人脸识别经典网络-MTCNN（含Python源码实现）

$12 \times 12$ 的图像并不能保证，方形框能够完美的框住人脸，所以输出包含的信息都是误差信息，通道数为4，有4个方面的信息，边框左上角的横坐标的相对偏移信息、边框左上角纵坐标的相对偏移信息、标定框宽度的误差、标定框高度的误差；第三个部分给出了人脸的5个关键点的位置，分别是左眼位置、右眼位置、鼻子位置、嘴巴左位置、嘴巴右位置，每个关键位置使用两个维度表示，故而输出是

1times 1times 10

$1 \times 1 \times 10$ 。

P-Net 应用举例

一张

70times 70

$70 \times 70$ 的图，经过P网络全卷积后，输出为

−

frac{70-2}{2} -2 -2 =30

$\frac{70 - 2}{2} - 2 - 2 = 30$ ，即一个5通道的

30times 30

$30 \times 30$ 的特征图。这就意味着该图经过p的一次滑窗操作，得到

900

30times 30=900

$30 \times 30 = 900$ 个建议框，而每个建议框对应1个置信度与4个偏移量。再经nms把置信度分数大于设定的阈值0.6对应的建议框保留下来，将其对应的边框偏移量经边框回归操作，得到在原图中的坐标信息，即得到符合P-Net的这些建议框了。之后传给R-Net。

2.5 R-Net

R-Net（Refine Network），从网络图可以看到，该网络结构只是和P-Net网络结构多了一个全连接层。图片在输入R-Net之前，都需要缩放到24x24x3。网络的输出与P-Net是相同的，R-Net的目的是为了去除大量的非人脸框。

在这里插入图片描述

2.6 O-Net

O-Net（Output Network），该层比R-Net层又多了一层卷积层，所以处理的结果会更加精细。输入的图像大小48x48x3，输出包括N个边界框的坐标信息，score以及关键点位置。

在这里插入图片描述

从P-Net到R-Net，再到最后的O-Net，网络输入的图像越来越大，卷积层的通道数越来越多，网络的深度（层数）也越来越深，因此识别人脸的准确率应该也是越来越高的。

3. 工程实践（基于Ker as）

点击此处下载人脸数据集。该数据集有32,203张图片，共有93,703张脸被标记。

在这里插入图片描述

MTCNN网络定义，按照上述网络结构完成定义，代码按照P-Net、R-Net、O-Net进行模块化设计，在mtcnn的网络构建过程中将其整合。mtcnn.py 代码如下：

from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense, Permute
from keras.layers.advanced_activations import PReLU
from keras.models import Model, Sequential
import tensorflow as tf
import numpy as np
import utils
import cv2
#-----------------------------#
#   粗略获取人脸框
#   输出bbox位置和是否有人脸
#-----------------------------#
def create_Pnet(weight_path):
    input = Input(shape=[None, None, 3])

    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='PReLU1')(x)
    x = MaxPool2D(pool_size=2)(x)

    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='PReLU2')(x)

    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='PReLU3')(x)

    classifier = Conv2D(2, (1, 1), activation='softmax', name='conv4-1')(x)
    # 无激活函数，线性。
    bbox_regress = Conv2D(4, (1, 1), name='conv4-2')(x)

    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model

#-----------------------------#
#   mtcnn的第二段
#   精修框
#-----------------------------#
def create_Rnet(weight_path):
    input = Input(shape=[24, 24, 3])
    # 24,24,3 -&gt; 11,11,28
    x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
    x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)

    # 11,11,28 -> 4,4,48
    x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)

    # 4,4,48 -> 3,3,64
    x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
    # 3,3,64 -> 64,3,3
    x = Permute((3, 2, 1))(x)
    x = Flatten()(x)
    # 576 -> 128
    x = Dense(128, name='conv4')(x)
    x = PReLU( name='prelu4')(x)
    # 128 -> 2 128 -> 4
    classifier = Dense(2, activation='softmax', name='conv5-1')(x)
    bbox_regress = Dense(4, name='conv5-2')(x)
    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model

#-----------------------------#
#   mtcnn的第三段
#   精修框并获得五个点
#-----------------------------#
def create_Onet(weight_path):
    input = Input(shape = [48,48,3])
    # 48,48,3 -> 23,23,32
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='prelu1')(x)
    x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
    # 23,23,32 -> 10,10,64
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)
    # 8,8,64 -> 4,4,64
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='prelu3')(x)
    x = MaxPool2D(pool_size=2)(x)
    # 4,4,64 -> 3,3,128
    x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
    x = PReLU(shared_axes=[1,2],name='prelu4')(x)
    # 3,3,128 -> 128,12,12
    x = Permute((3,2,1))(x)

