pyimkernel 1.1.0

Applying some image kernel(s) on a grayscale or RGB 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 RGB 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
imkernel.imshow(cv2.cvtColor(image1, cv2.COLOR_BGR2RGB), 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=cv2.cvtColor(blurred_image, cv2.COLOR_BGR2RGB))

sharpened_image = imkernel.apply_filter_on_color_img(image1, kernel_name='sharpen', with_resize=True)
imkernel.imshow(image=cv2.cvtColor(sharpened_image, cv2.COLOR_BGR2RGB), figsize=(6, 6))

imkernel.imshow(image=cv2.cvtColor(imkernel.apply_filter_on_color_img(sharpened_image, kernel_name='soften'),
                                   cv2.COLOR_BGR2RGB), figsize=(6, 6))

imkernel.imshow(image=cv2.cvtColor(imkernel.apply_filter_on_color_img(sharpened_image, kernel_name='unsharp masking'),
                                   cv2.COLOR_BGR2RGB), figsize=(6, 6))

The Grayscale Output

Before Applying a Blur Kernel on a grayscale image 9

After Applying a Blur Kernel on a grayscale image 9

The Color Scale Output

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

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

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

After Applying an Unsharp Masking Kernel on a color-scale image, which was filtered using the Sharpen Kernel before, and assigning False to the with_resize parameter

Package Status

All Tests Passed Successfully.

Maintainer Contact

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