def feature_extractor(): #Abrir arquivo que vai receber as features dos arquivos de imagens file = "image_features_train_20_15.arff" text_file = open(file, "a") trainPath = "C:/Python27/ARP/Dataset/train2" dirList = os.listdir(trainPath) edge = EdgeHistogramFeatureExtractor(20) for dirName in dirList: fileList = os.listdir(trainPath + '/' + dirName) maxsize = len(fileList) for i in range(0, maxsize): #print fileList[i] filePATH = (trainPath + '/' + dirName + '/' + fileList[i]) img = Image(filePATH) #Feature - Edge histogram edge_vecs = edge.extract(img) edge_fields = edge.getFieldNames() for y in range(0, edge.getNumFields()): #print edge_fields[y], '=', edge_vecs[y] text_file.write(str(edge_vecs[y]) + ",") #Feature - Hue hue = HueHistogramFeatureExtractor(15) hue_vecs = hue.extract(img) hue_fields = hue.getFieldNames() for i in range(0, hue.getNumFields()): text_file.write(str(hue_vecs[i]) + ",") text_file.write(dirName + "\n") text_file.close()
def hue(): hue = HueHistogramFeatureExtractor() a = np.array(hue.extract(watchman.imgbank[0])) if len(watchman.imgbank) > 1: b = np.array(hue.extract(watchman.imgbank[1])) else: b = np.array(hue.extract(watchman.imgbank[0])) AandB = np.sum(np.square(a-b)) return AandB
def hue(): hue = HueHistogramFeatureExtractor() a = np.array(hue.extract(watchman.imgbank[0])) if len(watchman.imgbank) > 1: b = np.array(hue.extract(watchman.imgbank[1])) else: b = np.array(hue.extract(watchman.imgbank[0])) AandB = np.sum(np.square(a - b)) return AandB
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
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.pipeline import Pipeline from sklearn.preprocessing import StandardScaler 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])