How to change the opacity of boxes (cv2.rectangle)?

I would like to add a small optimization to the @HansHirse answer, Instead of creating the canvas for whole image, we can crop the rectangle first from the src image and then later swap it with the cv2.addWeighted result as:

import cv2
import numpy as np

img = cv2.imread("lena.png")

# First we crop the sub-rect from the image
x, y, w, h = 100, 100, 200, 100
sub_img = img[y:y+h, x:x+w]
white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255

res = cv2.addWeighted(sub_img, 0.5, white_rect, 0.5, 1.0)

# Putting the image back to its position
img[y:y+h, x:x+w] = res

EDIT: Since this answer seems to have some importance, I decided to edit it again, incorporating the proper blending from ZdaR's answer, which initially was an improvement to my original answer (check the timeline if interested). Also, I incorporated Jon's comments to include an example of a non-rectangular shape.

At least from my point of view, built-in functions like cv2.rectangle don't support opacity, even on BGRA images, see here. So, as I described in the linked answer, the only possibility to achieve, what you want, is to use the cv2.addWeighted function. You can simply set up a blank mask image, and draw all possible shapes on that. Doing so, you can also use that as an actual mask to limit the blending to that part only.

An example could be:

import cv2
import numpy as np

# Load image
img = cv2.imread('images/paddington.png')

# Initialize blank mask image of same dimensions for drawing the shapes
shapes = np.zeros_like(img, np.uint8)

# Draw shapes
cv2.rectangle(shapes, (5, 5), (100, 75), (255, 255, 255), cv2.FILLED)
cv2.circle(shapes, (300, 300), 75, (255, 255, 255), cv2.FILLED)

# Generate output by blending image with shapes image, using the shapes
# images also as mask to limit the blending to those parts
out = img.copy()
alpha = 0.5
mask = shapes.astype(bool)
out[mask] = cv2.addWeighted(img, alpha, shapes, 1 - alpha, 0)[mask]

# Visualization
cv2.imshow('Image', img)
cv2.imshow('Shapes', shapes)
cv2.imshow('Output', out)
cv2.waitKey(0)
cv2.destroyAllWindows()

The original Paddington img:

The intermediate image to draw the shapes on shapes:

And, the final result out:

After drawing the shapes and blending the images, you can add your texts as before.

Hope that helps!

Simply install pyshine and use putBText, it has following inputs and output.

pip install pyshine

"""

Inputs:
    img: cv2 image img
    text_offset_x, text_offset_x: X,Y location of text start
    vspace, hspace: Vertical and Horizontal space between text and box boundaries
    font_scale: Font size
    background_RGB: Background R,G,B color
    text_RGB: Text R,G,B color
    font: Font Style e.g. cv2.FONT_HERSHEY_DUPLEX,cv2.FONT_HERSHEY_SIMPLEX,cv2.FONT_HERSHEY_PLAIN,cv2.FONT_HERSHEY_COMPLEX
          cv2.FONT_HERSHEY_TRIPLEX, etc
    thickness: Thickness of the text font
    alpha: Opacity 0~1 of the box around text
    gamma: 0 by default

Output:
    img: CV2 image with text and background
"""

Example tested on Python3 and complete demonstration is here:

lena.jpg

simple.py

import pyshine as ps
import cv2
image = cv2.imread('lena.jpg')
text  =  'HELLO WORLD!'
image =  ps.putBText(image,text,text_offset_x=20,text_offset_y=20,vspace=10,hspace=10, font_scale=2.0,background_RGB=(0,250,250),text_RGB=(255,250,250))
cv2.imshow('Output', image)
cv2.imwrite('out.jpg',image)
cv2.waitKey(0)

out.jpg

another.py

import pyshine as ps
import cv2
import time
image = cv2.imread('lena.jpg')


text  =  'ID: '+str(123)
image = ps.putBText(image,text,text_offset_x=20,text_offset_y=20,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(1,1,1))
text = str(time.strftime("%H:%M %p"))
image = ps.putBText(image,text,text_offset_x=image.shape[1]-170,text_offset_y=20,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(1,1,1))

text  =  '6842'
image = ps.putBText(image,text,text_offset_x=80,text_offset_y=372,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(255,255,255))

text  =  "Lena Fors'en"
image = ps.putBText(image,text,text_offset_x=80,text_offset_y=425,vspace=20,hspace=10, font_scale=1.0,background_RGB=(20,210,4),text_RGB=(255,255,255))

text  =  'Status: '
image = ps.putBText(image,text,text_offset_x=image.shape[1]-130,text_offset_y=200,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(255,255,255))
text  =  'On time'
image = ps.putBText(image,text,text_offset_x=image.shape[1]-130,text_offset_y=242,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(255,255,255))


text  =  'Attendence: '
image = ps.putBText(image,text,text_offset_x=image.shape[1]-200,text_offset_y=394,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(255,255,255))
text  =  '96.2%      '
image = ps.putBText(image,text,text_offset_x=image.shape[1]-200,text_offset_y=436,vspace=10,hspace=10, font_scale=1.0,background_RGB=(228,225,222),text_RGB=(255,255,255))

cv2.imshow('Output', image)
cv2.imwrite('out.jpg',image)
cv2.waitKey(0)

out.jpg

A simpler solution (although a bit less efficient in terms of memory) is:

1. create a copy of the original image
2. draw the desired shapes/text on the the original image
3. get the overlay with: alpha*img + (1-alpha)*img_cpy

In this way each original pixel will not change it's value (since alpha*px + (1-alpha)px = px), whereas pixels which were drawn on will be affected by the overlay.

This eliminates the need to perform crops and pesky calculations seen in the other answers.

...and applying to to the OP's code:

frame_cpy = frame.copy()
cv2.rectangle(frame, (500, 650), (800, 700), (42, 219, 151), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (510, 685), font, 1.0, (255, 255, 255), 1)
alpha = 0.4
frame_overlay=cv2.addWeighted(frame, alpha, frame_cpy,1-alpha, gamma=0)
cv2.imshow("overlay result",frame_overlay)
cv2.waitKey(0)

Disclaimer: this answer was inspired by a post on www.pyimagesearch.com