Question

我一直在仔细阅读文档并重新阅读/运行以下代码，以便准确了解正在发生的事情。尽管如此，我的知识仍然存在差距。我希望通过评论向您展示代码，这些评论意味着我的知识中存在的差距，希望有些人愿意填补。

所以这是我的要求朋友：
1）帮助我填补我的知识空白 2）以非技术性和简单的格式逐步解释这里发生的事情。

import numpy
import scipy.misc
import matplotlib.pyplot

lena = scipy.misc.lena()


''' Generates an artificial range within the framework of the original array (Which is an image)
This artificial range will be paired with another one and used to 'climb'
Through the original array and make changes'''

def get_indices(size):
    arr = numpy.arange(size)
    #This sets every fourth element to False? How?
    return arr % 4 == 0

lena1 = lena.copy()
xindices = get_indices(lena.shape[0])
yindices = get_indices(lena.shape[1])




'''I am unsure of HOW the below code is executing. I know something is being
Set to zero, but what? And how can I verify it?'''

lena[xindices, yindices] = 0

#What does the argument 211 do exactly?
matplotlib.pyplot.subplot(211)
matplotlib.pyplot.imshow(lena1)


matplotlib.pyplot.show()

感谢队友！

Answer 1

使用Python调试器在执行代码时逐步执行代码总是很有用。在您选择的任何地方写下以下内容：

import pdb; pdb.set_trace()

执行将停止，您可以检查任何变量，使用任何已定义的函数，并逐行前进。

在这里，您有一个评论版的代码。关于函数的注释被转换为docstring，其中包含可以执行的doctest。

import numpy
import scipy.misc
import matplotlib.pyplot

# Get classic image processing example image, Lena, at 8-bit grayscale
# bit-depth, 512 x 512 size.
lena = scipy.misc.lena()
# lena is now a Numpy array of integers, between 245 and 25, of 512 rows and
# 512 columns.


def get_indices(size):
    """
    Returns each fourth index in a Numpy vector of the passed in size.
    Specifically, return a vector of booleans, where all indices are set to
    False except those of every fourth element. This vector can be used to
    index another Numpy array and select *only* those elements. Example use:

        >>> import numpy as np
        >>> vector = np.array([0, 1, 2, 3, 4])
        >>> get_indices(vector.size)
        array([ True, False, False, False,  True], ...)

    """
    arr = numpy.arange(size)
    return arr % 4 == 0

# Keep a copy of the original image
lena1 = lena.copy()

# Use the defined function to get every fourth index, first in the x direction,
# then in the y direction
xindices = get_indices(lena.shape[0])
yindices = get_indices(lena.shape[1])


# Set every pixel that equals true in the vectors further up to 0. This
# selects **each fourth pixel on the diagonal** (from up left to bottom right).
lena[xindices, yindices] = 0

# Create a Matplotlib plot, with 2 subplots, and selects the one on the 1st
# colum, 1st row. The layout for all subplots is determined from all calls to
# subplot, i.e. if you later call `subplot(212)` you will get a vertical layout
# in one column and two rows; but if you call `subplot(221)` you will get a
# horizontal layout in two columns and one row.
matplotlib.pyplot.subplot(211)
# Show the unaltered image on the first subplot
matplotlib.pyplot.imshow(lena1)
# You could plot the modified original image in the second subplot, and compare
# to the unmodified copy by issuing:
#matplotlib.pyplot.subplot(212)
#matplotlib.pyplot.imshow(lena)

matplotlib.pyplot.show()

打破numpy代码

1 个答案: