899
357 | 606.0,0.6897
358 | 607.0,0.6894
359 | 608.0,0.6893
360 | 609.0,0.6897
361 | 610.0,0.6904
362 | 611.0,0.6907
363 | 612.0,0.6903
364 | 613.0,0.6897
365 | 614.0,0.6896
366 | 615.0,0.6906
367 | 616.0,0.6913
368 | 617.0,0.6914
369 | 618.0,0.6914
370 | 619.0,0.6914
371 | 620.0,0.6912
372 | 621.0,0.691
373 | 622.0,0.6911
374 | 623.0,0.6914
375 | 624.0,0.6915
376 | 625.0,0.6911
377 | 626.0,0.6909
378 | 627.0,0.6915
379 | 628.0,0.6921
380 | 629.0,0.6926
381 | 630.0,0.6926
382 | 631.0,0.6917
383 | 632.0,0.6911
384 | 633.0,0.6917
385 | 634.0,0.6924
386 | 635.0,0.6919
387 | 636.0,0.691
388 | 637.0,0.691
389 | 638.0,0.6911
390 | 639.0,0.691
391 | 640.0,0.6911
392 | 641.0,0.6914
393 | 642.0,0.6916
394 | 643.0,0.6916
395 | 644.0,0.6916
396 | 645.0,0.6914
397 | 646.0,0.6907
398 | 647.0,0.6904
399 | 648.0,0.6907
400 | 649.0,0.6907
401 | 650.0,0.6906
402 | 651.0,0.6905
403 | 652.0,0.6902
404 | 653.0,0.6902
405 | 654.0,0.6902
406 | 655.0,0.6898
407 | 656.0,0.6893
408 | 657.0,0.689
409 | 658.0,0.6885
410 | 659.0,0.6884
411 | 660.0,0.6895
412 | 661.0,0.6902
413 | 662.0,0.6904
414 | 663.0,0.6904
415 | 664.0,0.6899
416 | 665.0,0.6891
417 | 666.0,0.6887
418 | 667.0,0.6881
419 | 668.0,0.6876
420 | 669.0,0.6872
421 | 670.0,0.6866
422 | 671.0,0.6858
423 | 672.0,0.6853
424 | 673.0,0.6846
425 | 674.0,0.6847
426 | 675.0,0.6855
427 | 676.0,0.6854
428 | 677.0,0.6841
429 | 678.0,0.683
430 | 679.0,0.6829
431 | 680.0,0.6832
432 | 681.0,0.6837
433 | 682.0,0.6837
434 | 683.0,0.6835
435 | 684.0,0.6829
436 | 685.0,0.6818
437 | 686.0,0.6814
438 | 687.0,0.6823
439 | 688.0,0.6829
440 | 689.0,0.6828
441 | 690.0,0.6824
442 | 691.0,0.6816
443 | 692.0,0.6809
444 | 693.0,0.6812
445 | 694.0,0.6827
446 | 695.0,0.6837
447 | 696.0,0.6831
448 | 697.0,0.6819
449 | 698.0,0.6814
450 | 699.0,0.6814
451 | 700.0,0.6807
452 | 701.0,0.6805
453 | 702.0,0.6807
454 | 703.0,0.6807
455 | 704.0,0.6807
456 | 705.0,0.6806
457 | 706.0,0.6808
458 | 707.0,0.6811
459 | 708.0,0.6807
460 | 709.0,0.6802
461 | 710.0,0.6803
462 | 711.0,0.6804
463 | 712.0,0.6794
464 | 713.0,0.6786
465 | 714.0,0.6785
466 | 715.0,0.6785
467 | 716.0,0.6786
468 | 717.0,0.6792
469 | 718.0,0.6793
470 | 719.0,0.6785
471 | 720.0,0.6779
472 | 721.0,0.6783
473 | 722.0,0.6796
474 | 723.0,0.6799
475 | 724.0,0.6784
476 | 725.0,0.6773
477 | 726.0,0.6776
478 | 727.0,0.6783
479 | 728.0,0.6793
480 | 729.0,0.6799
481 | 730.0,0.6789
482 | 731.0,0.6774
483 | 732.0,0.6772
484 | 733.0,0.6772
485 | 734.0,0.6772
486 | 735.0,0.6769
487 | 736.0,0.6767
488 | 737.0,0.6766
489 | 738.0,0.6763
490 | 739.0,0.6758
491 | 740.0,0.6753
492 | 741.0,0.6752
493 | 742.0,0.6749
494 | 743.0,0.6745
495 | 744.0,0.6744
496 | 745.0,0.6747
497 | 746.0,0.6747
498 | 747.0,0.6732
499 | 748.0,0.6722
500 | 749.0,0.6718
501 | 750.0,0.6713
502 | 751.0,0.6711
503 | 752.0,0.6717
504 | 753.0,0.6721
505 | 754.0,0.6715
506 | 755.0,0.6708
507 | 756.0,0.6707
508 | 757.0,0.6707
509 | 758.0,0.6701
510 | 759.0,0.6697
511 | 760.0,0.6693
512 | 761.0,0.6688
513 | 762.0,0.6684
514 | 763.0,0.6681
515 | 764.0,0.6678
516 | 765.0,0.6678
517 | 766.0,0.6675
518 | 767.0,0.6667
519 | 768.0,0.6662
520 | 769.0,0.6665
521 | 770.0,0.6666
522 | 771.0,0.6663
523 | 772.0,0.6651
524 | 773.0,0.6642
525 | 774.0,0.6639
526 | 775.0,0.6636
527 | 776.0,0.6631
528 | 777.0,0.6625
529 | 778.0,0.6623
530 | 779.0,0.6619
531 | 780.0,0.6614
532 | 781.0,0.661
533 | 782.0,0.6605
534 | 783.0,0.6605
535 | 784.0,0.6606
536 | 785.0,0.6599
537 | 786.0,0.6588
538 | 787.0,0.6585
539 | 788.0,0.6585
540 | 789.0,0.6578
541 | 790.0,0.6575
542 | 791.0,0.6574
543 | 792.0,0.6568
544 | 793.0,0.6562
545 | 794.0,0.6553
546 | 795.0,0.6547
547 | 796.0,0.6549
548 | 797.0,0.655
549 | 798.0,0.6551
550 | 799.0,0.6556
551 | 800.0,0.6555
552 | 801.0,0.6546
553 | 802.0,0.6541
554 | 803.0,0.6541
555 | 804.0,0.6536
556 | 805.0,0.653
557 | 806.0,0.6518
558 | 807.0,0.6517
559 | 808.0,0.6522
560 | 809.0,0.6517
561 | 810.0,0.6504
562 | 811.0,0.6499
563 | 812.0,0.6505
564 | 813.0,0.6514
565 | 814.0,0.6515
566 | 815.0,0.6511
567 | 816.0,0.6503
568 | 817.0,0.6496
569 | 818.0,0.6494
570 | 819.0,0.6497
571 | 820.0,0.6505
572 | 821.0,0.6508
573 | 822.0,0.6497
574 | 823.0,0.648
575 | 824.0,0.647
576 | 825.0,0.6468
577 | 826.0,0.6459
578 | 827.0,0.6454
579 | 828.0,0.6467
580 | 829.0,0.6474
581 | 830.0,0.6473
582 | 831.0,0.6481
583 | 832.0,0.6491
584 | 833.0,0.6486
585 | 834.0,0.6472
586 | 835.0,0.6463
587 | 836.0,0.6447
588 | 837.0,0.6431
589 | 838.0,0.6431
590 | 839.0,0.6443
591 | 840.0,0.6459
592 | 841.0,0.6461
593 | 842.0,0.6448
594 | 843.0,0.6443
595 | 844.0,0.6442
596 | 845.0,0.6437
597 | 846.0,0.6446
598 | 847.0,0.6473
599 | 848.0,0.6486
600 | 849.0,0.6251
601 | 850.0,0.4684
602 | 851.0,0.1791
603 | 852.0,0.0231
604 | 853.0,0.0
605 | 854.0,0.0
606 | 855.0,0.0
607 | 856.0,0.0
608 | 857.0,0.0
609 | 858.0,0.0
610 | 859.0,0.0
611 | 860.0,0.0
612 | 861.0,0.0
613 | 862.0,0.0
614 | 863.0,0.0
615 | 864.0,0.0
616 | 865.0,0.0
617 | 866.0,0.0
618 | 867.0,0.0
619 | 868.0,0.0
620 | 869.0,0.0
621 | 870.0,0.0
622 | 871.0,0.0
623 | 872.0,0.0
624 | 873.0,0.0
625 | 874.0,0.0
626 | 875.0,0.0
627 | 876.0,0.0
628 | 877.0,0.0
629 | 878.0,0.0
630 | 879.0,0.0
631 | 880.0,0.0
632 | 881.0,0.0
633 | 882.0,0.0
634 | 883.0,0.0
635 | 884.0,0.0
636 | 885.0,0.0
637 | 886.0,0.0
638 | 887.0,0.0
639 | 888.0,0.0
640 | 889.0,0.0
641 | 890.0,0.0
642 | 891.0,0.0
643 | 892.0,0.0
644 | 893.0,0.0
645 | 894.0,0.0
646 | 895.0,0.0
647 | 896.0,0.0
648 | 897.0,0.0
649 | 898.0,0.0
650 | 899.0,0.0
651 | 900.0,0.0
652 | 


--------------------------------------------------------------------------------
/calibration/RP04-1806002-SC.csv:
--------------------------------------------------------------------------------
  1 | Wavelength (nm),Reflectance
  2 | 250,49.901862
  3 | 251,49.832032
  4 | 252,49.766288
  5 | 253,49.717399
  6 | 254,49.615813
  7 | 255,49.527184
  8 | 256,49.470175
  9 | 257,49.384848
 10 | 258,49.324898
 11 | 259,49.290495
 12 | 260,49.230678
 13 | 261,49.150224
 14 | 262,49.114494
 15 | 263,49.101499
 16 | 264,49.045483
 17 | 265,49.003076
 18 | 266,48.980207
 19 | 267,49.011184
 20 | 268,48.940621
 21 | 269,48.900661
 22 | 270,48.898757
 23 | 271,48.925217
 24 | 272,48.929311
 25 | 273,48.89483
 26 | 274,48.820243
 27 | 275,48.791543
 28 | 276,48.769115
 29 | 277,48.815296
 30 | 278,48.809855
 31 | 279,48.766377
 32 | 280,48.753984
 33 | 281,48.749944
 34 | 282,48.72047
 35 | 283,48.743811
 36 | 284,48.707487
 37 | 285,48.696994
 38 | 286,48.766163
 39 | 287,48.746015
 40 | 288,48.748846
 41 | 289,48.775368
 42 | 290,48.812023
 43 | 291,48.771199
 44 | 292,48.799693
 45 | 293,48.820744
 46 | 294,48.751855
 47 | 295,48.827653
 48 | 296,48.848083
 49 | 297,48.845934
 50 | 298,48.83592
 51 | 299,48.831873
 52 | 300,48.815778
 53 | 301,48.827736
 54 | 302,48.845061
 55 | 303,48.813947
 56 | 304,48.840886
 57 | 305,48.849711
 58 | 306,48.809554
 59 | 307,48.896175
 60 | 308,48.945884
 61 | 309,48.902407
 62 | 310,48.842354
 63 | 311,48.838909
 64 | 312,48.786016
 65 | 313,48.85805
 66 | 314,48.80662
 67 | 315,48.872551
 68 | 316,48.832587
 69 | 317,48.696978
 70 | 318,48.695546
 71 | 319,48.639999
 72 | 320,48.671532
 73 | 321,48.779213
 74 | 322,48.582865
 75 | 323,48.607938
 76 | 324,48.666362
 77 | 325,48.541926
 78 | 326,48.600089
 79 | 327,48.574099
 80 | 328,48.453977
 81 | 329,48.413214
 82 | 330,48.452426
 83 | 331,48.385727
 84 | 332,48.419893
 85 | 333,48.428104
 86 | 334,48.417844
 87 | 335,48.419071
 88 | 336,48.430646
 89 | 337,48.332083
 90 | 338,48.310058
 91 | 339,48.279598
 92 | 340,48.28381
 93 | 341,48.314246
 94 | 342,48.357884
 95 | 343,48.336439
 96 | 344,48.356472
 97 | 345,48.326084
 98 | 346,48.286254
 99 | 347,48.316462
100 | 348,48.29935
101 | 349,48.331439
102 | 350,48.299259
103 | 351,48.371994
104 | 352,48.28669
105 | 353,48.322813
106 | 354,48.318801
107 | 355,48.298261
108 | 356,48.276364
109 | 357,48.277978
110 | 358,48.258998
111 | 359,48.307042
112 | 360,48.277528
113 | 361,48.218594
114 | 362,48.278612
115 | 363,48.277062
116 | 364,48.307968
117 | 365,48.210622
118 | 366,48.287384
119 | 367,48.264492
120 | 368,48.338323
121 | 369,48.314075
122 | 370,48.26416
123 | 371,48.329519
124 | 372,48.191372
125 | 373,48.271729
126 | 374,48.313783
127 | 375,48.204573
128 | 376,48.299615
129 | 377,48.384155
130 | 378,48.358459
131 | 379,48.417144
132 | 380,48.354234
133 | 381,48.358213
134 | 382,48.384212
135 | 383,48.370969
136 | 384,48.381329
137 | 385,48.38559
138 | 386,48.364826
139 | 387,48.34676
140 | 388,48.339269
141 | 389,48.328116
142 | 390,48.334449
143 | 391,48.312791
144 | 392,48.328882
145 | 393,48.340994
146 | 394,48.315117
147 | 395,48.298929
148 | 396,48.308705
149 | 397,48.309548
150 | 398,48.291071
151 | 399,48.313812
152 | 400,48.346554
153 | 401,48.344698
154 | 402,48.340867
155 | 403,48.343119
156 | 404,48.333864
157 | 405,48.279082
158 | 406,48.314276
159 | 407,48.362913
160 | 408,48.322448
161 | 409,48.330502
162 | 410,48.316138
163 | 411,48.323258
164 | 412,48.038124
165 | 413,48.355781
166 | 414,48.286055
167 | 415,48.272801
168 | 416,48.278334
169 | 417,48.322142
170 | 418,48.31726
171 | 419,48.335462
172 | 420,48.326164
173 | 421,48.325002
174 | 422,48.354496
175 | 423,48.310591
176 | 424,48.343642
177 | 425,48.365068
178 | 426,48.374752
179 | 427,48.334929
180 | 428,48.356817
181 | 429,48.381074
182 | 430,48.407889
183 | 431,48.400647
184 | 432,48.388909
185 | 433,48.395997
186 | 434,48.367681
187 | 435,48.395099
188 | 436,48.400717
189 | 437,48.357291
190 | 438,48.359293
191 | 439,48.375422
192 | 440,48.347006
193 | 441,48.036478
194 | 442,48.369696
195 | 443,48.391377
196 | 444,48.409047
197 | 445,48.401593
198 | 446,48.412322
199 | 447,48.421272
200 | 448,48.430114
201 | 449,48.468935
202 | 450,48.500344
203 | 451,48.480246
204 | 452,48.483367
205 | 453,48.486221
206 | 454,48.499744
207 | 455,48.513576
208 | 456,48.506246
209 | 457,48.524053
210 | 458,48.540735
211 | 459,48.526469
212 | 460,48.509991
213 | 461,48.500729
214 | 462,48.488062
215 | 463,48.514728
216 | 464,48.528348
217 | 465,48.523823
218 | 466,48.53376
219 | 467,48.519198
220 | 468,48.50547
221 | 469,48.504732
222 | 470,48.496032
223 | 471,48.506687
224 | 472,48.51877
225 | 473,48.514463
226 | 474,48.509255
227 | 475,48.489268
228 | 476,48.483041
229 | 477,48.478538
230 | 478,48.485284
231 | 479,48.503033
232 | 480,48.507856
233 | 481,48.527548
234 | 482,48.556359
235 | 483,48.542056
236 | 484,48.552749
237 | 485,48.568022
238 | 486,48.581135
239 | 487,48.605629
240 | 488,48.580285
241 | 489,48.576883
242 | 490,48.577357
243 | 491,48.599234
244 | 492,48.630432
245 | 493,48.650965
246 | 494,48.62625
247 | 495,48.60958
248 | 496,48.629314
249 | 497,48.628052
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653 | 


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702 | 


--------------------------------------------------------------------------------
/config/config-base.yml:
--------------------------------------------------------------------------------
  1 | # This is an example yaml configuration for a metashape run
  2 | 
  3 | #### Project-level parameters:
  4 | 
  5 | # Project to load. If not a blank string, this will open an existing project at the path specified. If a blank string, creates a new empty project.
