mapdatasetgenerator 0.0.2

Map dataset generator for learning map representations and generation

MapDatasetGenerator

Generate and load dataset of road network maps.

Installation from pip

pip install mapdatasetgenerator

Creating patches

# Run this script to generate data in /output directory.
import logging
import sys

root = logging.getLogger()
root.setLevel(logging.INFO)

handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
root.addHandler(handler)

from mapdataset import ImageGroupReader, single_layer_converter, MapsDataset, MapReader


sfMap = MapReader('./data/input/sf_layered.txt', "SF_Layered")
mapsDataset = MapsDataset(
    patch_size=(32, 32), 
    stride=10, 
    sample_group_size=1280, 
    converter=single_layer_converter,
    outputDir="./data/output"
    ) 
    
mapsDataset.generate_patches(sfMap) #This will generate dill files which contain the saved sample lists.

Reading patches

# Script to read dill data objects as numpy arrays.
from PIL import Image
import os
import sys
import logging

root = logging.getLogger()
root.setLevel(logging.INFO)

handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
root.addHandler(handler)


from mapdataset import ImageGroupReader, single_layer_converter, MapsDataset, MapReader, ImageUtils

dillFolder = "./data/output/SF_Layered/32x32/group-1280-stride-10"

mapsDataset = MapsDataset(
    patch_size=(32, 32), 
    stride=10, 
    sample_group_size=1280, 
    converter=single_layer_converter,
    outputDir="./data/output"
    ) 

mapsDataset.loadPatches("./data/output/SF_Layered/32x32/group-1280-stride-10")
patchNo = randint(0, len(mapsDataset))
logging.info(f"reading patch {patchNo}")
patch = mapsDataset[patchNo]


im = ImageUtils.TorchNpPatchToPILImgGray(patch)
path = os.path.join(dillFolder, f"{patchNo}.png")
im.save(path)

Using for training

1. Create patches if you already do not have them
2. Create a MapsDataset object and load patches. Now you can use the dataset object as a regular Pytorch dataset or use it with a Dataloader.