def data_gen(input_path_root, target_path_root, batch_size):
input_dir_names = os.listdir(input_path_root)
target_dir_names = os.listdir(target_path_root)
# 共通のディレクトリのみを対象とする
dir_names = set(input_dir_names) & set(target_dir_names)
path_pairs = []
for dir_name in dir_names:
input_dir = os.path.join(input_path_root, dir_name)
target_dir = os.path.join(target_path_root, dir_name)
i_pathes = sorted(list_pictures(input_dir))
t_pathes = sorted(list_pictures(target_dir))
path_pairs += zip(i_pathes, t_pathes)
n_batch = math.ceil(len(path_pairs) / batch_size)
while True:
for i in range(n_batch):
batch_path_pairs = path_pairs[i * batch_size:(i + 1) * batch_size]
batch_size = len(batch_path_pairs)
batch_input_img = np.empty(shape=(batch_size, 256, 256, 1))
batch_target_img = np.empty(shape=(batch_size, 256, 256, 1))
for i, path_pair in enumerate(batch_path_pairs):
x = load_scaled_img(path_pair[0])
x -= np.mean(x, keepdims=True)
x /= (np.std(x, keepdims=True) + K.epsilon())
batch_input_img[i] = x
batch_target_img[i] = load_scaled_img(path_pair[1])
yield batch_input_img, batch_target_img
def prepare_train():
X = []
Y = []
# 対象Aの画像
for picture in list_pictures('./ordinary/'):
img = img_to_array(load_img(picture, target_size=(224, 224)))
X.append(img)
Y.append(0)
# 対象Bの画像
for picture in list_pictures('./jiro/'):
img = img_to_array(load_img(picture, target_size=(224, 224)))
X.append(img)
Y.append(1)
# arrayに変換
X = np.asarray(X)
Y = np.asarray(Y)
# 画素値を0から1の範囲に変換
X = X.astype('float32')
X = X / 255.0
# クラスの形式を変換
Y = np_utils.to_categorical(Y, 2)
# 学習用データとテストデータ
X_train, X_test, y_train, y_test = train_test_split(X,
Y,
test_size=0.33,
random_state=111)
return X_train, X_test, y_train, y_test
def __init__(self,
root_dir,
classes=None,
mean=IMAGENET_MEAN,
std=IMAGENET_STD,
color_mode="rgb"):
""" ILSVRC data generator.
# Arguments:
- root_dir: Root directory of the ILSVRC dataset, containing directories "ILSVRC2012_img_train" and "ILSVRC2012_img_val", both containing
sub-directories with names of synsets and the images for each synset in the corresponding sub-directories.
- classes: List of synsets to restrict the dataset to. Numeric labels will be assigned to these synsets in ascending order.
If set to `None`, all available synsets will be used and enumerated in the lexicographical order.
- mean: Channel-wise image mean for normalization (in "RGB" order). If set to `None`, mean and standard deviation will be computed from the images.
- std: Channel-wise standard deviation for normalization (in "RGB" order). If set to `None`, standard deviation will be computed from the images.
- color_mode: Image color mode, either "rgb" or "bgr".
"""
super(ILSVRCGenerator, self).__init__(root_dir,
classes,
default_target_size=256,
randzoom_range=(256, 480),
color_mode=color_mode)
self.train_dir = os.path.join(self.root_dir, 'ILSVRC2012_img_train')
self.test_dir = os.path.join(self.root_dir, 'ILSVRC2012_img_val')
# Search for classes
if classes is None:
classes = []
for subdir in sorted(os.listdir(self.train_dir)):
if os.path.isdir(os.path.join(self.train_dir, subdir)):
classes.append(subdir)
self.classes = classes
self.class_indices = dict(zip(self.classes, range(len(self.classes))))
# Search for images
for lbl, subdir in enumerate(self.classes):
cls_files = sorted(
list_pictures(os.path.join(self.train_dir, subdir),
'JPE?G|jpe?g'))
self.train_img_files += cls_files
self._train_labels += [lbl] * len(cls_files)
cls_files = sorted(
list_pictures(os.path.join(self.test_dir, subdir),
'JPE?G|jpe?g'))
self.test_img_files += cls_files
self._test_labels += [lbl] * len(cls_files)
print('Found {} training and {} validation images from {} classes.'.