  6 | # Even if opening an existing project, all processing on it is saved as a new project (path and name specified below). The original project file is not modified.
  7 | load_project: ""
  8 | 
  9 | # The path to the directory of flight photos
 10 | # If there are multiple photo folders, set path to the folder that contains all the photo folders,
 11 | # or provide a list of paths via the YAML list syntax (e.g., ["/path/to/photo/folder1", "/path/to/photo/folder2"])
 12 | # If there are no photos to add (e.g., this is an existing project that already has photos in it, set to an empty string ("")
 13 | photo_path: ""
 14 | 
 15 | # The path to a secondary directory of flight photos, which are only aligned to the project *after*
 16 | # all the other processing is done. This is useful if you want to use secondary photos for multiview
 17 | # analyses (e.g., species ID) without affecting the photogrammetry. Note that the secondary photos
 18 | # are processed in the same way as the primary (e.g., same resolution, same procedure regarding
 19 | # separate calibration per path) and that align_photos:reset_alignment must be False and
 20 | # align_photos:keep_keypoints must be True. If there are no secondary photos to add, set to an empty
 21 | # string ("").
 22 | photo_path_secondary: ""
 23 | 
 24 | # Path for exports (e.g., points, DSM, orthomosaic) and processing log. Will be created if does not exist.
 25 | output_path: ""
 26 | 
 27 | # Path to save Metashape project file (.psx). Will be created if does not exist
 28 | project_path: ""
 29 | 
 30 | # The identifier for the run. Will be used in naming output files. Recommended to include a photoset name and processing parameter set name.
 31 | # Optionally, set it to an empty string ("") to use the config file name (minus extension) as the run name
 32 | run_name: ""
 33 | 
 34 | # CRS EPSG code that project outputs should be in (projection should be in meter units and intended for the project area)
 35 | project_crs: "EPSG::26910" # 26910 is UTM 10N
 36 | 
 37 | # Enable metashape "fine-level task subdivision" which reduces memory use by breaking processing into independent chunks that are run in series.
 38 | # Assuming there's enough memory, it seems to run 10-20% faster by disabling subdividing. But large projects can run out memory and fail if subdivide is not enabled.
 39 | subdivide_task: True
 40 | 
 41 | # Should CUDA GPU driver be used? Alternative is OpenCL. Metashape uses CUDA by default but we have observed it can cause crashes on HPC infrastructure.
 42 | use_cuda: True
 43 | 
 44 | # What value to use for the Metashape tweak "depth_max_gpu_multiplier"? May help to mitigate GPU errors per: https://www.agisoft.com/forum/index.php?topic=11771.0, but doesn't appear to do anything per our testing. Metashape default is 2.
 45 | gpu_multiplier: 2
 46 | 
 47 | #### Processing parameters:
 48 | ## Steps can be run or skipped using the 'enabled' parameter. If enabled == False, everything else in the step is irrelevant.
 49 | ## The metashape functions powering each of these steps are listed in the comments in parentheses.
 50 | ## Refer to Metashape documentation for full parameter definitions: https://www.agisoft.com/pdf/metashape_python_api_1_5_0.pdf
 51 | ## Parameter names here generally follow the parameter names of the Metashape functions.
 52 | 
 53 | # Should the photos at the path(s) listed above be added to the project? Can disable if, for
 54 | # example, you only want to add GCPs (or do additional processing) to an existing project.
 55 | addPhotos: # (Metashape: addPhotos)
 56 |   enabled: True # This applies to the main photos specified in photo_path above. Secondary photos are always added and aligned if a path (or paths) is provided.
 57 |   separate_calibration_per_path: False # If True, each photo path (i.e. each element in the list supplied to 'photo_path' above) will be calibrated independently. Regardless whether True or False, separate camera *models* are calibrated separately; if True, identical camera *models* are calibrated separately if they are provided as separate paths. This addresses the case where two different instances of the same camera model are used in the same project. Note that when True, the logic for assigning separate calibration to each path assumes that the same camera is used for all photos in the path.
 58 |   multispectral: False # Is this a multispectral photo set? If RGB, set to False.
 59 |   use_rtk: False # Whether to use image EXIF RTK flags to make image geospatial accuracy more precise. If enabled but photos don't have RTK data, will treat them as regular photos and use the nofix accuracy.
 60 |   fix_accuracy: 3 # Accuracy to set for photos that have a RTK fix, in units of the CRS
 61 |   nofix_accuracy: 25 # Accuracy to set for photos that have no fix, in units of the CRS
 62 |   # Choose what type of camera you are using:
 63 |   #   Frame: default, uses radial distortion to handle lens distortion
 64 |   #   Spherical: expects image data to come in in the equirectangular format
 65 |   sensor_type: Metashape.Sensor.Type.Frame # Sets the camera type. Tested choices: Metashape.Sensor.Type.Frame, Metashape.Sensor.Type.Spherical
 66 | 
 67 | calibrateReflectance: # (Metahsape: calibrateReflectance)
 68 |     enabled: False
 69 |     panel_filename: "RP04-1923118-OB.csv" # The calibration file must be in the "calibration" folder in the top-level project photos directory. See example panel calibration file in the calibration directory of project repo.
 70 |     use_reflectance_panels: True
 71 |     use_sun_sensor: True
 72 | 
 73 | alignPhotos: # (Metashape: matchPhotos, alignCameras)
 74 |     enabled: True
 75 |     downscale: 2 # How much to coarsen the photos when searching for tie points. Higher number for blurrier photos or when there are small surfaces that may move between photos (such as leaves). Accepts numbers 2^x (and zero) (https://www.agisoft.com/forum/index.php?topic=11697.0).
 76 |     adaptive_fitting: True # Should the camera lens model be fit at the same time as aligning photos?
 77 |     keep_keypoints: True # Should keypoints from matching photos be stored in the project? Required if you later want to add more photos and align them to the previously aligned photos without redoing the original alignment.
 78 |     reset_alignment: False # When running an alignment, if any of the photos were already aligned, should we keep that alignment? Or reset it so we align everything anew?
 79 |     generic_preselection: True # When matching photos, use a much-coarsened version of each photo to narrow down the potential neighbors to pair? Works well if the photos have high altitude above the surface and high overlap (e.g. a 120m nadir 90/90 overlap mission), but doesn't work well for low-altitude and/or highly oblique photos (e.g. a 80m 25deg pitch 80/80 overlap mission)
 80 |     reference_preselection: True # When matching photos, use the camera location data to narrow down the potential neighbors to pair?
 81 |     reference_preselection_mode: Metashape.ReferencePreselectionSource # When matching photos, use the camera location data to narrow down the potential neighbors to pair?
 82 | 
 83 | # To use GCPs, a 'gcps' folder must exist in the root of the photo folder provided in photo_path
 84 | # above (or the first folder, if a list is passed). The contents of the 'gcps' folder are created by
 85 | # the prep_gcps.R script. See readme: https://github.com/ucdavis/metashape
 86 | addGCPs:
 87 |     enabled: False
 88 |     gcp_crs: "EPSG::26910" # CRS EPSG code of GCP coordinates. 26910 (UTM 10 N) is the CRS of the sample RGB photoset.
 89 |     marker_location_accuracy: 0.1 # Accuracy of GCPs real-world coordinates, in meters.
 90 |     marker_projection_accuracy: 8 # Accuracy of the identified locations of the GCPs within the images, in pixels.
 91 |     optimize_w_gcps_only: True # Optimize alignment using GCPs only: required for GCP locations to take precedence over photo GPS data. Disabling it makes GCPs essentially irrelevant.
 92 | 
 93 | filterPointsUSGS:
 94 |     enabled: False
 95 |     rec_thresh_percent: 20
 96 |     rec_thresh_absolute: 15
 97 |     proj_thresh_percent: 30
 98 |     proj_thresh_absolute: 2
 99 |     reproj_thresh_percent: 5
100 |     reproj_thresh_absolute: 0.3
101 | 
102 | optimizeCameras: # (Metashape: optimizeCameras)
103 |     enabled: True
104 |     adaptive_fitting: True # Should the camera lens model be fit at the same time as optimizing photos?
105 |     export: True # Export the camera locations, now updated from the initial alignment
106 | 
107 | # Should an xml file specifying estimated camera locations (transform matrices) be exported? If
108 | # enabled, it is exported once after all alignment-related steps (e.g., align, fliter points,
109 | # optimize cameras) -- even if these steps are disabled -- and then again after aligning the
110 | # secondary set of locations (if performed), overwriting the first file
111 | exportCameras: # (Metashape: exportCameras)
112 |     enabled: True
113 | 
114 | buildDepthMaps: # (Metashape: buildDepthMaps)
115 |     enabled: True
116 |     downscale: 4 # How much to coarsen the photos when searching for matches to build the point cloud. For large photosets, values < 4 likely take prohibitively long. Accepts numbers 2^x (https://www.agisoft.com/forum/index.php?topic=11697.0).
117 |     filter_mode: Metashape.ModerateFiltering # How to filter the depth map. Options are NoFiltering, MildFiltering, ModerateFiltering, AggressiveFiltering. Aggressive filtering removes detail and makes worse DEMs (at least for forest). NoFiltering takes very long. In trials, it never completed.
118 |     reuse_depth: False # Purpose unknown.
119 |     max_neighbors: 60 # Maximum number of neighboring photos to use for estimating depth map. Higher numbers may increase accuracy but dramatically increase processing time.
120 | 
121 | buildPointCloud: # (Metashape: buildPointCloud, (optionally) classifyGroundPoints, and exportPoints)
122 |     enabled: True
123 |     keep_depth: True # If False, removes depth maps from project data after building point cloud
124 |     max_neighbors: 60 # Maximum number of neighboring photos to use for estimating point cloud. Higher numbers may increase accuracy but dramatically increase processing time.
125 |     classify_ground_points: True # Should ground points be classified as a part of this step? Must be enabled (either here or in buildDem, below) if a digital terrain model (DTM) is needed either for orthomosaic or DTM export. Enabling here is an alternative to enabling as a component of buildDem (below). It depends on which stage you want the classification to be done at. If you already have a point cloud but it's unclassified, then don't do it as part of this stage as it would require computing the point cloud again.
126 |     export: False # Whether to export point cloud file.
127 |     export_format: Metashape.PointCloudFormatCOPC # Export format. Options: Metashape.PointCloudFormatCOPC, Metashape.PointCloudFormatLAZ, Metashape.PointCloudFormatLAS, or other options indicated in the Metashape Python module documentation. We have observed that COPC export takes about 6x longer than LAZ or LAS export on a 32-core machine, but this is still much faster than using Untwine when the files are not stored on a local volume. PDAL is proably faster but requires a lot of memory. COPC is cloud-optimized point cloud (a subset of LAZ format) and is recommended for cloud native visualization and analysis.
