import cv2
import numpy as np
import os
import csv
################################################################################

print 'Set Image Directory'

imgFile = 'images/skin_color_database/skin_color_database_1/'
################################################################################

# define variables for histogram

colorRange = 64

# skin color counts
countSkin = np.zeros(colorRange ** 3, np.double)
# non-skin color counts
countNonSkin = np.zeros(colorRange ** 3, np.double)

# label sets true if it's a label image, otherwise sets false if it's an original image
label = True
################################################################################

print 'Skin Color Histogram'

for fileName in os.listdir(imgFile):
        
    if label == True:
        # load a label image
        print 'Label File Name:',fileName
        imgLabel = cv2.imread(imgFile + fileName)
        
        # convert color space from bgr to gray                      
        imgLabel = cv2.cvtColor(imgLabel, cv2.COLOR_BGR2GRAY)

    else:
        # load an original image
        print 'Original File Name:',fileName       
        img = cv2.imread(imgFile + fileName)
    ############################################################################
    
    # count pixel color values
    
    if label == False:
        
        # get image shape
        rows,cols,channels = img.shape 
        
        for r in range(rows):
            for c in range(cols):
                
                # get values from rgb color space
                B = img.item(r,c,0) / 4
                G = img.item(r,c,1) / 4
                R = img.item(r,c,2) / 4
                
                color = B * 64 * 64 + G * 64 + R
                                
                if imgLabel.item(r,c) == 255:
                    countSkin[color] += 1
                else:
                    countNonSkin[color] += 1                                  

    label = not label
################################################################################

# data combination
# ---------countSkin------------
# ---------countNonSkin---------
data = [countSkin, countNonSkin]
################################################################################

# write csv file

csvFile = open('skin_nonskin.csv','wb')
writer = csv.writer(csvFile)

for row in data:
    writer.writerow([row[col] for col in range(colorRange ** 3)])
################################################################################

print 'Goodbye!'

（2）测试代码

import os
import cv2
import csv
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
################################################################################

print 'read csv file'

VRange = 64 * 64 * 64
print VRange

countSkin = np.zeros(VRange, np.double)
countNonSkin = np.zeros(VRange, np.double)

csvFile = open('skin_nonskin.csv', "rb")
reader = csv.reader(csvFile)

rowNum = 0
for row in reader:

    colNum = 0    
    for col in row:        
        # restore countSkin and countNonSkin
        modNum = rowNum - rowNum / 2 * 2
        
        if modNum == 1:                
            countNonSkin[((rowNum - 1) * VRange + colNum)] = float(col)                
        if modNum == 0:          
            countSkin[((rowNum - 1) * VRange + colNum)] = float(col)
                                            
        colNum += 1
                  
    rowNum += 1
################################################################################

print 'Skin Color Database Result'

# count
print 'count of skin pixels:',countSkin
print 'count of non-skin pixels:',countNonSkin
################################################################################

print 'prior probability'

skinPix = sum(countSkin)
nskinPix = sum(countNonSkin)

PCs = skinPix / (skinPix + nskinPix) # P(Cs)
PCns = 1 - PCs # P(Cns)

print 'probability of skin class:',PCs
print 'probability of non-skin class',PCns
################################################################################

print 'conditional probability variables'

PVCs = np.zeros(VRange, np.double)  # P(V|Cs)
PVCns = np.zeros(VRange, np.double) # P(V|Cns)

for i in range(VRange):
    # pixel color probability given skin color
    PVCs[i] = countSkin[i] / skinPix

    # pixel color probability given non-skin color
    PVCns[i] = countNonSkin[i] / nskinPix
################################################################################

print 'posterior probability'

PCsV = np.zeros(VRange, np.double) # P(Cs|V)

for i in range(VRange):
    # skin probability given pixel color
    PCsV[i] = (PVCs[i] * PCs) / (PVCs[i] * PCs + PVCns[i] * PCns + 0.00000001)
################################################################################

print 'determine skin distribution'

# pixel color range
v = range(VRange)
# skin class threshold
theta = [0.6,0.7,0.8,0.9]
# color of a single class
rgbColor = np.zeros(3, np.uint8)
# rgb color value
skinPix = np.zeros(VRange, np.uint8)

# prepare for 3d plot
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')

for i in v:
    
    # split 3 channels(64b * 64b * 64b) 
    B = i / (64 * 64)
    G = (i - B * 64 * 64) / 64
    R = i - B * 64 * 64 - G * 64
    
    # given probability as threshold
    if PCsV[i] > theta[3]:
        skinPix[i] = 4
        ax.scatter(R, G, B, c='r', marker='o')                  
    else:
        skinPix[i] = 0
################################################################################

print 'display distribution of skin color'

ax.set_xlabel('Red')
ax.set_ylabel('Green')
ax.set_zlabel('Blue')

plt.show()
################################################################################

print 'Goodbye!'<span style="font-family: Arial, Helvetica, sans-serif; background-color: rgb(255, 255, 255);">  </span>