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Python dtypes.uint8方法代码示例

本文整理汇总了Python中tensorflow.python.framework.dtypes.uint8方法的典型用法代码示例。如果您正苦于以下问题:Python dtypes.uint8方法的具体用法?Python dtypes.uint8怎么用?Python dtypes.uint8使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow.python.framework.dtypes的用法示例。


在下文中一共展示了dtypes.uint8方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: extract_labels

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:SalikLP,项目名称:classification-of-encrypted-traffic,代码行数:19,代码来源:dataset.py

示例2: GenerateImage

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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() 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:25,代码来源:tfexample_decoder_test.py

示例3: _RGBToGrayscale

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:20,代码来源:image_ops_test.py

示例4: testBasicGrayscaleToRGB

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:23,代码来源:image_ops_test.py

示例5: test_adjust_gamma_less_one

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:20,代码来源:image_ops_test.py

示例6: test_adjust_gamma_greater_one

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:20,代码来源:image_ops_test.py

示例7: testResizeDownArea

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:27,代码来源:image_ops_test.py

示例8: testConvertBetweenInt16AndInt8

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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]) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:19,代码来源:image_ops_test.py

示例9: visualize

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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) 
开发者ID:mauriceqch,项目名称:pcc_geo_cnn,代码行数:19,代码来源:entropy_models.py

示例10: __init__

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:SalikLP,项目名称:classification-of-encrypted-traffic,代码行数:33,代码来源:dataset.py

示例11: extract_images

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:GoogleCloudPlatform,项目名称:cloudml-samples,代码行数:17,代码来源:input_data.py

示例12: extract_labels

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:GoogleCloudPlatform,项目名称:cloudml-samples,代码行数:16,代码来源:input_data.py

示例13: __init__

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:GoogleCloudPlatform,项目名称:cloudml-samples,代码行数:40,代码来源:input_data.py

示例14: _assert_dtype

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:dojoteef,项目名称:glas,代码行数:9,代码来源:omniglot.py

示例15: _correct_images

# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint8 [as 别名]
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 
开发者ID:dojoteef,项目名称:glas,代码行数:10,代码来源:omniglot.py


注:本文中的tensorflow.python.framework.dtypes.uint8方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。