128 |     classes: "ALL" # Point classes to export. Must be a list. Or can set to "ALL" to use all points. An example of a specific class is: Metashape.PointClass.Ground
129 |     remove_after_export: False # Remove point cloud from project after export of all dependencies (DEMs) to reduce the metashape project file size
130 | 
131 | classifyGroundPoints: # (Metashape: classifyGroundPoints) # classify points, IF SPECIFIED as a component of buildPointCloud (above) or buildDem (below). Must be enabled (in either location) if a digital terrain model (DTM) is needed either for orthomosaic or DTM export. Definitions here: https://www.agisoft.com/forum/index.php?topic=9328.0
132 |     max_angle: 15.0
133 |     max_distance: 1.0
134 |     cell_size: 50.0
135 | 
136 | buildMesh:
137 |     enabled: True
138 |     face_count: "Metashape.MediumFaceCount" # How many faces to use, Metashape.LowFaceCount, MediumFaceCount, HighFaceCount, CustomFaceCount
139 |     face_count_custom: 100000 # Only used if custom number of faces set (above).
140 |     export: True # Export the georeferenced mesh.
141 |     export_extension: "ply" # Can be any supported 3D mesh extension
142 |     # If True, add in a shift to the CRS so the mesh origin is at the camera EXIF average location and points are reported in a local frame - shifted from the normal CRS origin.
143 |     # This is necessary when fine mesh detail is needed, since the natural export format of Metashape is float32 for meshes. In most CRS, that results in point quantization in the 5cm-20cm range
144 |     # In order to save finer detail, set shift_crs_to_cameras to True, and then apply the inverse shift to the mesh model to recreate the true CRS values
145 |     # The shift used is reported using the save_metadata_xml mechanism of exportModel
146 |     shift_crs_to_cameras: False
147 | 
148 | buildDem: # (Metashape: buildDem, (optionally) classifyGroundPoints, exportRaster)
149 |     enabled: True
150 |     classify_ground_points: False # Should ground points be classified as part of this step? Note that an alternative is to calculate them as a part of buildPointCloud (above)
151 |     surface: ["DTM-ptcloud", "DSM-ptcloud", "DSM-mesh"] # Options: "DTM-ptcloud", "DSM-ptcloud", and/or "DSM-mesh". Type of DEM to export and data to build it from (digital terrain model or digital surface model, and from point cloud or mesh)
152 |     resolution: 0 # DSM resolution. Only affects DSM built using the mesh (other DSM types are set by Metashape and not customizable). Note that this also sets the resolution of the orthomosaic built from this DSM, which is 1/4 of the DSM resolution. If using a mesh-derived DSM, and you also desire an orthomosaic with maximal detail, set the DEM resolution to 4x your GSD. Set to 0 to use Metashape-determined default.
153 |     export: True # Whether to export DEM(s)
154 |     tiff_big: True # Use BigTIFF format? Required for larger projects with large DEMs
155 |     tiff_tiled: False # Use tiled TIFF? This is related to internal file architecture.
156 |     nodata: -32767 # Value used to represent nodata.
157 |     tiff_overviews: True # Include coarse-scale raster data in file for quick display in GIS.
158 | 
159 | buildOrthomosaic: # (Metashape: buildOrthomosaic, exportRaster)
160 |     enabled: True
161 |     surface: ["DTM-ptcloud", "DSM-ptcloud", "DSM-mesh", "Mesh"] # Options: "DTM-ptcloud", "DSM-ptcloud", "DSM-mesh", and/or "Mesh". The surface to build the orthomosaic onto. DTM and DSM refer to elevation models built by Metashape and must be configured to be computed via buildDem, above. Mesh refers to using the mesh model directly rather than first computing a DEM.
162 |     blending: Metashape.MosaicBlending # Photo blending mode. Options include AverageBlending, MosaicBlending, MinBlending, MaxBlending, DisabledBlending
163 |     fill_holes: True # Fill holes in orthomosaic where no photo data exist by interpolating?
164 |     refine_seamlines: True # Use smart algorithm to identify photo seamlines where they will least distort.
165 |     export: True # Whether to export orthomosaic(s)
166 |     tiff_big: True # Use BigTIFF format? Required for larger projects with large DEMs
167 |     tiff_tiled: True # Use tiled TIFF? This is related to internal file architecture. Tiled may be (semi-)equivalent to COG.
168 |     nodata: -32767 # Value used to represent nodata.
169 |     tiff_overviews: True # Include coarse-scale raster data in file for quick display in GIS.
170 |     remove_after_export: True # Remove orthomosaic from project after export to reduce the metashape project file size
171 | 


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/prior-versions/metashape_v1.6-1.8/R/prep_configs.R:
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 1 | ### Author: Derek Young, UC Davis
 2 | 
 3 | ### This script does the following:
 4 | ### - Takes a base config YAML template and a set of alternate (derived) parameter values (partial YAMLs that only specify the parameters to change) and composes a set of config files to run in the metashape workflow)
 5 | 
 6 | library(yaml)
 7 | library(readr)
 8 | library(stringr)
 9 | 
10 | #### Determine paths and read YAML files ####
11 | 
12 | # If running manually, specify path to base and derived YAML templates
13 | manual_yaml_path = "/storage/forestuav/configs/set26"
14 | # also the path to metashape repo (this is used only in building the batch job script -- for the call to metashape)
15 | manual_metashape_path = "~/Documents/projects/metashape/python/metashape_workflow.py"
16 | 
17 | ## read paths from command line argument (otherwise use the hard-coded defaults above)
18 | command_args = commandArgs(trailingOnly=TRUE)
19 | 
20 | if(length(command_args) == 0) {
21 |   yaml_path = manual_yaml_path
22 |   metashape_path = manual_metashape_path
23 | } else if (length(command_args) == 1) {
24 |   yaml_path = command_args[1]
25 | } else {
26 |   metashape_path = command_args[2]
27 |   yaml_path = command_args[1]
28 | }
29 | 
30 | ## Read YAML files
31 | base_yaml_path = paste0(yaml_path,"/","base.yml")
32 | derived_yaml_path = paste0(yaml_path,"/","derived.yml")
33 | 
34 | base = read_yaml(base_yaml_path)
35 | 
36 | # read derived config lines as vector
37 | derived_data = read_lines(derived_yaml_path, skip_empty_rows = TRUE)
38 | 
39 | 
40 | 
41 | #### Store each derived set as a separate R object (interpreted from YAML) ####
42 | 
43 | # remove newlines at start of each line
44 | derived_data = str_replace(derived_data,"^\n","")
45 | 
46 | # search for vector elements (lines) that indicate the start of a derived parameter set
47 | start_rows = grep("^####CONFIG", derived_data)
48 | 
49 | # Vector to store config file locations to create a shell script
50 | config_files = NULL
51 | 
52 | # For each derived parameter set, replace the base parameters with the provided derived parameters, and write a config file
53 | for(i in 1:length(start_rows)) {
54 | 
55 |   # get first line of the current derived parameter set
56 |   first_line = start_rows[i] + 1
57 | 
58 |   # get last line of the current derived parameter set
59 |   if(i != length(start_rows)) {
60 |     last_line = start_rows[i+1] - 1
61 |   } else {
62 |     last_line = length(derived_data)
63 |   }
64 | 
65 | 
66 |   ## save as R object
67 |   yaml_string = paste(derived_data[first_line:last_line],collapse="\n")
68 |   derived_focal = yaml.load(yaml_string)
69 | 
70 |   ## take the template and replace each element specified in the derived with the value specified in the derived
71 |   base_derived = modifyList(base,derived_focal)
72 | 
73 | 
74 |   ## get the number (ID) of the derived set (just use the run name from the YAML)
75 |   id = base_derived$run_name
76 | 
77 |   ## write the derived set with its ID number
78 |   filename = paste0(yaml_path,"/cfg_",id,".yml")
79 | 
80 |   write_yaml(base_derived,filename)
81 | 
82 |   config_files = c(config_files,filename)
83 | 
84 | 
85 | }
86 | 
87 | 
88 | ## make a shell script to run all the config files (assume WD is the metashape repo)
89 | shell_lines = paste0("python ", metashape_path, " ", config_files)
90 | 
91 | writeLines(shell_lines,
92 |             con = paste0(yaml_path,"/config_batch.sh"), sep="\n")
93 | 


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/prior-versions/metashape_v1.6-1.8/R/prep_gcps.R:
--------------------------------------------------------------------------------
  1 | ### Author: Derek Young, UC Davis
  2 | 
  3 | ### This script does the following:
  4 | ### - Loads a user-created data file of the image files (and coordinates in each image) where GCPs are located
  5 | ### - Loads a geospatial file containing the locations of the GCPs
  6 | ### - Loads a 10 m DEM and extracts elevation values at each GCP
  7 | ### - Compiles all of the above into a file needed by Metashape for its GCP workflow
  8 | ### - Produces a PDF that shows each GCP image and the location of the GCP for QAQC
  9 | 
 10 | 
 11 | ### This script requires the following folders/files in the main mission imagery directory (the one containing 100MEDIA etc):
 12 | ### - gcps/raw/gcps.gpkg : geospatial data file with each gcp id in the column "gcp_id". Must be in the same projection as the whole Metashape project, and must be in meters xy (usually a UTM zone).
 13 | ### - gcps/raw/gcp_imagecoords.csv : data file listing the images and coordinates in each where a GCP is visible (created manually by technician via imagery inspection)
 14 | ### - dem_usgs/dem_usgs.tif : 10 m USGS dem extending well beyond the project flight area
 15 | 
 16 | ### This script assumes all images to be linked to GCPs have standard DJI naming and directory structure ("100MEDIA/DJI_xxxx.JPG").
 17 | ### In input data file gcp_imagecoords.csv, images are specified without "DJI_", leading zeros, and ".JPG". The image directory is specified without "MEDIA"
 18 | 
 19 | 
 20 | #### Load packages ####
 21 | 
 22 | library(sf)
 23 | library(raster)
 24 | library(dplyr)
 25 | library(stringr)
 26 | library(magick)
 27 | library(ggplot2)
 28 | 
 29 | #### User-defined vars (only used when running interactivesly) ####
 30 | 
 31 | dir_manual = "/home/derek/Downloads/crater_gcps"
 32 | 
 33 | 
 34 | 
 35 | #### Load data ####
 36 | 
 37 | ### All relevant GCP data should be in the top-level mission imagery folder
 38 | ### Load folder from the command line argument
 39 | 
 40 | 
 41 | dir = commandArgs(trailingOnly=TRUE)
 42 | 
 43 | if(length(dir) == 0) {
 44 |   dir = dir_manual
 45 | }
 46 | 
 47 | gcps = read_sf(paste0(dir,"/gcps/raw/gcps.geojson"))
 48 | imagecoords = read.csv(paste0(dir,"/gcps/raw/gcp_imagecoords.csv"),header=TRUE,stringsAsFactors=FALSE)
 49 | dem_usgs = raster(paste0(dir,"/dem_usgs/dem_usgs.tif"))
 50 | 
 51 | # remove blank lines from image coords file
 52 | imagecoords = imagecoords %>%
 53 |   filter(!is.na(x))
 54 | 
 55 | 
 56 | #### Make prepared data directory if it doesn't ecist ####
 57 | dir.create(paste0(dir,"/gcps/prepared"),showWarnings=FALSE)
 58 | 
 59 | 
 60 | 
 61 | #### Create GCP table in the format required by metashape_control and metashape_functions ####
 62 | 
 63 | # Extract elev
 64 | gcp_table = gcps
 65 | gcp_table$elev = suppressWarnings(extract(dem_usgs,gcp_table,method="bilinear"))
 66 | 
 67 | # Extract coords
 68 | coords = st_coordinates(gcp_table)
 69 | gcp_table = cbind(gcp_table,coords)
 70 | 
 71 | # Remove geospatial info
 72 | st_geometry(gcp_table) = NULL
 73 | 
 74 | # Reorder columns, add "point" prefix to GCP names
 75 | gcp_table = gcp_table %>%
 76 |   dplyr::select(gcp_id,x=X,y=Y,elev) %>%
 77 |   dplyr::mutate(gcp_id = paste0("point",gcp_id))
 78 |   
 79 | write.table(gcp_table,paste0(dir,"/gcps/prepared/gcp_table.csv"),row.names=FALSE,col.names=FALSE,sep=",")
 80 | 
 81 | 
 82 | #### Create image coordinate-to-gcp table in the format required by metashape_control and metashape_functions ####
 83 | 
 84 | imagecoords_table = imagecoords %>%
 85 |   mutate(gcp_id = paste0("point",gcp)) %>%
 86 |   mutate(image_text = paste0("DJI_",str_pad(image_file,4,pad="0"),".JPG")) %>%
 87 |   mutate(part_text = paste0("PART_",str_pad(part_folder,2,pad="0"))) %>%
 88 |   mutate(folder_text = paste0(media_folder,"MEDIA")) %>%
 89 |   mutate(image_path = paste0(part_text,"/",folder_text,"/",image_text)) %>%
 90 |   dplyr::select(gcp_id,image_path,x,y) %>%
 91 |   arrange(gcp_id,image_path)
 92 | 
 93 | # remove blank lines from image coords file
 94 | imagecoords = imagecoords %>%
 95 |   filter(!is.na(gcp))
 96 | 
 97 | 
 98 | write.table(imagecoords_table,paste0(dir,"/gcps/prepared/gcp_imagecoords_table.csv"),row.names=FALSE,col.names=FALSE,sep=",")
 99 | 
100 | 
101 | #### Export a PDF of images with the GCP circled on each ####
102 | 
103 | 
104 | pdf(paste0(dir,"/gcps/prepared/gcp_qaqc.pdf"))
105 | 
106 | for(i in 1:nrow(imagecoords_table)) {
107 |   
108 |   imagecoords_row = imagecoords_table[i,]
109 |   
110 |   img = image_read(paste0(dir,"/",imagecoords_row$image_path))
111 |   img = image_scale(img,"10%")
112 |   img = image_flip(img)
113 |   
114 |   img_x = imagecoords_row$x/10
115 |   img_y = imagecoords_row$y/10
116 |   img_gcp = imagecoords_row$gcp_id
117 |   img_path = imagecoords_row$image_path
118 |   
119 |   map = image_ggplot(img) +
120 |     geom_point(x=img_x,y=img_y,size=20,pch=1,fill=NA,color="red",stroke=1) +
121 |     geom_point(x=img_x,y=img_y,size=20,pch=3,fill=NA,color="red",stroke=0.5) +
122 |     labs(title=paste0(img_gcp,"\n",img_path))
123 | 
124 |   print(map)
125 |   
126 |   cat("Completed GCP ", i, " of ",nrow(imagecoords_table),"\r")
127 |   
128 | }
129 | 
130 | garbage = dev.off()
131 | 
132 | 
133 | 
134 | 


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/prior-versions/metashape_v1.6-1.8/config/base.yml:
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  1 | # This is an example yaml configuration for a metashape run
  2 | 
  3 | #### Project-level parameters:
  4 | 
  5 | # Project to load. If not a blank string, this will open an existing project at the path specified. If a blank string, creates a new empty project.
