Python 使用 OpenCV 或 Matplotlib/Pyplot 可视化 MNIST 数据集
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Visualize MNIST dataset using OpenCV or Matplotlib/Pyplot
提问by decipher
i have MNIST dataset and i am trying to visualise it using pyplot. The dataset is in cvsformat where each row is one image of 784 pixels. i want to visualise it in pyplotor opencvin the 28*28 image format. I am trying directly using :
我有 MNIST 数据集，我正在尝试使用 pyplot 对其进行可视化。数据集的cvs格式为每行是一个 784 像素的图像。我想以28*28 图像格式pyplot或opencv以 28*28 图像格式对其进行可视化。我正在尝试直接使用：
plt.imshow(X[2:],cmap =plt.cm.gray_r, interpolation = "nearest")
but i its not working? any ideas on how should i approach this.
但我不工作？关于我应该如何处理这个问题的任何想法。
回答by bakkal
Assuming you have a CSV file with this format, which is a format the MNIST dataset is available in
假设您有一个这种格式的 CSV 文件，这是 MNIST 数据集可用的格式
label, pixel_1_1, pixel_1_2, ...
Here's how you can visulize it in Python with Matplotlib and then OpenCV
以下是如何使用 Matplotlib 和 OpenCV 在 Python 中对其进行可视化
Matplotlib / Pyplot
Matplotlib / Pyplot
import numpy as np
import csv
import matplotlib.pyplot as plt
with open('mnist_test_10.csv', 'r') as csv_file:
for data in csv.reader(csv_file):
# The first column is the label
label = data[0]
# The rest of columns are pixels
pixels = data[1:]
# Make those columns into a array of 8-bits pixels
# This array will be of 1D with length 784
# The pixel intensity values are integers from 0 to 255
pixels = np.array(pixels, dtype='uint8')
# Reshape the array into 28 x 28 array (2-dimensional array)
pixels = pixels.reshape((28, 28))
# Plot
plt.title('Label is {label}'.format(label=label))
plt.imshow(pixels, cmap='gray')
plt.show()
break # This stops the loop, I just want to see one
OpenCV
OpenCV
You can take the pixelsnumpy array from above which is of dtype='uint8'(unsigned 8-bits integer) and shape 28 x 28 , and plot with cv2.imshow()
您可以pixels从上面获取numpy 数组，它是dtype='uint8'(unsigned 8-bits integer) 和形状 28 x 28 ，并用绘图cv2.imshow()
title = 'Label is {label}'.format(label=label)
cv2.imshow(title, pixels)
cv2.waitKey(0)
cv2.destroyAllWindows()
回答by cpury
For all like me who want a quick and dirty solution, simply to get a rough idea what a given input is about, in-console and without fancy libraries:
对于像我这样想要快速而肮脏的解决方案的人来说，只需粗略地了解给定的输入是关于什么的，在控制台中并且没有花哨的库：
def print_greyscale(pixels, width=28, height=28):
def get_single_greyscale(pixel):
val = 232 + round(pixel * 23)
return '\x1b[48;5;{}m \x1b[0m'.format(int(val))
for l in range(height):
line_pixels = pixels[l * width:(l+1) * width]
print(''.join(get_single_greyscale(p) for p in line_pixels))
(expects the input to be shaped like [784]and with float values from 0 to 1. If either is not the case, you can easily convert (e.g. pixels = pixels.reshape((784,))or pixels \= 255)
（期望输入的形状像[784]0 到 1 的浮点值。如果不是这种情况，您可以轻松转换（例如pixels = pixels.reshape((784,))或pixels \= 255）
The output is a bit distorted but you get the idea.
输出有点失真，但你明白了。
回答by Suraj Subramanian
Importing necessary packages
导入必要的包
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
Reading mnist train dataset ( which is csv formatted ) as a pandas dataframe
将 mnist 训练数据集（采用 csv 格式）作为 Pandas 数据框读取
s = pd.read_csv("mnist_train.csv")
Converting the pandas dataframe to a numpy matrix
将 Pandas 数据帧转换为 numpy 矩阵
data = np.matrix(s)
The first column contains the label, so store it in a separate array
第一列包含标签，因此将其存储在单独的数组中
output = data[:, 0]
And delete the first column from the data matrix
并从数据矩阵中删除第一列
data = np.delete(data, 0, 1)
The first row represents the first image, it is 28X28 image (stored as 784 pixels)
第一行代表第一张图片，是28X28的图片（存储为784像素）
img = data[0].reshape(28,28)
[And displaying the image][1]
plt.imshow(img, cmap="gray")
