.github/workflows/issue-triage.yml

@@ -13,7 +13,6 @@ jobs:
         with:
           ghToken: ${{ secrets.GITHUB_TOKEN }}
           openAI: ${{ secrets.OPENAI_TOKEN }}
-          model: gpt-4o-2024-08-06
           filter: |
             - "#"
           variables: |

.github/workflows/publish-windows.yml

@@ -9,7 +9,7 @@ on:
 
 jobs:
   build:
-    runs-on: windows-2022
+    runs-on: windows-2019
     steps:
     - name: Checkout
       uses: actions/checkout@v2
@@ -49,7 +49,7 @@ jobs:
       run: |
         python configure.py dist --code-sign-cert-path $env:CODE_SIGN_CERT_PATH
     - name: Upload Setup File
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: Setup
         path: dist\*.exe

.github/workflows/test-build-prs.yaml

@@ -81,7 +81,7 @@ jobs:
       run: |
         python configure.py dist
     - name: Upload Setup File
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: Setup
         path: dist\*.exe

.gitignore

@@ -34,5 +34,4 @@ vcpkg/
 venv/
 python38/
 dist/
-innosetup/
+innosetup/
-.DS_Store

README.md

@@ -15,19 +15,12 @@ If you would rather not type commands in a shell and are looking for a friendly
 
 ## Quickstart
 
-The easiest way to run ODM is via docker. To install docker, see [docs.docker.com](https://docs.docker.com). Once you have docker installed and [working](https://docs.docker.com/get-started/#test-docker-installation), you can get ODM by running from a Command Prompt / Terminal:
+The easiest way to run ODM on is via docker. To install docker, see [docs.docker.com](https://docs.docker.com). Once you have docker installed and [working](https://docs.docker.com/get-started/#test-docker-installation), you can run ODM by placing some images (JPEGs or TIFFs) in a folder named “images” (for example `C:\Users\youruser\datasets\project\images` or `/home/youruser/datasets/project/images`) and simply run from a Command Prompt / Terminal:
-
-```bash
-docker pull opendronemap/odm
-```
-
-Run ODM by placing some images (JPEGs, TIFFs or DNGs) in a folder named “images” (for example `C:\Users\youruser\datasets\project\images` or `/home/youruser/datasets/project/images`) and simply run from a Command Prompt / Terminal:
 
 ```bash
 # Windows
 docker run -ti --rm -v c:/Users/youruser/datasets:/datasets opendronemap/odm --project-path /datasets project
-```
+
-```bash
 # Mac/Linux
 docker run -ti --rm -v /home/youruser/datasets:/datasets opendronemap/odm --project-path /datasets project
 ```
@@ -95,7 +88,7 @@ run C:\Users\youruser\datasets\project  [--additional --parameters --here]
 ODM has support for doing SIFT feature extraction on a GPU, which is about 2x faster than the CPU on a typical consumer laptop. To use this feature, you need to use the `opendronemap/odm:gpu` docker image instead of `opendronemap/odm` and you need to pass the `--gpus all` flag:
 
 ```
-docker run -ti --rm -v c:/Users/youruser/datasets:/datasets --gpus all opendronemap/odm:gpu --project-path /datasets project --feature-type sift
+docker run -ti --rm -v c:/Users/youruser/datasets:/datasets --gpus all opendronemap/odm:gpu --project-path /datasets project
 ```
 
 When you run ODM, if the GPU is recognized, in the first few lines of output you should see:

SuperBuild/CMakeLists.txt

@@ -184,7 +184,7 @@ set(custom_libs OpenSfM
 
 externalproject_add(mve
     GIT_REPOSITORY  https://github.com/OpenDroneMap/mve.git
-    GIT_TAG         356
+    GIT_TAG         290
     UPDATE_COMMAND  ""
     SOURCE_DIR      ${SB_SOURCE_DIR}/mve
     CMAKE_ARGS      ${WIN32_CMAKE_ARGS} ${APPLE_CMAKE_ARGS}
@@ -244,7 +244,7 @@ externalproject_add(dem2points
 externalproject_add(odm_orthophoto
     DEPENDS           opencv 
     GIT_REPOSITORY    https://github.com/OpenDroneMap/odm_orthophoto.git
-    GIT_TAG           355
+    GIT_TAG           353
     PREFIX            ${SB_BINARY_DIR}/odm_orthophoto
     SOURCE_DIR        ${SB_SOURCE_DIR}/odm_orthophoto
     CMAKE_ARGS        -DCMAKE_INSTALL_PREFIX:PATH=${SB_INSTALL_DIR}

SuperBuild/cmake/External-Obj2Tiles.cmake

@@ -1,7 +1,7 @@
 set(_proj_name obj2tiles)
 set(_SB_BINARY_DIR "${SB_BINARY_DIR}/${_proj_name}")
 
-set(OBJ2TILES_VERSION v1.0.13)
+set(OBJ2TILES_VERSION v1.0.12)
 set(OBJ2TILES_EXT "")
 
 set(OBJ2TILES_ARCH "Linux64")
@@ -9,7 +9,7 @@ if (WIN32)
     set(OBJ2TILES_ARCH "Win64")
     set(OBJ2TILES_EXT ".exe")
 elseif(${CMAKE_SYSTEM_PROCESSOR} STREQUAL "aarch64")
-    set(OBJ2TILES_ARCH "LinuxArm64")
+    set(OBJ2TILES_ARCH "LinuxArm")
 elseif(APPLE)
     set(OBJ2TILES_ARCH "Osx64")
 endif()

SuperBuild/cmake/External-OpenMVS.cmake

@@ -53,7 +53,7 @@ ExternalProject_Add(${_proj_name}
   #--Download step--------------
   DOWNLOAD_DIR      ${SB_DOWNLOAD_DIR}
   GIT_REPOSITORY    https://github.com/OpenDroneMap/openMVS
-  GIT_TAG           355
+  GIT_TAG           320
   #--Update/Patch step----------
   UPDATE_COMMAND    ""
   #--Configure step-------------

SuperBuild/cmake/External-OpenSfM.cmake

@@ -25,7 +25,7 @@ ExternalProject_Add(${_proj_name}
   #--Download step--------------
   DOWNLOAD_DIR      ${SB_DOWNLOAD_DIR}
   GIT_REPOSITORY    https://github.com/OpenDroneMap/OpenSfM/
-  GIT_TAG           355
+  GIT_TAG           352
   #--Update/Patch step----------
   UPDATE_COMMAND    git submodule update --init --recursive
   #--Configure step-------------

VERSION

@@ -1 +1 @@
-3.5.6
+3.5.3

configure.sh

@@ -58,7 +58,7 @@ ensure_prereqs() {
     if [[ "$UBUNTU_VERSION" == *"20.04"* ]]; then
         echo "Enabling PPA for Ubuntu GIS"
         sudo $APT_GET install -y -qq --no-install-recommends software-properties-common
-        sudo add-apt-repository ppa:ubuntugis/ppa
+        sudo add-apt-repository -y ppa:ubuntugis/ubuntugis-unstable
         sudo $APT_GET update
     fi
 

contrib/blender/common.py

@@ -1,4 +1,5 @@
 import bpy
+import materials_utils
 
 def loadMesh(file):
 

contrib/dem-blend/README.md

@@ -1,13 +0,0 @@
-# DEM Blending
-
-Blend sets of DEMs by calculating euclidean distance to null values and weighting the combination of elevation models. Based on the split-merge tool within ODM.
-
-Requirements:
-* Directory full of images to blend together
-* NoData should be coded as a value of -9999
-
-## Usage
-
-```BASH
-docker run -ti --rm -v /home/youruser/folder_with_dems:/input --entrypoint /code/contrib/dem-blend/dem-blend.py opendronemap/odm /input
-```

contrib/dem-blend/dem-blend.py

@@ -1,30 +0,0 @@
-#!/usr/bin/env python3
-# Authors: Piero Toffanin, Stephen Mather
-# License: AGPLv3
-
-import os
-import glob
-import sys
-sys.path.insert(0, os.path.join("..", "..", os.path.dirname(__file__)))
-
-import argparse
-from opendm.dem import merge
-
-parser = argparse.ArgumentParser(description='Merge and blend DEMs using OpenDroneMap\'s approach.')
-parser.add_argument('input_dems',
-                type=str,
-                help='Path to input dems (.tif)')
-
-args = parser.parse_args()
-
-if not os.path.exists(args.input_dems):
-    print("%s does not exist" % args.input_dems)
-    exit(1)
-
-output_dem = os.path.join(args.input_dems, 'merged_blended_dem.tif')
-input_dem_path = os.path.join(args.input_dems, '*.tif')
-input_dems = glob.glob(input_dem_path)
-
-merge.euclidean_merge_dems(input_dems
-                    ,output_dem=output_dem
-                )

contrib/fix_ply/README.md

@@ -1,19 +0,0 @@
-# Fix Ply
-
-Use to translate a modified ply into a compatible format for subsequent steps in ODM. Via Jaime Chacoff, https://community.opendronemap.org/t/edited-point-cloud-with-cloudcompare-wont-rerun-from-odm-meshing/21449/6
-
-The basic idea is to process through ODM until the point cloud is created, use a 3rd party tool, like CloudCompare to edit the point cloud, and then continue processing in OpenDroneMap.
-
-This useful bit of python will convert the PLY exported from CloudCompare back into a compatible format for continued processing in OpenDroneMap.
-
-1. Run project in WebODM and add this to your settings: `end-with: odm-filterpoints`
-1. Once complete, go to your NodeODM container and copy `/var/www/data/[Task ID]/odm-filterpoints` directory
-1. Open CloudCompare and from `odm-filterpoints` directory you've copied, open `point_cloud.ply`
-1. In the box that pops up, add a scalar field `vertex - views`
-1. To see the actual colours again - select the point cloud, then in properties change colours from "Scalar field" to "RGB"
-1. Make your changes to the point cloud
-1. Compute normals (Edit > Normals > Compute)
-1. Save PLY file as ASCII
-1. Run Python file above to fix PLY file and convert to binary
-1. Copy `odm_filterpoints` directory (or just `point_cloud.ply`) back into NodeODM container
-1. Restart project in WebODM "From Meshing" (don't forget to edit settings to remove `end-with: odm-filterpoints` or it's not going to do anything).

