一、所需工具
**Python版本：**3.5.4(64bit)
二、相關模塊
- opencv_python模塊
- sklearn模塊
- numpy模塊
- dlib模塊
- 一些Python自帶的模塊。
三、環境搭建
（1）安裝相應版本的Python并添加到環境變量中；
（2）pip安裝相關模塊中提到的模塊。
例如：
若pip安裝報錯，請自行到：
http://www.lfd.uci.edu/~gohlke/pythonlibs/
下載pip安裝報錯模塊的whl文件，并使用：
pip install whl文件路徑+whl文件名安裝。
例如：
（已在相關文件中提供了編譯好的用于dlib庫安裝的whl文件——>因為這個庫最不好裝）
參考文獻鏈接
【1】xxxPh.D.的博客
http://www.learnopencv.com/computer-vision-for-predicting-facial-attractiveness/
【2】華南理工大學某實驗室
http://www.hcii-lab.net/data/SCUT-FBP/EN/introduce.html
四、主要思路
（1）模型訓練
用了PCA算法對特征進行了壓縮降維；
然后用隨機森林訓練模型。
數據源于網絡，據說數據“發源地”就是華南理工大學某實驗室，因此我在參考文獻上才加上了這個實驗室的鏈接。
（2）提取人臉關鍵點
主要使用了dlib庫。
使用官方提供的模型構建特征提取器。
（3）特征生成
完全參考了xxxPh.D.的博客。
（4）顏值預測
利用之前的數據和模型進行顏值預測。
使用方式
有特殊疾病者請慎重嘗試預測自己的顏值，本人不對顏值預測的結果和帶來的所有負面影響負責！！！
言歸正傳。
環境搭建完成后，解壓相關文件中的Face_Value.rar文件，cmd窗口切換到解壓后的*.py文件所在目錄。
例如：
打開test_img文件夾，將需要預測顏值的照片放入并重命名為test.jpg。
例如：
若嫌麻煩或者有其他需求，請自行修改：
getLandmarks.py文件中第13行。
最后依次運行：
train_model.py(想直接用我模型的請忽略此步)
# 模型訓練腳本
import numpy as np
from sklearn import decomposition
from sklearn.ensemble import RandomForestRegressor
from sklearn.externals import joblib
# 特征和對應的分數路徑
features_path = './data/features_ALL.txt'
ratings_path = './data/ratings.txt'
# 載入數據
# 共500組數據
# 其中前480組數據作為訓練集，后20組數據作為測試集
features = np.loadtxt(features_path, delimiter=',')
features_train = features[0: -20]
features_test = features[-20: ]
ratings = np.loadtxt(ratings_path, delimiter=',')
ratings_train = ratings[0: -20]
ratings_test = ratings[-20: ]
# 訓練模型
# 這里用PCA算法對特征進行了壓縮和降維。
# 降維之后特征變成了20維，也就是說特征一共有500行，每行是一個人的特征向量，每個特征向量有20個元素。
# 用隨機森林訓練模型
pca = decomposition.PCA(n_components=20)
pca.fit(features_train)
features_train = pca.transform(features_train)
features_test = pca.transform(features_test)
regr = RandomForestRegressor(n_estimators=50, max_depth=None, min_samples_split=10, random_state=0)
regr = regr.fit(features_train, ratings_train)
joblib.dump(regr, './model/face_rating.pkl', compress=1)
# 訓練完之后提示訓練結束
print('Generate Model Successfully!')
