Why CIFAR-10 images are not displayed properly using matplotlib?

Question

From the training set I took a image(\'img\') of size (3,32,32). I have used plt.imshow(img.T). The image is not clear. Now changes I have to make to image(\'img\') to make it m

Answer 1

try using

import matplotlib.pyplot as plt
from scipy.misc import toimage
plt.imshow(toimage(img))

I am not 100% sure of how the code works, but I think that because the images are stored in floating point numpy arrays, the imshow() function has a difficult time mapping them to the right colors. By typecasting them to image using toimage() you convert them into proper image format that imshow() expects, i.e not an array but an image encoded as .png or .jpg.

This code works for me every time I want to display images in python.

Answer 2

This file reads the cifar10 dataset and plots individual images using matplotlib.

import _pickle as pickle
import argparse
import numpy as np
import os
import matplotlib.pyplot as plt

cifar10 = "./cifar-10-batches-py/"

parser = argparse.ArgumentParser("Plot training images in cifar10 dataset")
parser.add_argument("-i", "--image", type=int, default=0, 
                    help="Index of the image in cifar10. In range [0, 49999]")
args = parser.parse_args()


def unpickle(file):
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict

def cifar10_plot(data, meta, im_idx=0):
    im = data[b'data'][im_idx, :]

    im_r = im[0:1024].reshape(32, 32)
    im_g = im[1024:2048].reshape(32, 32)
    im_b = im[2048:].reshape(32, 32)

    img = np.dstack((im_r, im_g, im_b))

    print("shape: ", img.shape)
    print("label: ", data[b'labels'][im_idx])
    print("category:", meta[b'label_names'][data[b'labels'][im_idx]])         

    plt.imshow(img) 
    plt.show()


def main():
    batch = (args.image // 10000) + 1
    idx = args.image - (batch-1)*10000

    data = unpickle(os.path.join(cifar10, "data_batch_" + str(batch)))
    meta = unpickle(os.path.join(cifar10, "batches.meta"))

    cifar10_plot(data, meta, im_idx=idx)


if __name__ == "__main__":
    main()

Answer 3

Make sure you don't normalize your dataset when you want to display the image.

Example :

The loader...

import torch
from torchvision import datasets, transforms
import matplotlib.pyplot as plt


train_loader = torch.utils.data.DataLoader(
    datasets.CIFAR10('../data', train=True, download=True,
                     transform=transforms.Compose([
                         transforms.RandomHorizontalFlip(),
                         transforms.ToTensor(),
                        #  transforms.Normalize(
                        #      (0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))
                     ])),
    batch_size=64, shuffle=True)

The code that shows the image...

img = next(iter(train_loader))[0][0]
plt.imshow(transforms.ToPILImage()(img))

Normalized

Wihtout normalization

Answer 4

I made a function to plot the RGB image from a row in the CIFAR10 dataset.The image will be blurry at best since the original size of the image is very small (32px X 32px).

def unpickle(file):
    with open(file, 'rb') as fo:
        dict1 = pickle.load(fo, encoding='bytes')
    return dict1

pd_tr = pd.DataFrame()
tr_y = pd.DataFrame()

for i in range(1,6):
    data = unpickle('data/data_batch_' + str(i))
    pd_tr = pd_tr.append(pd.DataFrame(data[b'data']))
    tr_y = tr_y.append(pd.DataFrame(data[b'labels']))
    pd_tr['labels'] = tr_y

tr_x = np.asarray(pd_tr.iloc[:, :3072])
tr_y = np.asarray(pd_tr['labels'])
ts_x = np.asarray(unpickle('data/test_batch')[b'data'])
ts_y = np.asarray(unpickle('data/test_batch')[b'labels'])    
labels = unpickle('data/batches.meta')[b'label_names']

def plot_CIFAR(ind):
    arr = tr_x[ind]
    sc_dpi = 157.35
    R = arr[0:1024].reshape(32,32)/255.0
    G = arr[1024:2048].reshape(32,32)/255.0
    B = arr[2048:].reshape(32,32)/255.0

    img = np.dstack((R,G,B))
    title = re.sub('[!@#$b]', '', str(labels[tr_y[ind]]))
    fig = plt.figure(figsize=(3,3))
    ax = fig.add_subplot(111)
    ax.imshow(img,interpolation='bicubic')
    ax.set_title('Category = '+ title,fontsize =15)

plot_CIFAR(4)

Answer 5

code result is: Try below code.

I found a very useful link about visualization of mnist and cifar images. You can find codes for various images : https://machinelearningmastery.com/how-to-load-and-visualize-standard-computer-vision-datasets-with-keras/ cifar10 image code is below: It works well. Image is above.

# example of loading the cifar10 dataset
from matplotlib import pyplot
from keras.datasets import cifar10
# load dataset
(trainX, trainy), (testX, testy) = cifar10.load_data()
# summarize loaded dataset
print('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))
print('Test: X=%s, y=%s' % (testX.shape, testy.shape))
# plot first few images
for i in range(9):
    # define subplot
    pyplot.subplot(330 + 1 + i)
    # plot raw pixel data
    pyplot.imshow(trainX[i])
# show the figure
pyplot.show()

Answer 6

Add 0.5:

plt.imshow(np.transpose(img, (1, 2, 0)) + 0.5)