基于深度学习算法实现视频人脸自动打码

企业开发 2023-04-07 23:25:59 阅读次数: 0

前言

1.在当下的环境上，短视频已是生活的常态，但这是很容易就侵犯别人肖像权，好多视频都会在后期给不相关的人打上码，这里是基于yolov5的人脸检测实现人脸自动打码功能。
2.开发环境是win10,显卡RTX3080，cuda10.2，cudnn7.1，OpenCV4.5,NCNN，IDE 是Vs2019。

一、人脸检测

1.首先最主要的一步是肯定是先检测到当前图像是否存在人脸，这个属于人脸检测的范围，目前有很多开源的人脸检测算法和模型，OpenCV本身也带有人脸检测的算法，但了帧率跟得上，这里使用更轻快些的yolov5。
2.实现代码

#ifndef YOLOFACE_H
#define YOLOFACE_H

#include <opencv2/core/core.hpp>
#include <opencv2/opencv.hpp>

#include <ncnn/net.h>

struct Object
{
    
    
    cv::Rect rect;
    int label;
    float prob;
    std::vector<cv::Point2f> pts;
    float live;
};


class YoloFace
{
    
    
public:
    YoloFace();

  
    int loadModel(std::string model, bool use_gpu = true);

    int detection(const cv::Mat& rgb, std::vector<Object>& objects, float prob_threshold = 0.25f, float nms_threshold = 0.45f);

    int drawFace(cv::Mat& rgb,cv::Mat &cv_dst, std::vector<Object>& objects);

private:

    ncnn::Net face_net;

    int target_size = 640;
    const float norm_vals[3] = {
    
     1 / 255.f, 1 / 255.f, 1 / 255.f };
    int image_w;
    int image_h;
    int in_w;
    int in_h;
};

#endif // NANODET_H


#include "yoloface.h"

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#define clip(x, y) (x < 0 ? 0 : (x > y ? y : x))


static inline float intersection_area(const Object& a, const Object& b)
{
    
    
    cv::Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)
{
    
    
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;

    while (i <= j)
    {
    
    
        while (faceobjects[i].prob > p)
            i++;

        while (faceobjects[j].prob < p)
            j--;

        if (i <= j)
        {
    
    
            // swap
            std::swap(faceobjects[i], faceobjects[j]);

            i++;
            j--;
        }
    }

    #pragma omp parallel sections
    {
    
    
        #pragma omp section
        {
    
    
            if (left < j) qsort_descent_inplace(faceobjects, left, j);
        }
        #pragma omp section
        {
    
    
            if (i < right) qsort_descent_inplace(faceobjects, i, right);
        }
    }
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects)
{
    
    
    if (faceobjects.empty())
        return;

    qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);
}

static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)
{
    
    
    picked.clear();

    const int n = faceobjects.size();

    std::vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
    
    
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
    
    
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
    
    
            const Object& b = faceobjects[picked[j]];

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}

static inline float sigmoid(float x)
{
    
    
    return static_cast<float>(1.f / (1.f + exp(-x)));
}

static void generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects)
{
    
    
    const int num_grid = feat_blob.h;

    int num_grid_x;
    int num_grid_y;
    if (in_pad.w > in_pad.h)
    {
    
    
        num_grid_x = in_pad.w / stride;
        num_grid_y = num_grid / num_grid_x;
    }
    else
    {
    
    
        num_grid_y = in_pad.h / stride;
        num_grid_x = num_grid / num_grid_y;
    }

    const int num_class = feat_blob.w - 5-10;

    const int num_anchors = anchors.w / 2;

    for (int q = 0; q < num_anchors; q++)
    {
    
    
        const float anchor_w = anchors[q * 2];
        const float anchor_h = anchors[q * 2 + 1];

        const ncnn::Mat feat = feat_blob.channel(q);

        for (int i = 0; i < num_grid_y; i++)
        {
    
    
            for (int j = 0; j < num_grid_x; j++)
            {
    
    
                const float* featptr = feat.row(i * num_grid_x + j);

                // find class index with max class score
                int class_index = 0;
                float class_score = -FLT_MAX;
                for (int k = 0; k < num_class; k++)
                {
    
    
                    float score = featptr[5 +10+ k];
                    if (score > class_score)
                    {
    
