### Introduction

When we talk about images, Images are more like matrices where data is stored row-wise and column-wise. Since the image is a replication of a matrix, we can do all sorts of matrix operations on the image.

**Credits of Cover Image** - Photo by Erwan Hesry on Unsplash

We will use the NumPy module extensively for this project and also for the following projects related to Image Processing.

### Matrix Creation

```
>>> import numpy as np
>>>
>>> mat = np.matrix(
... [[1, 2, 3, 4, 5],
... [3, 4, 5, 6, 1]]
... )
>>> mat
matrix([[1, 2, 3, 4, 5],
[3, 4, 5, 6, 1]])
>>>
>>> mat.shape
(2, 5)
>>>
```

### Transpose Operation

```
>>> mat.T
matrix([[1, 3],
[2, 4],
[3, 5],
[4, 6],
[5, 1]])
>>>
```

### Scalar Multiplication

```
>>> 3 * mat
matrix([[ 3, 6, 9, 12, 15],
[ 9, 12, 15, 18, 3]])
>>>
```

### Matrix to Image

For converting the matrix into an image, we can use the Matplotlib module. The method `imshow()`

is helpful to do the conversion.

```
>>> from matplotlib import pyplot as plt
>>>
>>> plt.figure(figsize=(10, 3))
>>> image_mat = plt.imshow(mat, cmap='Reds')
>>> plt.colorbar(image_mat)
>>> plt.show()
>>>
```

### Large matrix to Image

In the above example, we did the conversion for a small matrix. Now we shall try to do the conversion for a large matrix. We will use the Random module to generate random numbers for the matrix.

- There should be 30 rows and 50 columns.
- Each row of the matrix should have 50 numbers in the range of 1 and 200.

We shall use the method `randint(a, b)`

to create a random large matrix.

```
>>> import random
>>>
>>> big_mat = [
... [random.randint(1, 200) for i in range(50)]
... for j in range(30)
... ]
>>>
>>> big_mat = np.matrix(big_mat)
>>>
>>> big_mat
matrix([[ 16, 184, 130, ..., 35, 126, 104],
[198, 153, 172, ..., 109, 60, 174],
[ 90, 138, 108, ..., 192, 132, 103],
...,
[172, 83, 158, ..., 154, 4, 109],
[ 23, 3, 198, ..., 147, 12, 88],
[ 62, 106, 96, ..., 191, 83, 193]])
>>>
>>> big_mat.shape
(30, 50)
>>>
```

Let's convert the above large matrix into an image.

```
>>> plt.figure(figsize=(10, 5))
>>> image_mat = plt.imshow(big_mat, cmap=None)
>>> plt.colorbar(image_mat)
>>> plt.show()
```

### Image to Matrix

To convert an image into a matrix, we need to use the package called `opencv-python`

. With this package, we can read an image into a matrix and perform the matrix operations using the NumPy module.

**Installation**

```
pip install opencv-python --user
```

A typical colored image is comprised of pixels that are represented as `RGB`

pixels. A pixel is simply a number in the range of `0`

to`255`

for all `R`

, `G`

, and `B`

.

- R → Red → 0 to 255
- G → Green → 0 to 255
- B → Blue → 0 to 255

We will `cv2`

(OpenCV) module to read the image in the form of a matrix. The method `imread()`

is used to read the image either in `grayscale`

mode or `RGB`

mode.

`import`

the packages

```
import numpy as np
import cv2
import json
from matplotlib import pyplot as plt
```

The image that we will be reading is the original Lenna image. Function to read the image as a matrix.

```
def read_this(image_file, gray_scale=False):
image_src = cv2.imread(image_file)
if gray_scale:
image_src = cv2.cvtColor(image_src, cv2.COLOR_BGR2GRAY)
else:
image_src = cv2.cvtColor(image_src, cv2.COLOR_BGR2RGB)
return image_src
```

By default, `imread()`

reads the image in `BGR`

(Blue, Green, and Red) format. We shall convert into `RGB`

and hence the above function. Now that we have read the image, we can separate the pixels into 3 distinct matrices separating `R`

, `G`

, and `B`

.

Let's plot the same as we have done for the above examples.

```
def separate_rgb(image_file, gray_scale=False):
image_src = read_this(image_file=image_file, gray_scale=gray_scale)
if not gray_scale:
r_, g_, b_ = image_src[:, :, 0], image_src[:, :, 1], image_src[:, :, 2]
return [r_, g_, b_]
return [image_src]
```

The above function separates the pixels into 3 distinct matrices.

```
def plot_rgb_seperated(image_file, with_plot=True, gray_scale=False):
with_plot = True
image_src = read_this(image_file=image_file, gray_scale=False)
separates = separate_rgb(image_file=image_file, gray_scale=gray_scale)
pixel_matrices = [image_src]
pixel_matrices.extend(separates)
cmap_vals = [None, 'Reds', 'Greens', 'Blues'] if not gray_scale else [None, 'gray']
titles = ['Original', 'Red', 'Green', 'Blues'] if not gray_scale else ['Original', 'Grayscale']
n_cols = 4 if not gray_scale else 2
fig_size = (15, 10) if not gray_scale else (10, 20)
if with_plot:
fig, axes = plt.subplots(nrows=1, ncols=n_cols, figsize=fig_size)
for i, ax in zip(range(n_cols), axes):
ax.axis("off")
ax.set_title(titles[i])
ax.imshow(pixel_matrices[i], cmap=cmap_vals[i])
return True
return False
```

The above function plots the matrices along with the original matrix. Let's test the function.

**For color image:**

```
plot_rgb_seperated(image_file='lena_original.png')
```

**For grayscale image:**

```
plot_rgb_seperated(image_file='lena_original.png', gray_scale=True)
```

### END

That is it for this article. We have learned how to convert the matrix into an image and vice-versa.