RoadTrip

carResults

(

)

#Loads Car Results from Car Detection Model output

openCarPredictions

(

)

#CLI: open car_predictions.csv

openMap

(

)

#CLI: open stitched-images.png

openRoadMap

(

)

#CLI: open map.svg'

openRoadPremap

(

)

#CLI: open premap.bmp

runSh

(

path

)

#CLI: run shell script form path

sqrt

(

)

#Square Root

createDirectories

(

)

#Create missing directories for project

getMirrored

(

x = None

y = None

)

#Mirror matrix

loadLocations

(

path

)

#Load georefernces from csv file. csv format: image, latTopLeftCorner, longTopLeftCorner, latDownRightCorner, longDownRightCorner

dictGeoreferences

(

path

correction=False

)

#Add georefernces to dictionary

calcRes

(

path

correction=False

)

#Calculate resolution in meters from georefernces

randomPair

(

latRange

lonRange

)

#Random lat lon pair.

offset

(

lat

lon

offset

)

#Add earths radius as offset in meters to georefences

coordinatesToMeters

(

point

point2

)

#Distance bewtween lat lon coordinates in meters.

coordinatesToPixels

(

coordinates

)

#Distance bewtween lat lon coordinates in pixels.

outerPoints

(

path

)

#Order coorners out of 4 points

randomRectangle

(

latRange

lonRange

size

)

#Generate random rect

tileReferences

(

path

)

#Load tile georefernces

dictTileReferences

(

path

correction=False

)

#Add tile georefernces to dictionary

ConvexHull(object)

#Convex Hull

add

(

point

)

#Add points to convex hull

_get_orientation

(

origin

)

#Convex hull orientation

compute_hull

(

)

#Computes convex hull

get_hull_points

(

)

#Returns Convex Hull points

display

(

)

#Matplot lib display o Convex Hull Points

giftWrappedBoardSize

(

georeferencesPath

)

#Get size of map out of georefences

coordConvexHull

(

georeferencesPath

)

#Convex Hull fromg georeferences file

bmp_to_svg

(

path

pathOut

)

#CLI: potrace bitmap to vector graphics

sekeletonize

(

path

pathOut

)

#Lines out of street detection output

loadTiles

(

path

amt = None

correction=False

)

#Load Tile images from path

loadImages

(

path

amt = None

correction=False

whereFrom='images/image-{}.jpg'

)

#Load Satelite images from path

createBoard

(

path

res

)

#Create Empty Map size of all georefences

createBoardPerfectSquares

(

)

#Empty Square Map

fitTiles

(

board

tiles

res

)

#Fit tiles to empty map

stitchImages

(

path

res

amt = None

correction=False

where='images/image-{}.jpg'

outputPath='files/stitched-images.png'

)

#Stitch images toghether out of georefences

stitchMasks

(

path

res

amt = None

correction=False

)

#Stitched road detection output as masked map

scale

(

img

fullSize1

fullSize2

roadSize

)

#Scale image

multiple

(

georeferencesPath

res

correction=False

)

#Multiple satelital images

single

(

)

#Singe satelite image

separated

(

)

#Multiple satelite images that do not compose a map

multipleNoSkeleton

(

georeferencesPath

res

correction=False

)

#Multiple road detection from satelite images

singleNoSkeleton

(

)

#Single road detection from satelite image

separatedNoSkeleton

(

)

#Multiple road detection satelite images that do not compose a map

executeRoutine

(

)

#Full standardize routine: creates directories, loads georeferences and calculates resolution, stitches images, detects roads, generates a single map out of all satellital images, detects cars on masked map, outputs map depicting both road detecion and car detection.

cli

(

cmmd

)

#CLI input

index

(

)

#Web Interface main endpoint

allowed_file

(

filename

)

#Web Interface check for file upload

favicon

(

)

#Web Interface favicon

upload_file

(

)

#Web Interface upload files

delete_file

(

file

)

#Web Interface delete file

run

(

)

#Web Interface Run main routine with executeRoutine()

done

(

)

#Retun results

interface

(

)

#Web CLI

roads

(

)

#Web Interface display road detection

cars

(

)

#Web Interface display car detection

server

(

)

#Launch WSGI server for Web Interface

keep_alive

(

)

#Threading for server instance