def encode(self, img):
# encode first m rows
data = []
for row in range(0, self.num_rows):
data += img[row].tolist()
codec_first_rows = HuffmanCodec.from_data(data)
encoded_first_rows = codec_first_rows.encode(data)
# encode remain rows
prediction_error_img = self.predErrImg(img)
data = []
for row in range(0, img.shape[0] - self.num_rows):
data += prediction_error_img[row].tolist()
codec_remaining_rows = HuffmanCodec.from_data(data)
encoded_remaining_rows = codec_remaining_rows.encode(data)
# return {'height': self.img.shape[0],
# 'width': self.img.shape[1],
# 'codec_first_rows': codec_first_rows,
# 'encoded_first_rows': encoded_first_rows,
# 'codec_remaining_rows': codec_remaining_rows,
# 'encoded_remaining_rows': encoded_remaining_rows}
return (img.shape[0], img.shape[1], codec_first_rows,
encoded_first_rows, codec_remaining_rows,
encoded_remaining_rows)
def compute_compression_ratio(data, som, winners, width):
diff = Dataset.differential_coding(winners.flatten(), width)
codeNormal = HuffmanCodec.from_data(winners).encode(winners)
codeDiff = HuffmanCodec.from_data(diff).encode(diff)
hd = np.concatenate(som.get_som_as_list(), 0) * 255
hd = np.array(hd, 'uint8')
header = HuffmanCodec.from_data(hd).encode(hd)
return len(codeNormal) / len(codeDiff), len(data) * len(
data[0]) / (len(header) + len(codeDiff))
def compute_compression_metrics(data, som, winners, width):
diff = ImageData.differential_coding(winners.flatten(), width)
normal_code = HuffmanCodec.from_data(winners).encode(winners)
differential_code = HuffmanCodec.from_data(diff).encode(diff)
hd = np.concatenate(som.get_som_as_list(), 0) * 255
hd = np.array(hd, 'uint8')
header = HuffmanCodec.from_data(hd).encode(hd)
return len(normal_code) / len(differential_code), len(data) * len(
data[0]) / (len(header) + len(differential_code))
def test_string_data(data):
codec = HuffmanCodec.from_data(data)
encoded = codec.encode(data)
assert type(encoded) == type(b'')
assert len(encoded) < len(data)
decoded = codec.decode(encoded)
assert decoded == data
def strDict(self, strList, encStr):
lib = {}
for i in strList:
codec = HuffmanCodec.from_data(i)
encoded = codec.encode(encStr)
lib.update({i: encoded})
return lib
def huffmanCoding(self, model, quantized, k):
modelSize = 0
compressedModelSize = 0
for layer in model.layers:
if not layer.weights:
continue
else:
length = 1
shape = layer.weights[0].shape
for l in shape:
length = l * length
w_vec = np.reshape(layer.weights[0].numpy(), (1, length))[0]
codec = HuffmanCodec.from_data(w_vec[k:])
encoded = codec.encode(w_vec[k:])
compressedModelSize = compressedModelSize + len(encoded)
if quantized:
modelSize = modelSize + ((len(w_vec) - k) * quantized) // 8
else:
modelSize = modelSize + (len(w_vec) - k) * 4
self.modelSize = modelSize
self.compressedSize = compressedModelSize
def get_codec(self):
"""Generate the codec for encoding and decoding based on random sentence
Args: None
Output: the codec
"""
codec = HuffmanCodec.from_data(
"the quick brown fox jumps over the lazy dog")
return codec
def test_save(tmp_path: Path):
codec1 = HuffmanCodec.from_data('aabcbcdbabdbcbd')
path = str(tmp_path / 'foo' / 'bar.huff')
codec1.save(path)
output1 = codec1.encode('abcdabcd')
codec2 = PrefixCodec.load(path)
output2 = codec2.encode('abcdabcd')
assert output1 == output2
assert codec1.decode(output1) == codec2.decode(output2)
def encode(frame, huffman=False):