format(self.num_train, self.num_test, self.num_classes))
# Compute mean and standard deviation
self._compute_stats(mean, std)
def load_images(dir, grayscale=False):
imgs = []
for i in image_utils.list_pictures(dir):
imgs.append(
image_utils.img_to_array(
image_utils.load_img(i,
grayscale=grayscale,
target_size=i_shape)))
return image_utils.list_pictures(dir), np.array(imgs)
def data_gen(path_dir, batch_size):
dir_names = sorted(os.listdir(path_dir))
dir_pathes = [os.path.join(path_dir, d) for d in dir_names]
n_pair = len(dir_names) - 1
n_batches = math.ceil(n_pair / batch_size)
while True:
for i in range(n_batches):
start = i * batch_size
end = start + batch_size
if end > n_pair:
end = n_pair
file_pathes_list = [
list_pictures(d) for d in dir_pathes[start:end]
]
max_page_len = max([len(ps) for ps in file_pathes_list])
enc_inputs = [
load_img_batch(p, max_page_len) for p in file_pathes_list
]
batch_enc_inputs = np.empty(shape=(len(enc_inputs), max_page_len,
256, 256, 1))
for i, e in enumerate(enc_inputs):
batch_enc_inputs[i] = e
file_pathes_list = [
list_pictures(d) for d in dir_pathes[start + 1:end + 1]
]
max_page_len = max([len(ps) for ps in file_pathes_list])
# BOSあり
reshaped_bos = BOS.reshape(1, 256, 256, 1)
dec_inputs = [
np.vstack((reshaped_bos, load_img_batch(p, max_page_len)))
for p in file_pathes_list
]
batch_dec_inputs = np.empty(shape=(len(dec_inputs), max_page_len,
256, 256, 1))
for i, d in enumerate(dec_inputs):
batch_dec_inputs[i] = d[:-1]
batch_dec_outputs = np.empty(shape=(len(dec_inputs), max_page_len,
256, 256, 1))
for i, d in enumerate(dec_inputs):
batch_dec_outputs[i] = d[1:]
yield [batch_enc_inputs, batch_dec_inputs], batch_dec_outputs
def main():
X_train = []
y_train = []
X_test = []
y_test = []
for picture in list_pictures('./roboint/moji/train/pos/'):
img = img_to_array(
load_img(picture, target_size=(image_size, image_size)))
X_train.append(img)
y_train.append(0)
for picture in list_pictures('./roboint/moji/train/neg/'):
img = img_to_array(
load_img(picture, target_size=(image_size, image_size)))
X_train.append(img)
y_train.append(1)
for picture in list_pictures('./roboint/moji/test/pos/'):
img = img_to_array(
load_img(picture, target_size=(image_size, image_size)))
X_test.append(img)
y_test.append(0)
for picture in list_pictures('./roboint/moji/test/neg/'):
img = img_to_array(
load_img(picture, target_size=(image_size, image_size)))
X_test.append(img)
y_test.append(1)
X_train = np.array(X_train)
y_train = np.array(y_train)
X_test = np.array(X_test)
y_test = np.array(y_test)
X_train = X_train.astype('float32')
X_train = X_train / 255.0
X_test = X_test.astype('float32')
X_test = X_test / 255.0
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
# モデルを訓練し評価する
model = model_train(X_train, y_train)
model_eval(model, X_test, y_test)
#おまけ(テスト画像の予想を出力)
"""
def load_images(image_dir, W, H, image_array_divided_by=1):
from keras.preprocessing import image
# Get list of all files asuming that files are image type only
list_of_files = image.list_pictures(image_dir)
print("Count of image files read is ", len(list_of_files))
# Iterate over each images and read their sizes
list_images = []
for img_name in list_of_files: #[0:10] img_name = list_of_files[0]
img_path = img_name
img = np.NaN
#img = image.load_img(img_path, target_size=(W, H))
if W > 0 and H > 0:
img = image.load_img(img_path, target_size=(W, H))
else:
img = image.load_img(img_path)
x = image.img_to_array(img)
x /= image_array_divided_by
list_images.append(x)
del (img, x)
# Stack to have one complete list as required by NN
train = np.stack(list_images)
# Cleaning
del (list_images)
# Return all required
return ({"train": train, "list_of_files": list_of_files})
def generator(img_dir, cap_path, batch_size, max_len=30):
