In this tutorial – Alpha blending using OpenCV, we will learn how to alpha blend two images and overlay a transparent PNG image over another image in OpenCV. We are sharing code in both C++ and Python.
What is Alpha Blending?
Alpha blending is the process of overlaying a foreground image with transparency over a background image. Transparency is often the fourth channel of an image ( e.g. in a transparent PNG), but it can also be a separate image. This transparency mask is often called the alpha mask or the alpha matte.
In the feature image at the top of this article, the foreground image is shown on the top left, the grayscale alpha mask is shown on the top right, the background image is shown on the bottom left, and the composite image obtained by blending the foreground image and the background image using an alpha mask is shown in the bottom right.
The math behind alpha blending is straightforward. At every pixel of the image, we need to combine the foreground image color (F) and the background image color (B) using the alpha mask (
).
Note: The value of used in the equation is the pixel value in the alpha mask divided by 255. So, in the equation below, 
From the equation above, you can make the following observations.
- When
, the output pixel color is the background. - When
, the output pixel color is the foreground. - When
, the output pixel color is a mix of the background and the foreground. For realistic blending, the boundary of the alpha mask usually has pixels between 0 and 1.
, the output pixel color is the background.
, the output pixel color is the foreground.
, the output pixel color is a mix of the background and the foreground. For realistic blending, the boundary of the alpha mask usually has pixels between 0 and 1.How to Implement Alpha Blending Using OpenCV (C++ /Python)?
Let us review an example in C++ and Python to see how to implement alpha blending using OpenCV. In a later section, we also show an efficient implementation of the C++ code.
Alpha Blending: C++
#include opencv2/opencv.hpp
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
// Read the images
Mat foreground = imread("puppets.png");
Mat background = imread("ocean.png");
Mat alpha = imread("puppets_alpha.png");
// Convert Mat to float data type
foreground.convertTo(foreground, CV_32FC3);
background.convertTo(background, CV_32FC3);
// Normalize the alpha mask to keep intensity between 0 and 1
alpha.convertTo(alpha, CV_32FC3, 1.0/255); //
// Storage for output image
Mat ouImage = Mat::zeros(foreground.size(), foreground.type());
// Multiply the foreground with the alpha matte
multiply(alpha, foreground, foreground);
// Multiply the background with ( 1 - alpha )
multiply(Scalar::all(1.0)-alpha, background, background);
// Add the masked foreground and background.
add(foreground, background, ouImage);
// Display image
imshow("alpha blended image", ouImage/255);
waitKey(0);
return 0;
}
Alpha Blending: Python
import cv2
# Read the images
foreground = cv2.imread("puppets.png")
background = cv2.imread("ocean.png")
alpha = cv2.imread("puppets_alpha.png")
# Convert uint8 to float
foreground = foreground.astype(float)
background = background.astype(float)
# Normalize the alpha mask to keep intensity between 0 and 1
alpha = alpha.astype(float)/255
# Multiply the foreground with the alpha matte
foreground = cv2.multiply(alpha, foreground)
# Multiply the background with ( 1 - alpha )
background = cv2.multiply(1.0 - alpha, background)
# Add the masked foreground and background.
outImage = cv2.add(foreground, background)
# Display image
cv2.imshow("outImg", outImage/255)
cv2.waitKey(0)
Efficient Alpha Blending Using OpenCV (C++)
The above code is spotless but not as efficient as it can be. Note that we are making two passes over the foreground image — once while multiplying with alpha and once again while adding to the masked background. Similarly, we are making multiple passes over the background image.
For small (e.g. 250×250) and medium (e.g. 800×800) sized images, the above methods are fine, but if we are interested in efficiency, especially for large images (e.g. 2000×2000), we could manipulate the data directly to get much better performance. Below is the CPP code for the same.
void alphaBlend(Mat& foreground, Mat& background, Mat& alpha, Mat& outImage)
{
// Find number of pixels.
int numberOfPixels = foreground.rows * foreground.cols * foreground.channels();
// Get floating point pointers to the data matrices
float* fptr = reinterpret_cast<float*>(foreground.data);
float* bptr = reinterpret_cast<float*>(background.data);
float* aptr = reinterpret_cast<float*>(alpha.data);
float* outImagePtr = reinterpret_cast<float*>(outImage.data);
// Loop over all pixesl ONCE
for(
int i = 0;
i < numberOfPixels;
i++, outImagePtr++, fptr++, aptr++, bptr++
)
{
*outImagePtr = (*fptr)*(*aptr) + (*bptr)*(1 - *aptr);
}
}
Runtime Comparison
In the table below, we show the runtime comparison of the two methods on images of various sizes. The numbers are the average time taken over 3000 runs. As you can see for larger images, the efficient method that makes a single pass over the images takes less than half the time! It is not clear why the single-pass method is slightly worse for small images.
| Image Size | Using functions - multiply, add (in milliseconds) | Using direct access (in milliseconds) |
|---|---|---|
| 230 x 162 | 0.156 | 0.364 |
| 1000 x 704 | 7.856 | 7.102 |
| 2296 x 1617 | 54.014 | 39.985 |
| 4592 x 3234 | 355.502 | 161.34 |
Subscribe & Download Code
If you liked this article and would like to download code (C++ and Python) and example images used in this post, please click here. Alternately, sign up to receive a free Computer Vision Resource Guide. In our newsletter, we share OpenCV tutorials and examples written in C++/Python, and Computer Vision and Machine Learning algorithms and news.Image Credits
1. The foreground image and its alpha matte was obtained from the alpha matting evaluation website
2. The background image is in the public domain. Here is the link.
I guess the reason of faster alphablending small images with matrix multiplication is pretty straightforward, as while matrixes are fit in your RAM their are being computed with optimized techniques for matrix multiplication with good speed, but it requires much more RAM.
