人脸检测（一）

客官°小女子只卖身不卖艺 2022-06-16 05:17 301阅读 0赞

有天导师突然找我，让我搞一些关于人脸的应用，比如换个脸什么的……没办法那就先把人脸自动检测出来吧。人脸检测，即检测出图像中存在的人脸，并把它的位置准确地框出来。是人脸特征点检测、人脸识别的基础。可以谷歌Face Detection Benchmark寻找数据集和优秀论文，上thinkface论坛，搜集人脸检测数据集和方法。常用的人脸检测数据集，包括FDDB、AFLW、WIDER FACE等。随着近年来随着深度学习的快速发展，涌现出来很多优秀的人脸检测算法。  
例如，FDDB数据库就提交了很多出色的人脸检测算法，例如采用级联CNN网络的人脸检测方法：A Convolutioanal Neural Network Cascade，改进的faster rcnn做人脸检测：Face Detection using Deep Learning：An Improved Faster RCNN Approach，还有对小脸检测非常成功的Finding tiny faces等等，建议找个三篇左右认真研读就行了，也不需要去一一实现，没有太大意义。  
另外，像opencv、dlib、libfacedetect等也提供了人脸检测的接口。因为人脸检测是很基本的任务，所以很多公司都做了人脸检测的工作，而且做得很牛，例如face++。  
![这里写图片描述][SouthEast]

下面仅介绍本人尝试并实现了的几种常见的人脸检测方法：

**1.单个CNN人脸检测方法**  
**2.级联CNN人脸检测方法**  
**3.OpenCV人脸检测方法**  
**4.Dlib人脸检测方法**  
**5.libfacedetect人脸检测方法**  
**6.Seetaface人脸检测方法**

--------------------

**1.单个CNN人脸检测方法**

该人脸检测方法的有点在于，思路简单，实现简单；缺点是速度较慢（在一块普通的gpu上对一副1000x600的图像进行多尺度检测也可能花上一两秒），检测效果还可以，但得到的人脸框不够准确。  
首先训练一个判断人脸非人脸的二分类器。例如采用卷积神经网络caffenet进行二分类，可以在imagenet数据集训练过的模型，利用自己的人脸数据集，进行微调。也可以自定义卷积网络进行训练，为了能检测到更小的人脸目标，我们一般采用小一点的卷积神经网络作为二分类模型，减小图像输入尺寸，加快预测速度。  
然后将训练好的人脸判断分类网络的全连接层改为卷积层，这样网络变成了全卷积网络，可以接受任意输入图像大小，图像经过全卷积网络将得到特征图，特征图上每一个“点”对应该位置映射到原图上的感受野区域属于人脸的概率，将属于人脸概率大于设定阈值的视为人脸候选框。  
图像上人脸的大小是变化的，为了适应这种变化，最暴力的办法就是使用图像金字塔的方式，将待检测的图像缩放到不同大小，以进行多尺度人脸检测。对多个尺度下检测出来的所有人脸候选框，做非极大值抑制NMS，得到最后人脸检测的结果。

![这里写图片描述][SouthEast 1]

这里提供用caffe实现该方法的数据集、模型文件和代码打包的 [下载链接][Link 1]

下面介绍用caffe实现该方法的具体过程。因为需要训练判断是否为人脸的CNN分类器，准备好正负训练样本，然后得到caffe训练所需的的数据集文件（由于采用的是48x48的网络，原始数据集归一化到了48x48）。  
![这里写图片描述][SouthEast 2]  
这里CNN采用的是DeepID卷积神经网络，网络结构如下，它的输入只有48x48大小，而采用AlexNet或CaffeNet网络会增加时间开销。  
![这里写图片描述][SouthEast 3]  
准备好网络模型文件train\_val.prototxt和超参数配置文件solver.prototxt之后（下载链接中都有），开始训练，迭代10w次得到caffemodel。对测试集face\_test文件夹中的图像进行测试，准备好测试用的deploy.prototxt。  
测试单张图像的python脚本face\_test.py如下：

# -*- coding: utf-8 -*-
    """
    Created on Fri Mar 10 23:02:06 2017
    @author: Administrator
    """
    import numpy as np
    import caffe
    size = 48
    image_file = 'C:/Users/Administrator/Desktop/caffe/data/face/face_test/0/253_faceimage07068.jpg'#测试图片路径
    model_def = 'C:/Users/Administrator/Desktop/caffe/models/face/deploy.prototxt'
    model_weights = 'C:/Users/Administrator/Desktop/caffe/models/face/_iter_10000.caffemodel'
    net = caffe.Net(model_def, model_weights, caffe.TEST)    
    
