Hello,
I’m trying to process 143 images into a map, and when its about 2 minutes into the process, it stops and gives me a message that it cant process the images. Any suggestions?
Can you post the error from the console log so we can see what the issue might be?
Also post your computer specs please.
1 Like
You need to copy from the console log, as “cannot process dataset” isn’t very informative 
The relevant info is generally in the final few dozen lines, which you can copy and paste in here, although the entire file may fit, given it failed after 1m53s.
So I’m not the greatest at computer but i was only able to find this information, which I don’t think is what you needed.
{
"domain": "integer",
"help": "Legacy option (use --feature-quality instead). Resizes images by the largest side for feature extraction purposes only. Set to -1 to disable. This does not affect the final orthophoto resolution quality and will not resize the original images. Default: %(default)s",
"name": "resize-to",
"value": "2048",
"type": "int"
},
{
"domain": [
"dataset",
"split",
"merge",
"opensfm",
"openmvs",
"odm_filterpoints",
"odm_meshing",
"mvs_texturing",
"odm_georeferencing",
"odm_dem",
"odm_orthophoto",
"odm_report",
"odm_postprocess"
],
"help": "End processing at this stage. Can be one of: %(choices)s. Default: %(default)s",
"name": "end-with",
"value": "odm_postprocess",
"type": "enum"
},
{
"domain": [
"",
"dataset",
"split",
"merge",
"opensfm",
"openmvs",
"odm_filterpoints",
"odm_meshing",
"mvs_texturing",
"odm_georeferencing",
"odm_dem",
"odm_orthophoto",
"odm_report",
"odm_postprocess"
],
"help": "Rerun processing from this stage. Can be one of: %(choices)s. Default: %(default)s",
"name": "rerun-from",
"value": "",
"type": "enum"
},
{
"domain": "integer",
"help": "Minimum number of features to extract per image. 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",
"name": "min-num-features",
"value": "10000",
"type": "int"
},
{
"domain": [
"akaze",
"hahog",
"orb",
"sift"
],
"help": "Choose the algorithm for extracting keypoints and computing descriptors. Can be one of: %(choices)s. Default: %(default)s",
"name": "feature-type",
"value": "sift",
"type": "enum"
},
{
"domain": [
"ultra",
"high",
"medium",
"low",
"lowest"
],
"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",
"name": "feature-quality",
"value": "high",
"type": "enum"
},
{
"domain": [
"bow",
"bruteforce",
"flann"
],
"help": "Matcher algorithm, Fast Library for Approximate Nearest Neighbors or Bag of Words. FLANN is slower, but more stable. BOW is faster, but can sometimes miss valid matches. BRUTEFORCE is very slow but robust.Can be one of: %(choices)s. Default: %(default)s",
"name": "matcher-type",
"value": "flann",
"type": "enum"
},
{
"domain": "positive integer",
"help": "Perform image matching with the nearest images based on GPS exif data. Set to 0 to match by triangulation. Default: %(default)s",
"name": "matcher-neighbors",
"value": "0",
"type": "int"
},
{
"domain": "",
"help": "Turn off camera parameter optimization during bundle adjustment. This can be sometimes useful for improving results that exhibit doming/bowling or when images are taken with a rolling shutter camera. Default: %(default)s",
"name": "use-fixed-camera-params",
"value": "false",
"type": "bool"
},
{
"domain": "json",
"help": "Use the camera parameters computed from another dataset instead of calculating them. Can be specified either as path to a cameras.json file or as a JSON string representing the contents of a cameras.json file. Default: %(default)s",
"name": "cameras",
"value": "",
"type": "string"
},
{
"domain": [
"auto",
"perspective",
"brown",
"fisheye",
"spherical",
"equirectangular",
"dual"
],
"help": "Set a camera projection type. Manually setting a value can help improve geometric undistortion. By default the application tries to determine a lens type from the images metadata. Can be one of: %(choices)s. Default: %(default)s",
"name": "camera-lens",
"value": "auto",
"type": "enum"
},
{
"domain": [
"none",
"camera",
"camera+sun"
],
"help": "Set the radiometric calibration to perform on images. When processing multispectral and thermal images you should set this option to obtain reflectance/temperature values (otherwise you will get digital number values). [camera] applies black level, vignetting, row gradient gain/exposure compensation (if appropriate EXIF tags are found) and computes absolute temperature values. [camera+sun] is experimental, applies all the corrections of [camera], plus compensates for spectral radiance registered via a downwelling light sensor (DLS) taking in consideration the angle of the sun. Can be one of: %(choices)s. Default: %(default)s",
"name": "radiometric-calibration",
"value": "none",
"type": "enum"
},
{
"domain": "positive integer",
"help": "The maximum number of processes to use in various processes. Peak memory requirement is ~1GB per thread and 2 megapixel image resolution. Default: %(default)s",
"name": "max-concurrency",
"value": "8",
"type": "int"
},
{
"domain": "positive float",
"help": "Controls the density of the point cloud by setting the resolution of the depthmap images. Higher values take longer to compute but produce denser point clouds. Overrides the value calculated by --pc-quality.Default: %(default)s",
"name": "depthmap-resolution",
"value": "640",
"type": "float"
},
{
"domain": "",
"help": "Run local bundle adjustment for every image added to the reconstruction and a global adjustment every 100 images. Speeds up reconstruction for very large datasets. Default: %(default)s",
"name": "use-hybrid-bundle-adjustment",
"value": "false",
"type": "bool"
},
{
"domain": [
"incremental",
"triangulation",
"planar"
],
"help": "Choose the structure from motion algorithm. For aerial datasets, if camera GPS positions and angles are available, triangulation can generate better results. For planar scenes captured at fixed altitude with nadir-only images, planar can be much faster. Can be one of: %(choices)s. Default: %(default)s",
"name": "sfm-algorithm",
"value": "incremental",
"type": "enum"
},
{
"domain": "",
"help": "Automatically compute image masks using AI to remove the sky. Experimental. Default: %(default)s",
"name": "sky-removal",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Use a full 3D mesh to compute the orthophoto instead of a 2.5D mesh. This option is a bit faster and provides similar results in planar areas. Default: %(default)s",
"name": "use-3dmesh",
"value": "false",
"type": "bool"
},
{
"domain": "",
"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",
"name": "skip-3dmodel",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Skip generation of PDF report. This can save time if you don't need a report. Default: %(default)s",
"name": "skip-report",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Skip generation of the orthophoto. This can save time if you only need 3D results or DEMs. Default: %(default)s",
