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| 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:
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Image Loading and Display (using OpenCV):
pythonimport cv2 image_path = 'path_to_your_image.jpg' image = cv2.imread(image_path) cv2.imshow('Original Image', image) cv2.waitKey(0) cv2.destroyAllWindows() -
Grayscale Conversion (using OpenCV):
pythongray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow('Grayscale Image', gray_image) cv2.waitKey(0) cv2.destroyAllWindows() -
Image Filtering and Convolution (using OpenCV):
pythonimport 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() -
Image Blurring (using OpenCV):
pythonblurred_image = cv2.GaussianBlur(image, (5, 5), 0) cv2.imshow('Blurred Image', blurred_image) cv2.waitKey(0) cv2.destroyAllWindows() -
Image Thresholding (using OpenCV):
pythonret, thresholded_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY) cv2.imshow('Thresholded Image', thresholded_image) cv2.waitKey(0) cv2.destroyAllWindows() -
Edge Detection (using scikit-image):
pythonfrom skimage import filters edges = filters.sobel(gray_image) cv2.imshow('Edge Detection', edges) cv2.waitKey(0) cv2.destroyAllWindows() -
Histogram Equalization (using OpenCV):
pythonequ_image = cv2.equalizeHist(gray_image) cv2.imshow('Equalized Image', equ_image) cv2.waitKey(0) cv2.destroyAllWindows() -
Morphological Operations (using OpenCV):
pythonkernel = 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() -
Affine Transformations (using OpenCV):
pythonrows, 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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