def main(argv):
# discretize_channel(9)
# discretize_channel(10)
vals = discretize_channel(9)
print vals
rand_block = random_11_block(vals)
print rand_block
dctd = two_dim_DCT(rand_block)
print 'DCTd'
print_8_by_8(dctd)
jpeg = JPEG()
print
# For visualizing the quantization matrices.
#
# for quality in range(5, 96):
# quantized = jpeg.scaled_luminance_quant_matrix(quality)
# with open('quant_%s.log' % quality, 'w') as fh:
# for row in range(8):
# for col in range(8):
# fh.write('%d %d %d\n' % (row, col, quantized[row,col]))
quantized = jpeg.luminance_quantize(dctd, 75)
print_8_by_8(quantized)
print 'Dequanitzed'
dequantized = jpeg.luminance_dequantize(quantized, 75)
print_8_by_8(dequantized)
idctd = two_dim_DCT(dequantized, False)
print 'iDCT'
print_8_by_8(idctd)
print 'Diff'
print_8_by_8( idctd - rand_block)
print 'FS'
dithered = jpeg.fs_dither(idctd)
print_8_by_8(dithered)
print 'Diff Dither'
print_8_by_8( dithered - rand_block)
for col in range(8):
print '%5.1f' % orig_mat[row, col],
print ''
print
# Shift for [-128, 127]
mat = orig_mat - 128
for row in range(8):
for col in range(8):
print '%4d' % mat[row, col],
print ''
print
dct_mat = two_dim_DCT(mat)
for row in range(8):
for col in range(8):
print '%8.2f' % dct_mat[row, col],
print ''
print
scale_factor = jpeg_scale_factor(_QUALITY)
print scale_factor
quantized_mat = numpy.zeros((8,8))
for i, quant in enumerate(_QUANT_TABLE):
row = i / 8
col = i % 8
# temp = float(quant)