getLandmarks.py
# 人臉關鍵點提取腳本
import cv2
import dlib
import numpy
# 模型路徑
PREDICTOR_PATH = './model/shape_predictor_68_face_landmarks.dat'
# 使用dlib自帶的frontal_face_detector作為人臉提取器
detector = dlib.get_frontal_face_detector()
# 使用官方提供的模型構建特征提取器
predictor = dlib.shape_predictor(PREDICTOR_PATH)
face_img = cv2.imread("test_img/test.jpg")
# 使用detector進行人臉檢測，rects為返回的結果
rects = detector(face_img, 1)
# 如果檢測到人臉
if len(rects) >= 1:
print("{} faces detected".format(len(rects)))
else:
print('No faces detected')
exit()
with open('./results/landmarks.txt', 'w') as f:
f.truncate()
for faces in range(len(rects)):
# 使用predictor進行人臉關鍵點識別
landmarks = numpy.matrix([[p.x, p.y] for p in predictor(face_img, rects[faces]).parts()])
face_img = face_img.copy()
# 使用enumerate函數遍歷序列中的元素以及它們的下標
for idx, point in enumerate(landmarks):
pos = (point[0, 0], point[0, 1])
f.write(str(point[0, 0]))
f.write(',')
f.write(str(point[0, 1]))
f.write(',')
f.write('\n')
f.close()
# 成功后提示
print('Get landmarks successfully')
getFeatures.py
# 特征生成腳本
# 具體原理請參見參考論文
import math
import numpy
import itertools
def facialRatio(points):
x1 = points[0]
y1 = points[1]
x2 = points[2]
y2 = points[3]
x3 = points[4]
y3 = points[5]
x4 = points[6]
y4 = points[7]
dist1 = math.sqrt((x1-x2)**2 + (y1-y2)**2)
dist2 = math.sqrt((x3-x4)**2 + (y3-y4)**2)
ratio = dist1/dist2
return ratio
def generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates):
size = allLandmarkCoordinates.shape
if len(size) > 1:
allFeatures = numpy.zeros((size[0], len(pointIndices1)))
for x in range(0, size[0]):
landmarkCoordinates = allLandmarkCoordinates[x, :]
ratios = []
for i in range(0, len(pointIndices1)):
x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
points = [x1, y1, x2, y2, x3, y3, x4, y4]
ratios.append(facialRatio(points))
allFeatures[x, :] = numpy.asarray(ratios)
else:
allFeatures = numpy.zeros((1, len(pointIndices1)))
landmarkCoordinates = allLandmarkCoordinates
ratios = []
for i in range(0, len(pointIndices1)):
x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
points = [x1, y1, x2, y2, x3, y3, x4, y4]
ratios.append(facialRatio(points))
allFeatures[0, :] = numpy.asarray(ratios)
return allFeatures
def generateAllFeatures(allLandmarkCoordinates):
a = [18, 22, 23, 27, 37, 40, 43, 46, 28, 32, 34, 36, 5, 9, 13, 49, 55, 52, 58]
combinations = itertools.combinations(a, 4)
i = 0
pointIndices1 = []
pointIndices2 = []
pointIndices3 = []
pointIndices4 = []
for combination in combinations:
pointIndices1.append(combination[0])
pointIndices2.append(combination[1])
pointIndices3.append(combination[2])
pointIndices4.append(combination[3])
i = i+1
pointIndices1.append(combination[0])
pointIndices2.append(combination[2])
pointIndices3.append(combination[1])
pointIndices4.append(combination[3])
i = i+1
pointIndices1.append(combination[0])
pointIndices2.append(combination[3])
pointIndices3.append(combination[1])
pointIndices4.append(combination[2])
i = i+1
return generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates)
landmarks = numpy.loadtxt("./results/landmarks.txt", delimiter=',', usecols=range(136))
featuresALL = generateAllFeatures(landmarks)
numpy.savetxt("./results/my_features.txt", featuresALL, delimiter=',', fmt = '%.04f')
print("Generate Feature Successfully!")
Predict.py
# 顏值預測腳本
from sklearn.externals import joblib
import numpy as np
from sklearn import decomposition
pre_model = joblib.load('./model/face_rating.pkl')
features = np.loadtxt('./data/features_ALL.txt', delimiter=',')
my_features = np.loadtxt('./results/my_features.txt', delimiter=',')
pca = decomposition.PCA(n_components=20)
pca.fit(features)
predictions = []
if len(my_features.shape) > 1:
for i in range(len(my_features)):
feature = my_features[i, :]
feature_transfer = pca.transform(feature.reshape(1, -1))
predictions.append(pre_model.predict(feature_transfer))
print('照片中的人顏值得分依次為(滿分為5分)：')
k = 1
for pre in predictions:
print('第%d個人：' % k, end='')
print(str(pre)+'分')
k += 1
else:
feature = my_features
feature_transfer = pca.transform(feature.reshape(1, -1))
predictions.append(pre_model.predict(feature_transfer))
print('照片中的人顏值得分為(滿分為5分)：')
k = 1
for pre in predictions:
print(str(pre)+'分')
k += 1
到此這篇關于Python實現對照片中的人臉進行顏值預測的文章就介紹到這了,更多相關Python對人臉進行顏值預測內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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