    # 1152 -> 256
    x = Flatten()(x)
    x = Dense(256, name='conv5') (x)
    x = PReLU(name='prelu5')(x)

    # 鉴别
    # 256 -> 2 256 -> 4 256 -> 10 
    classifier = Dense(2, activation='softmax',name='conv6-1')(x)
    bbox_regress = Dense(4,name='conv6-2')(x)
    landmark_regress = Dense(10,name='conv6-3')(x)

    model = Model([input], [classifier, bbox_regress, landmark_regress])
    model.load_weights(weight_path, by_name=True)

    return model

class mtcnn():
    def __init__(self):
        self.Pnet = create_Pnet('model_data/pnet.h5')
        self.Rnet = create_Rnet('model_data/rnet.h5')
        self.Onet = create_Onet('model_data/onet.h5')

    def detectFace(self, img, threshold):
        #-----------------------------#
        #   归一化，加快收敛速度
        #   把[0,255]映射到(-1,1)
        #-----------------------------#
        copy_img = (img.copy() - 127.5) / 127.5
        origin_h, origin_w, _ = copy_img.shape
        #-----------------------------#
        #   计算原始输入图像
        #   每一次缩放的比例
        #-----------------------------#
        scales = utils.calculateScales(img)

        out = []
        #-----------------------------#
        #   粗略计算人脸框
        #   pnet部分
        #-----------------------------#
        for scale in scales:
            hs = int(origin_h * scale)
            ws = int(origin_w * scale)
            scale_img = cv2.resize(copy_img, (ws, hs))
            inputs = scale_img.reshape(1, *scale_img.shape)
            # 图像金字塔中的每张图片分别传入Pnet得到output
            output = self.Pnet.predict(inputs)
            # 将所有output加入out
            out.append(output)

        image_num = len(scales)
        rectangles = []
        for i in range(image_num):
            # 有人脸的概率
            cls_prob = out[i][0][0][:,:,1]
            # 其对应的框的位置
            roi = out[i][1][0]

            # 取出每个缩放后图片的长宽
            out_h, out_w = cls_prob.shape
            out_side = max(out_h, out_w)
            print(cls_prob.shape)
            # 解码过程
            rectangle = utils.detect_face_12net(cls_prob, roi, out_side, 1 / scales[i], origin_w, origin_h, threshold[0])
            rectangles.extend(rectangle)

        # 进行非极大抑制
        rectangles = utils.NMS(rectangles, 0.7)

        if len(rectangles) == 0:
            return rectangles

        #-----------------------------#
        #   稍微精确计算人脸框
        #   Rnet部分
        #-----------------------------#
        predict_24_batch = []
        for rectangle in rectangles:
            crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
            scale_img = cv2.resize(crop_img, (24, 24))
            predict_24_batch.append(scale_img)

        predict_24_batch = np.array(predict_24_batch)
        out = self.Rnet.predict(predict_24_batch)

        cls_prob = out[0]
        cls_prob = np.array(cls_prob)
        roi_prob = out[1]
        roi_prob = np.array(roi_prob)
        rectangles = utils.filter_face_24net(cls_prob, roi_prob, rectangles, origin_w, origin_h, threshold[1])

        if len(rectangles) == 0:
            return rectangles

        #-----------------------------#
        #   计算人脸框
        #   onet部分
        #-----------------------------#
        predict_batch = []
        for rectangle in rectangles:
            crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
            scale_img = cv2.resize(crop_img, (48, 48))
            predict_batch.append(scale_img)

        predict_batch = np.array(predict_batch)
        output = self.Onet.predict(predict_batch)
        cls_prob = output[0]
        roi_prob = output[1]
        pts_prob = output[2]

        rectangles = utils.filter_face_48net(cls_prob, roi_prob, pts_prob, rectangles, origin_w, origin_h, threshold[2])

        return rectangles

当有了mtcnn 定义之后，可以利用其作为自己的模块来进行调用，推理，detect.py 代码如下：

import cv2
import numpy as np
from mtcnn import mtcnn

img = cv2.imread('img/test1.jpg')

model = mtcnn()
threshold = [0.5,0.6,0.7]  # 三段网络的置信度阈值不同
rectangles = model.detectFace(img, threshold)
draw = img.copy()

for rectangle in rectangles:
    if rectangle is not None:
        W = -int(rectangle[0]) + int(rectangle[2])
        H = -int(rectangle[1]) + int(rectangle[3])
        paddingH = 0.01 * W
        paddingW = 0.02 * H
        crop_img = img[int(rectangle[1]+paddingH):int(rectangle[3]-paddingH), int(rectangle[0]-paddingW):int(rectangle[2]+paddingW)]
        if crop_img is None:
            continue
        if crop_img.shape[0] < 0 or crop_img.shape[1] < 0:
            continue
        cv2.rectangle(draw, (int(rectangle[0]), int(rectangle[1])), (int(rectangle[2]), int(rectangle[3])), (255, 0, 0), 1)

        for i in range(5, 15, 2):
            cv2.circle(draw, (int(rectangle[i + 0]), int(rectangle[i + 1])), 2, (0, 255, 0))

cv2.imwrite("img/out.jpg",draw)

cv2.imshow("test", draw)
c = cv2.waitKey(0)

