Exercises 3¶
Part 1¶
- Import Numpy, matplotlib.pyplot and skimage.data
- From the data module import the moon picture
- Plot that image
- Check the image dimensions
- Calculate the image mean, max and min
- Create a mask of pixels with values above half of the max
- Create a cropped version of the image containing the crater on the top left
- Crop the mask as well
- Plot the cropped mask on top of the cropped image using transparency and the colormaps you want.
- Create a list of the pixels and count them
Part 2¶
- Find how to generate normally distributed numbers (within numpy.random module)
- Create a matrix of the same size as the cropped images with normally distributed values
- Plot it
- Add the random matrix to the cropped image, and plot it.
- Adjust the noise level by multiplying the random matrix with different factors (1, 10, 100). Plot the result
- From skimage.data load the rocket image
- Check its size
- Crop the rocket image to the same size as the moon image
- Replace the first channel of the cropped rocket image, with the image of the cropped moon
- Plot the result
Solutions 3¶
Part 1¶
In [1]:
import numpy as np
import matplotlib.pyplot as plt
import skimage.data
In [2]:
#load moon image
image = skimage.data.moon()
In [3]:
plt.imshow(image, cmap = 'gray')
plt.show()
In [4]:
#get shape of image
image.shape
Out[4]:
(512, 512)
In [5]:
#get infos about image values
In [6]:
np.mean(image)
Out[6]:
112.16957092285156
In [7]:
image.mean()
Out[7]:
112.16957092285156
In [8]:
image.max()
Out[8]:
255
In [9]:
image.min()
Out[9]:
0
In [10]:
maxval = image.max()
In [11]:
#create a mask of pixels higher than 0.5*(max value)
mask = image>0.5*maxval
In [12]:
plt.imshow(mask)
plt.show()
In [13]:
#crop an image region
image_crop = image[50:150,50:150]
mask_crop = mask[50:150,50:150]
In [14]:
#superpose image and mask
plt.imshow(image_crop,cmap = 'gray')
plt.imshow(mask_crop,cmap = 'Reds', alpha = 0.5)
plt.show()
In [15]:
#get only positive mask pixels
pixels = image_crop[mask_crop]
In [16]:
len(pixels)
Out[16]:
1072
Part 2¶
In [23]:
#find the function to generate normally distributed samples (Google is your best friend)
np.random.standard_normal?
Docstring: standard_normal(size=None) Draw samples from a standard Normal distribution (mean=0, stdev=1). Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- out : float or ndarray A floating-point array of shape ``size`` of drawn samples, or a single sample if ``size`` was not specified. Notes ----- For random samples from :math:`N(\mu, \sigma^2)`, use one of:: mu + sigma * np.random.standard_normal(size=...) np.random.normal(mu, sigma, size=...) See Also -------- normal : Equivalent function with additional ``loc`` and ``scale`` arguments for setting the mean and standard deviation. Examples -------- >>> np.random.standard_normal() 2.1923875335537315 #random >>> s = np.random.standard_normal(8000) >>> s array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, # random -0.38672696, -0.4685006 ]) # random >>> s.shape (8000,) >>> s = np.random.standard_normal(size=(3, 4, 2)) >>> s.shape (3, 4, 2) Two-by-four array of samples from :math:`N(3, 6.25)`: >>> 3 + 2.5 * np.random.standard_normal(size=(2, 4)) array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random Type: builtin_function_or_method
In [24]:
image_crop.shape[1]
Out[24]:
100
In [26]:
#generate a matrix of the right size
normal_matrix = np.random.standard_normal((image_crop.shape[0], image_crop.shape[1]))
In [27]:
plt.imshow(normal_matrix)
plt.show()
In [28]:
im1 = image_crop +100* normal_matrix
plt.imshow(im1)
plt.show()
In [29]:
im1 = image_crop + 10*normal_matrix
plt.imshow(im1)
plt.show()
In [30]:
rocket = skimage.data.rocket()
In [31]:
rocket.shape
Out[31]:
(427, 640, 3)
In [32]:
rocket_cropped = rocket[50:150,50:150,:]
In [33]:
rocket_cropped.shape
Out[33]:
(100, 100, 3)
In [34]:
rocket_cropped[:,:,0] = image_crop
In [35]:
plt.imshow(rocket_cropped)
plt.show()
In [ ]: