def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
thresholds = np.arange(0, 4, 0.001)
val2, val_std2, far2 = facenet.calculate_val(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), 1e-2, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
val3, val_std3, far3 = facenet.calculate_val(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
return tpr, fpr, accuracy, val2, val_std2, far2, val3, val_std3, far3
def evaluate(embeddings,
actual_issame,
nrof_folds=10,
distance_metric=0,
subtract_mean=False,
labels=None,
logdir=None):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds,
distance_metric=distance_metric,
subtract_mean=subtract_mean,
labels=labels,
logdir=logdir)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
1e-3,
nrof_folds=nrof_folds,
distance_metric=distance_metric,
subtract_mean=subtract_mean)
return tpr, fpr, accuracy, val, val_std, far
def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
if distance_metric == 1:
thresholds = np.arange(0, 1, 0.001)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds,
distance_metric=distance_metric)
thresholds = np.arange(0, 4, 0.001)
if distance_metric == 1:
thresholds = np.arange(0, 1, 0.001)
val, val_std, far = facenet.calculate_val(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
1e-3,
nrof_folds=nrof_folds,
distance_metric=distance_metric)
f1 = 2 * np.mean(accuracy) * val / (np.mean(accuracy) + val)
return tpr, fpr, accuracy, val, val_std, far, f1
def evaluate(embeddings, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
# tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
# np.asarray(actual_issame), nrof_folds=nrof_folds)
# thresholds = np.arange(0, 4, 0.001)
# val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
# np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
tpr, fpr, accuracy, fp_idx, fn_idx, best_threshold_acc = facenet.calculate_roc(
thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far, threshold_val = facenet.calculate_val(
thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
1e-3,
nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far, fp_idx, fn_idx, best_threshold_acc, threshold_val
def validate(sess, paths, actual_issame, seed, batch_size, images_placeholder, phase_train_placeholder, embeddings, nrof_folds=10):
image_size = images_placeholder.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0*nrof_images / batch_size))
emb_list = []
for i in range(nrof_batches):
start_index = i*batch_size
end_index = min((i+1)*batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder: images, phase_train_placeholder: False }
emb_list += sess.run([embeddings], feed_dict=feed_dict)
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), seed, nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), 1e-3, seed, nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
def validate(sess, paths, actual_issame, seed, batch_size, images_placeholder, phase_train_placeholder, embeddings, nrof_folds=10):
image_size = images_placeholder.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0*nrof_images / batch_size))
emb_list = []
for i in range(nrof_batches):
start_index = i*batch_size
end_index = min((i+1)*batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder: images, phase_train_placeholder: False }
emb_list += sess.run([embeddings], feed_dict=feed_dict)
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), seed, nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), 1e-3, seed, nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
def evaluate(embeddings, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
def evaluate(embeddings, seed, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), seed, nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), 1e-3, seed, nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01) #用来判定TP,FP的阈值，从0-4，0.01间隔，交叉验证，可以得出最优的threadhold
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
return tpr, fpr, accuracy, val, val_std, far
def evaluate(embeddings, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01) # 0.01表示步长
# 四个点原来是取奇数和偶数列
embeddings1 = embeddings[0::2] # 取出奇数行的特征，取出偶数行的特征
embeddings2 = embeddings[1::2]
# print(len(embeddings1))
# print('wokao')
# print(len(embeddings2))
tpr, fpr, accuracy, threshold_acc = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds) # np.asarray将列表转换为数组
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, threshold_acc, embeddings1, embeddings2,
np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
def main(argv=None):
pairs = read_pairs(os.path.expanduser(FLAGS.lfw_pairs))
paths, actual_issame = get_paths(os.path.expanduser(FLAGS.lfw_dir), pairs)
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
print('Loading model "%s"' % FLAGS.model_file)
facenet.load_model(FLAGS.model_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
image_size = images_placeholder.get_shape()[1]
# Run forward pass to calculate embeddings
nrof_images = len(paths)
nrof_batches = int(nrof_images / FLAGS.batch_size)
emb_list = []
for i in range(nrof_batches):
start_time = time.time()
paths_batch = paths[i*FLAGS.batch_size:(i+1)*FLAGS.batch_size]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder: images, phase_train_placeholder: False }
emb_list += sess.run([embeddings], feed_dict=feed_dict)
duration = time.time() - start_time
print('Calculated embeddings for batch %d of %d: time=%.3f seconds' % (i+1,nrof_batches, duration))
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), FLAGS.seed)
print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy), np.std(accuracy)))
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), 1e-3, FLAGS.seed)
print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
facenet.plot_roc(fpr, tpr, 'NN4')
def evaluate(embeddings, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
# Note there that x[startAt:endBefore:skip] is the notation
# so embeddings1 and embeddings2 are simply two lists with
# embeddings1[i] and embeddings2[i] being the pairs that we need to validate
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
print("thresholds is: {}".format(thresholds))
print("embeddings1 is: {}".format(embeddings1))
print("embeddings2 is: {}".format(embeddings2))
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
# val is the percentage of samples that were classified to be correct
# val_std is the std of val
# far is the percentage of samples that were classified to be wrong
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
def evaluate(embeddings, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
# start from 0, step=2
embeddings1 = embeddings[0::2]
# start from 1, step=2
embeddings2 = embeddings[1::2]
# embeddings1 is corresponding with embeddings2
tpr, fpr, accuracy = facenet.calculate_roc(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
1e-3,
nrof_folds=nrof_folds)
return tpr, fpr, accuracy, val, val_std, far
def evaluate(embeddings, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2]
embeddings2 = embeddings[1::2]
# ipdb.set_trace()
tpr, fpr, accuracy = facenet.calculate_roc(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
1e-2,
nrof_folds=nrof_folds)
dot_product_threshold = np.arange(0.0, 1.0, 0.001)
# ipdb.set_trace()
best_threshold, acc_dot, recall, fpr_dot, precision_dot, dot_product_all, fp_idxs, fn_idxs, recall_th, precision_th,\
acc_th = facenet.calculate_acc_dot_product(dot_product_threshold, embeddings1, embeddings2, np.asarray(actual_issame))
return tpr, fpr, accuracy, val, val_std, far, best_threshold, acc_dot, recall, fpr_dot, precision_dot,\
dot_product_all, fp_idxs, fn_idxs, recall_th, precision_th, acc_th
