带年龄的版本 Keras 实现的性别年龄检测
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__author__ = 'pangzhiwei'
import cv2
import dlib
import numpy as np
import math
import itertools
from sklearn.externals import joblib
from sklearn import decomposition
import bottle
from bottle import request
import urllib.request
import json
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 = np.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, :] = np.asarray(ratios)
else:
allFeatures = np.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, :] = np.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)
def fetch_face_pic(face,predictor):
rects = detector(face, 1)
#str = ""
#strs = ""
arrs = []
face_arr = []
for faces in range(len(rects)):
# 使用predictor进行人脸关键点识别
#print(rects[faces])
landmarks = np.matrix([[p.x, p.y] for p in predictor(face, rects[faces]).parts()])
#face_img = face.copy()
# 使用enumerate函数遍历序列中的元素以及它们的下标
arr = []
for idx, point in enumerate(landmarks):
arr = np.append(arr,point[0,0])
arr = np.append(arr,point[0,1])
#strs += str(point[0, 0]) + ',' + str(point[0, 1]) + ','
#pos = (point[0, 0], point[0, 1])
#print(point)
#f.write(str(point[0, 0]))
#f.write(',')
#f.write(str(point[0, 1]))
#f.write(',')
#f.write('\n')
if len(arrs) == 0:
arrs = [arr]
else:
arrs = np.concatenate((arrs,[arr]),axis=0)
f = rects[faces]
[x1,x2,y1,y2]=[f.left(),f.right(),f.top(),f.bottom()]
a = [[x1,x2,y1,y2]]
if len(face_arr) == 0:
face_arr = a
else:
face_arr = np.concatenate((face_arr,a) ,axis=0)
return arrs,face_arr
def predict(my_features):
predictions = []
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).tolist())
print(i)
'''
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('照片中的人颜值得分依次为(满分为5分)：')
k = 1
for pre in predictions:
print('第%d个人：' % 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('照片中的人颜值得分为(满分为5分)：')
k = 1
for pre in predictions:
print(str(pre)+'分')
k += 1
'''
return predictions
PREDICTOR_PATH = './model/shape_predictor_68_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
# 使用官方提供的模型构建特征提取器
predictor = dlib.shape_predictor(PREDICTOR_PATH)
pre_model = joblib.load('./model/face_rating.pkl')
features = np.loadtxt('./data/features_ALL.txt', delimiter=',')
pca = decomposition.PCA(n_components=20)
pca.fit(features)
@bottle.route('/find', method='GET')
def do_find():
w = request.query.get("url")
#print(w)
resp = urllib.request.urlopen(w)
image = np.asarray(bytearray(resp.read()),dtype="uint8")
image = cv2.imdecode(image,cv2.IMREAD_COLOR)
arrs,faces = fetch_face_pic(image,predictor)
print(arrs)
my_features = generateAllFeatures(arrs)
if len(my_features.shape) > 1:
predictions = predict(my_features,)
print(faces)
print(predictions)
# print(type(predictions))
result =[
faces.tolist(),predictions
]
#print(image)
print(faces)
return json.dumps(result)
bottle.run(host='0.0.0.0', port=8888)
最新代码及model 文件见 https://github.com/endpang/xindong