# Image Flipping and Mirroring with NumPy and OpenCV  Mohammed Sameeruddin

Published on Nov 18, 2020

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• Introduction
• Package requirements
• Let's get started
• Mirroring the Image
• Flipping the Image
• The Complete Code
• Basic Image Operations Package
• What I have gained from this?

### Introduction

In this blog article, I will explain how we can mirror or flip an image in Python. All it is required is understanding the basic mathematics behind various matrix operations and matrix manipulations.

Credits of Cover Image - Photo by Michael Busch on Unsplash

### Package requirements

• NumPy - For matrix operations and manipulating the same.
• OpenCV - For reading the image and converting it into a 2D array (matrix).
• Matplotlib - For plotting the matrix as an image. 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 the `imread()` 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()` method reads the image in `BGR` (`Blue`, `Green`, `Red`) format. To convert the read image into regular format i.e., `RGB` (`Red`, `Green`, `Blue`) we use `cvtColor()` method from the same module `cv2`.

``````def read_this(image_file, gray_scale=False):
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_mirror = np.fliplr(image_rgb)
if with_plot:
fig = plt.figure(figsize=(10, 20))

ax1.axis("off")
ax1.title.set_text('Original')
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)
`````` ``````mirror_this(image_file="lena_original.png", gray_scale=True, with_plot=True)
`````` ### 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_flip = np.flipud(image_rgb)
if with_plot:
fig = plt.figure(figsize=(10, 20))

ax1.axis("off")
ax1.title.set_text('Original')
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)
`````` ``````flip_this(image_file='lena_original.png', gray_scale=True, with_plot=True)
`````` ### The Complete Code

``````class ImageOpsFromScratch(object):
def __init__(self, image_file):
self.image_file = 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_mirror = np.fliplr(image_rgb)

if with_plot:
return None
return image_mirror

def flip_this(self, with_plot=True, gray_scale=False):
image_flip = np.flipud(image_rgb)

if with_plot:
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.axis("off")
ax1.title.set_text('Original')

ax2.axis("off")

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.