def __getitem__(self, i, type=None): filename = self.files[i] image = load_image(filename) label = data_processor.binarize(self.annotations[i], self.num_classes) label = np.reshape(label, (self.num_classes)) return (image, label)
def __getitem__(self, i, type=None): files, labels = self.files_and_annotations image = self._get_image(files[i]) label = data_processor.binarize(labels[i], self.num_classes) label = np.reshape(label, (self.num_classes)) return (image, label)
def __getitem__(self, i, type=None): target_file = self.files[i] image = load_image(target_file) label = self.get_label(target_file) label = data_processor.binarize(label, self.num_classes) label = np.reshape(label, (self.num_classes)) return (image, label)
def __getitem__(self, i, type=None): filename = self.files[i] image = PIL.Image.open(filename) # sometime image data be gray. image = image.convert("RGB") image = np.array(image) label = data_processor.binarize(self.annotations[i], self.num_classes) label = np.reshape(label, (self.num_classes)) return (image, label)
def feed(self): """Returns numpy array of batch size data.""" images, labels = zip(*[self._element() for _ in range(self.batch_size)]) labels = data_processor.binarize(labels, self.num_classes) images = np.array(images) if self.data_format == 'NCHW': images = np.transpose(images, [0, 3, 1, 2]) return images, labels
def feed(self): """Batch size numpy array of images and ground truth boxes. Returns: images: images numpy array. shape is [batch_size, height, width] labels: labels numpy array. shape is [batch_size, num_classes] """ images, labels_list = zip( *[self._element() for _ in range(self.batch_size)]) images = np.array(images) labels_list = data_processor.binarize(labels_list, self.num_classes) if self.data_format == "NCHW": images = np.transpose(images, [0, 3, 1, 2]) return images, labels_list
def feed(self): """Returns numpy array of batch size data. Returns: images: images numpy array. shape is [batch_size, height, width] labels: one hot labels. shape is [batch_size, num_classes] """ images, labels = zip( *[self._element() for _ in range(self.batch_size)]) labels = data_processor.binarize(labels, self.num_classes) images = np.array(images) if self.data_format == "NCHW": images = np.transpose(images, [0, 3, 1, 2]) return images, labels
def _init_features(self): iterator = self.dataset.make_one_shot_iterator() next_element = iterator.get_next() self.features = [] with tf.Session() as sess: for _ in range(self.num_per_epoch): element = sess.run(next_element) image = element["image"] label = element["label"] # Converting grayscale images into RGB images. # This workaround is needed because the method PIL.Image.fromarray() # in pre_prpcessor requires images array to have a shape of (h, w, 3). if image.shape[2] == 1: image = np.stack([image] * 3, 3) image = image.reshape(image.shape[:2] + (3,)) label = data_processor.binarize(label, self.num_classes) label = np.reshape(label, (self.num_classes)) self.features.append((image, label))
def __getitem__(self, i, type=None): image = self._get_image(i) label = data_processor.binarize(self.labels[i], self.num_classes) label = np.reshape(label, (self.num_classes)) return (image, label)