Basic Operations on Images {#tutorial_py_basic_ops}
==========================

Goal
----

Learn to:

-   Access pixel values and modify them
-   Access image properties
-   Setting Region of Interest (ROI)
-   Splitting and Merging images

Almost all the operations in this section is mainly related to Numpy rather than OpenCV. A good
knowledge of Numpy is required to write better optimized code with OpenCV.

*( Examples will be shown in Python terminal since most of them are just single line codes )*

Accessing and Modifying pixel values
------------------------------------

Let's load a color image first:
@code{.py}
>>> import cv2
>>> import numpy as np

>>> img = cv2.imread('messi5.jpg')
@endcode
You can access a pixel value by its row and column coordinates. For BGR image, it returns an array
of Blue, Green, Red values. For grayscale image, just corresponding intensity is returned.
@code{.py}
>>> px = img[100,100]
>>> print( px )
[157 166 200]

# accessing only blue pixel
>>> blue = img[100,100,0]
>>> print( blue )
157
@endcode
You can modify the pixel values the same way.
@code{.py}
>>> img[100,100] = [255,255,255]
>>> print( img[100,100] )
[255 255 255]
@endcode

**warning**

Numpy is a optimized library for fast array calculations. So simply accessing each and every pixel
values and modifying it will be very slow and it is discouraged.

@note Above mentioned method is normally used for selecting a region of array, say first 5 rows and
last 3 columns like that. For individual pixel access, Numpy array methods, array.item() and
array.itemset() is considered to be better. But it always returns a scalar. So if you want to access
all B,G,R values, you need to call array.item() separately for all.

Better pixel accessing and editing method :
@code{.py}
# accessing RED value
>>> img.item(10,10,2)
59

# modifying RED value
>>> img.itemset((10,10,2),100)
>>> img.item(10,10,2)
100
@endcode

Accessing Image Properties
--------------------------

Image properties include number of rows, columns and channels, type of image data, number of pixels
etc.

Shape of image is accessed by img.shape. It returns a tuple of number of rows, columns and channels
(if image is color):
@code{.py}
>>> print( img.shape )
(342, 548, 3)
@endcode

@note If image is grayscale, tuple returned contains only number of rows and columns. So it is a
good method to check if loaded image is grayscale or color image.

Total number of pixels is accessed by `img.size`:
@code{.py}
>>> print( img.size )
562248
@endcode
Image datatype is obtained by \`img.dtype\`:
@code{.py}
>>> print( img.dtype )
uint8
@endcode

@note img.dtype is very important while debugging because a large number of errors in OpenCV-Python
code is caused by invalid datatype.

Image ROI
---------

Sometimes, you will have to play with certain region of images. For eye detection in images, first
face detection is done all over the image and when face is obtained, we select the face region alone
and search for eyes inside it instead of searching whole image. It improves accuracy (because eyes
are always on faces :D ) and performance (because we search for a small area)

ROI is again obtained using Numpy indexing. Here I am selecting the ball and copying it to another
region in the image:
@code{.py}
>>> ball = img[280:340, 330:390]
>>> img[273:333, 100:160] = ball
@endcode
Check the results below:

![image](images/roi.jpg)

Splitting and Merging Image Channels
------------------------------------

Sometimes you will need to work separately on B,G,R channels of image. Then you need to split the
BGR images to single planes. Or another time, you may need to join these individual channels to BGR
image. You can do it simply by:
@code{.py}
>>> b,g,r = cv2.split(img)
>>> img = cv2.merge((b,g,r))
@endcode
Or
@code
>>> b = img[:,:,0]
@endcode
Suppose, you want to make all the red pixels to zero, you need not split like this and put it equal
to zero. You can simply use Numpy indexing, and that is more faster.
@code{.py}
>>> img[:,:,2] = 0
@endcode

**warning**

cv2.split() is a costly operation (in terms of time). So do it only if you need it. Otherwise go
for Numpy indexing.

Making Borders for Images (Padding)
-----------------------------------

If you want to create a border around the image, something like a photo frame, you can use
**cv2.copyMakeBorder()** function. But it has more applications for convolution operation, zero
padding etc. This function takes following arguments:

-   **src** - input image
-   **top**, **bottom**, **left**, **right** - border width in number of pixels in corresponding
    directions

-   **borderType** - Flag defining what kind of border to be added. It can be following types:
    -   **cv2.BORDER_CONSTANT** - Adds a constant colored border. The value should be given
            as next argument.
        -   **cv2.BORDER_REFLECT** - Border will be mirror reflection of the border elements,
            like this : *fedcba|abcdefgh|hgfedcb*
        -   **cv2.BORDER_REFLECT_101** or **cv2.BORDER_DEFAULT** - Same as above, but with a
            slight change, like this : *gfedcb|abcdefgh|gfedcba*
        -   **cv2.BORDER_REPLICATE** - Last element is replicated throughout, like this:
            *aaaaaa|abcdefgh|hhhhhhh*
        -   **cv2.BORDER_WRAP** - Can't explain, it will look like this :
            *cdefgh|abcdefgh|abcdefg*

-   **value** - Color of border if border type is cv2.BORDER_CONSTANT

Below is a sample code demonstrating all these border types for better understanding:
@code{.py}
import cv2
import numpy as np
from matplotlib import pyplot as plt

BLUE = [255,0,0]

img1 = cv2.imread('opencv-logo.png')

replicate = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REPLICATE)
reflect = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT)
reflect101 = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT_101)
wrap = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_WRAP)
constant= cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_CONSTANT,value=BLUE)

plt.subplot(231),plt.imshow(img1,'gray'),plt.title('ORIGINAL')
plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE')
plt.subplot(233),plt.imshow(reflect,'gray'),plt.title('REFLECT')
plt.subplot(234),plt.imshow(reflect101,'gray'),plt.title('REFLECT_101')
plt.subplot(235),plt.imshow(wrap,'gray'),plt.title('WRAP')
plt.subplot(236),plt.imshow(constant,'gray'),plt.title('CONSTANT')

plt.show()
@endcode
See the result below. (Image is displayed with matplotlib. So RED and BLUE planes will be
interchanged):

![image](images/border.jpg)

Additional Resources
--------------------

Exercises
---------
