IOError: [Errno 22] invalid mode ('rb') or filename: '\x89PNG\n'
IOError: [Errno 22] invalid mode ('rb') or filename: '\x89PNG\n'
我刚开始编程（使用 Python），因为我需要开发一种允许我执行直径分布的可执行文件。我设法得到了一些有用的东西（下面的代码）：
# Put here all modules you would need in order to represent your data
import matplotlib.pylab as plt
import numpy as np
import collections as c
from collections import Counter
from PIL import Image
import matplotlib.mlab as mlab
from scipy.optimize import curve_fit
import Tkinter as tk
from tkFileDialog import askopenfilename
# This prints the plot containing the diameter distribution of our sample
root = tk.Tk() ; root.withdraw()
filename = askopenfilename(parent=root)
f = open(filename)
with f as input: #Change the Results.txt file for your own .txt file
a = zip(*(line.strip().split('\t') for i,line in enumerate(input) if i != 0))
areas = a[1]
diam = []
for area in areas:
diam.append(round((np.sqrt(float(area)/ np.pi) * 2), 3)) # The number 3 tells us how many decimals will be shown
hist, bins = np.histogram(diam, 50)
diam.sort()
counts = c.Counter(diam)
'''This prints the table which includes all diameter values
and how many of them we can find on our sample# '''
table = sorted(counts.items())
col_labels = ['Diameter (nm)', 'Counts'] # In Diameter column you can add the units inside the empty parenthesis
table_vals = table
q = diam
mu = sum(q)/float(len(q))
variance = np.var(q)
sigma = np.sqrt(variance)
# In the plt.suptitle part --> change the default name to your sample name
plt.subplot(121)
plt.bar(counts.keys(), counts.values(), 0.01, color="black")
plt.tick_params(direction = 'out', labeltop='off', labelright='off')
plt.xlabel('Diameter (nm)', fontsize=16, fontweight='bold')
plt.ylabel('Count (a.u.)', fontsize=16, fontweight='bold')
plt.title(r'$\mathregular{Diameter \ distribution\ of \ the\ sample:}\ \mu=%.3f,\ \sigma=%.3f$' % (mu, sigma), fontsize=18, fontweight='bold')
plt.suptitle('Silicon nanopillars grown epitaxially', fontsize=22, fontweight='bold')
plt.autoscale(enable=True, axis='x', tight=None)
the_table = plt.table(cellText = table_vals, colLabels = col_labels, loc = 'bottom', cellLoc = 'center', bbox = [0.67, 0.18, 0.30, 0.8])
the_table.auto_set_font_size(False)
the_table.set_fontsize(12)
# This adds a gaussian fit to our histogram
plt.plot()
x = diam
yhist, xhist = np.histogram(x, bins=np.arange(4096))
xh = np.where(yhist > 0)[0]
yh = yhist[xh]
def gaussian(x, a, mu, sigma):
return a * np.exp(-((x - mu)**2 / (2 * sigma**2)))
popt, pcov = curve_fit(gaussian, xh, yh, [1, mu, sigma])
i = np.linspace(min(diam)-20, max(diam), 1000)
plt.plot(i, gaussian(i, *popt), lw=3, ls=':', c='r')
plt.xlim(min(diam)-10, max(diam)+10)
# This adds the image from where the data is extracted (always use .png images otherwise this won't work)
im = Image.open('Dosi05.png') # Put here the original image
im2 = Image.open('Dosi05_Analyzed.png') # Put here the thresholded and analysed image
plt.subplot(222)
plt.imshow(im, cmap='gray')
plt.axis('off')
plt.title('Original Image', fontsize=14, fontweight='bold')
plt.subplot(224)
plt.imshow(im2, cmap='gray')
plt.axis('off')
plt.title('Processed Image', fontsize=14, fontweight='bold')
plt.show()
但我被要求做与 .txt 文件相同的操作，但使用 plt.subplot(222) 和 plt.subplot(224) 绘制的图像以避免触及代码。
我尝试使用 Tkinter 做类似的事情（见下面的代码）：
# Put here all modules you would need in order to represent your data
import matplotlib.pylab as plt
import numpy as np
import collections as c
from collections import Counter
from PIL import Image
import matplotlib.mlab as mlab
from scipy.optimize import curve_fit
import Tkinter as tk
