mapillary_tools is a command line tool that uploads geotagged images and videos to Mapillary.

# Install mapillary_tools
pip install mapillary_tools

# Process and upload images and videos in the directory
mapillary_tools process_and_upload MY_CAPTURE_DIR

# List all commands
mapillary_tools --help

• Supported File Formats
• Installation
• Usage
  • Process and Upload
  • Process
  • Upload
• Advanced Usage
  • Local Video Processing
  • Geotagging with GPX
  • Authenticate
  • Image Description
  • Zip Images
• Development

mapillary_tools can upload both images and videos.

mapillary_tools supports JPG/JEPG images (.jpg, .jpeg), with the following EXIF tags minimally required:

• GPS Longitude
• GPS Latitude
• Date/Time Original or GPS Date/Time

mapillary_tools supports videos (.mp4, .360) that contain any of the following telemetry structures:

• GPMF: mostly GoPro videos
  • GoPro HERO series (from 5 to 11)
  • GoPro MAX
• CAMM: an open-standard telemetry spec supported by a number of cameras
  • Insta360 Pro2
  • Insta360 Titan
  • Ricoh Theta X
  • Labpano
  • and more...
• BlackVue videos
  • DR900S-1CH
  • DR900X Plus

1. Download the latest executable for your platform from the releases.
2. Move the executable to your system $PATH

NOTE: If you see the error "mapillary_tools is damaged and can’t be opened" on macOS, try to clear the extended attributes:

xattr -c mapillary_tools

To install or upgrade to the latest stable version:

pip install --upgrade mapillary_tools

If you can't wait for the latest features in development, install it from GitHub:

pip install --upgrade git+https://github.com/mapillary/mapillary_tools

NOTE: If you see "Permission Denied" error, try to run the command above with sudo, or install it in your local virtualenv (recommended).

A command line program such as Termux is required. Installation can be done without root privileges. The following commands will install Python 3, pip3, git, and all required libraries for mapillary_tools on Termux:

pkg install python git build-essential libgeos openssl libjpeg-turbo libexpat libexpat-static
pip install --upgrade pip wheel
pip install --upgrade mapillary_tools

Termux must access the device's internal storage to process and upload images. To do this, use the following command:

termux-setup-storage

Finally, on devices running Android 11, using a command line program, mapillary_tools will process images very slowly if they are in shared internal storage during processing. It is advisable to first move images to the command line program’s native directory before running mapillary_tools. For an example using Termux, if imagery is stored in the folder Internal storage/DCIM/mapillaryimages the following command will move that folder from shared storage to Termux:

mv -v storage/dcim/mapillaryimages mapillaryimages

For most users, process_and_upload is the command to go:

# Process and upload all images and videos in MY_CAPTURE_DIR and its subfolders, and all videos under MY_VIDEO_DIR
mapillary_tools process_and_upload MY_CAPTURE_DIR MY_VIDEO_DIR/*.mp4

If any process error occurs, e.g. GPS not found in an image, mapillary_tools will exit with non-zero status code. To ignore these errors and continue uploading the rest:

# Skip process errors and upload to the specified user and organization
mapillary_tools process_and_upload MY_CAPTURE_DIR MY_VIDEO_DIR/*.mp4 \
    --skip_process_errors \
    --user_name "my_username" \
    --organization_key "my_organization_id"

The process_and_upload command will run the process and the upload commands consecutively with combined required and optional arguments. The command above is equivalent to:

mapillary_tools process MY_CAPTURE_DIR MY_VIDEO_DIR/*.mp4 \
    --skip_process_errors \
    --desc_path /tmp/mapillary_description_file.json

mapillary_tools upload MY_CAPTURE_DIR MY_VIDEO_DIR/*.mp4 \
    --desc_path /tmp/mapillary_description_file.json \
    --user_name "my_username" \
    --organization_key "my_organization_id"

The process command is an intermediate step that extracts the metadata from images and videos, and writes them in an image description file. Users should pass it to the upload command.

mapillary_tools process MY_CAPTURE_DIR MY_VIDEO_DIR/*.mp4

Duplicate check with custom distance and angle:

# Mark images that are 3 meters closer to its previous one as duplicates.
# Duplicates won't be uploaded
mapillary_tools process MY_CAPTURE_DIR \
    --duplicate_distance 3 \
    --duplicate_angle 360  # Set 360 to disable angle check

Split sequences with the custom cutoff distance or custom capture time gap:

# If two successive images are 100 meters apart,
# OR their capture times are 120 seconds apart,
# then split the sequence from there
mapillary_tools process MY_CAPTURE_DIR \
    --offset_angle 90 \
    --cutoff_distance 100 \
    --cutoff_time 120 \

After processing you should get the image description file. Pass it to the upload command to upload them:

# Upload processed images and videos to the specified user account and organization
mapillary_tools upload  MY_CAPTURE_DIR \
    --desc_path /tmp/mapillary_image_description.json \
    --user_name "my_username" \
    --organization_key "my_organization_id"

Local video processing samples a video into a sequence of sample images and ensures the images are geotagged and ready for uploading. It gives users more control over the sampling process, for example, you can specify the sampling distance to control the density. Also, the sample images have smaller file sizes than videos, hence saving bandwidth.

