Arithmetic Operations on Images {#tutorial_py_image_arithmetics}
===============================

Goal
----

-   Learn several arithmetic operations on images, like addition, subtraction, bitwise operations, and etc.
-   Learn these functions: **cv.add()**, **cv.addWeighted()**, etc.

Image Addition
--------------

You can add two images with the OpenCV function, cv.add(), or simply by the numpy operation
res = img1 + img2. Both images should be of same depth and type, or the second image can just be a
scalar value.

@note There is a difference between OpenCV addition and Numpy addition. OpenCV addition is a
saturated operation while Numpy addition is a modulo operation.

For example, consider the below sample:
@code{.py}
>>> x = np.uint8([250])
>>> y = np.uint8([10])

>>> print( cv.add(x,y) ) # 250+10 = 260 => 255
[[255]]

>>> print( x+y )          # 250+10 = 260 % 256 = 4
[4]
@endcode
This will be more visible when you add two images. Stick with OpenCV functions, because they will provide a better result.

Image Blending
--------------

This is also image addition, but different weights are given to images in order to give a feeling of
blending or transparency. Images are added as per the equation below:

\f[g(x) = (1 - \alpha)f_{0}(x) + \alpha f_{1}(x)\f]

By varying \f$\alpha\f$ from \f$0 \rightarrow 1\f$, you can perform a cool transition between one image to
another.

Here I took two images to blend together. The first image is given a weight of 0.7 and the second image
is given 0.3. cv.addWeighted() applies the following equation to the image:

\f[dst = \alpha \cdot img1 + \beta \cdot img2 + \gamma\f]

Here \f$\gamma\f$ is taken as zero.
@code{.py}
img1 = cv.imread('ml.png')
img2 = cv.imread('opencv-logo.png')
assert img1 is not None, "file could not be read, check with os.path.exists()"
assert img2 is not None, "file could not be read, check with os.path.exists()"

dst = cv.addWeighted(img1,0.7,img2,0.3,0)

cv.imshow('dst',dst)
cv.waitKey(0)
cv.destroyAllWindows()
@endcode
Check the result below:

![image](images/blending.jpg)

Bitwise Operations
------------------

This includes the bitwise AND, OR, NOT, and XOR operations. They will be highly useful while extracting
any part of the image (as we will see in coming chapters), defining and working with non-rectangular
ROI's, and etc. Below we will see an example of how to change a particular region of an image.

I want to put the OpenCV logo above an image. If I add two images, it will change the color. If I blend them,
I get a transparent effect. But I want it to be opaque. If it was a rectangular region, I could use
ROI as we did in the last chapter. But the OpenCV logo is a not a rectangular shape. So you can do it with
bitwise operations as shown below:
@code{.py}
# Load two images
img1 = cv.imread('messi5.jpg')
img2 = cv.imread('opencv-logo-white.png')
assert img1 is not None, "file could not be read, check with os.path.exists()"
assert img2 is not None, "file could not be read, check with os.path.exists()"

# I want to put logo on top-left corner, So I create a ROI
rows,cols,channels = img2.shape
roi = img1[0:rows, 0:cols]

# Now create a mask of logo and create its inverse mask also
img2gray = cv.cvtColor(img2,cv.COLOR_BGR2GRAY)
ret, mask = cv.threshold(img2gray, 10, 255, cv.THRESH_BINARY)
mask_inv = cv.bitwise_not(mask)

# Now black-out the area of logo in ROI
img1_bg = cv.bitwise_and(roi,roi,mask = mask_inv)

# Take only region of logo from logo image.
img2_fg = cv.bitwise_and(img2,img2,mask = mask)

# Put logo in ROI and modify the main image
dst = cv.add(img1_bg,img2_fg)
img1[0:rows, 0:cols ] = dst

cv.imshow('res',img1)
cv.waitKey(0)
cv.destroyAllWindows()
@endcode
See the result below. Left image shows the mask we created. Right image shows the final result. For
more understanding, display all the intermediate images in the above code, especially img1_bg and
img2_fg.

![image](images/overlay.jpg)

Exercises
---------

-#  Create a slide show of images in a folder with smooth transition between images using
    cv.addWeighted function