"name": "skip-orthophoto",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Ignore Ground Sampling Distance (GSD). GSD caps 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. Default: %(default)s",
"name": "ignore-gsd",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Do not use GPU acceleration, even if it's available. Default: %(default)s",
"name": "no-gpu",
"value": "false",
"type": "bool"
},
{
"domain": "positive integer",
"help": "The maximum vertex count of the output mesh. Default: %(default)s",
"name": "mesh-size",
"value": "200000",
"type": "int"
},
{
"domain": "integer: 1 <= x <= 14",
"help": "Octree depth used in the mesh reconstruction, increase to get more vertices, recommended values are 8-12. Default: %(default)s",
"name": "mesh-octree-depth",
"value": "11",
"type": "int"
},
{
"domain": "",
"help": "Skips dense reconstruction and 3D model generation. It generates an orthophoto directly from the sparse reconstruction. If you just need an orthophoto and do not need a full 3D model, turn on this option. Default: %(default)s",
"name": "fast-orthophoto",
"value": "false",
"type": "bool"
},
{
"domain": "positive float",
"help": "Automatically crop image outputs by creating a smooth buffer around the dataset boundaries, shrunk by N meters. Use 0 to disable cropping. Default: %(default)s",
"name": "crop",
"value": "3",
"type": "float"
},
{
"domain": "json",
"help": "GeoJSON polygon limiting the area of the reconstruction. Can be specified either as path to a GeoJSON file or as a JSON string representing the contents of a GeoJSON file. Default: %(default)s",
"name": "boundary",
"value": "",
"type": "string"
},
{
"domain": "",
"help": "Automatically set a boundary using camera shot locations to limit the area of the reconstruction. This can help remove far away background artifacts (sky, background landscapes, etc.). See also --boundary. Default: %(default)s",
"name": "auto-boundary",
"value": "false",
"type": "bool"
},
{
"domain": "positive float",
"help": "Specify the distance between camera shot locations and the outer edge of the boundary when computing the boundary with --auto-boundary. Set to 0 to automatically choose a value. Default: %(default)s",
"name": "auto-boundary-distance",
"value": "0",
"type": "float"
},
{
"domain": [
"ultra",
"high",
"medium",
"low",
"lowest"
],
"help": "Set point cloud quality. Higher quality generates better, denser point clouds, but requires more memory and takes longer. Each step up in quality increases processing time roughly by a factor of 4x.Can be one of: %(choices)s. Default: %(default)s",
"name": "pc-quality",
"value": "medium",
"type": "enum"
},
{
"domain": "",
"help": "Classify the point cloud outputs using a Simple Morphological Filter. You can control the behavior of this option by tweaking the --dem-* parameters. Default: %(default)s",
"name": "pc-classify",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Export the georeferenced point cloud in CSV format. Default: %(default)s",
"name": "pc-csv",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Export the georeferenced point cloud in LAS format. Default: %(default)s",
"name": "pc-las",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Export the georeferenced point cloud in Entwine Point Tile (EPT) format. Default: %(default)s",
"name": "pc-ept",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Save the georeferenced point cloud in Cloud Optimized Point Cloud (COPC) format. Default: %(default)s",
"name": "pc-copc",
"value": "false",
"type": "bool"
},
{
"domain": "positive float",
"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",
"name": "pc-filter",
"value": "2.5",
"type": "float"
},
{
"domain": "positive float",
"help": "Filters the point cloud by keeping only a single point around a radius N (in meters). This can be useful to limit the output resolution of the point cloud and remove duplicate points. Set to 0 to disable sampling. Default: %(default)s",
"name": "pc-sample",
"value": "0",
"type": "float"
},
{
"domain": "",
"help": "Reduce the memory usage needed for depthmap fusion by splitting large scenes into tiles. Turn this on if your machine doesn't have much RAM and/or you've set --pc-quality to high or ultra. Experimental. Default: %(default)s",
"name": "pc-tile",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Improve the accuracy of the point cloud by computing geometrically consistent depthmaps. This increases processing time, but can improve results in urban scenes. Default: %(default)s",
"name": "pc-geometric",
"value": "false",
"type": "bool"
},
{
"domain": "positive float",
"help": "Simple Morphological Filter elevation scalar parameter. Default: %(default)s",
"name": "smrf-scalar",
"value": "1.25",
"type": "float"
},
{
"domain": "positive float",
"help": "Simple Morphological Filter slope parameter (rise over run). Default: %(default)s",
"name": "smrf-slope",
"value": "0.15",
"type": "float"
},
{
"domain": "positive float",
"help": "Simple Morphological Filter elevation threshold parameter (meters). Default: %(default)s",
"name": "smrf-threshold",
"value": "0.5",
"type": "float"
},
{
"domain": "positive float",
"help": "Simple Morphological Filter window radius parameter (meters). Default: %(default)s",
"name": "smrf-window",
"value": "18",
"type": "float"
},
{
"domain": [
"gmi",
"area"
],
"help": "When texturing the 3D mesh, for each triangle, choose to prioritize images with sharp features (gmi) or those that cover the largest area (area). Default: %(default)s",
"name": "texturing-data-term",
"value": "gmi",
"type": "enum"
},
{
"domain": [
"none",
"gauss_clamping",
"gauss_damping"
],
"help": "Type of photometric outlier removal method. Can be one of: %(choices)s. Default: %(default)s",
"name": "texturing-outlier-removal-type",
"value": "gauss_clamping",
"type": "enum"
},
{
"domain": "",
"help": "Skip normalization of colors across all images. Useful when processing radiometric data. Default: %(default)s",
"name": "texturing-skip-global-seam-leveling",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Skip the blending of colors near seams. Default: %(default)s",
"name": "texturing-skip-local-seam-leveling",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Keep faces in the mesh that are not seen in any camera. Default: %(default)s",
"name": "texturing-keep-unseen-faces",
"value": "false",
"type": "bool"
},
{
"domain": [
"none",
"gamma"
],
"help": "Turn on gamma tone mapping or none for no tone mapping. Can be one of %(choices)s. Default: %(default)s ",
"name": "texturing-tone-mapping",
"value": "none",
"type": "enum"
},
{
"domain": "",
"help": "Use this tag if you have a GCP File but want to use the EXIF information for georeferencing instead. Default: %(default)s",
"name": "use-exif",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Use this tag to build a DTM (Digital Terrain Model, ground only) using a simple morphological filter. Check the --dem* and --smrf* parameters for finer tuning. Default: %(default)s",
"name": "dtm",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Use this tag to build a DSM (Digital Surface Model, ground + objects) using a progressive morphological filter. Check the --dem* parameters for finer tuning. Default: %(default)s",
"name": "dsm",
"value": "false",
"type": "bool"
},
{
"domain": "positive integer",
"help": "Number of steps used to fill areas with gaps. Set to 0 to disable gap filling. Starting with a radius equal to the output resolution, N different DEMs are generated with progressively bigger radius using the inverse distance weighted (IDW) algorithm and merged together. Remaining gaps are then merged using nearest neighbor interpolation. Default: %(default)s",
"name": "dem-gapfill-steps",
"value": "3",
"type": "int"
},
{
"domain": "float",
"help": "DSM/DTM resolution in cm / pixel. Note that this value is capped to 2x the ground sampling distance (GSD) estimate. To remove the cap, check --ignore-gsd also. Default: %(default)s",
"name": "dem-resolution",
"value": "5",
"type": "float"
},
{
"domain": "positive integer",
"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",
"name": "dem-decimation",
"value": "1",
"type": "int"
},
{
"domain": "",
"help": "Computes an euclidean raster map for each DEM. The map reports the distance from each cell to the nearest NODATA value (before any hole filling takes place). This can be useful to isolate the areas that have been filled. Default: %(default)s",
"name": "dem-euclidean-map",
"value": "false",
"type": "bool"
},
{
"domain": "float > 0.0",
"help": "Orthophoto resolution in cm / pixel. Note that this value is capped by a ground sampling distance (GSD) estimate. To remove the cap, check --ignore-gsd also. Default: %(default)s",
"name": "orthophoto-resolution",
"value": "5",
"type": "float"
},
{
"domain": "",
"help": "Set this parameter if you want a striped GeoTIFF. Default: %(default)s",
"name": "orthophoto-no-tiled",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Set this parameter if you want to generate a PNG rendering of the orthophoto. Default: %(default)s",
"name": "orthophoto-png",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Set this parameter if you want to generate a Google Earth (KMZ) rendering of the orthophoto. Default: %(default)s",
"name": "orthophoto-kmz",
"value": "false",
"type": "bool"
},
{
"domain": [
"JPEG",
"LZW",
"PACKBITS",
"DEFLATE",
"LZMA",
"NONE"
],
"help": "Set the compression to use for orthophotos. Can be one of: %(choices)s. Default: %(default)s",
"name": "orthophoto-compression",
"value": "DEFLATE",
"type": "enum"
},
{
"domain": "",
"help": "Generates a polygon around the cropping area that cuts the orthophoto around the edges of features. This polygon can be useful for stitching seamless mosaics with multiple overlapping orthophotos. Default: %(default)s",
"name": "orthophoto-cutline",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Generate static tiles for orthophotos and DEMs that are suitable for viewers like Leaflet or OpenLayers. Default: %(default)s",
"name": "tiles",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Generate OGC 3D Tiles outputs. Default: %(default)s",
"name": "3d-tiles",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Turn on rolling shutter correction. If the camera has a rolling shutter and the images were taken in motion, you can turn on this option to improve the accuracy of the results. See also --rolling-shutter-readout. Default: %(default)s",
"name": "rolling-shutter",
"value": "false",
"type": "bool"
},
{
"domain": "positive integer",
"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",
"name": "rolling-shutter-readout",
"value": "0",
"type": "float"
},
{
"domain": "",
"help": "Build orthophoto overviews for faster display in programs such as QGIS. Default: %(default)s",
"name": "build-overviews",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Create Cloud-Optimized GeoTIFFs instead of normal GeoTIFFs. Default: %(default)s",
"name": "cog",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Print additional messages to the console. Default: %(default)s",
"name": "verbose",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Print debug messages. Default: %(default)s",
"name": "debug",
"value": "false",
"type": "bool"
},
{
"domain": "positive integer",
"help": "Average number of images per submodel. When splitting a large dataset into smaller submodels, images are grouped into clusters. This value regulates the number of images that each cluster should have on average. Default: %(default)s",
"name": "split",
"value": "999999",
"type": "int"
},
{
"domain": "positive integer",
"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. Default: %(default)s",
"name": "split-overlap",
"value": "150",
"type": "float"
},
{
"domain": "string",
"help": "URL to a ClusterODM instance for distributing a split-merge workflow on multiple nodes in parallel. Default: %(default)s",
"name": "sm-cluster",
"value": "None",
"type": "string"
},
{
"domain": [
"all",
"pointcloud",
"orthophoto",
"dem"
],
"help": "Choose what to merge in the merge step in a split dataset. By default all available outputs are merged. Options: %(choices)s. Default: %(default)s",
"name": "merge",
"value": "all",
"type": "enum"
},
{
"domain": "",
"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",
"name": "force-gps",
"value": "false",
"type": "bool"
},
{
"domain": "positive float",
"help": "Set a value in meters for the GPS Dilution of Precision (DOP) information for all images. If your images are tagged 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",
"name": "gps-accuracy",
"value": "10",
"type": "float"
},
{
"domain": "",
"help": "Delete heavy intermediate files to optimize disk space usage. This affects the ability to restart the pipeline from an intermediate stage, but allows datasets to be processed on machines that don't have sufficient disk space available. Default: %(default)s",
"name": "optimize-disk-space",
"value": "false",
"type": "bool"
},
{
"domain": "",
"help": "Perform ground rectification on the point cloud. This means that wrongly classified ground points will be re-classified and gaps will be filled. Useful for generating DTMs. Default: %(default)s",
"name": "pc-rectify",
"value": "false",
"type": "bool"
},
{
"domain": "string",
"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. Default: %(default)s",
"name": "primary-band",
"value": "auto",
"type": "string"
},
{
"domain": "",
"help": "When processing multispectral datasets, ODM will automatically align the images for each band. If the images have been postprocessed and are already aligned, use this option. Default: %(default)s",
"name": "skip-band-alignment",
"value": "false",
"type": "bool"
}
]
I found how to do it, I just cant upload file to this reply for some reason. Can I email it to you?
