Python dtypes.uint8方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def extract_labels(dataframe, one_hot=False, num_classes=10):
    """Extract the labels into a 1D uint8 numpy array [index].

    Args:
    dataframe: A pandas dataframe object.
    one_hot: Does one hot encoding for the result.
    num_classes: Number of classes for the one hot encoding.

    Returns:
    labels: a 1D uint8 numpy array.
    """
    print('Extracting labels', )
    labels = dataframe['label'].values
    labels = _label_encoder.fit_transform(labels)
    if one_hot:
        return dense_to_one_hot(labels, num_classes)
    return labels 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def GenerateImage(self, image_format, image_shape):
    """Generates an image and an example containing the encoded image.

    Args:
      image_format: the encoding format of the image.
      image_shape: the shape of the image to generate.

    Returns:
      image: the generated image.
      example: a TF-example with a feature key 'image/encoded' set to the
        serialized image and a feature key 'image/format' set to the image
        encoding format ['jpeg', 'JPEG', 'png', 'PNG', 'raw'].
    """
    num_pixels = image_shape[0] * image_shape[1] * image_shape[2]
    image = np.linspace(
        0, num_pixels - 1, num=num_pixels).reshape(image_shape).astype(np.uint8)
    tf_encoded = self._Encoder(image, image_format)
    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image/encoded': self._EncodedBytesFeature(tf_encoded),
        'image/format': self._StringFeature(image_format)
    }))

    return image, example.SerializeToString() 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def _RGBToGrayscale(self, images):
    is_batch = True
    if len(images.shape) == 3:
      is_batch = False
      images = np.expand_dims(images, axis=0)
    out_shape = images.shape[0:3] + (1,)
    out = np.zeros(shape=out_shape, dtype=np.uint8)
    for batch in xrange(images.shape[0]):
      for y in xrange(images.shape[1]):
        for x in xrange(images.shape[2]):
          red = images[batch, y, x, 0]
          green = images[batch, y, x, 1]
          blue = images[batch, y, x, 2]
          gray = 0.2989 * red + 0.5870 * green + 0.1140 * blue
          out[batch, y, x, 0] = int(gray)
    if not is_batch:
      out = np.squeeze(out, axis=0)
    return out 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def testBasicGrayscaleToRGB(self):
    # 4-D input with batch dimension.
    x_np = np.array([[1, 2]], dtype=np.uint8).reshape([1, 1, 2, 1])
    y_np = np.array([[1, 1, 1], [2, 2, 2]],
                    dtype=np.uint8).reshape([1, 1, 2, 3])

    with self.test_session(use_gpu=True):
      x_tf = constant_op.constant(x_np, shape=x_np.shape)
      y = image_ops.grayscale_to_rgb(x_tf)
      y_tf = y.eval()
      self.assertAllEqual(y_tf, y_np)

    # 3-D input with no batch dimension.
    x_np = np.array([[1, 2]], dtype=np.uint8).reshape([1, 2, 1])
    y_np = np.array([[1, 1, 1], [2, 2, 2]], dtype=np.uint8).reshape([1, 2, 3])

    with self.test_session(use_gpu=True):
      x_tf = constant_op.constant(x_np, shape=x_np.shape)
      y = image_ops.grayscale_to_rgb(x_tf)
      y_tf = y.eval()
      self.assertAllEqual(y_tf, y_np) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def test_adjust_gamma_less_one(self):
    """Verifying the output with expected results for gamma
    correction with gamma equal to half"""
    with self.test_session():
      x_np = np.arange(0, 255, 4, np.uint8).reshape(8,8)
      y = image_ops.adjust_gamma(x_np, gamma=0.5)
      y_tf = np.trunc(y.eval())

