imgmagic 0.2.3

No project description provided

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.