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helper.py
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helper.py
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import numpy as np
import tensorflow as tf
import os
from scipy import misc
from ms_ssim_np import MultiScaleSSIM
import arithmeticcoding
def configure(args):
if args.l == 256:
I_level = 37
elif args.l == 512:
I_level = 32
elif args.l == 1024:
I_level = 27
elif args.l == 2048:
I_level = 22
elif args.l == 8:
I_level = 3
elif args.l == 16:
I_level = 3
elif args.l == 32:
I_level = 5
elif args.l == 64:
I_level = 7
path = args.path + '/'
path_com = args.path + '_' + args.mode + '_' + str(args.l) + '/frames/'
path_bin = args.path + '_' + args.mode + '_' + str(args.l) + '/bitstreams/'
path_lat = args.path + '_' + args.mode + '_' + str(args.l) + '/latents/'
os.makedirs(path_com, exist_ok=True)
os.makedirs(path_bin, exist_ok=True)
os.makedirs(path_lat, exist_ok=True)
F1 = misc.imread(path + 'f001.png')
Height = np.size(F1, 0)
Width = np.size(F1, 1)
batch_size = 1
Channel = 3
if (Height % 16 != 0) or (Width % 16 != 0):
raise ValueError('Height and Width must be a mutiple of 16.')
activation = tf.nn.relu
GOP_size = args.f_P + args.b_P + 1
GOP_num = int(np.floor((args.frame - 1)/GOP_size))
return I_level, Height, Width, batch_size, \
Channel, activation, GOP_size, GOP_num, \
path, path_com, path_bin, path_lat
def configure_decoder(args):
path = args.path + '/'
path_com = args.path + '_' + args.mode + '_' + str(args.l) + '/frames_dec/'
path_bin = args.path + '_' + args.mode + '_' + str(args.l) + '/bitstreams/'
path_lat = args.path + '_' + args.mode + '_' + str(args.l) + '/latents_dec/'
os.makedirs(path_com, exist_ok=True)
os.makedirs(path_lat, exist_ok=True)
activation = tf.nn.relu
GOP_size = args.f_P + args.b_P + 1
GOP_num = int(np.floor((args.frame - 1)/GOP_size))
return activation, GOP_size, GOP_num, \
path, path_com, path_bin, path_lat
def encode_I(args, frame_index, I_level, path, path_com, path_bin):
if args.mode == 'PSNR':
os.system('bpgenc -f 444 -m 9 ' + path + 'f' + str(frame_index).zfill(3) + '.png '
'-o ' + path_bin + 'f' + str(frame_index).zfill(3) + '.bin -q ' + str(I_level))
os.system('bpgdec ' + path_bin + 'f' + str(frame_index).zfill(3) + '.bin '
'-o ' + path_com + 'f' + str(frame_index).zfill(3) + '.png')
elif args.mode == 'MS-SSIM':
os.system(args.python_path + ' ' + args.CA_model_path + '/encode.py --model_type 1 '
'--input_path ' + path + 'f' + str(frame_index).zfill(3) + '.png' +
' --compressed_file_path ' + path_bin + 'f' + str(frame_index).zfill(3) + '.bin'
+ ' --quality_level ' + str(I_level))
os.system(args.python_path + ' ' + args.CA_model_path + '/decode.py --compressed_file_path '
+ path_bin + 'f' + str(frame_index).zfill(3) + '.bin'
+ ' --recon_path ' + path_com + 'f' + str(frame_index).zfill(3) + '.png')
# bits = os.path.getsize(path_bin + str(frame_index).zfill(3) + '.bin')
# bits = bits * 8
F0_com = misc.imread(path_com + 'f' + str(frame_index).zfill(3) + '.png')
F0_raw = misc.imread(path + 'f' + str(frame_index).zfill(3) + '.png')
F0_com = np.expand_dims(F0_com, axis=0)
F0_raw = np.expand_dims(F0_raw, axis=0)
if args.metric == 'PSNR':
mse = np.mean(np.power(np.subtract(F0_com / 255.0, F0_raw / 255.0), 2.0))
quality = 10 * np.log10(1.0 / mse)
elif args.metric == 'MS-SSIM':
quality = MultiScaleSSIM(F0_com, F0_raw, max_val=255)
print('Frame', frame_index, args.metric + ' =', quality)
return quality
def decode_I(args, frame_index, path_com, path_bin):
if args.mode == 'PSNR':
os.system('bpgdec ' + path_bin + 'f' + str(frame_index).zfill(3) + '.bin '
'-o ' + path_com + 'f' + str(frame_index).zfill(3) + '.png')
elif args.mode == 'MS-SSIM':
os.system(args.python_path + ' ' + args.CA_model_path + '/decode.py --compressed_file_path '
+ path_bin + 'f' + str(frame_index).zfill(3) + '.bin'
+ ' --recon_path ' + path_com + 'f' + str(frame_index).zfill(3) + '.png')
print('Decoded I-frame', frame_index)
def entropy_coding(frame_index, lat, path_bin, latent, sigma, mu):
if lat == 'mv':
bias = 50
else:
bias = 100
bin_name = 'f' + str(frame_index).zfill(3) + '_' + lat + '.bin'
bitout = arithmeticcoding.BitOutputStream(open(path_bin + bin_name, "wb"))
enc = arithmeticcoding.ArithmeticEncoder(32, bitout)
for h in range(latent.shape[1]):
for w in range(latent.shape[2]):
for ch in range(latent.shape[3]):
mu_val = mu[0, h, w, ch] + bias
sigma_val = sigma[0, h, w, ch]
symbol = latent[0, h, w, ch] + bias
freq = arithmeticcoding.logFrequencyTable_exp(mu_val, sigma_val, np.int(bias * 2 + 1))
enc.write(freq, symbol)
enc.finish()
bitout.close()
bits_value = os.path.getsize(path_bin + bin_name) * 8
return bits_value
def entropy_decoding(frame_index, lat, path_bin, path_lat, sigma, mu):
if lat == 'mv':
bias = 50
else:
bias = 100
bin_name = 'f' + str(frame_index).zfill(3) + '_' + lat + '.bin'
bitin = arithmeticcoding.BitInputStream(open(path_bin + bin_name, "rb"))
dec = arithmeticcoding.ArithmeticDecoder(32, bitin)
latent = np.zeros([1, mu.shape[1], mu.shape[2], mu.shape[3]])
for h in range(mu.shape[1]):
for w in range(mu.shape[2]):
for ch in range(mu.shape[3]):
mu_val = mu[0, h, w, ch] + bias
sigma_val = sigma[0, h, w, ch]
freq = arithmeticcoding.logFrequencyTable_exp(mu_val, sigma_val, np.int(bias * 2 + 1))
symbol = dec.read(freq)
latent[0, h, w, ch] = symbol - bias
bitin.close()
np.save(path_lat + '/f' + str(frame_index).zfill(3) + '_' + lat + '.npy', latent)
print('Decoded latent_' + lat + ' frame', frame_index)
return latent