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DL_model.py
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from __future__ import print_function
import numpy as np
import warnings
from keras.models import Model
from keras.layers import Flatten, Dense, Input
from keras.layers import Convolution2D, MaxPooling2D
from scipy import misc
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Activation
import copy
from PIL import Image, ImageDraw
import os
import cv2
import sys
import tensorflow as tf
class Are_U_Beatutifut:
def __init__(self):
self.path = 'weight/20170311-2.h5'
self.model = None
self.read_dir = 'image/'
self.save_dir = 'temp/'
self.face_cascade = cv2.CascadeClassifier("haarcascades_cuda/haarcascade_frontalface_alt2.xml")
#build the vgg-network
def build(self):
input_shape = (224, 224, 3)
img_input = Input(shape=input_shape)
# Block 1
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='conv1_1')(img_input)
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='conv1_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
# Block 2
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_1')(x)
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)
# Block 3
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_1')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_2')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x)
# Block 4
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_1')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_2')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x)
# Block 5
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_1')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_2')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool5')(x)
x = Flatten(name='flatten')(x)
x = Dense(1024, activation=None, name='fc6')(x)
x = BatchNormalization()(x)
x = Activation(activation='relu')(x)
x = Dense(512, activation=None, name='fc7')(x)
x = BatchNormalization()(x)
x = Activation(activation='relu')(x)
x = Dense(1, activation=None, name='fc8')(x)
x = BatchNormalization()(x)
out = Activation(activation='sigmoid')(x)
self.model = Model(img_input, out)
self.model.load_weights(self.path)
print('model build finish')
def predict(self,image_name): #此处传入numpy类型
im = misc.imresize(image_name, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.expand_dims(im, axis=0)
#im = im/255.
res = self.model.predict(im)
res = res*100
return res
def detectFaces(self,image_path): #图片路径
#img = cv2.imread(image_path)
img = misc.imread(image_path,mode='RGB')
#opencv 用于人脸检测的特征
#face_cascade = cv2.CascadeClassifier("haarcascades_cuda/haarcascade_frontalface_alt2.xml")
if img.ndim == 3:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img # if语句：如果img维度为3，说明不是灰度图，先转化为灰度图gray，如果不为3，也就是2，原图就是灰度图
faces = self.face_cascade.detectMultiScale(gray, 1.3,5) # 1.3和5是特征的最小、最大检测窗口，它改变检测结果也会改变
result = []
for (x, y, width, height) in faces:
result.append((x-width*0.1, y-height*0.15, x + width*1.05, y + height*1.05)) #设置框住人脸的大小
#result.append((x-width, y-height, x + width, y + height))
return result
def draw_image(self,image_name): #用于保存框住的图片
image_path = self.read_dir+image_name
faces = self.detectFaces(image_path) #检测人脸位置
img = Image.open(image_path)
if faces:
draw_instance = ImageDraw.Draw(img)
for (x1, y1, x2, y2) in faces:
draw_instance.rectangle((x1, y1, x2, y2), outline=(255,0,0))
img.save(self.save_dir + image_name)
#此处还差一个给人脸上标记的功能，有时间在写吧。。。。。。。
def rating(self,image_path):
positions = self.detectFaces(image_path)
img = Image.open(image_path).convert('RGB')
count = len(positions)
rating = []
flag = 0
result = []
if positions:
flag = 1
rating = np.zeros([count])
i = 0
for (x1,y1,x2,y2) in positions:
data = img.crop((x1,y1,x2,y2))
rating[i] = self.predict(data) #对人得分进行预测
i+=1
rating = rating.tolist()
else:
rating.append(-1.0)
print('no face in the image')
result.append(flag) #int 类型
result.append(count) #int 类型
result.extend(rating) #float 类型
return result