dct_frame = dct(frame)
if not huffman:
rle_frame = rle(dct_frame.flatten())
return rle_frame
else:
codec = HuffmanCodec.from_data(dct_frame.flatten())
rle_frame = codec.encode(dct_frame.flatten())
return rle_frame, codec
def test_print_code_table2():
codec = HuffmanCodec.from_data("aaaaa")
out = io.StringIO()
codec.print_code_table(out=out)
actual = out.getvalue().split('\n')
expected = "Bits Code Value Symbol\n 1 0 0 _EOF\n 1 1 1 'a'\n".split(
'\n')
assert actual[0] == expected[0]
assert set(actual[1:]) == set(expected[1:])
def generate_table(self,inputfilename):
self.generate_data_list(inputfilename)
self.codec = HuffmanCodec.from_data(self.__data_ns)
self.table = self.codec.get_code_table()
self.__strings_table = {}
for symbol in self.table.keys():
if not type(symbol) is int:
self.eof = symbol
bitsize, value = self.table[symbol]
self.__strings_table[symbol] = bin(value)[2:].rjust(bitsize, '0')
def huffman_coding_decoding(RLE_total):
RLE_total_new = str(RLE_total)
codec = HuffmanCodec.from_data(RLE_total_new)
print("Huffman Code Table: \n")
codec.print_code_table()
coded_string = codec.encode(RLE_total_new)
decoded_string = codec.decode(coded_string)
return codec, coded_string, decoded_string
def encodedStringDict(self, listOfStrings=[]):
codec = HuffmanCodec.from_data(listOfStrings)
encodeResults = codec.encode(listOfStrings)
def decodeListOfString(encode):
decodeResults = codec.decode(encode)
return decodeResults
decodedList = decodeListOfString(encodeResults)
encodedDict = dict(zip(listOfStrings, encodeResults))
return encodedDict, decodedList
def save_compressed(self, som, name):
# winners = np.zeros((neuron_nbr, neuron_nbr))
# for i in range(len(self.data)):
# w = som.winner(self.data[i]/255)
# winners[w] += 1
winners = som.winners()
file = open(output_path + name, "w")
# file.write(str(som.get_som_as_list()))
# res = ""
# str_win = ""
diff = Dataset.differential_coding(winners.flatten(),
self.nb_pictures[1])
# for i in range(len(self.data)):
# res += str(diff[i])+" "
# str_win += str(winners[i])+" "
# # Codebook compression
# codebook = som.get_som_as_list()
# str_codebook = ""
# for i in codebook:
# for j in range(len(i)):
# str_codebook += str(j)+" "
# str_codebook += "\n"
codeNormal = HuffmanCodec.from_data(winners).encode(winners)
codeDiff = HuffmanCodec.from_data(diff).encode(diff)
hd = np.concatenate(som.get_som_as_list(), 0) * 255
hd = np.array(hd, 'uint8')
header = HuffmanCodec.from_data(hd).encode(hd)
file.write(str(header))
file.write(str(codeDiff))
file.close()
print("Taux de compression du codage différentiel :",
len(codeNormal) / len(codeDiff))
print(
"Taux de compression total :",
len(self.data) * len(self.data[0]) / (len(header) + len(codeDiff)))
def Huffman_Encoder(binary_map, bits_group_num=64):
binary_map = float2bin(binary_map)
map_length = len(binary_map)
if map_length % bits_group_num:
bools_list = list(binary_map[:-(map_length % bits_group_num)].reshape(
-1, bits_group_num))
bools_list.append(binary_map[-(map_length % bits_group_num):])
else:
bools_list = list(binary_map.reshape(-1, bits_group_num))
bits_string = [b.tobytes() for b in bools_list]
codec = HuffmanCodec.from_data(bits_string)
output = codec.encode(bits_string)
return output, codec
def decode_huffman_encoded_string(self, encoded_item):
"""Decode a given string previously Huffman encoded and latin1 decoded.