Did you turn task output ON as per the image I posted? All the info is there in the console log.
Yes sir, and downloaded the file, but when I go to upload into this message, it’s not visible in my files. Too many lines to paste into this message
Let me know if you received the file
I didn’t receive it, where did you send it? You should be able to copy and paste it into a reply here, don’t try to upload it as an attachment.
As mentioned earlier, its probably only the final 30-50 lines required.
… output truncated at undefined lines …
2022-08-17 17:35:09,971 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:10,013 DEBUG: Found 10467 points in 0.651254415512085s
2022-08-17 17:35:10,014 DEBUG: done
2022-08-17 17:35:10,336 DEBUG: Found 3455 points in 0.49805736541748047s
2022-08-17 17:35:10,336 DEBUG: reducing threshold
2022-08-17 17:35:10,336 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:10,411 DEBUG: No segmentation for DJI_0415.JPG, no features masked.
2022-08-17 17:35:10,464 DEBUG: No segmentation for DJI_0345.JPG, no features masked.
2022-08-17 17:35:10,537 DEBUG: Found 4541 points in 0.5660505294799805s
2022-08-17 17:35:10,537 DEBUG: reducing threshold
2022-08-17 17:35:10,537 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:10,663 INFO: Extracting ROOT_SIFT features for image DJI_0416.JPG
2022-08-17 17:35:10,670 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:10,759 DEBUG: No segmentation for DJI_0309.JPG, no features masked.
2022-08-17 17:35:10,912 DEBUG: Found 6361 points in 0.5748286247253418s
2022-08-17 17:35:10,912 DEBUG: reducing threshold
2022-08-17 17:35:10,912 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:11,052 INFO: Extracting ROOT_SIFT features for image DJI_0311.JPG
2022-08-17 17:35:11,060 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:11,090 INFO: Extracting ROOT_SIFT features for image DJI_0347.JPG
2022-08-17 17:35:11,096 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:11,118 DEBUG: Found 6556 points in 0.5807061195373535s
2022-08-17 17:35:11,119 DEBUG: reducing threshold
2022-08-17 17:35:11,119 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:11,216 DEBUG: No segmentation for DJI_0380.JPG, no features masked.
2022-08-17 17:35:11,342 DEBUG: Found 11863 points in 0.6722004413604736s
2022-08-17 17:35:11,342 DEBUG: done
2022-08-17 17:35:11,437 INFO: Extracting ROOT_SIFT features for image DJI_0381.JPG
2022-08-17 17:35:11,444 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:11,503 DEBUG: Found 905 points in 0.44321274757385254s
2022-08-17 17:35:11,503 DEBUG: reducing threshold
2022-08-17 17:35:11,503 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:11,560 DEBUG: Found 9357 points in 0.6481244564056396s
2022-08-17 17:35:11,560 DEBUG: reducing threshold
2022-08-17 17:35:11,560 DEBUG: Computing sift with threshold 0.00869135802469136
2022-08-17 17:35:11,603 DEBUG: Found 2840 points in 0.506399393081665s
2022-08-17 17:35:11,603 DEBUG: reducing threshold
2022-08-17 17:35:11,603 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:11,823 DEBUG: Found 9681 points in 0.7043581008911133s
2022-08-17 17:35:11,823 DEBUG: reducing threshold
2022-08-17 17:35:11,823 DEBUG: Computing sift with threshold 0.00869135802469136
2022-08-17 17:35:11,894 INFO: Extracting ROOT_SIFT features for image DJI_0417.JPG
2022-08-17 17:35:11,905 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:12,031 DEBUG: Found 1890 points in 0.5277955532073975s
2022-08-17 17:35:12,031 DEBUG: reducing threshold
2022-08-17 17:35:12,031 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:12,070 DEBUG: Found 7952 points in 0.6254165172576904s
2022-08-17 17:35:12,070 DEBUG: reducing threshold
2022-08-17 17:35:12,070 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:12,220 DEBUG: Found 4178 points in 0.6172480583190918s
2022-08-17 17:35:12,220 DEBUG: reducing threshold
2022-08-17 17:35:12,220 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:12,339 DEBUG: Found 10937 points in 0.7796950340270996s
2022-08-17 17:35:12,339 DEBUG: done
2022-08-17 17:35:12,587 DEBUG: Found 11558 points in 0.7624943256378174s
2022-08-17 17:35:12,587 DEBUG: done
2022-08-17 17:35:12,590 DEBUG: Found 3403 points in 0.5589888095855713s
2022-08-17 17:35:12,590 DEBUG: reducing threshold
2022-08-17 17:35:12,591 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:12,625 DEBUG: Found 12175 points in 0.7204718589782715s
2022-08-17 17:35:12,626 DEBUG: done
2022-08-17 17:35:12,697 DEBUG: No segmentation for DJI_0416.JPG, no features masked.
2022-08-17 17:35:12,844 DEBUG: Found 5460 points in 0.6226780414581299s
2022-08-17 17:35:12,845 DEBUG: reducing threshold
2022-08-17 17:35:12,845 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:12,881 DEBUG: Found 9891 points in 0.8113460540771484s
2022-08-17 17:35:12,882 DEBUG: reducing threshold
2022-08-17 17:35:12,882 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:13,383 DEBUG: Found 6318 points in 0.7925529479980469s
2022-08-17 17:35:13,384 DEBUG: reducing threshold
2022-08-17 17:35:13,384 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:13,434 INFO: Extracting ROOT_SIFT features for image DJI_0312.JPG
2022-08-17 17:35:13,441 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:13,565 DEBUG: Found 6924 points in 0.7191512584686279s
2022-08-17 17:35:13,565 DEBUG: reducing threshold
2022-08-17 17:35:13,565 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:13,589 DEBUG: No segmentation for DJI_0310.JPG, no features masked.
2022-08-17 17:35:13,615 DEBUG: Found 10547 points in 0.7330195903778076s
2022-08-17 17:35:13,616 DEBUG: done
2022-08-17 17:35:13,933 DEBUG: Found 915 points in 0.49263787269592285s
2022-08-17 17:35:13,934 DEBUG: reducing threshold
2022-08-17 17:35:13,934 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:13,985 DEBUG: No segmentation for DJI_0346.JPG, no features masked.