contrib/fix_ply/fix_ply.py

@@ -1,68 +0,0 @@
-import os
-import logging
-from plyfile import PlyData, PlyElement
-import numpy as np
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-def pcd_ascii_to_binary_ply(ply_file: str, binary_ply: str) -> None:
-    """Converts ASCII PLY to binary, ensuring 'views' is present and of type uchar.
-       Raises ValueError if neither 'scalar_views' nor 'views' is found.
-    """
-
-    try:
-        logging.info(f"Reading ASCII PLY file: {ply_file}")
-        ply_data: PlyData = PlyData.read(ply_file)
-    except FileNotFoundError:
-        logging.error(f"File not found: {ply_file}")
-        return
-    except Exception as e:
-        logging.error(f"Error reading PLY file: {e}")
-        return
-
-    new_elements: list[PlyElement] = []
-
-    for element in ply_data.elements:
-        new_data = element.data.copy()
-
-        if 'scalar_views' in element.data.dtype.names:
-            new_data['views'] = new_data['scalar_views'].astype('u1')
-            del new_data['scalar_views']
-        elif 'views' in element.data.dtype.names:
-            new_data['views'] = new_data['views'].astype('u1')
-        else:
-            raise ValueError(f"Neither 'scalar_views' nor 'views' found - did you import them when opened the file in CloudCompare?")
-
-
-        new_element = PlyElement.describe(new_data, element.name)
-        new_elements.append(new_element)
-
-    new_ply_data = PlyData(new_elements, text=False)
-
-    try:
-        logging.info(f"Writing binary PLY file: {binary_ply}")
-        new_ply_data.write(binary_ply)
-    except Exception as e:
-        logging.error(f"Error writing PLY file: {e}")
-        return
-
-    logging.info("PLY conversion complete.")
-
-
-if __name__ == '__main__':
-
-    # Parameters
-    base: str = os.path.dirname(os.path.abspath(__file__))
-    ply_file: str = os.path.join(base, 'point_cloud_ascii.ply')
-    binary_ply_file: str = os.path.join(base, 'point_cloud.ply')
-
-    if not os.path.exists(ply_file):
-        logging.error(f"Input file not found: {ply_file}")
-        exit(1) # Exit with error code
-
-    try:
-        pcd_ascii_to_binary_ply(ply_file, binary_ply_file)
-    except ValueError as e:
-        logging.error(f"PLY conversion failed: {e}")
-        exit(1) # Exit with error code to indicate failure

contrib/mask-duplicator/mask-duplicator.py

@@ -4,7 +4,7 @@ import os
 
 import PIL
 
-from PIL import Image
+from PIL import Image, ExifTags
 
 import shutil
 

contrib/orthorectify/orthorectify.py

@@ -12,6 +12,7 @@ import numpy as np
 import numpy.ma as ma
 import multiprocessing
 import argparse
+import functools
 from skimage.draw import line
 from opensfm import dataset
 

contrib/time-sift/README.md

@@ -1,64 +0,0 @@
-# Plugin Time-SIFT
-
-This script does Time-SIFT processing with ODM. Time-SIFT is a method for multi-temporal analysis without the need to co-registrate the data.
-
->  D. Feurer, F. Vinatier, Joining multi-epoch archival aerial images in a single SfM block allows 3-D change detection with almost exclusively image information, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146, 2018, Pages 495-506, ISSN 0924-2716, doi: 10.1016/j.isprsjprs.2018.10.016
-(https://doi.org/10.1016/j.isprsjprs.2018.10.016)
-
-## Requirements
-* ODM ! :-) 
-* subprocess
-* json
-* os
-* shutil
-* pathlib
-* sys
-* argparse
-* textwrap
-
-## Usage
-
-### Provided example
-Download or clone [this repo](https://forge.inrae.fr/Denis.Feurer/timesift-odm-data-example.git) to get example data.
-
-Then execute
-```
-python Timesift_odm.py datasets --end-with odm_filterpoints
-```
-It should make the Time-SIFT processing on the downloaded example data, stopping after the filtered dense clouds step.
-
-In the destination dir, you should obtain new directories, ```0_before``` and ```1_after``` at the same level as the ```time-sift-block``` directory. These new directories contain all the results natively co-registered.
-
-You can then use [CloudCompare](https://cloudcompare.org/) to compute distance between the ```datasets/0_before/odm_filterpoints/point_cloud.ply``` and the ```datasets/1_after/odm_filterpoints/point_cloud.ply``` and obtain this image showing the difference between the two 3D surfaces. Here, two soil samples were excavated as can be seen on the image below.
-![](https://forge.inrae.fr/Denis.Feurer/timesift-odm-data-example/-/raw/main/Example.png?ref_type=heads)
-
-### Your own data
-In your dataset directory (usually ```datasets```, but you can have chosen another name) you have to prepare a Time-SIFT project directory (default name : ```time-sift-block```, *can be tuned via a parameter*) that contains :
-   * ```images/``` : a subdirectory with all images of all epochs. This directory name is fixed as it is the one expected by ODM
-   * ```images_epochs.txt``` : a file that has the same format as the file used for the split and merge ODM function. This file name *can be tuned via a parameter*.
-
-The ```images_epochs.txt``` file has two columns, the first column contains image names and the second contains the epoch name as follows
-```
-DSC_0368.JPG 0_before
-DSC_0369.JPG 0_before
-DSC_0370.JPG 0_before
-DSC_0389.JPG 1_after
-DSC_0390.JPG 1_after
-DSC_0391.JPG 1_after
-```
-
-Your directory, before running the script, should look like this :
-```
-$PWD/datasets/
-└── time-sift-block/
-    ├── images/
-    └── images_epochs.txt
-```
-
-At the end of the script you obtain a directory by epoch (at the same level as the Time-SIFT project directory). Each directory is processed with images of each epoch and all results are natively co-registered due to the initial sfm step done with all images.
-```
-$PWD/datasets/
-├── 0_before/
-├── 1_after/
-└── time-sift-block/
-```