Yes, sounds reasonable. With larger images, it would need more memory swapping even if the matrix operations are optimized.
Can you elaborate on how to get an alpha mask from the foreground image? it seems to me that the alpha mask would be a binary image therefore there is no blending?
Alpha mask should be a grayscale image, not binary. I will write about extracting alpha matte in a followup post. You could try running code for generating an alpha matte at https://github.com/np-csu/AlphaMatting
you still in your code need the input image and another segmentation image.
How did you get the segmentation mask image please?
Excellent article! I’m using a similar technique to your first example code in an art project creating time lapse collages: http://www.tommymintz.com/automated-digital-photo-collage-page/
Thanks for the info. I’m going to modify my code to be more efficient!
May I ask some questions by email? The code that I ran on iOS was different from what I expected, and I don’t know if it was a platform problem
Can you share your image here?
How do I subtract mat from scalar in java? im unable to do multiply(1-alpha, background, background); in java
Sorry, we do not use Java and have no idea.
hi did you get any solutions to this? i am also stuck ….
hi thanks for nice tutorial. we can achieve same above task using addweight function right? give me your idea..thanks
No because addweighted uses a single alpha value for the entire image. Here each pixel has a different alpha.
noted. thanks for the answer….!
Any opencv direct tutorial or code about how to to generate the Alpha image from just the real input image.
Most of the matting tutorials suppose having alpha is must, but I can’t find direct way to do it in opencv.
any suggestion please?
Yes, it is called natural image matting. There are several implementations available at
http://www.alphamatting.com/
Here is one that I had tried once.
https://github.com/atilimcetin/global-matting
in the Github code, it requires the Trimap image as input as well beside the input image.
int main()
{
cv::Mat image = cv::imread(“GT04-image.png”, CV_LOAD_IMAGE_COLOR);
cv::Mat trimap = cv::imread(“GT04-trimap.png”, CV_LOAD_IMAGE_GRAYSCALE);
The problem I don’t have the Trimap image and I don’t want to have it using semi-automatic way.
@Satya Mallick can you please look at my problem i am also facing same problem , i want to place one image on another image using image overlay and alpha blending suppose i have two images one is background image i.e is shirt image and other is foreground image i.e is logo image so the logo image should be place one shirt image such that it looks realistic and if there is uneven surface on shirt image so the logo here should blend according to curves on shirt, but i am not getting expected result,
please correct me here where i am doing mistake, my problem is here http://answers.opencv.org/question/185252/place-one-image-over-another-using-image-overlay-transparency/
@Satya Mallick can you please look at my problem i am also facing same problem , i want to place one image on another image using image overlay and alpha blending suppose i have two images one is background image i.e is shirt image and other is foreground image i.e is logo image so the logo image should be place one shirt image such that it looks realistic and if there is uneven surface on shirt image so the logo here should blend according to curves on shirt, but i am not getting expected result,
Please see the images below you will easily understand problem
please correct me here where i am doing mistake, my problem is here
here is my code snippet:
public void loadLogo(ActionEvent actionEvent) throws IOException, InterruptedException {
Mat imageROI, croppedImage, lines;
Rect roi;
double alpha = 0.5; double beta; double input;
FileChooser fileChooser = new FileChooser();
File selectedFile = fileChooser.showOpenDialog(null);
if (selectedFile != null) {
BufferedImage logoImage = ImageIO.read(selectedFile.toURL());
Image logo = logoImage;
Mat backgroundImageMatrics = createMat(img);
Mat logoImageMatrics = createMat(logoImage)
dst=new Mat();
imageROI = backgroundImageMatrics.submat(roi);
Core.addWeighted(imageROI,0.3,logoImageMatrics,1-0.3,0.0,dst);
dst.copyTo(imageROI);
HighGui.imshow(“Linear Blend”, backgroundImageMatrics);
HighGui.waitKey(0);
CloseApp();
}
}
here is actual result of my code
https://uploads.disquscdn.com/images/a903694210590362ad62cb8bc976d251c8c7af2bced20517e2c8b5b7d45c65f6.png
and here is what actually i want
https://uploads.disquscdn.com/images/0d0965ea17e2696b909bf29b8dc19a4c7d4d065c45873df0c939e02a772b91e8.png
This is a great read – I’m trying to write your alphaBlend in C# but, am not getting anywhere. Do you happen to have this function in C#? Kind Regards.