Images as high dimensional data

Images as high dimensional data#

Mahmood Amintoosi, Fall 2024

Computer Science Dept, Ferdowsi University of Mashhad

# Importing Necessary Libraries
import numpy as np
import matplotlib.pyplot as plt

img = np.random.randint(10, size=(5,6))
img

array([[3, 0, 5, 6, 3, 3],
       [4, 8, 6, 5, 4, 7],
       [6, 6, 1, 7, 8, 1],
       [9, 1, 0, 6, 7, 8],
       [4, 5, 1, 4, 0, 5]])

plt.figure(figsize=(3,4))
plt.imshow(img, cmap=plt.cm.gray)

<matplotlib.image.AxesImage at 0x1fac7673220>
../_images/26ed47d109d43310fc9d70b8a25dab68eea3383b2a94f9f1c7fdaebf5295cbc6.png

Image segmentation by thresholding#

seg_img = img > 5
type(seg_img), seg_img

(numpy.ndarray,
 array([[False, False, False,  True, False, False],
        [False,  True,  True, False, False,  True],
        [ True,  True, False,  True,  True, False],
        [ True, False, False,  True,  True,  True],
        [False, False, False, False, False, False]]))

# plot each image ...
# ... side by side
fig=plt.figure(figsize=(6, 4))
fig.add_subplot(1, 2, 1)   # subplot one
plt.imshow(img, cmap=plt.cm.gray)

fig.add_subplot(1, 2, 2)   # subplot two
# my data is OK to use gray colormap (0:black, 1:white)
plt.imshow(seg_img, cmap=plt.cm.gray)  # use appropriate colormap here

<matplotlib.image.AxesImage at 0x1fac7582cd0>
../_images/b255b09d4982bbf987f248a77b62877db8602c0b4eaa682e5afaee94a23ad3ee.png

https://www.geeksforgeeks.org/image-segmentation-using-pythons-scikit-image-module/

# !pip install scikit-image

RGB2Gray#

# Importing Necessary Libraries
from skimage import data
from skimage.color import rgb2gray
import matplotlib.pyplot as plt

# Sample Image of scikit-image package
coffee = data.coffee()
coffee.shape,  type(coffee[0,0,0])

((400, 600, 3), numpy.uint8)

# Setting the plot size to 15,15
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)

# Displaying the sample image
plt.imshow(coffee)

# Converting RGB image to Monochrome
gray_coffee = rgb2gray(coffee)
plt.subplot(1, 2, 2)

# Displaying the sample image - Monochrome
# Format
plt.axis('off')
plt.imshow(gray_coffee, cmap="gray")

<matplotlib.image.AxesImage at 0x1fac76e1970>
../_images/a5c78f60b4d105e17981190e2dceab564cd5586e27111dec013c76bd6e276e40.png 
plt.figure(figsize=(8, 12))

for i in range(10):

    # Iterating different thresholds
    binarized_gray = gray_coffee > i*0.1
    plt.subplot(5,2,i+1)

    # Rounding of the threshold
    # value to 1 decimal point
    plt.title("Threshold: >"+str(round(i*0.1,1)))

    # Displaying the binarized image
    # of various thresholds
    plt.imshow(binarized_gray, cmap = 'gray', vmin=0, vmax=1)

plt.tight_layout()
../_images/272c1a401dcd48c6deca360857d0e68cde0a14d32ff1b3c291e1c7f31f8a311d.png
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