Python lfw.get_paths方法代码示例

# 需要导入模块: import lfw [as 别名]
# 或者: from lfw import get_paths [as 别名]
def validate_on_lfw(model, lfw_160_path):
    # Read the file containing the pairs used for testing
    pairs = lfw.read_pairs('validation-LFW-pairs.txt')
    # Get the paths for the corresponding images
    paths, actual_issame = lfw.get_paths(lfw_160_path, pairs)
    num_pairs = len(actual_issame)

    all_embeddings = np.zeros((num_pairs * 2, 512), dtype='float32')
    for k in tqdm.trange(num_pairs):
        img1 = cv2.imread(paths[k * 2], cv2.IMREAD_COLOR)[:, :, ::-1]
        img2 = cv2.imread(paths[k * 2 + 1], cv2.IMREAD_COLOR)[:, :, ::-1]
        batch = np.stack([img1, img2], axis=0)
        embeddings = model.eval_embeddings(batch)
        all_embeddings[k * 2: k * 2 + 2, :] = embeddings

    tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(
        all_embeddings, actual_issame, distance_metric=1, subtract_mean=True)

    print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))

    auc = metrics.auc(fpr, tpr)
    print('Area Under Curve (AUC): %1.3f' % auc)
    eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.)
    print('Equal Error Rate (EER): %1.3f' % eer) 

# 需要导入模块: import lfw [as 别名]
# 或者: from lfw import get_paths [as 别名]
def load_testset(size):
    # Load images paths and labels
    pairs = lfw.read_pairs(pairs_path)
    paths, labels = lfw.get_paths(testset_path, pairs, file_extension)

    # Random choice
    permutation = np.random.choice(len(labels), size, replace=False)
    paths_batch_1 = []
    paths_batch_2 = []

    for index in permutation:
        paths_batch_1.append(paths[index * 2])
        paths_batch_2.append(paths[index * 2 + 1])

    labels = np.asarray(labels)[permutation]
    paths_batch_1 = np.asarray(paths_batch_1)
    paths_batch_2 = np.asarray(paths_batch_2)

    # Load images
    faces1 = facenet.load_data(paths_batch_1, False, False, image_size)
    faces2 = facenet.load_data(paths_batch_2, False, False, image_size)

    # Change pixel values to 0 to 1 values
    min_pixel = min(np.min(faces1), np.min(faces2))
    max_pixel = max(np.max(faces1), np.max(faces2))
    faces1 = (faces1 - min_pixel) / (max_pixel - min_pixel)
    faces2 = (faces2 - min_pixel) / (max_pixel - min_pixel)

    # Convert labels to one-hot vectors
    onehot_labels = []
    for index in range(len(labels)):
        if labels[index]:
            onehot_labels.append([1, 0])
        else:
            onehot_labels.append([0, 1])

    return faces1, faces2, np.array(onehot_labels) 

# 需要导入模块: import lfw [as 别名]
# 或者: from lfw import get_paths [as 别名]
def main(args):
  
    with tf.Graph().as_default():
      
        with tf.Session() as sess:
            
            # Read the file containing the pairs used for testing
            pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))

            # Get the paths for the corresponding images
            paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs)
            
            image_paths_placeholder = tf.placeholder(tf.string, shape=(None,1), name='image_paths')
            labels_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='labels')
            batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
            control_placeholder = tf.placeholder(tf.int32, shape=(None,1), name='control')
            phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
 
            nrof_preprocess_threads = 4
            image_size = (args.image_size, args.image_size)
            eval_input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
                                        dtypes=[tf.string, tf.int32, tf.int32],
                                        shapes=[(1,), (1,), (1,)],
                                        shared_name=None, name=None)
            eval_enqueue_op = eval_input_queue.enqueue_many([image_paths_placeholder, labels_placeholder, control_placeholder], name='eval_enqueue_op')
            image_batch, label_batch = facenet.create_input_pipeline(eval_input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
     
            # Load the model
            input_map = {'image_batch': image_batch, 'label_batch': label_batch, 'phase_train': phase_train_placeholder}
            facenet.load_model(args.model, input_map=input_map)

            # Get output tensor
            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
#              
            coord = tf.train.Coordinator()
            tf.train.start_queue_runners(coord=coord, sess=sess)

            evaluate(sess, eval_enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
                embeddings, label_batch, paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, args.distance_metric, args.subtract_mean,
                args.use_flipped_images, args.use_fixed_image_standardization) 

