Using openCV to overlay transparent image onto another image

How can I overlay a transparent PNG onto another image without losing it's transparency using openCV in python?

import cv2

background = cv2.imread('field.jpg')
overlay = cv2.imread('dice.png')

# Help please

cv2.imwrite('combined.png', background)

Desired output:

Sources:

Background Image

Overlay

import cv2

background = cv2.imread('field.jpg')
overlay = cv2.imread('dice.png')

added_image = cv2.addWeighted(background,0.4,overlay,0.1,0)

cv2.imwrite('combined.png', added_image)

The correct answer to this was far too hard to come by, so I'm posting this answer even though the question is really old. What you are looking for is "over" compositing, and the algorithm for this can be found on Wikipedia: https://en.wikipedia.org/wiki/Alpha_compositing

I am far from an expert with OpenCV, but after some experimentation this is the most efficient way I have found to accomplish the task:

import cv2

background = cv2.imread("background.png", cv2.IMREAD_UNCHANGED)
foreground = cv2.imread("overlay.png", cv2.IMREAD_UNCHANGED)

# normalize alpha channels from 0-255 to 0-1
alpha_background = background[:,:,3] / 255.0
alpha_foreground = foreground[:,:,3] / 255.0

# set adjusted colors
for color in range(0, 3):
    background[:,:,color] = alpha_foreground * foreground[:,:,color] + \
        alpha_background * background[:,:,color] * (1 - alpha_foreground)

# set adjusted alpha and denormalize back to 0-255
background[:,:,3] = (1 - (1 - alpha_foreground) * (1 - alpha_background)) * 255

# display the image
cv2.imshow("Composited image", background)
cv2.waitKey(0)

The following code will use the alpha channels of the overlay image to correctly blend it into the background image, use x and y to set the top-left corner of the overlay image.

import cv2
import numpy as np

def overlay_transparent(background, overlay, x, y):

    background_width = background.shape[1]
    background_height = background.shape[0]

    if x >= background_width or y >= background_height:
        return background

    h, w = overlay.shape[0], overlay.shape[1]

    if x + w > background_width:
        w = background_width - x
        overlay = overlay[:, :w]

    if y + h > background_height:
        h = background_height - y
        overlay = overlay[:h]

    if overlay.shape[2] < 4:
        overlay = np.concatenate(
            [
                overlay,
                np.ones((overlay.shape[0], overlay.shape[1], 1), dtype = overlay.dtype) * 255
            ],
            axis = 2,
        )

    overlay_image = overlay[..., :3]
    mask = overlay[..., 3:] / 255.0
    
    background[y:y+h, x:x+w] = (1.0 - mask) * background[y:y+h, x:x+w] + mask * overlay_image

    return background

This code will mutate background so create a copy if you wish to preserve the original background image.

Been a while since this question appeared, but I believe this is the right simple answer, which could still help somebody.

background = cv2.imread('road.jpg')
overlay = cv2.imread('traffic sign.png')
                
rows,cols,channels = overlay.shape
    
overlay=cv2.addWeighted(background[250:250+rows, 0:0+cols],0.5,overlay,0.5,0)

background[250:250+rows, 0:0+cols ] = overlay

This will overlay the image over the background image such as shown here:

Ignore the ROI rectangles

Note that I used a background image of size 400x300 and the overlay image of size 32x32, is shown in the x[0-32] and y[250-282] part of the background image according to the coordinates I set for it, to first calculate the blend and then put the calculated blend in the part of the image where I want to have it.

(overlay is loaded from disk, not from the background image itself,unfortunately the overlay image has its own white background, so you can see that too in the result)

You need to open the transparent png image using the flag IMREAD_UNCHANGED

Mat overlay = cv::imread("dice.png", IMREAD_UNCHANGED);

Then split the channels, group the RGB and use the transparent channel as an mask, do like that:

/**
 * @brief Draws a transparent image over a frame Mat.
 * 
 * @param frame the frame where the transparent image will be drawn
 * @param transp the Mat image with transparency, read from a PNG image, with the IMREAD_UNCHANGED flag
 * @param xPos x position of the frame image where the image will start.
 * @param yPos y position of the frame image where the image will start.
 */
void drawTransparency(Mat frame, Mat transp, int xPos, int yPos) {
    Mat mask;
    vector<Mat> layers;
    
    split(transp, layers); // seperate channels
    Mat rgb[3] = { layers[0],layers[1],layers[2] };
    mask = layers[3]; // png's alpha channel used as mask
    merge(rgb, 3, transp);  // put together the RGB channels, now transp insn't transparent 
    transp.copyTo(frame.rowRange(yPos, yPos + transp.rows).colRange(xPos, xPos + transp.cols), mask);
}

