torchgeo/tests/data

This directory contains fake data used to test torchgeo. Depending on the type of dataset, fake data can be created in multiple ways:

GeoDataset

GeoDataset data can be created like so. We first open an existing data example and use it to copy the driver/CRS/transform to the fake data.

Raster data

import os

import numpy as np
import rasterio as rio

ROOT = "data/landsat8"
FILENAME = "LC08_L2SP_023032_20210622_20210629_02_T1_SR_B1.TIF"
SIZE = 64

with rio.open(os.path.join(ROOT, FILENAME), "r") as src:
    dtype = src.profile["dtype"]
    Z = np.random.randint(np.iinfo(dtype).max, size=(SIZE, SIZE), dtype=dtype)
    with rio.open(FILENAME, "w", **src.profile) as dst:
        for i in dst.indexes:
            dst.write(Z, i)

Optionally, if the dataset has a colormap, this can be copied like so:

cmap = src.colormap(1)
dst.write_colormap(1, cmap)

Vector data

import os
from collections import OrderedDict

import fiona

ROOT = "data/cbf"
FILENAME = "Ontario.geojson"

rec = {"type": "Feature", "id": "0", "properties": OrderedDict(), "geometry": {"type": "Polygon", "coordinates": [[(0, 0), (0, 1), (1, 1), (1, 0), (0, 0)]]}}
with fiona.open(os.path.join(ROOT, FILENAME), "r") as src:
    src.meta["schema"]["properties"] = OrderedDict()
    with fiona.open(FILENAME, "w", **src.meta) as dst:
        dst.write(rec)

NonGeoDataset

NonGeoDataset data can be created like so.

RGB images

import numpy as np
from PIL import Image

DTYPE = np.uint8
SIZE = 64

arr = np.random.randint(np.iinfo(DTYPE).max, size=(SIZE, SIZE, 3), dtype=DTYPE)
img = Image.fromarray(arr)
img.save("01.png")

Grayscale images

import numpy as np
from PIL import Image

DTYPE = np.uint8
SIZE = 64

arr = np.random.randint(np.iinfo(DTYPE).max, size=(SIZE, SIZE), dtype=DTYPE)
img = Image.fromarray(arr)
img.save("02.jpg")

Audio wav files

import numpy as np
from scipy.io import wavfile

audio = np.random.randn(1).astype(np.float32)
wavfile.write("01.wav", rate=22050, data=audio)

HDF5 datasets

import h5py
import numpy as np

DTYPE = np.uint8
SIZE = 64
NUM_CLASSES = 10

images = np.random.randint(np.iinfo(DTYPE).max, size=(SIZE, SIZE, 3), dtype=DTYPE)
masks = np.random.randint(NUM_CLASSES, size=(SIZE, SIZE), dtype=DTYPE)
with h5py.File("data.hdf5", "w") as f:
    f.create_dataset("images", data=images)
    f.create_dataset("masks", data=masks)

LAS Point Cloud files

import laspy

num_points = 4

las = laspy.read("0.las")
las.points = las.points[:num_points]

points = np.random.randint(low=0, high=100, size=(num_points,), dtype=las.x.dtype)
las.x = points
las.y = points
las.z = points

if hasattr(las, "red"):
    colors = np.random.randint(low=0, high=10, size=(num_points,), dtype=las.red.dtype)
    las.red = colors
    las.green = colors
    las.blue = colors

las.write("0.las")