    # 加载均值文件  也可指定数值做相应的操作
    #mu = np.load('C:/Users/Administrator/Desktop/caffe/python/caffe/imagenet/ilsvrc_2012_mean.npy')  ###caffe 自带的文件
    #mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values
    
    transformer = caffe.io.Transformer({
       'data': net.blobs['data'].data.shape}) ##设定图片的shape格式(1,3,48,48)，大小由deploy 文件指定
    #transformer.set_mean('data', mu)            # 每个通道减去均值
    # python读取的图片文件格式为H×W×K，需转化为K×H×W
    transformer.set_transpose('data', (2,0,1))  #改变维度的顺序，由原始图片(48,48,3)变为(3,48,48) 
    # python中将图片存储为[0, 1]，而caffe中将图片存储为[0, 255]，所以需要一个转换
    transformer.set_raw_scale('data', 255)      # 缩放到【0，255】之间
    transformer.set_channel_swap('data', (2,1,0))   #交换通道，将图片由RGB变为BGR
    #net.blobs['data'].reshape(1,3,size, size)  # 将输入图片格式转化为合适格式（与deploy文件相同）
    #上面这句，第一参数：图片数量 第二个参数 ：通道数 第三个参数：图片高度 第四个参数：图片宽度
    
    image = caffe.io.load_image(image_file) #加载图片，始终是得到一副(h,w,3),rgb,0~1,float32的图像
    net.blobs['data'].data[...]  = transformer.preprocess('data', image) #用上面的transformer.preprocess来处理刚刚加载图片
    
    caffe.set_device(0)
    caffe.set_mode_gpu()
    output = net.forward()
    output_prob = output['prob'][0].argmax()  # 给出概率最高的是第几类，需要自己对应到我们约定的类别去
    print output_prob
    print output['prob'][0][0] #或print output['prob'][0,1]

批量测试计算准确度的matlab脚本face\_test.m如下：

%注意：caffe中维度顺序为(N,C,H,W),而matcaffe中Blob维度顺序为(W,H,C,N)，即完全相反
    %matlab加载图像为(h,w,c),得到的是rgb,而caffe使用的是bgr
    
    function test_face()
    clear;
    addpath('..');%添加上级目录搜索路径
    addpath('.');%添加当前目录搜索路径
    caffe.set_mode_gpu(); %设置gpu模式
    caffe.set_device(0); %gpu的id为0
    %caffe.set_mode_cpu();
    net_model = 'C:\Users\Administrator\Desktop\caffe\models\face\deploy.prototxt'; %网络模型deploy.prototxt
    net_weights = 'C:\Users\Administrator\Desktop\caffe\models\face\_iter_10000.caffemodel'; %训练好的模型文件
    %net_model = 'C:\Users\Administrator\Desktop\caffe\models\face2\deploy.prototxt'; %网络模型deploy.prototxt
    %net_weights = 'C:\Users\Administrator\Desktop\caffe\models\face2\_iter_100000.caffemodel'; %训练好的模型文件
    phase = 'test'; %不做训练，而是测试
    net = caffe.Net(net_model, net_weights, phase); %获取网络
    
    tic;
    error = 0;
    total = 0;
    %批量读取图像进行测试
    datadir = 'C:\Users\Administrator\Desktop\caffe\data\face\face_test\0';
    imagefiles = dir(datadir);
    for i = 3:length(imagefiles)
        im = imread(fullfile(datadir,imagefiles(i).name));
        [input_data,flag] = prepare_image(im); %图像数据预处理
        if flag ~= 1
            continue;
        end
        input_data ={input_data};
        net.forward(input_data); %做前向传播
    
        scores = net.blobs('prob').get_data();
    
        [best_score,best] = max(scores);
    %     fprintf('*****%.3f %d %d\n',best_score,best - 1,classid(i-2));
        best = best - 1; %matlab中从1开始，减1变成从0开始
        if best ~= 0
            error = error + 1;
            fprintf('-----error: %d\n',error);
            errorfile = ['error\' imagefiles(i).name];
            %imwrite(im,errorfile);
        end
        total = total + 1;
    end
    datadir_1 = 'C:\Users\Administrator\Desktop\caffe\data\face\face_test\1';
    imagefiles_1 = dir(datadir_1);
    for i = 3:length(imagefiles_1)
        im_1 = imread(fullfile(datadir_1,imagefiles_1(i).name));
        [input_data_1,flag] = prepare_image(im_1); %图像数据预处理
        if flag ~= 1
            continue;
        end
        input_data_1 = {input_data_1};
        net.forward(input_data_1); %做前向传播
    
        scores_1 = net.blobs('prob').get_data();
    