      y_np = np.array([[  0,  31,  45,  55,  63,  71,  78,  84],
          [ 90,  95, 100, 105, 110, 115, 119, 123],
          [127, 131, 135, 139, 142, 146, 149, 153],
          [156, 159, 162, 165, 168, 171, 174, 177],
          [180, 183, 186, 188, 191, 194, 196, 199],
          [201, 204, 206, 209, 211, 214, 216, 218],
          [221, 223, 225, 228, 230, 232, 234, 236],
          [238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.float32)

      self.assertAllClose(y_tf, y_np, 1e-6) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def test_adjust_gamma_greater_one(self):
    """Verifying the output with expected results for gamma
    correction with gamma equal to two"""
    with self.test_session():
      x_np = np.arange(0, 255, 4, np.uint8).reshape(8,8)
      y = image_ops.adjust_gamma(x_np, gamma=2)
      y_tf = np.trunc(y.eval())

      y_np = np.array([[  0,   0,   0,   0,   1,   1,   2,   3],
          [  4,   5,   6,   7,   9,  10,  12,  14],
          [ 16,  18,  20,  22,  25,  27,  30,  33],
          [ 36,  39,  42,  45,  49,  52,  56,  60],
          [ 64,  68,  72,  76,  81,  85,  90,  95],
          [100, 105, 110, 116, 121, 127, 132, 138],
          [144, 150, 156, 163, 169, 176, 182, 189],
          [196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.float32)

      self.assertAllClose(y_tf, y_np, 1e-6) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def testResizeDownArea(self):
    img_shape = [1, 6, 6, 1]
    data = [128, 64, 32, 16, 8, 4,
            4, 8, 16, 32, 64, 128,
            128, 64, 32, 16, 8, 4,
            5, 10, 15, 20, 25, 30,
            30, 25, 20, 15, 10, 5,
            5, 10, 15, 20, 25, 30]
    img_np = np.array(data, dtype=np.uint8).reshape(img_shape)

    target_height = 4
    target_width = 4
    expected_data = [73, 33, 23, 39,
                     73, 33, 23, 39,
                     14, 16, 19, 21,
                     14, 16, 19, 21]

    with self.test_session(use_gpu=True):
      image = constant_op.constant(img_np, shape=img_shape)
      y = image_ops.resize_images(image, [target_height, target_width],
                                  image_ops.ResizeMethod.AREA)
      expected = np.array(expected_data).reshape(
          [1, target_height, target_width, 1])
      resized = y.eval()
      self.assertAllClose(resized, expected, atol=1) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def testConvertBetweenInt16AndInt8(self):
    with self.test_session(use_gpu=True):
      # uint8, uint16
      self._convert([0, 255 * 256], dtypes.uint16, dtypes.uint8,
                    [0, 255])
      self._convert([0, 255], dtypes.uint8, dtypes.uint16,
                    [0, 255 * 256])
      # int8, uint16
      self._convert([0, 127 * 2 * 256], dtypes.uint16, dtypes.int8,
                    [0, 127])
      self._convert([0, 127], dtypes.int8, dtypes.uint16,
                    [0, 127 * 2 * 256])
      # int16, uint16
      self._convert([0, 255 * 256], dtypes.uint16, dtypes.int16,
                    [0, 255 * 128])
      self._convert([0, 255 * 128], dtypes.int16, dtypes.uint16,
                    [0, 255 * 256]) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def visualize(self):
    """Multi-channel visualization of densities as images.

    Creates and returns an image summary visualizing the current probabilty
    density estimates. The image contains one row for each channel. Within each
    row, the pixel intensities are proportional to probability values, and each
    row is centered on the median of the corresponding distribution.

