直男福利！手把手教你做一隻口紅色號識別器，秒變李佳琦
來自Github的口紅色號宇宙
先用番茄做個實驗？
提取這張純色圖片的RGB值在技術上是可行的，getcolor.py程式碼如下：
import colorsys import PIL.Image as Image def get_dominant_color(image): max_score = 0.0001 dominant_color = None for count,(r,g,b) in image.getcolors(image.size[0]*image.size[1]): # 轉為HSV標準 saturation = colorsys.rgb_to_hsv(r/255.0, g/255.0, b/255.0)[1] y = min(abs(r*2104+g*4130+b*802+4096+131072)>>13,235) y = (y-16.0)/(235-16) #忽略高亮色 if y > 0.9: continue score = (saturation+0.1)*count if score > max_score: max_score = score dominant_color = (r,g,b) return dominant_color
import os
import getcolor
from os.path import join as pjoin
from scipy import misc
def load_color(color_dir,list):
count = 0
for dir in os.listdir(color_dir):
img_dir = pjoin(color_dir, dir)
image = getcolor.Image.open(img_dir)
image = image.convert('RGB')
get=getcolor.get_dominant_color(image)
list.append(get)
count = count+1
#print(person_dir)
#print(count)
return count
def Mean_color(count,list):
Mean_R=Mean_G=Mean_B=0
for i in range(count):
tuple=list[i]
Mean_R+=tuple[0]
Mean_G+=tuple[1]
Mean_B+=tuple[2]
MeanC=((int)(Mean_R/count),(int)(Mean_G/count),(int)(Mean_B/count))
return Me當然是口紅的資料，文摘菌這兒用到了5個品牌，分別是聖羅蘭、香奈兒可可小姐、迪奧、美寶蓮、紀梵希，共17個系列，271個口紅色號，資料集是一個巢狀的字典資料結構，存為json串的形式，裡面記錄了每個口紅品牌系列下不同口紅色號的顏色id、名稱、和16進位制顏色值，lipstick.json部分資料集展示如下：
{"brands":[{"name":"聖羅蘭","series":
[{"name":"瑩亮純魅唇膏","lipsticks":
[{"color":"#D62352","id":"49","name":"撩騷"},
{"color":"#DC4B41","id":"14","name":"一見傾心"},
{"color":"#B22146","id":"05","name":"浮生若夢"}，資料集中儲存的RGB顏色是16進位制的字串形式，需要將其轉換成RGB值，比較兩個顏色相近與否，實際上是比較RGB三個分量維度上的誤差，最小的口紅輸出對應的品牌、系列、色號和id，程式碼如下：
import json
import getcolor
import numpy as np
import lipcolor
#filename = 'temp.txt'
##write the temp data to file##
def WtoFile(filename,RGB_temp):
num=len(RGB_temp)
with open(filename,'w') as f:
for i in range(num):
s = str(RGB_temp[i]).replace('[','').replace(']','')
f.write(s)
f.write("\n")
#operate the data #
##save the brand&series&color id&color name to sum_list##
##covert the color #D62352 to RGB_array##
##caculate the RGB difference to RGB_temp and write the value to file##
def data_operate():
with open('lipstick.json', 'r', encoding='utf-8') as f:
ret_dic = json.load(f)
#print(ret_dic['brands'])
#print(type(ret_dic)) # <class 'dict'>
#print(ret_dic['brands'][0]['name'])
b_num=len(ret_dic['brands'])
#print(b_num)#brands number
s_list=[]
#series brands#
for i in range(len(ret_dic['brands'])):
s_num=len(ret_dic['brands'][i]['series'])
s_list.append(s_num)
#print("{0} has {1} series".format((ret_dic['brands'][i]['name']),(s_list[i])))
#the lipstick color of every brands every series#
#the first loop calculate the total color numbers
sum=0
for b1 in range(b_num):
for s1 in range(s_list[b1]):
brand_name=ret_dic['brands'][b1]['name']
lip_name=ret_dic['brands'][b1]['series'][s1]['name']
color_num=len(ret_dic['brands'][b1]['series'][s1]['lipsticks'])
sum+=color_num#calculate the total color numbers
#the second loop save the message to a list#
