color_face.py

import cv2
import signal
import matplotlib.pyplot as plt

from bokeh.plotting import figure
from bokeh.io import output_notebook, show, push_notebook
import cv2
import time

# https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml
face_cascade = cv2.CascadeClassifier('/Users/k/Desktop/haarcascade_frontalface_default.xml')

output_notebook()

cap = cv2.VideoCapture(0)
ret, frame = cap.read()

frameColor=cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
frameColor=cv2.flip(frameColor, -1)

frameGrey=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frameGrey=cv2.flip(frameGrey, -1)

decolored = cv2.cvtColor(frameGrey, cv2.COLOR_GRAY2RGBA)

width=decolored.shape[1]
height=decolored.shape[0]

p = figure(x_range=(0,width), y_range=(0,height), output_backend="webgl", width=width, height=height)

myImage = p.image_rgba(image=[decolored], x=0, y=0, dw=width, dh=height)
show(p, notebook_handle=True)

while True:
    ret, frame = cap.read()
    
    frameColor=cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
    frameColor=cv2.flip(frameColor, -1)

    frameGrey=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    frameGrey=cv2.flip(frameGrey, -1)
    
    decolored = cv2.cvtColor(frameGrey, cv2.COLOR_GRAY2RGBA)

    faces = face_cascade.detectMultiScale(frameGrey, 1.1, 4)
    
    for (x, y, w, h) in faces:
        colorFace = frameColor[y:y+h,x:x+w]
        decolored[y:y+h,x:x+w] = colorFace    
    
    myImage.data_source.data['image']=[decolored]
    push_notebook()
    time.sleep(0.1)