def main():
cfg = load_yaml('./configs/arc_res50_mask.yaml')
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=False)
model.summary()
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] training from scratch.")
temp1 = np.ones((62,112,3))
temp2 = np.zeros((50,112,3))
masked_img = np.concatenate([temp1, temp2], axis =0)
path_img1 = '/home/anhdq23/Desktop/nguyen/data/AR/test2/M-002-12.bmp'
path_img2 = '/home/anhdq23/Desktop/nguyen/data/AR/test2/M-003-01.bmp'
img1 = Image.open(path_img1)
img1 = img1.resize((112, 112))
img1 = np.array(img1)/255.0
img2 = Image.open(path_img2)
img2 = img2.resize((112, 112))
img2 = np.array(img2)/255.0
mask_img2 = np.multiply(img2, masked_img)
fc1 = model.predict(mask_img2.reshape((1,112,112,3)))
norm_fc1 = preprocessing.normalize(fc1.reshape((1,512)), norm='l2', axis=1)
fc2 = model.predict(img2.reshape((1,112,112,3)))
norm_fc2 = preprocessing.normalize(fc2.reshape((1,512)), norm='l2', axis=1)
diff = np.subtract(norm_fc1, norm_fc2)
dist = np.sqrt(np.sum(np.square(diff), 1))/2
print(dist)
for i in np.arange(20):
print(np.sqrt(np.sum(np.square(diff[0][i*25:i*25+25]), 0))/2)
fig = plt.figure()
ax = fig.add_subplot(2,1,1)
ax.plot(np.arange(512), norm_fc1[0])
# ax = fig.add_subplot(2,1,2)
ax.plot(np.arange(512), norm_fc2[0])
ax = fig.add_subplot(2,1,2)
ax.plot(np.arange(512), diff[0])
plt.show()
def main(args):
ijbc_meta = np.load(args.meta_path)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
#cfg = load_yaml('configs/arc_res50.yaml')
cfg = load_yaml(args.config_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
training=False)
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] Cannot find ckpt from {}.".format(ckpt_path))
exit()
img_names = [
os.path.join(args.input_path,
img_name.split('/')[-1])
for img_name in ijbc_meta['img_names']
]
embedding_size = cfg['embd_shape']
batch_size = cfg['batch_size']
img_size = cfg['input_size']
def read_img(filename):
raw = tf.io.read_file(filename)
img = tf.image.decode_jpeg(raw, channels=3)
img = tf.cast(img, tf.float32)
img = img / 255
return img
dataset = tf.data.Dataset.from_tensor_slices(img_names)
dataset = dataset.map(read_img,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
embeddings = model.predict(dataset, batch_size=batch_size, verbose=1)
print('embeddings', embeddings.shape)
np.save(args.output_path, embeddings)
def main():
# with open('/home/anhdq23/Desktop/nguyen/VT_simulation/weights/arcface_ret50.json', 'r') as f:
# model_json = json.load(f)
# model = model_from_json(model_json)
# model.load_weights('/home/anhdq23/Desktop/nguyen/VT_simulation/weights/arcface_ret50.h5')
# model.summary()
cfg = load_yaml('./configs/arc_res50_new.yaml')
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=False)
model.summary()
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
print(ckpt_path)
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] training from scratch.")
model_mask = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=False)
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + 'arc_res50_mask')
print(ckpt_path)
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model_mask.load_weights(ckpt_path)
else:
print("[*] training from scratch.")
import sys
sys.path.append('/home/anhdq23/Desktop/nguyen/VT_simulation/')
from detector import get_detector
predictor = get_detector()
ICPR_dict = dict()
path_ICPR = '/home/anhdq23/Desktop/nguyen/data/ICPR_cropped_face'
for name_fold in os.listdir(path_ICPR):
print(name_fold)
path_fold = os.path.join(path_ICPR, name_fold)
if name_fold not in ICPR_dict.keys():
ICPR_dict[name_fold] = []
for name_image in os.listdir(path_fold):
path_image = os.path.join(path_fold, name_image)
if '60' not in name_image[-10:-4] and '90' not in name_image[-10:-4]\
and '75' not in name_image[-10:]:
image = Image.open(path_image)
image = expand2square(image, (255, 255, 255))
image = image.resize((112, 112))
image = np.array(image)/255.0
_, labels, _ = predictor.predict(image, 1500/2, 0.6)
if labels.numpy()[0] == 1:
fc1 = model_mask.predict(image.reshape((1,112,112,3)))
norm_fc1 = preprocessing.normalize(fc1.reshape((1,cfg['embd_shape'])), norm='l2', axis=1)
else:
fc1 = model.predict(image.reshape((1,112,112,3)))
norm_fc1 = preprocessing.normalize(fc1.reshape((1,cfg['embd_shape'])), norm='l2', axis=1)
ICPR_dict[name_fold].append(norm_fc1)
path_ICPR = '/home/anhdq23/Desktop/nguyen/data/ICPR_cropped_face'
anchor_list = []
name_list = []
for name_fold in os.listdir(path_ICPR):
print(name_fold)
path_fold = os.path.join(path_ICPR, name_fold)
for name_image in os.listdir(path_fold):
path_image = os.path.join(path_fold, name_image)
if '+0+0' in name_image[-10:] or '+0-15' in name_image[-10:] or\
'+0+15' in name_image[-10:] or '+15+0' in name_image[-10:] or\
'-15+0' in name_image[-10:]:
print(name_image)
image = Image.open(path_image)
image = expand2square(image, (255, 255, 255))
image = image.resize((112, 112))
image = np.array(image)/255.0
_, labels, _ = predictor.predict(image, 1500/2, 0.6)
if labels.numpy()[0] == 1:
fc1 = model_mask.predict(image.reshape((1,112,112,3)))
norm_fc1 = preprocessing.normalize(fc1.reshape((1,cfg['embd_shape'])), norm='l2', axis=1)
else:
fc1 = model.predict(image.reshape((1,112,112,3)))
norm_fc1 = preprocessing.normalize(fc1.reshape((1,cfg['embd_shape'])), norm='l2', axis=1)
anchor_list.append(norm_fc1)
name_list.append(name_fold)
# Init faiss
import faiss
count_true = 0
count_all = 0
res = faiss.StandardGpuResources() # use a single GPU
index_flat = faiss.IndexFlatL2(512)
# gpu_index_flat = faiss.index_cpu_to_gpu(res, 0, index_flat)
gpu_index_flat = index_flat
gpu_index_flat.add(np.array(anchor_list).reshape((-1,512)))
for key in list(ICPR_dict.keys()):
for feature in ICPR_dict[key]:
D, I = gpu_index_flat.search(feature, k=1) # actual search
print(key, name_list[I[0][0]])
if key == name_list[I[0][0]]:
count_true +=1
count_all +=1
print(count_true, count_all)
def main():
# with open('/home/anhdq23/Desktop/nguyen/VT_simulation/weights/arcface_ret50.json', 'r') as f:
# model_json = json.load(f)
# model = model_from_json(model_json)
# model.load_weights('/home/anhdq23/Desktop/nguyen/VT_simulation/weights/arcface_ret50.h5')
# model.summary()
cfg = load_yaml('./configs/arc_res50_mix.yaml')
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=False)
model.summary()
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
print(ckpt_path)
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] training from scratch.")
# # serialize model to JSON
# model_json = model.to_json()
# with open("/home/anhdq23/Desktop/nguyen/image-segmentation-keras/weights/arc_res50_new.json", "w") as json_file:
# json.dump(model_json, json_file)
# model_mask.save_weights("/home/anhdq23/Desktop/nguyen/VT_simulation/weights/arc_res50_mask.h5")
data_path = '/home/anhdq23/Desktop/nguyen/arcface-tf2/data'
lfw, lfw_issame = get_val_pair(data_path, 'lfw_align_112/lfw')
lfw = np.transpose(lfw, [0, 2, 3, 1]) * 0.5 + 0.5
image_1 = lfw[0::2]
image_2 = lfw[1::2]
dist_all = []
for idx in range(len(lfw_issame)):
print(idx)
fc1 = model.predict(image_1[idx].reshape((1,112,112,3)))
norm_fc1 = preprocessing.normalize(fc1.reshape((1,cfg['embd_shape'])), norm='l2', axis=1)
fc2 = model.predict(image_2[idx].reshape((1,112,112,3)))
norm_fc2 = preprocessing.normalize(fc2.reshape((1,cfg['embd_shape'])), norm='l2', axis=1)
# dist = tf.keras.losses.cosine_similarity(fc1.reshape((1,512)), fc2.reshape((1,512)))
diff = np.subtract(norm_fc1, norm_fc2)
dist = np.sqrt(np.sum(np.square(diff), 1))/2
dist_all.extend(dist)
plt.plot(dist_all)
plt.show()
thresholds = np.arange(0, 1, 0.01)
tpr_all = []
fpr_all = []
for thr in thresholds:
tpr, fpr, acc, f1 = calculate_accuracy(thr, np.array(dist_all), lfw_issame)
top_left = np.sqrt((1-tpr)**2 + fpr**2)
print('thr %.4f' % thr , 'tpr %.4f' % tpr, 'fpr %.4f' % fpr, \
'top left %.4f' % top_left, 'acc %.4f' % acc, 'f1_score %.4f'%f1)
# top_left_batch.append(top_left)
tpr_all.append(tpr)
fpr_all.append(fpr)
for threshold in thresholds:
predict_issame = np.less(np.array(dist_all), threshold)
conf_matrix = confusion_matrix(lfw_issame, predict_issame)
print(conf_matrix)
plt.figure()
lw = 2
plt.plot(fpr_all, tpr_all, color='darkorange',
lw=lw, label='ROC curve')
plt.xlim([0.0, 1.])
plt.ylim([0.0, 1.])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()