Image Flipping and Mirroring with NumPy and OpenCV
Tags: Image Mirroring, Image Flipping, Image Processing, Opencv

For this mini-project, I am using a famous Lena image which is mainly used for testing the computer vision models. Make sure to download this image and save it in the current working directory.

import cv2

import numpy as np

from matplotlib import pyplot as plt

Let’s get started

First, we read the image file using imread() the method from the module cv2 . To do that, we simply need to import the package and use so. Hence by doing this, we get the image in the form of a matrix.

By default, imread() the method reads the image in BGR ( Blue , Green , Red ) format. To convert the read image into a regular format i.e., RGB ( Red , Green , Blue ) we use cvtColor() the method from the same module cv2 .

def read_this(image_file, gray_scale=False):

image_src = cv2.imread(image_file)

if gray_scale:

image_rgb = cv2.cvtColor(image_src, cv2.COLOR_BGR2GRAY)

else:

image_rgb = cv2.cvtColor(image_src, cv2.COLOR_BGR2RGB)

return image_rgb

The above function returns an image matrix from the image file that is passed. It consists of regular if and else condition if we want to get the image matrix either in gray_scale or RGB format.

Mirroring the Image

To mirror the image basically, we need to reverse the matrix row-wise from left to right. For example, let’s consider a matrix A .

>>> A = [

[4, 1, 1],

[2, 8, 0],

[3, 8, 1]

]

If we want to mirror this matrix (row-wise), then it would be -

>>> import numpy as np

>>> mirror_ = np.fliplr(A)

>>> mirror_

[[1, 1, 4],

[0, 8, 2],

[1, 8, 3]]

We can also do this without using the NumPy module. If so, we get to use loops and reverse each row. This would take a while if performed the same on image matrices as they are very large matrices and we don't want our code to perform very slow.

def mirror_this(image_file, gray_scale=False, with_plot=False):

image_rgb = read_this(image_file=image_file, gray_scale=gray_scale)

image_mirror = np.fliplr(image_rgb)

if with_plot:

fig = plt.figure(figsize=(10, 20)) ax1 = fig.add_subplot(2, 2, 1)

ax1.axis("off")

ax1.title.set_text('Original')

ax2 = fig.add_subplot(2, 2, 2)

ax2.axis("off")

ax2.title.set_text("Mirrored") if not gray_scale:

ax1.imshow(image_rgb)

ax2.imshow(image_mirror)

else:

ax1.imshow(image_rgb, cmap='gray')

ax2.imshow(image_mirror, cmap='gray')

return True

return image_mirror

The above function returns an image matrix that is reversed or flipped row-wise from left to right.

Let’s plot the same -

mirror_this(image_file="lena_original.png", with_plot=True)

Image by Author

mirror_this(image_file="lena_original.png", gray_scale=True, with_plot=True)

Image by Author

Flipping the Image

To flip the image basically, we need to reverse the matrix column-wise from up to down. For example, let’s consider a matrix B .

>>> B = [

[4, 1, 1],

[2, 8, 0],

[3, 8, 1]

]

If we want to flip this matrix (column-wise), then it would be -

>>> import numpy as np

>>> flip_= np.flipud(B)

>>> flip_

[[3, 8, 1],

[2, 8, 0],

[4, 1, 1]]

We use NumPy for flipping the matrix in order to maintain the fastness of the code.

def flip_this(image_file, gray_scale=False, with_plot=False):

image_rgb = read_this(image_file=image_file, gray_scale=gray_scale)

image_flip = np.flipud(image_rgb)

if with_plot:

fig = plt.figure(figsize=(10, 20)) ax1 = fig.add_subplot(2, 2, 1)

ax1.axis("off")

ax1.title.set_text('Original')

ax2 = fig.add_subplot(2, 2, 2)

ax2.axis("off")

ax2.title.set_text("Flipped") if not gray_scale:

ax1.imshow(image_rgb)

ax2.imshow(image_flip)

else:

ax1.imshow(image_rgb, cmap='gray')

ax2.imshow(image_flip, cmap='gray')

return True

return image_flip

The above function returns an image matrix that is reversed or flipped column-wise from up to down.

Let’s plot the same -

flip_this(image_file='lena_original.png', with_plot=True)

Image by Author

flip_this(image_file='lena_original.png', gray_scale=True, with_plot=True)

Image by Author

The Complete Code

class ImageOpsFromScratch(object):

def __init__(self, image_file):

self.image_file = image_file def read_this(self, gray_scale=False):

image_src = cv2.imread(self.image_file)

if gray_scale:

image_rgb = cv2.cvtColor(image_src, cv2.COLOR_BGR2GRAY)

else:

image_rgb = cv2.cvtColor(image_src, cv2.COLOR_BGR2RGB)

return image_rgb def mirror_this(self, with_plot=True, gray_scale=False):

image_rgb = self.read_this(gray_scale=gray_scale)

image_mirror = np.fliplr(image_rgb) if with_plot:

self.plot_it(orig_matrix=image_rgb, trans_matrix=image_mirror, head_text='Mirrored', gray_scale=gray_scale)

return None

return image_mirror def flip_this(self, with_plot=True, gray_scale=False):

image_rgb = self.read_this(gray_scale=gray_scale)

image_flip = np.flipud(image_rgb) if with_plot:

self.plot_it(orig_matrix=image_rgb, trans_matrix=image_flip, head_text='Flipped', gray_scale=gray_scale)

return None

return image_flip def plot_it(self, orig_matrix, trans_matrix, head_text, gray_scale=False):

fig = plt.figure(figsize=(10, 20)) ax1 = fig.add_subplot(2, 2, 1)

ax1.axis("off")

ax1.title.set_text('Original') ax2 = fig.add_subplot(2, 2, 2)

ax2.axis("off")

ax2.title.set_text(head_text) if not gray_scale:

ax1.imshow(orig_matrix)

ax2.imshow(trans_matrix)

else:

ax1.imshow(orig_matrix, cmap='gray')

ax2.imshow(trans_matrix, cmap='gray') return True

Basic Image Operations Package

imo = ImageOpsFromScratch(image_file='lena_original.png')

### Mirroring ###

imo.mirror_this()

imo.mirror_this(gray_scale=True)

### Flipping ###

imo.flip_this()

imo.flip_this(gray_scale=True)

The above image results will be displayed. Now that everything is sorted we can create other image operations like equalize() , solarize() etc. I have not covered that in this article but definitely in the next one.

What I have gained from this?

By implementing this, I have learned and understood the logic behind various image operations and how to put that in the form of code.

We can also do the same using a well-known image library Pillow . But, as a developer, we need to know things work from start to end. This is something where everyone should be curious about.

Please do read my another article related to image processing and computer vision where I explain image convolution from scratch.