.. _imagefeaturesrst:

=================
Image to features
=================


.. only:: html

    **Links:** :download:`notebook <image_features.ipynb>`, :downloadlink:`html <image_features2html.html>`, :download:`PDF <image_features.pdf>`, :download:`python <image_features.py>`, :downloadlink:`slides <image_features.slides.html>`, :githublink:`GitHub|_doc/notebooks/cheat_sheets/image_features.ipynb|*`


Read an image with opencv.

.. code:: ipython3

    %matplotlib inline
    import matplotlib.pyplot as plt
    plt.style.use('ggplot')

.. code:: ipython3

    from jyquickhelper import add_notebook_menu
    add_notebook_menu()






.. contents::
    :local:





OpenCV
------

.. code:: ipython3

    import numpy as np
    import argparse
    import cv2
    from numpy import matrix
    import os
    from functools import reduce
    
    # create NumPy arrays from the boundaries
    lower = np.array([0, 0, 0], dtype = "uint8")
    upper = np.array([100, 115, 240], dtype = "uint8")
    
    i=0
    List= []
    folder = "tomates"
    
    if not os.path.exists(folder):
        raise FileNotFoundError(os.path.abspath(folder))
    
    for element in os.listdir(folder):
        i+=1
        # load the image
        image = cv2.imread(os.path.join(folder, element))
        res = cv2.resize(image,(100, 100), interpolation = cv2.INTER_CUBIC)
    
        # find the colors within the specified boundaries and apply the mask
        mask = cv2.inRange(res, lower, upper)
        output = cv2.bitwise_and(res, res, mask = mask)
        shape = output.shape
        nb = reduce(lambda a,b: a*b, shape)
        mat = output.reshape((1, nb))
        List.append(mat)
        print(element, mat.shape)
    
    bigmat = np.vstack(List)
    bigmat.shape


.. parsed-literal::
    imgt_61.jpg (1, 30000)
    imgt_66.jpg (1, 30000)




.. parsed-literal::

    (2, 30000)



.. code:: ipython3

    from pyquickhelper.helpgen import NbImage
    NbImage("tomates/imgt_61.jpg")




.. image:: image_features_5_0.jpg