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)
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_accuracy(sess, images_placeholder, phase_train_placeholder,
image_size, embeddings, paths, actual_issame,
augment_images, aug_value, batch_size, orig_image_size,
seed):
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, orig_image_size)
images_aug = augment_images(images, aug_value, image_size)
feed_dict = {
images_placeholder: images_aug,
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
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
_, _, accuracy = facenet.calculate_roc(thresholds, embeddings1,
embeddings2,
np.asarray(actual_issame), seed)
return accuracy
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, actual_issame, nrof_folds=10):
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = embeddings[0::2] # 6000张图片 是每一个Paris中的第一张
embeddings2 = embeddings[1::2] # 6000张图片 是每一个Paris中的第2张
# 计算roc曲线需要的数据和在测试数据上的每一折的测试精度
tpr, fpr, accuracy = facenet.calculate_roc(thresholds,
embeddings1,
embeddings2,
np.asarray(actual_issame),
nrof_folds=nrof_folds)
thresholds = np.arange(0, 4, 0.001)
# 计算验证率
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