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Images in Python





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%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

Working with images in python is essentially a visual way of working with 2-d arrays (matrices)

Matrices

$$ \left[ \begin{array}{ccc} 1 & 3 & 5 \\ 2 & 5 & 1 \\ 2 & 3 & 8 \end{array} \right] $$ 




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my_matrix = np.array([[1,3,5],[2,5,1],[2,3,8]])

print(my_matrix)

All of the normal numpy commands work with matrices (of any dimension)





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my_matrix.mean()        # mean of the whole matrix

You can work over just the rows or columns of the matrix

  • axis = 0 (COLUMNS)
  • axis = 1 (ROWS)




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my_matrix.mean(axis=0)   # mean of the columns



    











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my_matrix.mean(axis=0)[0]  # mean of the 0th column



    











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np.mean(my_matrix, axis=0)  # alternative



    











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my_matrix.mean(axis=1)   # mean of the rows



    











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my_matrix.flatten()      # convert to 1D (useful for some plotting)

imshow will display 2-d arrays as images





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plt.style.use('ggplot')
plt.rc('axes', grid=False)   # turn off the background grid for images



    











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plt.imshow(my_matrix, interpolation='nearest', cmap=plt.cm.Blues);

Read in some data





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test_image = np.load("./MyData/test_data.npy")    # load in a saved numpy array



    











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test_image.ndim, test_image.shape, test_image.dtype



    











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plt.imshow(test_image, cmap=plt.cm.gray);



    











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print("The minimum value of the image is {0:.2f}".format(test_image.min()))
print("The maximum value of the image is {0:.2f}".format(test_image.max()))
print("The mean value of the image is {0:.2f}".format(test_image.mean()))
print("The standard deviation of the image is {0:.2f}".format(test_image.std()))



    











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plt.hist(test_image.flatten(),bins=30);    #flatten array to get histogram of whole image

Math on images applies to every value (pixel)





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another_test_image = test_image + 8

print("The minimum value of the other image is {0:.2f}".format(another_test_image.min()))
print("The maximum value of the other image is {0:.2f}".format(another_test_image.max()))
print("The mean value of the other image is {0:.2f}".format(another_test_image.mean()))
print("The standard deviation of the other image is {0:.2f}".format(another_test_image.std()))

Show the image represenation of test_image with a colorbar





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plt.imshow(test_image, cmap=plt.cm.gray)
plt.colorbar();

Colormap reference: http://matplotlib.org/examples/color/colormaps_reference.html





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fig, ax = plt.subplots(1,5,sharey=True)

fig.set_size_inches(12,6)

fig.tight_layout()

ax[0].imshow(test_image, cmap=plt.cm.viridis)
ax[0].set_xlabel('viridis')

ax[1].imshow(test_image, cmap=plt.cm.hot)
ax[1].set_xlabel('hot')

ax[2].imshow(test_image, cmap=plt.cm.magma)
ax[2].set_xlabel('magma')

ax[3].imshow(test_image, cmap=plt.cm.spectral)
ax[3].set_xlabel('spectral')

ax[4].imshow(test_image, cmap=plt.cm.gray)
ax[4].set_xlabel('gray');

WARNING! Common image formats DO NOT preserve dynamic range of original data!!

  • Common image formats: jpg, gif, png, tiff
  • Common image formats will re-scale your data values to [0:1]
  • Common image formats are NOT suitable for scientific data!




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plt.imsave('Splash.png', test_image, cmap=plt.cm.gray)     # Write the array I to a PNG file

my_png = plt.imread('Splash.png')                          # Read in the PNG file

print("The original data has a min = {0:.2f} and a max = {1:.2f}".format(test_image.min(), test_image.max()))

print("The PNG file has a min = {0:.2f} and a max = {1:.2f}".format(my_png.min(), my_png.max()))

Creating images from math





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X = np.linspace(-5, 5, 500)
Y = np.linspace(-5, 5, 500)

X, Y = np.meshgrid(X, Y)     # turns two 1-d arrays (X, Y) into one 2-d grid

Z = np.sqrt(X**2+Y**2)+np.sin(X**2+Y**2)

Z.min(), Z.max(), Z.mean()

Fancy Image Display





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from matplotlib.colors import LightSource

ls = LightSource(azdeg=0,altdeg=40)
shadedfig = ls.shade(Z,plt.cm.copper)

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

fig.set_size_inches(8,4)

fig.tight_layout()

ax[0].imshow(shadedfig)

contlevels = [1,2,Z.mean()]

ax[1].imshow(shadedfig)
ax[1].contour(Z,contlevels);

Reading in images (imread) - Common Formats





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my_doctor = plt.imread('./MyData/doctor5.png')

print("The image my_doctor has a shape [height,width] of {0}".format(my_doctor.shape))
print("The image my_doctor is made up of data of type {0}".format(my_doctor.dtype))
print("The image my_doctor has a maximum value of {0}".format(my_doctor.max()))
print("The image my_doctor has a minimum value of {0}".format(my_doctor.min()))



    











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plt.imshow(my_doctor,cmap=plt.cm.gray);

Images are just arrays that can be sliced.