contrib/time-sift/Timesift_odm.py

@@ -1,167 +0,0 @@
-# Script for Time-SIFT multi-temporal images alignment with ODM
-#
-# This is python script for ODM, based on the following publication :
-#
-# D. Feurer, F. Vinatier, Joining multi-epoch archival aerial images in a single SfM block allows 3-D change detection
-#    with almost exclusively image information, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146, 2018,
-#    Pages 495-506, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2018.10.016.
-
-import subprocess
-import json
-import os
-import shutil
-from pathlib import Path
-import sys
-import argparse
-import textwrap
-
-def main(argv):
-    # Parsing and checking args
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent('''\
-            Timesift_odm.py datasetdir [-t <timesift-dir>] [-i <imageepochs-file>] [<options passed to ODM>]
-
-                you can add options passed to ODM, for instance [--end-with odm_filterpoints] so that the final step is point clouds
-                these options are not checked for before the final runs of each epoch, so use it carefully
-            '''))
-    parser.add_argument('datasetdir', help='dataset directory')
-    parser.add_argument('-t', '--timesift-dir',
-                        help='Time-SIFT directory ; default value : "time-sift-block" # must be in the datasetdir')
-    parser.add_argument('-i', '--imageepochs-file',
-                        help='Text file describing epochs ; default value : "images_epochs.txt" # must be in the TIMESIFT_DIR ')
-    args, additional_options_to_rerun = parser.parse_known_args()
-    datasets_DIR = Path(args.datasetdir).absolute().as_posix()
-    if args.timesift_dir:
-        timesift_DIR = args.timesift_dir
-    else:
-        timesift_DIR = 'time-sift-block'
-    if args.imageepochs_file:
-        images_epochs_file = args.imageepochs_file
-    else:
-        images_epochs_file = 'images_epochs.txt'
-    if '-h' in sys.argv or '--help' in sys.argv:
-        parser.print_help()
-        sys.exit()
-    if additional_options_to_rerun:  # for instance, --end-with odm_filterpoints
-        print(f'[Time-SIFT]    Options passed to ODM for the final steps: {additional_options_to_rerun}')
-        print(f'[Time-SIFT]    \033[93mWARNING there is no check of these options done before the last ODM call\033[0m')
-
-    def check_path_args(var: Path):
-        if not var.exists():
-            print(
-                f'\033[91m[Time-SIFT]    ERROR: the {var.as_posix()} directory does not exist. Exiting program\033[0m')
-            exit()
-
-    check_path_args(Path(datasets_DIR))
-    check_path_args(Path(datasets_DIR, timesift_DIR))
-    check_path_args(Path(datasets_DIR, timesift_DIR, images_epochs_file))
-
-    def clean_reconstruction_dict(subdict, key, images):
-        """
-        Delete subdict elements where the key do not match any name in the images list.
-        To create the {epoch} block with only images of this epoch
-        """
-        # The list of valid images is prepared by removing any extension (to be robust to the .tif added by ODM)
-        valid_images = {os.path.basename(image).split(os.extsep)[0] for image in images}
-        for item_key in list(subdict[key]):
-            image_name = os.path.basename(item_key).split(os.extsep)[0]
-            if image_name not in valid_images:
-                del subdict[key][item_key]
-
-    ### Read images.txt file and create a dict of images/epochs
-    images_epochs_dict = {}
-    with open(Path(datasets_DIR, timesift_DIR, images_epochs_file), 'r') as f:
-        for line in f:
-            line = line.strip()
-            if not line:
-                continue  # Empty lines are skipped
-            image, epoch = line.split()
-            if epoch not in images_epochs_dict:
-                images_epochs_dict[epoch] = []
-            images_epochs_dict[epoch].append(image)
-
-    ### Check for existing epochs directories before computing anything (these directories must be deleted by hand)
-    path_exists_error = False
-    for epoch in images_epochs_dict:
-        if Path(datasets_DIR, epoch).exists():
-            if path_exists_error:
-                print(f"sudo rm -rf {Path(datasets_DIR, epoch).as_posix()}")
-            else:
-                print(f'\033[91m[Time-SIFT]    ERROR: {Path(datasets_DIR, epoch).as_posix()} already exists.\033[0m')
-                print(f"               Other epochs probably also exist.")
-                print(
-                    f"               The problem is \033[93mI CAN'T\033[0m delete it by myself, it requires root privileges.")
-                print(
-                    f"               The good news is \033[92mYOU CAN\033[0m do it with the following command (be careful).")
-                print(f'\033[91m               => Consider doing it (at your own risks). Exiting program\033[0m')
-                print(f"- Commands to copy/paste (I'm kind, I prepared all the necessary commands for you).")
-                print(f"sudo rm -rf {Path(datasets_DIR, epoch).as_posix()}")
-                path_exists_error = True
-    if path_exists_error:
-        exit()
-
-    ### LAUNCH global alignment (Time-SIFT multitemporal block)
-    try:
-        subprocess.run(['docker', 'run', '-i', '--rm', '-v', datasets_DIR + ':/datasets',
-                        'opendronemap/odm', '--project-path', '/datasets', timesift_DIR, '--end-with', 'opensfm'])
-    except:
-        print(f'\033[91m[Time-SIFT]    ERROR: {sys.exc_info()[0]}\033[0m')
-        exit()
-    print('\033[92m[Time-SIFT]    Sfm on multi-temporal block done\033[0m')
-
-    print('[Time-SIFT]    Going to dense matching on all epochs...')
-    ### Loop on epochs for the dense matching
-    for epoch in images_epochs_dict:
-        #### We first duplicate the time-sift multitemporal block to save sfm results
-        shutil.copytree(Path(datasets_DIR, timesift_DIR),
-                        Path(datasets_DIR, epoch))
-
-        #### Reads the datasets/{epoch}/opensfm/undistorted/reconstruction.json file that has to be modified
-        with open(Path(datasets_DIR, epoch, 'opensfm', 'undistorted', 'reconstruction.json'), mode="r",
-                  encoding="utf-8") as read_file:
-            reconstruction_dict = json.load(read_file)
-
-        #### Removes images in this json dict (we delete the shot and the rig_instances that do not correspond to this epoch)
-        images = images_epochs_dict[epoch]
-        clean_reconstruction_dict(reconstruction_dict[0], 'shots', images)
-        clean_reconstruction_dict(reconstruction_dict[0], 'rig_instances', images)
-
-        #### Makes a backup of the reconstruction.json file and writes the modified json
-        shutil.copy(Path(datasets_DIR, epoch, 'opensfm', 'undistorted', 'reconstruction.json'),
-                    Path(datasets_DIR, epoch, 'opensfm', 'undistorted', 'reconstruction.json.bak'))
-        with open(Path(datasets_DIR, epoch, 'opensfm', 'undistorted', 'reconstruction.json'), mode="w",
-                  encoding="utf-8") as write_file:
-            json.dump(reconstruction_dict, write_file)
-
-        #### Launches dense matching from the good previous step, with possible options (e.g. => to stop at the point clouds)
-        command_rerun = ['docker', 'run', '-i', '--rm', '-v', datasets_DIR + ':/datasets',
-                             'opendronemap/odm',
-                             '--project-path', '/datasets', epoch,
-                             '--rerun-from', 'openmvs']
-        if additional_options_to_rerun:
-            print(f'[Time-SIFT]        Epoch {epoch}: Rerun with additionnal options: {additional_options_to_rerun}')
-            command_rerun.extend(additional_options_to_rerun)
-        else:
-            print(f'[Time-SIFT]        Epoch {epoch}: Default full rerun')
-        result = subprocess.run(command_rerun)
-        if result.returncode != 0:
-            print(f'\033[91m[Time-SIFT]    ERROR in processing epoch {epoch}\033[0m')
-            print(f'{result=}')
-            exit(result.returncode)
-        print(f'\033[92m[Time-SIFT]        Epoch {epoch} finished\033[0m')
-
-    print('§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§')
-    print('§§§      §§  §§§  §§§§§  §§§      §§§§§§§§§§      §§  §§      §§      §§§')
-    print('§§§§§  §§§§  §§§   §§§   §§§  §§§§§§§§§§§§§   §§§§§§  §§  §§§§§§§§  §§§§§')
-    print('§§§§§  §§§§  §§§    §    §§§    §§§§    §§§§§   §§§§  §§    §§§§§§  §§§§§')
-    print('§§§§§  §§§§  §§§  §§§§§  §§§  §§§§§§§§§§§§§§§§§   §§  §§  §§§§§§§§  §§§§§')
-    print('§§§§§  §§§§  §§§  §§§§§  §§§      §§§§§§§§§      §§§  §§  §§§§§§§§  §§§§§')
-    print('§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§')
-    print('  \033[92mTime-SIFT with ODM finished, congrats !\033[0m Want to cite the method ?')
-    print('=> D. Feurer, F. Vinatier, Joining multi-epoch archival aerial images in ')
-    print('   a single SfM block allows 3-D change detection with almost exclusively')
-    print('   image information, ISPRS Journal of Photogrammetry and Remote Sensing,')
-    print('   2018, https://doi.org/10.1016/j.isprsjprs.2018.10.016                ')
-
-if __name__ == "__main__":
-    main(sys.argv[1:])

gpu.Dockerfile

@@ -1,8 +1,8 @@
-FROM nvidia/cuda:11.8.0-devel-ubuntu20.04 AS builder
+FROM nvidia/cuda:11.2.2-devel-ubuntu20.04 AS builder
 
 # Env variables
 ENV DEBIAN_FRONTEND=noninteractive \
-    PYTHONPATH="/code/SuperBuild/install/lib/python3.9/dist-packages:/code/SuperBuild/install/lib/python3.8/dist-packages:/code/SuperBuild/install/bin/opensfm" \
+    PYTHONPATH="$PYTHONPATH:/code/SuperBuild/install/lib/python3.9/dist-packages:/code/SuperBuild/install/lib/python3.8/dist-packages:/code/SuperBuild/install/bin/opensfm" \
     LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/code/SuperBuild/install/lib"
 
 # Prepare directories
@@ -21,11 +21,12 @@ RUN bash configure.sh clean
 
 ### Use a second image for the final asset to reduce the number and
 # size of the layers.
-FROM nvidia/cuda:11.8.0-runtime-ubuntu20.04
+FROM nvidia/cuda:11.2.2-runtime-ubuntu20.04
+#FROM nvidia/cuda:11.2.0-devel-ubuntu20.04
 
 # Env variables
 ENV DEBIAN_FRONTEND=noninteractive \
-    PYTHONPATH="/code/SuperBuild/install/lib/python3.9/dist-packages:/code/SuperBuild/install/lib/python3.8/dist-packages:/code/SuperBuild/install/bin/opensfm" \
+    PYTHONPATH="$PYTHONPATH:/code/SuperBuild/install/lib/python3.9/dist-packages:/code/SuperBuild/install/lib/python3.8/dist-packages:/code/SuperBuild/install/bin/opensfm" \
     LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/code/SuperBuild/install/lib" \
     PDAL_DRIVER_PATH="/code/SuperBuild/install/bin"
 
@@ -37,7 +38,6 @@ COPY --from=builder /code /code
 # Copy the Python libraries installed via pip from the builder
 COPY --from=builder /usr/local /usr/local
 #COPY --from=builder /usr/lib/x86_64-linux-gnu/libavcodec.so.58 /usr/lib/x86_64-linux-gnu/libavcodec.so.58
-
 RUN apt-get update -y \
  && apt-get install -y ffmpeg libtbb2
 # Install shared libraries that we depend on via APT, but *not*

opendm/ai.py

@@ -4,25 +4,6 @@ from opendm import log
 import zipfile
 import time
 import sys
-import rawpy
-import cv2
-
-def read_image(img_path):
-    if img_path[-4:].lower() in [".dng", ".raw", ".nef"]:
-        try:
-            with rawpy.imread(img_path) as r:
-                img = r.postprocess(output_bps=8, use_camera_wb=True, use_auto_wb=False)
-        except:
-            return None
-    else:
-        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
-        if img is None:
-            return None
-
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    
-    return img
-
 
 def get_model(namespace, url, version, name = "model.onnx"):
     version = version.replace(".", "_")

opendm/bgfilter.py

@@ -5,7 +5,6 @@ import cv2
 import os
 import onnxruntime as ort
 from opendm import log
-from opendm.ai import read_image
 from threading import Lock
 
 mutex = Lock()
@@ -74,7 +73,11 @@ class BgFilter():
         return output
 
     def run_img(self, img_path, dest):
-        img = read_image(img_path)
+        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+        if img is None:
+            return None
+
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         mask  = self.get_mask(img)
         
         img_name = os.path.basename(img_path)