Can be called like that:

drawTransparency(background, overlay, 10, 10);

To overlay png image watermark over normal 3 channel jpeg image

import cv2
import numpy as np
​
def logoOverlay(image,logo,alpha=1.0,x=0, y=0, scale=1.0):
    (h, w) = image.shape[:2]
    image = np.dstack([image, np.ones((h, w), dtype="uint8") * 255])
​
    overlay = cv2.resize(logo, None,fx=scale,fy=scale)
    (wH, wW) = overlay.shape[:2]
    output = image.copy()
    # blend the two images together using transparent overlays
    try:
        if x<0 : x = w+x
        if y<0 : y = h+y
        if x+wW > w: wW = w-x  
        if y+wH > h: wH = h-y
        print(x,y,wW,wH)
        overlay=cv2.addWeighted(output[y:y+wH, x:x+wW],alpha,overlay[:wH,:wW],1.0,0)
        output[y:y+wH, x:x+wW ] = overlay
    except Exception as e:
        print("Error: Logo position is overshooting image!")
        print(e)
​
    output= output[:,:,:3]
    return output

Usage:

background = cv2.imread('image.jpeg')
overlay = cv2.imread('logo.png', cv2.IMREAD_UNCHANGED)
​
print(overlay.shape) # must be (x,y,4)
print(background.shape) # must be (x,y,3)

# downscale logo by half and position on bottom right reference
out = logoOverlay(background,overlay,scale=0.5,y=-100,x=-100) 
​
cv2.imshow("test",out)
cv2.waitKey(0)

import cv2
import numpy as np

background = cv2.imread('background.jpg')
overlay = cv2.imread('cloudy.png')
overlay = cv2.resize(overlay, (200,200))
# overlay = for_transparent_removal(overlay)
h, w = overlay.shape[:2]
shapes = np.zeros_like(background, np.uint8)
shapes[0:h, 0:w] = overlay
alpha = 0.8
mask = shapes.astype(bool)

# option first
background[mask] = cv2.addWeighted(shapes, alpha, shapes, 1 - alpha, 0)[mask]
cv2.imwrite('combined.png', background)
# option second
background[mask] = cv2.addWeighted(background, alpha, overlay, 1 - alpha, 0)[mask]
# NOTE : above both option will give you image overlays but effect would be changed
cv2.imwrite('combined.1.png', background)

If performance isn't a concern then you can iterate over each pixel of the overlay and apply it to the background. This isn't very efficient, but it does help to understand how to work with png's alpha layer.

slow version

import cv2

background = cv2.imread('field.jpg')
overlay = cv2.imread('dice.png', cv2.IMREAD_UNCHANGED)  # IMREAD_UNCHANGED => open image with the alpha channel

height, width = overlay.shape[:2]
for y in range(height):
    for x in range(width):
        overlay_color = overlay[y, x, :3]  # first three elements are color (RGB)
        overlay_alpha = overlay[y, x, 3] / 255  # 4th element is the alpha channel, convert from 0-255 to 0.0-1.0

        # get the color from the background image
        background_color = background[y, x]

        # combine the background color and the overlay color weighted by alpha
        composite_color = background_color * (1 - overlay_alpha) + overlay_color * overlay_alpha

        # update the background image in place
        background[y, x] = composite_color

cv2.imwrite('combined.png', background)

result:

fast version

I stumbled across this question while trying to add a png overlay to a live video feed. The above solution is way too slow for that. We can make the algorithm significantly faster by using numpy's vector functions.

note: This was my first real foray into numpy so there may be better/faster methods than what I've come up with.

import cv2
import numpy as np

background = cv2.imread('field.jpg')
overlay = cv2.imread('dice.png', cv2.IMREAD_UNCHANGED)  # IMREAD_UNCHANGED => open image with the alpha channel

# separate the alpha channel from the color channels
alpha_channel = overlay[:, :, 3] / 255 # convert from 0-255 to 0.0-1.0
overlay_colors = overlay[:, :, :3]

# To take advantage of the speed of numpy and apply transformations to the entire image with a single operation
# the arrays need to be the same shape. However, the shapes currently looks like this:
#    - overlay_colors shape:(width, height, 3)  3 color values for each pixel, (red, green, blue)
#    - alpha_channel  shape:(width, height, 1)  1 single alpha value for each pixel
# We will construct an alpha_mask that has the same shape as the overlay_colors by duplicate the alpha channel
# for each color so there is a 1:1 alpha channel for each color channel
alpha_mask = np.dstack((alpha_channel, alpha_channel, alpha_channel))

