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Detect Objects of Similar Color using OpenCV in Python

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detect similar color objects opencv python

In this project, we are going to make a basic Object Detector by color using OpenCV python. Here, we will create this using an image processing technique called Color Detection and Segmentation.

OpenCV is an open-source computer vision library. OpenCV is used in many real-time applications also. OpenCV has some built-in functions to perform Color detection and Segmentation operations.

So what are Color Detection and Segmentation Techniques in Image Processing?

Project Prerequisites

Download Project Code

Please download the source code of detecting objects of similar color in an image: Detect Objects of Similar Color Project Code

Ok, now we have everything we need to get started. So let’s start.

Steps to Detect Objects of Similar Color using OpenCV

  1. Import necessary packages and read the image.
  2. Detect the color from the input image and create a mask.
  3. Removing unnecessary noise from masks.
  4. Apply the mask to the image.
  5. Draw a Boundary of the detected objects.

Step 1 – Import necessary packages and Initialize the camera:

# TechVidvan Object detection of similar color

import cv2
import numpy as np

# Reading the image
img = cv2.imread('image.jpg')

# Showing the output

cv2.imshow("Image", img)

cv2.waitKey(0)
cv2.destroyAllWindows()

Explanation:

Output:

Step 2 – Detect the color from the input image and create a mask:

At first, we’ll detect green objects in this image and then we’ll detect yellow objects. OpenCV reads the frame as BGR colorspace. But to detect any color, first, we have to convert the frame to HSV colorspace using cv2.cvtColor function.

So why HSV?

HSV color space is useful when we’re working with color information. It stands for HUE, SATURATION, and VALUE (or brightness). It is a cylindrical color space.

# convert to hsv colorspace
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# lower bound and upper bound for Green color
lower_bound = np.array([50, 20, 20])	 
upper_bound = np.array([100, 255, 255])

# find the colors within the boundaries
mask = cv2.inRange(hsv, lower_bound, upper_bound)

Explanation:

Here we can see that in the frame wherever the green color is detected the mask shows that as white and the rest of the region as black.

Step 3 – Removing unnecessary noise from masks:

In the mask, we can see that there is lots of unnecessary noise. So we have to remove it to get a better result.

#define kernel size  
kernel = np.ones((7,7),np.uint8)

# Remove unnecessary noise from mask

mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)

Explanation:

Step 4 – Apply the mask on the image:

Now we’ll segment the green region from the image.

# Segment only the detected region
segmented_img = cv2.bitwise_and(img, img, mask=mask)

cv2.bitwise_and() applies mask on frame in only that region where the mask is true means white.

so we have successfully detected all the green objects from the image. Now we’ll draw boundaries over the detected regions.

Step 5 – Draw a Boundary of the detected objects:

# Find contours from the mask

contours, hierarchy = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

output = cv2.drawContours(segmented_img, contours, -1, (0, 0, 255), 3)

# Showing the output

cv2.imshow("Output", output)

Explanation:

Let’s detect yellow objects in the image.

# lower bound and upper bound for Yellow color

lower_bound = np.array([20, 80, 80])	 
upper_bound = np.array([30, 255, 255])

Finally, we’ll draw boundaries in the main image.

# Draw contour on original image

output = cv2.drawContours(img, contours, -1, (0, 0, 255), 3)

Summary

In this project, we’ve created a basic object detector using OpenCV Python. Here we implemented the Color detection and Segmentation Technique. We learned about colors, color filtering techniques, contour detection, and some segmentation techniques.

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