.. _Contour_Properties:

Contour Properties
*********************

Here we will learn to extract some frequently used properties of objects like Solidity, Equivalent Diameter, Mask image, Mean Intensity etc. More features can be found at `Matlab regionprops documentation <http://www.mathworks.in/help/images/ref/regionprops.html>`_.

*(NB : Centroid, Area, Perimeter etc also belong to this category, but we have seen it in last chapter)*

1. Aspect Ratio
================

It is the ratio of width to height of bounding rect of the object.

.. math::

    Aspect \; Ratio = \frac{Width}{Height}

.. code-block:: python

    x,y,w,h = cv2.boundingRect(cnt)
    aspect_ratio = float(w)/h

2. Extent
==========

Extent is the ratio of contour area to bounding rectangle area.

.. math::
    Extent = \frac{Object \; Area}{Bounding \; Rectangle \; Area}

.. code-block:: python

    area = cv2.contourArea(cnt)
    x,y,w,h = cv2.boundingRect(cnt)
    rect_area = w*h
    extent = float(area)/rect_area

3. Solidity
============

Solidity is the ratio of contour area to its convex hull area.

.. math::
    Solidity = \frac{Contour \; Area}{Convex \; Hull \; Area}

.. code-block:: python

    area = cv2.contourArea(cnt)
    hull = cv2.convexHull(cnt)
    hull_area = cv2.contourArea(hull)
    solidity = float(area)/hull_area

4. Equivalent Diameter
=======================

Equivalent Diameter is the diameter of the circle whose area is same as the contour area.

.. math::
    Equivalent \; Diameter = \sqrt{\frac{4 \times Contour \; Area}{\pi}}

.. code-block:: python

    area = cv2.contourArea(cnt)
    equi_diameter = np.sqrt(4*area/np.pi)

5. Orientation
================

Orientation is the angle at which object is directed. Following method also gives the Major Axis and Minor Axis lengths.
::

    (x,y),(MA,ma),angle = cv2.fitEllipse(cnt)

6. Mask and Pixel Points
=========================

In some cases, we may need all the points which comprises that object. It can be done as follows:
::

    mask = np.zeros(imgray.shape,np.uint8)
    cv2.drawContours(mask,[cnt],0,255,-1)
    pixelpoints = np.transpose(np.nonzero(mask))
    #pixelpoints = cv2.findNonZero(mask)

Here, two methods, one using Numpy functions, next one using OpenCV function (last commented line) are given to do the same. Results are also same, but with a slight difference. Numpy gives coordinates in **(row, column)** format, while OpenCV gives coordinates in **(x,y)** format. So basically the answers will be interchanged. Note that, **row = x** and **column = y**.

7. Maximum Value, Minimum Value and their locations
=======================================================

We can find these parameters using a mask image.
::

    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(imgray,mask = mask)

8. Mean Color or Mean Intensity
===================================

Here, we can find the average color of an object. Or it can be average intensity of the object in grayscale mode. We again use the same mask to do it.
::

    mean_val = cv2.mean(im,mask = mask)

9. Extreme Points
==================

Extreme Points means topmost, bottommost, rightmost and leftmost points of the object.
::

    leftmost = tuple(cnt[cnt[:,:,0].argmin()][0])
    rightmost = tuple(cnt[cnt[:,:,0].argmax()][0])
    topmost = tuple(cnt[cnt[:,:,1].argmin()][0])
    bottommost = tuple(cnt[cnt[:,:,1].argmax()][0])

For eg, if I apply it to an Indian map, I get the following result :

    .. image:: images/extremepoints.jpg
        :alt: Extreme Points
        :align: center

Additional Resources
======================

Exercises
===========
#. There are still some features left in matlab regionprops doc. Try to implement them.