  • ### For common image formats the origin is the upper left hand corner




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fig, ax = plt.subplots(1,4)
fig.set_size_inches(12,6)

fig.tight_layout()

# You can show just slices of the image - Rememeber: The origin is the upper left corner

ax[0].imshow(my_doctor, cmap=plt.cm.gray)
ax[0].set_xlabel('Original')

ax[1].imshow(my_doctor[0:300,0:100], cmap=plt.cm.gray)
ax[1].set_xlabel('[0:300,0:100]')                 # 300 rows, 100 columns

ax[2].imshow(my_doctor[:,0:100], cmap=plt.cm.gray)       # ":" = whole range
ax[2].set_xlabel('[:,0:100]')                     # all rows, 100 columns

ax[3].imshow(my_doctor[:,::-1], cmap=plt.cm.gray);
ax[3].set_xlabel('[:,::-1]') ;                     # reverse the columns



    











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fig, ax = plt.subplots(1,2)
fig.set_size_inches(12,6)

fig.tight_layout()

CutLine = 300

ax[0].imshow(my_doctor, cmap=plt.cm.gray)
ax[0].hlines(CutLine, 0, 194, color='b', linewidth=3)

ax[1].plot(my_doctor[CutLine,:], color='b', linewidth=3)
ax[1].set_xlabel("X Value")
ax[1].set_ylabel("Pixel Value")

Simple image manipulation





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from scipy import ndimage



    











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fig, ax = plt.subplots(1,5)
fig.set_size_inches(14,6)

fig.tight_layout()

ax[0].imshow(my_doctor, cmap=plt.cm.gray)

my_doctor_2 = ndimage.rotate(my_doctor,45,cval=0.75)               # cval is the value to set pixels outside of image
ax[1].imshow(my_doctor_2, cmap=plt.cm.gray)                 # Rotate and reshape

my_doctor_3 = ndimage.rotate(my_doctor,45,reshape=False,cval=0.75) # Rotate and do not reshape
ax[2].imshow(my_doctor_3, cmap=plt.cm.gray)

my_doctor_4 = ndimage.shift(my_doctor,(10,30),cval=0.75)           # Shift image      
ax[3].imshow(my_doctor_4, cmap=plt.cm.gray)

my_doctor_5 = ndimage.gaussian_filter(my_doctor,5)                # Blur image
ax[4].imshow(my_doctor_5, cmap=plt.cm.gray);

ndimage can do much more: http://scipy-lectures.github.io/advanced/image_processing/

Pseudocolor - All color astronomy images are fake.

Color images are composed of three 2-d images:

JPG images are 3-d, even grayscale images





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redfilter = plt.imread("./MyData/sphereR.jpg")

redfilter.shape,redfilter.dtype

We just want to read in one of the three channels





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redfilter = plt.imread("./MyData/sphereR.jpg")[:,:,0]

redfilter.shape,redfilter.dtype



    











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plt.imshow(redfilter,cmap=plt.cm.gray);



    











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greenfilter = plt.imread("./MyData/sphereG.jpg")[:,:,0]
bluefilter = plt.imread("./MyData/sphereB.jpg")[:,:,0]



    











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fig, ax = plt.subplots(1,3)
fig.set_size_inches(12,3)

fig.tight_layout()

ax[0].set_title("Red Filter")
ax[1].set_title("Green Filter")
ax[2].set_title("Blue Filter")

ax[0].imshow(redfilter,cmap=plt.cm.gray)
ax[1].imshow(greenfilter,cmap=plt.cm.gray)
ax[2].imshow(bluefilter,cmap=plt.cm.gray);

Need to create a blank 3-d array to hold all of the images





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rgb = np.zeros((480,640,3),dtype='uint8')

print(rgb.shape, rgb.dtype)

plt.imshow(rgb,cmap=plt.cm.gray);

Fill the array with the filtered images





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rgb[:,:,0] = redfilter
rgb[:,:,1] = greenfilter
rgb[:,:,2] = bluefilter



    











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fig, ax = plt.subplots(1,4)
fig.set_size_inches(14,3)

fig.tight_layout()

ax[0].set_title("Red Filter")
ax[1].set_title("Green Filter")
ax[2].set_title("Blue Filter")
ax[3].set_title("All Filters Stacked")

ax[0].imshow(redfilter,cmap=plt.cm.gray)
ax[1].imshow(greenfilter,cmap=plt.cm.gray)
ax[2].imshow(bluefilter,cmap=plt.cm.gray)
ax[3].imshow(rgb,cmap=plt.cm.gray);



    











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print("The image rgb has a shape [height,width] of {0}".format(rgb.shape))
print("The image rgb is made up of data of type {0}".format(rgb.dtype))
print("The image rgb has a maximum value of {0}".format(rgb.max()))
print("The image rgb has a minimum value of {0}".format(rgb.min()))



    











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rgb[:,:,0] = redfilter * 1.5

plt.imshow(rgb)

Content source: UWashington-Astro300/Astro300-A17

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