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Color detection using python and OpenCV | Color Detection with Python
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Title: Multiple Color Detection in Real-Time using Python-OpenCV
Introduction:
● Colour detection is the process of detecting the name of any colour. Simple isn’t it? Well, for humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into colour. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the colour. Since childhood, we have mapped certain lights with their colour names. We will be using the somewhat same strategy to detect colour names.
● For a robot to visualize the environment, along with the object detection, detection of its colour in real-time is also very important.
Why this is important? : Some Real-world Applications
●In a self-driving car, to detect the traffic signals.
Multiple colour detection is used in some industrial robots, to performing pick-and-place task in separating different coloured objects.
●This is an implementation of detecting multiple colours (here, only red, green and blue colours have been considered) in real-time using Python programming language.
Python Libraries Used:
●NumPy
●OpenCV-Python
Work Flow Description:
●Step 1:
Input: Capture video through webcam.
●Step 2:
Read the video stream in image frames.
●Step 3:
Convert the image frame in BGR(RGB colour space represented as three matrices of red, green and blue with integer values from 0 to 255) to HSV(hue-saturation-value) colour space.
Hue
describes a colour in terms of
saturation
, represents the amount of grey colour in that colour and
value
describes the brightness or intensity of the colour. This can be represented as three matrices in the range of 0-179, 0-255 and 0-255 respectively.
●Step 4:
Define the range of each colour and create the corresponding mask.
●Step 5:
Morphological Transform: Dilation, to remove noises from the images.
●Step 6:
bitwise_and between the image frame and the mask is performed to specifically detect that particular colour and discard others.
●Step 7:
Create contour for the individual colours to display the detected coloured region distinguish.
●Step 8:
Output: Detection of the colours in real-time.
Source Code & Link:
#Colourdetection, #Colordetectionopencv, #Colourdetectionusingpython, #Colordetection, #Opencv
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Introduction:
● Colour detection is the process of detecting the name of any colour. Simple isn’t it? Well, for humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into colour. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the colour. Since childhood, we have mapped certain lights with their colour names. We will be using the somewhat same strategy to detect colour names.
● For a robot to visualize the environment, along with the object detection, detection of its colour in real-time is also very important.
Why this is important? : Some Real-world Applications
●In a self-driving car, to detect the traffic signals.
Multiple colour detection is used in some industrial robots, to performing pick-and-place task in separating different coloured objects.
●This is an implementation of detecting multiple colours (here, only red, green and blue colours have been considered) in real-time using Python programming language.
Python Libraries Used:
●NumPy
●OpenCV-Python
Work Flow Description:
●Step 1:
Input: Capture video through webcam.
●Step 2:
Read the video stream in image frames.
●Step 3:
Convert the image frame in BGR(RGB colour space represented as three matrices of red, green and blue with integer values from 0 to 255) to HSV(hue-saturation-value) colour space.
Hue
describes a colour in terms of
saturation
, represents the amount of grey colour in that colour and
value
describes the brightness or intensity of the colour. This can be represented as three matrices in the range of 0-179, 0-255 and 0-255 respectively.
●Step 4:
Define the range of each colour and create the corresponding mask.
●Step 5:
Morphological Transform: Dilation, to remove noises from the images.
●Step 6:
bitwise_and between the image frame and the mask is performed to specifically detect that particular colour and discard others.
●Step 7:
Create contour for the individual colours to display the detected coloured region distinguish.
●Step 8:
Output: Detection of the colours in real-time.
Source Code & Link:
#Colourdetection, #Colordetectionopencv, #Colourdetectionusingpython, #Colordetection, #Opencv
====*====
✔ Now donation is open, you can donate to help us grow ✔
Google Pay - 7418713984
====*====
For More Videos:
● Sentiment Analysis using Python
● Make Your Own Haar Cascade Object Detector Using GUI In Windows
● How to Encrypt and Decrypt in Python
● Blur human face Real-Time using OpenCV and Python
● How to create link shortener using python
● CREATING STUDENT MARKSHEET APPLICATION IN PYTHON
● How to download YouTube videos using python
● How to convert an image into a PDF file using Python
● Create Street Maps with Folium and Python
● Python Tutorial In Detail
● Data Visualisation Running Graph
● Computer Hacks and Tricks
====*====
Follow Me Here For More Help or Queries
====*====
SUBSCRIBE NOW for weekly videos on Python Tutorial, Computer Hacks and Tricks, Data Visualisation, Technology, and Many More.
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