def main_multicard(spath, cutno, total_num):
#model = builders.build_arch()
model = builders.MetricModel()
print(model)
load_checkpoint(MODEL_WEIGHTS, model)
if torch.cuda.is_available():
model.cuda()
model.eval()
feadic = {}
for index, imgfile in enumerate(walkfile(spath)):
if index % total_num != cutno - 1:
continue
ext = os.path.splitext(imgfile)[-1]
name = os.path.basename(imgfile)
if ext.lower() in ['.jpg', '.jpeg', '.bmp', '.png', '.pgm']:
embedding = extract(imgfile, model)
feadic[name] = embedding
#print(feadic)
if index % 5000 == cutno - 1:
print(index, embedding.shape)
with open(
COMBINE_DIR + spath.split("/")[-1] + "fea.pickle" + '_%d' % cutno,
"wb") as fout:
pickle.dump(feadic, fout, protocol=2)
def main(spath):
model = setup_model()
feadic = {}
for index, imgfile in enumerate(walkfile(spath)):
ext = os.path.splitext(imgfile)[-1]
name = os.path.basename(imgfile)
print(index, name)
if ext.lower() in ['.jpg', '.jpeg', '.bmp', '.png', '.pgm']:
im = cv2.imread(imgfile)
im = im.astype(np.float32, copy=False)
data = delg_extract(im, model)
feadic[name] = data
with open(spath.split("/")[-2] + "localfea.pickle", "wb") as fout:
pickle.dump(feadic, fout, protocol=2)
def main_multicard(spath, cutno, total_num):
model = setup_model()
feadic = {}
for index, imgfile in enumerate(walkfile(spath)):
if index % total_num != cutno - 1:
continue
ext = os.path.splitext(imgfile)[-1]
name = os.path.basename(imgfile)
print(index, name)
if ext.lower() in ['.jpg', '.jpeg', '.bmp', '.png', '.pgm']:
im = cv2.imread(imgfile)
im = im.astype(np.float32, copy=False)
data = delg_extract(im, model)
print(data['locations'].shape, data['descriptors'].shape)
feadic[name] = data
with open(COMBINE_DIR + "localfea.pickle" + '_%d' % cutno, "wb") as fout:
pickle.dump(feadic, fout, protocol=2)
def main(spath):
model = builders.MetricModel()
print(model)
load_checkpoint(MODEL_WEIGHTS, model)
if torch.cuda.is_available():
model.cuda()
model.eval()
result = {}
for index, imgfile in enumerate(walkfile(spath)):
ext = os.path.splitext(imgfile)[-1]
name = os.path.basename(imgfile)
if ext.lower() in ['.jpg', '.jpeg', '.bmp', '.png', '.pgm']:
label = extract(imgfile, model)
result[name] = label
if index % 5000 == 0:
print(index, name, label)
with open(spath.split("/")[-1] + "label.pickle", "wb") as fout:
pickle.dump(result, fout, protocol=2)