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Getting started with OpenCV in python

Open Source Computer Vision Library (OpenCV) is a classic and sate of the art vision library that utilizes machine learning. It has the power to build applications such as: identify objects, classify human actions in videos, track camera movements, track moving objects, and many more. It is provided in python and C++, there is likely other wrappers around on Github or similar.

First we're going to need python version 3.6, if you're not on this version you can download it at:

We're also going to need a few libraries, first being the OpenCV library, to install this enter the following:

pip install opencv-python

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You can additionally install the contributor kit if you wish (Not required)

pip install opencv-contrib-python

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In OpenCV projects you may find that you'll be using Number systems a lot, I recommend using the library Numpy. In this example it will not be required but you can install numpy by entering the following into your terminal

pip install Numpy

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Now that we have our libraries lets get to the fun stuff. In this example we will be taking a picture of multiple people (or yourself) and applying Split HSV, Saturation and hue filters, as well as showing a bitwise filter. The outcome should look something like this

If this shows I probably broke something

The code

import cv2

img = cv2.imread("mult.jpg", 1)  # image reading
# converting it into Hue, saturation, value (HSV)

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  

# the : in an array in python means that we're going to slice that part of the array

h = hsv[:, :, 0]
s = hsv[:, :, 1]
v = hsv[:, :, 2]

hsv_split = np.concatenate((h, s, v), axis=1)
cv2.imshow("Split hsv", hsv_split) 

#  some of the values require multiple variables, hence why ret is shown multiple times

ret, min_sat = cv2.threshold(s, 40, 255, cv2.THRESH_BINARY)
#  showing an image is very simple, first argument is the name, second is the image we wish to show
cv2.imshow("Sat filter", min_sat) 

ret, max_hue = cv2.threshold(h, 15, 255, cv2.THRESH_BINARY_INV)  # will do the inverse of the normal threshold

cv2.imshow("Hue filter", max_hue)

# the final image is the min saturation and the max hue put together

final = cv2.bitwise_and(min_sat, max_hue)
cv2.imshow("Final", final)

cv2.imshow("Original image", img)

#  the windows will display until a key is pressed, this is using key characters, in this case we're using escape, which is 27 but 0 also works
#  destroy all windows will prevent you from having to mass spam the kill keys

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And we're done. To test this simply run


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In some operating systems you may need to run


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Very simple introduction to OpenCV, the library has much potential.
Some useful links:
OpenCV documentation
Numpy/Spicy documentation
Python documentation
Link to image used in example

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