  6 | # Even if opening an existing project, all processing on it is saved as a new project (path and name specified below). The original project file is not modified.
  7 | load_project: ""
  8 | 
  9 | # The path to the directory of flight photos
 10 | # If there are multiple photo folders, set path to the folder that contains all the photo folders
 11 | # If there are no photos to add (e.g., this is an existing project that already has photos in it, set to an empty string ("")
 12 | photo_path: "/storage/uav/photosets/sample_rgb_photoset"
 13 | multispectral: False # Is this a multispectral photo set? If RGB, set to False.
 14 | 
 15 | # Path for exports (e.g., points, DSM, orthomosaic) and processing log. Will be created if does not exist.
 16 | output_path: "/storage/uav/metashape_outputs/sample"
 17 | 
 18 | # Path to save Metashape project file (.psx). Will be created if does not exist
 19 | project_path: "/storage/uav/metashape_projects/sample"
 20 | 
 21 | # The identifier for the run. Will be used in naming output files. Recommended to include a photoset name and processing parameter set name.
 22 | run_name: "sample_rgb_photoset_run001"
 23 | 
 24 | # CRS EPSG code that project outputs should be in (projection should be in meter units and intended for the project area)
 25 | project_crs: "EPSG::26910" # 26910 is UTM 10N
 26 | 
 27 | # Enable metashape "fine-level task subdivision" which reduces memory use by breaking processing into independent chunks that are run in series.
 28 | # Assuming there's enough memory, it seems to run 10-20% faster by disabling subdividing. But large projects can run out memory and fail if subdivide is not enabled.
 29 | subdivide_task: True
 30 | 
 31 | # Should CUDA GPU driver be used? Alternative is OpenCL. Metashape uses CUDA by default but we have observed it can cause crashes on HPC infrastructure
 32 | use_cuda: True # Recommended: True (Metashape default)
 33 | 
 34 | # What value to use for the Metashape tweak "depth_max_gpu_multiplier"? May help to mitigate GPU errors per: https://www.agisoft.com/forum/index.php?topic=11771.0
 35 | gpu_multiplier: 2 # Recommended: 2 (Metashape default)
 36 | 
 37 | #### Processing parameters:
 38 | ## Steps can be run or skipped using the 'enabled' parameter. If enabled == False, everything else in the step is irrelevant.
 39 | ## The metashape functions powering each of these steps are listed in the comments in parentheses.
 40 | ## Refer to Metashape documentation for full parameter definitions: https://www.agisoft.com/pdf/metashape_python_api_1_5_0.pdf
 41 | ## Parameter names here generally follow the parameter names of the Metashape functions.
 42 | 
 43 | ### Whether to use image EXIF RTK flags to make image geospatial accuracy more precise
 44 | use_rtk: True # Recommended: True
 45 | fix_accuracy: 3
 46 | nofix_accuracy: 25
 47 | 
 48 | # To use GCPs, a 'gcps' folder must exist in the top level photos folder. The contents of the 'gcps' folder are created by the prep_gcps.R script. See readme: https://github.com/ucdavis/metashape
 49 | addGCPs:
 50 |     enabled: True
 51 |     gcp_crs: "EPSG::26910" # CRS EPSG code of GCP coordinates. 26910 (UTM 10 N) is the CRS of the sample RGB photoset.
 52 |     marker_location_accuracy: 0.1 # Recommended: 0.1. Accuracy of GCPs real-world coordinates, in meters.
 53 |     marker_projection_accuracy: 8 # Recommended: 8. Accuracy of the identified locations of the GCPs within the images, in pixels.
 54 |     optimize_w_gcps_only: True # Optimize alignment using GCPs only: required for GCP locations to take precedence over photo GPS data. Disabling it makes GCPs essentially irrelevant.
 55 | 
 56 | calibrateReflectance: # (Metahsape: calibrateReflectance)
 57 |     enabled: True
 58 |     panel_filename: "RP04-1923118-OB.csv" # The calibration file must be in the "calibration" folder in the top-level project photos directory. See example panel calibration file in the calibration directory of project repo.
 59 |     use_reflectance_panels: True
 60 |     use_sun_sensor: True
 61 | 
 62 | alignPhotos: # (Metashape: alignPhotos)
 63 |     enabled: True
 64 |     downscale: 2 # Recommended: 2. How much to coarsen the photos when searching for tie points. Higher number for blurrier photos or when there are small surfces that may move between photos (such as leaves). Accepts numbers 2^x (and zero) (https://www.agisoft.com/forum/index.php?topic=11697.0).
 65 |     adaptive_fitting: True # Recommended: True. Should the camera lens model be fit at the same time as aligning photos?
 66 |     keep_keypoints: True # Recommended: True. Should keypoints from matching photos be stored in the project? Required if you later want to add more photos and align them to the previously aligned photos without redoing the original alignment.
 67 |     reset_alignment: False # Recommended: False. When running an alignment, if any of the photos were already aligned, should we keep that alignment? Or reset it so we align everything anew?
 68 | 
 69 | filterPointsUSGS:
 70 |     enabled: False
 71 |     rec_thresh_percent: 20
 72 |     rec_thresh_absolute: 15
 73 |     proj_thresh_percent: 30
 74 |     proj_thresh_absolute: 2
 75 |     reproj_thresh_percent: 5
 76 |     reproj_thresh_absolute: 0.3
 77 | 
 78 | optimizeCameras: # (Metashape: optimizeCameras)
 79 |     enabled: True
 80 |     adaptive_fitting: True # Recommended: True. Should the camera lens model be fit at the same time as optinizing photos?
 81 | 
 82 | buildDenseCloud: # (Metashape: buildDepthMaps, buildDenseCloud, (optionally) classifyGroundPoints, and exportPoints)
 83 |     enabled: True
 84 |     ## For depth maps (buldDepthMaps)
 85 |     downscale: 2 # Recommended: 2. How much to coarsen the photos when searching for matches to build the dense cloud. For large photosets, values < 4 likely take prohibitively long. Accepts numbers 2^x (https://www.agisoft.com/forum/index.php?topic=11697.0).
 86 |     filter_mode: Metashape.MildFiltering # Recommended: Metashape.MildFiltering. How to filter the point cloud. Options are NoFiltering, MildFiltering, ModerateFiltering, AggressiveFiltering. Aggressive filtering removes detail and makes worse DEMs (at least for forest). NoFiltering takes very long. In trials, it never completed.
 87 |     reuse_depth: False # Recommended: False. Purpose unknown.
 88 |     ## For dense cloud (buildDenseCloud)
 89 |     keep_depth: False # Recommended: False. Purpose unknown.
 90 |     ## For both
 91 |     max_neighbors: 100 # Recommended: 100. Maximum number of neighboring photos to use for estimating point cloud. Higher numbers may increase accuracy but dramatically increase processing time.
 92 |     ## For ground point classification (classifyGroundPoints). Definitions here: https://www.agisoft.com/forum/index.php?topic=9328.0
 93 |     classify_ground_points: False # Should ground points be classified as a part of this step? Must be enabled (either here or in buldDem, below) if a digital terrain model (DTM) is needed either for orthomosaic or DTM export. Enabling here is an alternative to enabling as a component of buildDem (below). It depends on which stage you want the classification to be done at. If you already have a point cloud but it's unclassified, then don't do it as part of this stage as it would require computing the point cloud again.
 94 |     ## For dense cloud export (exportPoints)
 95 |     export: True # Whether to export dense cloud file.
 96 |     format: Metashape.PointsFormatLAS # Recommended: PointsFormatLAS. The file format to export points in.
 97 |     classes: "ALL" # Recommended: "ALL". Point classes to export. Must be a list. Or can set to "ALL" to use all points. An example of a specific class is: Metashape.PointClass.Ground
 98 | 
 99 | classifyGroundPoints: # (Meatshape: classifyGroundPoints) # classify points, IF SPECIFIED as a component of buildDenseCloud (above) or buldDem (below). Must be enabled (either here or in buldDem, below) if a digital terrain model (DTM) is needed either for orthomosaic or DTM export. 
100 |     max_angle: 15.0 # Recommended: 15.0
101 |     max_distance: 1.0 # Recommended: 1.0
102 |     cell_size: 50.0 # Recommended: 50.0
103 | 
104 | buildDem: # (Metashape: buildDem, (optionally) classifyGroundPoints, exportRaster)
105 |     enabled: True
106 |     classify_ground_points: True # Should ground points be classified as part of this step? Note that an alternative is to calculate them as a part of buildDenseCloud (above)
107 |     ## For building DEM (buildDem)
108 |     type: "both" # Recommended: "both". Options: "DSM" or "DTM" or "both". Type of DEM to exporot (digital surface model, digital terrain model, or both).
109 |     ## For exporting DEM (exportRaster)
110 |     export: True # Whether to export DEM(s)
111 |     tiff_big: True # Recommended: True. Use BigTIFF format? Required for larger projects with large DEMs
112 |     tiff_tiled: False # Recommended: False. Use tiled TIFF? This is related to internal file architecture.
113 |     nodata: -32767 # Recommended: -32767. Value used to represent nodata.
114 |     tiff_overviews: True # Recommended: True. Include coarse-scale raster data in file for quick display in GIS.
115 | 
116 | buildOrthomosaic: # (Metashape: buildOrthomosaic, exportRaster)
117 |     enabled: True
118 |     ## For building orthomosaic (buildOrthomosaic)
119 |     surface: "USGS" # Recommended: "USGS" (assuming a USGS DEM is available and GCPs with accurate elvevation data are being used). The surface to build the orthomosaic onto. "DTM", "DSM", "USGS", or "DTMandDSM. DTM and DSM refer to elevation models built by Metashape (buildDem step above) and stored in the project. If USGS, you must use GCPs with accurate elevations (ideally extracted from the USGS DEM).
120 |     usgs_dem_path: "dem_usgs/dem_usgs.tif" # Path to USGS DEM for the project area. Needed if surface (parameter above) is "USGS".
121 |     usgs_dem_crs: "EPSG::4269" # CRS of the USGS DEM. Needed if surface (parameter above) is "USGS". For sample RGB photoset, crs is 4269 (Geographic NAD83)
122 |     blending: Metashape.MosaicBlending # Recommended: Metashape.MosaicBlending. Photo blending mode. Options include AverageBlending, MosaicBlending, MinBlending, MaxBlending, DisabledBlending
123 |     fill_holes: True # Recommended: True. Fill holes in orthomosaic where no photo data exist by interpolating?
124 |     refine_seamlines: True # Recommended: True. Use smart algorithm to identify photo seamlines where they will least distort.
125 |     ## For exporting orthomosaic (exportRaster)
126 |     export: True # Whether to export orthomosaic
127 |     tiff_big: True # Recommended: True. Use BigTIFF format? Required for larger projects with large DEMs
128 |     tiff_tiled: False # Recommended: False. Use tiled TIFF? This is related to internal file architecture.
129 |     nodata: -32767 # Recommended: -32767. Value used to represent nodata.
130 |     tiff_overviews: True # Recommended: True. Include coarse-scale raster data in file for quick display in GIS.
131 | 


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/prior-versions/metashape_v1.6-1.8/config/derived.yml:
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 1 | ####CONFIG_0001####
 2 | photo_path: "/storage/uav/photosets/sample_rgb_photoset_01"
 3 | multispectral: False
 4 | buildDenseCloud: # (Metashape: buildDepthMaps, buildDenseCloud, classifyGroundPoints, and exportPoints)
 5 |     enabled: False
 6 | 
 7 | ####CONFIG_0002####
 8 | photo_path: "/storage/uav/photosets/sample_rgb_photoset_02"
 9 | multispectral: True
10 | 
11 | ####CONFIG_0003####
12 | photo_path: "/storage/uav/photosets/sample_rgb_photoset_03"
13 | multispectral: False
14 | 


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/prior-versions/metashape_v1.6-1.8/config/example.yml:
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  1 | # This is an example yaml configuration for a metashape run
  2 | 
  3 | #### Project-level parameters:
  4 | 
  5 | # Project to load. If not a blank string, this will open an existing project at the path specified. If a blank string, creates a new empty project.
  6 | # Even if opening an existing project, all processing on it is saved as a new project (path and name specified below). The original project file is not modified.
  7 | load_project: ""
  8 | 
  9 | # The path to the directory of flight photos
 10 | # If there are multiple photo folders, set path to the folder that contains all the photo folders
 11 | # If there are no photos to add (e.g., this is an existing project that already has photos in it, set to an empty string ("")
 12 | photo_path: "/storage/uav/photosets/sample_rgb_photoset"
 13 | multispectral: False # Is this a multispectral photo set? If RGB, set to False.
 14 | 
 15 | # Path for exports (e.g., points, DSM, orthomosaic) and processing log. Will be created if does not exist.
