h5image 0.4.1

Load and save images to HDF5 files

h5image

This package provides standard functions for interacting with HDF5 files as part of the CriticalMAAS project.

This package will create HDF5 files with the following structure:

• group : all data associated with a single map
  • json : the raw json file used to create the map and layers
  • map : the actual map image
    • data : the actual bytes of the image
    • corners : the corners of the map where layers have data
    • patches : for each layer (key) a list of patches as a tuple (x, y)
    • layers_patch : for each patch (key is x_y) a list of layers
    • valid_patche : a list of locations across all layers as a tuple (x, y)
  • <layer> : a layer of the map, the name of the layer is the name of file
    • data : the actual layer data
    • patches : a list of patches as a tuple (x, y) for this specific layer

Installation

Install using pip:

pip install h5image

To convert a folder of images to a HDF5 file use the h5create program.

Quickstart example

The following code will create a new HDF5 file with a map and a layer, find the patch with most layers, and load the map and the layers at that patch.

import matplotlib.pyplot as plt
import numpy as np
import rasterio
from h5image import H5Image

# create map file
map = "CA_Sage"
h5i = H5Image(f"{map}.hdf5", "w")
h5i.add_image(f"{map}.json", folder="./data")

# get biggest patch
patches = h5i.get_patches(map, True)
sorted_patches = {k: v for k, v in sorted(patches.items(), key=lambda item: len(item[1]), reverse=True)}
loc, loc_layers = next(iter(sorted_patches.items()))
row = int(loc.split("_")[0])
col = int(loc.split("_")[1])
print(f"Biggest number of layers ({len(loc_layers)}) for {map} is at ( {row}, {col})")

# plot map
rgb = h5i.get_patch(row, col, map)
plt.imshow(rgb)
plt.title('map')
plt.axis('off')
plt.show()

# plot layers and legend
for layer in loc_layers:
    rgb = h5i.get_patch(row, col, map, layer=layer)
    plt.imshow(rgb, cmap=plt.colormaps['plasma'])
    plt.title(layer)
    plt.axis('off')
    plt.show()

    rgb = h5i.get_legend(map, layer)
    plt.imshow(rgb)
    plt.title("legend")
    plt.axis('off')
    plt.show()

# saving image as geotiff
# can also use h5i.save_image(map, 'map')
dset = h5i.get_map(map)
if dset.ndim == 3:
    image = dset[...].transpose(2, 0, 1)  # rasterio expects bands first
else:
    image = np.array(dset[...], ndmin=3)
rasterio.open(f"{map}.tif", 'w', driver='GTiff', compress='lzw',
              height=image.shape[1], width=image.shape[2], count=image.shape[0], dtype=image.dtype,
              crs=h5i.get_crs(map, layer), transform=h5i.get_transform(map, layer)).write(image)

# close file
h5i.close()