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‎module03/exercises/m03ex00.tex

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\chapter{Exercise 00}
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\extitle{NumPyCreator}
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\turnindir{ex00}
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\exnumber{00}
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\exfiles{NumPyCreator.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================== %
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\section*{Objective}
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% ---------------------------------- %
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Introduction to the Numpy library.
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% ================================== %
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\section*{Instructions}
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% ---------------------------------- %
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Write a class named \texttt{NumPyCreator}, that implements all of the following methods.\\
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\\
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Each method receives as an argument a different type of data structure and transforms it into a Numpy array:
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\begin{itemize}
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\item \texttt{from\_list(self, lst)}: takes a list or nested lists and returns its corresponding Numpy array.
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\item \texttt{from\_tuple(self, tpl)}: takes a tuple or nested tuples and returns its corresponding Numpy array.
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\item \texttt{from\_iterable(self, itr)}: takes an iterable and returns an array which contains all of its elements.
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\item \texttt{from\_shape(self, shape, value)}: returns an array filled with the same value.
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The first argument is a tuple which specifies the shape of the array, and the second argument specifies the value of the elements.
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This value must be 0 by default.
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\item \texttt{random(self, shape)}: returns an array filled with random values.
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It takes as an argument a tuple which specifies the shape of the array.
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\item \texttt{identity(self, n)}: returns an array representing the identity matrix of size n.
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\end{itemize}
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\textit{\textbf{BONUS:}} Add to these methods an optional argument which specifies the datatype (dtype) of the array (e.g. to represent its elements as integers, floats, ...)
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\hint{Each of these methods can be implemented in one line. You only need to find the right Numpy functions.}
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% ================================== %
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\section*{Examples}
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% ---------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from NumpyCreator import NumpyCreator
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npc = NumpyCreator()
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npc.from_list([[1,2,3],[6,3,4]])
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# Output :
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array([[1, 2, 3],
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[6, 3, 4]])
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npc.from_list([[1,2,3],[6,4]])
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# Output :
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None
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npc.from_list([[1,2,3],['a','b','c'],[6,4,7]])
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# Output :
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array([['1','2','3'],
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['a','b','c'],
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['6','4','7'], dtype='<U21'])
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npc.from_list(((1,2),(3,4)))
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# Output :
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None
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npc.from_tuple(("a", "b", "c"))
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# Output :
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array(['a', 'b', 'c'])
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npc.from_tuple(["a", "b", "c"])
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# Output :
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None
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npc.from_iterable(range(5))
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# Output :
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array([0, 1, 2, 3, 4])
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shape=(3,5)
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npc.from_shape(shape)
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# Output :
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array([[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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npc.random(shape)
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# Output :
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array([[0.57055863, 0.23519999, 0.56209311, 0.79231567, 0.213768 ],
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[0.39608366, 0.18632147, 0.80054602, 0.44905766, 0.81313615],
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[0.79585328, 0.00660962, 0.92910958, 0.9905421 , 0.05244791]])
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npc.identity(4)
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# Output :
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array([[1., 0., 0., 0.],
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[0., 1., 0., 0.],
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[0., 0., 1., 0.],
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[0., 0., 0., 1.]])
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\end{minted}