from tkFileDialog import askopenfilename, askopenfile
from skimage import data
# This prints the plot containing the diameter distribution of our sample
root = tk.Tk() ; root.withdraw()
filename = askopenfilename(parent=root)
f = open(filename)
with f as input: #Change the Results.txt file for your own .txt file
a = zip(*(line.strip().split('\t') for i,line in enumerate(input) if i != 0))
areas = a[1]
diam = []
for area in areas:
diam.append(round((np.sqrt(float(area)/ np.pi) * 2), 3)) # The number 3 tells us how many decimals will be shown
hist, bins = np.histogram(diam, 50)
diam.sort()
counts = c.Counter(diam)
'''This prints the table which includes all diameter values
and how many of them we can find on our sample# '''
table = sorted(counts.items())
col_labels = ['Diameter (nm)', 'Counts'] # In Diameter column you can add the units inside the empty parenthesis
table_vals = table
q = diam
mu = sum(q)/float(len(q))
variance = np.var(q)
sigma = np.sqrt(variance)
# In the plt.suptitle part --> change the default name to your sample name
plt.subplot(121)
plt.bar(counts.keys(), counts.values(), 0.01, color="black")
plt.tick_params(direction = 'out', labeltop='off', labelright='off')
plt.xlabel('Diameter (nm)', fontsize=16, fontweight='bold')
plt.ylabel('Count (a.u.)', fontsize=16, fontweight='bold')
plt.title(r'$\mathregular{Diameter \ distribution\ of \ the\ sample:}\ \mu=%.3f,\ \sigma=%.3f$' % (mu, sigma), fontsize=18, fontweight='bold')
plt.suptitle('Silicon nanopillars grown epitaxially', fontsize=22, fontweight='bold')
plt.autoscale(enable=True, axis='x', tight=None)
the_table = plt.table(cellText = table_vals, colLabels = col_labels, loc = 'bottom', cellLoc = 'center', bbox = [0.67, 0.18, 0.30, 0.8])
the_table.auto_set_font_size(False)
the_table.set_fontsize(12)
# This adds a gaussian fit to our histogram
plt.plot()
x = diam
yhist, xhist = np.histogram(x, bins=np.arange(4096))
xh = np.where(yhist > 0)[0]
yh = yhist[xh]
def gaussian(x, a, mu, sigma):
return a * np.exp(-((x - mu)**2 / (2 * sigma**2)))
popt, pcov = curve_fit(gaussian, xh, yh, [1, mu, sigma])
i = np.linspace(min(diam)-20, max(diam), 1000)
plt.plot(i, gaussian(i, *popt), lw=3, ls=':', c='r')
plt.xlim(min(diam)-10, max(diam)+10)
# This adds the image from where the data is extracted (always use .png images otherwise this won't work)
image_formats = [('PNG','*.png')]
file_path_list = askopenfilename(filetypes=image_formats, initialdir='/', title='Please select a picture to analyze')
for file_path in file_path_list:
image = data.imread(file_path)
image2 = data.imread(file_path)
plt.subplot(222)
plt.imshow(image, cmap='gray')
plt.axis('off')
plt.title('Original Image', fontsize=14, fontweight='bold')
plt.subplot(224)
plt.imshow(image2, cmap='gray')
plt.axis('off')
plt.title('Processed Image', fontsize=14, fontweight='bold')
plt.show()
但是在选择第一个图像文件后，Python 抛出以下内容：
[IOError: [Errno 22] 无效模式 ('rb') 或文件名: '\x89PNG\n' 1
谁能告诉我我能做些什么来解决这个问题，或者是否有其他方法可以通过从文件浏览器中选择而不是在代码本身中更改它们来显示图像？
希望问题够清楚，提前感谢您的帮助！
是
askopenfilename的返回值为字符串。当您遍历一个字符串（可能是 C:\Somepath\someimage.png 之类的路径）时，您会收到以下错误。
Errno2: No such file or directory: u'C'
您需要使用askopenfilenames模块来获取多个文件。
image_formats = [('PNG','*.png')]
file_path_list = askopenfilenames(filetypes=image_formats, initialdir='/', title='Please select a picture to analyze')
for file_path in file_path_list:
file_path = os.path.normpath(file_path) # To normalize path
image = data.imread(file_path)
image2 = data.imread(file_path)
对 python<2.7.7 使用 for file_path in file_path_list.split()，因为 return 值是 string instead of tuple.