FFmpeg is required for local video processing. You can download ffmpeg and ffprobe from here, or install them with your favorite package manager.

mapillary_tools first extracts the GPS track from the video's telemetry structure, and then locates video frames along the GPS track. When all are located, it then extracts one frame (image) every 3 meters by default.

# Sample videos in MY_VIDEO_DIR and write the sample images in MY_SAMPLES with a custom sampling distance
mapillary_tools video_process MY_VIDEO_DIR MY_SAMPLES --video_sample_distance 5
# The command above is equivalent to
mapillary_tools sample_video MY_VIDEO_DIR MY_SAMPLES --video_sample_distance 5
mapillary_tools process MY_SAMPLES

To process and upload the sample images consecutively, run:

mapillary_tools video_process_and_upload MY_VIDEO_DIR MY_SAMPLES --video_sample_distance 5
# The command above is equivalent to
mapillary_tools video_process MY_VIDEO_DIR MY_SAMPLES --video_sample_distance 5 --desc_path=/tmp/mapillary_description.json
mapillary_tools upload MY_SAMPLES --desc_path=/tmp/mapillary_description.json

If you use external GPS devices for mapping, you will need to geotag your captures with the external GPS tracks.

To geotag images with a GPX file, the capture time (extracted from EXIF tag "Date/Time Original" or "GPS Date/Time") is minimally required. It is used to locate the images along the GPS tracks.

mapillary_tools process MY_IMAGE_DIR --geotag_source "gpx" --geotag_source_path MY_EXTERNAL_GPS.gpx

To geotag videos with a GPX file, video start time (video creation time minus video duration) is required to locate the sample images along the GPS tracks.

# Geotagging with GPX works with interval-based sampling only,
# the two options --video_sample_distance -1 --video_sample_interval 2 are therefore required
# to switch from the default distance-based sampling to the legacy interval-based sampling
mapillary_tools video_process MY_VIDEO_DIR \
    --geotag_source "gpx" \
    --geotag_source_path MY_EXTERNAL_GPS.gpx \
    --video_sample_distance -1 --video_sample_interval 2

Ideally, the GPS device and the capture device should use the same clock to get the timestamps synchronized. If not, as is often the case, the image locations will be shifted. To solve that, mapillary_tools provides an option --interpolation_offset_time N that adds N seconds to image capture times for synchronizing the timestamps.

# The capture device's clock is 8 hours (i.e. -8 * 3600 = -28800 seconds) ahead of the GPS device's clock
mapillary_tools process MY_IMAGE_DIR \
    --geotag_source "gpx" \
    --geotag_source_path MY_EXTERNAL_GPS.gpx \
    --interpolation_offset_time -28800

Another option --interpolation_use_gpx_start_time moves your images to align with the beginning of the GPS track. This is useful when you can confirm that you start GPS recording and capturing at the same time, or with a known delay.

# Start capturing 2.5 seconds after start GPS recording
mapillary_tools video_process MY_VIDEO_DIR \
    --geotag_source "gpx" \
    --geotag_source_path MY_EXTERNAL_GPS.gpx \
    --interpolation_use_gpx_start_time \
    --interpolation_offset_time 2.5 \
    --video_sample_distance -1 --video_sample_interval 2

As experimental features, mapillary_tools can now:

• Geotag videos from tracks recorded in GPX and NMEA files
• Invoke exiftool internally, if available on the system. exiftool can extract geolocation data from a wide range of video formats, allowing us to support more cameras with less trouble for the end user.
• Try several geotagging sources sequentially, until proper data is found.

These features apply to the process command, which analyzes the video for direct upload instead of sampling it into images. They are experimental and will be subject to change in future releases.

The new video processing is triggered with the --video_geotag_source SOURCE option. It can be specified multiple times: In this case, each source will be tried in turn, until one returns good quality data.

SOURCE can be:

1. the plain name of the source - one of video, camm, gopro, blackvue, gpx, nmea, exiftool_xml, exiftool_runtime
2. a JSON object that includes optional parameters

{
  "source": "SOURCE",
  "pattern": "FILENAME_PATTERN" // optional
}

PATTERN specifies how the data source file is named, starting from the video filename:

• %f: the full video filename
• %g: the video filename without extension
• %e: the video filename extension

Supported sources and their default pattern are:

• video: parse the video, in order, as camm, gopro, blackvue. Pattern: %f
• camm: parse the video looking for a CAMM track. Pattern: %f
• gopro: parse the video looking for geolocation in GoPro format. Pattern: %f
• blackvue: parse the video looking for geolocation in BlackVue format. Pattern: %f
• gpx: external GPX file. Pattern: %g.gpx
• nmea: external NMEA file. Pattern: %g.nmea
• exiftool_xml: external XML file generated by exiftool. Pattern: %g.xml
• exiftool_runtime: execute exiftool on the video file. Pattern: %f

