About python project
In this project we going to create or build an application which will automatically show the name of color just by clicking on color
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:
Screenshots:
Output:
Double click on the window to know the name of the pixel color
Colors Dataset
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
Steps for Building a Project in Python – Color Detection
Here are the steps to build an application in Python that can detect colors:
1. Download and unzip the zip file
Color Detection Zip File
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.
2. Taking an image from the user
We are using argparse library to create an argument parser. We can directly give an image path from the command prompt:
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True, help="Image Path")
args = vars(ap.parse_args())
img_path = args['image']
#Reading image with opencv
img = cv2.imread(img_path)
3. Next, we read the CSV file with pandas
The pandas library is very useful when we need to perform various operations on data files like CSV. pd.read_csv() reads the CSV file and loads it into the pandas DataFrame. We have assigned each column with a name for easy accessing.
#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)
4. Set a mouse callback event on a window
First,
we created a window in which the input image will display. Then, we set a callback function which will be called when a mouse event happens.
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_function)
With these lines, we named our window as ‘image’ and set a callback function which will call the draw_function() whenever a mouse event occurs.
5. Create the draw_function
It will calculate the rgb values of the pixel which we double click. The function parameters have the event name, (x,y) coordinates of the mouse position, etc. In the function, we check if the event is double-clicked then we calculate and set the r,g,b values along with x,y positions of the mouse.
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)
6. Calculate distance to get color name
We have the r,g and b values. Now, we need another function which will return us the color name from RGB values. To get the color name, we calculate a distance(d) which tells us how close we are to color and choose the one having minimum distance.
Our distance is calculated by this formula:
d = abs(Red – ithRedColor) + (Green – ithGreenColor) + (Blue – ithBlueColor)
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
7. Display image on the window
Whenever a double click event occurs, it will update the color name and RGB values on the window
Using the cv2.imshow() function, we draw the image on the window. When the user double clicks the window, we draw a rectangle and get the color name to draw text on the window using cv2.rectangle and cv2.putText() functions.
while(1):
cv2.imshow("image",img)
if (clicked):
#cv2.rectangle(image, startpoint, endpoint, color, thickness) -1 thickness fills rectangle entirely
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, (optional bottomLeft bool) )
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()
8. Run Python File
Give real-time directions
The beginner Python project is now complete, you can run the Python file from the command prompt. Make sure to give an image path using ‘-i’ argument. If the image is in another directory, then you need to give full path of the image:
python color_detection.py -i
Summary
In this Python project with source code, we learned about colors and how we can extract color RGB values and the color name of a pixel. We learned how to handle events like double-clicking on the window and saw how to read CSV files with pandas and perform operations on data. This is used in numerous image editing and drawing apps.