https://docs.python.org/2/library/os.path.html#os.path.normpath
原题
Op 用 askopenfile 而不是 askopenfilename
粘贴了代码
tkFileDialog.askopenfile return 如果未指定 mode，则选择的文件以 r 模式打开。
def askopenfile(mode = "r", **options):
"Ask for a filename to open, and returned the opened file"
filename = Open(**options).show()
if filename:
return open(filename, mode)
return None
而imread的输入是图像文件路径。
您需要使用 askopenfilenames 其中 return 选择的文件路径。
我刚开始编程（使用 Python），因为我需要开发一种允许我执行直径分布的可执行文件。我设法得到了一些有用的东西（下面的代码）：
# Put here all modules you would need in order to represent your data
import matplotlib.pylab as plt
import numpy as np
import collections as c
from collections import Counter
from PIL import Image
import matplotlib.mlab as mlab
from scipy.optimize import curve_fit
import Tkinter as tk
from tkFileDialog import askopenfilename
# This prints the plot containing the diameter distribution of our sample
root = tk.Tk() ; root.withdraw()
filename = askopenfilename(parent=root)
f = open(filename)
with f as input: #Change the Results.txt file for your own .txt file
a = zip(*(line.strip().split('\t') for i,line in enumerate(input) if i != 0))
areas = a[1]
diam = []
for area in areas:
diam.append(round((np.sqrt(float(area)/ np.pi) * 2), 3)) # The number 3 tells us how many decimals will be shown
hist, bins = np.histogram(diam, 50)
diam.sort()
counts = c.Counter(diam)
'''This prints the table which includes all diameter values
and how many of them we can find on our sample# '''
table = sorted(counts.items())
col_labels = ['Diameter (nm)', 'Counts'] # In Diameter column you can add the units inside the empty parenthesis
table_vals = table
q = diam
mu = sum(q)/float(len(q))
variance = np.var(q)
sigma = np.sqrt(variance)
# In the plt.suptitle part --> change the default name to your sample name
plt.subplot(121)
plt.bar(counts.keys(), counts.values(), 0.01, color="black")
plt.tick_params(direction = 'out', labeltop='off', labelright='off')
plt.xlabel('Diameter (nm)', fontsize=16, fontweight='bold')
plt.ylabel('Count (a.u.)', fontsize=16, fontweight='bold')
plt.title(r'$\mathregular{Diameter \ distribution\ of \ the\ sample:}\ \mu=%.3f,\ \sigma=%.3f$' % (mu, sigma), fontsize=18, fontweight='bold')
plt.suptitle('Silicon nanopillars grown epitaxially', fontsize=22, fontweight='bold')
plt.autoscale(enable=True, axis='x', tight=None)
the_table = plt.table(cellText = table_vals, colLabels = col_labels, loc = 'bottom', cellLoc = 'center', bbox = [0.67, 0.18, 0.30, 0.8])
the_table.auto_set_font_size(False)
the_table.set_fontsize(12)
# This adds a gaussian fit to our histogram
plt.plot()
x = diam
yhist, xhist = np.histogram(x, bins=np.arange(4096))
xh = np.where(yhist > 0)[0]
yh = yhist[xh]
def gaussian(x, a, mu, sigma):
return a * np.exp(-((x - mu)**2 / (2 * sigma**2)))
popt, pcov = curve_fit(gaussian, xh, yh, [1, mu, sigma])
i = np.linspace(min(diam)-20, max(diam), 1000)
plt.plot(i, gaussian(i, *popt), lw=3, ls=':', c='r')
plt.xlim(min(diam)-10, max(diam)+10)
# This adds the image from where the data is extracted (always use .png images otherwise this won't work)
im = Image.open('Dosi05.png') # Put here the original image
im2 = Image.open('Dosi05_Analyzed.png') # Put here the thresholded and analysed image
plt.subplot(222)
plt.imshow(im, cmap='gray')
plt.axis('off')
plt.title('Original Image', fontsize=14, fontweight='bold')
plt.subplot(224)
plt.imshow(im2, cmap='gray')
plt.axis('off')
plt.title('Processed Image', fontsize=14, fontweight='bold')
plt.show()
但我被要求做与 .txt 文件相同的操作，但使用 plt.subplot(222) 和 plt.subplot(224) 绘制的图像以避免触及代码。
我尝试使用 Tkinter 做类似的事情（见下面的代码）：
# Put here all modules you would need in order to represent your data
import matplotlib.pylab as plt
import numpy as np
import collections as c
from collections import Counter
from PIL import Image
import matplotlib.mlab as mlab
from scipy.optimize import curve_fit
import Tkinter as tk
from tkFileDialog import askopenfilename, askopenfile
