pyimkernel 0.8.0

Applying some image kernel(s) on a grayscale or color scale image

Pyimkernel

With this package, You can apply various image kernels such as Blur, Sobel, Scharr and so forth (The comprehensive list of image kernels is mentioned below) on a grayscale or color-scale image, and then show them. These effects and enhancements in digital images can be achieved using the "ApplyKernels" class, allowing for a wide range of transformations.

Image kernels

Image kernels are listed below:

• blur
• bottom sobel
• emboss
• identity
• left sobel
• outline
• right sobel
• sharpen
• top sobel
• horizontal edge
• vertical edge
• box blur
• laplacian
• prewitt horizontal edge
• prewitt vertical edge
• high-pass filter
• unsharp masking
• dilate
• soften
• scharr horizontal edge
• scharr vertical edge
• motion blur

Installation

pip install pyimkernel

Usage

from pyimkernel import ApplyKernels
import mnist # pip install mnist
import cv2
import os

# Load data
X_train, X_test, y_train, y_test = mnist.train_images(), mnist.test_images(), mnist.train_labels(), mnist.test_labels()

# Create an instance
imkernel = ApplyKernels(random_seed=0)

# Grayscale
# Show image 9 
imkernel.imshow(X_train[19], cmap=plt.cm.gray)

# Apply blur kernel on a grayscale image 9
filtered_image = imkernel.apply_filter_on_gray_img(X_train[19], kernel_name='blur')

# Show the filtered image 9
imkernel.imshow(image=filtered_image, cmap='gray')

# the Color-Scale image
# Read the flower image
image1 = cv2.imread(os.path.join('Images', '1.jpg'))

# Show the flower image
# Convert a color-scale image to a grayscale one and then visualize it
imkernel.imshow(cv2.cvtColor(image1, cv2.COLOR_BGRA2GRAY), cmap='gray', figsize=(6, 6))

# Show the filtered flower image
blurred_image = imkernel.apply_filter_on_color_img(image1, kernel_name='box blur', with_resize=True, dsize=(100, 100))
imkernel.imshow(image=blurred_image)

sharpened_image = imkernel.apply_filter_on_color_img(image1, kernel_name='sharpen', with_resize=True)
imkernel.imshow(image=sharpened_image, figsize=(6, 6))

imkernel.imshow(image=imkernel.apply_filter_on_color_img(sharpened_image, kernel_name='soften'),
                figsize=(6, 6))

The Grayscale Output

Before Applying the Blur Kernel on a grayscale image 9

After Applying the Blur Kernel on a grayscale image 9

The Color Scale Output

Before Applying the Box Blur Kernel on a color-scale flower image

After Applying the Box Blur Kernel on a color-scale flower image and assigning True to the with_resize parameter and (100, 100) to the dsize parameter

After Applying the Sharpen Kernel on a color-scale flower image and assigning True to the with_resize parameter and 'auto' to the dsize parameter

After Applying the Soften Kernel on a color-scale image, which was filtered using the Sharpen Blur Kernel before, and assigning True to the with_resize parameter

Package Status

All Tests Passed.

Maintainer Contact

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