2022-08-17 17:35:14,238 DEBUG: Found 9148 points in 0.8543815612792969s
2022-08-17 17:35:14,241 DEBUG: reducing threshold
2022-08-17 17:35:14,241 DEBUG: Computing sift with threshold 0.00869135802469136
2022-08-17 17:35:14,243 DEBUG: No segmentation for DJI_0417.JPG, no features masked.
2022-08-17 17:35:14,324 DEBUG: Found 9413 points in 0.7585592269897461s
2022-08-17 17:35:14,325 DEBUG: reducing threshold
2022-08-17 17:35:14,325 DEBUG: Computing sift with threshold 0.00869135802469136
2022-08-17 17:35:14,459 INFO: Extracting ROOT_SIFT features for image DJI_0348.JPG
2022-08-17 17:35:14,470 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:14,510 DEBUG: Found 1927 points in 0.5759730339050293s
2022-08-17 17:35:14,510 DEBUG: reducing threshold
2022-08-17 17:35:14,510 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:14,837 INFO: Extracting ROOT_SIFT features for image DJI_0382.JPG
2022-08-17 17:35:14,847 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:14,995 DEBUG: Found 10704 points in 0.7543487548828125s
2022-08-17 17:35:14,996 DEBUG: done
2022-08-17 17:35:14,999 DEBUG: No segmentation for DJI_0381.JPG, no features masked.
2022-08-17 17:35:15,089 DEBUG: Found 3240 points in 0.6195249557495117s
2022-08-17 17:35:15,090 DEBUG: reducing threshold
2022-08-17 17:35:15,090 INFO: Extracting ROOT_SIFT features for image DJI_0418.JPG
2022-08-17 17:35:15,091 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:15,099 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:15,123 DEBUG: Found 3516 points in 0.6126635074615479s
2022-08-17 17:35:15,123 DEBUG: Found 11264 points in 0.7986452579498291s
2022-08-17 17:35:15,124 DEBUG: reducing threshold
2022-08-17 17:35:15,124 DEBUG: done
2022-08-17 17:35:15,124 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:15,540 DEBUG: Found 5161 points in 0.6936826705932617s
2022-08-17 17:35:15,541 DEBUG: reducing threshold
2022-08-17 17:35:15,541 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:15,804 DEBUG: Found 4689 points in 0.7138187885284424s
2022-08-17 17:35:15,804 DEBUG: reducing threshold
2022-08-17 17:35:15,804 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:15,823 INFO: Extracting ROOT_SIFT features for image DJI_0383.JPG
2022-08-17 17:35:15,836 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:15,931 DEBUG: Found 6549 points in 0.8063459396362305s
2022-08-17 17:35:15,931 DEBUG: reducing threshold
2022-08-17 17:35:15,931 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:15,954 DEBUG: Found 10486 points in 0.8556058406829834s
2022-08-17 17:35:15,954 DEBUG: done
2022-08-17 17:35:16,255 DEBUG: Found 6881 points in 0.713383674621582s
2022-08-17 17:35:16,255 DEBUG: reducing threshold
2022-08-17 17:35:16,255 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:16,292 DEBUG: No segmentation for DJI_0311.JPG, no features masked.
2022-08-17 17:35:16,373 DEBUG: Found 2528 points in 0.537362813949585s
2022-08-17 17:35:16,373 DEBUG: reducing threshold
2022-08-17 17:35:16,374 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:16,466 DEBUG: Found 6026 points in 0.6602504253387451s
2022-08-17 17:35:16,466 DEBUG: reducing threshold
2022-08-17 17:35:16,466 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:16,586 DEBUG: No segmentation for DJI_0347.JPG, no features masked.
2022-08-17 17:35:16,731 DEBUG: Found 9138 points in 0.7995429039001465s
2022-08-17 17:35:16,731 DEBUG: reducing threshold
2022-08-17 17:35:16,731 DEBUG: Computing sift with threshold 0.00869135802469136
2022-08-17 17:35:16,962 DEBUG: Found 7612 points in 0.7053999900817871s
2022-08-17 17:35:16,962 DEBUG: reducing threshold
2022-08-17 17:35:16,962 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:17,116 DEBUG: Found 3880 points in 0.7415645122528076s
2022-08-17 17:35:17,116 DEBUG: reducing threshold
2022-08-17 17:35:17,116 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:17,278 INFO: Extracting ROOT_SIFT features for image DJI_0419.JPG
2022-08-17 17:35:17,286 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:17,304 DEBUG: Found 7445 points in 0.8381350040435791s
2022-08-17 17:35:17,304 DEBUG: reducing threshold
2022-08-17 17:35:17,304 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:17,397 DEBUG: No segmentation for DJI_0418.JPG, no features masked.
2022-08-17 17:35:17,529 INFO: Extracting ROOT_SIFT features for image DJI_0384.JPG
2022-08-17 17:35:17,536 DEBUG: Computing sift with threshold 0.066
2022-08-17 17:35:17,623 DEBUG: Found 10458 points in 0.8920714855194092s
2022-08-17 17:35:17,624 DEBUG: done
2022-08-17 17:35:17,802 DEBUG: Found 4722 points in 0.684722900390625s
2022-08-17 17:35:17,802 DEBUG: reducing threshold
2022-08-17 17:35:17,803 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:17,845 DEBUG: Found 8825 points in 0.8834223747253418s
2022-08-17 17:35:17,845 DEBUG: reducing threshold
2022-08-17 17:35:17,845 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:18,072 DEBUG: Found 920 points in 0.5351011753082275s
2022-08-17 17:35:18,073 DEBUG: reducing threshold
2022-08-17 17:35:18,073 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:18,138 DEBUG: Found 9749 points in 0.8340408802032471s
2022-08-17 17:35:18,138 DEBUG: reducing threshold
2022-08-17 17:35:18,139 DEBUG: Computing sift with threshold 0.00869135802469136
2022-08-17 17:35:18,188 DEBUG: Found 9926 points in 0.9016075134277344s
2022-08-17 17:35:18,188 DEBUG: reducing threshold
2022-08-17 17:35:18,188 DEBUG: Computing sift with threshold 0.044000000000000004
2022-08-17 17:35:18,671 DEBUG: Found 6997 points in 0.8684194087982178s
2022-08-17 17:35:18,672 DEBUG: reducing threshold
2022-08-17 17:35:18,672 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:18,777 DEBUG: Found 1815 points in 0.7041652202606201s
2022-08-17 17:35:18,777 DEBUG: reducing threshold
2022-08-17 17:35:18,778 DEBUG: Computing sift with threshold 0.029333333333333336
2022-08-17 17:35:18,792 DEBUG: Found 12137 points in 0.9461865425109863s
2022-08-17 17:35:18,792 DEBUG: done
2022-08-17 17:35:19,035 DEBUG: No segmentation for DJI_0312.JPG, no features masked.
