Open himynamesdave opened 9 months ago
The local SQLite database is structured with the following tables
input
id
timestamp
cli_mode
cli_input_path
cli_extract_fps
cli_keep_extracted_frames
cli_path_to_nadir
cli_size_of_nadir
cli_outlier_speed_meters_sec
cli_max_output_video_secs
cli_output_filepath
cli_upload_to_streetview
output_directory
streetview_id
streetview_status
steetview_error
video2video
input_id
length
Width
Height
frames2video
ProjectionType
GPSLatitudeRef
GPSLatitude
GPSLongitudeRef
GPSLongitude
GPSAltitudeRef
GPSAltitude
GPSTimeStamp
GPSDateStamp
time_secs_to_previous
time_secs_to_next
distance_km_to_previous
distance_km_to_next
speed_kmh_to_previous
speed_kmh_to_next
error_reported
gsvauth
Can be done easily with geopy for speed/distance --> https://pypi.org/project/geopy/
The local SQLite database is structured with the following tables
input
id
: UUID v4 for inputtimestamp
: time of importcli_mode
: selected via CLIcli_input_path
: selected via CLI (either directory to images, or video path)cli_extract_fps
: selected via CLIcli_keep_extracted_frames
: selected via CLIcli_path_to_nadir
: selected via CLIcli_size_of_nadir
: selected via CLIcli_outlier_speed_meters_sec
: selected via CLIcli_max_output_video_secs
: selected via CLIcli_output_filepath
: selected via CLIcli_upload_to_streetview
: selected via CLIoutput_directory
streetview_id
streetview_status
steetview_error
video2video
(for mode 1)input_id
: input ID video linked toid
: UUID v4 for videolength
: extracted via exiftoolWidth
: extracted via exiftoolHeight
: extracted via exiftoolframes2video
(for mode 2, 3, 4)input_id
: input ID video linked toid
: UUID v4 for videoWidth
Height
ProjectionType
GPSLatitudeRef
GPSLatitude
GPSLongitudeRef
GPSLongitude
GPSAltitudeRef
GPSAltitude
GPSTimeStamp
GPSDateStamp
time_secs_to_previous
time_secs_to_next
distance_km_to_previous
distance_km_to_next
speed_kmh_to_previous
speed_kmh_to_next
error_reported
gsvauth
Can be done easily with geopy for speed/distance --> https://pypi.org/project/geopy/