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extract_features.py
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extract_features.py
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import cv2
import numpy as np
import data
import shutil,sys
import skimage.feature
from sklearn import preprocessing as prep
from sklearn.decomposition import PCA
from skimage import measure
from skimage import morphology
from skimage.feature import peak_local_max
from skimage import segmentation
from skimage.morphology import watershed
import mahotas as mh
if not len(sys.argv) == 2:
subset = "train"
print "Usage extract_features.py <subset:train or test (default=train)>"
else:
subset = sys.argv[1]
SAVE_PATH = "data/features_%s.pkl"%subset
scale = True
images = data.load(subset)
# images_test = data.load('test')
print images.shape
def pause():
import time
time.sleep(10000)
def getLargestRegion(props, labelmap, imagethres):
regionmaxprop = None
for regionprop in props:
# check to see if the region is at least 50% nonzero
if sum(imagethres[labelmap == regionprop.label])*1.0/regionprop.area < 0.50:
continue
if regionmaxprop is None:
regionmaxprop = regionprop
if regionmaxprop.filled_area < regionprop.filled_area:
regionmaxprop = regionprop
return regionmaxprop
def getMinorMajorRatio_2(image):
image = image.copy()
# Create the thresholded image to eliminate some of the background
imagethr = np.where(image > np.mean(image),0.,1.0)
#Dilate the image
imdilated = morphology.dilation(imagethr, np.ones((4,4)))
# Create the label list
label_list = measure.label(imdilated)
label_list = imagethr*label_list
label_list = label_list.astype(int)
region_list = measure.regionprops(label_list)
maxregion = getLargestRegion(region_list, label_list, imagethr)
# guard against cases where the segmentation fails by providing zeros
ratio = 0.0
minor_axis_length = 0.0
major_axis_length = 0.0
area = 0.0
convex_area = 0.0
eccentricity = 0.0
equivalent_diameter = 0.0
euler_number = 0.0
extent = 0.0
filled_area = 0.0
orientation = 0.0
perimeter = 0.0
solidity = 0.0
centroid = [0.0,0.0]
if ((not maxregion is None) and (maxregion.major_axis_length != 0.0)):
ratio = 0.0 if maxregion is None else maxregion.minor_axis_length*1.0 / maxregion.major_axis_length
minor_axis_length = 0.0 if maxregion is None else maxregion.minor_axis_length
major_axis_length = 0.0 if maxregion is None else maxregion.major_axis_length
area = 0.0 if maxregion is None else maxregion.area
convex_area = 0.0 if maxregion is None else maxregion.convex_area
eccentricity = 0.0 if maxregion is None else maxregion.eccentricity
equivalent_diameter = 0.0 if maxregion is None else maxregion.equivalent_diameter
euler_number = 0.0 if maxregion is None else maxregion.euler_number
extent = 0.0 if maxregion is None else maxregion.extent
filled_area = 0.0 if maxregion is None else maxregion.filled_area
orientation = 0.0 if maxregion is None else maxregion.orientation
perimeter = 0.0 if maxregion is None else maxregion.perimeter
solidity = 0.0 if maxregion is None else maxregion.solidity
centroid = [0.0,0.0] if maxregion is None else maxregion.centroid
return ratio,minor_axis_length,major_axis_length,area,convex_area,eccentricity,\
equivalent_diameter,euler_number,extent,filled_area,orientation,perimeter,solidity, centroid[0], centroid[1]
features = {"hu":[],"ORB":[],"tutorial":[],"haralick":[],"lbp":[],"pftas":[],
"zernike_moments":[],"image_size":[]}
image_shapes = np.asarray([img.shape for img in data.load('train')]).astype(np.float32)
# print image_shapes.shape
# pause()
# img_shp = (64,64)
# resized_imgs = np.empty((len(images),np.prod(img_shp)),dtype="uint8")
report = [int((j+1)*images.shape[0]/100.) for j in range(100)]
# count = 0
for i,img in enumerate(images):
img_o = img.copy()
img = data.uint_to_float(img)
# Hu moments
hu = cv2.HuMoments(cv2.moments(img)).flatten()
features["hu"].append(hu)
# ORB
# img_ = cv2.resize(img_o,img_shp)
# orb = cv2.ORB(nfeatures=1,patchSize=5,nlevels=4,edgeThreshold=5)
# kp, des = orb.detectAndCompute(img_,None)
# if (des is None):
# des = np.zeros((32,))
# count+=1
# else:
# des = des.flatten()
# features["ORB"].append(des)
#Tutorial features
tut_features = np.array(getMinorMajorRatio_2(img_o))
# image2 = mh.imread(nameFileImage, as_grey=True)
haralick = mh.features.haralick(img_o, ignore_zeros=False,
preserve_haralick_bug=False, compute_14th_feature=False).flatten()
lbp = mh.features.lbp(img_o, radius=20, points=7, ignore_zeros=False)
pftas = mh.features.pftas(img_o)
zernike_moments = mh.features.zernike_moments(img_o, radius=20, degree=8)
# print tut_features.shape, tut_features.dtype
# print haralick.shape, haralick.dtype
# print lbp.shape, lbp.dtype
# print pftas.shape, pftas.dtype
# print zernike_moments.shape, zernike_moments.dtype
# print np.array(img_o.shape).shape
features["tutorial"].append(tut_features)
features["haralick"].append(haralick)
features["lbp"].append(lbp)
features["pftas"].append(pftas)
features["zernike_moments"].append(zernike_moments)
features["image_size"].append(np.array(img_o.shape))
# pause()
if i in report: print np.ceil(i *100.0 / images.shape[0]), "% done"
# resized_imgs[i] = img_.flatten()
# print count
# print "PCA"
# pca = PCA(n_components=10)
# pca.fit(resized_imgs)
# x_pca = pca.transform(resized_imgs)
# # print x_pca[0]
# # pause()
# if scale:
# scaler = prep.StandardScaler().fit(x_pca)
# x_pca = scaler.transform(x_pca)
for k,v in features.items():
x = np.array(v,dtype="float32")
if scale:
scaler = prep.StandardScaler().fit(x)
x = scaler.transform(x)
features.update({k:x})
# features["PCA"] = x_pca
np.save(open(SAVE_PATH,"wb"),features)
# shutil.move("/home/lpigou/temp.np",SAVE_PATH)