KartaView Tools

Description

Tools developed by KartaView to help contributors.

Requirements

• Python 3.6+
• Dependencies from requirements.txt file. The dependencies can be installed by running:

pip3 install virtualenv

virtualenv -p python3 .

source bin/activate

pip3 install -r requirements.txt

1. Upload photos to KartaView

Description

This script is used to upload sequences from a local directory. The available formats are:

• Sequences taken with the OSC mobile apps
• Exif images

Usage

cd /path_to_scripts/osc_tools

# help
python osc_tools.py -h

# help for upload
python osc_tools.py upload -h

# upload all sequences from ~/OSC_sequences folder
python osc_tools.py upload -p ~/OSC_seqences

2. Generate Exif info

Description

This script generates GPS Exif for each image. It has two options generating exif info from metadata file or generating exif info from a custom geojson file.

cd /path_to_scripts

# help
python osc_tools.py -h

# help for Exif generation
python osc_tools.py generate_exif -h

Option I. From an KV metadata format file

# Exif generation for mobile recorded sequence having metadata files in ~/OSC_sequences/Sequence1 folder
python osc_tools.py generate_exif -exif_source metadata -p ~/OSC_seqences/Sequence1

Option II. From custom geojson file and the images that require the exif data

# Exif generation for custom geojson + imagery 
python osc_tools.py generate_exif -exif_source custom_geojson -p ~/CustomFolderContainingGeoJsonAndImages

Folder structure

~/CustomFolderContainingGeoJsonAndImages
~/CustomFolderContainingGeoJsonAndImages/a_file.geojson
~/CustomFolderContainingGeoJsonAndImages/folder_with_images
~/CustomFolderContainingGeoJsonAndImages/folder_with_images/image1.jpg ~/CustomFolderContainingGeoJsonAndImages/folder_with_images/image2.jpg ~/CustomFolderContainingGeoJsonAndImages/folder_with_images/image3.jpg

Expand to see the custom geojson sample:

     
{
        "type":"FeatureCollection",
        "features":[
           {
              "type":"Feature",
              "properties":{
                 "order":1.0,
                 "path":"folder_with_images/image1.jpg",
                 "direction":236.0,
                 "Lat":1.910309,
                 "Lon":1.503069,
                 "Timestamp":"2020-01-20T08:00:01Z"
              },
              "geometry":{ 
                 "type":"Point",
                 "coordinates":[ 1.503069408072847, 1.910308570011793 ]
              }
           },
           {
              "type":"Feature",
              "properties":{
                 "order":2.0,
                 "path":"folder_with_images/image2.jpg",
                 "direction":236.0,
                 "Lat":1.910199,
                 "Lon":1.502908,
                 "Timestamp":"2020-01-20T08:01:21Z"
              },
              "geometry":{
                 "type":"Point",
                 "coordinates":[ 1.502907515952158, 1.910198963742701 ]
              }
           },
           {
              "type":"Feature",
              "properties":{
                 "order":3.0,
                 "path":"folder_with_images/image3.jpg",
                 "direction":236.0,
                 "Lat":1.910096,
                 "Lon":1.502764,
                 "Timestamp":"2020-01-20T08:12:10Z"
              },
              "geometry":{
                 "type":"Point",
                 "coordinates":[ 1.50276400212099, 1.910095961756973 ]
              }
           }
        ]
     
}

Docker Support

To run the scripts inside a Docker container:

make docker
docker run -Pit osc-up osc_tools.py
docker run -Pit --mount type=bind,source="$(pwd)",target=/opt/osc osc-up /opt/osc/osc_tools.py

The 'images' directory in the repo will be available in the container at /opt/osc/images

3. Downloading OpenStreetMap and OpenStreetCam data

Minimally tested with test_coordinates.txt

Create the environment:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Provide a list of all coordinates which should be searched for as lat-long pairs in a .txt file like this:

35.225784 -114.295925
35.225527 -114.289817
35.225294 -114.286065
35.225285 -114.281114
35.224792 -114.275691

Then, run the provided script:

python3 create_custom_database.py -f my_coordinates.txt -o downloads

This script can take a while (since the public overpass api has limited bandwith). This will create a new directory downloads where it saves a database.csv and all downloaded images like this:

	coordinates	photos	name	tiger:county	oneway	tiger:name_type	adot_name	ref	highway	source	lanes	tiger:name_base	tiger:reviewed	tiger:cfcc	maxspeed
0	[35.224792, -114.275691]	['downloads/photos/-114.275691_35.224792/982415_1cc50_27.jpg']	Bullhead City-Kingman Highway	nan	yes	nan	Bullhead City-Kingman Highway	AZ 68	trunk	survey	2	nan	nan	nan	65 mph
1	[35.225285, -114.281114]	['downloads/photos/-114.281114_35.225285/1250637_ea38d_5b26725fbf35c.jpg']	Bullhead City-Kingman Highway	nan	yes	nan	Bullhead City-Kingman Highway	AZ 68	trunk	survey	2	nan	nan	nan	65 mph
2	[35.225527, -114.289817]	['downloads/photos/-114.289817_35.225527/982415_0fa40_29.jpg']	Bullhead City-Kingman Highway	nan	yes	nan	Bullhead City-Kingman Highway	AZ 68	trunk	survey	2	nan	nan	nan	65 mph
3	[35.225784, -114.295925]	['downloads/photos/-114.295925_35.225784/982415_0fa40_8.jpg']	Bullhead City-Kingman Highway	nan	yes	nan	Bullhead City-Kingman Highway	AZ 68	trunk	survey	2	nan	nan	nan	65 mph
4	[35.225294, -114.286065]	['downloads/photos/-114.286065_35.225294/982415_0fa40_42.jpg']	Cibola Road	Mohave, AZ	nan	Rd	nan	nan	residential	nan	nan	Cibola	no	A41	nan

It automatically retrieves all available attributes for the given coordinates.