# 需要导入模块: import lfw [as 别名]
# 或者: from lfw import get_paths [as 别名]
def load_testset(size):
  # Load images paths and labels
  pairs = lfw.read_pairs(pairs_path)
  paths, labels = lfw.get_paths(testset_path, pairs)

  # Random choice
  permutation = np.random.choice(len(labels), size, replace=False)
  paths_batch_1 = []
  paths_batch_2 = []

  for index in permutation:
    paths_batch_1.append(paths[index * 2])
    paths_batch_2.append(paths[index * 2 + 1])

  labels = np.asarray(labels)[permutation]
  paths_batch_1 = np.asarray(paths_batch_1)
  paths_batch_2 = np.asarray(paths_batch_2)

  # Load images
  faces1 = facenet.load_data(paths_batch_1, False, False, image_size)
  faces2 = facenet.load_data(paths_batch_2, False, False, image_size)

  # Change pixel values to 0 to 1 values
  min_pixel = min(np.min(faces1), np.min(faces2))
  max_pixel = max(np.max(faces1), np.max(faces2))
  faces1 = (faces1 - min_pixel) / (max_pixel - min_pixel)
  faces2 = (faces2 - min_pixel) / (max_pixel - min_pixel)

  # Convert labels to one-hot vectors
  onehot_labels = []
  for index in range(len(labels)):
    if labels[index]:
      onehot_labels.append([1, 0])
    else:
      onehot_labels.append([0, 1])

  return faces1, faces2, np.array(onehot_labels) 

# 需要导入模块: import lfw [as 别名]
# 或者: from lfw import get_paths [as 别名]
def test(args):
    with tf.Graph().as_default():
        with tf.Session() as sess:
            #saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
            saver = tf.train.Saver(tf.global_variables())
            saver.restore(sess, args.model)
            # Read the file containing the pairs used for testing
            pairs = lfw.read_pairs(os.path.expanduser(args.test_list_dir))
            # Get the paths for the corresponding images
            paths, actual_issame = lfw.get_paths(os.path.expanduser(args.test_data_dir), pairs, args.test_list_dir)
            image_size = args.image_size
            print('image size',image_size)
            images_placeholder = tf.placeholder(tf.float32,shape=(None,args.image_height,args.image_width,args.image_width),name='image')
            phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
            #network definition.
            prelogits1 = network.infer(images_placeholder,args.embedding_size)
            if args.fc_bn:
                print('do batch norm after network')
                prelogits = slim.batch_norm(prelogits1, is_training=phase_train_placeholder,epsilon=1e-5, scale=True,scope='softmax_bn')
            #embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
            embeddings = tf.identity(prelogits)
            embedding_size = embeddings.get_shape()[1]
            # Run forward pass to calculate embeddings
            print('Runnning forward pass on testing images')
            batch_size = args.test_batch_size
            nrof_images = len(paths)
            nrof_batches = int(math.ceil(1.0*nrof_images / batch_size))
            emb_array = np.zeros((nrof_images, embedding_size))

            for i in range(nrof_batches):
                start_index = i*batch_size
                print('handing {}/{}'.format(start_index,nrof_images))
                end_index = min((i+1)*batch_size, nrof_images)
                paths_batch = paths[start_index:end_index]
                images = utils.load_data(paths_batch, False, False, args.image_height,args.image_width,False,\
                    (args.image_height,args.image_width))
                feed_dict = { images_placeholder:images, phase_train_placeholder:False }
                feats,a = sess.run([embeddings,prelogits], feed_dict=feed_dict)
                # do not know for sure whether we should turn this on? it depends.
                feats = utils.l2_normalize(feats)
                emb_array[start_index:end_index,:] = feats
        
            tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(emb_array, 
                actual_issame, 0.001, nrof_folds=args.test_nrof_folds)
            print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy), np.std(accuracy)))
            print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
            auc = metrics.auc(fpr, tpr)
            print('Area Under Curve (AUC): %1.3f' % auc) #
            eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.)#fill_value="extrapolate"
            print('Equal Error Rate (EER): %1.3f' % eer)


            tpr1, fpr1, accuracy1, val1, val_std1, far1 = lfw.evaluate(emb_array, 
                actual_issame, 0.0001, nrof_folds=args.test_nrof_folds)
            print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy1), np.std(accuracy1)))
            print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val1, val_std1, far1))
            auc = metrics.auc(fpr1, tpr1)
            print('Area Under Curve (AUC): %1.3f' % auc) #
            eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr1, tpr1)(x), 0., 1.)#fill_value="extrapolate"
            print('Equal Error Rate (EER): %1.3f' % eer)