这是我在stackoverflow中提出的第三个问题，因为每次我对python引发运行时错误的方式进行一些更改。
以前的问题是：here和here。在第一个问题中，我认为是内存问题，因为我对许多图像进行了分类，在第二种情况下，运行时错误发生在这一行
p2 = numpy.percentile(img, 2)
我认为这是一个 numpy 模块问题。
但现在我运行时错误发生在这里：
imgbnbin = mh.morph.dilate(gray, disk7)
在 mahotas 函数扩张。
我在 90 张图片中只有三张是这样的：
这些是 2 个示例图像，代码可以正常工作：
下面是我得到运行时错误的函数 skelfeatures 的代码：
import os
import glob
import scipy
import numpy as np
import pymorph as pm
import pylab as plb
import matplotlib
from matplotlib import pyplot as plt
import cv2
import mahotas as mh
from skimage import morphology
from skimage import io
from math import sqrt
from skimage import data, img_as_float
from skimage import exposure
from skimage import color
from skimage import io, filter
from skimage.morphology import erosion, dilation, opening, closing, white_tophat
from skimage.morphology import black_tophat, skeletonize, convex_hull_image
from skimage.morphology import disk
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
import urllib, cStringIO
listarough = list()
def skelfeatures(path):
import copy
if (path[0] == "h"):
#URL
req = urllib.urlopen(path) #cv2.imdecode(arr,0) # load as grayscale
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
gray = cv2.imdecode(arr,0) # 'load it as it is'
#cv2.imshow('lalala',gray)
#if cv2.waitKey() & 0xff == 27: quit()
else:
#local path
print("prima di cv2imread")
gray = cv2.imread(path,0)
stampac = "no"
originale = copy.copy(gray)
oshape = list(gray.shape)
if (oshape[0] <= 140):
#print(oshape[0]/100)
ow = int ((oshape[0]/100 )*2.5 )
oh = int ((oshape[0]/100 )*2.5 )
#print("ow ",ow)
elif (oshape[0] <= 300):
#print(oshape[0]/100)
ow = int ((oshape[0]/100 )*3.5 )
oh = int ((oshape[0]/100 )*3.5 )
#print("ow ",ow)
else:
ow = int ((oshape[0]/100 )*7 )
oh = int ((oshape[0]/100 )*7 )
owclose = ow * 2
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(ow,oh))
graydilate = cv2.erode(gray, element) #imgbnbin
ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV)
gray = thresh
proxMax = int((oshape[0]/100 )*22)
proxMin = int((oshape[0]/100 )*19)
proxGE = 5
proxGEmin = 2
oshape = list(gray.shape)
if (oshape[0] <= 140):
#print(oshape[0]/100)
ow = int ((oshape[0]/100 )*2.5 )
oh = int ((oshape[0]/100 )*2.5 )
#print("ow ",ow)
elif (oshape[0] <= 300):
#print(oshape[0]/100)
ow = int ((oshape[0]/100 )*3.5 )
oh = int ((oshape[0]/100 )*3.5 )
#print("ow ",ow)
else:
ow = int ((oshape[0]/100 )*7 )
oh = int ((oshape[0]/100 )*7 )
#print(ow)
owclose = ow * 2
disko = pm.sedisk(0.2)
imgbnbin7 = gray
############################################################
# OPEN ImAGE PATH
############################################################
print("mahotas")
img = color.rgb2gray(io.imread(path))
print(type(img))
print("FINE ioimread")
from skimage import exposure
#print dir(exposure)
# Contrast stretching
###########################################################################
# THE FIRST TIME WHERE I GOT RUNTIME ERROR, the following line
###########################################################################
p2 = np.percentile(img, 2)
p98 = np.percentile(img, 98)
img_rescale = exposure.rescale_intensity(img, out_range=(0, 1))
# 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()'''
if ( stampac =="no" ):
plt.gray()
plt.subplot(121)
plt.title("dopo histo")
plt.imshow(img)
plt.show()
binimg = img_adapteq
if ( stampac =="no" ):
plt.gray()
plt.subplot(121)
plt.title("dopo conversione")
plt.imshow(binimg)
plt.show()
binimgafter = copy.copy(binimg)
threshold = filter.threshold_otsu(img_rescale)
gray =( img_rescale< threshold)
if ( stampac =="si" ):
plt.gray()
plt.subplot(121)
plt.title("dopo otsu")
plt.imshow(gray)
plt.show()
shape = 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)
print("bau2")
imgbnbin = mh.morph.dilate(gray, disk7)
if ( stampac =="no" ): #2
plt.gray()
plt.subplot(121)
plt.title("dopo dilate prima di close")
plt.imshow(imgbnbin)
plt.show()
########################################################
#RUNTIME ERROR HER. After that python does not show bau3
###########################################################
print("bau3")
imgbnbin = mh.morph.close(imgbnbin, disko)
if ( stampac =="no" ): #2
plt.gray()
plt.subplot(121)
plt.title("dopo close prima di skeletonize")
plt.imshow(imgbnbin)
plt.show()
out = morphology.skeletonize(imgbnbin>0)
# the function continue...
path = "http://i.stack.imgur.com/pzBWU.jpg"