2022-08-17 17:35:19,092 DEBUG: Found 11375 points in 0.9528765678405762s
2022-08-17 17:35:19,093 DEBUG: done
2022-08-17 17:35:19,097 DEBUG: Found 11805 points in 0.9084527492523193s
2022-08-17 17:35:19,097 DEBUG: done
2022-08-17 17:35:19,387 DEBUG: Found 3073 points in 0.6089258193969727s
2022-08-17 17:35:19,387 DEBUG: reducing threshold
2022-08-17 17:35:19,387 DEBUG: Computing sift with threshold 0.01955555555555556
2022-08-17 17:35:19,503 DEBUG: Found 11737 points in 0.8315324783325195s
2022-08-17 17:35:19,503 DEBUG: done
2022-08-17 17:35:20,098 DEBUG: Found 7155 points in 0.7109668254852295s
2022-08-17 17:35:20,099 DEBUG: reducing threshold
2022-08-17 17:35:20,099 DEBUG: Computing sift with threshold 0.01303703703703704
2022-08-17 17:35:20,210 DEBUG: No segmentation for DJI_0382.JPG, no features masked.
2022-08-17 17:35:20,385 DEBUG: No segmentation for DJI_0348.JPG, no features masked.
2022-08-17 17:35:20,429 DEBUG: No segmentation for DJI_0419.JPG, no features masked.
2022-08-17 17:35:20,727 DEBUG: No segmentation for DJI_0383.JPG, no features masked.
2022-08-17 17:35:20,768 DEBUG: Found 12402 points in 0.6685011386871338s
2022-08-17 17:35:20,768 DEBUG: done
2022-08-17 17:35:21,589 DEBUG: No segmentation for DJI_0384.JPG, no features masked.
Traceback (most recent call last):
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\bin\opensfm_main.py”, line 25, in
commands.command_runner(
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\commands\command_runner.py”, line 38, in command_runner
command.run(data, args)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\commands\command.py”, line 13, in run
self.run_impl(data, args)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\commands\detect_features.py”, line 13, in run_impl
detect_features.run_dataset(dataset)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\actions\detect_features.py”, line 15, in run_dataset
features_processing.run_features_processing(data, data.images(), False)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\features_processing.py”, line 79, in run_features_processing
parallel_map(process, arguments, processes, 1)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\context.py”, line 58, in parallel_map
res = Parallel(batch_size=batch_size)(delayed(func)(arg) for arg in args)
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\joblib\parallel.py”, line 1061, in call
self.retrieve()
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\joblib\parallel.py”, line 940, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File “multiprocessing\pool.py”, line 768, in get
File “multiprocessing\pool.py”, line 125, in worker
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\joblib_parallel_backends.py”, line 595, in call
return self.func(*args, **kwargs)
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\joblib\parallel.py”, line 262, in call
return [func(*args, **kwargs)
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\joblib\parallel.py”, line 262, in
return [func(*args, **kwargs)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\features_processing.py”, line 149, in process
run_detection(queue)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\features_processing.py”, line 184, in run_detection
detect(image, image_array, segmentation_array, instances_array, data, force)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\features_processing.py”, line 294, in detect
data.save_features(image, features_data)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\dataset.py”, line 322, in save_features
self._save_features(self._feature_file(image), features_data)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\dataset.py”, line 305, in _save_features
features_data.save(fwb, self.config)
File “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\opensfm\features.py”, line 126, in save
np.savez_compressed(
File “<array_function internals>”, line 180, in savez_compressed
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\numpy\lib\npyio.py”, line 666, in savez_compressed
_savez(file, args, kwds, True)
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\numpy\lib\npyio.py”, line 699, in _savez
format.write_array(fid, val,
File “C:\WebODM\resources\app\apps\ODM\venv\lib\site-packages\numpy\lib\format.py”, line 694, in write_array
fp.write(chunk.tobytes(‘C’))
File “zipfile.py”, line 1139, in write
OSError: [Errno 28] No space left on device
Exception ignored in: <function ZipFile.del at 0x000001BE00005AF0>
Traceback (most recent call last):
File “zipfile.py”, line 1819, in del
File “zipfile.py”, line 1836, in close
ValueError: seek of closed file
[INFO] running “C:\WebODM\resources\app\apps\ODM\SuperBuild\install\bin\opensfm\bin\opensfm” match_features “C:\WebODM\resources\app\apps\NodeODM\data\059afb06-388d-46e1-81a5-8ee03aaf4f9e\opensfm”
2022-08-17 17:35:24,791 INFO: Matching 1238 image pairs
2022-08-17 17:35:24,791 INFO: Computing pair matching with 5 processes
2022-08-17 17:35:24,873 DEBUG: No segmentation for DJI_0294.JPG, no features masked.
2022-08-17 17:35:24,874 DEBUG: No segmentation for DJI_0363.JPG, no features masked.
2022-08-17 17:35:24,875 DEBUG: No segmentation for DJI_0296.JPG, no features masked.
2022-08-17 17:35:24,876 DEBUG: No segmentation for DJI_0323.JPG, no features masked.
2022-08-17 17:35:24,892 DEBUG: No segmentation for DJI_0383.JPG, no features masked.
2022-08-17 17:35:24,952 DEBUG: No segmentation for DJI_0319.JPG, no features masked.
2022-08-17 17:35:24,964 DEBUG: No segmentation for DJI_0297.JPG, no features masked.
2022-08-17 17:35:24,965 DEBUG: No segmentation for DJI_0389.JPG, no features masked.
2022-08-17 17:35:24,974 DEBUG: No segmentation for DJI_0321.JPG, no features masked.