Parameters:
encoded_item (dictionary): a dictionary with a string key and a
Huffman encoded and latin1 decoded value.
Returns:
A Huffman decoded string as JSON.
"""
encoded_item = json.loads(encoded_item)
key = next(iter(encoded_item))
item_codec = HuffmanCodec.from_data(key)
return json.dumps(item_codec.decode(
encoded_item[key].encode('latin1')))
def compress_pic(self, quant_blocks):
"""
compress the matrix data
:param quant_blocks: written quantified matrix
:return:huffman tree and encoded data
"""
sequence = list(
chain(*[
list(chain(*[self.mat2sequence(mat) for mat in line]))
for line in quant_blocks
]))
codec = HuffmanCodec.from_data(sequence)
encoded = codec.encode(sequence)
return codec, encoded
def huffman_encode_strings(self, items):
"""Build a dictionary of strings - the key being the original string,
and the value being a (Huffman) encoded version of that string.
Parameters:
items (list): a list of strings.
Returns:
A dictionary of Huffman encoded strings with original strings as
keys and encoded strings as latin1 decoded values as JSON.
"""
encoded_items = {}
for item in items:
item_codec = HuffmanCodec.from_data(item)
encoded_item = item_codec.encode(item)
encoded_items.update({item: encoded_item.decode('latin1')})
return json.dumps(encoded_items)
def encode(model, out_file='out.pkl'):
print('Start encoding...')
quanti_dic = OrderedDict()
for k, v in model.state_dict().items():
print(k)
if 'running' in k or 'batches' in k:
print("Ignoring {}".format(k))
quanti_dic[k] = v
continue
else:
layer_w = v.data.cpu().numpy().flatten()
codec = HuffmanCodec.from_data(layer_w)
encoded = codec.encode(layer_w)
quanti_dic[k] = [encoded, codec]
outfile = open(out_file, 'wb')
pickle.dump(quanti_dic, outfile)
outfile.close()
print('Done. Save to {}'.format(out_file))
def decodeStr(self, strLis, decStr):
codec = HuffmanCodec.from_data(strLis)
decoded = codec.decode(decStr)
decoded2 = decoded.decode("utf-8")
return decoded2
def encode(self, index):
self.codebook = HuffmanCodec.from_data(index)
self.encoded_index = self.codebook.encode(index)
return self.encoded_index
def test_decode_concat():
codec = HuffmanCodec.from_data([1, 2, 3])
encoded = codec.encode([1, 2, 1, 2, 3, 2, 1])
decoded = codec.decode(encoded, concat=sum)
assert decoded == 12
def test_non_string_symbols(data):
codec = HuffmanCodec.from_data(data)
encoded = codec.encode(data)
assert type(encoded) == type(b'')
decoded = codec.decode(encoded)
assert decoded == data
'''
Microservice that performs a variety of functions:
1) Squares every odd number in a vector of integers
2) Generate string:encoding key store from a list of strings
3) Decode an encoded string
'''
# Nameko import
from nameko.rpc import rpc
# Huffman encoder/decoder
from dahuffman import HuffmanCodec
### Use NLTK Gutenberg corpus to create a frequency distribution of letters
### Use that to perform static Huffman encoding
from nltk.corpus import gutenberg
codec = HuffmanCodec.from_data(gutenberg.raw())
# Define the service
class InvictusService():
name = "invictus_service"
# Function that squares a number if it's odd
def odd_square(self, number):
if (number % 2 != 0):
return number * number
return number
