Face Detection and masking filter program with fake webcam

facedetect_masking_filter.py

import cv2
import numpy
import pyfakewebcam as fakecam
import threading, queue
from time import sleep

imq = queue.Queue()
rectq = queue.Queue()

def faceDetector():
    CASCADE_DIR = "/usr/share/opencv/haarcascades/" # Data path (maybe depends on your distro)
    CASCADE_FILE = "haarcascade_frontalface_default.xml"    # Face model data
    cascade = cv2.CascadeClassifier(CASCADE_DIR + CASCADE_FILE)
    # Noise reduction
    px = 0
    py = 0
    while True:
        sleep(0.1)  # Reduce %CPU, keep cool, stay home.
        img = imq.get()
        if img is None:
            break
        gscale = cv2.cvtColor(sframe, cv2.COLOR_RGB2GRAY)

        facerect = cascade.detectMultiScale(gscale)
        if len(facerect) > 0:
            max_rect = facerect[0]
            for rect in facerect:
                if rect[3] > max_rect[3]:
                    max_rect = rect
            px = px*2/3 + max_rect[2]/3
            py = py*2/3 + max_rect[3]/3
            max_rect[2] = px
            max_rect[3] = py
        else:
            max_rect = None
        rectq.put(max_rect)


# Open capture source
videodev = "/dev/video1"    # Change to your own video device
cap = cv2.VideoCapture(videodev)

height, width = 720, 1280   # 720p
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
cap.set(cv2.CAP_PROP_FPS, 30)

# Open loopback device
v4l2loopback = "/dev/video20"
fake = fakecam.FakeWebcam(v4l2loopback, width, height)

# Read background image
bgfile = "./EVFe-qiUUAA1EuH.jpeg"   # Change to your own image file
bgimg = cv2.imread(bgfile)
bgimg = cv2.resize(bgimg, (width, height))

# Sound only image (https://twitter.com/evangelion_co/status/1247883138849624069)
sofile = "./EVFe99XUMAoR28L.jpeg"
soimg = cv2.imread(sofile)
soimg = cv2.resize(soimg, (width, height))

# Start face detection thread
frect = None
t1 = threading.Thread(target = faceDetector)
t1.start()

while True:
    success, frame = cap.read()

    if imq.empty(): # Feed new image when worker is waiting
        sframe = cv2.resize(frame, None, fx=0.25, fy=0.25)
        imq.put(sframe)

    if not rectq.empty():   # Update face rectangle
        frect = rectq.get()

    if frect is not None:
        # Make a mask (simple ellipse mask)
        center = int((frect[0] + frect[2]/2))*4, int(frect[1] + frect[3]/2.5)*4
        size = int(frect[2])*2, int(frect[3])*3
        mask = numpy.zeros((height, width, 3), numpy.uint8)
        cv2.ellipse(mask, center, size, 0,0,360, (255,255,255), -1)
        # Blend 2 images
        frame = cv2.addWeighted(cv2.bitwise_and(frame, mask), 1.0,
                                cv2.bitwise_and(bgimg, cv2.bitwise_not(mask)), 1.0, 0)
    else:
        frame = soimg

    # Zoom expects RGB and mirrored image
    fake.schedule_frame(cv2.flip(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), 1))

    # showing result in window (and get key input)
    cv2.imshow('camera capture', frame)
    k = cv2.waitKey(10)
    if k == 27:
        break

imq.put(None)
t1.join()
cap.release()
cv2.destroyAllWindows()

This requires opencv and pyfakewebcam modules.

Note that we need to setup v4l2loopback before run.

sudo modprobe v4l2loopback devices=1 video_nr=20 card_label="v4l2loopback" exclusive_caps=1