python之带有灰度数据的 Enthought Canopy Chaco img_plot

我终于开始尝试 Chaco，所以这个问题可能很天真。目前，我正在尝试绘制类型为 numpy.uint8 的非常大的 8 位(也称为灰度或单 channel )图像。似乎无论我做什么，图像都是彩色的。这是我基于 Chaco 附带的 image_plot.py 示例的代码:

#!/usr/bin/env python 
""" 
Draws an simple RGB image 
 - Left-drag pans the plot. 
 - Mousewheel up and down zooms the plot in and out. 
 - Pressing "z" brings up the Zoom Box, and you can click-drag a rectangular 
   region to zoom.  If you use a sequence of zoom boxes, pressing alt-left-arrow 
   and alt-right-arrow moves you forwards and backwards through the "zoom 
   history". 
""" 
 
# Major library imports 
from numpy import zeros, uint8 
 
 
 
# Enthought library imports 
from enable.api import Component, ComponentEditor 
from traits.api import HasTraits, Instance 
from traitsui.api import Item, Group, View 
 
# Chaco imports 
from chaco.api import ArrayPlotData, Plot, ImageData 
from chaco.tools.api import PanTool, ZoomTool 
from chaco.tools.image_inspector_tool import ImageInspectorTool, \ 
     ImageInspectorOverlay 
 
#=============================================================================== 
# # Create the Chaco plot. 
#=============================================================================== 
def _create_plot_component(): 
 
    # Create some uint8 image data 
    imageSize = 10000 
    image = zeros((imageSize,imageSize), dtype=uint8) 
    for x in range(0,imageSize): 
        image[x,x] = 255 
 
    print image.shape 
    print type(image[0][0]) 
 
    # Create a plot data obect and give it this data 
    pd = ArrayPlotData() 
    pd.set_data("imagedata", image) 
 
    # Create the plot 
    plot = Plot(pd, default_origin="top left") 
    plot.x_axis.orientation = "top" 
    img_plot = plot.img_plot("imagedata")[0] 
 
    # Tweak some of the plot properties 
    plot.bgcolor = "white" 
 
    # Attach some tools to the plot 
    plot.tools.append(PanTool(plot, constrain_key="shift")) 
    plot.overlays.append(ZoomTool(component=plot, 
                                    tool_mode="box", always_on=False)) 
 
    # imgtool = ImageInspectorTool(img_plot) 
    # img_plot.tools.append(imgtool) 
    # plot.overlays.append(ImageInspectorOverlay(component=img_plot, 
    #                                            image_inspector=imgtool)) 
    return plot 
 
#=============================================================================== 
# Attributes to use for the plot view. 
size = (600, 600) 
title="Simple image plot" 
bg_color="lightgray" 
 
#=============================================================================== 
# # Demo class that is used by the demo.py application. 
#=============================================================================== 
class Demo(HasTraits): 
    plot = Instance(Component) 
 
    traits_view = View( 
                    Group( 
                        Item('plot', editor=ComponentEditor(size=size, 
                                                            bgcolor=bg_color), 
                             show_label=False), 
                        orientation = "vertical"), 
                    resizable=True, title=title 
                    ) 
 
    def _plot_default(self): 
         return _create_plot_component() 
 
demo = Demo() 
 
if __name__ == "__main__": 
    demo.configure_traits() 
 
#--EOF--- 

请您参考如下方法:

您看到的只是伪彩色:2D 图像需要通过某种方式从像素值变为“颜色”(在这种情况下，灰度仍然是彩色)。默认颜色图是彩色的，但您可以指定 colormap 关键字参数来获取灰度图像。这是一个简化的示例:

import numpy as np 
 
from enable.api import Component, ComponentEditor 
from traits.api import HasTraits, Instance 
from traitsui.api import UItem, View 
 
from chaco.api import ArrayPlotData, Plot, gray 
 
 
class Demo(HasTraits): 
 
    plot = Instance(Component) 
 
    traits_view = View( 
        UItem('plot', editor=ComponentEditor(size=(600, 600))), 
        resizable=True, title="Simple image plot" 
    ) 
 
    def _plot_default(self): 
        image = np.random.random_integers(0, 255, size=(100, 100)) 
        image = image.astype(np.uint8) 
        data = ArrayPlotData(imagedata=image) 
 
        plot = Plot(data, default_origin="top left") 
        plot.img_plot("imagedata", colormap=gray) 
        return plot 
 
 
demo = Demo() 
demo.configure_traits()