 16 | output_path: "/storage/uav/metashape_outputs/sample"
 17 | 
 18 | # Path to save Metashape project file (.psx). Will be created if does not exist
 19 | project_path: "/storage/uav/metashape_projects/sample"
 20 | 
 21 | # The identifier for the run. Will be used in naming output files. Recommended to include a photoset name and processing parameter set name.
 22 | run_name: "sample_rgb_photoset_run001"
 23 | 
 24 | # CRS EPSG code that project outputs should be in (projection should be in meter units and intended for the project area)
 25 | project_crs: "EPSG::26910" # 26910 is UTM 10N
 26 | 
 27 | # Enable metashape "fine-level task subdivision" which reduces memory use by breaking processing into independent chunks that are run in series.
 28 | # Assuming there's enough memory, it seems to run 10-20% faster by disabling subdividing. But large projects can run out memory and fail if subdivide is not enabled.
 29 | subdivide_task: True
 30 | 
 31 | # Should CUDA GPU driver be used? Alternative is OpenCL. Metashape uses CUDA by default but we have observed it can cause crashes on HPC infrastructure
 32 | use_cuda: True # Recommended: True (Metashape default)
 33 | 
 34 | # What value to use for the Metashape tweak "depth_max_gpu_multiplier"? May help to mitigate GPU errors per: https://www.agisoft.com/forum/index.php?topic=11771.0
 35 | gpu_multiplier: 2 # Recommended: 2 (Metashape default)
 36 | 
 37 | #### Processing parameters:
 38 | ## Steps can be run or skipped using the 'enabled' parameter. If enabled == False, everything else in the step is irrelevant.
 39 | ## The metashape functions powering each of these steps are listed in the comments in parentheses.
 40 | ## Refer to Metashape documentation for full parameter definitions: https://www.agisoft.com/pdf/metashape_python_api_1_5_0.pdf
 41 | ## Parameter names here generally follow the parameter names of the Metashape functions.
 42 | 
 43 | ### Whether to use image EXIF RTK flags to make image geospatial accuracy more precise
 44 | use_rtk: True # Recommended: True
 45 | fix_accuracy: 3
 46 | nofix_accuracy: 25
 47 | 
 48 | # To use GCPs, a 'gcps' folder must exist in the top level photos folder. The contents of the 'gcps' folder are created by the prep_gcps.R script. See readme: https://github.com/ucdavis/metashape
 49 | addGCPs:
 50 |     enabled: True
 51 |     gcp_crs: "EPSG::26910" # CRS EPSG code of GCP coordinates. 26910 (UTM 10 N) is the CRS of the sample RGB photoset.
 52 |     marker_location_accuracy: 0.1 # Recommended: 0.1. Accuracy of GCPs real-world coordinates, in meters.
 53 |     marker_projection_accuracy: 8 # Recommended: 8. Accuracy of the identified locations of the GCPs within the images, in pixels.
 54 |     optimize_w_gcps_only: True # Optimize alignment using GCPs only: required for GCP locations to take precedence over photo GPS data. Disabling it makes GCPs essentially irrelevant.
 55 | 
 56 | calibrateReflectance: # (Metahsape: calibrateReflectance)
 57 |     enabled: True
 58 |     panel_filename: "RP04-1923118-OB.csv" # The calibration file must be in the "calibration" folder in the top-level project photos directory. See example panel calibration file in the calibration directory of project repo.
 59 |     use_reflectance_panels: True
 60 |     use_sun_sensor: True
 61 | 
 62 | alignPhotos: # (Metashape: alignPhotos)
 63 |     enabled: True
 64 |     downscale: 2 # Recommended: 2. How much to coarsen the photos when searching for tie points. Higher number for blurrier photos or when there are small surfces that may move between photos (such as leaves). Accepts numbers 2^x (and zero) (https://www.agisoft.com/forum/index.php?topic=11697.0).
 65 |     adaptive_fitting: True # Recommended: True. Should the camera lens model be fit at the same time as aligning photos?
 66 |     keep_keypoints: True # Recommended: True. Should keypoints from matching photos be stored in the project? Required if you later want to add more photos and align them to the previously aligned photos without redoing the original alignment.
 67 |     reset_alignment: False # Recommended: False. When running an alignment, if any of the photos were already aligned, should we keep that alignment? Or reset it so we align everything anew?
 68 | 
 69 | filterPointsUSGS:
 70 |     enabled: False
 71 |     rec_thresh_percent: 20
 72 |     rec_thresh_absolute: 15
 73 |     proj_thresh_percent: 30
 74 |     proj_thresh_absolute: 2
 75 |     reproj_thresh_percent: 5
 76 |     reproj_thresh_absolute: 0.3
 77 | 
 78 | optimizeCameras: # (Metashape: optimizeCameras)
 79 |     enabled: True
 80 |     adaptive_fitting: True # Recommended: True. Should the camera lens model be fit at the same time as optinizing photos?
 81 | 
 82 | buildDenseCloud: # (Metashape: buildDepthMaps, buildDenseCloud, (optionally) classifyGroundPoints, and exportPoints)
 83 |     enabled: True
 84 |     ## For depth maps (buldDepthMaps)
 85 |     downscale: 2 # Recommended: 2. How much to coarsen the photos when searching for matches to build the dense cloud. For large photosets, values < 4 likely take prohibitively long. Accepts numbers 2^x (https://www.agisoft.com/forum/index.php?topic=11697.0).
 86 |     filter_mode: Metashape.MildFiltering # Recommended: Metashape.MildFiltering. How to filter the point cloud. Options are NoFiltering, MildFiltering, ModerateFiltering, AggressiveFiltering. Aggressive filtering removes detail and makes worse DEMs (at least for forest). NoFiltering takes very long. In trials, it never completed.
 87 |     reuse_depth: False # Recommended: False. Purpose unknown.
 88 |     ## For dense cloud (buildDenseCloud)
 89 |     keep_depth: False # Recommended: False. Purpose unknown.
 90 |     ## For both
 91 |     max_neighbors: 100 # Recommended: 100. Maximum number of neighboring photos to use for estimating point cloud. Higher numbers may increase accuracy but dramatically increase processing time.
 92 |     ## For ground point classification (classifyGroundPoints). Definitions here: https://www.agisoft.com/forum/index.php?topic=9328.0
 93 |     classify_ground_points: False # Should ground points be classified as a part of this step? Must be enabled (either here or in buldDem, below) if a digital terrain model (DTM) is needed either for orthomosaic or DTM export. Enabling here is an alternative to enabling as a component of buildDem (below). It depends on which stage you want the classification to be done at. If you already have a point cloud but it's unclassified, then don't do it as part of this stage as it would require computing the point cloud again.
 94 |     ## For dense cloud export (exportPoints)
 95 |     export: True # Whether to export dense cloud file.
 96 |     format: Metashape.PointsFormatLAS # Recommended: PointsFormatLAS. The file format to export points in.
 97 |     classes: "ALL" # Recommended: "ALL". Point classes to export. Must be a list. Or can set to "ALL" to use all points. An example of a specific class is: Metashape.PointClass.Ground
 98 | 
 99 | classifyGroundPoints: # (Meatshape: classifyGroundPoints) # classify points, IF SPECIFIED as a component of buildDenseCloud (above) or buldDem (below). Must be enabled (either here or in buldDem, below) if a digital terrain model (DTM) is needed either for orthomosaic or DTM export. 
100 |     max_angle: 15.0 # Recommended: 15.0
101 |     max_distance: 1.0 # Recommended: 1.0
102 |     cell_size: 50.0 # Recommended: 50.0
103 | 
104 | buildDem: # (Metashape: buildDem, (optionally) classifyGroundPoints, exportRaster)
105 |     enabled: True
106 |     classify_ground_points: True # Should ground points be classified as part of this step? Note that an alternative is to calculate them as a part of buildDenseCloud (above)
107 |     ## For building DEM (buildDem)
108 |     type: "both" # Recommended: "both". Options: "DSM" or "DTM" or "both". Type of DEM to exporot (digital surface model, digital terrain model, or both).
109 |     ## For exporting DEM (exportRaster)
110 |     export: True # Whether to export DEM(s)
111 |     tiff_big: True # Recommended: True. Use BigTIFF format? Required for larger projects with large DEMs
112 |     tiff_tiled: False # Recommended: False. Use tiled TIFF? This is related to internal file architecture.
113 |     nodata: -32767 # Recommended: -32767. Value used to represent nodata.
114 |     tiff_overviews: True # Recommended: True. Include coarse-scale raster data in file for quick display in GIS.
115 | 
116 | buildOrthomosaic: # (Metashape: buildOrthomosaic, exportRaster)
117 |     enabled: True
118 |     ## For building orthomosaic (buildOrthomosaic)
119 |     surface: "USGS" # Recommended: "USGS" (assuming a USGS DEM is available and GCPs with accurate elvevation data are being used). The surface to build the orthomosaic onto. "DTM", "DSM", "USGS", or "DTMandDSM. DTM and DSM refer to elevation models built by Metashape (buildDem step above) and stored in the project. If USGS, you must use GCPs with accurate elevations (ideally extracted from the USGS DEM).
120 |     usgs_dem_path: "dem_usgs/dem_usgs.tif" # Path to USGS DEM for the project area. Needed if surface (parameter above) is "USGS".
121 |     usgs_dem_crs: "EPSG::4269" # CRS of the USGS DEM. Needed if surface (parameter above) is "USGS". For sample RGB photoset, crs is 4269 (Geographic NAD83)
122 |     blending: Metashape.MosaicBlending # Recommended: Metashape.MosaicBlending. Photo blending mode. Options include AverageBlending, MosaicBlending, MinBlending, MaxBlending, DisabledBlending
123 |     fill_holes: True # Recommended: True. Fill holes in orthomosaic where no photo data exist by interpolating?
124 |     refine_seamlines: True # Recommended: True. Use smart algorithm to identify photo seamlines where they will least distort.
125 |     ## For exporting orthomosaic (exportRaster)
126 |     export: True # Whether to export orthomosaic
127 |     tiff_big: True # Recommended: True. Use BigTIFF format? Required for larger projects with large DEMs
128 |     tiff_tiled: False # Recommended: False. Use tiled TIFF? This is related to internal file architecture.
129 |     nodata: -32767 # Recommended: -32767. Value used to represent nodata.
130 |     tiff_overviews: True # Recommended: True. Include coarse-scale raster data in file for quick display in GIS.