opendm/config.py

@@ -41,7 +41,6 @@ rerun_stages = {
     'geo': 'dataset',
     'gltf': 'mvs_texturing',
     'gps_accuracy': 'dataset',
-    'gps_z_offset': 'dataset',
     'help': None,
     'ignore_gsd': 'opensfm',
     'matcher_neighbors': 'opensfm',
@@ -128,7 +127,7 @@ def url_string(string):
         r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip
         r'(?::\d+)?' # optional port
         r'(?:/?|[/?]\S+)$', re.IGNORECASE)
-
+        
     if re.match(regex, string) is None:
         raise argparse.ArgumentTypeError("%s is not a valid URL. The URL must be in the format: http(s)://host[:port]/[?token=]" % string)
     return string
@@ -165,7 +164,7 @@ def config(argv=None, parser=None):
         parser = SettingsParser(description='ODM is a command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images.',
                             usage='%s [options] <dataset name>' % usage_bin,
                             yaml_file=open(context.settings_path))
-
+    
     parser.add_argument('--project-path',
                         metavar='<path>',
                         action=StoreValue,
@@ -214,7 +213,7 @@ def config(argv=None, parser=None):
                               'More features can be useful for finding more matches between images, '
                               'potentially allowing the reconstruction of areas with little overlap or insufficient features. '
                               'More features also slow down processing. Default: %(default)s'))
-
+    
     parser.add_argument('--feature-type',
                         metavar='<string>',
                         action=StoreValue,
@@ -223,7 +222,7 @@ def config(argv=None, parser=None):
                         help=('Choose the algorithm for extracting keypoints and computing descriptors. '
                             'Can be one of: %(choices)s. Default: '
                             '%(default)s'))
-
+    
     parser.add_argument('--feature-quality',
                         metavar='<string>',
                         action=StoreValue,
@@ -232,7 +231,7 @@ def config(argv=None, parser=None):
                         help=('Set feature extraction quality. Higher quality generates better features, but requires more memory and takes longer. '
                             'Can be one of: %(choices)s. Default: '
                             '%(default)s'))
-
+    
     parser.add_argument('--matcher-type',
                         metavar='<string>',
                         action=StoreValue,
@@ -248,7 +247,7 @@ def config(argv=None, parser=None):
                         default=0,
                         type=int,
                         help='Perform image matching with the nearest images based on GPS exif data. Set to 0 to match by triangulation. Default: %(default)s')
-
+    
     parser.add_argument('--matcher-order',
                         metavar='<positive integer>',
                         action=StoreValue,
@@ -332,7 +331,7 @@ def config(argv=None, parser=None):
                 nargs=0,
                 default=False,
                 help='Automatically compute image masks using AI to remove the sky. Experimental. Default: %(default)s')
-
+    
     parser.add_argument('--bg-removal',
                 action=StoreTrue,
                 nargs=0,
@@ -350,19 +349,19 @@ def config(argv=None, parser=None):
                     nargs=0,
                     default=False,
                     help='Skip generation of a full 3D model. This can save time if you only need 2D results such as orthophotos and DEMs. Default: %(default)s')
-
+    
     parser.add_argument('--skip-report',
                     action=StoreTrue,
                     nargs=0,
                     default=False,
                     help='Skip generation of PDF report. This can save time if you don\'t need a report. Default: %(default)s')
-
+    
     parser.add_argument('--skip-orthophoto',
                     action=StoreTrue,
                     nargs=0,
                     default=False,
                     help='Skip generation of the orthophoto. This can save time if you only need 3D results or DEMs. Default: %(default)s')
-
+    
     parser.add_argument('--ignore-gsd',
                         action=StoreTrue,
                         nargs=0,
@@ -372,13 +371,13 @@ def config(argv=None, parser=None):
                         'Ordinarily, GSD estimates are used to cap the maximum resolution of image outputs and resizes images when necessary, resulting in faster processing and lower memory usage. '
                         'Since GSD is an estimate, sometimes ignoring it can result in slightly better image output quality. '
                         'Never set --ignore-gsd to true unless you are positive you need it, and even then: do not use it. Default: %(default)s')
-
+    
     parser.add_argument('--no-gpu',
                     action=StoreTrue,
                     nargs=0,
                     default=False,
                     help='Do not use GPU acceleration, even if it\'s available. Default: %(default)s')
-
+    
     parser.add_argument('--mesh-size',
                         metavar='<positive integer>',
                         action=StoreValue,
@@ -463,7 +462,7 @@ def config(argv=None, parser=None):
                         nargs=0,
                         default=False,
                         help='Export the georeferenced point cloud in CSV format. Default: %(default)s')
-
+    
     parser.add_argument('--pc-las',
                 action=StoreTrue,
                 nargs=0,
@@ -489,7 +488,7 @@ def config(argv=None, parser=None):
                         default=5,
                         help='Filters the point cloud by removing points that deviate more than N standard deviations from the local mean. Set to 0 to disable filtering. '
                              'Default: %(default)s')
-
+    
     parser.add_argument('--pc-sample',
                         metavar='<positive float>',
                         action=StoreValue,
@@ -520,7 +519,7 @@ def config(argv=None, parser=None):
         default=0.15,
         help='Simple Morphological Filter slope parameter (rise over run). '
                 'Default: %(default)s')
-
+    
     parser.add_argument('--smrf-threshold',
         metavar='<positive float>',
         action=StoreValue,
@@ -528,7 +527,7 @@ def config(argv=None, parser=None):
         default=0.5,
         help='Simple Morphological Filter elevation threshold parameter (meters). '
                 'Default: %(default)s')
-
+    
     parser.add_argument('--smrf-window',
         metavar='<positive float>',
         action=StoreValue,
@@ -587,7 +586,7 @@ def config(argv=None, parser=None):
                               'EPSG:<code> or <+proj definition>\n'
                               'image_name geo_x geo_y geo_z [yaw (degrees)] [pitch (degrees)] [roll (degrees)] [horz accuracy (meters)] [vert accuracy (meters)]\n'
                               'Default: %(default)s'))
-
+    
     parser.add_argument('--align',
                     metavar='<path string>',
                     action=StoreValue,
@@ -643,7 +642,7 @@ def config(argv=None, parser=None):
                         type=int,
                         help='Decimate the points before generating the DEM. 1 is no decimation (full quality). '
                              '100 decimates ~99%% of the points. Useful for speeding up generation of DEM results in very large datasets. Default: %(default)s')
-
+    
     parser.add_argument('--dem-euclidean-map',
             action=StoreTrue,
             nargs=0,
@@ -676,13 +675,13 @@ def config(argv=None, parser=None):
                         default=False,
                         help='Set this parameter if you want to generate a PNG rendering of the orthophoto. '
                              'Default: %(default)s')
-
+    
     parser.add_argument('--orthophoto-kmz',
                         action=StoreTrue,
                         nargs=0,
                         default=False,
                         help='Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. '
-                             'Default: %(default)s')
+                             'Default: %(default)s')    
 
     parser.add_argument('--orthophoto-compression',
                         metavar='<string>',
@@ -691,7 +690,7 @@ def config(argv=None, parser=None):
                         choices=['JPEG', 'LZW', 'PACKBITS', 'DEFLATE', 'LZMA', 'NONE'],
                         default='DEFLATE',
                         help='Set the compression to use for orthophotos. Can be one of: %(choices)s. Default: %(default)s')
-
+    
     parser.add_argument('--orthophoto-cutline',
             action=StoreTrue,
             nargs=0,
@@ -730,7 +729,7 @@ def config(argv=None, parser=None):
                         action=StoreValue,
                         metavar='<positive integer>',
                         default=0,
-                    help='Override the rolling shutter readout time for your camera sensor (in milliseconds), instead of using the rolling shutter readout database. '
+                    help='Override the rolling shutter readout time for your camera sensor (in milliseconds), instead of using the rolling shutter readout database. ' 
                     'Note that not all cameras are present in the database. Set to 0 to use the database value. '
                     'Default: %(default)s')
 
@@ -769,7 +768,7 @@ def config(argv=None, parser=None):
                         default=4000,
                         metavar='<positive integer>',
                         help='The maximum output resolution of extracted video frames in pixels. Default: %(default)s')
-
+    
     parser.add_argument('--split',
                         type=int,
                         action=StoreValue,
@@ -786,12 +785,11 @@ def config(argv=None, parser=None):
                         action=StoreValue,
                         metavar='<positive integer>',
                         default=150,
-                        help='Radius of the overlap between submodels in meters. '
+                        help='Radius of the overlap between submodels. '
                         'After grouping images into clusters, images '
                         'that are closer than this radius to a cluster '
                         'are added to the cluster. This is done to ensure '
-                        'that neighboring submodels overlap. All images' \
+                        'that neighboring submodels overlap. Default: %(default)s')
-                        'need GPS information. Default: %(default)s')
 
     parser.add_argument('--split-image-groups',
                         metavar='<path string>',
@@ -835,7 +833,7 @@ def config(argv=None, parser=None):
                     help=('Use images\' GPS exif data for reconstruction, even if there are GCPs present.'
                           'This flag is useful if you have high precision GPS measurements. '
                           'If there are no GCPs, this flag does nothing. Default: %(default)s'))
-
+    
     parser.add_argument('--gps-accuracy',
                         type=float,
                         action=StoreValue,
@@ -846,17 +844,7 @@ def config(argv=None, parser=None):
                         'with high precision GPS information (RTK), this value will be automatically '
                         'set accordingly. You can use this option to manually set it in case the reconstruction '
                         'fails. Lowering this option can sometimes help control bowling-effects over large areas. Default: %(default)s')
-    
+
-    parser.add_argument('--gps-z-offset',
-                        type=float,
-                        action=StoreValue,
-                        metavar='<float>',
-                        default=0,
-                        help='Set a GPS offset in meters for the vertical axis (Z) '
-                        'by adding it to the altitude value of the GPS EXIF data. This does not change the value of any GCPs. '
-                        'This can be useful for example when adjusting from ellipsoidal to orthometric height. '
-                        'Default: %(default)s')
-    
     parser.add_argument('--optimize-disk-space',
                 action=StoreTrue,
                 nargs=0,
@@ -880,7 +868,7 @@ def config(argv=None, parser=None):
                         default="auto",
                         type=str,
                         help=('When processing multispectral datasets, you can specify the name of the primary band that will be used for reconstruction. '
-                              'It\'s recommended to choose a band which has sharp details and is in focus. '
+                              'It\'s recommended to choose a band which has sharp details and is in focus. ' 
                               'Default: %(default)s'))
 
     parser.add_argument('--skip-band-alignment',
@@ -935,5 +923,5 @@ def config(argv=None, parser=None):
         except exceptions.NodeConnectionError as e:
             log.ODM_ERROR("Cluster node seems to be offline: %s"  % str(e))
             sys.exit(1)
-
+    
     return args

opendm/context.py

@@ -40,7 +40,7 @@ odm_orthophoto_path = os.path.join(superbuild_bin_path, "odm_orthophoto")
 settings_path = os.path.join(root_path, 'settings.yaml')
 
 # Define supported image extensions
-supported_extensions = {'.jpg','.jpeg','.png', '.tif', '.tiff', '.bmp', '.raw', '.dng', '.nef'}
+supported_extensions = {'.jpg','.jpeg','.png', '.tif', '.tiff', '.bmp'}
 supported_video_extensions = {'.mp4', '.mov', '.lrv', '.ts'}
 
 # Define the number of cores

opendm/dem/commands.py

@@ -1,5 +1,4 @@
 import os
-import subprocess
 import sys
 import rasterio
 import numpy
@@ -21,8 +20,6 @@ from opendm import log
 from .ground_rectification.rectify import run_rectification
 from . import pdal
 
