It has never as easy as it is nowadays to take a picture. All it usually needs is a mobile phone. These are the bare essentials to shoot and to view an image. Taking a photograph is free, if we don't take the costs for the mobile phone into considerations. Just a generation ago, hobby artists and real artists needed special and often expensive and the costs per picture were far from being free.
We take pictures to preserve great moments in time. Pickled memories ready to be "opened" in the future at will.
Similar to pickling things, we have to pay attention to the right preservatives. Of course, mobile phone also provide us with a range of image processing software, but as soon as we need to manipulate a huge quantity of photographs we need other tools. This is when programming and Python comes into play. Python and its modules like Numpy, Scipy, Matplotlib and other special modules provide the optimal functionality to be able to cope with the flood of pictures.
To provide you with the necessary knowledge this chapter of our Python tutorial deals with basic image processing and manipulation. For this purpose we use the modules NumPy, Matplotlib and SciPy.
We start with the scipy package misc. The helpfile says that scipy.misc contains "various utilities that don't have another home".
# the following line is only necessary in Python notebook: %matplotlib inline from scipy import misc lena = misc.lena() import matplotlib.pyplot as plt plt.gray() plt.imshow(lena) plt.show()
Additionally to the image, we can see the axis with the ticks. This may be very interesting, if you need some orientations about the size and the pixel position, but in most cases, you want to see the image without this information. We can get rid of the ticks and the axis by adding the command
from scipy import misc lena = misc.lena() import matplotlib.pyplot as plt plt.axis("off") # removes the axis and the ticks plt.gray() plt.imshow(lena) plt.show()
We can see that the type of this image is an integer array:
lena.dtypeWir erhalten die folgende Ergebnisse:
We can also check the size of the image:
lena.shapeDer obige Code führt zu folgendem Ergebnis:
The misc package contains an image of a racoon as well:
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()
(768, 1024, 3) <built-in method max of numpy.ndarray object at 0x2ca42b0> uint8
import matplotlib.pyplot as plt import matplotlib.image as mpimg
Only png images are supported by matplotlib
[[[ 0.41176471 0.56862748 0.80000001] [ 0.40392157 0.56078434 0.79215688] [ 0.40392157 0.56862748 0.79607844] ..., [ 0.48235294 0.62352943 0.81960785] [ 0.47843137 0.627451 0.81960785] [ 0.47843137 0.62352943 0.82745099]] [[ 0.40784314 0.56470591 0.79607844] [ 0.40392157 0.56078434 0.79215688] [ 0.40392157 0.56862748 0.79607844] ..., [ 0.48235294 0.62352943 0.81960785] [ 0.47843137 0.627451 0.81960785] [ 0.48235294 0.627451 0.83137256]] [[ 0.40392157 0.56862748 0.79607844] [ 0.40392157 0.56862748 0.79607844] [ 0.40392157 0.56862748 0.79607844] ..., [ 0.48235294 0.62352943 0.81960785] [ 0.48235294 0.62352943 0.81960785] [ 0.48627451 0.627451 0.83137256]]]
plt.axis("off") imgplot = plt.imshow(img)
lum_img = img[:,:,1] print(lum_img)
[[ 0.56862748 0.56078434 0.56862748 ..., 0.62352943 0.627451 0.62352943] [ 0.56470591 0.56078434 0.56862748 ..., 0.62352943 0.627451 0.627451 ] [ 0.56862748 0.56862748 0.56862748 ..., 0.62352943 0.62352943 0.627451 ] ..., [ 0.31764707 0.32941177 0.32941177 ..., 0.30588236 0.3137255 0.31764707] [ 0.31764707 0.3137255 0.32941177 ..., 0.3019608 0.32156864 0.33725491] [ 0.31764707 0.3019608 0.33333334 ..., 0.30588236 0.32156864 0.33333334]]
plt.axis("off") imgplot = plt.imshow(lum_img)
Now, we will show how to tint an image. Tint is an expression from colour theory and an often used technique by painters. Thinking about painters and not think about the Netherlands is hard to imagine. So we will use a picture with Dutch windmills in our next example. (The image has been taken at Kinderdijk, a village in the Netherlands, about 15 km east of Rotterdam and about 50 kilometres from Den Haag (The Hague). It's a UNESCO World Heritage Site since 1997.)
windmills = mpimg.imread('windmills.png') plt.axis("off") plt.imshow(windmills) plt.imshow(windmills)Der obige Code liefert folgendes Ergebnis:
<matplotlib.image.AxesImage at 0x7f81410ba9e8>
We want to tint the image now. This means we will "mix" our colours with white. This will increase the lightness of our image. For this purpose, we write a Python function, which takes an image and a percentage value as a parameter. Setting 'percentage' to 0 will not change the image, setting it to one means that the image will be completely whitened:
import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg 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 = mpimg.imread('windmills.png') tinted_windmills = tint(windmills, 0.8) plt.axis("off") plt.imshow(tinted_windmills) plt.imshow(tinted_windmills)Der obige Python-Code liefert folgendes Ergebnis:
<matplotlib.image.AxesImage at 0x7f81412718d0>
A shade is the mixture of a color with black, which reduces lightness.
import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg 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 = mpimg.imread('windmills.png') tinted_windmills = shade(windmills, 0.7) plt.imshow(tinted_windmills)Wir erhalten die folgende Ausgabe:
<matplotlib.image.AxesImage at 0x7f814124a128>
def vertical_gradient_line(image, reverse=False): """ We create a horizontal gradient line with the shape (1, image.shape, 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 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)Führt man obigen Code aus, erhält man Folgendes:
<matplotlib.image.AxesImage at 0x7f8141221cc0>