pyimkernel 0.2.0

Apply image kernel(s) on a 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 gray scale 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

# 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)

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

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

# Show the filtered image
imkernel.imshow(image=filtered_image, cmap=plt.cm.gray)

Output

Before Applying the blur kernel on a gray scale image 9

After Applying the blur kernel on a gray scale image 9

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