fh = open(cap_path)
raw_data = fh.read()
data = json.loads(raw_data)
img_groups = {}
all_captions = []
for img_group in data['images']:
img_groups[img_group['filename']] = []
for sentence in img_group['sentences']:
img_groups[img_group['filename']].append(sentence['raw'])
all_captions.append(sentence['raw'])
preprocessor = CaptionPreprocessor(rare_words_handling='nothing')
preprocessor.fit_on_captions(all_captions)
image_files = list_pictures(img_dir)
image_processor = ImagePreprocessor(is_training=True, img_size=(299, 299))
'''
global img_array
if os.path.exists('./img_array.npy'):
img_array = np.load('./img_array.npy')
else:
print("Preprocessing images...\n")
img_array = image_processor.process_images(image_files)
print("\nImages preprocessed.")
np.save('./img_array', img_array)
'''
return ImgSequence(img_groups, image_files, img_dir, batch_size,
(image_processor, preprocessor), max_len)
def extract_inception():
model = InceptionV3(weights='imagenet', include_top=False)
print(model.summary())
X_dirname = '../../411a3/train'
Y_filename = '../../411a3/train.csv'
X_filelist = image.list_pictures(X_dirname)
Y_list = np.loadtxt(Y_filename, dtype='str', delimiter=',')[1:]
X_inception = np.zeros((train_size, 2048, 8, 8))
y_inception = Y_list[:, 1].astype('int64').reshape(-1, 1) - 1
for i in range(train_size):
img = image.load_img(X_filelist[i], target_size=target_size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
inception = model.predict(x)
X_inception[i, :, :, :] = inception
print('Read image: ' + X_filelist[i])
# shuffle inputs and targets
rnd_idx = np.arange(X_inception.shape[0])
np.random.shuffle(rnd_idx)
X_train = X_inception[rnd_idx]
y_train = y_inception[rnd_idx]
return X_train, y_train
def load_image(nb_test=0):
X_dirname = '../411a3/train'
Y_filename = '../411a3/train.csv'
X_filelist = list_pictures(X_dirname)
Y_list = np.loadtxt(Y_filename, dtype='str', delimiter=',')[1:]
X_train = np.zeros((train_size, image_channel, image_size, image_size))
y_train = Y_list[:, 1].astype('int64').reshape(-1, 1) - 1
for i in range(train_size):
img = load_img(X_filelist[i])
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
X_train[i, :, :, :] = x
print('Read image: ' + X_filelist[i])
# shuffle inputs and targets
rnd_idx = np.arange(X_train.shape[0])
np.random.shuffle(rnd_idx)
X_train = X_train[rnd_idx]
y_train = y_train[rnd_idx]
if K.image_dim_ordering() == 'tf':
X_train = X_train.transpose(0, 2, 3, 1)
# print('X_train shape:', X_train.shape)
# print('y_train shape:', y_train.shape)
return (X_train[nb_test:], y_train[nb_test:]), (X_train[:nb_test], y_train[:nb_test])
def getImage():
X = []
for picture in list_pictures('./eroimages/'):
img = img_to_array(load_img(picture, target_size=(128, 128, 3)))
X.append(img)
npX = np.array(X)
return npX
def setUp(self):
self.motion_detection_test_dir = path.join(temp_data_dir,
'motion_detection')
self.images = list_pictures(self.motion_detection_test_dir)
with open(path.join(self.motion_detection_test_dir,
'annotations')) as f:
self.annotations = [line.split(' ')[1].strip('\n') for line in f]
self.positive_annotation = 'person_visible'
self.negative_annotation = 'no_person_visible'
def getImage():
X = []
for picture in list_pictures('./gazo/'):
img = img_to_array(load_img(picture, target_size=(96, 96, 3)))
X.append(img)
if len(X) % 100 == 0:
print(len(X))
npX = np.array(X)
return npX
def load_mnist(width, height, channels):
X = []
for image in list_pictures('./MNIST/0/'):
img = img_to_array(
load_img(image, grayscale=True, target_size=(28, 28)))
X.append(img)
X = np.asarray(X)
X = (X.astype(np.float32) - 127.5) / 127.5
img_rows = width
img_cols = height
img_shape = [img_rows, img_cols, channels]
return X, img_shape
def load_data():
x_data = []
