satsense.extract¶
Module for computing features.
satsense.extract.extract_features(features: Iterator[satsense.features.Feature], generator: satsense.generators.FullGenerator, n_jobs: int = -1)[source]¶Compute features.
Parameters:
- features – Iterable of features.
- generator – Generator providing the required windows on the image.
- n_jobs – The maximum number of processes to use. The default is to use the value returned by
os.cpu_count().Yields:
satsense.FeatureVector– The requested feature vectors.Examples
Extracting features from an image:
import numpy as np from satsense import Image from satsense.generators import FullGenerator from satsense.extract import extract_features from satsense.features import NirNDVI, HistogramOfGradients, Pantex # Define the features to calculate features = [ HistogramOfGradients(((50, 50), (100, 100))), NirNDVI(((50, 50),)), Pantex(((50, 50), (100, 100))), ] # Load the image into a generator # This generator splits the image into chunks of 10x10 pixels image = Image('test/data/source/section_2_sentinel.tif', 'quickbird') image.precompute_normalization() generator = FullGenerator(image, (10, 10)) # Calculate all the features and append them to a list vector = [] for feature_vector in extract_features(features, generator): # The shape returned is (x, y, w, v) # where x is the number of chunks in the x direction # y is the number of chunks in the y direction # w is the number of windows the feature uses # v is the length of the feature per window # Reshape the resulting vector so it is (x, y, w * v) # e.g. flattened along the windows and features data = feature_vector.vector.reshape( *feature_vector.vector.shape[0:2], -1) vector.append(data) # dstack reshapes the vector into and (x, y, n) # where n is the total length of all features featureset = np.dstack(vector)