        [best_score_1,best_1] = max(scores_1);
    %     fprintf('*****%.3f %d %d\n',best_score,best - 1,classid(i-2));
        best_1 = best_1 - 1; %matlab中从1开始，减1变成从0开始
        if best_1 ~= 1
            error = error + 1;
            fprintf('error: %d-----\n',error);
            errorfile = ['face_error\' imagefiles_1(i).name];
            %imwrite(im,errorfile);
        end
        total = total + 1;
    end
    total_time = toc;
    %打印到屏幕上
    fprintf('total_time: %.3f s\n',total_time);
    fprintf('aver_time: %.3f s\n',total_time/total);
    fprintf('error/total: %d/%d\n',error,total);
    fprintf('accurary: %.4f\n',1.0 - (error*1.0)/total);
    %disp(['error/total: ',num2str(error),'/',num2str(length(imagefiles)-2)]);
    end
    
    function [im_data,flag] = prepare_image(im)
    %d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
    %mean_data = d.mean_data;
    
    %resize to 227 x 227
    im_data = [];
    im = imresize(im,[227 227],'bilinear');
    %im = imresize(im,[48 48],'bilinear');
    [h,w,c] = size(im);
    if c ~= 3
        flag = 0;
        return;
    end
    flag = 1;
    %caffe的blob顺序是[w h c num]
    %matlab:[h w c] rgb -> caffe:[w h c] bgr
    im_data = im(:,:,[3,2,1]); %rgb -> bgr
    im_data = permute(im_data,[2,1,3]); %[h w c] -> [w h c]
    [w,h,~] = size(im_data);
    %ImageNet数据集的均值具有统计规律，这里可以直接拿来使用
    mean_data(:,:,1) = ones(w,h) .* 104; %b
    mean_data(:,:,2) = ones(w,h) .* 117; %g
    mean_data(:,:,3) = ones(w,h) .* 123; %r
    
    im_data = single(im_data);
    %im_data = im_data - single(mean_data); %因为训练集和测试集都没有做去均值，所以这里也不做（如果只是这里做了去均值效果会变差）
    end

在测试集上进行批量测试，准确率达到了98%。  
![这里写图片描述][SouthEast 4]  
为了利用CNN分类器来检测人脸，需要将CNN网络中的全连接层替换为卷积层得到全卷积网络，修改好的全卷积网络deploy\_full\_conv.prototxt内容如下：

