Python matplotlib.image方法代码示例

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def switch_backend(newbackend):
    """
    Switch the default backend.  This feature is **experimental**, and
    is only expected to work switching to an image backend.  e.g., if
    you have a bunch of PostScript scripts that you want to run from
    an interactive ipython session, you may want to switch to the PS
    backend before running them to avoid having a bunch of GUI windows
    popup.  If you try to interactively switch from one GUI backend to
    another, you will explode.

    Calling this command will close all open windows.
    """
    close('all')
    global _backend_mod, new_figure_manager, draw_if_interactive, _show
    matplotlib.use(newbackend, warn=False, force=True)
    from matplotlib.backends import pylab_setup
    _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def clim(vmin=None, vmax=None):
    """
    Set the color limits of the current image.

    To apply clim to all axes images do::

      clim(0, 0.5)

    If either *vmin* or *vmax* is None, the image min/max respectively
    will be used for color scaling.

    If you want to set the clim of multiple images,
    use, for example::

      for im in gca().get_images():
          im.set_clim(0, 0.05)

    """
    im = gci()
    if im is None:
        raise RuntimeError('You must first define an image, eg with imshow')

    im.set_clim(vmin, vmax)
    draw_if_interactive() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def set_cmap(cmap):
    """
    Set the default colormap.  Applies to the current image if any.
    See help(colormaps) for more information.

    *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or
    the name of a registered colormap.

    See :func:`matplotlib.cm.register_cmap` and
    :func:`matplotlib.cm.get_cmap`.
    """
    cmap = cm.get_cmap(cmap)

    rc('image', cmap=cmap.name)
    im = gci()

    if im is not None:
        im.set_cmap(cmap)

    draw_if_interactive() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def get_plot_formats(config):
    default_dpi = {'png': 80, 'hires.png': 200, 'pdf': 200}
    formats = []
    plot_formats = config.plot_formats
    if isinstance(plot_formats, six.string_types):
        # String Sphinx < 1.3, Split on , to mimic
        # Sphinx 1.3 and later. Sphinx 1.3 always
        # returns a list.
        plot_formats = plot_formats.split(',')
    for fmt in plot_formats:
        if isinstance(fmt, six.string_types):
            if ':' in fmt:
                suffix, dpi = fmt.split(':')
                formats.append((str(suffix), int(dpi)))
            else:
                formats.append((fmt, default_dpi.get(fmt, 80)))
        elif type(fmt) in (tuple, list) and len(fmt) == 2:
            formats.append((str(fmt[0]), int(fmt[1])))
        else:
            raise PlotError('invalid image format "%r" in plot_formats' % fmt)
    return formats 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def get_plot_formats(config):
    default_dpi = {'png': 80, 'hires.png': 200, 'pdf': 200}
    formats = []
    plot_formats = config.plot_formats
    for fmt in plot_formats:
        if isinstance(fmt, str):
            if ':' in fmt:
                suffix, dpi = fmt.split(':')
                formats.append((str(suffix), int(dpi)))
            else:
                formats.append((fmt, default_dpi.get(fmt, 80)))
        elif isinstance(fmt, (tuple, list)) and len(fmt) == 2:
            formats.append((str(fmt[0]), int(fmt[1])))
        else:
            raise PlotError('invalid image format "%r" in plot_formats' % fmt)
    return formats 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def clim(vmin=None, vmax=None):
    """
    Set the color limits of the current image.

    To apply clim to all axes images do::

      clim(0, 0.5)

    If either *vmin* or *vmax* is None, the image min/max respectively
    will be used for color scaling.

    If you want to set the clim of multiple images,
    use, for example::

      for im in gca().get_images():
          im.set_clim(0, 0.05)

    """
    im = gci()
    if im is None:
        raise RuntimeError('You must first define an image, e.g., with imshow')

    im.set_clim(vmin, vmax)
    draw_if_interactive() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def gci():
    """
    Get the current colorable artist.  Specifically, returns the
    current :class:`~matplotlib.cm.ScalarMappable` instance (image or
    patch collection), or *None* if no images or patch collections
    have been defined.  The commands :func:`~matplotlib.pyplot.imshow`
    and :func:`~matplotlib.pyplot.figimage` create
    :class:`~matplotlib.image.Image` instances, and the commands
    :func:`~matplotlib.pyplot.pcolor` and
    :func:`~matplotlib.pyplot.scatter` create
    :class:`~matplotlib.collections.Collection` instances.  The
    current image is an attribute of the current axes, or the nearest
    earlier axes in the current figure that contains an image.
    """
    return gcf()._gci()


## Any Artist ##


# (getp is simply imported) 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def clim(vmin=None, vmax=None):
    """
    Set the color limits of the current image.

    To apply clim to all axes images do::

      clim(0, 0.5)

    If either *vmin* or *vmax* is None, the image min/max respectively
    will be used for color scaling.

