lizecillie

import matplotlib.pyplot as plt
import numpy as np

A = np.zeros((256, 256, 3))
for i in xrange(0, 255, 3):
    A[i, :, 0] = 255*np.ones((1, A.shape[1]))
    A[i+1, :, 1] = 255*np.ones((1, A.shape[1]))
    A[i+2, :, 2] = 255*np.ones((1, A.shape[1]))
A = np.dstack((A[:, :, 0], A[:, :, 1], A[:, :, 2]))

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io

def contrast_stretching(A, c1, c2, c3, c4):
    A = c1*np.tanh(c2*A - c3) + c4
    return A

A = io.imread('castle1.jpg', plugin='pil')

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, img_as_float

def salt_and_pepper_noise_gray(A, d):
    rows = np.shape(A)[0]
    columns = np.shape(A)[1]
    number_of_pixels = int(d*rows*columns)

lizecillie

from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, img_as_float


def median_filter(A, filter_size):
    rows = np.shape(A)[0]
    columns = np.shape(A)[1]
    half_length = int((filter_size-1)/2)

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io,color, img_as_float

def brighten(A):
    gamma = 0.4
    c = 255
    A = c*(A**gamma) 
    return A

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, color, img_as_float

def histogram_equalization(A):
    rows = A.shape[0]
    columns = A.shape[1]
    histogram, bin_edges = np.histogram(A, range=(0,1), bins=255)
    c = (np.cumsum(histogram/np.max(histogram)))*255

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io,color, img_as_float

def averaging_mask(A, mask_size):
    rows = np.shape(A)[0]
    columns = np.shape(A)[1]
    half_length = int((mask_size-1)/2)

lizecillie

def histogram_equalization_point_transformation(A):
    B = A.copy()
    P1 = B[:, :, 0]
    P2 = B[:, :, 1]
    P3 = B[:, :, 2]
    G = (np.sum(B * (1/3, 1/3, 1/3), axis=2))/255

    rows = G.shape[0]
    columns = G.shape[1]
    histogram, bin_edges = np.histogram(G.flatten(), range=(0, 1), bins=256)

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, color, img_as_float

def histogram_equalization(A):
    rows = A.shape[0]
    columns = A.shape[1]
    histogram, bin_edges = np.histogram(A.flatten(), range=(0, 1), bins=256)
    histogram = histogram/np.max(histogram)

lizecillie

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, color, img_as_float

def histogram_equalization(A):
    rows = A.shape[0]
    columns = A.shape[1]
    histogram, bin_edges = np.histogram(A.flatten(), range=(0, 1), bins=256)
    histogram = histogram/np.max(histogram)