name: "face_full_conv_net"
    layer {
      name: "data"
      type: "Input"
      top: "data"
      input_param { shape: { dim: 1 dim: 3 dim: 48 dim: 48 } }
    }
    layer {
      name: "conv1"
      type: "Convolution"
      bottom: "data"
      top: "conv1"
      convolution_param {
        num_output: 20
        kernel_size: 3
        stride: 1
        pad: 1
      }
    }
    layer {
      name: "relu1"
      type: "ReLU"
      bottom: "conv1"
      top: "conv1"
    }
    layer {
      name: "norm1"
      type: "LRN"
      bottom: "conv1"
      top: "conv1"
      lrn_param {
        local_size: 5
        alpha: 0.0001
        beta: 0.75
      }
    }
    layer {
      name: "pool1"
      type:  "Pooling"
      bottom: "conv1"
      top: "pool1"
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layer {
      name: "conv2"
      type: "Convolution"
      bottom: "pool1"
      top: "conv2"
      convolution_param {
        num_output: 40
        kernel_size: 3
        pad: 1
      }
    }
    layer {
      name: "relu2"
      type: "ReLU"
      bottom: "conv2"
      top: "conv2"
    }
    layer {
      name: "norm2"
      type: "LRN"
      bottom: "conv2"
      top: "conv2"
      lrn_param {
        local_size: 5
        alpha: 0.0001
        beta: 0.75
      }
    }
    layer {
      name: "pool2"
      type: "Pooling"
      bottom: "conv2"
      top: "pool2"
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layer {
      name: "conv3"
      type: "Convolution"
      bottom: "pool2"
      top: "conv3"
      convolution_param {
        num_output: 60
        kernel_size: 3
        pad: 1
      }
    }
    layer {
      name: "relu3"
      type: "ReLU"
      bottom: "conv3"
      top: "conv3"
    }
    layer {
      name: "norm3"
      type: "LRN"
      bottom: "conv3"
      top: "conv3"
      lrn_param {
        local_size: 5
        alpha: 0.0001
        beta: 0.75
      }
    }
    layer {
      name: "pool3"
      type: "Pooling"
      bottom: "conv3"
      top: "pool3"
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layer {
      name: "conv4"
      type: "Convolution"
      bottom: "pool3"
      top: "conv4"
      convolution_param {
        num_output: 80
        kernel_size: 3
        pad: 1
      }
    }
    layer {
      name: "relu4"
      type: "ReLU"
      bottom: "conv4"
      top: "conv4"
    }
    layer {
      name: "norm4"
      type: "LRN"
      bottom: "conv4"
      top: "conv4"
      lrn_param {
        local_size: 5
        alpha: 0.0001
        beta: 0.75
      }
    }
    layer {
      name: "pool4"
      type: "Pooling"
      bottom: "conv4"
      top: "pool4"
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    #修改为卷积层
    layer {
      name: "fc5-conv" ### fc5
      type: "Convolution" ### InnerProduct
      bottom: "pool4"
      top: "fc5-conv" ### fc5
      #inner_product_param {
       # num_output: 160
      #}
      convolution_param {
        num_output: 160
        kernel_size: 3
      }
    }
    layer {
      name: "relu5"
      type: "ReLU"
      bottom: "fc5-conv"
      top: "fc5-conv"
    }
    layer {
      name: "drop5"
      type:  "Dropout"
      bottom: "fc5-conv"
      top: "fc5-conv"
      dropout_param {
        dropout_ratio: 0.5
      }
    }
    #修改为卷积层
    layer {
      name: "fc6-conv" ### fc6
      type:  "Convolution" ### InnerProduct
      bottom: "fc5-conv"
      top: "fc6-conv"
      #inner_product_param {
       # num_output: 2
      #}
      convolution_param {
        num_output: 2
        kernel_size: 1
      }
    }
    layer {
      name: "prob"
      type: "Softmax"
      bottom: "fc6-conv"
      top: "prob"
    }

还需要将训练好的\_iter\_100000.caffemodel模型文件也转化为全卷积的，得到的\_iter\_100000\_full\_conv.caffemodel，转换脚本convert\_full\_conv.py如下：

# -*- coding: utf-8 -*-
    """
    Created on Fri Mar 10 21:14:09 2017
    @author: Administrator
    """
    ###首先需要手动将deploy.prototxt修改成全卷积的deploy_full_conv.prorotxt，特别要注意全连接层修改成卷积层的细节
    ###将训练好的分类模型caffemodel转换成可以接受任意输入大小，最后输出特征图的全卷积模型caffemodel
    
    import numpy as np
    import caffe
    
    model_def = 'C:/Users/Administrator/Desktop/caffe/models/face/deploy.prototxt'
    model_weights = 'C:/Users/Administrator/Desktop/caffe/models/face/_iter_100000.caffemodel'
    net = caffe.Net(model_def,
                    model_weights,
                    caffe.TEST)
    params = ['fc5', 'fc6']
    # fc_params = {name: (weights, biases)}
    fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}
    for fc in params:
        print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)
    
    # Load the fully convolutional network to transplant the parameters.
    net_full_conv = caffe.Net('./deploy_full_conv.prototxt', 
                              './_iter_100000.caffemodel',
                              caffe.TEST)
    params_full_conv = ['fc5-conv', 'fc6-conv']
    # conv_params = {name: (weights, biases)}
    conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}
    for conv in params_full_conv:
        print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)
    
    for pr, pr_conv in zip(params, params_full_conv):
        conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays
        conv_params[pr_conv][1][...] = fc_params[pr][1]
    
    net_full_conv.save('./_iter_100000_full_conv.caffemodel')
    print 'success'

最后，就可以用deploy\_full\_conv.prototxt和\_iter\_100000\_full\_conv.caffemodel对任意输入尺寸的图像进行人脸检测了。对单张图像进行人脸检测的python脚本face\_detect如下：

