OpenCV及rembg去除图像背景

OpenCV去除图像背景

去除图像背景，需要综合使用二值化（thresholding）、腐蚀（erosion）、膨胀（dilation）以及位运算（bitwise operations），代码如下：

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>using namespace cv;int main(int argc, char** argv)
{CommandLineParser parser(argc, argv, "{@input | dog.jpg | input image}");// Read an imageMat src = imread(samples::findFile(parser.get<String>("@input")));if (src.empty()){std::cout << "Could not open or find the image!\n" << std::endl;std::cout << "Usage: " << argv[0] << " <Input image>" << std::endl;return EXIT_FAILURE;}// Convert the image to grayscaleMat grayImg;cvtColor(src, grayImg, COLOR_BGR2GRAY);// Remove the background using a threshold// median filter is applied to reduce noise in the image// ksize is 5Mat grayImgBlurred;medianBlur(grayImg, grayImgBlurred, 5);// A binary threshold is applied to the grayscale image using a thresholdMat binaryImg;double thresh = threshold(grayImgBlurred, binaryImg, 150, 255, THRESH_BINARY_INV);// Output the threshstd::cout << thresh << std::endl;// The binary image is eroded to remove small objects and fill in small gaps using erodeMat erodedMask;erode(binaryImg, erodedMask, getStructuringElement(MORPH_RECT, Size(3, 3)), Point(-1, 1), 2);// The binary image is dilated to expand the remaining foreground objects # and fill in gaps using dilateMat mask;dilate(erodedMask, mask, getStructuringElement(MORPH_RECT, Size(3, 3)), Point(-1, 1), 2);// The original input image is combined with the binary mask using bitwise_andMat backgroundRemovedImg;bitwise_and(src, src, backgroundRemovedImg, mask);// Display the processed imagesimshow("Background Removed Image", backgroundRemovedImg);waitKey(0);return EXIT_SUCCESS;
}

###Background removal is removing the background from an imageimport cv2# Read an image
img = cv2.imread('../data/dog.jpg')
# Convert the image to grayscale
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# Remove the background using a threshold
# median filter is applied to reduce noise in the image
gray_img = cv2.medianBlur(gray_img, 5)# A binary threshold is applied to the grayscale image using a threshold
ret, thresh = cv2.threshold(gray_img, 150, 255, cv2.THRESH_BINARY_INV)# The binary image is eroded to remove small objects and fill in small gaps using erode
mask = cv2.erode(thresh, None, iterations=2)# The binary image is dilated to expand the remaining foreground objects # and fill in gaps using dilate
mask = cv2.dilate(mask, None, iterations=2)
# The original input image is combined with the binary mask using bitwise_and
background_removed_img = cv2.bitwise_and(img, img, mask=mask)# Display the processed images 
cv2.imshow('Background Removed Image', background_removed_img)# Wait for a key press and then close the windows
cv2.waitKey(0)
cv2.destroyAllWindows()

原图：
原来的狗狗
处理后：
处理后的狗狗
显然，这个结果并不美丽，我们可以尝试修改参数修正，但结果总是难以令人满意。于是采用更好更省事的办法，引用rembg库，调用u2net模型，去除背景。

使用`rembg`库去除图像背景

rembg库地址为：https://pypi.org/project/rembg/，这是一个基于机器学习模型的库，安装命令如下：

pip install rembg

如果有CUDA，可以安装GPU版：

pip install rembg[gpu]

使用该库，去除图像背景的代码如下：

### For background removal using rembg libraryfrom rembg import remove
import cv2#input path for image
input_path = '../data/dog.jpg'
output_path = 'output.png'#read the image
input = cv2.imread(input_path)
output = remove(input)
# save the image 
cv2.imwrite(output_path, output)# Display the processed images 
img = cv2.imread('output.png')
cv2.imshow('Background Removed Image', img)# Wait for a key press and then close the windows
cv2.waitKey(0)
cv2.destroyAllWindows()