我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用tensorflow.substr()。
def read_image(self, image_path): # tf.decode_image does not return the image size, this is an ugly workaround to handle both jpeg and png path_length = string_length_tf(image_path)[0] file_extension = tf.substr(image_path, path_length - 3, 3) file_cond = tf.equal(file_extension, 'jpg') image = tf.cond(file_cond, lambda: tf.image.decode_jpeg(tf.read_file(image_path)), lambda: tf.image.decode_png(tf.read_file(image_path))) # if the dataset is cityscapes, we crop the last fifth to remove the car hood if self.dataset == 'cityscapes': o_height = tf.shape(image)[0] crop_height = (o_height * 4) / 5 image = image[:crop_height,:,:] image = tf.image.convert_image_dtype(image, tf.float32) image = tf.image.resize_images(image, [self.params.height, self.params.width], tf.image.ResizeMethod.AREA) return image
def read_image(self, image_path): # tf.decode_image does not return the image size, this is an ugly workaround to handle both jpeg and png path_length = string_length_tf(image_path)[0] file_extension = tf.substr(image_path, path_length - 3, 3) file_cond = tf.equal(file_extension, 'jpg') image = tf.cond(file_cond, lambda: tf.image.decode_jpeg(tf.read_file(image_path)), lambda: tf.image.decode_png(tf.read_file(image_path))) # if the dataset is cityscapes, we crop the last fifth to remove the car hood if self.dataset == 'cityscapes': o_height = tf.shape(image)[0] crop_height = (o_height * 4) // 5 image = image[:crop_height,:,:] image = tf.image.convert_image_dtype(image, tf.float32) image = tf.image.resize_images(image, [self.params.height, self.params.width], tf.image.ResizeMethod.AREA) return image
def __init__(self, config, batch_size, one_hot=False): self.lookup = None reader = tf.TextLineReader() filename_queue = tf.train.string_input_producer(["chargan.txt"]) key, x = reader.read(filename_queue) vocabulary = self.get_vocabulary() table = tf.contrib.lookup.string_to_index_table_from_tensor( mapping = vocabulary, default_value = 0) x = tf.string_join([x, tf.constant(" " * 64)]) x = tf.substr(x, [0], [64]) x = tf.string_split(x,delimiter='') x = tf.sparse_tensor_to_dense(x, default_value=' ') x = tf.reshape(x, [64]) x = table.lookup(x) self.one_hot = one_hot if one_hot: x = tf.one_hot(x, len(vocabulary)) x = tf.cast(x, dtype=tf.float32) x = tf.reshape(x, [1, int(x.get_shape()[0]), int(x.get_shape()[1]), 1]) else: x = tf.cast(x, dtype=tf.float32) x -= len(vocabulary)/2.0 x /= len(vocabulary)/2.0 x = tf.reshape(x, [1,1, 64, 1]) num_preprocess_threads = 8 x = tf.train.shuffle_batch( [x], batch_size=batch_size, num_threads=num_preprocess_threads, capacity= 5000, min_after_dequeue=500, enqueue_many=True) self.x = x self.table = table
def read_image(self, image_path): # tf.decode_image does not return the image size, this is an ugly workaround to handle both jpeg and png path_length = string_length_tf(image_path)[0] file_extension = tf.substr(image_path, path_length - 3, 3) file_cond = tf.equal(file_extension, 'jpg') image = tf.cond(file_cond, lambda: tf.image.decode_jpeg(tf.read_file(image_path)), lambda: tf.image.decode_png(tf.read_file(image_path))) image = tf.image.convert_image_dtype(image, tf.float32) image = tf.image.resize_images(image, [self.params.height, self.params.width], tf.image.ResizeMethod.AREA) return image
def decode_image(contents, channels=None, name=None): """Convenience function for `decode_gif`, `decode_jpeg`, and `decode_png`. Detects whether an image is a GIF, JPEG, or PNG, and performs the appropriate operation to convert the input bytes `string` into a `Tensor` of type `uint8`. Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as opposed to `decode_jpeg` and `decode_png`, which return 3-D arrays `[height, width, num_channels]`. Make sure to take this into account when constructing your graph if you are intermixing GIF files with JPEG and/or PNG files. Args: contents: 0-D `string`. The encoded image bytes. channels: An optional `int`. Defaults to `0`. Number of color channels for the decoded image. name: A name for the operation (optional) Returns: `Tensor` with type `uint8` with shape `[height, width, num_channels]` for JPEG and PNG images and shape `[num_frames, height, width, 3]` for GIF images. """ with ops.name_scope(name, 'decode_image') as scope: if channels not in (None, 0, 1, 3): raise ValueError('channels must be in (None, 0, 1, 3)') substr = tf.substr(contents, 0, 4) def _gif(): # Create assert op to check that bytes are GIF decodable is_gif = tf.equal(substr, b'\x47\x49\x46\x38', name='is_gif') decode_msg = 'Unable to decode bytes as JPEG, PNG, or GIF' assert_decode = control_flow_ops.Assert(is_gif, [decode_msg]) # Create assert to make sure that channels is not set to 1 # Already checked above that channels is in (None, 0, 1, 3) gif_channels = 0 if channels is None else channels good_channels = tf.not_equal(gif_channels, 1, name='check_channels') channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_decode, assert_channels]): return gen_image_ops.decode_gif(contents) def _png(): return gen_image_ops.decode_png(contents, channels) def check_png(): is_png = tf.equal(substr, b'\211PNG', name='is_png') return control_flow_ops.cond(is_png, _png, _gif, name='cond_png') def _jpeg(): return gen_image_ops.decode_jpeg(contents, channels) is_jpeg = tf.logical_or(tf.equal(substr, b'\xff\xd8\xff\xe0', name='is_jpeg0'), tf.equal(substr, b'\xff\xd8\xff\xe1', name='is_jpeg0')) return control_flow_ops.cond(is_jpeg, _jpeg, check_png, name='cond_jpeg')
