Python facenet.load_data方法代碼示例

# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import load_data [as 別名]
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 

# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import load_data [as 別名]
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) 

# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import load_data [as 別名]
def main():
    image_size = 96
    old_dataset = '/home/david/datasets/facescrub/fs_aligned_new_oean/'
    new_dataset = '/home/david/datasets/facescrub/facescrub_110_96/'
    eq = 0
    num = 0
    l = []
    dataset = facenet.get_dataset(old_dataset)
    for cls in dataset:
        new_class_dir = os.path.join(new_dataset, cls.name)
        for image_path in cls.image_paths:
          try:
            filename = os.path.splitext(os.path.split(image_path)[1])[0]
            new_filename = os.path.join(new_class_dir, filename+'.png')
            #print(image_path)
            if os.path.exists(new_filename):
                a = facenet.load_data([image_path, new_filename], False, False, image_size, do_prewhiten=False)
                if np.array_equal(a[0], a[1]):
                  eq+=1
                num+=1
                err = np.sum(np.square(np.subtract(a[0], a[1])))
                #print(err)
                l.append(err)
                if err>2000:
                  fig = plt.figure(1)
                  p1 = fig.add_subplot(121)
                  p1.imshow(a[0])
                  p2 = fig.add_subplot(122)
                  p2.imshow(a[1])
                  print('%6.1f: %s\n' % (err, new_filename))
                  pass
            else:
                pass
                #print('File not found: %s' % new_filename)
          except:
            pass 

# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import load_data [as 別名]
def compute_facial_encodings(sess,images_placeholder,embeddings,phase_train_placeholder,image_size,
                    embedding_size,nrof_images,nrof_batches,emb_array,batch_size,paths):
    """ Compute Facial Encodings

        Given a set of images, compute the facial encodings of each face detected in the images and
        return them. If no faces, or more than one face found, return nothing for that image.

        Inputs:
            image_paths: a list of image paths

        Outputs:
            facial_encodings: (image_path, facial_encoding) dictionary of facial encodings

    """

    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_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)

    facial_encodings = {}
    for x in range(nrof_images):
        facial_encodings[paths[x]] = emb_array[x,:]


    return facial_encodings 

# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import load_data [as 別名]
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) 

# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import load_data [as 別名]
def main(args):

	with tf.Graph().as_default():

		with tf.Session() as sess:

			# create output directory if it doesn't exist
			output_dir = os.path.expanduser(args.output_dir)
			if not os.path.isdir(output_dir):
				os.makedirs(output_dir)

			# load the model
			print("Loading trained model...\n")
			meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.trained_model_dir))
			facenet.load_model(args.trained_model_dir, meta_file, ckpt_file)

			# grab all image paths and labels
			print("Finding image paths and targets...\n")
			data = load_files(args.data_dir, load_content=False, shuffle=False)
			labels_array = data['target']
			paths = data['filenames']

			# Get input and output tensors
			images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
			embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
			phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

			image_size = images_placeholder.get_shape()[1]
			embedding_size = embeddings.get_shape()[1]

			# Run forward pass to calculate embeddings
			print('Generating embeddings from images...\n')
			start_time = time.time()
			batch_size = args.batch_size
			nrof_images = len(paths)
			nrof_batches = int(np.ceil(1.0*nrof_images / batch_size))
			emb_array = np.zeros((nrof_images, embedding_size))
			for i in xrange(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, do_random_crop=False, do_random_flip=False, image_size=image_size, do_prewhiten=True)
				feed_dict = { images_placeholder:images, phase_train_placeholder:False}
				emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)

			time_avg_forward_pass = (time.time() - start_time) / float(nrof_images)
			print("Forward pass took avg of %.3f[seconds/image] for %d images\n" % (time_avg_forward_pass, nrof_images))

			print("Finally saving embeddings and gallery to: %s" % (output_dir))
			# save the gallery and embeddings (signatures) as numpy arrays to disk
			np.save(os.path.join(output_dir, "gallery.npy"), labels_array)
			np.save(os.path.join(output_dir, "signatures.npy"), emb_array)