Python Project on Color Detection

Python Project on Color Detection

What is Colour Detection?

 Colour detection is the process of detecting the name of any color. 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 color. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the color. Since childhood, we have mapped certain lights with their color names. We will be using the somewhat same strategy to detect color names.

In this color detection Python project, we are going to build an application through which you can automatically get the name of the color by clicking on them. So for this, we will have a data file that contains the color name and its values. Then we will calculate the distance from each color and find the shortest one.

The Dataset

Colors are made up of 3 primary colors; red, green, and blue. In computers, we define each color value within a range of 0 to 255. So in how many ways we can define a color? The answer is 256*256*256 = 16,581,375. There are approximately 16.5 million different ways to represent a color. In our dataset, we need to map each color’s values with their corresponding names. But don’t worry, we don’t need to map all the values. We will be using a dataset that contains RGB values with their corresponding names. The CSV file for our dataset has been taken from this link:

Prerequisites

Before starting with this Python project with source code, you should be familiar with the computer vision library of Python that is OpenCV and Pandas.

OpenCV, Pandas, and numpy are the Python packages that are necessary for this project in Python. To install them, simply run this pip command in your terminal:

The project folder contains 3 files:

Color_detection.py – main source code of our project.

Colorpic.jpg – sample image for experimenting.

Colors.csv – a file that contains our dataset.

Code:

import cv2

import numpy as np

import pandas as pd

import argparse

#Creating argument parser to take image path from command line

ap = argparse.ArgumentParser()

ap.add_argument('-i', '--image', required=True, help="Image Path")

args = vars(ap.parse_args())

img_path = args['image']

#Reading the image with opencv

img = cv2.imread(img_path)

#declaring global variables (are used later on)

clicked = False

r = g = b = xpos = ypos = 0

#Reading csv file with pandas and giving names to each column

index=["color","color_name","hex","R","G","B"]

csv = pd.read_csv('colors.csv', names=index, header=None)

#function to calculate minimum distance from all colors and get the most matching color

def getColorName(R,G,B):

    minimum = 10000

    for i in range(len(csv)):

        d = abs(R- int(csv.loc[i,"R"])) + abs(G- int(csv.loc[i,"G"]))+ abs(B- int(csv.loc[i,"B"]))

        if(d<=minimum):

            minimum = d

            cname = csv.loc[i,"color_name"]

    return cname

#function to get x,y coordinates of mouse double click

def draw_function(event, x,y,flags,param):

    if event == cv2.EVENT_LBUTTONDBLCLK:

        global b,g,r,xpos,ypos, clicked

        clicked = True

        xpos = x

        ypos = y

        b,g,r = img[y,x]

        b = int(b)

        g = int(g)

        r = int(r)

       

cv2.namedWindow('image')

cv2.setMouseCallback('image',draw_function)

while(1):

    cv2.imshow("image",img)

    if (clicked):

   

        #cv2.rectangle(image, startpoint, endpoint, color, thickness)-1 fills entire rectangle 

        cv2.rectangle(img,(20,20), (750,60), (b,g,r), -1)

        #Creating text string to display( Color name and RGB values )

        text = getColorName(r,g,b) + ' R='+ str(r) +  ' G='+ str(g) +  ' B='+ str(b)

        

        #cv2.putText(img,text,start,font(0-7),fontScale,color,thickness,lineType )

        cv2.putText(img, text,(50,50),2,0.8,(255,255,255),2,cv2.LINE_AA)

        #For very light colours we will display text in black colour

        if(r+g+b>=600):

            cv2.putText(img, text,(50,50),2,0.8,(0,0,0),2,cv2.LINE_AA)

            

        clicked=False

    #Break the loop when user hits 'esc' key    

    if cv2.waitKey(20) & 0xFF ==27:

        break

    

cv2.destroyAllWindows()