21. Image Processing in Python with Matplotlib

from scipy import misc
import matplotlib.pyplot as plt

ascent = misc.ascent()
plt.gray()
plt.imshow(ascent)
plt.show()

from scipy import misc

ascent = misc.ascent()
import matplotlib.pyplot as plt
plt.axis("off") # removes the axis and the ticks
plt.gray()
plt.imshow(ascent)
plt.show()

ascent.dtype

ascent.shape

import scipy.misc
face = scipy.misc.face()
print(face.shape)
print(face.max)
print(face.dtype)
plt.axis("off")
plt.gray()
plt.imshow(face)
plt.show()

import matplotlib.pyplot as plt

img = plt.imread('frankfurt.png')

print(img[:3])

plt.axis("off")
imgplot = plt.imshow(img)

lum_img = img[:,:,1]
print(lum_img)

plt.axis("off")
imgplot = plt.imshow(lum_img)

windmills = plt.imread('windmills.png')

plt.axis("off")
plt.imshow(windmills)

import numpy as np
import matplotlib.pyplot as plt

def tint(imag, percent):
    """
    imag: the image which will be shaded
    percent: a value between 0 (image will remain unchanged
             and 1 (image will completely white)
    """
    tinted_imag = imag + (np.ones(imag.shape) - imag) * percent
    return tinted_imag

windmills = plt.imread('windmills.png')

tinted_windmills = tint(windmills, 0.8)
plt.axis("off")
plt.imshow(tinted_windmills)

import numpy as np
import matplotlib.pyplot as plt


def shade(imag, percent):
    """
    imag: the image which will be shaded
    percent: a value between 0 (image will remain unchanged
             and 1 (image will be blackened)
    """
    tinted_imag = imag * (1 - percent)
    return tinted_imag

windmills = plt.imread('windmills.png')

tinted_windmills = shade(windmills, 0.7)
plt.imshow(tinted_windmills)

def vertical_gradient_line(image, reverse=False):
    """
    We create a horizontal gradient line with the shape (1, image.shape[1], 3))
    The values are incremented from 0 to 1, if reverse is False,
    otherwise the values are decremented from 1 to 0.
    """
    number_of_columns = image.shape[1]
    if reverse:
        C = np.linspace(1, 0, number_of_columns)
    else:
        C = np.linspace(0, 1, number_of_columns)
    C = np.dstack((C, C, C))
    return C

horizontal_brush = vertical_gradient_line(windmills)
tinted_windmills =  windmills * horizontal_brush
plt.axis("off")
plt.imshow(tinted_windmills)

def vertical_gradient_line(image, reverse=False):
    """
    We create a horizontal gradient line with the shape (1, image.shape[1], 3))
    The values are incremented from 0 to 1, if reverse is False,
    otherwise the values are decremented from 1 to 0.
    """
    number_of_columns = image.shape[1]
    if reverse:
        C = np.linspace(1, 0, number_of_columns)
    else:
        C = np.linspace(0, 1, number_of_columns)
    C = np.dstack((C, C, C))
    return C

horizontal_brush = vertical_gradient_line(windmills, reverse=True)
tinted_windmills =  windmills * horizontal_brush
plt.axis("off")
plt.imshow(tinted_windmills)

def horizontal_gradient_line(image, reverse=False):
    """
    We create a vertical gradient line with the shape (image.shape[0], 1, 3))
    The values are incremented from 0 to 1, if reverse is False,
    otherwise the values are decremented from 1 to 0.
    """
    number_of_rows, number_of_columns = image.shape[:2]
    C = np.linspace(1, 0, number_of_rows)
    C = C[np.newaxis,:]
    C = np.concatenate((C, C, C)).transpose()
    C = C[:, np.newaxis]
    return C

vertical_brush = horizontal_gradient_line(windmills)
tinted_windmills =  windmills 
plt.imshow(tinted_windmills)

charlie = plt.imread('Chaplin.png')
plt.gray()
print(charlie)
plt.imshow(charlie)

colored = np.dstack((charlie*0.1, charlie*1, charlie*0.5))

plt.imshow(colored)

plt.cm.datad.keys()

import numpy as np
import matplotlib.pyplot as plt

charlie = plt.imread('Chaplin.png')

#  colormaps plt.cm.datad
# cmaps = set(plt.cm.datad.keys())
cmaps = {'afmhot', 'autumn', 'bone', 'binary', 'bwr', 'brg', 
         'CMRmap', 'cool', 'copper', 'cubehelix', 'Greens'}

X = [(4, 3, 1, (1, 0, 0)), 
     (4, 3, 2, (0.5, 0.5, 0)), 
     (4, 3, 3, (0, 1, 0)), 
     (4, 3, 4, (0, 0.5, 0.5)),  
     (4, 3, (5, 8), (0, 0, 1)), 
     (4, 3, 6, (1, 1, 0)), 
     (4, 3, 7, (0.5, 1, 0) ),
     (4, 3, 9, (0, 0.5, 0.5)),
     (4, 3, 10, (0, 0.5, 1)), 
     (4, 3, 11, (0, 1, 1)),
     (4, 3, 12, (0.5, 1, 1))]

fig = plt.figure(figsize=(6, 5))

#fig.subplots_adjust(bottom=0, left=0, top = 0.975, right=1)
for nrows, ncols, plot_number, factor in X:
    sub = fig.add_subplot(nrows, ncols, plot_number)
    sub.set_xticks([])
    
    sub.imshow(charlie*0.0002, cmap=cmaps.pop())
    sub.set_yticks([])