131 | 


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/prior-versions/metashape_v1.6-1.8/python/metashape_workflow.py:
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 1 | # -*- coding: utf-8 -*-
 2 | # File for running a metashape workflow
 3 | 
 4 | # Derek Young and Alex Mandel
 5 | # University of California, Davis
 6 | # 2021
 7 | 
 8 | import sys
 9 | 
10 | # ---- If this is a first run from the standalone python module, need to copy the license file from the full metashape install: from python import metashape_license_setup
11 | 
12 | ## Define where to get the config file (only used if running interactively)
13 | manual_config_file = "config/example_dev.yml"
14 | # ---- If not running interactively, the config file should be supplied as the command-line argument after the python script, e.g.: python metashape_workflow.py config.yml
15 | 
16 | 
17 | ## Load custom modules and config file: slightly different depending whether running interactively or via command line
18 | try:  # running interactively (in linux) or command line (windows)
19 |     from python import metashape_workflow_functions as meta
20 |     from python import read_yaml
21 | except:  # running from command line (in linux) or interactively (windows)
22 |     import metashape_workflow_functions as meta
23 |     import read_yaml
24 | 
25 | if sys.stdin.isatty():
26 |     config_file = sys.argv[1]
27 | else:
28 |     config_file = manual_config_file
29 | 
30 | ## Parse the config file
31 | cfg = read_yaml.read_yaml(config_file)
32 | 
33 | ### Run the Metashape workflow
34 | 
35 | doc, log, run_id = meta.project_setup(cfg)
36 | 
37 | meta.enable_and_log_gpu(log, cfg)
38 | 
39 | if cfg["photo_path"] != "":  # only add photos if there is a photo directory listed
40 |     meta.add_photos(doc, cfg)
41 | 
42 | if cfg["calibrateReflectance"]["enabled"]:
43 |     meta.calibrate_reflectance(doc, cfg)
44 | 
45 | if cfg["alignPhotos"]["enabled"]:
46 |     meta.align_photos(doc, log, cfg)
47 |     meta.reset_region(doc)
48 | 
49 | if cfg["filterPointsUSGS"]["enabled"]:
50 |     meta.filter_points_usgs_part1(doc, cfg)
51 |     meta.reset_region(doc)
52 | 
53 | if cfg["addGCPs"]["enabled"]:
54 |     meta.add_gcps(doc, cfg)
55 |     meta.reset_region(doc)
56 | 
57 | if cfg["optimizeCameras"]["enabled"]:
58 |     meta.optimize_cameras(doc, cfg)
59 |     meta.reset_region(doc)
60 | 
61 | if cfg["filterPointsUSGS"]["enabled"]:
62 |     meta.filter_points_usgs_part2(doc, cfg)
63 |     meta.reset_region(doc)
64 | 
65 | if cfg["buildDenseCloud"]["enabled"]:
66 |     meta.build_dense_cloud(doc, log, run_id, cfg)
67 | 
68 | if cfg["buildDem"]["enabled"]:
69 |     meta.build_dem(doc, log, run_id, cfg)
70 | 
71 | if cfg["buildOrthomosaic"]["enabled"]:
72 |     meta.build_orthomosaics(doc, log, run_id, cfg)
73 | 
74 | meta.export_report(doc, run_id, cfg)
75 | 
76 | meta.finish_run(log, config_file)
77 | 


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/prior-versions/metashape_v1.6-1.8/python/metashape_workflow_functions.py:
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  1 | # Derek Young and Alex Mandel
  2 | # University of California, Davis
  3 | # 2021
  4 | 
  5 | #### Import libraries
  6 | 
  7 | import datetime
  8 | import glob
  9 | import os
 10 | import platform
 11 | import re
 12 | 
 13 | # import the fuctionality we need to make time stamps to measure performance
 14 | import time
 15 | 
 16 | ### import the Metashape functionality
 17 | import Metashape
 18 | import yaml
 19 | 
 20 | #### Helper functions and globals
 21 | 
 22 | # Set the log file name-value separator
 23 | # Chose ; as : is in timestamps
 24 | # TODO: Consider moving log to json/yaml formatting using a dict
 25 | sep = "; "
 26 | 
 27 | 
 28 | def stamp_time():
 29 |     """
 30 |     Format the timestamps as needed
 31 |     """
 32 |     stamp = datetime.datetime.now().strftime("%Y%m%dT%H%M")
 33 |     return stamp
 34 | 
 35 | 
 36 | def diff_time(t2, t1):
 37 |     """
 38 |     Give a end and start time, subtract, and round
 39 |     """
 40 |     total = str(round(t2 - t1, 1))
 41 |     return total
 42 | 
 43 | 
 44 | # Used by add_gcps function
 45 | def get_marker(chunk, label):
 46 |     for marker in chunk.markers:
 47 |         if marker.label == label:
 48 |             return marker
 49 |     return None
 50 | 
 51 | 
 52 | # Used by add_gcps function
 53 | def get_camera(chunk, label):
 54 |     for camera in chunk.cameras:
 55 |         if camera.label.lower() == label.lower():
 56 |             return camera
 57 |     return None
 58 | 
 59 | 
 60 | #### Functions for each major step in Metashape
 61 | 
 62 | 
 63 | def project_setup(cfg):
 64 |     """
 65 |     Create output and project paths, if they don't exist
 66 |     Define a project ID based on photoset name and timestamp
 67 |     Define a project filename and a log filename
 68 |     Create the project
 69 |     Start a log file
 70 |     """
 71 | 
 72 |     # Make project directories (necessary even if loading an existing project because this workflow saves a new project based on the old one, leaving the old one intact
 73 |     if not os.path.exists(cfg["output_path"]):
 74 |         os.makedirs(cfg["output_path"])
 75 |     if not os.path.exists(cfg["project_path"]):
 76 |         os.makedirs(cfg["project_path"])
 77 | 
 78 |     ### Set a filename template for project files and output files
 79 |     ## Get the first parts of the filename (the photoset ID and location string)
 80 | 
 81 |     run_name = cfg["run_name"]
 82 | 
 83 |     ## Project file example to make: "projectID_YYYYMMDDtHHMM-jobID.psx"
 84 |     timestamp = stamp_time()
 85 |     run_id = "_".join([run_name, timestamp])
 86 |     # TODO: If there is a slurm JobID, append to time (separated with "-", not "_"). This will keep jobs initiated in the same minute distinct
 87 | 
 88 |     project_file = os.path.join(cfg["project_path"], ".".join([run_id, "psx"]))
 89 |     log_file = os.path.join(cfg["output_path"], ".".join([run_id + "_log", "txt"]))
 90 | 
 91 |     """
 92 |     Create a doc and a chunk
 93 |     """
 94 | 
 95 |     # create a handle to the Metashape object
 96 |     doc = (
 97 |         Metashape.Document()
 98 |     )  # When running via Metashape, can use: doc = Metashape.app.document
 99 | 
100 |     # If specified, open existing project
101 |     if cfg["load_project"] != "":
102 |         doc.open(cfg["load_project"])
103 |     else:
104 |         # Initialize a chunk, set its CRS as specified
105 |         chunk = doc.addChunk()
106 |         chunk.crs = Metashape.CoordinateSystem(cfg["project_crs"])
107 |         chunk.marker_crs = Metashape.CoordinateSystem(cfg["addGCPs"]["gcp_crs"])
108 | 
109 |     # Save doc doc as new project (even if we opened an existing project, save as a separate one so the existing project remains accessible in its original state)
110 |     doc.save(project_file)
111 | 
112 |     """
113 |     Log specs except for GPU
114 |     """
115 | 
116 |     # log Metashape version, CPU specs, time, and project location to results file
117 |     # open the results file
118 |     # TODO: records the Slurm values for actual cpus and ram allocated
119 |     # https://slurm.schedmd.com/sbatch.html#lbAI
120 |     with open(log_file, "a") as file:
121 | 
122 |         # write a line with the Metashape version
123 |         file.write(sep.join(["Project", run_id]) + "\n")
124 |         file.write(
125 |             sep.join(["Agisoft Metashape Professional Version", Metashape.app.version])
126 |             + "\n"
127 |         )
128 |         # write a line with the date and time
129 |         file.write(sep.join(["Processing started", stamp_time()]) + "\n")
130 |         # write a line with CPU info - if possible, improve the way the CPU info is found / recorded
131 |         file.write(sep.join(["Node", platform.node()]) + "\n")
132 |         file.write(sep.join(["CPU", platform.processor()]) + "\n")
133 |         # write two lines with GPU info: count and model names - this takes multiple steps to make it look clean in the end
134 | 
135 |     return doc, log_file, run_id
136 | 
137 | 
138 | def enable_and_log_gpu(log_file, cfg):
139 |     """
140 |     Enables GPU and logs GPU specs
141 |     """
142 | 
143 |     gpustringraw = str(Metashape.app.enumGPUDevices())
144 |     gpucount = gpustringraw.count("name': '")
145 |     gpustring = ""
146 |     currentgpu = 1
147 |     while gpucount >= currentgpu:
148 |         if gpustring != "":
149 |             gpustring = gpustring + ", "
150 |         gpustring = (
151 |             gpustring + gpustringraw.split("name': '")[currentgpu].split("',")[0]
152 |         )
153 |         currentgpu = currentgpu + 1
154 |     # gpustring = gpustringraw.split("name': '")[1].split("',")[0]
155 |     gpu_mask = Metashape.app.gpu_mask
156 | 
157 |     with open(log_file, "a") as file:
158 |         file.write(sep.join(["Number of GPUs Found", str(gpucount)]) + "\n")
159 |         file.write(sep.join(["GPU Model", gpustring]) + "\n")
160 |         file.write(sep.join(["GPU Mask", str(gpu_mask)]) + "\n")
161 | 
162 |         # If a GPU exists but is not enabled, enable the 1st one
163 |         if (gpucount > 0) and (gpu_mask == 0):
164 |             Metashape.app.gpu_mask = 1
165 |             gpu_mask = Metashape.app.gpu_mask
166 |             file.write(sep.join(["GPU Mask Enabled", str(gpu_mask)]) + "\n")
167 | 
168 |         # This writes down all the GPU devices available
169 |         # file.write('GPU(s): '+str(Metashape.app.enumGPUDevices())+'\n')
170 | 
171 |     # set Metashape to *not* use the CPU during GPU steps (appears to be standard wisdom)
172 |     Metashape.app.cpu_enable = False
173 | 
174 |     # Disable CUDA if specified
175 |     if not cfg["use_cuda"]:
176 |         Metashape.app.settings.setValue("main/gpu_enable_cuda", "0")
177 | 
178 |     # Set GPU multiplier to value specified (2 is default)
179 |     Metashape.app.settings.setValue(
180 |         "main/depth_max_gpu_multiplier", cfg["gpu_multiplier"]
181 |     )
182 | 
183 |     return True
184 | 
185 | 
186 | def add_photos(doc, cfg):
187 |     """
188 |     Add photos to project and change their labels to include their containing folder
189 |     """
190 | 
191 |     ## Get paths to all the project photos
192 |     a = glob.iglob(
193 |         os.path.join(cfg["photo_path"], "**", "*.*"), recursive=True
194 |     )  # (([jJ][pP][gG])|([tT][iI][fF]))
195 |     b = [path for path in a]
196 |     photo_files = [
197 |         x
198 |         for x in b
199 |         if (
200 |             re.search("(.tif$)|(.jpg$)|(.TIF$)|(.JPG$)", x)
201 |             and (not re.search("dem_usgs.tif", x))
202 |         )
203 |     ]
204 | 
205 |     ## Add them
206 |     if cfg["multispectral"]:
207 |         doc.chunk.addPhotos(photo_files, layout=Metashape.MultiplaneLayout)
208 |     else:
209 |         doc.chunk.addPhotos(photo_files)
210 | 
211 |     ## Need to change the label on each camera so that it includes the containing folder(S)
212 |     for camera in doc.chunk.cameras:
213 |         path = camera.photo.path
214 |         # remove the base imagery dir from this string
215 |         rel_path = path.replace(cfg["photo_path"], "")
216 |         # if it starts with a '/', remove it
217 |         newlabel = re.sub("^/", "", rel_path)
218 |         camera.label = newlabel
219 | 
220 |     ## If specified, change the accuracy of the cameras to match the RTK flag (RTK fix if flag = 50, otherwise no fix
221 |     if cfg["use_rtk"]:
222 |         for cam in doc.chunk.cameras:
223 |             rtkflag = cam.photo.meta["DJI/RtkFlag"]
224 |             if rtkflag == "50":
225 |                 cam.reference.location_accuracy = Metashape.Vector(
226 |                     [cfg["fix_accuracy"], cfg["fix_accuracy"], cfg["fix_accuracy"]]
227 |                 )
228 |                 cam.reference.accuracy = Metashape.Vector(
229 |                     [cfg["fix_accuracy"], cfg["fix_accuracy"], cfg["fix_accuracy"]]
230 |                 )
231 |             else:
232 |                 cam.reference.location_accuracy = Metashape.Vector(
233 |                     [
234 |                         cfg["nofix_accuracy"],
235 |                         cfg["nofix_accuracy"],
236 |                         cfg["nofix_accuracy"],
237 |                     ]
238 |                 )
239 |                 cam.reference.accuracy = Metashape.Vector(
240 |                     [
241 |                         cfg["nofix_accuracy"],
242 |                         cfg["nofix_accuracy"],
243 |                         cfg["nofix_accuracy"],
244 |                     ]
245 |                 )
246 | 
247 |     doc.save()
248 | 
249 |     return True
250 | 
251 | 
252 | def calibrate_reflectance(doc, cfg):
253 |     # TODO: Handle failure to find panels, or mulitple panel images by returning error to user.
254 |     doc.chunk.locateReflectancePanels()
255 |     doc.chunk.loadReflectancePanelCalibration(
256 |         os.path.join(
257 |             cfg["photo_path"],
258 |             "calibration",
259 |             cfg["calibrateReflectance"]["panel_filename"],
260 |         )
261 |     )
262 |     # doc.chunk.calibrateReflectance(use_reflectance_panels=True,use_sun_sensor=True)
263 |     doc.chunk.calibrateReflectance(
264 |         use_reflectance_panels=cfg["calibrateReflectance"]["use_reflectance_panels"],
265 |         use_sun_sensor=cfg["calibrateReflectance"]["use_sun_sensor"],
266 |     )
267 |     doc.save()
268 | 
269 |     return True
270 | 
271 | 
272 | def add_gcps(doc, cfg):
273 |     """
274 |     Add GCPs (GCP coordinates and the locations of GCPs in individual photos.