# RPC to apply odd_square to a list of integers
@rpc
def apply_odd_square(self, array):
def encodeAudio(fileName):
audiofile = fileName
print("Archivo audio=", audiofile)
if len(sys.argv) == 3:
quality = float(sys.argv[2])
else:
quality = 100.0
fs, x = wav.read(audiofile)
try:
channels = x.shape[
1] #numero de canales, 2 para estereo(2 columnas en x)
except IndexError:
channels = 1 # 1 para mono
x = np.expand_dims(x, axis=1) #añade canales dimension 1
print("Canales=", channels)
N = 1024 #numero de MDCY subbands
nfilts = 64 #numero de subbands en bark domain
#Sine window:
fb = np.sin(np.pi / (2 * N) * (np.arange(int(1.5 * N)) + 0.5))
#Guarda en archivo pickle binario:
#Quita extension del nombre del archivo
name, ext = os.path.splitext(audiofile)
#nueva extension .acod para el archivo encode
encfile = name + '.acod'
print("Archivo comprimido:", encfile)
totalbytes = 0
with open(encfile, 'wb') as codedfile: #abrimos archivo comprimido
pickle.dump(fs, codedfile)
pickle.dump(channels, codedfile)
for chan in range(channels): #loop sobre canales
print("Canal ", chan)
#Calcular cuantificado en el dominio Bark y subbandas cuantificadas
yq, y, mTbarkquant = MDCT_psayac_quant_enc(x[:, chan],
fs,
fb,
N,
nfilts,
quality=quality)
print("Huffman Coding")
mTbarkquantflattened = np.reshape(mTbarkquant, (1, -1), order='F')
mTbarkquantflattened = mTbarkquantflattened[0] #quitar dimension 0
codecmTbarkquant = HuffmanCodec.from_data(mTbarkquantflattened)
#Huffman tabla
tablemTbarkquant = codecmTbarkquant.get_code_table()
#Huffman encoded
mTbarkquantc = codecmTbarkquant.encode(mTbarkquantflattened)
#Calcula Huffman coder para cuantificado valor subbandas samples:
yqflattened = np.reshape(yq, (1, -1), order='F')
yqflattened = yqflattened[0] #quitar dimension 0
codecyq = HuffmanCodec.from_data(yqflattened)
#Huffman tabla
tableyq = codecyq.get_code_table()
#Huffman encoded
yqc = codecyq.encode(yqflattened)
pickle.dump(tablemTbarkquant, codedfile) #factor de escala tabla
pickle.dump(tableyq, codedfile) #subanda huffman tabla samples
pickle.dump(mTbarkquantc,
codedfile) #Huffman coded factor de escala
pickle.dump(yqc, codedfile) #Huffman coded subandas samples
totalbytes += len(tablemTbarkquant) + len(tableyq) + len(
mTbarkquantc) + len(yqc)
numsamples = np.prod(x.shape)
print("Numero total de bytes=", totalbytes)
print("Numero total de samples:", numsamples)
print("bytes por sample=", totalbytes / numsamples)
with open(encfile, 'wb') as codedfile: #open compressed file
pickle.dump(fs,codedfile) #write sampling rate
pickle.dump(channels,codedfile) #write number of channels
for chan in range(channels): #loop over channels:
print("channel ", chan)
#Compute quantized masking threshold in the Bark domain and quantized subbands
yq, y, mTbarkquant=MDCT_psayac_quant_enc(x[:,chan],fs,fb,N, nfilts,quality=quality)
print("Huffman Coding")
#Train Huffman coder for quantized masking threshold in the Bark domain (scalefactors),
#with flattening the masking threshold array in column (subband) order:
mTbarkquantflattened=np.reshape(mTbarkquant, (1,-1),order='F')
mTbarkquantflattened=mTbarkquantflattened[0] #remove dimension 0
codecmTbarkquant=HuffmanCodec.from_data(mTbarkquantflattened)