2022-08-17 17:35:24,990 DEBUG: No segmentation for DJI_0400.JPG, no features masked.
2022-08-17 17:35:26,052 DEBUG: Matching DJI_0323.JPG and DJI_0297.JPG. Matcher: FLANN (symmetric) T-desc: 1.216 T-robust: 0.031 T-total: 1.254 Matches: 2365 Robust: 2338 Success: True
2022-08-17 17:35:26,063 DEBUG: Matching DJI_0363.JPG and DJI_0389.JPG. Matcher: FLANN (symmetric) T-desc: 1.263 T-robust: 0.002 T-total: 1.265 Matches: 324 Robust: 290 Success: True
2022-08-17 17:35:26,099 DEBUG: Matching DJI_0294.JPG and DJI_0319.JPG. Matcher: FLANN (symmetric) T-desc: 1.295 T-robust: 0.004 T-total: 1.301 Matches: 1438 Robust: 1391 Success: True
2022-08-17 17:35:26,109 DEBUG: Matching DJI_0296.JPG and DJI_0321.JPG. Matcher: FLANN (symmetric) T-desc: 1.305 T-robust: 0.004 T-total: 1.312 Matches: 1414 Robust: 1383 Success: True
2022-08-17 17:35:26,168 DEBUG: No segmentation for DJI_0370.JPG, no features masked.
2022-08-17 17:35:26,172 DEBUG: No segmentation for DJI_0412.JPG, no features masked.
2022-08-17 17:35:26,203 DEBUG: No segmentation for DJI_0293.JPG, no features masked.
2022-08-17 17:35:26,203 DEBUG: No segmentation for DJI_0361.JPG, no features masked.
2022-08-17 17:35:26,273 DEBUG: Matching DJI_0383.JPG and DJI_0400.JPG. Matcher: FLANN (symmetric) T-desc: 1.475 Matches: FAILED
2022-08-17 17:35:26,282 DEBUG: No segmentation for DJI_0419.JPG, no features masked.
2022-08-17 17:35:26,291 DEBUG: No segmentation for DJI_0292.JPG, no features masked.
2022-08-17 17:35:26,292 DEBUG: No segmentation for DJI_0340.JPG, no features masked.
2022-08-17 17:35:26,297 DEBUG: No segmentation for DJI_0396.JPG, no features masked.
2022-08-17 17:35:26,368 DEBUG: No segmentation for DJI_0397.JPG, no features masked.
2022-08-17 17:35:26,469 DEBUG: No segmentation for DJI_0385.JPG, no features masked.
2022-08-17 17:35:27,678 DEBUG: Matching DJI_0293.JPG and DJI_0292.JPG. Matcher: FLANN (symmetric) T-desc: 1.551 T-robust: 0.011 T-total: 1.569 Matches: 2773 Robust: 2722 Success: True
2022-08-17 17:35:27,724 DEBUG: Matching DJI_0361.JPG and DJI_0340.JPG. Matcher: FLANN (symmetric) T-desc: 1.620 T-robust: 0.003 T-total: 1.625 Matches: 748 Robust: 684 Success: True
2022-08-17 17:35:27,769 DEBUG: Matching DJI_0412.JPG and DJI_0419.JPG. Matcher: FLANN (symmetric) T-desc: 1.702 T-robust: 0.002 T-total: 1.705 Matches: 74 Robust: 58 Success: True
2022-08-17 17:35:27,773 DEBUG: No segmentation for DJI_0331.JPG, no features masked.
2022-08-17 17:35:27,828 DEBUG: No segmentation for DJI_0371.JPG, no features masked.
2022-08-17 17:35:27,860 DEBUG: No segmentation for DJI_0328.JPG, no features masked.
2022-08-17 17:35:27,863 DEBUG: No segmentation for DJI_0380.JPG, no features masked.
2022-08-17 17:35:27,890 DEBUG: Matching DJI_0370.JPG and DJI_0396.JPG. Matcher: FLANN (symmetric) T-desc: 1.830 T-robust: 0.004 T-total: 1.837 Matches: 1391 Robust: 1344 Success: True
2022-08-17 17:35:27,976 DEBUG: No segmentation for DJI_0367.JPG, no features masked.
2022-08-17 17:35:28,048 DEBUG: Matching DJI_0397.JPG and DJI_0385.JPG. Matcher: FLANN (symmetric) T-desc: 1.767 T-robust: 0.005 T-total: 1.774 Matches: 1339 Robust: 1316 Success: True
2022-08-17 17:35:28,061 DEBUG: No segmentation for DJI_0341.JPG, no features masked.
2022-08-17 17:35:28,117 DEBUG: No segmentation for DJI_0320.JPG, no features masked.
2022-08-17 17:35:28,185 DEBUG: No segmentation for DJI_0298.JPG, no features masked.
2022-08-17 17:35:28,623 DEBUG: Matching DJI_0380.JPG and DJI_0383.JPG. Matcher: FLANN (symmetric) T-desc: 0.817 T-robust: 0.035 T-total: 0.852 Matches: 37 Robust: 17 Success: False
2022-08-17 17:35:28,687 DEBUG: Matching DJI_0383.JPG and DJI_0371.JPG. Matcher: FLANN (symmetric) T-desc: 0.949 T-robust: 0.008 T-total: 0.961 Matches: 2112 Robust: 2068 Success: True
2022-08-17 17:35:28,712 DEBUG: No segmentation for DJI_0322.JPG, no features masked.
2022-08-17 17:35:28,771 DEBUG: No segmentation for DJI_0372.JPG, no features masked.
2022-08-17 17:35:28,872 DEBUG: Matching DJI_0331.JPG and DJI_0328.JPG. Matcher: FLANN (symmetric) T-desc: 1.186 T-robust: 0.004 T-total: 1.192 Matches: 1419 Robust: 1395 Success: True
2022-08-17 17:35:28,946 DEBUG: No segmentation for DJI_0301.JPG, no features masked.
2022-08-17 17:35:29,084 DEBUG: Matching DJI_0367.JPG and DJI_0341.JPG. Matcher: FLANN (symmetric) T-desc: 1.184 T-robust: 0.005 T-total: 1.192 Matches: 1194 Robust: 1158 Success: True
2022-08-17 17:35:29,138 DEBUG: Matching DJI_0320.JPG and DJI_0298.JPG. Matcher: FLANN (symmetric) T-desc: 1.073 T-robust: 0.010 T-total: 1.089 Matches: 2629 Robust: 2602 Success: True
2022-08-17 17:35:29,192 DEBUG: No segmentation for DJI_0346.JPG, no features masked.
