本文整理汇总了Python中matplotlib.cm.colors方法的典型用法代码示例。如果您正苦于以下问题：Python cm.colors方法的具体用法？Python cm.colors怎么用？Python cm.colors使用的例子？那么恭喜您, 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类matplotlib.cm的用法示例。
在下文中一共展示了cm.colors方法的4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: colorize
# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import colors [as 别名]
def colorize(value, vmin=None, vmax=None, cmap=None):
# normalize
vmin = tf.reduce_min(value) if vmin is None else vmin
vmax = tf.reduce_max(value) if vmax is None else vmax
value = (value - vmin) / (vmax - vmin) # vmin..vmax
# squeeze last dim if it exists
value = tf.squeeze(value)
# quantize
indices = tf.to_int32(tf.round(value * 255))
# gather
cm = matplotlib.cm.get_cmap(cmap if cmap is not None else 'gray')
colors = tf.constant(cm.colors, dtype=tf.float32)
value = tf.gather(colors, indices)
return value 示例2: colorize
# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import colors [as 别名]
def colorize(img, vmin=None, vmax=None, cmap='plasma'):
# normalize
vmin = tf.reduce_min(img) if vmin is None else vmin
vmax = tf.contrib.distributions.percentile(img, 99.) if vmax is None else vmax
img = (img - vmin) / (vmax - vmin)
img = tf.squeeze(img, axis=[-1])
# quantize
indices = tf.clip_by_value(tf.to_int32(tf.round(img * 255)), 0, 255)
# gather
cm = matplotlib.cm.get_cmap(cmap if cmap is not None else 'gray')
colors = tf.constant(cm.colors, dtype=tf.float32)
img = tf.gather(colors, indices)
return img 示例3: tf_colorize
# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import colors [as 别名]
def tf_colorize(value, factor=1, vmin=None, vmax=None, cmap=None):
"""
A utility function for TensorFlow that maps a grayscale image to a matplotlib
colormap for use with TensorBoard image summaries.
By default it will normalize the input value to the range 0..1 before mapping
to a grayscale colormap.
Arguments:
- value: 2D Tensor of shape [height, width] or 3D Tensor of shape
[height, width, 1].
- factor: resize factor, scalar
- vmin: the minimum value of the range used for normalization.
(Default: value minimum)
- vmax: the maximum value of the range used for normalization.
(Default: value maximum)
- cmap: a valid cmap named for use with matplotlib's `get_cmap`.
(Default: 'gray')
Example usage:
```
output = tf.random_uniform(shape=[256, 256, 1])
output_color = colorize(output, vmin=0.0, vmax=1.0, cmap='viridis')
tf.summary.image('output', output_color)
```
Returns a 3D tensor of shape [height, width, 3].
"""
# normalize
vmin = tf.reduce_min(value) if vmin is None else vmin
vmax = tf.reduce_max(value) if vmax is None else vmax
value = (value - vmin) / (vmax - vmin) # vmin..vmax
# squeeze last dim if it exists
value = tf.squeeze(value)
# quantize
indices = tf.to_int32(tf.round(value * 255))
# gather
cm = matplotlib.cm.get_cmap(cmap if cmap is not None else 'gray')
colors = tf.constant(cm.colors, dtype=tf.float32)
value = tf.gather(colors, indices)
return value 示例4: colorize_image
# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import colors [as 别名]
def colorize_image(value, vmin=None, vmax=None, cmap='viridis'):
"""
A utility function for TensorFlow that maps a grayscale image to a matplotlib
colormap for use with TensorBoard image summaries.
By default it will normalize the input value to the range 0..1 before mapping
to a grayscale colormap.
Arguments:
- value: 2D Tensor of shape [height, width] or 3D Tensor of shape
[height, width, 1].
- vmin: the minimum value of the range used for normalization.
(Default: value minimum)
- vmax: the maximum value of the range used for normalization.
(Default: value maximum)
- cmap: a valid cmap named for use with matplotlib's `get_cmap`.
(Default: 'gray')
Example usage:
```
output = tf.random_uniform(shape=[256, 256, 1])
output_color = colorize(output, vmin=0.0, vmax=1.0, cmap='viridis')
tf.summary.image('output', output_color)
```
Returns a 3D tensor of shape [height, width, 3].
"""
# normalize
vmin = tf.reduce_min(value) if vmin is None else vmin
vmax = tf.reduce_max(value) if vmax is None else vmax
value = (value - vmin) / (vmax - vmin) # vmin..vmax
# squeeze last dim if it exists
value = tf.squeeze(value)
# quantize
indices = tf.to_int32(tf.round(value * 255))
# gather
import matplotlib.cm
cm = matplotlib.cm.get_cmap(cmap if cmap is not None else 'gray')
colors = tf.constant(cm.colors, dtype=tf.float32)
value = tf.gather(colors, indices)
return value