#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_cell_magic('javascript', '', "$.getScript('../ipython_notebook_toc.js');\n")


# In[2]:


get_ipython().run_line_magic('matplotlib', 'inline')
import sys
sys.path.insert(0,'..')
from IPython.display import HTML
from helpers import header

HTML(header())


# Exercices and practical works
# =============================

# Most of the samples given in this course use python and numpy package, an example is given below where the gray level histogram is computed for an image using standard `histogram` numpy function.

# In[3]:


get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from skimage.data import camera
plt.style.use('ggplot')

plt.figure(figsize=[10,5])
ima = camera() #use a test image provided by the skimage library
hist,bins = np.histogram(ima.flatten(),range(256)) # histogram is computed on a 1D distribution --> flatten()
norm_hist = 1.*hist/np.sum(hist) # normalized histogram

# display the results
plt.subplot(1,2,1)
plt.imshow(camera(),cmap=cm.gray)
plt.subplot(1,2,2)
plt.plot(norm_hist)
plt.xlabel('gray level');