def main():
args = arg_parse()
docs = [
os.path.join(dir, f) for dir in os.listdir(args.root)
for f in os.listdir(os.path.join(args.root, dir))
]
docs = sorted(docs)
index = Indexer(docs, args.index, args.root)
embedder = Embedder()
# L0
q_pos, q_neg = query_expand(args.query)
# Get all OR-ed tokens
q_pos_expand = re.sub(r" ", " AND ", q_pos)
hits = index.query_boolean(q_pos_expand.split())
# Remove all NOT-ed tokens
if q_neg:
for token in q_neg.split():
term = index.stemmer.stem(token)
try:
not_posting = index.tfidf(index.index[term])
except KeyError:
not_posting = []
hits = not_and_postings(not_posting, hits)
if not hits:
print("nothing found")
return
hits = sorted(hits, key=lambda item: item[1], reverse=True)
hits = hits[:args.l0_size]
# L1
doc_ids = [x[0] for x in hits]
filenames = [os.path.join(args.root, docs[i]) for i in doc_ids]
texts = [get_text_reduced(x, maxlen=512) for x in filenames]
if args.batch_size >= args.l0_size:
embeddings = embedder.embed(texts)
else:
embeddings = batch_embed(embedder, texts, args.batch_size)
query_emb = embedder.embed([q_pos])[0]
dist_cos = [cosine(query_emb, e) for e in embeddings]
idx_cos = np.argsort(dist_cos)
# Render
q_red = query_reduce(args.query)
resorted = [doc_ids[i] for i in idx_cos]
for i, id in enumerate(resorted[:args.l1_size]):
print("\n{}:".format(i))
index.render_file(q_red.split(), docs[id])
orig_pos = idx_cos[i]
print("\tL0 rank = {}; tf-idf = {:.3f}; cos-sim = {:.3f}".format(
orig_pos, hits[orig_pos][1], 1 - dist_cos[orig_pos]))
def calc_embeddings(docs: List[str], batch_size: int, root: str) -> np.ndarray:
"""Calculate embeddings (in batches).
Args:
docs: List of documents filenames.
batch_size: Batch size.
root: Root directory.
Returns:
Numpy array of (N, 768) of texts embeddings.
"""
embedder = Embedder()
all_embeddings = np.zeros((len(docs), 768), dtype=np.float32)
iters = len(docs) // batch_size
if len(docs) % batch_size > 0:
iters += 1
for i in trange(iters):
batch = docs[i * batch_size:(i + 1) * batch_size]
filenames = [os.path.join(root, doc) for doc in batch]
texts = [get_text_reduced(x, maxlen=512) for x in filenames]
embeddings = embedder.embed(texts)
all_embeddings[i * batch_size:(i + 1) * batch_size] = embeddings
return all_embeddings
def embed(stego, secret, chromosome):
"""Embed secret message into the host using the chromosome"""
if len(chromosome) > 7:
chromosome = helper_individual.packchromosome(chromosome)
# Convert to a flattened pixel sequence
stego_sequence = MatScanner.scan_genetic(stego, chromosome)
secret = secret.flatten()
stego_sequence = Embedder.embed(stego_sequence, secret, chromosome)
# Reshape the stego image
return MatScanner.reshape_genetic(stego_sequence, stego.shape, chromosome)
def main():
embedder = Embedder()
parser = DocParser()
# iterate through grobid
with open('grobid_data.pkl', 'wb') as output:
for subdir, dirs, files in os.walk(grobid_path):
print(len(files))
count = 0
for file in files:
print(count)
count += 1
# print(os.path.join(subdir, file))
doc = parser.parseXML(os.path.join(subdir, file))
doc.id = str(file).split('.')[0]
if len(doc.abstract) == 0:
continue
doc.embedding = embedder.embed(doc.abstract)
# pair = variablesFromPair((doc.abstract, doc.title), word_index, embedding_map)
# if (len(pair[0]) == 0 or len(pair[1]) == 0):
# continue
# doc.embedding = encode(encoder, pair[0])
pickle.dump(doc, output, pickle.HIGHEST_PROTOCOL)
def batch_embed(embedder: Embedder, texts: List[str],
batch_size: int) -> np.ndarray:
"""Get embeddings in batches if GPU memory is not enough.
Args:
embedder: Embedder with DistilBERT model.
texts: List of songs' texts.
batch_size: Batch size.
Returns:
Numpy array of (N, 768) of texts embeddings.
"""
embeddings = np.zeros((len(texts), 768))
iters = len(texts) // batch_size
if len(texts) % batch_size > 0:
iters += 1
for i in range(iters):
batch = texts[i * batch_size:(i + 1) * batch_size]
emb_batch = embedder.embed(batch)
embeddings[i * batch_size:(i + 1) * batch_size] = emb_batch
return embeddings
biometric = cv2.imread(BIOMETRIC)
carrier = cv2.imread(CARRIER)
expected = cv2.cvtColor(cv2.imread(EXPECTED), cv2.COLOR_BGR2GRAY)
BIOMETRIC_SIZE = biometric.shape[0]
actualSize = carrier.shape
corners = [[0, 0], [0, 0], [0, 0], [0, 0]]
embedded = carrier.copy()
em = Embedder(PASSES, KEY, CHANNELS, DEPTH, MASK)
ex = Extractor(PASSES, KEY, ~MASK, SHIFT)
em_psnr = []
ex_psnr = []
for iteration in range(ITERATIONS):
embedded = em.embed(biometric, embedded)
filename = './CarrierVsRecoved/Iteration_' + str(iteration) + '.jpg'
cv2.imwrite(filename, embedded)
embedded = cv2.imread(filename)
em_psnr.append(cv2.PSNR(carrier, embedded))
extracted = ex.extract(embedded, corners, actualSize, BIOMETRIC_SIZE)
ex_psnr.append(cv2.PSNR(expected, extracted))
print(iteration, em_psnr[-1], ex_psnr[-1])
cv2.imwrite('./CarrierVsRecoved/final.png', extracted)
plt.plot(em_psnr, 'b')
plt.plot(ex_psnr, 'r')
plt.show()
x1 = int(CARRIER_SIZE[0] / 2) - int(CARRIER_SIZE[0] / 4)
x2 = int(CARRIER_SIZE[0] / 2) + int(CARRIER_SIZE[0] / 4)
y1 = int(CARRIER_SIZE[1] / 2) - int(CARRIER_SIZE[1] / 4)
y2 = int(CARRIER_SIZE[1] / 2) + int(CARRIER_SIZE[1] / 4)
crop = (x1, y1, x2, y2)
untouched = carrier[crop[0]:crop[2], crop[1]:crop[3]]
em_psnr = []
ex_psnr = []
for passes in range(MAX_PASSES):
em = Embedder(passes, KEY, CHANNELS, DEPTH, MASK)
ex = Extractor(passes, KEY, ~MASK, SHIFT)
embedded = em.embed(biometric, carrier)
cropped = embedded[crop[0]:crop[2], crop[1]:crop[3]]
margins = gl.getMargins(cropped, GUIDE_MASK)
corners = gl.marginsToCorners(margins)
actualSize = gl.getActualSize(cropped, margins)
extracted = ex.extract(cropped, corners, actualSize, BIOMETRIC_SIZE)
ex_psnr.append(cv2.PSNR(expected, extracted))
em_psnr.append(cv2.PSNR(np.uint8(cropped), np.uint8(untouched)))
print(passes, em_psnr[-1], ex_psnr[-1])
cv2.imwrite('./final.png', extracted)
plt.plot(em_psnr, 'b')
plt.plot(ex_psnr, 'r')
plt.show()