y_data = []
# appleの画像
for picture in list_pictures('./dataset/apple/'):
img = img_to_array(load_img(picture, target_size=img_size))
x_data.append(img)
y_data.append(0)
# bananaの画像
for picture in list_pictures('./dataset/banana/'):
img = img_to_array(load_img(picture, target_size=img_size))
x_data.append(img)
y_data.append(1)
# orangeの画像
for picture in list_pictures('./dataset/orange/'):
img = img_to_array(load_img(picture, target_size=img_size))
x_data.append(img)
y_data.append(2)
# arrayに変換
x_data = np.asarray(x_data)
y_data = np.asarray(y_data)
# 画素値を0から1の範囲に変換
x_data = x_data.astype('float32')
x_data = x_data / 255.0
# クラスの形式を変換
y_data = np_utils.to_categorical(y_data, category_size)
# 学習用データとテストデータの振り分け
x_train, x_test, y_train, y_test = train_test_split(x_data,
y_data,
test_size=0.33,
random_state=123)
return x_train, x_test, y_train, y_test
def load_dataset(directory, crop_size=320, batch_size=32):
"""
load data from directory
:param directory: jpg files directory
:param crop_size: cropped image size
:param batch_size: batch size
:return: python generator object, a batch training data, img, y_cls_mask_lable, y_regr_cls_mask_label
"""
jpg_list = list_pictures(directory, 'jpg')
generator = img_txtreg_generator(jpg_list, crop_size, scale=1 / 255.0)
generator = image_ylabel_generator(generator)
generator = group_by_batch(generator, batch_size)
return generator
def load_val():
X_dirname = '../411a3/val'
X_filelist = image.list_pictures(X_dirname)
X_val = np.zeros((970, gist_size))
for i in range(970):
im = Image.open(X_filelist[i])
descriptors = leargist.color_gist(im)
X_val[i, :] = descriptors
print('Load image: ' + X_filelist[i])
return X_val
def train(self):
images = list_pictures(self._image_path)
num_image = len(images)
total_seq = num_image * self._num_seq_per_image
steps_per_epoch = total_seq // self._batch_size
self._build_generator()
image_captioning_model = ImageCaptioningModel(
self._max_seq_length,
rnn_mode=self._rnn_mode,
drop_rate=self._drop_rate,
hidden_dim=self._hidden_dim,
rnn_state_size=self._rnn_state_size,
embedding_size=self._embedding_size,
rnn_activation=self._activation,
cnn_model=self._cnn_model,
optimizer=self._optimizer,
initializer=self._initializer,
learning_rate=self._lr,
mode=self._mode,
reg_l1=self._reg_l1,
reg_l2=self._reg_l2,
num_word=len(self._generator.caption_processor.word_index) + 1,
is_trainable=self._is_trainable,
metrics=self._metrics,
loss=self._loss)
image_captioning_model.build_model()
model = image_captioning_model.image_captioning_model
save_path = self._model_path
ckpt_path = self._ckpt_path
if ckpt_path and os.path.isfile(ckpt_path):
print("Load Check Point")
model.load_weights(ckpt_path)
self._image_captioning_model = model
model.fit_generator(generator=self._generator,
steps_per_epoch=steps_per_epoch,
epochs=self._epoch,
callbacks=callback(ckpt_path, './logs/'))
model.save(save_path)
def generate_batches(batchq, imdir, num_batches, batch_size, image_size):
image_paths = list_pictures(imdir)
if not image_paths:
print("Error: no images found in {}".format(imdir))
sys.exit(1)
for _ in range(num_batches):
batch_image_paths = sample(image_paths, batch_size)
batch = np.vstack([
load_and_preprocess_img(image_path,
image_size,
center_crop=True)
for image_path in batch_image_paths
])
batchq.put(batch)
def save_images(self, image_dir):
image_files = list_pictures(image_dir)
print("Preprocessing images...\n")
pbar = tqdm(total=len(image_files))
h5f = h5py.File('../tmp/img_array.h5', 'w')
img_cache = []
for image_file in image_files:
pbar.update()
array = image_processor.process_image(image_file)
img_cache.append((image_file.split('/')[-1], array))
h5f.create_dataset('img_data', data=img_cache)
h5f.close()
print("\nImages preprocessed.")
