Let’s see how to Convert an image to NumPy array and then save that array into CSV file in Python? First, we will learn about how to convert an image to a numpy ndarray. There are many methods to convert an image to ndarray, few of them are:

**Method 1: **Using **PIL **and **NumPy **library.

We will use PIL.Image.open() and numpy.asarray().

**Example:**

## Python3

`# import required libraries` `from` `PIL ` `import` `Image` `import` `numpy as gfg` ` ` `# read an image` `img ` `=` `Image.` `open` `(` `'geeksforgeeks.jpg'` `)` ` ` `# convert image object into array` `imageToMatrice ` `=` `gfg.asarray(img)` ` ` `# printing shape of image` `print` `(imageToMatrice.shape)` |

**Output:**

(251, 335, 3)

**Method 2: **Using **Matplotib **library.

We will use matplotlib.image.imread() method.

**Example:**

## Python3

`# import library` `from` `matplotlib.image ` `import` `imread` ` ` `# read an image` `imageToMatrice ` `=` `imread(` `'geeksforgeeks.jpg'` `)` ` ` `# show shape of the image` `print` `(imageToMatrice.shape)` |

**Output:**

(251, 335, 3)

Now, imageToMatrice variable contains the ndarray which is obtained after the conversion from the given image.

The dimension of the matrice obtained is decided by how many channels are present in the image:

**For a black and white or gray scale image:**There is only one channel present, thus, the shape of the matrices would be (n, n) where n represents the dimension of the images (pixels), and values inside the matrix range from 0 to 255.**For color or RGB image:**It will render a tensor of 3 channels, thus the shape of the matrices would be (n, n,3). Each channel is an (n, n) matrix where each entry represents respectively the level of Red, Green, or Blue at the actual location inside the image.

We will use two methods to do the same, first method using numpy library and the second method using pandas library:

**Note: **we can save only 1D or 2D matrix in a file, therefore, there would be no issue in the gray scale or black and white image as it is a 2D matrix, but we need to make sure that this works for a colored or RGB image, which is a 3D matrix.

**Method 1:** Using **NumPy** library.

We will use numpy.savetxt() and numpy.loadtxt().

**Example:**

## Python3

`# import required libraries` `import` `numpy as gfg` `import` `matplotlib.image as img` ` ` `# read an image` `imageMat ` `=` `img.imread(` `'gfg.jpg'` `)` `print` `(` `"Image shape:"` `, imageMat.shape)` ` ` `# if image is colored (RGB)` `if` `(imageMat.shape[` `2` `] ` `=` `=` `3` `):` ` ` ` ` `# reshape it from 3D matrice to 2D matrice` ` ` `imageMat_reshape ` `=` `imageMat.reshape(imageMat.shape[` `0` `],` ` ` `-` `1` `)` ` ` `print` `(` `"Reshaping to 2D array:"` `,` ` ` `imageMat_reshape.shape)` ` ` `# if image is grayscale` `else` `:` ` ` `# remain as it is` ` ` `imageMat_reshape ` `=` `imageMat` ` ` `# saving matrice to .csv file` `gfg.savetxt(` `'geek.csv'` `,` ` ` `imageMat_reshape)` ` ` `# retrieving matrice from the .csv file` `loaded_2D_mat ` `=` `gfg.loadtxt(` `'geek.csv'` `)` ` ` `# reshaping it to 3D matrice` `loaded_mat ` `=` `loaded_2D_mat.reshape(loaded_2D_mat.shape[` `0` `], ` ` ` `loaded_2D_mat.shape[` `1` `] ` `/` `/` `imageMat.shape[` `2` `],` ` ` `imageMat.shape[` `2` `])` ` ` `print` `(` `"Image shape of loaded Image:"` `, ` ` ` `loaded_mat.shape)` ` ` `# check if both matrice have same shape or not` `if` `((imageMat ` `=` `=` `loaded_mat).` `all` `()):` ` ` ` ` `print` `(` `"\n\nYes"` `,` ` ` `"The loaded matrice from CSV file is same as original image matrice"` `)` |

**Output:**

Image shape: (251, 335, 3)

Reshaping to 2D array: (251, 1005)

Image shape of loaded Image: (251, 335, 3)Yes The loaded matrice from CSV file is same as original image matrice

**Method 2: **Using **Pandas** library.

We will use pandas.Dataframe() and pandas.Dataframe() .to_csv() method.

## Python3

`# import required libraries` `import` `numpy as gfg` `import` `matplotlib.image as img` `import` `pandas as pd` ` ` `# read an image` `imageMat ` `=` `img.imread(` `'gfg.jpg'` `)` `print` `(` `"Image shape:"` `, ` ` ` `imageMat.shape)` ` ` `# if image is colored (RGB)` `if` `(imageMat.shape[` `2` `] ` `=` `=` `3` `):` ` ` ` ` `# reshape it from 3D matrice to 2D matrice` ` ` `imageMat_reshape ` `=` `imageMat.reshape(imageMat.shape[` `0` `],` ` ` `-` `1` `)` ` ` `print` `(` `"Reshaping to 2D array:"` `, ` ` ` `imageMat_reshape.shape)` ` ` `# if image is grayscale` `else` `:` ` ` `# remain as it is` ` ` `imageMat_reshape ` `=` `imageMat` ` ` `# converting it to dataframe.` `mat_df ` `=` `pd.DataFrame(imageMat_reshape)` ` ` `# exporting dataframe to CSV file.` `mat_df.to_csv(` `'gfgfile.csv'` `,` ` ` `header ` `=` `None` `,` ` ` `index ` `=` `None` `)` ` ` `# retrieving dataframe from CSV file` `loaded_df ` `=` `pd.read_csv(` `'gfgfile.csv'` `, ` ` ` `sep ` `=` `','` `,` ` ` `header ` `=` `None` `)` `# getting matrice values.` `loaded_2D_mat ` `=` `loaded_df.values` ` ` `# reshaping it to 3D matrice` `loaded_mat ` `=` `loaded_2D_mat.reshape(loaded_2D_mat.shape[` `0` `],` ` ` `loaded_2D_mat.shape[` `1` `] ` `/` `/` `imageMat.shape[` `2` `],` ` ` `imageMat.shape[` `2` `])` ` ` `print` `(` `"Image shape of loaded Image :"` `,` ` ` `loaded_mat.shape)` ` ` `# check if both matrice have same shape or not` `if` `((imageMat ` `=` `=` `loaded_mat).` `all` `()):` ` ` `print` `(` `"\n\nYes"` `,` ` ` `"The loaded matrice from CSV file is same as original image matrice"` `)` |

**Output:**

Image shape: (251, 335, 3)

Reshaping to 2D array: (251, 1005)

Image shape of loaded Image : (251, 335, 3)Yes The loaded matrice from CSV file is same as original image matrice

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