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(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