Week 1: Introduction to Python and Images

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# Know your TA

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* Eng. Eslam Adel, graduate of 2016 class.

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* e-mail: `eslam.adel.mahmoud.ali@gmail.com`

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* Office hours and materials are avaiable on the course page.

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## Python Basics

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### Variables

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Variables in python are very flexible, unlike C++:

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* Interpreted language.

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* No need to declare the variable type.

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* The same variable can be assigned to different types.

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```python
x = 5
y = 'Hello SBME'
z = 5
z = "Hello SBME"
```

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### Lists

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```python
# List construction
subjects = ['Technical Writing','Computer Vision']

# Append an element
subjects.append('Biochemistry')

# Append another list
subjects.extend(['Bioelectronics','Stochastic'])

print( subjects ) # ??
print( subjects[0] ) # ??
print( subjects[1] ) # ??
```

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### Arithmetic Operations

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```python

x = 19
y = 18

z = x / y
z = x * y
z = x + y
z = x - y
```

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### Logical Opertations

```python
x = 17 % 2 == 1

y = 9 / 3 < 1

b = x or y ## True

b = x and y ## False
```

- `and` => `&&` in C++

- `or` => `||` in C++

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### If, elif, else

```python

x = 23 
y = 22

if x < y:
  z = 13 # Local scope

elif x % 2 == 1 and x > y :
  h = 17 # Local scope

else:
  v = 80 # Local scope

print( z ) # Error: z is out of scope
print( h ) # Error: h is out of scope
```

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### Loops

```python

for i in range(10):
  print( i )

i = 0
while i < 10 :
  print( i )
  i += 1
```

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### Functions

```python
def mean( list ):
  sum = 0
  for element in list:
    sum += element
  return sum / len( sum )

m = mean([1,12,42,1,23,12])
print( m )
```

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### Importing Libraries

```python 
import numpy as np
```

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### Numpy

Reference: [{NumPy Reference}](https://docs.scipy.org/doc/numpy-1.13.0/reference/#numpy-reference)

```python
import numpy as np

a = np.array([12,23,44,21,23])

print( np.mean( a ))
print( np.std( a ))
```

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###  Matplotlib

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* `Matplotlib` is Matlab-like visualization library.

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```python
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

image1 = mpimg.imread("images.jpg")
image2 = mpimg.imread("cameraman1.png")

print( image1.shape )
print( image2.shape )

plt.imshow(image2)
plt.show()

```

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## Getting Started with Python: Installing Anaconda

### Anaconda

* Shipped +1000 Data Science Packages (DSP, Image Processing, Machine Learning, AI, Statistics).

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* Shipped with Python IDE (Spyder).

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* Shipped with Jupyter Notebook.

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[{Download Anaconda 3 (Python 3)}](https://www.anaconda.com/download)

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### References: Image Processing with Python

[{Scikit-image}](http://scikit-image.org/docs/dev/user_guide)

[{Programming Computer Vision with Python, *Jan Erik Solem*}](http://programmingcomputervision.com/downloads/ProgrammingComputerVision_CCdraft.pdf)

[{Image Processing in Numpy, *Computer Vision Laboratory, Link ̈oping Universit*}](https://www.cvl.isy.liu.se/education/undergraduate/tsbb15/computer-exercises/lektion_python.pdf)

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## Demo

You can find the demo files on this repository at *github* [{cv-week1-demo}](https://github.com/sbme-tutorials/cv-week1-demo)

### Prerequisites

1. Anaconda3 installed.
2. git installed

### Downloading the Demo

Issue the following command:

```terminal
$ git clone https://github.com/sbme-tutorials/cv-week1-demo.git
$ cd cv-week1-demo
```

Expect soon migration to Python notebooks for more convenience.