Image Manipulation

NumPy indexing enables powerful image transformations.

Channel Extraction

Separate RGB and alpha channels using indexing.

1. RGBA Channels

import numpy as np
import matplotlib.pyplot as plt
import PIL
import urllib

def main():
    url = "https://upload.wikimedia.org/wikipedia/en/4/43/Pok%C3%A9mon_Mewtwo_art.png"
    img = np.array(PIL.Image.open(urllib.request.urlopen(url)))
    print(f"{img.shape = }")

    fig, axes = plt.subplots(1, 4, figsize=(12, 3))
    for i, title in enumerate(["R Channel", "G Channel", "B Channel", "Alpha"]):
        axes[i].set_title(title, fontsize=15)
        axes[i].imshow(img[:, :, i])
        axes[i].axis('off')
    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    main()

2. Single Channel

Access one channel with img[:, :, 0] for red, 1 for green, 2 for blue.

Basic Transforms

Apply geometric transformations using slicing.

1. Flip and Slice

import numpy as np
import matplotlib.pyplot as plt
import PIL
import urllib

def main():
    url = "https://upload.wikimedia.org/wikipedia/en/4/43/Pok%C3%A9mon_Mewtwo_art.png"
    img = np.array(PIL.Image.open(urllib.request.urlopen(url)))

    imgs = (img, img[::-1], img[:, ::-1], 
            img[:, :, ::-1], img[::7, ::7], img[50:-50, 50:-50])
    titles = ("Original", "Flip Vertical", "Flip Horizontal",
              "Swap Channels", "Downsample", "Crop")

    fig, axes = plt.subplots(2, 3, figsize=(8, 5))
    for ax, im, title in zip(axes.flat, imgs, titles):
        ax.set_title(title, fontsize=12)
        ax.imshow(im)
        ax.axis('off')
    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    main()

2. Operation Summary

• img[::-1] — flip vertically
• img[:, ::-1] — flip horizontally
• img[:, :, ::-1] — reverse channel order (RGB→BGR)
• img[::7, ::7] — downsample by factor of 7
• img[50:-50, 50:-50] — crop 50 pixels from each edge

Grid Overlay

Draw grid lines on an image using slice assignment.

1. Manual Grid

import numpy as np
import matplotlib.pyplot as plt
import PIL
import urllib

def main():
    url = "https://upload.wikimedia.org/wikipedia/en/4/43/Pok%C3%A9mon_Mewtwo_art.png"
    img = np.array(PIL.Image.open(urllib.request.urlopen(url)))

    fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(6, 4))

    ax0.imshow(img)

    spacing = 50
    img_copy = img.copy()
    img_copy[spacing::spacing, :] = [0, 0, 0, 255]
    img_copy[:, spacing::spacing] = [0, 0, 0, 255]
    ax1.imshow(img_copy)

    for ax in (ax0, ax1):
        ax.axis('off')

    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    main()

2. Slice Assignment

img[::50, :] = color sets every 50th row to the specified color.

np.where Function

Conditional element replacement based on a condition.

1. Syntax Pattern

\[\begin{array}{cccccccc} b&=&\text{np.where(}&\text{condition}&,&\text{if\_true}&,&\text{if\_false}&\text{)} \end{array}\]

2. Add Noise Example

import numpy as np
import matplotlib.pyplot as plt
import PIL
import urllib

def main():
    url = "https://upload.wikimedia.org/wikipedia/en/4/43/Pok%C3%A9mon_Mewtwo_art.png"
    img = np.array(PIL.Image.open(urllib.request.urlopen(url)))

    img_noisy = img + np.random.randint(-100, 101, size=img.shape)
    img_noisy = np.where(img_noisy >= 0, img_noisy, 0)
    img_noisy = np.where(img_noisy <= 255, img_noisy, 255)

    fig, ax = plt.subplots()
    ax.imshow(img_noisy.astype(np.uint8))
    ax.axis('off')
    plt.show()

if __name__ == "__main__":
    main()

3. Clipping Values

Chain np.where to clamp values within valid range [0, 255].

np.transpose

Swap array axes for transposition.

1. Image Transpose

import numpy as np
import matplotlib.pyplot as plt
import PIL
import urllib

def main():
    url = "https://upload.wikimedia.org/wikipedia/en/4/43/Pok%C3%A9mon_Mewtwo_art.png"
    img = np.array(PIL.Image.open(urllib.request.urlopen(url)))

    img_T = np.empty((img.shape[1], img.shape[0], 3), dtype=np.uint8)
    img_T[:, :, 0] = img[:, :, 0].T
    img_T[:, :, 1] = img[:, :, 1].T
    img_T[:, :, 2] = img[:, :, 2].T

    fig, ax = plt.subplots()
    ax.imshow(img_T)
    ax.axis('off')
    plt.show()

if __name__ == "__main__":
    main()

2. Channel-wise

Transpose each color channel separately to maintain RGB structure.

np.roll Function

Circular shift of array elements along an axis.

1. Roll Example

import numpy as np
import matplotlib.pyplot as plt
import PIL
import urllib

def main():
    url = "https://upload.wikimedia.org/wikipedia/en/4/43/Pok%C3%A9mon_Mewtwo_art.png"
    img = np.array(PIL.Image.open(urllib.request.urlopen(url)))

    fig, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(12, 3))

    ax0.imshow(img)
    ax1.imshow(np.roll(img, 50, axis=0))
    ax2.imshow(np.roll(img, 50, axis=1))

    for ax in (ax0, ax1, ax2):
        ax.axis('off')

    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    main()

2. Axis Parameter

axis=0 rolls vertically; axis=1 rolls horizontally.