用keras实现人脸关键点检测

转载于：http://www.cnblogs.com/ansang/p/8583122.html

上一个代码只能实现小数据的读取与训练，在大数据训练的情况下。会造内存紧张，于是我根据keras的官方文档，对上一个代码进行了改进。

用keras实现人脸关键点检测

数据集：https://pan.baidu.com/s/1cnAxJJmN9nQUVYj8w0WocA

第一步：准备好需要的库

• tensorflow  1.4.0
• h5py 2.7.0 
• hdf5 1.8.15.1
• Keras     2.0.8
• opencv-python     3.3.0
• numpy    1.13.3+mkl

第二步：准备数据集：

我对每一张图像进行了剪裁，使图像的大小为178*178的正方形。

并且对于原有的lable进行了优化

第三步：将图片和标签转成numpy array格式：

参数

1 trainpath = 'E:/pycode/facial-keypoints-master/data/50000train/'
2 testpath = 'E:/pycode/facial-keypoints-master/data/50000test/'
3 imgsize = 178
4 train_samples =40000
5 test_samples = 200
6 batch_size = 32

 

 1 def __data_label__(path):
 2     f = open(path + "lable-40.txt", "r")
 3     j = 0
 4     i = -1
 5     datalist = []
 6     labellist = []
 7     while True:
 8 
 9         for line in f.readlines():
10             i += 1
11             j += 1
12             a = line.replace("\n", "")
13             b = a.split(",")
14             lable = b[1:]
15             # print(b[1:])
16             #对标签进行归一化（不归一化也行）
17             # for num in b[1:]:
18             #     lab = int(num) / 255.0
19             #     labellist.append(lab)
20             # lab = labellist[i * 10:j * 10]
21             imgname = path + b[0]
22             images = load_img(imgname)
23             images = img_to_array(images).astype('float32')
24             # 对图片进行归一化（不归一化也行）
25             # images /= 255.0
26             image = np.expand_dims(images, axis=0)
27             lables = np.array(lable)
28 
29             # lable =keras.utils.np_utils.to_categorical(lable)
30             # lable = np.expand_dims(lable, axis=0)
31             lable = lables.reshape(1, 10)
32 　　　　　　　#这里使用了生成器
33             yield (image,lable)

第四步：搭建网络：

这里使用非常简单的网络

 1     def __CNN__(self):
 2         model = Sequential()#178*178*3
 3         model.add(Conv2D(32, (3, 3), input_shape=(imgsize, imgsize, 3)))
 4         model.add(Activation('relu'))
 5         model.add(MaxPooling2D(pool_size=(2, 2)))
 6 
 7         model.add(Conv2D(32, (3, 3)))
 8         model.add(Activation('relu'))
 9         model.add(MaxPooling2D(pool_size=(2, 2)))
10 
11         model.add(Conv2D(64, (3, 3)))
12         model.add(Activation('relu'))
13         model.add(MaxPooling2D(pool_size=(2, 2)))
14 
15         model.add(Flatten())
16         model.add(Dense(64))
17         model.add(Activation('relu'))
18         model.add(Dropout(0.5))
19         model.add(Dense(10))
20         return model
21 #因为是回归问题，抛弃了softmax

_________________________________________________________________
Layer (type) Output Shape Param # 
=================================================================
conv2d_1 (Conv2D) (None, 176, 176, 32) 896 
_________________________________________________________________
activation_1 (Activation) (None, 176, 176, 32) 0 
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 88, 88, 32) 0 
_________________________________________________________________
conv2d_2 (Conv2D) (None, 86, 86, 32) 9248 
_________________________________________________________________
activation_2 (Activation) (None, 86, 86, 32) 0 
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 43, 43, 32) 0 
_________________________________________________________________
conv2d_3 (Conv2D) (None, 41, 41, 64) 18496 
_________________________________________________________________
activation_3 (Activation) (None, 41, 41, 64) 0 
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 20, 20, 64) 0 
_________________________________________________________________
flatten_1 (Flatten) (None, 25600) 0 
_________________________________________________________________
dense_1 (Dense) (None, 64) 1638464 
_________________________________________________________________
activation_4 (Activation) (None, 64) 0 
_________________________________________________________________
dropout_1 (Dropout) (None, 64) 0 
_________________________________________________________________
dense_2 (Dense) (None, 10) 650 
=================================================================
Total params: 1,667,754
Trainable params: 1,667,754
Non-trainable params: 0
_________________________________________________________________