-gdal_proximity = None
-
 try:
     # GDAL >= 3.3
     from osgeo_utils.gdal_proximity import main as gdal_proximity
@@ -30,13 +27,8 @@ except ModuleNotFoundError:
     # GDAL <= 3.2
     try:
         from osgeo.utils.gdal_proximity import main as gdal_proximity
-    except ModuleNotFoundError:
+    except:
-        # GDAL <= 3.0
+        pass
-        gdal_proximity_script = shutil.which("gdal_proximity.py")
-        if gdal_proximity_script is not None:
-            def gdal_proximity(args):
-                subprocess.run([gdal_proximity_script] + args[1:], check=True)
-
 
 def classify(lasFile, scalar, slope, threshold, window):
     start = datetime.now()
@@ -271,4 +263,4 @@ def get_dem_radius_steps(stats_file, steps, resolution, multiplier = 1.0):
     for _ in range(steps - 1):
         radius_steps.append(radius_steps[-1] * math.sqrt(2))
     
-    return radius_steps
+    return radius_steps

opendm/get_image_size.py

@@ -1,6 +1,6 @@
 from PIL import Image
 import cv2
-import rawpy
+
 from opendm import log
 
 Image.MAX_IMAGE_PIXELS = None
@@ -9,18 +9,12 @@ def get_image_size(file_path, fallback_on_error=True):
     """
     Return (width, height) for a given img file
     """
-    
     try:
-        if file_path[-4:].lower() in [".dng", ".raw", ".nef"]:
+        with Image.open(file_path) as img:
-            with rawpy.imread(file_path) as img:
+            width, height = img.size
-                s = img.sizes
-                width, height = s.raw_width, s.raw_height
-        else:
-            with Image.open(file_path) as img:
-                width, height = img.size
     except Exception as e:
         if fallback_on_error:
-            log.ODM_WARNING("Cannot read %s with image library, fallback to cv2: %s" % (file_path, str(e)))
+            log.ODM_WARNING("Cannot read %s with PIL, fallback to cv2: %s" % (file_path, str(e)))
             img = cv2.imread(file_path)
             width = img.shape[1]
             height = img.shape[0]

opendm/gltf.py

@@ -6,7 +6,6 @@ import numpy as np
 import pygltflib
 from opendm import system
 from opendm import io
-from opendm import log
 
 warnings.filterwarnings("ignore", category=rasterio.errors.NotGeoreferencedWarning)
 

opendm/gpu.py

@@ -19,17 +19,14 @@ def has_popsift_and_can_handle_texsize(width, height):
             log.ODM_INFO("CUDA compute platform is not supported (detected: %s.%s but we need at least 3.5)" % (compute_major, compute_minor))
             return False
     except Exception as e:
-        log.ODM_WARNING(str(e))
+        log.ODM_INFO("Using CPU for feature extraction: %s" % str(e))
         return False
 
     try:
         from opensfm import pypopsift
-        if pypopsift.fits_texture(int(width * 1.02), int(height * 1.02)):
+        return pypopsift.fits_texture(int(width * 1.02), int(height * 1.02))
-            log.ODM_INFO("popsift can handle texture size %dx%d" % (width, height))
+    except (ModuleNotFoundError, ImportError):
-            return True
+        return False
-        else:
-            log.ODM_INFO("popsift cannot handle texture size %dx%d" % (width, height))
-            return False
     except Exception as e:
         log.ODM_WARNING(str(e))
         return False
@@ -84,12 +81,11 @@ def has_gpu(args):
             log.ODM_INFO("CUDA drivers detected")
             return True
         else:
-            log.ODM_INFO("No CUDA drivers detected")
+            log.ODM_INFO("No CUDA drivers detected, using CPU")
             return False
     else:
         if shutil.which('nvidia-smi') is not None:
             log.ODM_INFO("nvidia-smi detected")
             return True
         else:
-            log.ODM_INFO("No nvidia-smi detected")
             return False

opendm/gsd.py

@@ -5,7 +5,6 @@ import math
 from repoze.lru import lru_cache
 from opendm import log
 from opendm.shots import get_origin
-from scipy import spatial
 
 def rounded_gsd(reconstruction_json, default_value=None, ndigits=0, ignore_gsd=False):
     """
@@ -112,12 +111,16 @@ def opensfm_reconstruction_average_gsd(reconstruction_json, use_all_shots=False)
     with open(reconstruction_json) as f:
         data = json.load(f)
 
+    # Calculate median height from sparse reconstruction
     reconstruction = data[0]
-    points = np.array([reconstruction['points'][pointId]['coordinates'] for pointId in reconstruction['points']])
+    point_heights = []
-    tdpoints = points.copy()
+
-    tdpoints[:,2] = 0
+    for pointId in reconstruction['points']:
-    tree = spatial.cKDTree(tdpoints)
+        point = reconstruction['points'][pointId]
-    
+        point_heights.append(point['coordinates'][2])
+
+    ground_height = np.median(point_heights)
+
     gsds = []
     for shotImage in reconstruction['shots']:
         shot = reconstruction['shots'][shotImage]
@@ -129,17 +132,10 @@ def opensfm_reconstruction_average_gsd(reconstruction_json, use_all_shots=False)
             if not focal_ratio:
                 log.ODM_WARNING("Cannot parse focal values from %s. This is likely an unsupported camera model." % reconstruction_json)
                 return None
-            
+                
-            shot_origin[2] = 0
+            gsds.append(calculate_gsd_from_focal_ratio(focal_ratio, 
-            distances, neighbors = tree.query(
+                                                        shot_height - ground_height, 
-                shot_origin, k=9
+                                                        camera['width']))
-            )
-
-            if len(distances) > 0:
-                ground_height = np.median(points[neighbors][:,2])
-                gsds.append(calculate_gsd_from_focal_ratio(focal_ratio, 
-                                                            shot_height - ground_height, 
-                                                            camera['width']))
     
     if len(gsds) > 0:
         mean = np.mean(gsds)
@@ -149,6 +145,7 @@ def opensfm_reconstruction_average_gsd(reconstruction_json, use_all_shots=False)
     
     return None
 
+
 def calculate_gsd(sensor_width, flight_height, focal_length, image_width):
     """
     :param sensor_width in millimeters

opendm/log.py

@@ -6,7 +6,6 @@ import datetime
 import dateutil.parser
 import shutil
 import multiprocessing
-from repoze.lru import lru_cache
 
 from opendm.arghelpers import double_quote, args_to_dict
 from vmem import virtual_memory
@@ -31,7 +30,6 @@ else:
 
 lock = threading.Lock()
 
-@lru_cache(maxsize=None)
 def odm_version():
     with open(os.path.join(os.path.dirname(__file__), "..", "VERSION")) as f:
         return f.read().split("\n")[0].strip()

opendm/multispectral.py

@@ -273,10 +273,7 @@ def compute_band_maps(multi_camera, primary_band):
                 # Quick check
                 if filename_without_band == p.filename:
                     raise Exception("Cannot match bands by filename on %s, make sure to name your files [filename]_band[.ext] uniformly." % p.filename)
-                
+
-                if not filename_without_band in filename_map:
-                    raise Exception("Cannot match bands by filename on %s, make sure to name your files [filename]_band[.ext] uniformly, check that your images have the appropriate CaptureUUID XMP tag and that no images are missing." % p.filename)
-                    
                 s2p[p.filename] = filename_map[filename_without_band]
 
                 if band['name'] != band_name:

opendm/orthophoto.py

@@ -13,9 +13,7 @@ from rasterio.mask import mask
 from opendm import io
 from opendm.tiles.tiler import generate_orthophoto_tiles
 from opendm.cogeo import convert_to_cogeo
-from opendm.utils import add_raster_meta_tags
 from osgeo import gdal
-from osgeo import ogr
 
 
 def get_orthophoto_vars(args):
@@ -45,50 +43,33 @@ def generate_png(orthophoto_file, output_file=None, outsize=None):
         output_file = base + '.png'
     
     # See if we need to select top three bands
-    params = []
+    bandparam = ""
 
-    try:
+    gtif = gdal.Open(orthophoto_file)
-        gtif = gdal.Open(orthophoto_file)
+    if gtif.RasterCount > 4:
         bands = []
         for idx in range(1, gtif.RasterCount+1):
             bands.append(gtif.GetRasterBand(idx).GetColorInterpretation())
         bands = dict(zip(bands, range(1, len(bands)+1)))
 
-        if gtif.RasterCount >= 3:
+        try:
             red = bands.get(gdal.GCI_RedBand)
             green = bands.get(gdal.GCI_GreenBand)
             blue = bands.get(gdal.GCI_BlueBand)
             if red is None or green is None or blue is None:
-                params.append("-b 1 -b 2 -b 3")
+                raise Exception("Cannot find bands")
-            else:
-                params.append("-b %s -b %s -b %s" % (red, green, blue))                
-        elif gtif.RasterCount <= 2:
-            params.append("-b 1")
-        
-        alpha = bands.get(gdal.GCI_AlphaBand)
-        if alpha is not None:
-            params.append("-b %s" % alpha)
-        else:
-            params.append("-a_nodata 0")
 
-        dtype = gtif.GetRasterBand(1).DataType
+            bandparam = "-b %s -b %s -b %s -a_nodata 0" % (red, green, blue)
-        if dtype != gdal.GDT_Byte:
+        except:
-            params.append("-ot Byte")
+            bandparam = "-b 1 -b 2 -b 3 -a_nodata 0"
-            if gtif.RasterCount >= 3:
+    gtif = None
-                params.append("-scale_1 -scale_2 -scale_3")
-            elif gtif.RasterCount <= 2:
-                params.append("-scale_1")
-        
-        gtif = None
-    except Exception as e:
-        log.ODM_WARNING("Cannot read orthophoto information for PNG generation: %s" % str(e))
 
+    osparam = ""
     if outsize is not None:
-        params.append("-outsize %s 0" % outsize)
+        osparam = "-outsize %s 0" % outsize
 
-    system.run('gdal_translate -of png "%s" "%s" %s '
+    system.run('gdal_translate -of png "%s" "%s" %s %s '
-               '-co WORLDFILE=YES '
+               '--config GDAL_CACHEMAX %s%% ' % (orthophoto_file, output_file, osparam, bandparam, get_max_memory()))
-               '--config GDAL_CACHEMAX %s%% ' % (orthophoto_file, output_file, " ".join(params), get_max_memory()))
 
 def generate_kmz(orthophoto_file, output_file=None, outsize=None):
     if output_file is None:
@@ -103,71 +84,8 @@ def generate_kmz(orthophoto_file, output_file=None, outsize=None):
 
     system.run('gdal_translate -of KMLSUPEROVERLAY -co FORMAT=PNG "%s" "%s" %s '
                '--config GDAL_CACHEMAX %s%% ' % (orthophoto_file, output_file, bandparam, get_max_memory()))    
-
+    
-def generate_extent_polygon(orthophoto_file):
+def post_orthophoto_steps(args, bounds_file_path, orthophoto_file, orthophoto_tiles_dir, resolution):
-    """Function to return the orthophoto extent as a polygon into a gpkg file
-
-    Args:
-        orthophoto_file (str): the path to orthophoto file
-    """
-    base, ext = os.path.splitext(orthophoto_file)
-    output_file = base + '_extent.dxf'
-
-    try:
-        gtif = gdal.Open(orthophoto_file)
-        srs =  gtif.GetSpatialRef()
-        geoTransform = gtif.GetGeoTransform()
-
-        # calculate the coordinates
-        minx = geoTransform[0]
-        maxy = geoTransform[3]
-        maxx = minx + geoTransform[1] * gtif.RasterXSize
-        miny = maxy + geoTransform[5] * gtif.RasterYSize
-        
-        # create polygon in wkt format
-        poly_wkt = "POLYGON ((%s %s, %s %s, %s %s, %s %s, %s %s))" % (minx, miny, minx, maxy, maxx, maxy, maxx, miny, minx, miny)
-        
-        # create vector file
-        # just the DXF to support AutoCAD users
-        # to load the geotiff raster correctly.
-        driver = ogr.GetDriverByName("DXF")
-        ds = driver.CreateDataSource(output_file)
-        layer = ds.CreateLayer("extent", srs, ogr.wkbPolygon)
-
-        # create the feature and set values
-        featureDefn = layer.GetLayerDefn()
-        feature = ogr.Feature(featureDefn)
-        feature.SetGeometry(ogr.CreateGeometryFromWkt(poly_wkt))
-
-        # add feature to layer
-        layer.CreateFeature(feature)
-
-        # save and close everything
-        feature = None
-        ds = None
-        gtif = None
-        log.ODM_INFO("Wrote %s" % output_file)
-    except Exception as e:
-        log.ODM_WARNING("Cannot create extent layer for %s: %s" % (orthophoto_file, str(e)))
-
-
-def generate_tfw(orthophoto_file):
-    base, ext = os.path.splitext(orthophoto_file)
-    tfw_file = base + '.tfw'
-
-    try:
-        with rasterio.open(orthophoto_file) as ds:
-            t = ds.transform
-            with open(tfw_file, 'w') as f:
-                # rasterio affine values taken by
-                # https://mharty3.github.io/til/GIS/raster-affine-transforms/
-                f.write("\n".join([str(v) for v in [t.a, t.d, t.b, t.e, t.c, t.f]]) + "\n")
-            log.ODM_INFO("Wrote %s" % tfw_file)
-    except Exception as e:
-        log.ODM_WARNING("Cannot create .tfw for %s: %s" % (orthophoto_file, str(e)))
-
-
-def post_orthophoto_steps(args, bounds_file_path, orthophoto_file, orthophoto_tiles_dir, resolution, reconstruction, tree, embed_gcp_meta=False):
     if args.crop > 0 or args.boundary:
         Cropper.crop(bounds_file_path, orthophoto_file, get_orthophoto_vars(args), keep_original=not args.optimize_disk_space, warp_options=['-dstalpha'])
 
@@ -180,17 +98,12 @@ def post_orthophoto_steps(args, bounds_file_path, orthophoto_file, orthophoto_ti
     if args.orthophoto_kmz:
         generate_kmz(orthophoto_file)
 
-    add_raster_meta_tags(orthophoto_file, reconstruction, tree, embed_gcp_meta=embed_gcp_meta)
-
     if args.tiles:
         generate_orthophoto_tiles(orthophoto_file, orthophoto_tiles_dir, args.max_concurrency, resolution)
 
     if args.cog:
         convert_to_cogeo(orthophoto_file, max_workers=args.max_concurrency, compression=args.orthophoto_compression)
 
-    generate_extent_polygon(orthophoto_file)
-    generate_tfw(orthophoto_file)
-
 def compute_mask_raster(input_raster, vector_mask, output_raster, blend_distance=20, only_max_coords_feature=False):
     if not os.path.exists(input_raster):
         log.ODM_WARNING("Cannot mask raster, %s does not exist" % input_raster)

opendm/osfm.py

@@ -296,23 +296,19 @@ class OSFMContext:
             config.append("matcher_type: %s" % osfm_matchers[matcher_type])
 
             # GPU acceleration?
-            if feature_type == "SIFT":
+            if has_gpu(args) and max_dims is not None:
-                log.ODM_INFO("Checking for GPU as using SIFT for extracting features")
+                w, h = max_dims
-                if has_gpu(args) and max_dims is not None:
+                if w > h:
-                    w, h = max_dims
+                    h = int((h / w) * feature_process_size)
-                    if w > h:
+                    w = int(feature_process_size)
-                        h = int((h / w) * feature_process_size)
+                else:
-                        w = int(feature_process_size)
+                    w = int((w / h) * feature_process_size)
-                    else:
+                    h = int(feature_process_size)
-                        w = int((w / h) * feature_process_size)
+
-                        h = int(feature_process_size)
+                if has_popsift_and_can_handle_texsize(w, h) and feature_type == "SIFT":
-                    
+                    log.ODM_INFO("Using GPU for extracting SIFT features")
-                    if has_popsift_and_can_handle_texsize(w, h):
+                    feature_type = "SIFT_GPU"
-                        log.ODM_INFO("Using GPU for extracting SIFT features")
+                    self.gpu_sift_feature_extraction = True
-                        feature_type = "SIFT_GPU"
-                        self.gpu_sift_feature_extraction = True
-                    else:
-                        log.ODM_INFO("Using CPU for extracting SIFT features as texture size is too large or GPU SIFT is not available")
             
             config.append("feature_type: %s" % feature_type)
 

opendm/photo.py

@@ -21,17 +21,6 @@ from opensfm.geo import ecef_from_lla
 
 projections = ['perspective', 'fisheye', 'fisheye_opencv', 'brown', 'dual', 'equirectangular', 'spherical']
 
-def find_mean_utc_time(photos):
-    utc_times = []
-    for p in photos: 
-        if p.utc_time is not None:
-            utc_times.append(p.utc_time / 1000.0)
-    if len(utc_times) == 0:
-        return None
-    
-    return np.mean(utc_times)
-
-
 def find_largest_photo_dims(photos):
     max_mp = 0
     max_dims = None
@@ -785,12 +774,6 @@ class ODM_Photo:
 
     def override_gps_dop(self, dop):
         self.gps_xy_stddev = self.gps_z_stddev = dop
-    
-    def adjust_z_offset(self, z_offset):
-        if self.altitude is not None:
-            self.altitude += z_offset
-        else:
-            self.altitude = z_offset
 
     def override_camera_projection(self, camera_projection):
         if camera_projection in projections:

opendm/point_cloud.py

@@ -327,7 +327,7 @@ def post_point_cloud_steps(args, tree, rerun=False):
                     tree.odm_georeferencing_model_laz,
                     tree.odm_georeferencing_model_las))
         else:
-            log.ODM_WARNING("Found existing LAS file %s" % tree.odm_georeferencing_model_las)
+            log.ODM_WARNING("Found existing LAS file %s" % tree.odm_georeferencing_xyz_file)
 
     # EPT point cloud output
     if args.pc_ept:

opendm/rollingshutter.py

@@ -22,13 +22,7 @@ RS_DATABASE = {
     'hasselblad l2d-20c': 16.6, # DJI Mavic 3 (not enterprise version)
 
     'dji fc3582': lambda p: 26 if p.get_capture_megapixels() < 48 else 60, # DJI Mini 3 pro (at 48MP readout is 60ms, at 12MP it's 26ms) 
-    'dji fc8482': lambda p: (
+
-        16 if p.get_capture_megapixels() < 12 else  # 12MP 16:9 mode (actual 9.1MP)
-        21 if p.get_capture_megapixels() < 20 else  # 12MP 4:3 mode (actual 12.2MP)
-        43 if p.get_capture_megapixels() < 45 else  # 48MP 16:9 mode (actual 36.6MP)
-        58                                          # 48MP 4:3 mode (actual 48.8MP)
-    ), # DJI Mini 4 Pro (readout varies by resolution and aspect ratio, image heights all different)
-    
     'dji fc350': 30, # Inspire 1
     
     'dji mavic2-enterprise-advanced': 31, # DJI Mavic 2 Enterprise Advanced

opendm/shots.py

@@ -148,16 +148,3 @@ def merge_geojson_shots(geojson_shots_files, output_geojson_file):
     
     with open(output_geojson_file, "w") as f:
         f.write(json.dumps(result))
-
-def merge_cameras(cameras_json_files, output_cameras_file):
-    result = {}
-    for cameras_file in cameras_json_files:
-        with open(cameras_file, "r") as f:
-            cameras = json.loads(f.read())
-        
-        for cam_id in cameras:
-            if not cam_id in result:
-                result[cam_id] = cameras[cam_id]
-    
-    with open(output_cameras_file, "w") as f:
-        f.write(json.dumps(result))

opendm/skyremoval/skyfilter.py

@@ -6,7 +6,6 @@ import os
 import onnxruntime as ort
 from .guidedfilter import guided_filter
 from opendm import log
-from opendm.ai import read_image
 from threading import Lock
 
 mutex = Lock()
@@ -73,7 +72,11 @@ class SkyFilter():
 
     def run_img(self, img_path, dest):
 
-        img = read_image(img_path)
+        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+        if img is None:
+            return None
+
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         img = np.array(img / 255., dtype=np.float32)
 
         mask  = self.get_mask(img)

opendm/tiles/tiler.py

@@ -39,7 +39,7 @@ def generate_colored_hillshade(geotiff):
 
         system.run('gdaldem color-relief "%s" "%s" "%s" -alpha -co ALPHA=YES' % (geotiff, relief_file, colored_dem))
         system.run('gdaldem hillshade "%s" "%s" -z 1.0 -s 1.0 -az 315.0 -alt 45.0' % (geotiff, hillshade_dem))
-        system.run('"%s" "%s" "%s" "%s" "%s"' % (sys.executable, hsv_merge_script, colored_dem, hillshade_dem, colored_hillshade_dem))
+        system.run('%s "%s" "%s" "%s" "%s"' % (sys.executable, hsv_merge_script, colored_dem, hillshade_dem, colored_hillshade_dem))
         
         return outputs
     except Exception as e:

opendm/utils.py

@@ -1,12 +1,8 @@
 import os, shutil
 import numpy as np
 import json
-import rasterio
-from osgeo import gdal
-from datetime import datetime
-
 from opendm import log
-from opendm.photo import find_largest_photo_dims, find_mean_utc_time
+from opendm.photo import find_largest_photo_dims
 from osgeo import gdal
 from opendm.arghelpers import double_quote
 
