pyimkernel 0.5.0

Applying some image kernel(s) on an image

Pyimkernel

With this package, You can apply various image kernels such as Blur, Sobel, Scharr and so forth (The list of image kernels is mentioned below) on a grayscale or color image, and show images using the class ApplyKernels in this package to reach a wide range of effects and enhancements in digital images.

Installation

pip install pyimkernel

Usage

from pyimkernel import ApplyKernels
import mnist
import cv2

# 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')

# Color Scale
# Read the flower image
image1 = cv2.imread('examples/Images/1.jpg')

# Show the flower image
imkernel.imshow(image1.reshape(image1.shape[0], -1), cmap='gray', figsize=(20, 10))

# Show the filtered flower image
imkernel.imshow(image=imkernel.apply_filter_on_color_img(image1, kernel_name='laplacian', with_resize=True),
                figsize=(7, 6), cmap='gray')

Grayscale Output

Before Applying the blur kernel on a grayscale image 9

After Applying the blur kernel on a grayscale image 9

Color Scale Output

Before Applying the laplacian kernel on a color scale flower image

After Applying the laplacian kernel on a color scale flower image and assigning True to the with_resize parameter

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