def load_train():
X_dirname = '../411a3/train'
Y_filename = '../411a3/train.csv'
X_filelist = image.list_pictures(X_dirname)
Y_list = np.loadtxt(Y_filename, dtype='str', delimiter=',')[1:]
X_train = np.zeros((7000, gist_size))
y_train = Y_list[:, 1].astype('int64').reshape(-1, 1) - 1
for i in range(7000):
im = Image.open(X_filelist[i])
descriptors = leargist.color_gist(im)
X_train[i, :] = descriptors
print('Load image: ' + X_filelist[i])
return X_train, y_train
def generate_image(source_directory_path, generate_directory_path, target_size, count_per_image):
if not os.path.exists(generate_directory_path):
os.mkdir(generate_directory_path)
for picture in list_pictures(source_directory_path):
img = load_img(picture,target_size=target_size)
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
generator = keras.preprocessing.image.ImageDataGenerator( rescale= 1.0 / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
g = generator.flow(x, batch_size=1, save_to_dir=generate_directory_path, save_prefix='img', save_format='bmp')
for i in range(count_per_image):
g.next()
def extract_inception_test():
model = InceptionV3(weights='imagenet', include_top=False)
print(model.summary())
X_dirname = '../../411a3/test'
X_filelist = image.list_pictures(X_dirname)
X_inception_test = np.zeros((test_size, 2048, 8, 8))
for i in range(test_size):
img = image.load_img(X_filelist[i], target_size=target_size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
inception = model.predict(x)
X_inception_test[i, :, :, :] = inception
print('Read image: ' + X_filelist[i])
return X_inception_test
def make_generator(filepath, labeldata, batch_size, img_size, test_size):
"""
画像を読み込むジェネレータを作成します
"""
# データ一覧の取得
import sys
from keras.preprocessing.image import list_pictures
from progressbar import ProgressBar
from pathlib import Path
# 全画像枚数
num_pics = 0
list_pics = []
list_labels = []
print('count pictures.', file=sys.stderr)
prog = ProgressBar(0, len(labeldata))
for index, label in enumerate(labeldata):
path = Path(filepath).joinpath(label)
pics = list_pictures(path)
for p in pics:
list_pics.append(p)
list_labels.append(index)
num_pics += len(pics)
prog.update(index)
prog.finish()
print('{} pictures.'.format(num_pics), file=sys.stderr)
# データジェネレータの作成
split = int(num_pics * test_size)
train_gen = ImageGenerator(data_paths=list_pics[split:],
data_classes=list_labels[split:],
batch_size=batch_size,
width=img_size,
height=img_size,
num_of_class=len(labeldata))
val_gen = ImageGenerator(data_paths=list_pics[:split],
data_classes=list_labels[:split],
batch_size=batch_size,
width=img_size,
height=img_size,
num_of_class=len(labeldata))
return (train_gen, val_gen)
def extract_vgg16_val():
model = VGG16(weights='imagenet', include_top=False)
print(model.summary())
X_dirname = '../../411a3/val'
X_filelist = image.list_pictures(X_dirname)
X_vgg_val = np.zeros((val_size, 512, 7, 7))
for i in range(val_size):
img = image.load_img(X_filelist[i], target_size=target_size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
vgg16 = model.predict(x)
X_vgg_val[i, :, :, :] = vgg16
print('Read image: ' + X_filelist[i])
return X_vgg_val
def count_pictures(labeldata, datapath):
"""
datapathで与えられたディレクトリ内に存在するlabeldataで指定されたディレクトリ以下の画像の枚数を取得します
