Show Grayscale images and Color images by plt.imshow

In computer vision, images are typically represented as 2D arrays of pixel values. However, images can have different numbers of channels, depending on whether they are grayscale or color images.

Grayscale images have only one channel, which represents the intensity of each pixel. The pixel values in grayscale images range from 0 (black) to 255 (white).

Color images, on the other hand, have three channels: red, green, and blue (RGB). Each pixel in a color image is represented as a combination of these three primary colors, with values ranging from 0 to 255. This means that a single pixel in a color image is represented by a tuple of three values (R, G, B).

When using the imshow function in a library like Matplotlib, you need to ensure that the number of channels in the image is correctly specified. For grayscale images, you can simply pass the 2D array of pixel values to imshow and it will be automatically displayed as a grayscale image.

For color images, you need to ensure that the pixel values are represented as a 3D array of shape (height, width, 3), where the third dimension represents the three color channels (R, G, B). You can then pass this array to imshow and it will be displayed as a color image.

Here's an example of how to display a grayscale image using imshow:

import matplotlib.pyplot as plt
import numpy as np

# Create a grayscale image
img = np.random.randint(0, 256, size=(100, 100))

# Display the image using imshow
plt.imshow(img, cmap='gray')
plt.show()

And here's an example of how to display a color image using imshow:

import matplotlib.pyplot as plt
import numpy as np

# Create a color image
img = np.random.randint(0, 256, size=(100, 100, 3))

# Display the image using imshow
plt.imshow(img)
plt.show()

In both examples, the imshow function correctly interprets the number of channels in the image and displays it appropriately.