其中，用到的工具类助手如下，实现了非极大值抑制已经网络的后处理等过程逻辑。

import sys
from operator import itemgetter
import numpy as np
import cv2
import matplotlib.pyplot as plt
#-----------------------------#
#   计算原始输入图像
#   每一次缩放的比例
#-----------------------------#
def calculateScales(img):
    copy_img = img.copy()
    pr_scale = 1.0
    h,w,_ = copy_img.shape
    # 引申优化项  = resize(h*500/min(h,w), w*500/min(h,w))
    if min(w,h)>500:
        pr_scale = 500.0/min(h,w)
        w = int(w*pr_scale)
        h = int(h*pr_scale)
    elif max(w,h)<500:
        pr_scale = 500.0/max(h,w)
        w = int(w*pr_scale)
        h = int(h*pr_scale)

    scales = []
    factor = 0.709
    factor_count = 0
    minl = min(h,w)
    while minl >= 12:
        scales.append(pr_scale*pow(factor, factor_count))
        minl *= factor
        factor_count += 1
    return scales

#-------------------------------------#
#   对pnet处理后的结果进行处理
#-------------------------------------#
def detect_face_12net(cls_prob,roi,out_side,scale,width,height,threshold):
    cls_prob = np.swapaxes(cls_prob, 0, 1)
    roi = np.swapaxes(roi, 0, 2)

    stride = 0
    # stride略等于2
    if out_side != 1:
        stride = float(2*out_side-1)/(out_side-1)
    (x,y) = np.where(cls_prob>=threshold)

    boundingbox = np.array([x,y]).T
    # 找到对应原图的位置
    bb1 = np.fix((stride * (boundingbox) + 0 ) * scale)
    bb2 = np.fix((stride * (boundingbox) + 11) * scale)
    # plt.scatter(bb1[:,0],bb1[:,1],linewidths=1)
    # plt.scatter(bb2[:,0],bb2[:,1],linewidths=1,c='r')
    # plt.show()
    boundingbox = np.concatenate((bb1,bb2),axis = 1)
    
    dx1 = roi[0][x,y]
    dx2 = roi[1][x,y]
    dx3 = roi[2][x,y]
    dx4 = roi[3][x,y]
    score = np.array([cls_prob[x,y]]).T
    offset = np.array([dx1,dx2,dx3,dx4]).T

    boundingbox = boundingbox + offset*12.0*scale
    
    rectangles = np.concatenate((boundingbox,score),axis=1)
    rectangles = rect2square(rectangles)
    pick = []
    for i in range(len(rectangles)):
        x1 = int(max(0     ,rectangles[i][0]))
        y1 = int(max(0     ,rectangles[i][1]))
        x2 = int(min(width ,rectangles[i][2]))
        y2 = int(min(height,rectangles[i][3]))
        sc = rectangles[i][4]
        if x2>x1 and y2>y1:
            pick.append([x1,y1,x2,y2,sc])
    return NMS(pick,0.3)
#-----------------------------#
#   将长方形调整为正方形
#-----------------------------#
def rect2square(rectangles):
    w = rectangles[:,2] - rectangles[:,0]
    h = rectangles[:,3] - rectangles[:,1]
    l = np.maximum(w,h).T
    rectangles[:,0] = rectangles[:,0] + w*0.5 - l*0.5
    rectangles[:,1] = rectangles[:,1] + h*0.5 - l*0.5 
    rectangles[:,2:4] = rectangles[:,0:2] + np.repeat([l], 2, axis = 0).T 
    return rectangles
#-------------------------------------#
#   非极大抑制
#-------------------------------------#
def NMS(rectangles,threshold):
    if len(rectangles)==0:
        return rectangles
    boxes = np.array(rectangles)
    x1 = boxes[:,0]
    y1 = boxes[:,1]
    x2 = boxes[:,2]
    y2 = boxes[:,3]
    s  = boxes[:,4]
    area = np.multiply(x2-x1+1, y2-y1+1)
    I = np.array(s.argsort())
    pick = []
    while len(I)>0:
        xx1 = np.maximum(x1[I[-1]], x1[I[0:-1]]) #I[-1] have hightest prob score, I[0:-1]->others
        yy1 = np.maximum(y1[I[-1]], y1[I[0:-1]])
        xx2 = np.minimum(x2[I[-1]], x2[I[0:-1]])
        yy2 = np.minimum(y2[I[-1]], y2[I[0:-1]])
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        o = inter / (area[I[-1]] + area[I[0:-1]] - inter)
        pick.append(I[-1])
        I = I[np.where(o<=threshold)[0]]
    result_rectangle = boxes[pick].tolist()
    return result_rectangle


