Unitoi01

import cv2
import sys
import os
import random
import time

colArr= videoArr()
play=False
playing=False;
#cascade for face finding

Unitoi01

def videoArr():
    colArr=[0 for x in range(4)]
    colArr[0]= "blue.MP4"
    colArr[1]= "green (1).MP4"
    colArr[2]= "pink.MP4"
    colArr[3]= "white.MP4"
    return colArr

Unitoi01

def randomVideo(colArr):
    rand= random.randint(0,2)
    answer= colArr[rand]
    return answer

Unitoi01

def videoPlay(str):
    try:
        os.startfile(str)
    except Exception, e:
        print str(e)
#closes VLC

Unitoi01

def selectionSort(arr):
    for i in range(len(arr)):
        minId= i
        for j in range(i+1, len(arr)):
            if(arr[minId][0]>arr[j][0]):
                minId=j
        arr[i], arr[minId]= arr[minId], arr[i]

Unitoi01

def colorAnswer(r,g,b):
    colArr= colorArray()
    for i in range(len(colArr)):
        colArr[i]= (colorDifference(r,g,b,colArr[i][0], colArr[i][1],colArr[i][2]),colArr[i][3])
    selectionSort(colArr)
    return(colArr[0][1])

Unitoi01

def colorArray():
    colArr=[[0 for x in range(3)] for y in range(12)]
    #these colors are from darkest to lightest skin tone. The number at the end is to tell which value we are considering after sorting
    #black skin
    colArr[0]=[117,85,61,0]
    colArr[1]=[159,136,121,1]
    #bronze Skin
    colArr[2]=[156,115,69,2]
    colArr[3]=[182,157,69,3]
    #medium brown skin

Unitoi01

def colorDifference(r,g,b,r1,g1,b1):
    red= abs((r-r1)**2)
    blue= abs((b-b1)**2)
    green= abs((g-g1)**2)
    answer=math.sqrt(red+blue+green)

    return answer;

Unitoi01

for (x,y,w,h) in faces:
    pic = cv.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    gray= cv.rectangle(gray,(x,y),(x+w,y+h),(0),2)
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = img[y:y+h, x:x+w]
    
cv.imshow('pic',pic)
cv.waitKey(0)
cv.destroyAllWindows()

Unitoi01

import numpy as np 
import cv2 as cv
import math


face_cascade= cv.CascadeClassifier('C:\Program Files\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml')
#direct path to the haarcascade, which is included in openCV
pic= cv.imread('face.jpg')
#the path to one image that OpenCV will convert to a numpy array
gray= cv.imread('face.jpg,0)