# -*- coding: utf-8 -*-
    import numpy as np
    import cv2 #需要安装opencv，然后将opencv安装目录下build\python\2.7\x64\cv2.pyd拷贝到python的安装目录下Anaconda2\Lib\site-packages文件夹下
    from operator import itemgetter
    import time
    import caffe
    caffe.set_device(0)
    caffe.set_mode_gpu()
    
    def IoU(rect_1, rect_2):
        '''
        :param rect_1: list in format [x11, y11, x12, y12, confidence]
        :param rect_2:  list in format [x21, y21, x22, y22, confidence]
        :return:    returns IoU ratio (intersection over union) of two rectangles
        '''
        x11 = rect_1[0]    # first rectangle top left x
        y11 = rect_1[1]    # first rectangle top left y
        x12 = rect_1[2]    # first rectangle bottom right x
        y12 = rect_1[3]    # first rectangle bottom right y
        x21 = rect_2[0]    # second rectangle top left x
        y21 = rect_2[1]    # second rectangle top left y
        x22 = rect_2[2]    # second rectangle bottom right x
        y22 = rect_2[3]    # second rectangle bottom right y
        x_overlap = max(0, min(x12,x22) -max(x11,x21))
        y_overlap = max(0, min(y12,y22) -max(y11,y21))
        intersection = x_overlap * y_overlap
        union = (x12-x11) * (y12-y11) + (x22-x21) * (y22-y21) - intersection
        return float(intersection) / union
    
    def IoM(rect_1, rect_2):
        '''
        :param rect_1: list in format [x11, y11, x12, y12, confidence]
        :param rect_2:  list in format [x21, y21, x22, y22, confidence]
        :return:    returns IoM ratio (intersection over min-area) of two rectangles
        '''
        x11 = rect_1[0]    # first rectangle top left x
        y11 = rect_1[1]    # first rectangle top left y
        x12 = rect_1[2]    # first rectangle bottom right x
        y12 = rect_1[3]    # first rectangle bottom right y
        x21 = rect_2[0]    # second rectangle top left x
        y21 = rect_2[1]    # second rectangle top left y
        x22 = rect_2[2]    # second rectangle bottom right x
        y22 = rect_2[3]    # second rectangle bottom right y
        x_overlap = max(0, min(x12,x22) -max(x11,x21))
        y_overlap = max(0, min(y12,y22) -max(y11,y21))
        intersection = x_overlap * y_overlap
        rect1_area = (y12 - y11) * (x12 - x11)
        rect2_area = (y22 - y21) * (x22 - x21)
        min_area = min(rect1_area, rect2_area)
        return float(intersection) / min_area
    
    def NMS(rectangles,threshold=0.3):
        '''
        :param rectangles:  list of rectangles, which are lists in format [x11, y11, x12, y12, confidence]
        :return:    list of rectangles after local NMS
        '''
        rectangles = sorted(rectangles, key=itemgetter(4), reverse=True) #按照confidence降序排列
        result_rectangles = rectangles[:]  # list to return
    
        '''
        while not result_rectangles == []:
            rect = result_rectangles[0]
            for index in range(1,len(result_rectangles)):
                iou = IoU(rect,result_rectangles[index])
                if
        '''
        number_of_rects = len(result_rectangles)
        #threshold = 0.3     # threshold of IoU of two rectangles
        cur_rect = 0
        while cur_rect < number_of_rects - 1:     # start from first element to second last element
            rects_to_compare = number_of_rects - cur_rect - 1      # elements after current element to compare
            cur_rect_to_compare = cur_rect + 1    # start comparing with element after current
            while rects_to_compare > 0:      # while there is at least one element after current to compare
                if (IoU(result_rectangles[cur_rect], result_rectangles[cur_rect_to_compare]) >= threshold or IoM(result_rectangles[cur_rect], result_rectangles[cur_rect_to_compare]) >= 0.3):
                    del result_rectangles[cur_rect_to_compare]      # delete the rectangle
                    number_of_rects -= 1
                else:
                    cur_rect_to_compare += 1    # skip to next rectangle
                rects_to_compare -= 1
            cur_rect += 1   # finished comparing for current rectangle
    
        return result_rectangles
    
    def face_detection(imgFile) :
        #model_def = 'C:/Users/Administrator/Desktop/caffe/models/face/deploy_full_conv.prototxt' 
        #model_weights = 'C:/Users/Administrator/Desktop/caffe/models/face/_iter_10000_full_conv.caffemodel'
        model_def = 'C:/Users/Administrator/Desktop/caffe/models/face2/deploy_full_conv.prototxt' 
        model_weights = 'C:/Users/Administrator/Desktop/caffe/models/face2/_iter_100000_full_conv.caffemodel'
        net_full_conv = caffe.Net(model_def,
                                  model_weights,
                                  caffe.TEST)
    
