如何解决阈值化后的 imshow (Scikit Image) 问题
from skimage.io import imread,imshow
from skimage.filters import threshold_otsu
imgtest = imread('image.tif')
img_threshold = threshold_otsu(imgtest)
imshow(img_threshold)
返回
Traceback (most recent call last):
File "<input>",line 8,in <module>
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/skimage/io/_io.py",line 159,in imshow
return call_plugin('imshow',arr,plugin=plugin,**plugin_args)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/skimage/io/manage_plugins.py",line 209,in call_plugin
return func(*args,**kwargs)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/skimage/io/_plugins/matplotlib_plugin.py",line 158,in imshow
ax_im = ax.imshow(image,**kwargs)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/matplotlib/__init__.py",line 1447,in inner
return func(ax,*map(sanitize_sequence,args),**kwargs)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/matplotlib/axes/_axes.py",line 5523,in imshow
im.set_data(X)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/matplotlib/image.py",line 711,in set_data
raise TypeError("Invalid shape {} for image data"
TypeError: Invalid shape () for image data
还有
img_threshold2 = akt1 > 100
imshow(img_threshold2)
返回另一个错误：
Traceback (most recent call last):
File "<input>",line 2,line 150,in imshow
lo,hi,cmap = _get_display_range(image)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/skimage/io/_plugins/matplotlib_plugin.py",line 97,in _get_display_range
ip = _get_image_properties(image)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/skimage/io/_plugins/matplotlib_plugin.py",line 55,in _get_image_properties
is_low_contrast(image))
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/skimage/exposure/exposure.py",line 637,in is_low_contrast
limits = np.percentile(image,[lower_percentile,upper_percentile])
File "<__array_function__ internals>",line 5,in percentile
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/numpy/lib/function_base.py",line 3818,in percentile
return _quantile_unchecked(
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/numpy/lib/function_base.py",line 3937,in _quantile_unchecked
r,k = _ureduce(a,func=_quantile_ureduce_func,q=q,axis=axis,out=out,File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/numpy/lib/function_base.py",line 3515,in _ureduce
r = func(a,**kwargs)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/numpy/lib/function_base.py",line 4064,in _quantile_ureduce_func
r = _lerp(x_below,x_above,weights_above,out=out)
File "/Users/Simo/opt/anaconda3/envs/segmentation/lib/python3.8/site-packages/numpy/lib/function_base.py",line 3961,in _lerp
diff_b_a = subtract(b,a)
TypeError: numpy boolean subtract,the `-` operator,is not supported,use the bitwise_xor,the `^` operator,or the logical_xor function instead.
也一样。
我对 Python 很陌生，所以在重新安装错误（numpy、skimage）中提到的包后，我用尽了我的知识..
我正在使用 conda 环境和 PyCharm，如果有帮助的话。
干杯
附言我使用的是 macOS Catalina
解决方法
threshold_otsu 适用于灰色图像。
我正在加载一个彩色图像并在示例中转换为灰色，如果您已经有灰色图像，则可以忽略该步骤。
from skimage.io import imread,imshow
from skimage.filters import threshold_otsu
from skimage.color import rgb2gray
imgtest = imread('00000001.jpg') # load my rgb image
gray = rgb2gray(imgtest) # convert to gray
img_threshold = threshold_otsu(gray) # apply thresholding
print(img_threshold) # 0.369140625 this is a number can't use imshow over this
binary = gray > img_threshold # converting to binary based on threshold -- this can be passed to imshow
imshow(binary) # image will be displayed now.!
threshold_otsu 需要灰度输入图像，它返回一个阈值，即单个标量值。值 >= 阈值的所有像素都被假定为前景。
import matplotlib.pyplot as plt
from skimage.io import imread,imshow
from skimage.filters import threshold_otsu
imgurl = 'https://i.picsum.photos/id/732/200/300.jpg?grayscale&hmac=ZeormnImNpZEXzeLNhI0BCcadwMVGAwJLPRh_Sl-7Wg'
img = imread(imgurl) # Input image mast be grayscale
threshold = threshold_otsu(img) # 132
binary = threshold <= img # [[True True ...],...,[False True...]]
# Pixel assumed to be forground where value >= threshould,otherwise background
imshow(binary)
# Btw,You can show images side by side by matplotlib.plt
import matplotlib.pyplot as plt
f,(ax0,ax1) = plt.subplots(1,2)
ax0.imshow(img,cmap='gray')
ax1.imshow(binary,cmap='gray')
plt.show() # attached below
我认为这个 answer 可以解决这个问题，因为它是我过去处理的。
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