    
                        class_index = k;
                        class_score = score;
                    }
                }

                float box_score = featptr[4];

		float confidence = sigmoid(box_score); //* sigmoid(class_score);

                if (confidence >= prob_threshold)
                {
    
    
                    // yolov5/models/yolo.py Detect forward
                    // y = x[i].sigmoid()
                    // y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
                    // y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh

                    float dx = sigmoid(featptr[0]);
                    float dy = sigmoid(featptr[1]);
                    float dw = sigmoid(featptr[2]);
                    float dh = sigmoid(featptr[3]);

                    float pb_cx = (dx * 2.f - 0.5f + j) * stride;
                    float pb_cy = (dy * 2.f - 0.5f + i) * stride;

                    float pb_w = pow(dw * 2.f, 2) * anchor_w;
                    float pb_h = pow(dh * 2.f, 2) * anchor_h;

                    float x0 = pb_cx - pb_w * 0.5f;
                    float y0 = pb_cy - pb_h * 0.5f;
                    float x1 = pb_cx + pb_w * 0.5f;
                    float y1 = pb_cy + pb_h * 0.5f;

                    Object obj;
                    obj.rect.x = x0;
                    obj.rect.y = y0;
                    obj.rect.width = x1 - x0;
                    obj.rect.height = y1 - y0;
                    obj.label = class_index;
                    obj.prob = confidence;

		    for (int l = 0; l < 5; l++)
		    {
    
    
			float x = featptr[2 * l + 5] * anchor_w + j * stride;
			float y = featptr[2 * l + 1 + 5] * anchor_h + i * stride;
			obj.pts.push_back(cv::Point2f(x, y));
		    }
	            objects.push_back(obj);
                }
            }
        }
    }
}



YoloFace::YoloFace()
{
    
    

}

int YoloFace::loadModel(std::string model, bool use_gpu)
{
    
    
    bool has_gpu = false;
    face_net.clear();

    face_net.opt = ncnn::Option();

#if NCNN_VULKAN
    ncnn::create_gpu_instance();
    has_gpu = ncnn::get_gpu_count() > 0;
#endif
    bool to_use_gpu = has_gpu && use_gpu;
    face_net.opt.use_vulkan_compute = to_use_gpu;
    face_net.load_param((model+".param").c_str());
    face_net.load_model((model + ".bin").c_str());

    return 0;
}



int YoloFace::detection(const cv::Mat& rgb, std::vector<Object>& objects, float prob_threshold, float nms_threshold)
{
    
    
    int img_w = rgb.cols;
    int img_h = rgb.rows;

    // letterbox pad to multiple of 32
    int w = img_w;
    int h = img_h;
    float scale = 1.f;
    if (w > h)
    {
    
    
        scale = (float)target_size / w;
        w = target_size;
        h = h * scale;
    }
    else
    {
    
    
        scale = (float)target_size / h;
        h = target_size;
        w = w * scale;
    }

    ncnn::Mat in = ncnn::Mat::from_pixels_resize(rgb.data, ncnn::Mat::PIXEL_RGB, img_w, img_h, w, h);

    int wpad = (w + 31) / 32 * 32 - w;
    int hpad = (h + 31) / 32 * 32 - h;
    ncnn::Mat in_pad;
    ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);

    in_pad.substract_mean_normalize(0, norm_vals);

    ncnn::Extractor ex = face_net.create_extractor();

    ex.input("data", in_pad);

    std::vector<Object> proposals;

    // anchor setting from yolov5/models/yolov5s.yaml

    // stride 8
    {
    
    
        ncnn::Mat out;
        ex.extract("981", out);

        ncnn::Mat anchors(6);
        anchors[0] = 4.f;
        anchors[1] = 5.f;
        anchors[2] = 8.f;
        anchors[3] = 10.f;
        anchors[4] = 13.f;
        anchors[5] = 16.f;

        std::vector<Object> objects8;
        generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8);

        proposals.insert(proposals.end(), objects8.begin(), objects8.end());
    }

    // stride 16
    {
    
    
        ncnn::Mat out;
        ex.extract("983", out);

        ncnn::Mat anchors(6);
        anchors[0] = 23.f;
        anchors[1] = 29.f;
        anchors[2] = 43.f;
        anchors[3] = 55.f;
        anchors[4] = 73.f;
        anchors[5] = 105.f;

