def main():
I = read_image('camera.jpg')
display_image(I, "Original image")
# Arrange image in column vector.
I = I.flatten(1).T
# Generate Haar basis vector (rows of H).
H = numpy.asmatrix(haar.haar(len(I)))
# Project image on the new basis.
I_haar = H * I
# Remove the second half of the coefficient.
I_haar[len(I_haar)/2:, 0] = 0
# Recover the image by inverting change of basis.
I_haar = H.T * I_haar
error = I - I_haar
distance = numpy.sqrt(error.T * error)
# Rearrange pixels of the image.
I_haar = numpy.reshape(I_haar, (64, 64), 'F')
display_image(I_haar, "Recoevered image, distance = {}".format(distance))
pylab.show()
def apply(infile, outfile): im = Image.open(infile) pixels = list(im.getdata()) print "Olength", len(pixels) #print "Orig", pixels res = h.haar(pixels) print "Tlen", len(res) print "Tav", res[0] #print "Trans", res im.putdata(res) im.save(outfile)