from skimage import data
# This prints the plot containing the diameter distribution of our sample
root = tk.Tk() ; root.withdraw()
filename = askopenfilename(parent=root)
f = open(filename)
with f as input: #Change the Results.txt file for your own .txt file
a = zip(*(line.strip().split('\t') for i,line in enumerate(input) if i != 0))
areas = a[1]
diam = []
for area in areas:
diam.append(round((np.sqrt(float(area)/ np.pi) * 2), 3)) # The number 3 tells us how many decimals will be shown
hist, bins = np.histogram(diam, 50)
diam.sort()
counts = c.Counter(diam)
'''This prints the table which includes all diameter values
and how many of them we can find on our sample# '''
table = sorted(counts.items())
col_labels = ['Diameter (nm)', 'Counts'] # In Diameter column you can add the units inside the empty parenthesis
table_vals = table
q = diam
mu = sum(q)/float(len(q))
variance = np.var(q)
sigma = np.sqrt(variance)
# In the plt.suptitle part --> change the default name to your sample name
plt.subplot(121)
plt.bar(counts.keys(), counts.values(), 0.01, color="black")
plt.tick_params(direction = 'out', labeltop='off', labelright='off')
plt.xlabel('Diameter (nm)', fontsize=16, fontweight='bold')
plt.ylabel('Count (a.u.)', fontsize=16, fontweight='bold')
plt.title(r'$\mathregular{Diameter \ distribution\ of \ the\ sample:}\ \mu=%.3f,\ \sigma=%.3f$' % (mu, sigma), fontsize=18, fontweight='bold')
plt.suptitle('Silicon nanopillars grown epitaxially', fontsize=22, fontweight='bold')
plt.autoscale(enable=True, axis='x', tight=None)
the_table = plt.table(cellText = table_vals, colLabels = col_labels, loc = 'bottom', cellLoc = 'center', bbox = [0.67, 0.18, 0.30, 0.8])
the_table.auto_set_font_size(False)
the_table.set_fontsize(12)
# This adds a gaussian fit to our histogram
plt.plot()
x = diam
yhist, xhist = np.histogram(x, bins=np.arange(4096))
xh = np.where(yhist > 0)[0]
yh = yhist[xh]
def gaussian(x, a, mu, sigma):
return a * np.exp(-((x - mu)**2 / (2 * sigma**2)))
popt, pcov = curve_fit(gaussian, xh, yh, [1, mu, sigma])
i = np.linspace(min(diam)-20, max(diam), 1000)
plt.plot(i, gaussian(i, *popt), lw=3, ls=':', c='r')
plt.xlim(min(diam)-10, max(diam)+10)
# This adds the image from where the data is extracted (always use .png images otherwise this won't work)
image_formats = [('PNG','*.png')]
file_path_list = askopenfilename(filetypes=image_formats, initialdir='/', title='Please select a picture to analyze')
for file_path in file_path_list:
image = data.imread(file_path)
image2 = data.imread(file_path)
plt.subplot(222)
plt.imshow(image, cmap='gray')
plt.axis('off')
plt.title('Original Image', fontsize=14, fontweight='bold')
plt.subplot(224)
plt.imshow(image2, cmap='gray')
plt.axis('off')
plt.title('Processed Image', fontsize=14, fontweight='bold')
plt.show()
但是在选择第一个图像文件后，Python 抛出以下内容：
[IOError: [Errno 22] 无效模式 ('rb') 或文件名: '\x89PNG\n' 1
谁能告诉我我能做些什么来解决这个问题，或者是否有其他方法可以通过从文件浏览器中选择而不是在代码本身中更改它们来显示图像？
希望问题够清楚，提前感谢您的帮助！
是
askopenfilename的返回值为字符串。当您遍历一个字符串（可能是 C:\Somepath\someimage.png 之类的路径）时，您会收到以下错误。
Errno2: No such file or directory: u'C'
您需要使用askopenfilenames模块来获取多个文件。
image_formats = [('PNG','*.png')]
file_path_list = askopenfilenames(filetypes=image_formats, initialdir='/', title='Please select a picture to analyze')
for file_path in file_path_list:
file_path = os.path.normpath(file_path) # To normalize path
image = data.imread(file_path)
image2 = data.imread(file_path)
对 python<2.7.7 使用 for file_path in file_path_list.split()，因为 return 值是 string instead of tuple.
https://docs.python.org/2/library/os.path.html#os.path.normpath
原题
Op 用 askopenfile 而不是 askopenfilename
tkFileDialog.askopenfile return 如果未指定 mode，则选择的文件以 r 模式打开。
def askopenfile(mode = "r", **options):
"Ask for a filename to open, and returned the opened file"
filename = Open(**options).show()
if filename:
return open(filename, mode)
return None
而imread的输入是图像文件路径。
您需要使用 askopenfilenames 其中 return 选择的文件路径。