    If you want to set the clim of multiple images,
    use, for example::

      for im in gca().get_images():
          im.set_clim(0, 0.05)

    """
    im = gci()
    if im is None:
        raise RuntimeError('You must first define an image, e.g., with imshow')

    im.set_clim(vmin, vmax) 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def test(train_loader, model, model2, dir):
    totalNumber = 0
    errorSum = {'MSE': 0, 'RMSE': 0, 'ABS_REL': 0, 'LG10': 0,
                'MAE': 0,  'DELTA1': 0, 'DELTA2': 0, 'DELTA3': 0}
    model.eval()
    model2.eval()

    # if not os.path.exists(dir):
    #     os.mkdir(dir)

    for i, sample_batched in enumerate(train_loader):
        image, depth_ = sample_batched['image'], sample_batched['depth']

        image = torch.autograd.Variable(image, volatile=True).cuda()
        depth_ = torch.autograd.Variable(depth_, volatile=True).cuda(async=True)
 
        depth = model(image)

        mask = model2(image)
        output = model(image*mask)

        batchSize = depth.size(0)
        errors = util.evaluateError(output,depth_)
        errorSum = util.addErrors(errorSum, errors, batchSize)
        totalNumber = totalNumber + batchSize
        averageError = util.averageErrors(errorSum, totalNumber)

        # mask = mask.squeeze().view(228,304).data.cpu().float().numpy()
        # matplotlib.image.imsave(dir+'/mask'+str(i)+'.png', mask)
 
    print('rmse:',np.sqrt(averageError['MSE'])) 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def region(self, region):
        "Extract a subset of the SARData image defined by a Region object"
        s = SARData()
        s.hhhh = self.hhhh.reshape(self.shape)[np.ix_(region.range_i, region.range_j)].flatten()
        s.hhhv = self.hhhv.reshape(self.shape)[np.ix_(region.range_i, region.range_j)].flatten()
        s.hvhv = self.hvhv.reshape(self.shape)[np.ix_(region.range_i, region.range_j)].flatten()
        s.hhvv = self.hhvv.reshape(self.shape)[np.ix_(region.range_i, region.range_j)].flatten()
        s.hvvv = self.hvvv.reshape(self.shape)[np.ix_(region.range_i, region.range_j)].flatten()
        s.vvvv = self.vvvv.reshape(self.shape)[np.ix_(region.range_i, region.range_j)].flatten()
        s.shape = (len(region.range_i), len(region.range_j))
        s.size = len(region.range_i) * len(region.range_j)
        return s 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def masked_region(self, mask):
        "Extract a subset of the SARData image defined by a mask"
        assert(mask.shape == self.hhhh.shape)

        s = SARData()
        for c in ["hhhh", "hhhv", "hvhv", "hhvv", "hvvv", "vvvv"]:
            s.__dict__[c] = self.__dict__[c][mask]
        s.shape = None
        s.size = mask.sum()
        return s 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def color_composite(self):
        "Color composite of a EMISAR image"

        # Take logarithm
        green = 10*np.log(self.hhhh.reshape(self.shape)) / np.log(10)
        blue = 10*np.log(self.vvvv.reshape(self.shape)) / np.log(10)
        red = 10*np.log(self.hvhv.reshape(self.shape)) / np.log(10)

        # Normalize
        green = matplotlib.colors.normalize(-30, 0, clip=True)(green)
        blue = matplotlib.colors.normalize(-30, 0, clip=True)(blue)
        red = matplotlib.colors.normalize(-36, -6, clip=True)(red)

        # Return as a RGB image
        return np.concatenate((red[:,:,None], green[:,:,None], blue[:,:,None]), axis=2) 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def rcdefaults():
    matplotlib.rcdefaults()
    draw_if_interactive()


# The current "image" (ScalarMappable) is retrieved or set
# only via the pyplot interface using the following two
# functions: 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def gci():
    """
    Get the current colorable artist.  Specifically, returns the
    current :class:`~matplotlib.cm.ScalarMappable` instance (image or
    patch collection), or *None* if no images or patch collections
    have been defined.  The commands :func:`~matplotlib.pyplot.imshow`
    and :func:`~matplotlib.pyplot.figimage` create
    :class:`~matplotlib.image.Image` instances, and the commands
    :func:`~matplotlib.pyplot.pcolor` and
    :func:`~matplotlib.pyplot.scatter` create
    :class:`~matplotlib.collections.Collection` instances.  The
    current image is an attribute of the current axes, or the nearest
    earlier axes in the current figure that contains an image.
    """
    return gcf()._gci() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import image [as 别名]
def sci(im):
    """
    Set the current image.  This image will be the target of colormap
    commands like :func:`~matplotlib.pyplot.jet`,
    :func:`~matplotlib.pyplot.hot` or
    :func:`~matplotlib.pyplot.clim`).  The current image is an
    attribute of the current axes.
    """
    gca()._sci(im)


## Any Artist ##
# (getp is simply imported)