@@ -117,42 +113,4 @@ def np_to_json(arr):
     return json.dumps(arr, cls=NumpyEncoder)
 
 def np_from_json(json_dump):
-    return np.asarray(json.loads(json_dump))
+    return np.asarray(json.loads(json_dump))
-
-def add_raster_meta_tags(raster, reconstruction, tree, embed_gcp_meta=True):
-    try:
-        if os.path.isfile(raster):
-            mean_capture_time = find_mean_utc_time(reconstruction.photos)
-            mean_capture_dt = None
-            if mean_capture_time is not None:
-                mean_capture_dt = datetime.fromtimestamp(mean_capture_time).strftime('%Y:%m:%d %H:%M:%S') + '+00:00'
-
-            log.ODM_INFO("Adding TIFFTAGs to {}".format(raster))
-            with rasterio.open(raster, 'r+') as rst:
-                if mean_capture_dt is not None:
-                    rst.update_tags(TIFFTAG_DATETIME=mean_capture_dt)
-                rst.update_tags(TIFFTAG_SOFTWARE='ODM {}'.format(log.odm_version()))
-
-            if embed_gcp_meta:
-                # Embed GCP info in 2D results via
-                # XML metadata fields
-                gcp_gml_export_file = tree.path("odm_georeferencing", "ground_control_points.gml")
-
-                if reconstruction.has_gcp() and os.path.isfile(gcp_gml_export_file):
-                    gcp_xml = ""
-
-                    with open(gcp_gml_export_file) as f:
-                        gcp_xml = f.read()
-
-                    ds = gdal.Open(raster)
-                    if ds is not None:
-                        if ds.GetMetadata('xml:GROUND_CONTROL_POINTS') is None or self.rerun():
-                            ds.SetMetadata(gcp_xml, 'xml:GROUND_CONTROL_POINTS')
-                            ds = None
-                            log.ODM_INFO("Wrote xml:GROUND_CONTROL_POINTS metadata to %s" % raster)
-                        else:
-                            log.ODM_WARNING("Already embedded ground control point information")
-                    else:
-                        log.ODM_WARNING("Cannot open %s for writing, skipping GCP embedding" % raster)
-    except Exception as e:
-        log.ODM_WARNING("Cannot write raster meta tags to %s: %s" % (raster, str(e)))

opendm/video/srtparser.py

@@ -120,7 +120,6 @@ class SrtFileParser:
         # <font size="36">SrtCnt : 1, DiffTime : 16ms
         # 2023-01-06 18:56:48,380,821
         # [iso : 3200] [shutter : 1/60.0] [fnum : 280] [ev : 0] [ct : 3925] [color_md : default] [focal_len : 240] [latitude: 0.000000] [longitude: 0.000000] [altitude: 0.000000] </font>
-        # </font> 
 
         # DJI Mavic Mini
         # 1
@@ -165,10 +164,9 @@ class SrtFileParser:
             end = None
 
             for line in f:
-                # Remove html tags, spaces
-                line = re.sub('<[^<]+?>', '', line).strip()
 
-                if not line:
+                # Check if line is empty
+                if not line.strip():
                     if start is not None:
                         self.data.append({
                             "start": start,
@@ -195,6 +193,9 @@ class SrtFileParser:
 
                     continue
 
+                # Remove html tags
+                line = re.sub('<[^<]+?>', '', line)
+
                 # Search this "00:00:00,000 --> 00:00:00,016"
                 match = re.search("(\d{2}:\d{2}:\d{2},\d+) --> (\d{2}:\d{2}:\d{2},\d+)", line)
                 if match:
@@ -224,14 +225,14 @@ class SrtFileParser:
                     ("GPS \([\d\.\-]+,? ([\d\.\-]+),? [\d\.\-]+\)", lambda v: float(v) if v != 0 else None),
                     ("RTK \([-+]?\d+\.\d+, (-?\d+\.\d+), -?\d+\)", lambda v: float(v) if v != 0 else None),
                 ], line)
-                
+
                 longitude = match_single([
                     ("longitude: ([\d\.\-]+)", lambda v: float(v) if v != 0 else None),
                     ("longtitude : ([\d\.\-]+)", lambda v: float(v) if v != 0 else None),
                     ("GPS \(([\d\.\-]+),? [\d\.\-]+,? [\d\.\-]+\)", lambda v: float(v) if v != 0 else None),
                     ("RTK \((-?\d+\.\d+), [-+]?\d+\.\d+, -?\d+\)", lambda v: float(v) if v != 0 else None),
                 ], line)
-                
+
                 altitude = match_single([
                     ("altitude: ([\d\.\-]+)", lambda v: float(v) if v != 0 else None),
                     ("GPS \([\d\.\-]+,? [\d\.\-]+,? ([\d\.\-]+)\)", lambda v: float(v) if v != 0 else None),

requirements.txt

@@ -23,7 +23,6 @@ rasterio==1.2.3 ; sys_platform == 'linux'
 rasterio==1.3.6 ; sys_platform == 'darwin'
 https://github.com/OpenDroneMap/windows-deps/raw/main/rasterio-1.2.3-cp38-cp38-win_amd64.whl ; sys_platform == 'win32'
 https://github.com/OpenDroneMap/windows-deps/raw/main/GDAL-3.2.3-cp38-cp38-win_amd64.whl ; sys_platform == 'win32'
-odmrawpy==0.24.1
 repoze.lru==0.7
 scikit-learn==1.1.1
 Pywavelets==1.3.0
@@ -33,7 +32,7 @@ xmltodict==0.12.0
 fpdf2==2.4.6
 Shapely==1.7.1
 onnxruntime==1.12.1
-pygltflib==1.16.5
+pygltflib==1.15.3
 codem==0.24.0
 trimesh==3.17.1
 pandas==1.5.2

stages/dataset.py

@@ -187,9 +187,6 @@ class ODMLoadDatasetStage(types.ODM_Stage):
                             p.compute_opk()
                             updated += 1
                     log.ODM_INFO("Updated %s image positions" % updated)
-                # Warn if a file path is specified but it does not exist
-                elif tree.odm_geo_file is not None and not os.path.isfile(tree.odm_geo_file):
-                    log.ODM_WARNING("Image geolocation file %s does not exist" % tree.odm_geo_file) 
 
                 # GPSDOP override if we have GPS accuracy information (such as RTK)
                 if 'gps_accuracy_is_set' in args:
@@ -234,9 +231,9 @@ class ODMLoadDatasetStage(types.ODM_Stage):
                                         item['p'].set_mask(os.path.basename(mask_file))
                                         log.ODM_INFO("Wrote %s" % os.path.basename(mask_file))
                                     else:
-                                        log.ODM_WARNING("Cannot generate mask for %s" % item['file'])
+                                        log.ODM_WARNING("Cannot generate mask for %s" % img)
                                 except Exception as e:
-                                    log.ODM_WARNING("Cannot generate mask for %s: %s" % (item['file'], str(e)))
+                                    log.ODM_WARNING("Cannot generate mask for %s: %s" % (img, str(e)))
 
                             parallel_map(parallel_sky_filter, sky_images, max_workers=args.max_concurrency)
 
@@ -338,10 +335,3 @@ class ODMLoadDatasetStage(types.ODM_Stage):
         if args.rolling_shutter and not reconstruction.is_georeferenced():
             log.ODM_WARNING("Reconstruction is not georeferenced, disabling rolling shutter correction")
             args.rolling_shutter = False
-        
-        # GPS Z offset
-        if 'gps_z_offset_is_set' in args:
-            log.ODM_INFO("Adjusting GPS Z offset by %s for all images" % args.gps_z_offset)
-
-            for p in photos:
-                p.adjust_z_offset(args.gps_z_offset)

stages/odm_dem.py

@@ -12,8 +12,6 @@ from opendm.cropper import Cropper
 from opendm import pseudogeo
 from opendm.tiles.tiler import generate_dem_tiles
 from opendm.cogeo import convert_to_cogeo
-from opendm.utils import add_raster_meta_tags
-
 
 class ODMDEMStage(types.ODM_Stage):
     def process(self, args, outputs):
@@ -88,8 +86,6 @@ class ODMDEMStage(types.ODM_Stage):
 
                     if pseudo_georeference:
                         pseudogeo.add_pseudo_georeferencing(dem_geotiff_path)
-                    
-                    add_raster_meta_tags(dem_geotiff_path, reconstruction, tree, embed_gcp_meta=not outputs['large'])
 
                     if args.tiles:
                         generate_dem_tiles(dem_geotiff_path, tree.path("%s_tiles" % product), args.max_concurrency, resolution)

stages/odm_georeferencing.py

@@ -6,7 +6,6 @@ import fiona
 import fiona.crs
 import json
 import zipfile
-import math
 from collections import OrderedDict
 from pyproj import CRS
 