275 |     See the helper script (and the comments therein) for details on how to prepare the data needed by this function: R/prep_gcps.R
276 |     """
277 | 
278 |     ## Tag specific pixels in specific images where GCPs are located
279 |     path = os.path.join(
280 |         cfg["photo_path"], "gcps", "prepared", "gcp_imagecoords_table.csv"
281 |     )
282 |     file = open(path)
283 |     content = file.read().splitlines()
284 | 
285 |     for line in content:
286 |         marker_label, camera_label, x_proj, y_proj = line.split(",")
287 |         if (
288 |             marker_label[0] == '"'
289 |         ):  # if it's in quotes (from saving CSV in Excel), remove quotes
290 |             marker_label = marker_label[
291 |                 1:-1
292 |             ]  # need to get it out of the two pairs of quotes
293 |         if (
294 |             camera_label[0] == '"'
295 |         ):  # if it's in quotes (from saving CSV in Excel), remove quotes
296 |             camera_label = camera_label[1:-1]
297 | 
298 |         marker = get_marker(doc.chunk, marker_label)
299 |         if not marker:
300 |             marker = doc.chunk.addMarker()
301 |             marker.label = marker_label
302 | 
303 |         camera = get_camera(doc.chunk, camera_label)
304 |         if not camera:
305 |             print(camera_label + " camera not found in project")
306 |             continue
307 | 
308 |         marker.projections[camera] = Metashape.Marker.Projection(
309 |             (float(x_proj), float(y_proj)), True
310 |         )
311 | 
312 |     ## Assign real-world coordinates to each GCP
313 |     path = os.path.join(cfg["photo_path"], "gcps", "prepared", "gcp_table.csv")
314 | 
315 |     file = open(path)
316 |     content = file.read().splitlines()
317 | 
318 |     for line in content:
319 |         marker_label, world_x, world_y, world_z = line.split(",")
320 |         if (
321 |             marker_label[0] == '"'
322 |         ):  # if it's in quotes (from saving CSV in Excel), remove quotes
323 |             marker_label = marker_label[
324 |                 1:-1
325 |             ]  # need to get it out of the two pairs of quotes
326 | 
327 |         marker = get_marker(doc.chunk, marker_label)
328 |         if not marker:
329 |             marker = doc.chunk.addMarker()
330 |             marker.label = marker_label
331 | 
332 |         marker.reference.location = (float(world_x), float(world_y), float(world_z))
333 |         marker.reference.accuracy = (
334 |             cfg["addGCPs"]["marker_location_accuracy"],
335 |             cfg["addGCPs"]["marker_location_accuracy"],
336 |             cfg["addGCPs"]["marker_location_accuracy"],
337 |         )
338 | 
339 |     doc.chunk.marker_location_accuracy = (
340 |         cfg["addGCPs"]["marker_location_accuracy"],
341 |         cfg["addGCPs"]["marker_location_accuracy"],
342 |         cfg["addGCPs"]["marker_location_accuracy"],
343 |     )
344 |     doc.chunk.marker_projection_accuracy = cfg["addGCPs"]["marker_projection_accuracy"]
345 | 
346 |     doc.save()
347 | 
348 |     return True
349 | 
350 | 
351 | def align_photos(doc, log_file, cfg):
352 |     """
353 |     Match photos, align cameras, optimize cameras
354 |     """
355 | 
356 |     #### Align photos
357 | 
358 |     # get a beginning time stamp
359 |     timer1a = time.time()
360 | 
361 |     # Align cameras
362 |     doc.chunk.matchPhotos(
363 |         downscale=cfg["alignPhotos"]["downscale"],
364 |         subdivide_task=cfg["subdivide_task"],
365 |         keep_keypoints=cfg["alignPhotos"]["keep_keypoints"],
366 |     )
367 |     doc.chunk.alignCameras(
368 |         adaptive_fitting=cfg["alignPhotos"]["adaptive_fitting"],
369 |         subdivide_task=cfg["subdivide_task"],
370 |         reset_alignment=cfg["alignPhotos"]["reset_alignment"],
371 |     )
372 |     doc.save()
373 | 
374 |     # get an ending time stamp
375 |     timer1b = time.time()
376 | 
377 |     # calculate difference between end and start time to 1 decimal place
378 |     time1 = diff_time(timer1b, timer1a)
379 | 
380 |     # record results to file
381 |     with open(log_file, "a") as file:
382 |         file.write(sep.join(["Align Photos", time1]) + "\n")
383 | 
384 |     return True
385 | 
386 | 
387 | def reset_region(doc):
388 |     """
389 |     Reset the region and make it much larger than the points; necessary because if points go outside the region, they get clipped when saving
390 |     """
391 | 
392 |     doc.chunk.resetRegion()
393 |     region_dims = doc.chunk.region.size
394 |     region_dims[2] *= 3
395 |     doc.chunk.region.size = region_dims
396 | 
397 |     return True
398 | 
399 | 
400 | def optimize_cameras(doc, cfg):
401 |     """
402 |     Optimize cameras
403 |     """
404 | 
405 |     # Disable camera locations as reference if specified in YML
406 |     if cfg["addGCPs"]["enabled"] and cfg["addGCPs"]["optimize_w_gcps_only"]:
407 |         n_cameras = len(doc.chunk.cameras)
408 |         for i in range(0, n_cameras):
409 |             doc.chunk.cameras[i].reference.enabled = False
410 | 
411 |     # Currently only optimizes the default parameters, which is not all possible parameters
412 |     doc.chunk.optimizeCameras(
413 |         adaptive_fitting=cfg["optimizeCameras"]["adaptive_fitting"]
414 |     )
415 |     doc.save()
416 | 
417 |     return True
418 | 
419 | 
420 | def filter_points_usgs_part1(doc, cfg):
421 | 
422 |     doc.chunk.optimizeCameras(
423 |         adaptive_fitting=cfg["optimizeCameras"]["adaptive_fitting"]
424 |     )
425 | 
426 |     rec_thresh_percent = cfg["filterPointsUSGS"]["rec_thresh_percent"]
427 |     rec_thresh_absolute = cfg["filterPointsUSGS"]["rec_thresh_absolute"]
428 |     proj_thresh_percent = cfg["filterPointsUSGS"]["proj_thresh_percent"]
429 |     proj_thresh_absolute = cfg["filterPointsUSGS"]["proj_thresh_absolute"]
430 |     reproj_thresh_percent = cfg["filterPointsUSGS"]["reproj_thresh_percent"]
431 |     reproj_thresh_absolute = cfg["filterPointsUSGS"]["reproj_thresh_absolute"]
432 | 
433 |     fltr = Metashape.PointCloud.Filter()
434 |     fltr.init(doc.chunk, Metashape.PointCloud.Filter.ReconstructionUncertainty)
435 |     values = fltr.values.copy()
436 |     values.sort()
437 |     thresh = values[int(len(values) * (1 - rec_thresh_percent / 100))]
438 |     if thresh < rec_thresh_absolute:
439 |         thresh = (
440 |             rec_thresh_absolute  # don't throw away too many points if they're all good
441 |         )
442 |     fltr.removePoints(thresh)
443 | 
444 |     doc.chunk.optimizeCameras(
445 |         adaptive_fitting=cfg["optimizeCameras"]["adaptive_fitting"]
446 |     )
447 | 
448 |     fltr = Metashape.PointCloud.Filter()
449 |     fltr.init(doc.chunk, Metashape.PointCloud.Filter.ProjectionAccuracy)
450 |     values = fltr.values.copy()
451 |     values.sort()
452 |     thresh = values[int(len(values) * (1 - proj_thresh_percent / 100))]
453 |     if thresh < proj_thresh_absolute:
454 |         thresh = (
455 |             proj_thresh_absolute  # don't throw away too many points if they're all good
456 |         )
457 |     fltr.removePoints(thresh)
458 | 
459 |     doc.chunk.optimizeCameras(
460 |         adaptive_fitting=cfg["optimizeCameras"]["adaptive_fitting"]
461 |     )
462 | 
463 |     fltr = Metashape.PointCloud.Filter()
464 |     fltr.init(doc.chunk, Metashape.PointCloud.Filter.ReprojectionError)
465 |     values = fltr.values.copy()
466 |     values.sort()
467 |     thresh = values[int(len(values) * (1 - reproj_thresh_percent / 100))]
468 |     if thresh < reproj_thresh_absolute:
469 |         thresh = reproj_thresh_absolute  # don't throw away too many points if they're all good
470 |     fltr.removePoints(thresh)
471 | 
472 |     doc.chunk.optimizeCameras(
473 |         adaptive_fitting=cfg["optimizeCameras"]["adaptive_fitting"]
474 |     )
475 | 
476 |     doc.save()
477 | 
478 | 
479 | def filter_points_usgs_part2(doc, cfg):
480 | 
481 |     doc.chunk.optimizeCameras(
482 |         adaptive_fitting=cfg["optimizeCameras"]["adaptive_fitting"]
483 |     )
484 | 
485 |     reproj_thresh_percent = cfg["filterPointsUSGS"]["reproj_thresh_percent"]
486 |     reproj_thresh_absolute = cfg["filterPointsUSGS"]["reproj_thresh_absolute"]
487 | 
488 |     fltr = Metashape.PointCloud.Filter()
489 |     fltr.init(doc.chunk, Metashape.PointCloud.Filter.ReprojectionError)
490 |     values = fltr.values.copy()
491 |     values.sort()
492 |     thresh = values[int(len(values) * (1 - reproj_thresh_percent / 100))]
493 |     if thresh < reproj_thresh_absolute:
494 |         thresh = reproj_thresh_absolute  # don't throw away too many points if they're all good
495 |     fltr.removePoints(thresh)
496 | 
497 |     doc.chunk.optimizeCameras(
498 |         adaptive_fitting=cfg["optimizeCameras"]["adaptive_fitting"]
499 |     )
500 | 
501 |     doc.save()
502 | 
503 | 
504 | def classify_ground_points(doc, log_file, run_id, cfg):
505 | 
506 |     # get a beginning time stamp for the next step
507 |     timer_a = time.time()
508 | 
509 |     doc.chunk.dense_cloud.classifyGroundPoints(
510 |         max_angle=cfg["classifyGroundPoints"]["max_angle"],
511 |         max_distance=cfg["classifyGroundPoints"]["max_distance"],
512 |         cell_size=cfg["classifyGroundPoints"]["cell_size"],
513 |     )
514 |     doc.save()
515 | 
516 |     # get an ending time stamp for the previous step
517 |     timer_b = time.time()
518 | 
519 |     # calculate difference between end and start time to 1 decimal place
520 |     time_tot = diff_time(timer_b, timer_a)
521 | 
522 |     # record results to file
523 |     with open(log_file, "a") as file:
524 |         file.write(sep.join(["Classify Ground Points", time_tot]) + "\n")
525 | 
526 | 
527 | def build_dense_cloud(doc, log_file, run_id, cfg):
528 |     """
529 |     Build depth maps and dense cloud
530 |     """
531 | 
532 |     ### Build depth maps
533 | 
534 |     # get a beginning time stamp for the next step
535 |     timer2a = time.time()
536 | 
537 |     # build depth maps only instead of also building the dense cloud ##?? what does
538 |     doc.chunk.buildDepthMaps(
539 |         downscale=cfg["buildDenseCloud"]["downscale"],
540 |         filter_mode=cfg["buildDenseCloud"]["filter_mode"],
541 |         reuse_depth=cfg["buildDenseCloud"]["reuse_depth"],
542 |         max_neighbors=cfg["buildDenseCloud"]["max_neighbors"],
543 |         subdivide_task=cfg["subdivide_task"],
544 |     )
545 |     doc.save()
546 | 
547 |     # get an ending time stamp for the previous step
548 |     timer2b = time.time()
549 | 
550 |     # calculate difference between end and start time to 1 decimal place
551 |     time2 = diff_time(timer2b, timer2a)
552 | 
553 |     # record results to file
554 |     with open(log_file, "a") as file:
555 |         file.write(sep.join(["Build Depth Maps", time2]) + "\n")
556 | 
557 |     ### Build dense cloud
558 | 
559 |     # get a beginning time stamp for the next step
560 |     timer3a = time.time()
561 | 
562 |     # build dense cloud
563 |     doc.chunk.buildDenseCloud(
564 |         max_neighbors=cfg["buildDenseCloud"]["max_neighbors"],
565 |         keep_depth=cfg["buildDenseCloud"]["keep_depth"],
566 |         subdivide_task=cfg["subdivide_task"],
567 |         point_colors=True,
568 |     )
569 |     doc.save()
570 | 
571 |     # classify ground points if specified
572 |     if cfg["buildDenseCloud"]["classify_ground_points"]:
573 |         classify_ground_points(doc, log_file, run_id, cfg)
574 | 
575 |     ### Export points
576 | 
577 |     if cfg["buildDenseCloud"]["export"]:
578 | 
579 |         output_file = os.path.join(cfg["output_path"], run_id + "_points.las")
580 | 
581 |         if cfg["buildDenseCloud"]["classes"] == "ALL":
582 |             # call without classes argument (Metashape then defaults to all classes)
583 |             doc.chunk.exportPoints(
584 |                 path=output_file,
585 |                 source_data=Metashape.DenseCloudData,
586 |                 format=Metashape.PointsFormatLAS,
587 |                 crs=Metashape.CoordinateSystem(cfg["project_crs"]),
588 |                 subdivide_task=cfg["subdivide_task"],
589 |             )
590 |         else:
591 |             # call with classes argument
592 |             doc.chunk.exportPoints(
593 |                 path=output_file,
594 |                 source_data=Metashape.DenseCloudData,
595 |                 format=Metashape.PointsFormatLAS,
596 |                 crs=Metashape.CoordinateSystem(cfg["project_crs"]),
597 |                 clases=cfg["buildDenseCloud"]["classes"],
598 |                 subdivide_task=cfg["subdivide_task"],
599 |             )
600 | 
601 |     # get an ending time stamp for the previous step
602 |     timer3b = time.time()
603 | 
604 |     # calculate difference between end and start time to 1 decimal place
605 |     time3 = diff_time(timer3b, timer3a)
606 | 
607 |     # record results to file
608 |     with open(log_file, "a") as file:
609 |         file.write(sep.join(["Build Dense Cloud", time3]) + "\n")
610 | 
611 |     return True
612 | 
613 | 
614 | def build_dem(doc, log_file, run_id, cfg):
615 |     """
616 |     Build end export DEM
617 |     """
618 | 
619 |     # classify ground points if specified
620 |     if cfg["buildDem"]["classify_ground_points"]:
621 |         classify_ground_points(doc, log_file, run_id, cfg)
622 | 
623 |     # get a beginning time stamp for the next step
624 |     timer5a = time.time()
625 | 
626 |     # prepping params for buildDem
627 |     projection = Metashape.OrthoProjection()
628 |     projection.crs = Metashape.CoordinateSystem(cfg["project_crs"])
629 | 
630 |     # prepping params for export
631 |     compression = Metashape.ImageCompression()
632 |     compression.tiff_big = cfg["buildDem"]["tiff_big"]
633 |     compression.tiff_tiled = cfg["buildDem"]["tiff_tiled"]
634 |     compression.tiff_overviews = cfg["buildDem"]["tiff_overviews"]
635 | 
636 |     if (cfg["buildDem"]["type"] == "DSM") | (cfg["buildDem"]["type"] == "both"):
637 |         # call without classes argument (Metashape then defaults to all classes)
638 |         doc.chunk.buildDem(
639 |             source_data=Metashape.DenseCloudData,
640 |             subdivide_task=cfg["subdivide_task"],
641 |             projection=projection,
642 |         )
643 |         output_file = os.path.join(cfg["output_path"], run_id + "_dsm.tif")
644 |         if cfg["buildDem"]["export"]:
645 |             doc.chunk.exportRaster(
646 |                 path=output_file,
647 |                 projection=projection,
648 |                 nodata_value=cfg["buildDem"]["nodata"],
649 |                 source_data=Metashape.ElevationData,
650 |                 image_compression=compression,
651 |             )
652 |     if (cfg["buildDem"]["type"] == "DTM") | (cfg["buildDem"]["type"] == "both"):
653 |         # call with classes argument
654 |         doc.chunk.buildDem(
655 |             source_data=Metashape.DenseCloudData,
656 |             classes=Metashape.PointClass.Ground,
657 |             subdivide_task=cfg["subdivide_task"],
658 |             projection=projection,
659 |         )
660 |         output_file = os.path.join(cfg["output_path"], run_id + "_dtm.tif")
661 |         if cfg["buildDem"]["export"]:
662 |             doc.chunk.exportRaster(
663 |                 path=output_file,
664 |                 projection=projection,
665 |                 nodata_value=cfg["buildDem"]["nodata"],
666 |                 source_data=Metashape.ElevationData,
667 |                 image_compression=compression,
668 |             )
669 |     if (
670 |         (cfg["buildDem"]["type"] != "DTM")
671 |         & (cfg["buildDem"]["type"] == "both")
672 |         & (cfg["buildDem"]["type"] == "DSM")
673 |     ):
674 |         raise ValueError("DEM type must be either 'DSM' or 'DTM' or 'both'")
675 | 
676 |     doc.save()
677 | 
678 |     # get an ending time stamp for the previous step
679 |     timer5b = time.time()
680 | 
681 |     # calculate difference between end and start time to 1 decimal place
682 |     time5 = diff_time(timer5b, timer5a)
683 | 
684 |     # record results to file
685 |     with open(log_file, "a") as file:
686 |         file.write(sep.join(["Build DEM", time5]) + "\n")
687 | 
688 |     return True
689 | 
690 | 
691 | def build_export_orthomosaic(doc, log_file, run_id, cfg, file_ending):
692 |     """
693 |     Helper function called by build_orthomosaics. build_export_orthomosaic builds and exports an ortho based on the current elevation data.