第五步：训练网络：

 1 def train(model):
 2     # print(lable.shape)
 3     model.compile(loss='mse', optimizer='adam')
 4     # optimizer = SGD(lr=0.03, momentum=0.9, nesterov=True)
 5     # model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])
 6     epoch_num = 14
 7     learning_rate = np.linspace(0.03, 0.01, epoch_num)
 8     change_lr = LearningRateScheduler(lambda epoch: float(learning_rate[epoch]))
 9     early_stop = EarlyStopping(monitor='val_loss', patience=20, verbose=1, mode='auto')
10     check_point = ModelCheckpoint('CNN_model_final.h5', monitor='val_loss', verbose=0, save_best_only=True,
11                                   save_weights_only=False, mode='auto', period=1)
12 
13     model.fit_generator(__data_label__(trainpath),callbacks=[check_point,early_stop,change_lr],samples_per_epoch=int(train_samples // batch_size),
14                         epochs=epoch_num,validation_steps = int(test_samples // batch_size),validation_data=__data_label__(testpath))
15 
16     # model.fit(traindata, trainlabel, batch_size=32, epochs=50,
17     #           validation_data=(testdata, testlabel))
18     model.evaluate_generator(__data_label__(testpath),steps=10)
19 
20 def save(model, file_path=FILE_PATH):
21     print('Model Saved.')
22     model.save_weights(file_path)
23 
24 
25 def predict(model,image):
26     # 预测样本分类
27     image = cv2.resize(image, (imgsize, imgsize))
28     image.astype('float32')
29     image /= 255
30 
31     #归一化
32     result = model.predict(image)
33     result = result*1000+20
34 
35     print(result)
36     return result

 

使用了fit_generator这一方法，加入了learning_rate,LearningRateScheduler,early_stop等参数。

第六步：图像验证

 1 import tes_main
 2 from keras.preprocessing.image import load_img, img_to_array
 3 import numpy as np
 4 import cv2
 5 FILE_PATH = 'E:\\pycode\\facial-keypoints-master\\code\\CNN_model_final.h5'
 6 imgsize =178
 7 def point(img,x, y):
 8     cv2.circle(img, (x, y), 1, (0, 0, 255), 10)
 9 
10 Model = tes_main.Model()
11 model = Model.__CNN__()
12 Model.load(model,FILE_PATH)
13 img = []
14 # path = "D:\\Users\\a\\Pictures\\face_landmark_data\data\\test\\000803.jpg"
15 path = "E:\pycode\\facial-keypoints-master\data\\50000test\\049971.jpg"
16 # image = load_img(path)
17 # img.append(img_to_array(image))
18 # img_data = np.array(img)
19 imgs = cv2.imread(path)
20 # img_datas = np.reshape(imgs,(imgsize, imgsize,3))
21 image = cv2.resize(imgs, (imgsize, imgsize))
22 rects = Model.predict(model,imgs)
23 
24 for x, y, w, h, a,b,c,d,e,f in rects:
25     point(image,x,y)
26     point(image,w, h)
27     point(image,a,b)
28     point(image,c,d)
29     point(image,e,f)
30 
31 cv2.imshow('img', image)
32 cv2.waitKey(0)
33 cv2.destroyAllWindows()

完整代码如下

  1 from tensorflow.contrib.keras.api.keras.preprocessing.image import ImageDataGenerator,img_to_array
  2 from keras.models import Sequential
  3 from keras.layers.core import Dense, Dropout, Activation, Flatten
  4 from keras.layers.advanced_activations import PReLU
  5 from keras.layers.convolutional import Conv2D, MaxPooling2D,ZeroPadding2D
  6 from keras.preprocessing.image import load_img, img_to_array
  7 from keras.optimizers import  SGD
  8 import numpy as np
  9 import cv2
 10 from keras.callbacks import *
 11 import keras
 12 
 13 FILE_PATH = 'E:\\pycode\\facial-keypoints-master\\code\\CNN_model_final.h5'
 14 trainpath = 'E:/pycode/facial-keypoints-master/data/50000train/'
 15 testpath = 'E:/pycode/facial-keypoints-master/data/50000test/'
 16 imgsize = 178
 17 train_samples =40000
 18 test_samples = 200
 19 batch_size = 32
 20 def __data_label__(path):
 21     f = open(path + "lable-40.txt", "r")
 22     j = 0
 23     i = -1
 24     datalist = []
 25     labellist = []
 26     while True:
 27 
 28         for line in f.readlines():
 29             i += 1
 30             j += 1
 31             a = line.replace("\n", "")
 32             b = a.split(",")
 33             lable = b[1:]
 34             # print(b[1:])
 35             #对标签进行归一化（不归一化也行）
 36             # for num in b[1:]:
 37             #     lab = int(num) / 255.0
 38             #     labellist.append(lab)
 39             # lab = labellist[i * 10:j * 10]
 40             imgname = path + b[0]
 41             images = load_img(imgname)
 42             images = img_to_array(images).astype('float32')
 43             # 对图片进行归一化（不归一化也行）
 44             # images /= 255.0
 45             image = np.expand_dims(images, axis=0)
 46             lables = np.array(lable)
 47 
 48             # lable =keras.utils.np_utils.to_categorical(lable)
 49             # lable = np.expand_dims(lable, axis=0)
 50             lable = lables.reshape(1, 10)
 51 
 52             yield (image,lable)
 53 
 54 ###############:
 55 
 56 # 开始建立CNN模型
 57 ###############
 58 
 59 # 生成一个model
 60 class Model(object):
 61     def __CNN__(self):
 62         model = Sequential()#218*178*3
 63         model.add(Conv2D(32, (3, 3), input_shape=(imgsize, imgsize, 3)))
 64         model.add(Activation('relu'))
 65         model.add(MaxPooling2D(pool_size=(2, 2)))
 66 
 67         model.add(Conv2D(32, (3, 3)))
 68         model.add(Activation('relu'))
 69         model.add(MaxPooling2D(pool_size=(2, 2)))
 70 
 71         model.add(Conv2D(64, (3, 3)))
 72         model.add(Activation('relu'))
 73         model.add(MaxPooling2D(pool_size=(2, 2)))
 74 
 75         model.add(Flatten())
 76         model.add(Dense(64))
 77         model.add(Activation('relu'))
 78         model.add(Dropout(0.5))
 79         model.add(Dense(10))
 80         model.summary()
 81         return model
 82 
 83 
 84     def train(self,model):
 85         # print(lable.shape)
 86         model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
 87         # optimizer = SGD(lr=0.03, momentum=0.9, nesterov=True)
 88         # model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])
 89         epoch_num = 10
 90         learning_rate = np.linspace(0.03, 0.01, epoch_num)
 91         change_lr = LearningRateScheduler(lambda epoch: float(learning_rate[epoch]))
 92         early_stop = EarlyStopping(monitor='val_loss', patience=20, verbose=1, mode='auto')
 93         check_point = ModelCheckpoint('CNN_model_final.h5', monitor='val_loss', verbose=0, save_best_only=True,
 94                                       save_weights_only=False, mode='auto', period=1)
 95 
 96         model.fit_generator(__data_label__(trainpath),callbacks=[check_point,early_stop,change_lr],samples_per_epoch=int(train_samples // batch_size),
 97                             epochs=epoch_num,validation_steps = int(test_samples // batch_size),validation_data=__data_label__(testpath))
 98 
 99         # model.fit(traindata, trainlabel, batch_size=32, epochs=50,
100         #           validation_data=(testdata, testlabel))
101         model.evaluate_generator(__data_label__(testpath))
102 
103     def save(self,model, file_path=FILE_PATH):
104         print('Model Saved.')
105         model.save_weights(file_path)
106 
107     def load(self,model, file_path=FILE_PATH):
108         print('Model Loaded.')
109         model.load_weights(file_path)
110 
111     def predict(self,model,image):
112         # 预测样本分类
113         print(image.shape)
114         image = cv2.resize(image, (imgsize, imgsize))
115         image.astype('float32')
116         image = np.expand_dims(image, axis=0)
117 
118         #归一化
119         result = model.predict(image)
120 
121         print(result)
122         return result