@@ -126,35 +125,13 @@ class ODMGeoreferencingStage(types.ODM_Stage):
 
                 stages.append("transformation")
                 utmoffset = reconstruction.georef.utm_offset()
-
-                # Establish appropriate las scale for export
-                las_scale = 0.001
-                filtered_point_cloud_stats = tree.path("odm_filterpoints", "point_cloud_stats.json")
-                # Function that rounds to the nearest 10
-                # and then chooses the one below so our
-                # las scale is sensible
-                def powerr(r):
-                    return pow(10,round(math.log10(r))) / 10
-
-                if os.path.isfile(filtered_point_cloud_stats):
-                    try:
-                        with open(filtered_point_cloud_stats, 'r') as stats:
-                             las_stats = json.load(stats)
-                             spacing = powerr(las_stats['spacing'])
-                             log.ODM_INFO("las scale calculated as the minimum of 1/10 estimated spacing or %s, which ever is less." % las_scale)
-                             las_scale = min(spacing, 0.001)
-                    except Exception as e:
-                        log.ODM_WARNING("Cannot find file point_cloud_stats.json. Using default las scale: %s" % las_scale)
-                else:
-                    log.ODM_INFO("No point_cloud_stats.json found. Using default las scale: %s" % las_scale)
-
                 params += [
                     f'--filters.transformation.matrix="1 0 0 {utmoffset[0]} 0 1 0 {utmoffset[1]} 0 0 1 0 0 0 0 1"',
                     f'--writers.las.offset_x={reconstruction.georef.utm_east_offset}' ,
                     f'--writers.las.offset_y={reconstruction.georef.utm_north_offset}',
-                    f'--writers.las.scale_x={las_scale}',
+                    '--writers.las.scale_x=0.001',
-                    f'--writers.las.scale_y={las_scale}',
+                    '--writers.las.scale_y=0.001',
-                    f'--writers.las.scale_z={las_scale}',
+                    '--writers.las.scale_z=0.001',
                     '--writers.las.offset_z=0',
                     f'--writers.las.a_srs="{reconstruction.georef.proj4()}"' # HOBU this should maybe be WKT
                 ]
@@ -280,4 +257,3 @@ class ODMGeoreferencingStage(types.ODM_Stage):
             os.remove(tree.filtered_point_cloud)
 
 
-

stages/odm_orthophoto.py

@@ -132,8 +132,7 @@ class ODMOrthoPhotoStage(types.ODM_Stage):
                     else:
                         log.ODM_INFO("Not a submodel run, skipping mask raster generation")
 
-                orthophoto.post_orthophoto_steps(args, bounds_file_path, tree.odm_orthophoto_tif, tree.orthophoto_tiles, resolution, 
+                orthophoto.post_orthophoto_steps(args, bounds_file_path, tree.odm_orthophoto_tif, tree.orthophoto_tiles, resolution)
-                    reconstruction, tree, not outputs["large"])
 
                 # Generate feathered orthophoto also
                 if args.orthophoto_cutline and submodel_run:

stages/odm_postprocess.py

@@ -1,5 +1,6 @@
 import os
 
+from osgeo import gdal
 from opendm import io
 from opendm import log
 from opendm import types
@@ -13,6 +14,37 @@ class ODMPostProcess(types.ODM_Stage):
 
         log.ODM_INFO("Post Processing")
 
+        if not outputs['large']:
+            # TODO: support for split-merge?
+
+            # Embed GCP info in 2D results via
+            # XML metadata fields
+            gcp_gml_export_file = tree.path("odm_georeferencing", "ground_control_points.gml")
+
+            if reconstruction.has_gcp() and io.file_exists(gcp_gml_export_file):
+                skip_embed_gcp = False
+                gcp_xml = ""
+
+                with open(gcp_gml_export_file) as f:
+                    gcp_xml = f.read()
+
+                for product in [tree.odm_orthophoto_tif,
+                                tree.path("odm_dem", "dsm.tif"),
+                                tree.path("odm_dem", "dtm.tif")]:
+                    if os.path.isfile(product):
+                        ds = gdal.Open(product)
+                        if ds is not None:
+                            if ds.GetMetadata('xml:GROUND_CONTROL_POINTS') is None or self.rerun():
+                                ds.SetMetadata(gcp_xml, 'xml:GROUND_CONTROL_POINTS')
+                                ds = None
+                                log.ODM_INFO("Wrote xml:GROUND_CONTROL_POINTS metadata to %s" % product)
+                            else:
+                                skip_embed_gcp = True
+                                log.ODM_WARNING("Already embedded ground control point information")
+                                break
+                        else:
+                            log.ODM_WARNING("Cannot open %s for writing, skipping GCP embedding" % product)
+
         if getattr(args, '3d_tiles'):
             build_3dtiles(args, tree, reconstruction, self.rerun())
 
@@ -21,4 +53,3 @@ class ODMPostProcess(types.ODM_Stage):
                 copy_paths([os.path.join(args.project_path, p) for p in get_processing_results_paths()], args.copy_to, self.rerun())
             except Exception as e:
                 log.ODM_WARNING("Cannot copy to %s: %s" % (args.copy_to, str(e)))
-

stages/openmvs.py

@@ -63,13 +63,11 @@ class ODMOpenMVSStage(types.ODM_Stage):
             densify_ini_file = os.path.join(tree.openmvs, 'Densify.ini')
             subres_levels = 2 # The number of lower resolutions to process before estimating output resolution depthmap.
             filter_point_th = -20
-            min_resolution = 320 if args.pc_quality in ["low", "lowest"] else 640
 
             config = [
                 "--resolution-level %s" % int(resolution_level),
                 '--dense-config-file "%s"' % densify_ini_file,
                 "--max-resolution %s" % int(outputs['undist_image_max_size']),
-                "--min-resolution %s" % min_resolution,
                 "--max-threads %s" % args.max_concurrency,
                 "--number-views-fuse %s" % number_views_fuse,
                 "--sub-resolution-levels %s" % subres_levels,

stages/splitmerge.py

@@ -1,19 +1,23 @@
 import os
 import shutil
+import json
+import yaml
 from opendm import log
 from opendm.osfm import OSFMContext, get_submodel_argv, get_submodel_paths, get_all_submodel_paths
 from opendm import types
 from opendm import io
 from opendm import system
 from opendm import orthophoto
-from opendm.dem import utils
+from opendm.gcp import GCPFile
+from opendm.dem import pdal, utils
 from opendm.dem.merge import euclidean_merge_dems
 from opensfm.large import metadataset
 from opendm.cropper import Cropper
+from opendm.concurrency import get_max_memory
 from opendm.remote import LocalRemoteExecutor
-from opendm.shots import merge_geojson_shots, merge_cameras
+from opendm.shots import merge_geojson_shots
 from opendm import point_cloud
-from opendm.utils import double_quote, add_raster_meta_tags
+from opendm.utils import double_quote
 from opendm.tiles.tiler import generate_dem_tiles
 from opendm.cogeo import convert_to_cogeo
 from opendm import multispectral
@@ -263,8 +267,7 @@ class ODMMergeStage(types.ODM_Stage):
 
                         orthophoto_vars = orthophoto.get_orthophoto_vars(args)
                         orthophoto.merge(all_orthos_and_ortho_cuts, tree.odm_orthophoto_tif, orthophoto_vars)
-                        orthophoto.post_orthophoto_steps(args, merged_bounds_file, tree.odm_orthophoto_tif, tree.orthophoto_tiles, args.orthophoto_resolution,
+                        orthophoto.post_orthophoto_steps(args, merged_bounds_file, tree.odm_orthophoto_tif, tree.orthophoto_tiles, args.orthophoto_resolution)
-                            reconstruction, tree, False)
                     elif len(all_orthos_and_ortho_cuts) == 1:
                         # Simply copy
                         log.ODM_WARNING("A single orthophoto/cutline pair was found between all submodels.")
@@ -306,8 +309,6 @@ class ODMMergeStage(types.ODM_Stage):
                         if args.tiles:
                             generate_dem_tiles(dem_file, tree.path("%s_tiles" % human_name.lower()), args.max_concurrency, args.dem_resolution)
                         
-                        add_raster_meta_tags(dem_file, reconstruction, tree, embed_gcp_meta=False)
-
                         if args.cog:
                             convert_to_cogeo(dem_file, max_workers=args.max_concurrency)
                     else:
@@ -336,15 +337,6 @@ class ODMMergeStage(types.ODM_Stage):
             else:
                 log.ODM_WARNING("Found merged shots.geojson in %s" % tree.odm_report)
 
-            # Merge cameras
-            cameras_json = tree.path("cameras.json")
-            if not io.file_exists(cameras_json) or self.rerun():
-                cameras_json_files = get_submodel_paths(tree.submodels_path, "cameras.json")
-                log.ODM_INFO("Merging %s cameras.json files" % len(cameras_json_files))
-                merge_cameras(cameras_json_files, cameras_json)
-            else:
-                log.ODM_WARNING("Found merged cameras.json in %s" % tree.root_path)
-
             # Stop the pipeline short by skipping to the postprocess stage.
             # Afterwards, we're done.
             self.next_stage = self.last_stage()

winpostinstall.bat

@@ -1,18 +1,14 @@
 set ODMBASE=%~dp0
 set VIRTUAL_ENV=%ODMBASE%venv
-IF "%~1"=="" (set WRITABLE_VIRTUAL_ENV=%VIRTUAL_ENV%) ELSE (set WRITABLE_VIRTUAL_ENV=%~1)
+set PYENVCFG=%VIRTUAL_ENV%\pyvenv.cfg
-mkdir "%WRITABLE_VIRTUAL_ENV%"
+set SBBIN=%ODMBASE%SuperBuild\install\bin
 
 rem Hot-patching pyvenv.cfg
-set PYENVCFG=%WRITABLE_VIRTUAL_ENV%\pyvenv.cfg
+echo home = %ODMBASE%venv\Scripts> "%PYENVCFG%"
-echo home = %VIRTUAL_ENV%\Scripts> "%PYENVCFG%"
 echo include-system-site-packages = false>> "%PYENVCFG%"
 
 rem Hot-patching cv2 extension configs
-set SBBIN=%ODMBASE%SuperBuild\install\bin
+echo BINARIES_PATHS = [r"%SBBIN%"] + BINARIES_PATHS> venv\Lib\site-packages\cv2\config.py
-set CV2=%WRITABLE_VIRTUAL_ENV%\Lib\site-packages\cv2
+echo PYTHON_EXTENSIONS_PATHS = [r'''%VIRTUAL_ENV%\lib\site-packages\cv2\python-3.8'''] + PYTHON_EXTENSIONS_PATHS> venv\Lib\site-packages\cv2\config-3.8.py
-mkdir "%CV2%"
-echo BINARIES_PATHS = [r"%SBBIN%"] + BINARIES_PATHS> "%CV2%\config.py"
-echo PYTHON_EXTENSIONS_PATHS = [r'''%VIRTUAL_ENV%\lib\site-packages\cv2\python-3.8'''] + PYTHON_EXTENSIONS_PATHS> "%CV2%\config-3.8.py"
 
 cls