From f7d18a56adbd7df161b9fa201771e9c9f1af4184 Mon Sep 17 00:00:00 2001 From: Himanshu Mishra Date: Sun, 24 Jan 2016 15:01:30 +0530 Subject: [PATCH] Replace normalise kwarg in hog with transform_sqrt --- skimage/feature/_hog.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/skimage/feature/_hog.py b/skimage/feature/_hog.py index 760102e7..75bdad6a 100644 --- a/skimage/feature/_hog.py +++ b/skimage/feature/_hog.py @@ -2,10 +2,11 @@ from __future__ import division import numpy as np from .._shared.utils import assert_nD from . import _hoghistogram +import warnings def hog(image, orientations=9, pixels_per_cell=(8, 8), - cells_per_block=(3, 3), visualise=False, normalise=False, + cells_per_block=(3, 3), visualise=False, transform_sqrt=False, feature_vector=True): """Extract Histogram of Oriented Gradients (HOG) for a given image. @@ -29,9 +30,9 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8), Number of cells in each block. visualise : bool, optional Also return an image of the HOG. - normalise : bool, optional + transform_sqrt : bool, optional Apply power law compression to normalise the image before - processing. + processing. DO NOT use this if the image contains negative values. feature_vector : bool, optional Return the data as a feature vector by calling .ravel() on the result just before returning. @@ -66,7 +67,10 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8), assert_nD(image, 2) - if normalise: + if transform_sqrt: + if image.min() < 0: + warnings.warn("The input image contains negative values. \ + This will produce NaNs in the result!") image = np.sqrt(image) """