2022-08-17 17:35:29,225 DEBUG: No segmentation for DJI_0339.JPG, no features masked.
2022-08-17 17:35:29,280 DEBUG: No segmentation for DJI_0310.JPG, no features masked.
2022-08-17 17:35:29,511 DEBUG: Matching DJI_0322.JPG and DJI_0323.JPG. Matcher: FLANN (symmetric) T-desc: 0.865 T-robust: 0.014 T-total: 0.889 Matches: 4635 Robust: 4611 Success: True
2022-08-17 17:35:29,585 DEBUG: Matching DJI_0383.JPG and DJI_0372.JPG. Matcher: FLANN (symmetric) T-desc: 0.889 T-robust: 0.006 T-total: 0.897 Matches: 1823 Robust: 1777 Success: True
2022-08-17 17:35:29,620 DEBUG: No segmentation for DJI_0377.JPG, no features masked.
2022-08-17 17:35:29,653 DEBUG: No segmentation for DJI_0335.JPG, no features masked.
2022-08-17 17:35:29,681 DEBUG: Matching DJI_0293.JPG and DJI_0301.JPG. Matcher: FLANN (symmetric) T-desc: 0.797 T-robust: 0.007 T-total: 0.808 Matches: 2339 Robust: 2291 Success: True
2022-08-17 17:35:29,769 DEBUG: No segmentation for DJI_0379.JPG, no features masked.
2022-08-17 17:35:29,848 DEBUG: No segmentation for DJI_0399.JPG, no features masked.
2022-08-17 17:35:29,934 DEBUG: Matching DJI_0363.JPG and DJI_0339.JPG. Matcher: FLANN (symmetric) T-desc: 0.789 T-robust: 0.004 T-total: 0.796 Matches: 1047 Robust: 1007 Success: True
2022-08-17 17:35:30,464 DEBUG: Matching DJI_0346.JPG and DJI_0310.JPG. Matcher: FLANN (symmetric) T-desc: 1.368 T-robust: 0.005 T-total: 1.379 Matches: 1680 Robust: 1653 Success: True
2022-08-17 17:35:30,546 DEBUG: No segmentation for DJI_0406.JPG, no features masked.
2022-08-17 17:35:30,560 DEBUG: Matching DJI_0322.JPG and DJI_0335.JPG. Matcher: FLANN (symmetric) T-desc: 0.969 T-robust: 0.004 T-total: 0.975 Matches: 1079 Robust: 1050 Success: True
2022-08-17 17:35:30,598 DEBUG: Matching DJI_0370.JPG and DJI_0372.JPG. Matcher: FLANN (symmetric) T-desc: 0.653 T-robust: 0.006 T-total: 0.664 Matches: 1779 Robust: 1757 Success: True
2022-08-17 17:35:30,660 DEBUG: Matching DJI_0377.JPG and DJI_0380.JPG. Matcher: FLANN (symmetric) T-desc: 1.111 T-robust: 0.036 T-total: 1.147 Matches: 30 Robust: 10 Success: False
2022-08-17 17:35:30,676 DEBUG: No segmentation for DJI_0349.JPG, no features masked.
2022-08-17 17:35:30,767 DEBUG: No segmentation for DJI_0368.JPG, no features masked.
2022-08-17 17:35:30,900 DEBUG: No segmentation for DJI_0394.JPG, no features masked.
2022-08-17 17:35:31,160 DEBUG: Matching DJI_0379.JPG and DJI_0399.JPG. Matcher: FLANN (symmetric) T-desc: 1.426 T-robust: 0.053 T-total: 1.479 Matches: 28 Robust: 9 Success: False
2022-08-17 17:35:31,275 DEBUG: No segmentation for DJI_0411.JPG, no features masked.
2022-08-17 17:35:31,559 DEBUG: Matching DJI_0406.JPG and DJI_0412.JPG. Matcher: FLANN (symmetric) T-desc: 1.090 T-robust: 0.002 T-total: 1.095 Matches: 831 Robust: 815 Success: True
2022-08-17 17:35:31,566 DEBUG: Matching DJI_0383.JPG and DJI_0406.JPG. Matcher: FLANN (symmetric) T-desc: 1.002 T-robust: 0.003 T-total: 1.005 Matches: 506 Robust: 480 Success: True
2022-08-17 17:35:31,672 DEBUG: No segmentation for DJI_0376.JPG, no features masked.
2022-08-17 17:35:31,687
1 Like
I think this is the problem:
Can you post your computer specs please, including disk drive details- size and free space.
| Device name | DESKTOP-CEVIQR8 |
|---|---|
| Processor | Intel(R) Core™ i5-1035G1 CPU @ 1.00GHz 1.19 GHz |
| Installed RAM | 12.0 GB (11.8 GB usable) |
| Device ID | 1CEE0CE9-47D4-48DF-8053-9955E47574C5 |
| Product ID | 00325-81673-90591-AAOEM |
| System type | 64-bit operating system, x64-based processor |
| Pen and touch | Touch support with 10 touch points |
| Edition | Windows 10 Home |
|---|---|
| Version | 21H2 |
| Installed on | 3/25/2021 |
| OS build | 19044.1889 |
| Experience | Windows Feature Experience Pack 120.2212.4180.0 |
1 Like
That isn’t really enough RAM, are you able to increase it at all?
What about:
Yes, I was actually thinking about adding Ram or just getting a more powerful computer. I’ll probably try the ram increase first. I do need to clean up some of my files as well to get more space. I appreciate all of your time and assistance.
1 Like
I suspect your hard drive/SSD is near full too.
yes it is.
Depending on the number of images you expect to be processing, you’ll need quite a bit more RAM. I’d suggest 32GB minimum for a few hundred images at high quality, more if you want to extract maximum detail with ultra quality in a reasonable time. A fast 1 or 2TB SSD with plenty of free space is ideal for virtual memory to increase the task size with more images, but is much slower than RAM.
A faster processor with more cores/logical processors would help a lot too.
2 Likes
This is great information. I’m not a tech person, but definatley helps me.
3 Likes
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