    Returns:
      The created image summary.
    """
    with ops.name_scope(self._name_scope()):
      image = self._pmf
      image *= 255 / math_ops.reduce_max(image, axis=1, keepdims=True)
      image = math_ops.cast(image + .5, dtypes.uint8)
      image = image[None, :, :, None]
    return summary.image("pmf", image, max_outputs=1) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def __init__(self,
                 payloads,
                 labels,
                 dtype=dtypes.float32,
                 seed=None):
        """Construct a DataSet.
        one_hot arg is used only if fake_data is true.  `dtype` can be either
        `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
        `[0, 1]`.  Seed arg provides for convenient deterministic testing.
        """
        seed1, seed2 = random_seed.get_seed(seed)
        # If op level seed is not set, use whatever graph level seed is returned
        np.random.seed(seed1 if seed is None else seed2)
        dtype = dtypes.as_dtype(dtype).base_dtype
        if dtype not in (dtypes.uint8, dtypes.float32):
            raise TypeError('Invalid payload dtype %r, expected uint8 or float32' %
                            dtype)

        assert payloads.shape[0] == labels.shape[0], (
                'payloads.shape: %s labels.shape: %s' % (payloads.shape, labels.shape))
        self._num_examples = payloads.shape[0]

        if dtype == dtypes.float32:
            # Convert from [0, 255] -> [0.0, 1.0].
            payloads = payloads.astype(np.float32)
            payloads = np.multiply(payloads, 1.0 / 255.0)

        self._payloads = payloads
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def extract_images(filename):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
  print('Extracting', filename)
  with open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
    magic = _read32(bytestream)
    if magic != 2051:
      raise ValueError('Invalid magic number %d in MNIST image file: %s' %
                       (magic, filename))
    num_images = _read32(bytestream)
    rows = _read32(bytestream)
    cols = _read32(bytestream)
    buf = bytestream.read(rows * cols * num_images)
    data = numpy.frombuffer(buf, dtype=numpy.uint8)
    data = data.reshape(num_images, rows, cols, 1)
    return data 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def extract_labels(filename, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index]."""
  print('Extracting', filename)
  with open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
    magic = _read32(bytestream)
    if magic != 2049:
      raise ValueError('Invalid magic number %d in MNIST label file: %s' %
                       (magic, filename))
    num_items = _read32(bytestream)
    buf = bytestream.read(num_items)
    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
    if one_hot:
      return dense_to_one_hot(labels, num_classes)
    return labels 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def __init__(self,
               images,
               labels,
               start_id=0,
               fake_data=False,
               one_hot=False,
               dtype=dtypes.float32):
    """Construct a DataSet.
    one_hot arg is used only if fake_data is true.  `dtype` can be either
    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
    `[0, 1]`.
    """
    dtype = dtypes.as_dtype(dtype).base_dtype
    if dtype not in (dtypes.uint8, dtypes.float32):
      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
                      dtype)
    if fake_data:
      self._num_examples = 10000
      self.one_hot = one_hot
    else:
      assert images.shape[0] == labels.shape[0], (
          'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
      self._num_examples = images.shape[0]

      # Convert shape from [num examples, rows, columns, depth]
      # to [num examples, rows*columns] (assuming depth == 1)
      assert images.shape[3] == 1
      images = images.reshape(images.shape[0],
                              images.shape[1] * images.shape[2])
      if dtype == dtypes.float32:
        # Convert from [0, 255] -> [0.0, 1.0].
        images = images.astype(numpy.float32)
        images = numpy.multiply(images, 1.0 / 255.0)
    self._ids = numpy.arange(start_id, start_id + self._num_examples)
    self._images = images
    self._labels = labels
    self._epochs_completed = 0
    self._index_in_epoch = 0 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def _assert_dtype(images):
    """ Make sure the images are of the correct data type """
    dtype = dtypes.as_dtype(images.dtype).base_dtype
    if dtype not in (dtypes.uint8, dtypes.float32):
        raise TypeError('Invalid image dtype {0}, expected uint8 or float32'.format(dtype))

    return dtype 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 別名]
def _correct_images(images):
    """ Convert images to be correct """
    # From the MNIST website: "Pixels are organized row-wise. Pixel values are 0 to 255. 0 means
    # background (white), 255 means foreground (black)."
    # The dataset does not transform the image such that 255 is black, so do that here.
    dtype = _assert_dtype(images)
    max_val = 255 if dtype == dtypes.uint8 else 1.0
    return max_val - images