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Pengolahan citra berwarna dan saluran warna

 

a. Membuat list peta warna

[in]

import numpy as np

import matplotlib.pyplot as plt

from matplotlib import cm

from matplotlib.colors import ListedColormap, LinearSegmentedColormap

 

viridis = cm.get_cmap('viridis', 8)

[in]

print(viridis(0.56))

[out]

 
(0.122312, 0.633153, 0.530398, 1.0)

 

[in]

def plot_examples(colormaps):

    """

    Helper function to plot data with associated colormap.

    """

    np.random.seed(19680801)

    data = np.random.randn(30, 30)

    n = len(colormaps)

    fig, axs = plt.subplots(1, n, figsize=(n * 2 + 2, 3),

                            constrained_layout=True, squeeze=False)

    for [ax, cmap] in zip(axs.flat, colormaps):

        psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)

        fig.colorbar(psm, ax=ax)

    plt.show()

[in]

cmap = ListedColormap(["darkorange", "gold", "lawngreen", "lightseagreen"])

plot_examples([cmap])

[out]

[in]

viridis = cm.get_cmap('viridis', 256)

newcolors = viridis(np.linspace(0, 1, 256))

pink = np.array([248/256, 24/256, 148/256, 1])

newcolors[:25, :] = pink

newcmp = ListedColormap(newcolors)

 

plot_examples([viridis, newcmp])

[out]

[in]

top = cm.get_cmap('Oranges_r', 128)

bottom = cm.get_cmap('Blues', 128)

 

newcolors = np.vstack((top(np.linspace(0, 1, 128)),

                       bottom(np.linspace(0, 1, 128))))

newcmp = ListedColormap(newcolors, name='OrangeBlue')

plot_examples([viridis, newcmp])

[out]

[in]

N = 256

vals = np.ones((N, 4))

vals[:, 0] = np.linspace(90/256, 1, N)

vals[:, 1] = np.linspace(40/256, 1, N)

vals[:, 2] = np.linspace(40/256, 1, N)

newcmp = ListedColormap(vals)

plot_examples([viridis, newcmp])

[out]

[in]

cdict = {'red':   [[0.0,  0.0, 0.0],

                   [0.5,  1.0, 1.0],

                   [1.0,  1.0, 1.0]],

         'green': [[0.0,  0.0, 0.0],

                   [0.25, 0.0, 0.0],

                   [0.75, 1.0, 1.0],

                   [1.0,  1.0, 1.0]],

         'blue':  [[0.0,  0.0, 0.0],

                   [0.5,  0.0, 0.0],

                   [1.0,  1.0, 1.0]]}

 

 

def plot_linearmap(cdict):

    newcmp = LinearSegmentedColormap('testCmap', segmentdata=cdict, N=256)

    rgba = newcmp(np.linspace(0, 1, 256))

    fig, ax = plt.subplots(figsize=(4, 3), constrained_layout=True)

    col = ['r', 'g', 'b']

    for xx in [0.25, 0.5, 0.75]:

        ax.axvline(xx, color='0.7', linestyle='--')

    for i in range(3):

        ax.plot(np.arange(256)/256, rgba[:, i], color=col[i])

    ax.set_xlabel('index')

    ax.set_ylabel('RGB')

    plt.show()

 

plot_linearmap(cdict)

[out]

[in]

colors = ["darkorange", "gold", "lawngreen", "lightseagreen"]

cmap1 = LinearSegmentedColormap.from_list("mycmap", colors)

[in]

nodes = [0.0, 0.4, 0.8, 1.0]

cmap2 = LinearSegmentedColormap.from_list("mycmap", list(zip(nodes, colors))) 

 

plot_examples([cmap1, cmap2])

[out]

 

b. Colormap Normalization

[in]

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.colors as colors

import matplotlib.cbook as cbook

from matplotlib import cm

 

N = 100

X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]

 

# Logarithmic

Z1 = np.exp(-X**2 - Y**2)

Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)

Z = Z1 + 50 * Z2

 

fig, ax = plt.subplots(2, 1)

 

pcm = ax[0].pcolor(X, Y, Z,

                   norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()),

                   cmap='PuBu_r', shading='auto')

fig.colorbar(pcm, ax=ax[0], extend='max')

 

pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r', shading='auto')

fig.colorbar(pcm, ax=ax[1], extend='max')

plt.show()

