matplotlib quickly display small images

matplotlib quickly display small images.md

Running this in a jupyter notebook

• (NOTE: this data is from one of the Keras Hello World datasets) , per below

import matplotlib.pyplot as plt

image = [[0, 0, 0, 0, 0, 0, 0, 0, 33, 96, 175, 156, 64, 14, 54, 137, 204, 194, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 73, 186, 177, 183, 175, 188, 232, 255, 223, 219, 194, 179, 186, 213, 146, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 35, 163, 140, 150, 152, 150, 146, 175, 175, 173, 171, 156, 152, 148, 129, 156, 140, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 150, 142, 140, 152, 160, 156, 146, 142, 127, 135, 133, 140, 140, 137, 133, 125, 169, 75, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 54, 167, 146, 129, 142, 137, 137, 131, 148, 148, 133, 131, 131, 131, 125, 140, 140, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 110, 188, 133, 146, 152, 133, 125, 127, 119, 129, 133, 119, 140, 131, 150, 14, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 221, 158, 137, 135, 123, 110, 110, 114, 108, 112, 117, 127, 142, 77, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 25, 158, 137, 125, 119, 119, 110, 117, 117, 110, 119, 127, 144, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 123, 156, 129, 112, 110, 102, 112, 100, 121, 117, 129, 114, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 125, 169, 127, 119, 106, 108, 104, 94, 121, 114, 129, 91, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 98, 171, 129, 112, 104, 114, 106, 102, 112, 104, 133, 64, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 66, 173, 135, 129, 98, 100, 119, 102, 108, 98, 135, 60, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 56, 171, 135, 127, 100, 108, 117, 85, 106, 110, 135, 66, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 52, 150, 129, 110, 100, 91, 102, 94, 83, 104, 123, 66, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 66, 167, 140, 148, 148, 127, 137, 152, 146, 146, 148, 96, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 45, 123, 94, 104, 96, 119, 121, 106, 98, 112, 87, 114, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 106, 89, 58, 50, 37, 50, 66, 56, 50, 75, 75, 137, 22, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 29, 148, 114, 106, 125, 89, 100, 133, 117, 131, 131, 131, 125, 112, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 100, 106, 114, 91, 137, 62, 102, 131, 89, 135, 112, 131, 108, 135, 37, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 146, 100, 108, 98, 144, 62, 106, 131, 87, 133, 104, 160, 117, 121, 68, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 33, 121, 108, 96, 100, 140, 71, 106, 127, 85, 140, 104, 150, 140, 114, 89, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 62, 119, 112, 102, 110, 137, 75, 106, 144, 81, 144, 108, 117, 154, 117, 104, 18, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 66, 121, 102, 112, 117, 131, 73, 104, 156, 77, 137, 135, 83, 179, 129, 121, 35, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 85, 127, 81, 125, 133, 119, 79, 100, 169, 83, 129, 175, 60, 163, 135, 146, 39, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 106, 129, 62, 140, 144, 108, 85, 83, 158, 85, 129, 175, 48, 146, 133, 135, 64, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 117, 119, 79, 140, 152, 102, 89, 110, 137, 96, 150, 196, 83, 144, 135, 133, 77, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 154, 121, 87, 140, 154, 112, 94, 52, 142, 100, 83, 152, 85, 160, 133, 100, 12, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 35, 4, 33, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]


plt.figure()
plt.imshow(train_images[3])
plt.colorbar()
plt.grid(False)
plt.show()

• And wow that displays...

And the matplot grid , wow this is cool too

• Example code from this tutorial
• According to help(plt.subplot) , plt.subplot(5, 5, i) below is an instruction to place the ith thing, within a 5x5 grid, so basically the count starts at 0 from the upper left corner and spreads the grid as if it were a tape, from 0 to 5*5 - 1

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]])
plt.show()

Obtaining image data

from tensorflow import keras

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

image = train_mages[3]