def detect(self,num,classifier):
p = PCA()
imagename = self.GetRealtimeInput(0)
cv2.imshow('you', imagename)
cv2.waitKey(0)
cv2.imwrite('test.bmp',imagename)
facenum = num
avgvals = pickle.load(open(self.dir+'/average.db','r'))
eigenfaces = pickle.load(open(self.dir+'/eigenfaces.db','r'))
weights = p.inputWeight(imagename,facenum,avgvals,eigenfaces)
s = Predictor(classifier)
return s.predict(weights)
from eigenfaces import PCA import glob import sys ''' Test plotting weights with 3 eigenface reconstruction ''' if __name__ == "__main__": ''' 1 -> dir name 2 -> number of eigenfaces ''' P = PCA() files = [] for filename in glob.glob(sys.argv[1] + '/*.bmp'): files.append(filename) P.createFaceVal(files) P.calculateWeights(int(sys.argv[2])) P.plot()
from eigenfaces import PCA import glob import sys ''' Test for finding matched image ''' if __name__ == "__main__": ''' 1 -> image name 2 -> dir name 3 -> number of eigenfaces 4 -> threshold value ''' P = PCA() files = [] for filename in glob.glob(sys.argv[2] + '/*.bmp'): files.append(filename) P.createFaceVal(files) P.calculateWeights(int(sys.argv[3])) dist, result = P.findmatchingimage(sys.argv[1], int(sys.argv[3]), int(sys.argv[4])) print result