"""
from keras.preprocessing.image import list_pictures
import sys
from pathlib import Path
from progressbar import ProgressBar
print('count pictures.', file=sys.stderr)
result = 0
prog = ProgressBar(0, len(labeldata))
for index, label in enumerate(labeldata):
path = Path(datapath).joinpath(label)
pics = list_pictures(path)
result += len(pics)
prog.update(index + 1)
prog.finish()
print('{} pictures.'.format(result), file=sys.stderr)
return result
def load_aug_image_train():
X_file_base = '../411a3/data_classify/train/'
X_train = np.empty((0, gist_size))
y_train = np.empty((0, 1))
for i_class in range(nb_classes):
X_file_part = X_file_base + str(i_class)
filelist = list_pictures(X_file_part)
nb_part = len(filelist)
X_part = np.zeros((nb_part, gist_size))
for i in range(nb_part):
im = Image.open(filelist[i])
descriptors = leargist.color_gist(im)
X_part[i, :] = descriptors
print('Load image: ' + filelist[i])
X_train = np.concatenate((X_train, X_part), axis=0)
y_train = np.concatenate((y_train, np.ones((nb_part, 1)) * i_class), axis=0)
print('X_train shape:', X_train.shape)
print('y_train shape:', y_train.shape)
return X_train, y_train
def load_val_image():
X_dirname = '../411a3/val'
X_filelist = image.list_pictures(X_dirname)
val_samples = len(X_filelist)
X_val = np.zeros((val_samples, 3, image_load_size, image_load_size))
for i in range(val_samples):
img = image.load_img(X_filelist[i],
target_size=(image_load_size, image_load_size))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
X_val[i, :, :, :] = x
print('Predict image: ' + X_filelist[i])
if K.image_dim_ordering() == 'tf':
X_val = X_val.transpose(0, 2, 3, 1)
# print('X_val shape:', X_val.shape)
return X_val
def get_data_list(data_list, target_size, category_size):
X = []
y = []
for item in data_list:
for picture in list_pictures(item['directory_path']):
img = img_to_array(load_img(picture, target_size=target_size))
X.append(img)
y.append(item['correct_value'])
# arrayに変換
X = np.asarray(X)
y = np.asarray(y)
# 画素値を0から1の範囲に変換
X = X.astype('float32')
X = X / 255.0
# クラスの形式を変換
y = np_utils.to_categorical(y, category_size)
return (X, y)
def make_dataset_reaction(out_x, out_y, labeldata, datapath, size):
"""
datapathで指定されたディレクトリ内のllabeldataで指定されたディレクトリ以下の画像ファイルをsize*sizeにリサイズしout_x・out_yに格納します
また、読み込みに失敗した画像パスのリストを返します
"""
from keras.preprocessing.image import img_to_array, list_pictures, load_img
import sys
import numpy as np
from pathlib import Path
from progressbar import ProgressBar
IMG = np.empty((1, size, size, 3), dtype='int8')
POSITION = 0
ERROR = []
for index, label in enumerate(labeldata):
path = Path(datapath).joinpath(label)
pics = list_pictures(path)
prog = ProgressBar(0, len(pics))
print('[{}/{}] load {} {} pictures.'.format(index + 1, len(labeldata),
path, len(pics)),
file=sys.stderr)
count = 0
for picture in pics:
try:
IMG[0] = img_to_array(
load_img(picture, target_size=(size, size)))
out_x[POSITION + count] = IMG
prog.update(count + 1)
count += 1
except Exception as identifier:
print(picture, identifier)
pics.remove(picture)
ERROR.append(picture)
out_y[POSITION:POSITION + len(pics)] = np.full(len(pics),
index,
dtype=int)
POSITION += len(pics)
prog.finish()
return ERROR