sum_list=np.zeros((sum,4), dtype=(str,8))
value_array=np.zeros((sum,6), dtype=int)
i=0
for b2 in range(b_num):
for s2 in range(s_list[b2]):
brand_name=ret_dic['brands'][b2]['name']
#print(type(brand_name))
lip_name=ret_dic['brands'][b2]['series'][s2]['name']
color_num=len(ret_dic['brands'][b2]['series'][s2]['lipsticks'])
for c in range(color_num):
color_value=ret_dic['brands'][b2]['series'][s2]['lipsticks'][c]['color']
color_name=ret_dic['brands'][b2]['series'][s2]['lipsticks'][c]['name']
color_id=ret_dic['brands'][b2]['series'][s2]['lipsticks'][c]['id']
#print("{0} series {1} has {2} colors,color {3}：{4}".format(brand_name,lip_name,color_num,c+1,color_name))
sum_list[i][0]=brand_name
sum_list[i][1]=lip_name
sum_list[i][2]=color_id
sum_list[i][3]=color_name
#value_array[i]=value_array[i][1]
#convert "#D62352" to [13 6 2 3 5 2]#
for l in range(6):
temp=color_value[l+1]
if(temp>='A'and temp<='F'):
temp1=ord(temp)-ord('A')+10
else:
temp1=ord(temp)-ord('0')
value_array[i][l]=temp1
i+=1
#the third loop covert value_array to RGB_array#
RGB_array=np.zeros((sum,3), dtype=int)
for i in range(sum):
RGB_array[i][0]=value_array[i][0]*16+value_array[i][1]
RGB_array[i][1]=value_array[i][2]*16+value_array[i][3]
RGB_array[i][2]=value_array[i][4]*16+value_array[i][5]
#calculate the similar and save to RGB_temp
#RGB_temp=np.zeros((sum,1), dtype=int)
RGB_temp=np.zeros((sum,1), dtype=float)
for i in range(sum):
R=RGB_array[i][0]
G=RGB_array[i][1]
B=RGB_array[i][2]
RGB_temp[i]=abs(get[0]-R)+abs(get[1]*3/4-G)+abs(get[2]-B)
RGB_temp.tolist();#covert array to list
#print(RGB_temp)
filename="temp.txt"
WtoFile(filename,RGB_temp)
#sort the RGB_temp#
result=sorted(range(len(RGB_temp)), key=lambda k: RGB_temp[k])
#print(result)
#output the three max prob of the lipsticks#
print("The first three possible lipstick brand and color id&name are as follows:")
for i in range(3):
idex=result[i]
print(sum_list[idex])
print("The first three possible lipstick brand RGB value are as follows:")
for i in range(3):
idex=result[i]
R=RGB_array[idex][0]
G=RGB_array[idex][1]
B=RGB_array[idex][2]
tuple=(R,G,B)
print(tuple)
if __name__ == '__main__':
#image = getcolor.Image.open(inputpath)
#image = image.convert('RGB')
#get=getcolor.get_dominant_color(image)#tuple #get=(231, 213, 211)
list=[]
color_dir="output"
count=lipcolor.load_color(color_dir,list)
get=lipcolor.Mean_color(count,list)
print("the extracted RGB value of the color is {0}".format(get))
#operate the data#
data_operat
提取到的番茄顏色：
'迪奧' '烈豔藍金唇膏' '080' '微笑正紅’的顏色：
'聖羅蘭' '純口紅' '56' '橙紅織錦'的顏色：
'紀梵希' '高定香榭天鵝絨唇' '325' '聖水紅'的顏色：
文摘菌已經眼花繚亂，三個顏色……有區別嗎？！以後不如準備統一叫它們，番茄色！
不過，這也正說明了，剛剛的提取&對比方法可行！
擷取到的嘴唇區域如下：
聖羅蘭官網#842C71口紅
人臉識別和擷取嘴唇區域的程式碼如下：
import numpy as np
import cv2
import dlib
from PIL import Image
def crop(source,pos):
x1=pos[2][0]
y1=pos[2][1]
x2=pos[1][0]
y2=pos[1][1]
d=abs(x2-x1)
region = source[(int)(y1-d*0.75):y2,x1:x2]
# save the image
cv2.imwrite("output/Mouth1.jpg", region)
x1=pos[1][0]
