imgmagic 0.2.0

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Image Processing

Toolbox to perform image processing.

Libraries

• Install numpy, scipy, matplotlib, pillow.

Documentation

imgmagic.display.greyscale(x: numpy.ndarray)

Function that converts image into greyscale.    Parameters:      x : numpy.ndarray       Image to convert.    Returns:      output : numpy.ndarray       Returns the greyscale image.

imgmagic.geometric.crop(img:numpy.ndarray, x:Tuple[int], width:int, height:int)

Function that crops an image.    Parameters:      img : numpy.ndarray       Image to crop.      x : Tuple[int]       Coordiates of starting point.      width : int       Width of the cropped image.      height : int       Height of the cropped image.    Returns:      output : numpy.ndarray       Cropped image.

imgmagic.geometric.rotate(img:numpy.ndarray, degree:float)

Function that rotates an image.    Parameters:      img : numpy.ndarray       Image to rotate.      degree : int       Angle of rotaion (degrees). This parameter can be positive or negative.    Returns:      output : numpy.ndarray       Rotated image.

imgmagic.geometric.flipping(img:numpy.ndarray)

Function that flips an image.    Parameters:      img : numpy.ndarray       Image to rotate.    Returns:      output : numpy.ndarray       Flipped image.

imgmagic.geometric.scaling(img:numpy.ndarray, scale:float)

Function that scales an image.    Parameters:      img : numpy.ndarray       Image to scale.      scale : float       Factor of scaling (ex. 0.5, 1.25, 2...etc).    Returns:      output : numpy.ndarray       Scaled image.

imgmagic.geometric.reverse(img:numpy.ndarray)

Function that reverts an image.    Parameters:      img : numpy.ndarray       Image to reverse.    Returns:      output : numpy.ndarray       Reverted image.

imgmagic.histogram.adjustContrast(img:numpy.ndarray)

Function that performs contrast stretching.    Parameters:      img : numpy.ndarray       Input image.    Returns:      output : numpy.ndarray       Image (with contrat stretching).

imgmagic.histogram.equalization(img:numpy.ndarray)

Function that performs histogram equalization.    Parameters:      img : numpy.ndarray       Input image.    Returns:      output : numpy.ndarray       Image (with equalization).

imgmagic.histogram.negative(img:numpy.ndarray)

Function that performs computes digital negative of an image.     s = T( r ) = (L – 1) – r    Parameters:      img : numpy.ndarray       Input image.    Returns:      output : numpy.ndarray       Image (negative).

imgmagic.filter.GaussianNoise(img:numpy.ndarray, mean:float, sigma:float)

Function that adds gaussian noise to an image.    Parameters:      img : numpy.ndarray       Input image.      mean, sigma : float       Parameters for gaussian distribution.    Returns:      output : numpy.ndarray       Image (with noise).

imgmagic.filter.SaltnPepperNoise(img:numpy.ndarray, prob:float)

Function that adds salt and pepper noise.    Parameters:      img : numpy.ndarray       Input image.      prob : float       Probability for salting (ex. 0.01 ...etc).    Returns:      output : numpy.ndarray       Image (with noise).

imgmagic.filter.filtering(img:numpy.ndarray, types:str, mode:str, *args: Tuple[Any])

Function that perfoms filtering on an image.    Parameters:      img : numpy.ndarray       Input image.      types : {"lowpass", "highpass", "avg", "gauss"}       Type of filter.      mode : {"reflect", "constant", "nearest", "mirror", "wrap"}       Type of padding.      args : float       Extra arguments (optional). For instance, it can be:           n : size for avg filter    Returns:      output : numpy.ndarray       Image (filtered).

imgmagic.filter.sharpening(img:numpy.ndarray, types:str, mode:str)

Function that perfoms sharpening on an image.    Parameters:      img : numpy.ndarray       Input image.      types : {"laplacian", "sobel", "roberts"}       Type of filter.      mode : {"reflect", "constant", "wrap"}       Type of padding.    Returns:      output : numpy.ndarray       Image (filtered).

imgmagic.filter.crosscorrelation(img:numpy.ndarray, filters:str, sub:numpy.ndarray)

Function that find a subimage in an image.    Parameters:      img : numpy.ndarray       Input image.      filters : {"laplacian", "sobel", "roberts"}       Type of filter.      sub : numpy.ndarray       Subimage image.    Returns:      output : numpy.ndarray       Image (found).

imgmagic.fourier.ftfiltering(img:numpy.ndarray, filters:str, *args: Tuple[Any])

Function that perfoms sharpening on an image.    Parameters:      img : numpy.ndarray       Input image.      filters : {"lowpass", "blowpass", "glowpass", "highpass", "bhighpass", "ghighpass", "bandreject", "bandpass", "homomorphic"}       Type of filter.      args : float       Extra arguments (optional). For instance, it can be:           D0 : cutoff frequency for Ideal filter ("lowpass", "highpass")           D0, n : cutoff frequency, order for Butterworth filter ("blowpass", "bhighpass")           sigma : cutoff frequency for Gaussian filter ("glowpass", "ghighpass")    Returns:      output : numpy.ndarray       Image (filtered).

imgmagic.fourier.UnsharpMasking(img:numpy.ndarray, D0:int, k:int)

Function that performs Unsharp Masking and Highboost filtering.    Parameters:      img : numpy.ndarray       Input image.      k : float       Parameter:           k=1 - unsharp masking           k>1 - highboost filtering    Returns:      output : numpy.ndarray       Image.

imgmagic.fourier.HighFreqEmph(img:numpy.ndarray, D0:int, k1:int, k2:int)

Function that performs High-frequency emphasis.    Parameters:      img : numpy.ndarray       Input image.      k1, k2 : float       Parameters:           k1=1           k2=1 - unsharp masking           k2>1 - highboost filtering    Returns:      output : numpy.ndarray       Image.

Example

import imgmagic
import numpy
import matplotlib.image as mpimg
from PIL import Image

filename = "img.jpeg"
img1 = mpimg.imread(filename)
img2 = imgmagic.filter.filtering(img1, "avg", "wrap", 9)

im = Image.fromarray(img2)
im.save("new.jpeg")

see Image Processing toolbox on github.