Notes:

• exiftool_runtime only works if exiftool is installed on the system. You can find it at https://exiftool.org/ or through your software manager. If exiftool is installed, but is not in the default execution path, it is possible to specify its location by setting the environment variable MAPILLARY_TOOLS_EXIFTOOL_PATH.
• Pattern are case-sensitive or not depending on the filesystem - in Windows, %g.gpx will match both basename.gpx and basename.GPX, in MacOs, Linux or other Unix systems no.
• If both --video_geotag_source and --geotag_source are specified, --video_geotag_source will apply to video files and --geotag_source to image files.

Process all videos in a directory, trying to parse them as CAMM, GoPro or BlackVue:

mapillary_tools process --video_geotag_source video VIDEO_DIR/

Process all videos in a directory, taking geolocation data from GPX files. A video named foo.mp4 will be associated with a GPX file called foo.gpx.

mapillary_tools process --video_geotag_source gpx VIDEO_DIR/

The videos to process have been stitched by Insta360 Studio; the geolocation data is in the original videos in the parent directory, and there may be GPX files alongside the stitched video. First look for GPX, then fallback to running exiftool against the original videos.

mapillary_tools process \
--video_geotag_source gpx \
--video_geotag_source '{"source": "exiftool_runtime", "pattern": "../%g.insv"}' \
VIDEO_DIR/

External geolocation sources will be aligned with the start of video, there is currently no way of applying offsets or scaling the time. This means, for instance, that GPX tracks must begin precisely at the instant of the video, and that timelapse videos are supported only for sources camm, gopro, blackvue.

The command authenticate will update the user credentials stored in the config file.

Authenticate new user:

mapillary_tools authenticate

Authenticate for user my_username. If the user is already authenticated, it will update the credentials in the config:

mapillary_tools authenticate --user_name "my_username"

The output of the process command is a JSON array of objects that describes metadata for each image or video. The metadata is validated by the image description schema. Here is a minimal example:

[
  {
    "MAPLatitude": 58.5927694,
    "MAPLongitude": 16.1840944,
    "MAPCaptureTime": "2021_02_13_13_24_41_140",
    "filename": "/MY_IMAGE_DIR/IMG_0291.jpg"
  },
  {
    "error": {
      "type": "MapillaryGeoTaggingError",
      "message": "Unable to extract GPS Longitude or GPS Latitude from the image"
    },
    "filename": "/MY_IMAGE_DIR/IMG_0292.jpg"
  }
]

Users may create or manipulate the image description file before passing them to the upload command. Here are a few examples:

# Remove images outside the bounding box and map matching the rest images on the road network
mapillary_tools process MY_IMAGE_DIR | \
    ./filter_by_bbox.py 5.9559,45.818,10.4921,47.8084  | \
    ./map_match.py > /tmp/mapillary_image_description.json

# Upload the processed images
mapillary_tools upload  MY_IMAGE_DIR --desc_path /tmp/mapillary_image_description.json

# Converts captures.csv to an image description file
./custom_csv_to_description.sh captures.csv | mapillary_tools upload MY_IMAGE_DIR --desc_path -

When uploading an image directory, internally the upload command will zip sequences in the temporary directory (TMPDIR) and then upload these zip files.

mapillary_tools provides zip command that allows users to specify where to store the zip files, usually somewhere with faster IO or more free space.

# Zip processed images in MY_IMAGE_DIR and write zip files in MY_ZIPFILES
mapillary_tools zip MY_IMAGE_DIR MY_ZIPFILES

# Upload all the zip files (*.zip) in MY_ZIPFILES:
mapillary_tools upload --file_types zip MY_ZIPFILES

Clone the repository:

git clone [email protected]:mapillary/mapillary_tools.git
cd mapillary_tools

Set up the virtual environment. It is optional but recommended:

pip install pipenv

Install dependencies:

pipenv install -r requirements.txt
pipenv install -r requirements-dev.txt

Enter the virtualenv shell:

pipenv shell

Run the code from the repository:

python3 -m mapillary_tools.commands --version

Run tests:

# test all cases
python3 -m pytest -s -vv tests
# or test a single case specifically
python3 -m pytest -s -vv tests/unit/test_camm_parser.py::test_build_and_parse

Run linting:

# format code
black mapillary_tools tests
# sort imports
usort format mapillary_tools tests

# Assume you are releasing v0.9.1a2 (alpha2)

# Tag your local branch
# Use -f here to replace the existing one
git tag -f v0.9.1a2

# Push the tagged commit first if it is not there yet
git push origin

# Push ALL local tags (TODO: How to push a specific tag?)
# Use -f here to replace the existing tags in the remote repo
git push origin --tags -f

# The last step will trigger CI to publish a draft release with binaries built
# in https://github.com/mapillary/mapillary_tools/releases