from sklearn import cross_validation
from sklearn.linear_model.logistic import LogisticRegression
import numpy as np
from glob import glob
from edginess import edginess_sobel
basedir = 'simple-dataset'
def features_for(im):
im = mh.imread(im,as_grey=True).astype(np.uint8)
return mh.features.haralick(im).mean(0)
features = []
sobels = []
labels = []
images = glob('{}/*.jpg'.format(basedir))
for im in images:
features.append(features_for(im))
sobels.append(edginess_sobel(mh.imread(im, as_grey=True)))
labels.append(im[:-len('00.jpg')])
features = np.array(features)
labels = np.array(labels)
scores = cross_validation.cross_val_score(LogisticRegression(), features, labels, cv=5)
print('Accuracy (5 fold x-val) with Logistic Regrssion [std features]: {}%'.format(0.1* round(1000*scores.mean())))
scores = cross_validation.cross_val_score(LogisticRegression(), np.hstack([np.atleast_2d(sobels).T,features]), labels, cv=5).mean()
print('Accuracy (5 fold x-val) with Logistic Regrssion [std features + sobel]: {}%'.format(0.1* round(1000*scores.mean())))
fs : ndarray
1-D array of features
'''
im = mh.imread(im, as_grey=True).astype(np.uint8)
return mh.features.haralick(im).mean(0)
haralicks = []
sobels = []
labels = []
# Use glob to get all the images
images = glob('{}/*.jpg'.format(basedir))
for fname in images:
haralicks.append(features_for(fname))
sobels.append(edginess_sobel(mh.imread(fname, as_grey=True)))
labels.append(fname[:-len('00.jpg')])
haralicks = np.array(haralicks)
sobels = np.array(sobels)
labels = np.array(labels)
# We use logistic regression because it is very fast.
# Feel free to experiment with other classifiers
scores = cross_validation.cross_val_score(LogisticRegression(),
haralicks,
labels,
cv=5)
print('Accuracy (5 fold x-val) with Logistic Regrssion [std features]: {}%'.
format(0.1 * round(1000 * scores.mean())))
print('picking ' + str(im) + ' ...')
shutil.copy(im, dst)
####################################################
# Xtrain1, Xtrain2, Ytrain
basedir = 'train-dogs-vs-cats'
features = []
sobels = []
labels = []
labelVect = [] # cat 0 , dog 1
images = glob('{}/*.jpg'.format(basedir))
for im in images:
features.append(features_for(im))
sobels.append(edginess_sobel(mh.imread(im, as_grey=True)))
#labels.append(im[:-len('00.jpg')])
if ("train-dogs-vs-cats/cat" in im[:-len('.jpg')]):
labels.append('cat')
labelVect.append(np.array([0]))
elif ("train-dogs-vs-cats/dog" in im[:-len('.jpg')]):
labels.append('dog')
labelVect.append(np.array([1]))
else:
raise Exception("unrecognized label:" + str(im[:-len('.jpg')]))
features = np.array(features)
labels = np.array(labels)
n = features.shape
nl = labels.shape
print('features=' + str(n))
def edginess_sobel_from_fname(fname):
from edginess import edginess_sobel
return edginess_sobel(mh.imread(fname, as_grey=True))
def edginess_sobel_from_fname(fname):
from edginess import edginess_sobel
return edginess_sobel(mh.imread(fname, as_grey=True))
