Image Normalization
Image normalization is a process of scaling the pixel values in an image to a specific range.This is often done to improve the performance of image processing algorithms, as many algorithms work better when the pixel values are within a certain range.
- In OpenCV, the
cv2.normalize()function is used to normalize an image. This function takes the following arguments:- The input image.
- The output image.
- The minimum and maximum values of the normalized image.
- The normalization type.
- The dtype of the output image.
- The normalization type specifies how the pixel values are scaled. There are several different normalization types available, each with its own trade-offs between accuracy and speed.
- Image normalization is a common preprocessing step in many image processing tasks. It can help to improve the performance of algorithms such as image classification, object detection, and image segmentation.
# Import the necessary Libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Load the image
image = cv2.imread('Ganesh.jpg')
# Convert BGR image to RGB
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Split the image into channels
b, g, r = cv2.split(image_rgb)
# Normalization parameter
min_value = 0
max_value = 1
norm_type = cv2.NORM_MINMAX
# Normalize each channel
b_normalized = cv2.normalize(b.astype('float'), None, min_value, max_value, norm_type)
g_normalized = cv2.normalize(g.astype('float'), None, min_value, max_value, norm_type)
r_normalized = cv2.normalize(r.astype('float'), None, min_value, max_value, norm_type)
# Merge the normalized channels back into an image
normalized_image = cv2.merge((b_normalized, g_normalized, r_normalized))
# Normalized image
print(normalized_image[:,:,0])
plt.imshow(normalized_image)
plt.xticks([])
plt.yticks([])
plt.title('Normalized Image')
plt.show()
Output:
[[0.0745098 0.0745098 0.0745098 ... 0.07843137 0.07843137 0.07843137]
[0.0745098 0.0745098 0.0745098 ... 0.07843137 0.07843137 0.07843137]
[0.0745098 0.0745098 0.0745098 ... 0.07843137 0.07843137 0.07843137]
...
[0.00392157 0.00392157 0.00392157 ... 0.0745098 0.0745098 0.0745098 ]
[0.00392157 0.00392157 0.00392157 ... 0.0745098 0.0745098 0.0745098 ]
[0.00392157 0.00392157 0.00392157 ... 0.0745098 0.0745098 0.0745098 ]]
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