Image Operators: Image Processing in Python by Jason M. Kinser PDF Book Free Download

Image Operators: Image Processing in Python by Jason M. Kinser PDF Book Free Download

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Image Operators: Image Processing in Python by Jason M. Kinser Book Details

Book Name	Image Operators: Image Processing in Python
Author	Jason M. Kinser
Category	Electronics & Communication Engineering, Education Books
Book Language	English
Publisher	Taylor & Francis Group
Pages	366
ISBN	9781498796187
Country	India
Book Size	14 MB

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About Image Operators: Image Processing in Python by Jason M. Kinser Book

Image operators in image processing refer to mathematical or algorithmic operations applied to images to enhance, manipulate, or analyze them. Python provides a rich ecosystem of libraries and tools for image processing, making it a popular choice for working with image operators. Some commonly used libraries for image processing in Python include OpenCV, scikit-image, and Pillow. Let's explore some basic image operators and how to use them in Python:

1. Image Loading and Display (using OpenCV):

   python

   import cv2 image_path = 'path_to_your_image.jpg' image = cv2.imread(image_path) cv2.imshow('Original Image', image) cv2.waitKey(0) cv2.destroyAllWindows()

2. Grayscale Conversion (using OpenCV):

   python

   gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow('Grayscale Image', gray_image) cv2.waitKey(0) cv2.destroyAllWindows()

3. Image Filtering and Convolution (using OpenCV):

   python

   import numpy as np kernel = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]) filtered_image = cv2.filter2D(image, -1, kernel) cv2.imshow('Filtered Image', filtered_image) cv2.waitKey(0) cv2.destroyAllWindows()

4. Image Blurring (using OpenCV):

   python

   blurred_image = cv2.GaussianBlur(image, (5, 5), 0) cv2.imshow('Blurred Image', blurred_image) cv2.waitKey(0) cv2.destroyAllWindows()

5. Image Thresholding (using OpenCV):

   python

   ret, thresholded_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY) cv2.imshow('Thresholded Image', thresholded_image) cv2.waitKey(0) cv2.destroyAllWindows()

6. Edge Detection (using scikit-image):

   python

   from skimage import filters edges = filters.sobel(gray_image) cv2.imshow('Edge Detection', edges) cv2.waitKey(0) cv2.destroyAllWindows()

7. Histogram Equalization (using OpenCV):

   python

   equ_image = cv2.equalizeHist(gray_image) cv2.imshow('Equalized Image', equ_image) cv2.waitKey(0) cv2.destroyAllWindows()

8. Morphological Operations (using OpenCV):

   python

   kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) morph_image = cv2.morphologyEx(thresholded_image, cv2.MORPH_CLOSE, kernel) cv2.imshow('Morphological Operation', morph_image) cv2.waitKey(0) cv2.destroyAllWindows()

9. Affine Transformations (using OpenCV):

   python

   rows, cols = image.shape[:2] M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1) rotated_image = cv2.warpAffine(image, M, (cols, rows)) cv2.imshow('Rotated Image', rotated_image) cv2.waitKey(0) cv2.destroyAllWindows()

These are just a few examples of image operators and their implementations using Python libraries. The OpenCV and scikit-image libraries provide extensive documentation and tutorials for a wide range of image processing tasks. By combining different image operators, you can create more complex image processing pipelines to achieve your desired results.

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