‎module03/exercises/m03ex01.tex

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\chapter{Exercise 01}
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\extitle{ImageProcessor}
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\turnindir{ex01}
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\exnumber{01}
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\exfiles{ImageProcessor.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================= %
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\section*{Objective}
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% --------------------------------- %
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Basic manipulation of images via the Matplotlib library.
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% ================================= %
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\section*{Instructions}
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% --------------------------------- %
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Build a tool that will be helpful to load and display images in the upcoming exercises.\\
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\\
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Write a class named \texttt{ImageProcessor} that implements the following methods:
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\begin{itemize}
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\item \texttt{load(path)}: opens the PNG file specified by the \texttt{path} argument and returns an array with the RGB values of the pixels in the image. It must display a message specifying the dimensions of the image (e.g. 340 x 500).
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\item \texttt{display(array)}: takes a numpy array as an argument and displays the corresponding RGB image.
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\end{itemize}
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You must handle these errors: if the file passed as argument does not exist or if it can't be read as an image, with an appropriate message of your choice.
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\hint{You can use the library of your choice for this exercise, but converting the image to a numpy array is mandatory.
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The goal of this exercise is to dispense with the technicality of loading and displaying images,
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so that you can focus on array manipulations in the upcoming exercises.
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}
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% ================================= %
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\section*{Examples}
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% --------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from ImageProcessor import ImageProcessor
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imp = ImageProcessor()
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arr = imp.load("non_existing_file.png")
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# Output :
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Exception: FileNotFoundError -- strerror: No such file or directory
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print(arr)
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# Output :
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None
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arr = imp.load("empty_file.png")
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# Output :
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Exception: OSError -- strerror: None
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print(arr)
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# Output :
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None
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arr = imp.load("../resources/42AI.png")
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# Output :
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Loading image of dimensions 200 x 200
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arr
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# Output :
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array([[[0.03529412, 0.12156863, 0.3137255 ],
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[0.03921569, 0.1254902 , 0.31764707],
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[0.04313726, 0.12941177, 0.3254902 ],
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...,
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[0.02745098, 0.07450981, 0.22745098],
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[0.02745098, 0.07450981, 0.22745098],
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[0.02352941, 0.07058824, 0.22352941]],
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[[0.03921569, 0.11764706, 0.30588236],
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[0.03529412, 0.11764706, 0.30980393],
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[0.03921569, 0.12156863, 0.30980393],
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...,
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[0.02352941, 0.07450981, 0.22745098],
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[0.02352941, 0.07450981, 0.22745098],
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[0.02352941, 0.07450981, 0.22745098]],
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[[0.03137255, 0.10980392, 0.2901961 ],
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[0.03137255, 0.11372549, 0.29803923],
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[0.03529412, 0.11764706, 0.30588236],
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...,
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[0.02745098, 0.07450981, 0.23137255],
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[0.02352941, 0.07450981, 0.22745098],
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[0.02352941, 0.07450981, 0.22745098]],
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...,
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[[0.03137255, 0.07450981, 0.21960784],
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[0.03137255, 0.07058824, 0.21568628],
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[0.03137255, 0.07058824, 0.21960784],
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...,
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[0.03921569, 0.10980392, 0.2784314 ],
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[0.03921569, 0.10980392, 0.27450982],
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[0.03921569, 0.10980392, 0.27450982]],
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[[0.03137255, 0.07058824, 0.21960784],
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[0.03137255, 0.07058824, 0.21568628],
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[0.03137255, 0.07058824, 0.21568628],
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...,
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[0.03921569, 0.10588235, 0.27058825],
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[0.03921569, 0.10588235, 0.27058825],
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[0.03921569, 0.10588235, 0.27058825]],
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[[0.03137255, 0.07058824, 0.21960784],
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[0.03137255, 0.07058824, 0.21176471],
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[0.03137255, 0.07058824, 0.21568628],
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...,
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[0.03921569, 0.10588235, 0.26666668],
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[0.03921569, 0.10588235, 0.26666668],
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[0.03921569, 0.10588235, 0.26666668]]], dtype=float32)
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imp.display(arr)
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\end{minted}
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\begin{figure}[h!]
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\centering
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\includegraphics[scale=0.5]{assets/capture_display_img.png}
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\end{figure}
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The image must be displayed in a separate window when running in the console.