y1=pos[1][1]
x2=pos[0][0]
y2=pos[0][1]
d=abs(x1-x2)
region = source[y1-d:y2,x1:x2]
# save the image
cv2.imwrite("output/Mouth2.jpg", region)
def detect_mouth(img,pos):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
detector = dlib.get_frontal_face_detector()
#use the predictor
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
for a in dets:
cv2.rectangle(img,(a.left(),a.top()),(a.right(),a.bottom()),(255,0,0))
#point_list=[]#save the mouth point to point_list[]#
#Extract 68 feature points of the face and crop the lip image#
for index, face in enumerate(dets):
print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(), face.bottom()))
shape = predictor(gray, face)
for i, pt in enumerate(shape.parts()):
#print('Part {}: {}'.format(i, pt))
#print(i)
pt_pos = (pt.x, pt.y)
if i>=48 and i<=67:
cv2.circle(img, pt_pos, 2, (255, 0, 0), 1)
if i>=56 and i<=58:
#print(pt_pos)
pos[i-56][0]=pt.x
pos[i-56][1]=pt.y
#cv2.circle(img, pt_pos, 2, (255, 0, 0), 1)
return img
if __name__ == "__main__":
img = cv2.imread("test3.png")
#copy the input image for the later crop#
img_clone = np.copy(img)
cv2.imwrite("input/source.jpg",img_clone)
#save the lip position to pos array#
pos=np.zeros((3,2), dtype=int)
result=detect_mouth(img,pos)
cv2.imwrite("input/source2.jpg",result)
#crop the lip areas#
source = cv2.imread("input/source.jpg")
crop(source,pos)
# show the result
cv2.imshow('FaceDetect',result)
cv2.waitKey(0)
cv2.destroyAllWindow
誤差分析
嘴唇區域擷取不可避免會擷取到皮膚中的一部分顏色，雖然演算法已經將那種可能降到最低； 顏色提取上，雖然擷取多個嘴唇圖片求平均值，但是本身的提取演算法還是和實際值稍有偏差； RGB顏色相似度比對的演算法也不夠精確； 最最重要的是，照片必須是原圖，而且光線要自然，加了濾鏡的圖是怎麼也不可能識別出來的。
文末福利：實時人像口紅色號預測
#coding=utf8
import cv2
import time
print('Press Esc to exit')
imgWindow = cv2.namedWindow('FaceDetect', cv2.WINDOW_NORMAL)
import sys
import os
import dlib
import glob
import numpy
from skimage import io
def detect_face():
capInput = cv2.VideoCapture(0)
#nextCaptureTime = time.time()
faces = []
feas = []
if not capInput.isOpened(): print('Capture failed because of camera')
while 1:
ret, img = capInput.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
time=0
eTime = time.time() + 0.1
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
dets = detector(gray, 1)
print("Number of faces detected: {}".format(len(dets)))
for a in dets:
cv2.rectangle(img,(a.left(),a.top()),(a.right(),a.bottom()),(255,0,0))
for index, face in enumerate(dets):
print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(), face.bottom()))
shape = predictor(gray, face)
for i, pt in enumerate(shape.parts()):
#print('Part {}: {}'.format(i, pt))
pt_pos = (pt.x, pt.y)
cv2.circle(img, pt_pos, 2, (255, 0, 0), 1)
cv2.imshow('FaceDetect',img)
if cv2.waitKey(1) & 0xFF == 27: break
capInput.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
detect_face()好啦，佳期如夢，雙星良夜，在這個充滿愛意的日子裡，定位好女神常用的口紅色號，和那個她來場華麗的邂逅吧！
來自 “ ITPUB部落格 ” ，連結：http://blog.itpub.net/31562039/viewspace-2652990/，如需轉載，請註明出處，否則將追究法律責任。
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