        mu = np.load('C:/Users/Administrator/Desktop/caffe/python/caffe/imagenet/ilsvrc_2012_mean.npy')
        mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values
        #print 'mean-subtracted values:' , zip('BGR', mu)
    
        start_time = time.time()
        scales = [] #尺度变换和尺度变换因子
        factor = 0.793700526
    
        img = cv2.imread(imgFile) #opencv读取的图像为(h,w,c),bgr,caffe的blob维度为(n,c,h,w),使用的也是rgb
        print img.shape
    
        largest = min(2, 4000/max(img.shape[0:2])) #4000是人脸检测的经验值
        scale = largest
        minD = largest*min(img.shape[0:2])
        while minD >= 48:  #网络的输入是227x227？？？ #多尺度变换 
            scales.append(scale) #添加当前尺度
            scale *= factor #乘以尺度变换因子
            minD *= factor #得到新的尺度
    
        true_boxes = []
    
        for scale in scales:
            scale_img = cv2.resize(img,((int(img.shape[1] * scale), int(img.shape[0] * scale)))) #将图像缩放到各尺度
            cv2.imwrite('C:/Users/Administrator/Desktop/caffe/scale_img.jpg',scale_img)
            im = caffe.io.load_image('C:/Users/Administrator/Desktop/caffe/scale_img.jpg') #利用caffe的io接口加载图像，始终是得到一副(h,w,3),rgb,0~1,float32的图像
    
            net_full_conv.blobs['data'].reshape(1,3,scale_img.shape[0],scale_img.shape[1]) #重新设置网络data层Blob维度为：1,3,height,width            
            transformer = caffe.io.Transformer({
       'data': net_full_conv.blobs['data'].data.shape}) #为data层创建transformer
            transformer.set_transpose('data', (2,0,1)) #(h,w,3)->(3,h,w)
            #transformer.set_mean('data', mu) #设置均值,由于训练集没有去均值，这里也不去均值
            transformer.set_raw_scale('data', 255.0) #rescale from [0,1] to [0,255]
            transformer.set_channel_swap('data', (2,1,0)) #RGB -> BGR
    
            net_full_conv.blobs['data'].data[...] = transformer.preprocess('data', im)
            out = net_full_conv.forward()
    
            print out['prob'][0,0].shape #输出层prob结果,行x列
            #print out['prob'][0].argmax(axis=0)
            featureMap = out['prob'][0,0] #out['prob'][0][0]属于人脸的概率特征图
            stride = 16 #特征图感受野大小
            cellSize = 48 #网络输入尺寸
            thresh = 0.95
            for (y,x),prob in np.ndenumerate(featureMap):
                if prob > thresh :
                    true_boxes.append([float(x*stride)/scale,
                                        float(y*stride)/scale,
                                        float(x*stride + cellSize - 1)/scale,
                                        float(y*stride + cellSize - 1)/scale,
                                        prob])
    
        true_boxes = NMS(true_boxes,0.2) #非极大值抑制
        for true_box in true_boxes:
            (x1, y1, x2, y2) = true_box[0:4] #取出人脸框的坐标
            cv2.rectangle(img, (int(x1),int(y1)), (int(x2),int(y2)), (0,255,0)) #画人脸框
    
        end_time = time.time()
        print (end_time-start_time)*1000,'ms'
    
        cv2.imwrite('output.jpg',img)
        cv2.namedWindow('test win')  
        cv2.imshow('test win', img)          
        cv2.waitKey(0)  
        cv2.destroyWindow('test win')
    
    if __name__ == "__main__":
        imgFile = 'C:/Users/Administrator/Desktop/caffe/matlab/demo/1.jpg'
        face_detection(imgFile)

[SouthEast]: /images/20220616/cfbbb4b9129045f885b45719d556bc9e.png
[SouthEast 1]: /images/20220616/f3c415369e18454aadb9824d96781f95.png
[Link 1]: http://pan.baidu.com/s/1skDxBXz
[SouthEast 2]: /images/20220616/a29c33fc7e9644d6a8e3652389004a33.png
[SouthEast 3]: /images/20220616/01bbede6a70440f5bac6b09cdb13cd16.png
[SouthEast 4]: /images/20220616/f6fc11d54b0c4126a283ce876c9a3a95.png