Skin Detection with Naive Bayes on Colab

skin_detection.py

import cv2
import os  
import numpy as np
import csv
import pandas as pd

dir = "/content/drive/MyDrive/Applied_data_Science"
def absoluteFilePaths(directory):
  filePaths = []
  for dirpath,_,filenames in os.walk(directory):
    for f in filenames:
      filePaths.append(os.path.abspath(os.path.join(dirpath, f))) 
  
  return filePaths

imagePaths = absoluteFilePaths("/content/drive/MyDrive/Applied_data_Science/Pratheepan_Dataset/FacePhoto")
maskPaths = absoluteFilePaths("/content/drive/MyDrive/Applied_data_Science/Ground_Truth/GroundT_FacePhoto")

skinPixelNumber = np.zeros((256,256,256))
nonskinPixelNumber = np.zeros((256,256,256))

## calculate skin and nonskinpixel number

for i in range(len(imagePaths)):
  image = cv2.imread(imagePaths[i])
  mask_image = cv2.imread(maskPaths[i])
  print(image.shape)

  for y in range(image.shape[1]):
      for x in range(image.shape[0]):
        blue, green, red = image[x,y]

        maskBlue, maskGreen , maskRed = mask_image[x,y]
        
        if(maskRed>250 and maskGreen>250 and maskBlue>250):  ##check the rgb combination is greater skin or not
          skinPixelNumber[red][green][blue] += 1  ## incrementing skinPixekNumber value(default = 0) of that rgb combination.. like, skinPixelNumber[10][20][30] += 1 
        else:
          nonskinPixelNumber[red][green][blue] += 1  ##incrementing non_skin value(default = 0) of that rgb combination.. like, nonskinPixelNumber[10][20][30] += 1
          
  print('image no: ' + str(i) + ' processed Complete!')

## Calculate Probability

skinPixels = 0
nonSkinPixels = 0
probability = 0


for i in range(256):
  for j in range(256):
    for k in range(256):
      skinPixelNumber[i][j][k] += 1                             ## Laplacian Smothing
      skinPixels += skinPixelNumber[i][j][k]
      nonskinPixelNumber[i][j][k] += 1
      nonSkinPixels += nonskinPixelNumber[i][j][k]

probabilityOfSkin = skinPixels / (skinPixels + nonSkinPixels)

f = open("./mynu.text", 'w')
f.write("")
for i in range(256):
  for j in range(256):
    for k in range(256):
      probability = skinPixelNumber[i][j][k] * probabilityOfSkin / (skinPixelNumber[i][j][k] + nonskinPixelNumber[i][j][k])
      f.write(str(probability) + "\n")
      # print(probability)

print("Process Complete")

f.close()

ratio = np.zeros((256,256,256))

with open("/content/mynu.text", "r") as file:
    for i in range(256):
      for j in range(256):
        for k in range(256):
          probability = file.readline()
          ratio[i][j][k] = float(probability)

image = cv2.imread("/content/corey.jpg")

for y in range(image.shape[1]):
  for x in range(image.shape[0]):
    blue , green, red = image[x,y]
    if(ratio[red][green][blue] > .12):
      image[x,y]= (250, 250, 250)
    else:
      image[x,y] = (0, 0, 0)
  print("-", end="")

cv2.imwrite("mask.jpg",image)

## Orginal Image
from PIL import Image
img = Image.open("/content/corey.jpg")
img

## Masked image

from PIL import Image
img = Image.open("/content/mask.jpg")
img