/
feature_match_efficient.py
173 lines (140 loc) · 6.2 KB
/
feature_match_efficient.py
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import argparse
import shutil
from datetime import datetime
from os import makedirs, path
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
class Matcher:
def __init__(self, probe_path, gallery_path, dataset_name):
# lenght of ids to get from feature files
self.id_length = -1
self.dataset_name = dataset_name
# load features, subject ids, feature labels from probe file
probe_file = np.sort(np.loadtxt(probe_path, dtype=np.str))
self.probe, self.probe_ids, self.probe_labels = self.get_features(probe_file)
if gallery_path is not None:
print(f"Matching {probe_path} to {gallery_path}")
gallery_file = np.sort(np.loadtxt(args.gallery, dtype=np.str))
# if matching different files, load gallery features, ids and labels
self.probe_equal_gallery = False
self.gallery, self.gallery_ids, self.gallery_labels = self.get_features(
gallery_file
)
else:
print(f"Matching {probe_path} to {probe_path}")
# if matching to the same file, just create a simbolic link to save memory
self.probe_equal_gallery = True
self.gallery = self.probe
self.gallery_ids = self.probe_ids
self.gallery_labels = self.probe_labels
# initiate a matrix NxM with zeros representing impostor matches
self.authentic_impostor = np.zeros(shape=(len(self.probe), len(self.gallery)))
for i in range(len(self.probe)):
# convert authentic matches to 1
self.authentic_impostor[i, self.probe_ids[i] == self.gallery_ids] = 1
# remove same feature files
self.authentic_impostor[i, self.probe_labels[i] == self.gallery_labels] = -1
if gallery_path is None:
# remove duplicate matches if matching probe to probe
self.authentic_impostor[i, 0 : min(i + 1, len(self.gallery))] = -1
# Remove same day matches from authentic pairs
if self.dataset_name == "ND_GENDERS_V3":
if gallery_path is None:
range_check = range(i + 1, len(self.gallery))
else:
range_check = range(0, len(self.gallery))
for j in range_check:
if self.probe_ids[i] != self.gallery_ids[j]:
continue
day_a = path.split(self.probe_labels[i])[1].split("_")[4]
day_b = path.split(self.gallery_labels[j])[1].split("_")[4]
if day_a == day_b:
self.authentic_impostor[i, j] = -1
self.matches = None
def get_features_label(self, feature_path):
subject_id = path.split(feature_path)[1]
feature_label = path.join(
path.split(path.split(feature_path)[0])[1], subject_id[:-4]
)
if self.dataset_name == "CHIYA":
subject_id = subject_id[:-5]
elif self.dataset_name == "CHIYA_VAL":
subject_id = feature_label[1:-4]
elif self.dataset_name == "AGEDB":
subject_id = subject_id.split("_")[1]
elif (
self.dataset_name == "PUBLIC_IVS"
or self.dataset_name == "VGGFACE2"
or self.dataset_name == "ASIANCELEB"
):
subject_id = path.split(feature_label)[0]
elif self.id_length > 0:
subject_id = subject_id[: self.id_length]
else:
subject_id = subject_id.split("_")[0]
return subject_id, feature_label
def get_features(self, file):
all_features = []
all_labels = []
all_subject_ids = []
for j in range(len(file)):
image_path = file[j]
features = np.load(image_path)
subject_id, feature_label = self.get_features_label(image_path)
all_features.append(features)
all_subject_ids.append(subject_id)
all_labels.append(feature_label)
return (
np.asarray(all_features),
np.asarray(all_subject_ids),
np.asarray(all_labels),
)
def match_features(self):
self.matches = cosine_similarity(self.probe, self.gallery)
def create_label_indices(self, labels):
indices = np.linspace(0, len(labels) - 1, len(labels)).astype(int)
return np.transpose(np.vstack([indices, labels]))
def get_indices_score(self, auth_or_imp):
x, y = np.where(self.authentic_impostor == auth_or_imp)
return np.transpose(
np.vstack(
[
x,
y,
np.round(self.matches[self.authentic_impostor == auth_or_imp], 6),
]
)
)
def save_matches(self, output, group):
np.save(path.join(output, f"{group}_authentic.npy"), self.get_indices_score(1))
np.save(path.join(output, f"{group}_impostor.npy"), self.get_indices_score(0))
np.savetxt(
path.join(output, f"{group}_labels.txt"),
self.create_label_indices(self.probe_labels),
delimiter=" ",
fmt="%s",
)
if not self.probe_equal_gallery:
np.savetxt(
path.join(output, f"{group}_gallery_labels.txt"),
self.create_label_indices(self.gallery_labels),
delimiter=" ",
fmt="%s",
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Match Extracted Features")
parser.add_argument("-probe", "-p", help="Probe image list.")
parser.add_argument("-gallery", "-g", help="Gallery image list.")
parser.add_argument("-output", "-o", help="Output folder.")
parser.add_argument("-dataset", "-d", help="Dataset name.")
parser.add_argument("-group", "-gr", help="Group name, e.g. AA")
args = parser.parse_args()
time1 = datetime.now()
# remove if already exists, to get new copy
shutil.rmtree(args.output, ignore_errors=True)
makedirs(args.output)
matcher = Matcher(args.probe, args.gallery, args.dataset.upper())
matcher.match_features()
matcher.save_matches(args.output, args.group)
time2 = datetime.now()
print(time2 - time1)