交互式 skimage 查看器画布工具 RectangleTool

Question

我运行遇到了 skimage.viewer.canvastools.RectangleTool() 的麻烦，如果能提供任何帮助，我将不胜感激。

我希望选择框是交互式的，即在绘制之后，可以使用手柄编辑所选区域。此功能 appears to work 与 matplotlib.widgets.RectangleSelector()...例如

from matplotlib.widgets import  RectangleSelector
from pylab import *

def onselect(eclick, erelease):
  'Dummy function'

x = arange(100)/(99.0)
y = sin(x)
fig = figure
ax = subplot(111)
ax.plot(x,y)

test = RectangleSelector(ax, onselect,
                         drawtype='box',
                         interactive=True)
show()

然而，随着 skimage.viewer.canvastools.RectangleTool() 当我使用

rect_tool = RectangleTool(viewer,
                      on_enter=save_region,
                      interactive=True)

我被扔了：

TypeError: init() got an unexpected keyword argument 'interactive'

...如果我使用

rect_tool = RectangleTool(viewer,
                      on_enter=save_region,
                      rect_props=dict(interactive=True))

我遇到了这个错误：

AttributeError: Unknown property interactive

我是不是误解了manpage？

非常感谢！

这是我所在的位置（RectangleTool 不是交互式的）：

import skimage.io
from skimage.viewer import ImageViewer
from skimage.viewer.canvastools import RectangleTool

import numpy as np

from tkinter import Tk
from tkinter.filedialog import askopenfilename
from tkinter.simpledialog import askfloat


Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file
print(filename)

image_rgb = skimage.io.imread(filename)
image_r = image_rgb[:,:,0] # extract red channel


########
# Does this need to be initialised differently to make the rectangle interactive?
viewer = ImageViewer(image_r)


intensity_dumps = [] # used to store pixel values for selected regions

def save_region(extents):
    global image_r, intensity_dumps
    xmin = np.floor(extents[0]).astype('uint16')
    xmax = np.ceil(extents[1]).astype('uint16')
    ymin = np.floor(extents[2]).astype('uint16')
    ymax = np.ceil(extents[3]).astype('uint16')
    region = image_r[ymin:ymax,xmin:xmax]
    intensity_dumps.append(np.ndarray.flatten(region))
    print('Mean:',np.mean(region))
    print('Std. dev.:',np.std(region))
    print('Max:',np.max(region))


########
# Here is where I believe the problem lies...
rect_tool = RectangleTool(viewer,
                          on_enter=save_region)


thresholded = viewer.show()[0][0]

Answer 1

是的，我想你误会了。请注意 RectangleTool 没有 interactive= 参数。相反，有一个 rect_props 参数，它是一个字典。字典项将作为关键字参数传递给 RectangleSelector。所以你想要：RectangleTool(..., rect_props=dict(interactive=True).

Answer 2

感谢 Juan 提出的一些有用的建议。

我的目标是拥有滑块和交互式矩形选择器。最后我决定不使用 scikit-image 库，而是直接使用 matplotlib。

这是我的代码的简化版本，供将来遇到类似问题时可能会觉得有用的其他人使用：

from matplotlib.widgets import  RectangleSelector
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider

def onselect(eclick, erelease):
  'Dummy function'

# Lists to store pixel values
intensity_dumps = []

def toggle_selector(event):
    #print(' Key pressed.')
    if event.key in ['Q', 'q'] and toggle_selector.RS.active:
        print(' RectangleSelector deactivated.')
        toggle_selector.RS.set_active(False)
    if event.key in ['B', 'b']:
        extents = toggle_selector.RS.extents
        xmin = np.floor(extents[0]).astype('uint16')
        xmax = np.ceil(extents[1]).astype('uint16')
        ymin = np.floor(extents[2]).astype('uint16')
        ymax = np.ceil(extents[3]).astype('uint16')
#        print(xmin,xmax,ymin,ymax)
        intensity_dumps.append(image_cropped[ymin:ymax,xmin:xmax])


ax = plt.subplot(111)
plt.subplots_adjust(left=0.2, bottom=0.11, top=0.99)

#plt.xlabel('x (pixels)')
#plt.ylabel('y (pixels)')
ax.set_title('Select region; type <b> to save to list')

img = ax.imshow(image, interpolation='nearest', cmap='terrain')
cb = plt.colorbar(img)


ax_cmin = plt.axes([0.2, 0.01, 0.65, 0.03])
ax_cmax  = plt.axes([0.2, 0.06, 0.65, 0.03])

s_cmin = Slider(ax_cmin, 'min percentile', 0, 100, valinit=1)
s_cmax = Slider(ax_cmax, 'max percentile', 0, 100, valinit=99)

def update(val, s=None):
    min_display = np.percentile(image, s_cmin.val)
    max_display = np.percentile(image, s_cmax.val)
    img.set_clim([min_display, max_display])
    plt.draw()

s_cmin.on_changed(update)
s_cmax.on_changed(update)

toggle_selector.RS = RectangleSelector(ax,
                                       onselect,
                                       spancoords='pixels',
                                       drawtype='box',
                                       interactive=True)
plt.connect('key_press_event', toggle_selector)

plt.show()

交互式 skimage 查看器画布工具 RectangleTool

Interactive skimage viewer canvastools RectangleTool

python

image-processing

interactive

scikit-image

viewer