1. Understand image cutout and OpenCV library
Matting is one of the important tasks in the field of image processing, aiming to separate objects from other parts. OpenCV is an open source computer vision library. It provides a wealth of functions and tools for image editing and processing. It can realize the cutout function simply and quickly, and can also perform more image processing and analysis. Below we will introduce in detail how to use Python to implement the background removal function based on OpenCV.
2. Obtaining images and processing methods
Before cutting out the image, we need to select the image and processing method. Here we take an image that contains foreground and background and the background is relatively clear as an example.
import cv2
import numpy as np
# Load the image
img = cv2.imread('example_image.jpg')
# Show the original image
cv2.imshow('Original Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Define the method for background removal
method = cv2.bgsegm.createBackgroundSubtractorMOG()
3. Implement background removal
With the image and method in hand, we can start working on background removal.
The first thing to do is to obtain a binary image of the foreground part. We use the background subtraction method to achieve this, using the cv2.createBackgroundSubtractorMOG() function to obtain a background subtractor to separate the foreground and background of the image.
# Create the mask
mask = method.apply(img)
# Show the mask
cv2.imshow('Mask', mask)
cv2.waitKey(0)
cv2.destroyAllWindows()Next, we need to process the foreground part to clearly distinguish the dividing line between foreground and background. Morphological operations such as dilation, edge detection, and closure are used here.
# Perform morphology operation
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
# Show the processed mask
cv2.imshow('Processed Mask', mask)
cv2.waitKey(0)
cv2.destroyAllWindows()Finally, we superimpose the processed foreground image and the original image to remove the background.
# Remove the background
res = cv2.bitwise_and(img, img, mask=mask)
# Show the result
cv2.imshow('Result', res)
cv2.waitKey(0)
cv2.destroyAllWindows()4. Summary
This article describes how to use the OpenCV library to implement background removal. During the implementation process, we need to first select the image and processing method, process the foreground according to the method, and finally superimpose the foreground and the original image to generate the final result. Through the implementation of this method, not only background removal can be performed, but more image editing processing and analysis can also be achieved.