python-SVD压缩图像文件方法

def printMat(inMat, thresh=0.7):

    for i in range(32):
        for k in range(32):
            if float(inMat[i,k]) > thresh:
                print (1,end=' ')
            else: 
                print (0,end=' ')
        print(end=' ')
#########################################################
def imgCompress(numSV=3, thresh=0.7):
    myl = []
    for line in open('d:\\0_5.txt').readlines():
        newRow = []
        for i in range(32):
            newRow.append(int(line[i]))
        myl.append(newRow)
    myMat = mat(myl)
    #print "****original matrix******"
    printMat(myMat, thresh)
    U,Sigma,VT = la.svd(myMat)
    SigRecon = mat(zeros((numSV, numSV)))
    for k in range(numSV):  
    #construct diagonal matrix from vector
        SigRecon[k,k] = Sigma[k]
    reconMat = U[:,:numSV]*SigRecon*VT[:numSV,:]
    #print "****reconstructed matrix using %d singular values******" % numSV
    print("shape(reconMat)",shape(reconMat))
    printMat(reconMat,thresh) 
    #只需要存储U和VT以及Sigma数据，一行即可.

    #降为2维，32*32 变为32*2*2  而原先32*32，所以压缩能得到1/8.

imgCompress()