This post shows some of the various tools in Python for visualizing images. There are usually two steps to the visualization process. First, you’ll need to read in the image, usually as a numpy array or something similar. Then, you can visualize it with various libraries.
Table of contents
Libraries
There are many libraries in Python to help with loading and processing images. Let’s look at a few of them.
SKImage
SKImage is used for turning an image on disk into a numpy array, like so.
import skimage
from skimage.io import imread
image_path = '../roo.jpg'
img = imread(image_path)
img[:, : , 0][:3]
array([[232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232,
232, 232, 232, 234, 234, 234, 234, 234, 234, 234, 234, 231, 231,
231, 231, 231, 231, 231, 231, 234, 234, 234, 234, 234, 234, 234,
234, 237, 237, 237, 237, 237, 237, 237, 237, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 234, 234, 234, 234, 234, 234, 234, 234, 232,
232, 232, 232, 232, 232, 232, 232, 229, 229, 229, 229, 229, 229,
229, 229, 227, 227, 227, 227, 227, 227, 227, 227],
[232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232,
232, 232, 232, 234, 234, 234, 234, 234, 234, 234, 234, 232, 232,
232, 232, 232, 232, 232, 232, 234, 234, 234, 234, 234, 234, 234,
234, 237, 237, 237, 237, 237, 237, 237, 237, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 234, 234, 234, 234, 234, 234, 234, 234, 232,
232, 232, 232, 232, 232, 232, 232, 229, 229, 229, 229, 229, 229,
229, 229, 227, 227, 227, 227, 227, 227, 227, 227],
[232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232, 232,
232, 232, 232, 234, 234, 234, 234, 234, 234, 234, 234, 232, 232,
232, 232, 232, 232, 232, 232, 234, 234, 234, 234, 234, 234, 234,
234, 237, 237, 237, 237, 237, 237, 237, 237, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241, 241,
241, 241, 241, 241, 234, 234, 234, 234, 234, 234, 234, 234, 232,
232, 232, 232, 232, 232, 232, 232, 229, 229, 229, 229, 229, 229,
229, 229, 227, 227, 227, 227, 227, 227, 227, 227]], dtype=uint8)
Matplotlib
from matplotlib import pyplot as plt
Matplotlib is my default for displaying images.
plt.imshow(img);
To have more control:
fix, ax = plt.subplots()
ax.imshow(img);
plt.figure(figsize=(10, 10))
plt.imshow(img);
fig, ax = plt.subplots(figsize=(30, 10))
ax.imshow(img)
ax.axis('off');
plt.rcParams['figure.figsize'] = (30, 10)
fig, ax = plt.subplots()
ax.imshow(img)
ax.axis('off');
PIL
from PIL import Image
pil_img = Image.fromarray(img)
pil_img
Open CV
You can also read images off disk using OpenCV.
import cv2
img = cv2.imread(image_path)
plt.imshow(img);
OpenCV uses BGR internally while most other libraries use RGB. This usually isn’t an issue unless you read an image with opencv and try to plot it with, say, matplotlib (note the flipped color channels in the image above). Then you’ll need to switch the channels around. Here is how to do that.
plt.imshow(img[:, :, ::-1]);
ImageIO
ImageIO is nice because it has a common interface for different image types.
import imageio
import numpy as np
image_path = '../roo.jpg'
img_arr = imageio.imread(image_path)
img_arr.shape
(256, 192, 3)
The three axes are the height, width, and number of color channels. So is image is 256 pixels high, 192 pixels wide, and has 3 color channels.
type(img_arr)
imageio.core.util.Array
isinstance(img_arr, np.ndarray)
True
As you can see, imageio.core.util.Array is a NumPy ndarray.
Visualizing from TensorFlow Datasets
import tensorflow as tf
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
print("Number of training examples:", len(x_train))
print("Number of test examples:", len(x_test))
Number of training examples: 60000
Number of test examples: 10000
print(y_train[0])
plt.imshow(x_train[0, :, :])
plt.colorbar();
5
Plotting Multiple Images
Here’s an example of plotting multiple images with the label below it. It’s commonly done to visualize datasets.
import tensorflow as tf
from tensorflow.keras import datasets
import matplotlib.pyplot as plt
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i][0]])
plt.show()
