tensorflow+OpenCV+Dlib实现人脸颜值预测

参考：
1、http://blog.csdn.net/wc781708249/article/details/78604740
2、http://blog.csdn.net/xingchenbingbuyu/article/details/52804013

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数据下载：http://www.hcii-lab.net/data/SCUT-FBP/EN/introduce.html

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小结：其实该方法也可以做人脸识别，只需要数据，稍微改下代码，即可使用！

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图片与标签转成pickle文件

# -*- coding: UTF-8 -*-"""生成图像数据和标签数据下载：http://www.hcii-lab.net/data/SCUT-FBP/EN/introduce.html"""import csvimport pandas as pdimport numpy as npimport osimport cv2import globfrom sklearn import preprocessingfrom sklearn import decompositionimport dlibfile_path="./Data_Collection/*.jpg"img_files=glob.glob(file_path)img_names=[]imgs=[]# 1.使用dlib自带的frontal_face_detector作为我们的人脸提取器detector = dlib.get_frontal_face_detector()for img_file in img_files:    img_name = img_file.split('\\')[-1].split('.')[0].split('-')[-1]  # Linux 为'/'    img_names.append(img_name) # 记录图片名 后续对应标签    img = cv2.imread(img_file)    rects = detector(img, 1)    assert len(rects)==1,print('一张图片上有多个人脸出现')    face=rects[0]    [x1, x2, y1, y2] = [face.left(), face.right(), face.top(), face.bottom()]    img=img[np.max((y1-50,0)):(y2+50),np.max((x1-50,0)):(x2+50)] # 提取人脸图片    img=cv2.resize(img,(100,100)) # 统一尺寸  尺寸也可以放大点    # cv2.imshow('dst',img)    # cv2.waitKey(0)    img = img.flatten()  # 按行展成一行 即每一行代表一张图片    imgs.append(img)imgs=np.array(imgs,np.float32)  # 转成 500x230400# 数据归一化normalizer = preprocessing.Normalizer(norm='l2').fit(imgs)imgs=normalizer.transform(imgs)# 将使用tensorflow cnn 卷积，不对数据降维# 提取图像对应的标签labels_path="./Rating_Collection/Attractiveness label.xlsx"labels=pd.read_excel(labels_path)labels=labels._values[:,:2]labels=dict(labels)labels2=[]# 数据与标签对应[labels2.append(labels[int(img_name)]) for img_name in img_names]# 现在 imgs 与 labels2 是对应的labels2=np.array(labels2,np.float32)[:,np.newaxis]data=np.hstack((imgs,labels2)) # 最后一列为对应的标签# 保存到文件中pd.to_pickle(data,'./data.pkl')

加载pkl文件训练模型

# -*- coding: UTF-8 -*-from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport pandas as pdimport numpy as npimport tensorflow as tfimport tensorflow.contrib.slim as slim# 加载数据data=pd.read_pickle('./data.pkl')# 分成train_datas 与 test_datasnp.random.shuffle(data) # 随机打乱数据train_datas=data[0:-50]test_datas=data[-50:]# --------------模型----------------# 参考：http://blog.csdn.net/wc781708249/article/details/78414028def cifarnet(images,num_classes=10,is_training=False,             dropout_keep_prob=0.5,             prediction_fn=slim.softmax,             scope='CifarNet'):    """    Creates a variant of the CifarNet model.    :param images: 输入图像数据 形状[n,h,w,c]    :param num_classes: 类别数    :param is_training: 是否训练 模型训练设置为True，测试、推理设置为False    :param dropout_keep_prob: droupout保持率    :param prediction_fn: 输出层的激活函数    :param scope: 节点名    :return:         net：2D Tensor ,logits （pre-softmax激活）如果num_classes            是非零整数，或者如果num_classes为0或None输入到逻辑层                   end_points：从网络组件到相应的字典激活。    """    end_points = {}    # images的shape为[n,100,100,3]    with tf.variable_scope(scope, 'CifarNet', [images]): # 其中[images]为传入的数据        net = slim.conv2d(images, 64, [5,5], scope='conv1') # 5x5卷核，输出节点64 默认stride为1  ;shape [n,100,100,64]        end_points['conv1'] = net        net = slim.max_pool2d(net, [2, 2], 2, scope='pool1') # 2x2池化核  stride为2  ;shape [n,50,50,64]        end_points['pool1'] = net        net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') # 本地响应规范化  一般可以不使用        net = slim.conv2d(net, 64, [5, 5], scope='conv2') # 5x5卷核，输出节点64 默认stride为1  ;shape [n,50,50,64]        end_points['conv2'] = net        net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')        net = slim.max_pool2d(net, [2, 2], 2, scope='pool2') # 2x2池化核  stride为2  ;shape [n,25,25,64]        end_points['pool2'] = net        net = slim.flatten(net) # 改变shape ;shape [n,25*25*64]        end_points['Flatten'] = net        net = slim.fully_connected(net, 384, scope='fc3') # 全连接层，输出节点384 ；shape [n,384]        end_points['fc3'] = net        # net = slim.dropout(net, dropout_keep_prob, is_training=is_training,scope='dropout3')  # droupout 层 ；shape [n,384]    这句竟然报错 下面那句却不报错 不解！        net = tf.nn.dropout(net, dropout_keep_prob, name='dropout3')        net = slim.fully_connected(net, 192, scope='fc4') # # 全连接层，输出节点192 ；shape [n,192]        end_points['fc4'] = net        if not num_classes:  # num_classes为0、Nnoe，返回的不是最终的输出层，            return net, end_points        logits = slim.fully_connected(net, num_classes,                                      biases_initializer=tf.zeros_initializer(),                                      weights_initializer=tf.truncated_normal_initializer(1 / 192.0),                                      weights_regularizer=None,                                      activation_fn=None,                                      scope='logits') # 输出层 ；shape [n,num_classes]        end_points['Logits'] = logits        end_points['Predictions'] = prediction_fn(logits, scope='Predictions')    return logits, end_pointscifarnet.default_image_size = 100def inception_resnet_v2_arg_scope(weight_decay=0.00004,                                  batch_norm_decay=0.9997,                                  batch_norm_epsilon=0.001,                                  activation_fn=tf.nn.relu):  """Returns the scope with the default parameters for inception_resnet_v2.  使用了batch_norm，相对来说要比cifarnet_arg_scope效果更佳，推荐使用该方式进行各层参数配置  Args:    weight_decay: the weight decay for weights variables.    batch_norm_decay: decay for the moving average of batch_norm momentums.    batch_norm_epsilon: small float added to variance to avoid dividing by zero.    activation_fn: Activation function for conv2d.  Returns:    a arg_scope with the parameters needed for inception_resnet_v2.  """  # Set weight_decay for weights in conv2d and fully_connected layers.  with slim.arg_scope([slim.conv2d, slim.fully_connected],                      weights_regularizer=slim.l2_regularizer(weight_decay),                      biases_regularizer=slim.l2_regularizer(weight_decay)):    batch_norm_params = {        'decay': batch_norm_decay,        'epsilon': batch_norm_epsilon,        'fused': None,  # Use fused batch norm if possible.    }    # Set activation_fn and parameters for batch_norm.    with slim.arg_scope([slim.conv2d], activation_fn=activation_fn,                        normalizer_fn=slim.batch_norm,                        normalizer_params=batch_norm_params) as scope:      return scope# 自己编写next_batch 实现按批次训练数据start_index=0def next_batch(data,batch_size,img_pixel=100,channels=3):    global start_index  # 必须定义成全局变量    global second_index  # 必须定义成全局变量    second_index=start_index+batch_size    if second_index>len(data):        second_index=len(data)    data1=data[start_index:second_index]    # lab=labels[start_index:second_index]    start_index=second_index    if start_index>=len(data):        start_index = 0    # 将每次得到batch_size个数据按行打乱    index = [i for i in range(len(data1))]  # len(data1)得到的行数    np.random.shuffle(index)  # 将索引打乱    data1 = data1[index]    # 提起出数据和标签    img = data1[:, 0:-1].astype(np.float32)    label = data1[:, -1][:,np.newaxis]    label = label.astype(np.float32)  # 类型转换    return img,labelwith tf.name_scope('input'):   x = tf.placeholder(tf.float32, [None, 100 * 100 * 3], 'x')   y_ = tf.placeholder(tf.float32, [None, 1], 'y_')   keep = tf.placeholder(tf.float32)   is_training = tf.placeholder(tf.bool, name='MODE')image_shaped_input = tf.reshape(x, [-1, 100, 100, 3])with slim.arg_scope(inception_resnet_v2_arg_scope()):   y, _ = cifarnet(images=image_shaped_input, num_classes=1, is_training=is_training, dropout_keep_prob=keep)cross_entropy=tf.reduce_mean(tf.reduce_sum(                tf.square(y- y_), reduction_indices=[1]))global_step = tf.Variable(0, trainable=False)train_op=tf.train.AdamOptimizer(1e-3).minimize(cross_entropy,global_step=global_step)init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())if __name__=="__main__":   with tf.Session() as sess:      sess.run(init)      for step in range(10000):         batch_xs, batch_ys = next_batch(train_datas,30)         train_op.run({x: batch_xs, y_: batch_ys, keep: 0.7, is_training: True})         if step % 100 == 0:            # acc = accuracy.eval({x: batch_xs, y_: batch_ys, keep: 1., is_training: False})            print("step", step,                  'loss', cross_entropy.eval({x: batch_xs, y_: batch_ys, keep: 1., is_training: False}))      # test      test_x, test_y = test_datas[:,:-1],test_datas[:,-1][:,np.newaxis]      # acc = accuracy.eval({x: test_x, y_: test_y, keep: 1., is_training: False})      print("pred value:",y.eval({x: test_x, y_: test_y, keep: 1., is_training: False}))      print('True value', test_y)print("Generate Model Successfully!")