# Arrays and images¶

The main point of this class is to show that you can consider arrays as images, and images as arrays.

Let’s make an array of numbers between 0 through 99:

an_array = np.array([[ 0,  0,  0,  0,  0,  0,  0,  0],
[ 0,  0,  0,  9, 99, 99, 94,  0],
[ 0,  0,  0, 25, 99, 99, 79,  0],
[ 0,  0,  0,  0,  0,  0,  0,  0],
[ 0,  0,  0, 56, 99, 99, 49,  0],
[ 0,  0,  0, 73, 99, 99, 31,  0],
[ 0,  0,  0, 91, 99, 99, 13,  0],
[ 0,  0,  9, 99, 99, 94,  0,  0],
[ 0,  0, 27, 99, 99, 77,  0,  0],
[ 0,  0, 45, 99, 99, 59,  0,  0],
[ 0,  0, 63, 99, 99, 42,  0,  0],
[ 0,  0, 80, 99, 99, 24,  0,  0],
[ 0,  1, 96, 99, 99,  6,  0,  0],
[ 0, 16, 99, 99, 88,  0,  0,  0],
[ 0,  0,  0,  0,  0,  0,  0,  0]])
an_array.shape


In fact this array represents a monochrome picture of a letter.

As we’ve seen, we can show arrays as images using the plt.imshow command.

plt.imshow(an_array)


The image looks rather blurry. This is because matplotlib is drawing an image with many more pixels than the array has values. For the pixels in-between array values, matplotlib is using interpolation to estimate a good value. I suggest you always turn off interpolation like this:

plt.imshow(an_array, interpolation='nearest')


The image is weirdly colorful. That is because matplotlib is using the default colormap. A colormap is a mapping from values in the array to colors. In this case the default colormap is called jet and maps low numbers in the image (0 in our case) to blue, and high numbers (99 in our case) to red.

We can see what the colormap is doing by asking for a color bar:

plt.imshow(an_array, interpolation='nearest')
plt.colorbar()


In our case, our image would make more sense as grayscale, so we use the gray colormap, like this:

plt.imshow(an_array, interpolation='nearest', cmap=plt.cm.gray)
plt.colorbar()


We can specify the colormap with a string, if we know it. This gives the same output as the command above:

plt.imshow(an_array, interpolation='nearest', cmap='gray')
plt.colorbar()


A grayscale image is an array containing numbers giving the pixel intensity values - in our case between 0 and 99.

We can also plot lines from the array. For example, we might want to plot row 8 out of this array (the 9th row):

plt.plot(an_array[8])


The x axis is the position in the array (0 through 7) and the y axis is the value of the array row at that position.

The plot shows us the 0 values at the edges of the bar of the “i”, an the ramp up to the peak at the middle of the bar of the “i”, in columns number 3 and 4.

A transpose in numpy uses the .T method on the array. This has the effect of flipping the rows and columns (in 2D):

an_array.T

plt.imshow(an_array.T, interpolation='nearest', cmap='gray')


We can also reshape the original array to a 1D array, by stacking all the rows end to end:

n_pixels = an_array.shape[0] * an_array.shape[1]
a_1d_array = np.reshape(an_array, (n_pixels,))
a_1d_array

a_1d_array.shape


Reshaping the array to one dimension is a common operation, so there is a separate numpy command for that, np.ravel:

np.ravel(an_array)


One use of the 1D version of the array, is for getting a histogram of the distribution of values in the array:

plt.hist(a_1d_array)


By default, the plt.hist function uses 50 bins, but you can specify how many bins you want with the bins keyword:

plt.hist(a_1d_array, bins=75)


As you can imagine, it’s easy to go back to the 2D shape, by splitting the 1D array back into 15 rows of 8 values each (and therefore 8 columns):

array_back = np.reshape(a_1d_array, (15, 8))
array_back

plt.imshow(array_back, interpolation='nearest', cmap='gray')


In numpy, basic operations like multiplication, addition, comparison, are always elementwise. For example, this multiplies every array value by 10:

an_array * 10


Comparison is also elementwise. For example, this gives True for every value > 50, and False for every value <= 50:

an_array > 50


Matplotlib will treat False as 0 and True as 1, so this is one way of binarizing the image at a threshold (of 50 in this case):

plt.imshow(an_array > 50, interpolation='nearest', cmap='gray')


We can slice arrays as we slice strings or lists. The difference for arrays is that we can slice in any or all dimensions at the same time. For example, to get the dot of the “i” it looks (from the numbers at the sides of the plot) that we want to the top 4 rows, and the last 5 columns:

an_array[0:4, 3:]

plt.imshow(an_array[0:4, 3:], interpolation='nearest', cmap='gray')


## Some final notes for the exercises¶

Converting strings to floating point values:

float('1.34')


This is integer division:

1 // 2


This is reading the lines of a text file into a list:

fobj = open('myfile.txt', 'rt')  # 'rt' = Read Text mode