#Huffman table for it:
tablemTbarkquant=codecmTbarkquant.get_code_table()
#Huffman encoded:
mTbarkquantc=codecmTbarkquant.encode(mTbarkquantflattened)
#Compute Huffman coder for the quantized subband values:
#Train with flattened quantized subband samples
yqflattened=np.reshape(yq,(1,-1),order='F')
yqflattened=yqflattened[0] #remove dimension 0
codecyq=HuffmanCodec.from_data(yqflattened)
#Huffman table for it:
tableyq=codecyq.get_code_table()
#Huffman encoded:
yqc=codecyq.encode(yqflattened)
def compress_pic_before(self):
sequence = tuple([tuple(line) for line in self.zig_data])
codec = HuffmanCodec.from_data(sequence)
encoded = codec.encode(sequence)
return codec, encoded
def PC_Encode(self, line):
line = line.replace('\n', '')
codec = HuffmanCodec.from_data(line)
encoded = codec.encode(line)
return (encoded, codec)
def main(string):
QF = 1
start = time.time()
B = 8
im = cv2.imread(string)
h,w=np.array(im.shape[:2])/B * B
h = int(h)
w = int(w)
jpg_len = 0
b = b""
b += h.to_bytes(HEIGHT_BITS, byteorder='big')
b += w.to_bytes(WIDTH_BITS, byteorder='big')
block=np.array([[B,B]]) #first component is col, second component is row
scol=block[0,0]
srow=block[0,1]
imYCC=cv2.cvtColor(im, cv2.COLOR_BGR2YCR_CB)
#*Subsample Chrominance Channels======================
SSV=2
SSH=2
crf=cv2.boxFilter(imYCC[:,:,1],ddepth=-1,ksize=(2,2))
cbf=cv2.boxFilter(imYCC[:,:,2],ddepth=-1,ksize=(2,2))
y = imYCC[:,:,0]
crsub=crf[::SSV,::SSH]
cbsub=cbf[::SSV,::SSH]
imSub=[y,crsub,cbsub]
#*=====================================================
ch=['Y','Cr','Cb']
it = 0
for idx,channel in enumerate(imSub):
channelrows=channel.shape[0]
channelcols=channel.shape[1]
TransQuant = np.zeros((channelrows,channelcols), np.float32)
blocksV=channelrows/B
blocksH=channelcols/B
vis0 = np.zeros((channelrows,channelcols), np.float32)
vis0[:channelrows, :channelcols] = channel
vis0=vis0 - 128
dc_c = []
ac_c = []
rle_ecoded = []
for row in range(int(blocksV)):
for col in range(int(blocksH)):
#* Using DCT
currentblock = cv2.dct(vis0[row*B:(row+1)*B,col*B:(col+1)*B])
#*Quantization
TransQuant[row*B:(row+1)*B,col*B:(col+1)*B]=np.round(currentblock/Q[idx])
#*Zigzag
zz = zigzag(TransQuant[row*B:(row+1)*B,col*B:(col+1)*B])
#*DPCM - Vectorizing
dc_c.append(zz[0])
ac_c.append(zz[1:])
#*RLE on ac encoded as (skip, value)
#*(0, 0) end the block
rle_ecoded.extend(rle(ac_c[-1]))
#Save dc table
dc_codec = HuffmanCodec.from_data(dc_c)
dc_codec.save("dc_table/dc_table{0}.tb" .format(it))
jpg_len += os.stat("dc_table/dc_table{0}.tb" .format(it)).st_size
encoded = dc_codec.encode(dc_c)
l = len(encoded)
b += l.to_bytes(DC_BITS, byteorder='big')
b += encoded
#Save ac table
ac_codec = HuffmanCodec.from_data(rle_ecoded)
ac_codec.save("ac_table/ac_table{0}.tb" .format(it))
jpg_len += os.stat("ac_table/ac_table{0}.tb" .format(it)).st_size
encoded = ac_codec.encode(rle_ecoded)
#string += encoded
l = len(encoded)
b += l.to_bytes(AC_BITS, byteorder='big')
b += encoded
it += 1
file = open("encoded.jpg", "wb")
file.write(b)
end = time.time()
r = (len(b) + jpg_len) / (w * h)
#print("Hệ số nén r : {0}" .format(r))
#print("time: {0}s" .format(end - start))
return r, end - start
