Color Detection using Pandas & OpenCV

In this blog, we will use OpenCV and Pandas for detecting colors in image.

What is Color Detection?

Color 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 colors.csv file includes 865 color names along with their RGB and hex values.

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:

pip install opencv-python numpy pandas

main.py

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:
        breakcv2.destroyAllWindows()

Output:

Source Code: https://github.com/sarjakmodi39/color-detection