694 |     build_orthomosaics sets the current elevation data and calls build_export_orthomosaic (one or more times depending on how many orthomosaics requested)
695 |     """
696 | 
697 |     # get a beginning time stamp for the next step
698 |     timer6a = time.time()
699 | 
700 |     # prepping params for buildDem
701 |     projection = Metashape.OrthoProjection()
702 |     projection.crs = Metashape.CoordinateSystem(cfg["project_crs"])
703 | 
704 |     doc.chunk.buildOrthomosaic(
705 |         surface_data=Metashape.ElevationData,
706 |         blending_mode=cfg["buildOrthomosaic"]["blending"],
707 |         fill_holes=cfg["buildOrthomosaic"]["fill_holes"],
708 |         refine_seamlines=cfg["buildOrthomosaic"]["refine_seamlines"],
709 |         subdivide_task=cfg["subdivide_task"],
710 |         projection=projection,
711 |     )
712 | 
713 |     doc.save()
714 | 
715 |     ## Export orthomosaic
716 |     if cfg["buildOrthomosaic"]["export"]:
717 |         output_file = os.path.join(
718 |             cfg["output_path"], run_id + "_ortho_" + file_ending + ".tif"
719 |         )
720 | 
721 |         compression = Metashape.ImageCompression()
722 |         compression.tiff_big = cfg["buildOrthomosaic"]["tiff_big"]
723 |         compression.tiff_tiled = cfg["buildOrthomosaic"]["tiff_tiled"]
724 |         compression.tiff_overviews = cfg["buildOrthomosaic"]["tiff_overviews"]
725 | 
726 |         projection = Metashape.OrthoProjection()
727 |         projection.crs = Metashape.CoordinateSystem(cfg["project_crs"])
728 | 
729 |         doc.chunk.exportRaster(
730 |             path=output_file,
731 |             projection=projection,
732 |             nodata_value=cfg["buildOrthomosaic"]["nodata"],
733 |             source_data=Metashape.OrthomosaicData,
734 |             image_compression=compression,
735 |         )
736 | 
737 |     # get an ending time stamp for the previous step
738 |     timer6b = time.time()
739 | 
740 |     # calculate difference between end and start time to 1 decimal place
741 |     time6 = diff_time(timer6b, timer6a)
742 | 
743 |     # record results to file
744 |     with open(log_file, "a") as file:
745 |         file.write(sep.join(["Build Orthomosaic", time6]) + "\n")
746 | 
747 |     return True
748 | 
749 | 
750 | def build_orthomosaics(doc, log_file, run_id, cfg):
751 |     """
752 |     Build orthomosaic. This function just calculates the needed elevation data(s) and then calls build_export_orthomosaic to do the actual building and exporting. It does this multiple times if orthos based on multiple surfaces were requsted
753 |     """
754 | 
755 |     # prep projection for export step below (in case export is enabled)
756 |     projection = Metashape.OrthoProjection()
757 |     projection.crs = Metashape.CoordinateSystem(cfg["project_crs"])
758 | 
759 |     # get a beginning time stamp for the next step
760 |     timer6a = time.time()
761 | 
762 |     # what should the orthomosaic filename end in? e.g., DSM, DTM, USGS to indicate the surface it was built on
763 |     file_ending = cfg["buildOrthomosaic"]["surface"]
764 | 
765 |     # Import USGS DEM as surface for orthomosaic if specified
766 |     if cfg["buildOrthomosaic"]["surface"] == "USGS":
767 |         path = os.path.join(cfg["photo_path"], cfg["buildOrthomosaic"]["usgs_dem_path"])
768 |         crs = Metashape.CoordinateSystem(cfg["buildOrthomosaic"]["usgs_dem_crs"])
769 |         doc.chunk.importRaster(path=path, crs=crs, raster_type=Metashape.ElevationData)
770 |         build_export_orthomosaic(doc, log_file, run_id, cfg, file_ending="USGS")
771 |     # Otherwise use Metashape point cloud to build elevation model
772 |     # DTM: use ground points only
773 |     if (cfg["buildOrthomosaic"]["surface"] == "DTM") | (
774 |         cfg["buildOrthomosaic"]["surface"] == "DTMandDSM"
775 |     ):
776 |         doc.chunk.buildDem(
777 |             source_data=Metashape.DenseCloudData,
778 |             classes=Metashape.PointClass.Ground,
779 |             subdivide_task=cfg["subdivide_task"],
780 |             projection=projection,
781 |         )
782 |         build_export_orthomosaic(doc, log_file, run_id, cfg, file_ending="dtm")
783 |     # DSM: use all point classes
784 |     if (cfg["buildOrthomosaic"]["surface"] == "DSM") | (
785 |         cfg["buildOrthomosaic"]["surface"] == "DTMandDSM"
786 |     ):
787 |         doc.chunk.buildDem(
788 |             source_data=Metashape.DenseCloudData,
789 |             subdivide_task=cfg["subdivide_task"],
790 |             projection=projection,
791 |         )
792 |         build_export_orthomosaic(doc, log_file, run_id, cfg, file_ending="dsm")
793 | 
794 |     return True
795 | 
796 | 
797 | def export_report(doc, run_id, cfg):
798 |     """
799 |     Export report
800 |     """
801 | 
802 |     output_file = os.path.join(cfg["output_path"], run_id + "_report.pdf")
803 | 
804 |     doc.chunk.exportReport(path=output_file)
805 | 
806 |     return True
807 | 
808 | 
809 | def finish_run(log_file, config_file):
810 |     """
811 |     Finish run (i.e., write completed time to log)
812 |     """
813 | 
814 |     # finish local results log and close it for the last time
815 |     with open(log_file, "a") as file:
816 |         file.write(sep.join(["Run Completed", stamp_time()]) + "\n")
817 | 
818 |     # open run configuration again. We can't just use the existing cfg file because its objects had already been converted to Metashape objects (they don't write well)
819 |     with open(config_file) as file:
820 |         config_full = yaml.safe_load(file)
821 | 
822 |     # write the run configuration to the log file
823 |     with open(log_file, "a") as file:
824 |         file.write("\n\n### CONFIGURATION ###\n")
825 |         documents = yaml.dump(config_full, file, default_flow_style=False)
826 |         file.write("### END CONFIGURATION ###\n")
827 | 
828 |     return True
829 | 


--------------------------------------------------------------------------------
/prior-versions/metashape_v1.6-1.8/python/read_yaml.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Created on Mon Oct 21 13:45:15 2019
 3 | 
 4 | @author: Alex Mandel
 5 | """
 6 | 
 7 | import Metashape
 8 | import yaml
 9 | 
10 | 
11 | def convert_objects(a_dict):
12 |     """
13 |     Based on
14 |     https://stackoverflow.com/a/25896596/237354
15 |     """
16 |     for k, v in a_dict.items():
17 |         if not isinstance(v, dict):
18 |             if isinstance(v, str):
19 |                 # TODO look for Metashape.
20 |                 if (
21 |                     v
22 |                     and "Metashape" in v
23 |                     and not ("path" in k)
24 |                     and not ("project" in k)
25 |                 ):  # allow "path" and "project" keys from YAML to include "Metashape" (e.g., Metashape in the filename)
26 |                     a_dict[k] = eval(v)
27 |             elif isinstance(v, list):
28 |                 # TODO skip if no item have metashape
29 |                 a_dict[k] = [eval(item) for item in v if ("Metashape" in item)]
30 |         else:
31 |             convert_objects(v)
32 | 
33 | 
34 | def read_yaml(yml_path):
35 |     with open(yml_path, "r") as ymlfile:
36 |         cfg = yaml.load(ymlfile, Loader=yaml.SafeLoader)
37 | 
38 |     # TODO: wrap in a Try to catch errors
39 |     convert_objects(cfg)
40 | 
41 |     return cfg
42 | 
43 | 
44 | if __name__ == "__main__":
45 | 
46 |     yml_path = "config/example.yml"
47 |     cfg = read_yaml(yml_path)
48 |     # with open("config/example.yml",'r') as ymlfile:
49 |     #    cfg = yaml.load(ymlfile)
50 | 
51 |     # catch = convert_objects(cfg)
52 | 
53 |     # Get a String value
54 |     Photo_path = cfg["Photo_path"]
55 | 
56 |     # Get a True/False
57 |     GPU_use = cfg["GPU"]["GPU_use"]
58 | 
59 |     # Get a Num
60 |     GPU_num = cfg["GPU"]["GPU_num"]
61 | 
62 |     # Convert a to a Metashape Object
63 |     accuracy = eval(cfg["matchPhotos"]["accuracy"])
64 | 


--------------------------------------------------------------------------------
/python/metashape_workflow.py:
--------------------------------------------------------------------------------
 1 | # -*- coding: utf-8 -*-
 2 | # File for running a metashape workflow
 3 | 
 4 | import argparse
 5 | import contextlib
 6 | import sys
 7 | from pathlib import Path
 8 | 
 9 | # ---- If this is a first run from the standalone python module, need to copy the license file from the full metashape install: from python import metashape_license_setup
10 | 
11 | ## Define where to get the config file (only used if running interactively)
12 | # manual_config_file = "config/config-base.yml"
13 | manual_config_file = Path(
14 |     Path(__file__).parent, "..", "config", "config-base.yml"
15 | ).resolve()
16 | # ---- If not running interactively, the config file should be supplied as the command-line argument after the python script, e.g.: python metashape_workflow.py config.yml
17 | 
18 | 
19 | # Load custom modules and config file: slightly different depending whether running interactively or via command line
20 | try:  # running interactively (in linux) or command line (windows)
21 |     from python.metashape_workflow_functions import MetashapeWorkflow
22 | except:  # running from command line (in linux) or interactively (windows)
23 |     from metashape_workflow_functions import MetashapeWorkflow
24 | 
25 | 
26 | def parse_args():
27 |     parser = argparse.ArgumentParser(
28 |         description="The first required argument is the path to the config file. "
29 |         + "All other arguments are optional overrides to the corresponding entry in that config"
30 |     )
31 |     parser.add_argument(
32 |         "--config_file",
33 |         default=manual_config_file,
34 |         help="A path to a yaml config file.",
35 |     )
36 |     parser.add_argument(
37 |         "--photo-path",
38 |         nargs="+",
39 |         help="One or more absolute paths to load photos from, separated by spaces.",
40 |     )
41 |     parser.add_argument(
42 |         "--photo-path-secondary",
43 |         help="A path to a folder of images to add after alignment. "
44 |         + "For more information, see the description in the example config file.",
45 |     )
46 |     parser.add_argument(
47 |         "--project-path",
48 |         help="Path to save Metashape project file (.psx). Will be created if does not exist",
49 |     )
50 |     parser.add_argument(
51 |         "--output-path",
52 |         help="Path for exports (e.g., points, DSM, orthomosaic) and processing log. "
53 |         + "Will be created if does not exist.",
54 |     )
55 |     parser.add_argument(
56 |         "--run-name",
57 |         help="The identifier for the run. Will be used in naming output files.",
58 |     )
59 |     parser.add_argument(
60 |         "--project-crs",
61 |         help="CRS EPSG code that project outputs should be in "
62 |         + "(projection should be in meter units and intended for the project area). "
63 |         + "It should be specified in the following format: 'EPSG::<EPSG code>'.",
64 |     )
65 | 
66 |     args = parser.parse_args()
67 |     return args
68 | 
69 | 
70 | if __name__ == "__main__":
71 |     args = parse_args()
72 | 
73 |     # Get the non-None overrides provided on the command line
74 |     override_dict = {k: v for k, v in args.__dict__.items() if v is not None}
75 | 
76 |     # Initialize the workflow instance with the configuration file and the dictionary representation of
77 |     # CLI overrides
78 |     meta = MetashapeWorkflow(config_file=args.config_file, override_dict=override_dict)
79 | 
80 |     ### Run the Metashape workflow
81 |     # The argo workflow requires that all stdout is json formatted. Since this isn't the case for the
82 |     # metashape logs, we redirect to standard error.
83 |     with contextlib.redirect_stdout(sys.stderr):
84 |         # Actually run the processing step
85 |         try:
86 |             meta.run()
87 |         except Exception as e:
88 |             # TODO make this error message more descriptive
89 |             print(
90 |                 "Metashape errored while processing, the completed paths will still be reported. "
91 |                 + "The error was: \n"
92 |                 + e.__str__()
93 |             )
94 | 
95 |     # Log where the data files were written as json dict
96 |     print(meta.get_written_paths(as_json=True))
97 | 


--------------------------------------------------------------------------------
/settings.json:
--------------------------------------------------------------------------------
1 | {
2 | 
3 |     "editor.rulers": [
4 |     
5 |         100
6 |     ],
7 |     "rewrap.autoWrap.enabled": false
8 | 
9 | }


--------------------------------------------------------------------------------