我使用直方图均衡和自适应来消除灰度图像中的照明，但是在直方图均衡（我使用 scikit 图像 python 库）很好之后，在mahotas中的图像转换过程中出现了问题。我得到了一张全黑的照片。我该如何解决？
- 源图像：
- 直方图均衡和适应；
- mahotas 转换后的结果。
从 scikit 到 mahotas 的转换代码：
binimg = np.array(img_adapteq, dtype=np.bool)
源代码：
import scipy
import numpy as np
import pymorph as pm
import mahotas as mh
from skimage import morphology
from skimage import io
from matplotlib import pyplot as plt
from skimage import data, img_as_float
from skimage import exposure
def plot_img_and_hist(img, axes, bins=256):
"""Plot an image along with its histogram and cumulative histogram.
"""
img = img_as_float(img)
ax_img, ax_hist = axes
ax_cdf = ax_hist.twinx()
# Display image
ax_img.imshow(img, cmap=plt.cm.gray)
ax_img.set_axis_off()
# Display histogram
ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black')
ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
ax_hist.set_xlabel('Pixel intensity')
ax_hist.set_xlim(0, 1)
ax_hist.set_yticks([])
# Display cumulative distribution
img_cdf, bins = exposure.cumulative_distribution(img, bins)
ax_cdf.plot(bins, img_cdf, 'r')
ax_cdf.set_yticks([])
return ax_img, ax_hist, ax_cdf
mhgray = mh.imread(path,0)
binimg = mhgray[:,:,0]
print(type(binimg[0][0]))
thresh = mh.otsu(binimg)
gray =( binimg< thresh)
shape = list(gray.shape)
w = 0
if (shape[0] > shape[1]):
shape = shape[0]
else:
shape = shape[1]
if (shape < 100):
w = int((shape/100 )*1.5)
elif(shape > 100 and shape <420):
w = int((shape/100 )*2.5)
else:
w = int((shape/100)*4)
disk7 = pm.sedisk(w)
img = binimg
# Contrast stretching
p2 = np.percentile(img, 2)
p98 = np.percentile(img, 98)
img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
# Equalization
img_eq = exposure.equalize_hist(img)
# Adaptive Equalization
img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
# Display results
f, axes = plt.subplots(2, 4, figsize=(8, 4))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title('Low contrast image')
y_min, y_max = ax_hist.get_ylim()
ax_hist.set_ylabel('Number of pixels')
ax_hist.set_yticks(np.linspace(0, y_max, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
ax_img.set_title('Contrast stretching')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
ax_img.set_title('Histogram equalization')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])
ax_img.set_title('Adaptive equalization')
ax_cdf.set_ylabel('Fraction of total intensity')
ax_cdf.set_yticks(np.linspace(0, 1, 5))
# prevent overlap of y-axis labels
plt.subplots_adjust(wspace=0.4)
plt.show()
plt.gray()
plt.subplot(121)
plt.title("after histo")
plt.imshow(img_adapteq)
plt.show()
binimg = np.array(img_adapteq, dtype=np.bool)#uint16
plt.gray()
plt.subplot(121)
plt.title("after otsu")
plt.imshow(binimg)
plt.show()
imgbnbin = mh.morph.dilate(binimg, disk7)
#2
plt.gray()
plt.subplot(121)
plt.title("after dilate before close")
plt.imshow(imgbnbin)
plt.show()
imgbnbin = mh.morph.close(imgbnbin, disk7)
#2
plt.gray()
plt.subplot(121)
plt.title("before skeletonize")
plt.imshow(imgbnbin)
plt.show()
imgbnbin = mh.morph.close(imgbnbin, disk7)
out = morphology.skeletonize(imgbnbin>0)