def getFeatureExtractors(self):
hueHistogramFeatureExtractor = HueHistogramFeatureExtractor(10)
edgeHistogramFeatureExtractor = EdgeHistogramFeatureExtractor(10)
haarLikeFeatureExtractor = HaarLikeFeatureExtractor(
fname='/Users/burton/Downloads/SimpleCV/SimpleCV/Features/haar.txt'
)
return [
hueHistogramFeatureExtractor, edgeHistogramFeatureExtractor,
haarLikeFeatureExtractor
]
def createExtractor(self, extractorName, trainPaths=[]):
if (extractorName == 'hue'):
extractor = HueHistogramFeatureExtractor(10)
elif (extractorName == 'edge'):
extractor = EdgeHistogramFeatureExtractor(10)
elif (extractorName == 'haar'):
extractor = HaarLikeFeatureExtractor(fname='haar.txt')
elif (extractorName == 'bof'):
extractor = BOFFeatureExtractor()
extractor.generate(trainPaths, imgs_per_dir=40)
# need to build the vocabuary (feature words) for bag of feature
# extractor.generate(trainPaths, imgs_per_dir=40)
return extractor
from sklearn.ensemble import AdaBoostClassifier
from sklearn import cross_validation
from SimpleCV import Image
from SimpleCV import HueHistogramFeatureExtractor
from SimpleCV import HaarLikeFeatureExtractor
from SimpleCV import EdgeHistogramFeatureExtractor
import numpy as np
from glob import glob
import pickle
# model to extract feature
hhfe = HueHistogramFeatureExtractor(10)
ehfe = EdgeHistogramFeatureExtractor(10)
haarfe = HaarLikeFeatureExtractor('haar.txt')
# Give path of the training folder
images = glob('./fruits/*')
# Extract features and target labels for training
def get_feature_labels():
features = list()
labels = list()
for im in images:
try:
img = Image(im)
labels.append(im[:-2])
features.append(
np.concatenate([
from glob import glob
from SimpleCV import Image
from SimpleCV import HueHistogramFeatureExtractor
from SimpleCV import HaarLikeFeatureExtractor
from SimpleCV import EdgeHistogramFeatureExtractor
from matplotlib import pyplot as plt
import os
from Services.MainServices import make_histogram
dataDir = os.path.dirname(os.path.abspath(__file__))
haar_file = os.path.join(dataDir, "../data/haar.txt")
haarfe = HaarLikeFeatureExtractor(fname=haar_file)
hhfe = HueHistogramFeatureExtractor(10)
ehfe = EdgeHistogramFeatureExtractor(10)
classifier = pickle.load(
open(os.path.join(dataDir, "../data/classifier.pkl"), 'rb'))
labels = pickle.load(open(os.path.join(dataDir, "../data/featLabel.pkl"),
'rb'))
def predictor():
for img in glob('/home/linuxsagar/tempTest/*'):
#List to hold the feature of new Image
_new_features = []