[out]

[in]

# Symmetric logarithmic

 

N = 100

X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]

Z1 = np.exp(-X**2 - Y**2)

Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)

Z = (Z1 - Z2) * 2

 

fig, ax = plt.subplots(2, 1)

 

pcm = ax[0].pcolormesh(X, Y, Z,

                       norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,

                                              vmin=-1.0, vmax=1.0, base=10),

                       cmap='RdBu_r', shading='auto')

fig.colorbar(pcm, ax=ax[0], extend='both')

 

pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z), shading='auto')

fig.colorbar(pcm, ax=ax[1], extend='both')

plt.show()

 

 

N = 100

X, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)]

Z1 = (1 + np.sin(Y * 10.)) * X**2

 

fig, ax = plt.subplots(2, 1, constrained_layout=True)

 

pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=0.5),

                       cmap='PuBu_r', shading='auto')

fig.colorbar(pcm, ax=ax[0], extend='max')

ax[0].set_title('PowerNorm()')

 

pcm = ax[1].pcolormesh(X, Y, Z1, cmap='PuBu_r', shading='auto')

fig.colorbar(pcm, ax=ax[1], extend='max')

ax[1].set_title('Normalize()')

plt.show()

 

 

import matplotlib.colors as colors

 

bounds = np.array([-0.25, -0.125, 0, 0.5, 1])

norm = colors.BoundaryNorm(boundaries=bounds, ncolors=4)

print(norm([-0.2, -0.15, -0.02, 0.3, 0.8, 0.99]))

 

 
[0 0 1 2 3 3]

 

 

 
# Discrete bounds
N = 100
X, Y = np.meshgrid(np.linspace(-3, 3, N), np.linspace(-2, 2, N))
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = ((Z1 - Z2) * 2)[:-1, :-1]
 
fig, ax = plt.subplots(2, 2, figsize=(8, 6), constrained_layout=True)
ax = ax.flatten()
 
# Default norm:
pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[0], orientation='vertical')
ax[0].set_title('Default norm')
 
# Even bounds give a contour-like effect:
bounds = np.linspace(-1.5, 1.5, 7)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')
ax[1].set_title('BoundaryNorm: 7 boundaries')
 
# Bounds may be unevenly spaced:
bounds = np.array([-0.2, -0.1, 0, 0.5, 1])
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[2].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')
ax[2].set_title('BoundaryNorm: nonuniform')
 
# With out-of-bounds colors:
bounds = np.linspace(-1.5, 1.5, 7)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256, extend='both')
pcm = ax[3].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
# The colorbar inherits the "extend" argument from BoundaryNorm.
fig.colorbar(pcm, ax=ax[3], orientation='vertical')
ax[3].set_title('BoundaryNorm: extend="both"')
plt.show()

 

 

 

c. Transformasi Jarak

[in]

import numpy as np

import mahotas

 

f = np.ones((256,256), bool)

f[200:,240:] = False

f[128:144,32:48] = False

 

[in]

from pylab import imshow, gray, show

import numpy as np

 

f = np.ones((256,256), bool)

f[200:,240:] = False

f[128:144,32:48] = False

 

gray()

imshow(f)

show()

[out]

[in]

import mahotas

dmap = mahotas.distance(f)

 

[in]

from __future__ import print_function

 

import pylab as p

import numpy as np

import mahotas

 

f = np.ones((256,256), bool)

f[200:,240:] = False

f[128:144,32:48] = False

 

dmap = mahotas.distance(f)

p.imshow(dmap)

p.show()

 

[out]

 

d. Konversi Ruang Warna

[in]

import mahotas as mh

lena = mh.demos.load('lena')

print(lena.shape)

 

[out]

(512, 512, 3)

 

[in]

import mahotas as mh

lena = mh.demos.load('lena')

lenag = mh.colors.rgb2grey(lena)

 

[in]

from pylab import imshow

import mahotas as mh

lena = mh.demos.load('lena')

lenag = mh.colors.rgb2grey(lena)

 

imshow(lenag)

[out]

[in]

lenas = mh.colors.rgb2sepia(lena)

 

from pylab import imshow

import mahotas as mh

lena = mh.demos.load('lena')

 

lenas = mh.colors.rgb2sepia(lena)

 

imshow(lenas)

 

[out]

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