# The background image is larger than the overlay so we'll take a subsection of the background that matches the
# dimensions of the overlay.
# NOTE: For simplicity, the overlay is applied to the top-left corner of the background(0,0). An x and y offset
# could be used to place the overlay at any position on the background.
h, w = overlay.shape[:2]
background_subsection = background[0:h, 0:w]

# combine the background with the overlay image weighted by alpha
composite = background_subsection * (1 - alpha_mask) + overlay_colors * alpha_mask

# overwrite the section of the background image that has been updated
background[0:h, 0:w] = composite

cv2.imwrite('combined.png', background)

How much faster? On my machine the slow method takes ~3 seconds and the optimized method takes ~ 30 ms. So about 100 times faster!

Wrapped up in a function

This function handles foreground and background images of different sizes and also supports negative and positive offsets the move the overlay across the bounds of the background image in any direction.

import cv2
import numpy as np

def add_transparent_image(background, foreground, x_offset=None, y_offset=None):
    bg_h, bg_w, bg_channels = background.shape
    fg_h, fg_w, fg_channels = foreground.shape

    assert bg_channels == 3, f'background image should have exactly 3 channels (RGB). found:{bg_channels}'
    assert fg_channels == 4, f'foreground image should have exactly 4 channels (RGBA). found:{fg_channels}'

    # center by default
    if x_offset is None: x_offset = (bg_w - fg_w) // 2
    if y_offset is None: y_offset = (bg_h - fg_h) // 2

    w = min(fg_w, bg_w, fg_w + x_offset, bg_w - x_offset)
    h = min(fg_h, bg_h, fg_h + y_offset, bg_h - y_offset)

    if w < 1 or h < 1: return

    # clip foreground and background images to the overlapping regions
    bg_x = max(0, x_offset)
    bg_y = max(0, y_offset)
    fg_x = max(0, x_offset * -1)
    fg_y = max(0, y_offset * -1)
    foreground = foreground[fg_y:fg_y + h, fg_x:fg_x + w]
    background_subsection = background[bg_y:bg_y + h, bg_x:bg_x + w]

    # separate alpha and color channels from the foreground image
    foreground_colors = foreground[:, :, :3]
    alpha_channel = foreground[:, :, 3] / 255  # 0-255 => 0.0-1.0

    # construct an alpha_mask that matches the image shape
    alpha_mask = np.dstack((alpha_channel, alpha_channel, alpha_channel))

    # combine the background with the overlay image weighted by alpha
    composite = background_subsection * (1 - alpha_mask) + foreground_colors * alpha_mask

    # overwrite the section of the background image that has been updated
    background[bg_y:bg_y + h, bg_x:bg_x + w] = composite

example usage:

background = cv2.imread('field.jpg')
overlay = cv2.imread('dice.png', cv2.IMREAD_UNCHANGED)  # IMREAD_UNCHANGED => open image with the alpha channel

x_offset = 0
y_offset = 0
print("arrow keys to move the dice. ESC to quit")
while True:
    img = background.copy()
    add_transparent_image(img, overlay, x_offset, y_offset)

    cv2.imshow("", img)
    key = cv2.waitKey()
    if key == 0: y_offset -= 10  # up
    if key == 1: y_offset += 10  # down
    if key == 2: x_offset -= 10  # left
    if key == 3: x_offset += 10  # right
    if key == 27: break  # escape

**Use this function to place your overlay on any background image. if want to resize overlay use this overlay = cv2.resize(overlay, (200,200)) and then pass resized overlay into the function. **

import cv2
import numpy as np


def image_overlay_second_method(img1, img2, location, min_thresh=0, is_transparent=False):
    h, w = img1.shape[:2]
    h1, w1 = img2.shape[:2]
    x, y = location
    roi = img1[y:y + h1, x:x + w1]

    gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    _, mask = cv2.threshold(gray, min_thresh, 255, cv2.THRESH_BINARY)
    mask_inv = cv2.bitwise_not(mask)

    img_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
    img_fg = cv2.bitwise_and(img2, img2, mask=mask)
    dst = cv2.add(img_bg, img_fg)
    if is_transparent:
        dst = cv2.addWeighted(img1[y:y + h1, x:x + w1], 0.1, dst, 0.9, None)
    img1[y:y + h1, x:x + w1] = dst
    return img1

if __name__ == '__main__':
    background = cv2.imread('background.jpg')
    overlay = cv2.imread('overlay.png')
    output = image_overlay_third_method(background, overlay, location=(800,50), min_thresh=0, is_transparent=True)
    cv2.imwrite('output.png', output)

background.jpg

output.png