Numpy¶

Export to numpy arrays¶

pyotb objects can be exported to numpy array.

import pyotb
import numpy as np

# The following is a pyotb object
calibrated = pyotb.OpticalCalibration('image.tif', level='toa')

# The following is a numpy array
arr = np.asarray(calibrated)

# Note that the following is equivalent:
arr = calibrated.to_numpy()

Interact with numpy functions¶

pyotb objects can be transparently used in numpy functions.

For example:

import pyotb
import numpy as np

# The following is a pyotb object
inp = pyotb.Input('image.tif')

# Creating a numpy array of noise. The following is a numpy object
white_noise = np.random.normal(0, 50, size=inp.shape)

# Adding the noise to the image
noisy_image = inp + white_noise
# Magically, this is a pyotb object that has the same geo-reference as `inp`. 
# Note the `np.add(inp, white_noise)` would have worked the same

# Finally we can write the result like any pyotb object
noisy_image.write('image_plus_noise.tif')

Rasterio¶

pyotb objects can also be exported in a format usable by rasterio.

For example:

import pyotb
import rasterio
from scipy import ndimage

# Pansharpening + NDVI + creating bare soils mask
pxs = pyotb.BundleToPerfectSensor(
    inp='panchromatic.tif', 
    inxs='multispectral.tif'
)
ndvi = pyotb.RadiometricIndices({
    'in': pxs, 
    'channels.red': 3, 
    'channels.nir': 4, 
    'list': 'Vegetation:NDVI'
})
bare_soil_mask = (ndvi < 0.3)

# Exporting the result as array & profile usable by rasterio
mask_array, profile = bare_soil_mask.to_rasterio()

# Doing something in Python that is not possible with OTB, e.g. gathering 
# the contiguous groups of pixels with an integer index
labeled_mask_array, nb_groups = ndimage.label(mask_array)

# Writing the result to disk
with rasterio.open('labeled_bare_soil.tif', 'w', **profile) as f:
    f.write(labeled_mask_array)

This way of exporting pyotb objects is more flexible that exporting to numpy, as the user gets the profile dictionary. If the georeference or pixel size is modified, the user can update the profile accordingly.

Tensorflow¶

We saw that numpy operations had some limitations. To bypass those limitations, it is possible to use some Tensorflow operations on pyotb objects.

You need a working installation of OTBTF >=3.0 for this and then the code is like this:

import pyotb

def scalar_product(x1, x2):
    """This is a function composed of tensorflow operations."""
    import tensorflow as tf
    return tf.reduce_sum(tf.multiply(x1, x2), axis=-1)

# Compute the scalar product
res = pyotb.run_tf_function(scalar_product)('image1.tif', 'image2.tif')  

# Magically, `res` is a pyotb object
res.write('scalar_product.tif')

For some easy syntax, one can use pyotb.run_tf_function as a function decorator, such as:

import pyotb

# The `pyotb.run_tf_function` decorator enables the use of pyotb objects as 
# inputs/output of the function
@pyotb.run_tf_function
def scalar_product(x1, x2):
    import tensorflow as tf
    return tf.reduce_sum(tf.multiply(x1, x2), axis=-1)

res = scalar_product('image1.tif', 'image2.tif')
# Magically, `res` is a pyotb object

Advantages :

• The process supports streaming, hence the whole image is not loaded into memory
• Can be integrated in OTB pipelines