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Python: Background MattingΒΆ

Background matting use only one segmentation model to generate a human body mask. This mask can figure out which pixels belong to humans and which belong to the background. This output can be used for background replacement (like in the Google Meet app). The segmentation model was taken from this implementation by nihui, the author of the NCNN framework. The author also has a webpage for a live demo on web browsers.

https://github.com/nihui/ncnn-webassembly-portrait-segmentation.

Source code:

import cv2
import json
from daisykit.utils import get_asset_file
from daisykit import BackgroundMattingFlow
 
config = {
    "background_matting_model": {
        "model": get_asset_file("models/background_matting/erd/erdnet.param"),
        "weights": get_asset_file("models/background_matting/erd/erdnet.bin"),
        "input_width": 256,
        "input_height": 256,
        "use_gpu": False
    }
}
 
# Load background
default_bg_file = get_asset_file("images/background.jpg")
background = cv2.imread(default_bg_file)
background = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
 
background_matting_flow = BackgroundMattingFlow(json.dumps(config), background)
 
# Open video stream from webcam
vid = cv2.VideoCapture(0)
 
while(True):
 
    # Capture the video frame
    ret, frame = vid.read()
 
    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
    mask = background_matting_flow.Process(image)
    background_matting_flow.DrawResult(image, mask)
 
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
    # Display the result frame
    cv2.imshow('frame', frame)
    cv2.imshow('result', image)
 
    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

In the source code, we use get_asset_file("images/background.jpg") to download the default background. You can use another image for the background by replacing it with a path to an image file.

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