‎module03/exercises/m03ex02.tex

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\chapter{Exercise 02}
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\extitle{ScrapBooker}
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\turnindir{ex02}
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\exnumber{02}
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\exfiles{ScrapBooker.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================= %
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\section*{Objective}
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% --------------------------------- %
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Manipulation and initiation to slicing on numpy arrays.
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% ================================= %
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\section*{Instructions}
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% --------------------------------- %
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Implement a class named \texttt{ScrapBooker} with the following methods:
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\begin{itemize}
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\item \texttt{crop},
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\item \texttt{thin},
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\item \texttt{juxtapose},
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\item \texttt{mosaic}.
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\end{itemize}
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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# within the class
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def crop(self, array, dim, position=(0,0)):
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"""
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Crops the image as a rectangle via dim arguments (being the new height
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and width of the image) from the coordinates given by position arguments.
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Args:
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-----
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array: numpy.ndarray
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dim: tuple of 2 integers.
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position: tuple of 2 integers.
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Returns:
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-------
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new_arr: the cropped numpy.ndarray.
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None: (if the combination of parameters is not possible).
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Raises:
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------
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This function should not raise any Exception.
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"""
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... your code ...
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def thin(self, array, n, axis):
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"""
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Deletes every n-th line pixels along the specified axis (0: vertical, 1: horizontal)
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Args:
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-----
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array: numpy.ndarray.
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n: non null positive integer lower than the number of row/column of the array
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(depending of axis value).
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axis: positive non null integer.
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Returns:
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-------
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new_arr: thined numpy.ndarray.
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None: (if the combination of parameters is not possible).
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Raises:
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------
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This function should not raise any Exception.
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"""
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... your code ...
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def juxtapose(self, array, n, axis):
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"""
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Juxtaposes n copies of the image along the specified axis.
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Args:
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-----
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array: numpy.ndarray.
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n: positive non null integer.
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axis: integer of value 0 or 1.
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Returns:
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-------
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new_arr: juxtaposed numpy.ndarray.
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None: (if the combination of parameters is not possible).
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Raises:
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-------
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This function should not raise any Exception.
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"""
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... your code ...
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\end{minted}
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\newpage
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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def mosaic(self, array, dim):
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"""
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Makes a grid with multiple copies of the array. The dim argument specifies
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the number of repetition along each dimensions.
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Args:
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-----
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array: numpy.ndarray.
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dim: tuple of 2 integers.
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Return:
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-------
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new_arr: mosaic numpy.ndarray.
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None (combinaison of parameters not compatible).
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Raises:
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-------
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This function should not raise any Exception.
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"""
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... your code ...
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\end{minted}
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\newline
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In this exercise, when specifying positions or dimensions, we will assume
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that the first coordinate is counted along the vertical axis starting from
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the top, and that the second coordinate is counted along the horizontal axis
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starting from the left. Indexing starts from 0.\\
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\parbox{\textwidth}{
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e.g.:\\
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(1,3)\\
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.....\\
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...x.\\
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.....}
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% ================================= %
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\section*{Examples}
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% --------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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import numpy as np
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from ScrapBooker import ScrapBooker
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spb = ScrapBooker()
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arr1 = np.arange(0,25).reshape(5,5)
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spb.crop(arr1, (3,1),(1,0))
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#Output :
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array([[ 5],
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[10],
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[15]])
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arr2 = np.array("A B C D E F G H I".split() * 6).reshape(-1,9)
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spb.thin(arr2,3,0)
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#Output :
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array([['A', 'B', 'D', 'E', 'G', 'H', 'J', 'K'],
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['A', 'B', 'D', 'E', 'G', 'H', 'J', 'K'],
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['A', 'B', 'D', 'E', 'G', 'H', 'J', 'K'],
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['A', 'B', 'D', 'E', 'G', 'H', 'J', 'K'],
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['A', 'B', 'D', 'E', 'G', 'H', 'J', 'K'],
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['A', 'B', 'D', 'E', 'G', 'H', 'J', 'K']], dtype='<U1')
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arr3 = np.array([[1, 2, 3],[1, 2, 3],[1, 2, 3]])
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spb.juxtapose(arr3, 3, 1)
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#Output :
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array([[1, 2, 3, 1, 2, 3, 1, 2, 3],
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[1, 2, 3, 1, 2, 3, 1, 2, 3],
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[1, 2, 3, 1, 2, 3, 1, 2, 3]])
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\end{minted}

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