#-------------------------------------#
#   对Rnet处理后的结果进行处理
#-------------------------------------#
def filter_face_24net(cls_prob,roi,rectangles,width,height,threshold):
    
    prob = cls_prob[:,1]
    pick = np.where(prob>=threshold)
    rectangles = np.array(rectangles)

    x1  = rectangles[pick,0]
    y1  = rectangles[pick,1]
    x2  = rectangles[pick,2]
    y2  = rectangles[pick,3]
    
    sc  = np.array([prob[pick]]).T

    dx1 = roi[pick,0]
    dx2 = roi[pick,1]
    dx3 = roi[pick,2]
    dx4 = roi[pick,3]

    w   = x2-x1
    h   = y2-y1

    x1  = np.array([(x1+dx1*w)[0]]).T
    y1  = np.array([(y1+dx2*h)[0]]).T
    x2  = np.array([(x2+dx3*w)[0]]).T
    y2  = np.array([(y2+dx4*h)[0]]).T

    rectangles = np.concatenate((x1,y1,x2,y2,sc),axis=1)
    rectangles = rect2square(rectangles)
    pick = []
    for i in range(len(rectangles)):
        x1 = int(max(0     ,rectangles[i][0]))
        y1 = int(max(0     ,rectangles[i][1]))
        x2 = int(min(width ,rectangles[i][2]))
        y2 = int(min(height,rectangles[i][3]))
        sc = rectangles[i][4]
        if x2>x1 and y2>y1:
            pick.append([x1,y1,x2,y2,sc])
    return NMS(pick,0.3)
#-------------------------------------#
#   对onet处理后的结果进行处理
#-------------------------------------#
def filter_face_48net(cls_prob,roi,pts,rectangles,width,height,threshold):
    
    prob = cls_prob[:,1]
    pick = np.where(prob>=threshold)
    rectangles = np.array(rectangles)

    x1  = rectangles[pick,0]
    y1  = rectangles[pick,1]
    x2  = rectangles[pick,2]
    y2  = rectangles[pick,3]

    sc  = np.array([prob[pick]]).T

    dx1 = roi[pick,0]
    dx2 = roi[pick,1]
    dx3 = roi[pick,2]
    dx4 = roi[pick,3]

    w   = x2-x1
    h   = y2-y1

    pts0= np.array([(w*pts[pick,0]+x1)[0]]).T
    pts1= np.array([(h*pts[pick,5]+y1)[0]]).T
    pts2= np.array([(w*pts[pick,1]+x1)[0]]).T
    pts3= np.array([(h*pts[pick,6]+y1)[0]]).T
    pts4= np.array([(w*pts[pick,2]+x1)[0]]).T
    pts5= np.array([(h*pts[pick,7]+y1)[0]]).T
    pts6= np.array([(w*pts[pick,3]+x1)[0]]).T
    pts7= np.array([(h*pts[pick,8]+y1)[0]]).T
    pts8= np.array([(w*pts[pick,4]+x1)[0]]).T
    pts9= np.array([(h*pts[pick,9]+y1)[0]]).T

    x1  = np.array([(x1+dx1*w)[0]]).T
    y1  = np.array([(y1+dx2*h)[0]]).T
    x2  = np.array([(x2+dx3*w)[0]]).T
    y2  = np.array([(y2+dx4*h)[0]]).T

    rectangles=np.concatenate((x1,y1,x2,y2,sc,pts0,pts1,pts2,pts3,pts4,pts5,pts6,pts7,pts8,pts9),axis=1)

    pick = []
    for i in range(len(rectangles)):
        x1 = int(max(0     ,rectangles[i][0]))
        y1 = int(max(0     ,rectangles[i][1]))
        x2 = int(min(width ,rectangles[i][2]))
        y2 = int(min(height,rectangles[i][3]))
        if x2>x1 and y2>y1:
            pick.append([x1,y1,x2,y2,rectangles[i][4],
                 rectangles[i][5],rectangles[i][6],rectangles[i][7],rectangles[i][8],rectangles[i][9],rectangles[i][10],rectangles[i][11],rectangles[i][12],rectangles[i][13],rectangles[i][14]])
    return NMS(pick,0.3)