        std::vector<Object> objects16;
        generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16);

        proposals.insert(proposals.end(), objects16.begin(), objects16.end());
    }

    // stride 32
    {
    
    
        ncnn::Mat out;
        ex.extract("985", out);

        ncnn::Mat anchors(6);
        anchors[0] = 146.f;
        anchors[1] = 217.f;
        anchors[2] = 231.f;
        anchors[3] = 300.f;
        anchors[4] = 335.f;
        anchors[5] = 433.f;

        std::vector<Object> objects32;
        generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32);

        proposals.insert(proposals.end(), objects32.begin(), objects32.end());
    }

    // sort all proposals by score from highest to lowest
    qsort_descent_inplace(proposals);

    // apply nms with nms_threshold
    std::vector<int> picked;
    nms_sorted_bboxes(proposals, picked, nms_threshold);

    int count = picked.size();

    objects.resize(count);
    for (int i = 0; i < count; i++)
    {
    
    
        objects[i] = proposals[picked[i]];

        // adjust offset to original unpadded
        float x0 = (objects[i].rect.x - (wpad / 2)) / scale;
        float y0 = (objects[i].rect.y - (hpad / 2)) / scale;
        float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;
        float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;

        for (int j = 0; j < objects[i].pts.size(); j++)
	{
    
    
	    float ptx = (objects[i].pts[j].x - (wpad / 2)) / scale;
	    float pty = (objects[i].pts[j].y - (hpad / 2)) / scale;
	    objects[i].pts[j] = cv::Point2f(ptx, pty);
	}

        // clip
        x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
        y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
        x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
        y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);

        objects[i].rect.x = x0;
        objects[i].rect.y = y0;
        objects[i].rect.width = x1 - x0;
        objects[i].rect.height = y1 - y0;
    }


    return 0;
}

int YoloFace::drawFace(cv::Mat& rgb,cv::Mat &cv_dst, std::vector<Object>& objects)
{
    
    
    cv_dst = rgb.clone();
    static const unsigned char colors[19][3] = {
    
    
        {
    
     54,  67, 244},
        {
    
     99,  30, 233},
        {
    
    176,  39, 156},
        {
    
    183,  58, 103},
        {
    
    181,  81,  63},
        {
    
    243, 150,  33},
        {
    
    244, 169,   3},
        {
    
    212, 188,   0},
        {
    
    136, 150,   0},
        {
    
     80, 175,  76},
        {
    
     74, 195, 139},
        {
    
     57, 220, 205},
        {
    
     59, 235, 255},
        {
    
      7, 193, 255},
        {
    
      0, 152, 255},
        {
    
     34,  87, 255},
        {
    
     72,  85, 121},
        {
    
    158, 158, 158},
        {
    
    139, 125,  96}
    };

    int color_index = 0;

    for (size_t i = 0; i < objects.size(); i++)
    {
    
    
        Object& obj = objects[i];

        const unsigned char* color = colors[color_index % 19];
        color_index++;

        cv::Scalar cc(color[0], color[1], color[2]);

        cv::rectangle(cv_dst,obj.rect, cc, 2);

        char text[256];

        sprintf(text, "Face = %.1f%%", obj.prob * 100);
        
        int baseLine = 0;
        cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

        int x = obj.rect.x;
        int y = obj.rect.y - label_size.height - baseLine;
        if (y < 0)
            y = 0;
        if (x + label_size.width > rgb.cols)
            x = rgb.cols - label_size.width;

        cv::rectangle(cv_dst, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)), cc, -1);

        cv::Scalar textcc = (color[0] + color[1] + color[2] >= 381) ? cv::Scalar(0, 0, 0) : cv::Scalar(255, 255, 255);

        cv::putText(cv_dst, text, cv::Point(x, y + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.5, textcc, 1);
        for (int j = 0; j < obj.pts.size(); j++)
        {
    
    
            cv::circle(cv_dst, obj.pts[j], 2, cv::Scalar(0, 255, 0), -1);
        }
    }

    return 0;
}

2.人脸检测效果
在这里插入图片描述

二、人脸打码

代码：

#include <opencv2/opencv.hpp>
#include "yoloface.h"
#include <opencv2/video/video.hpp>

cv::Rect targetResize(const cv::Mat& cv_src, cv::Mat& cv_dst, int target_w, int target__h);

bool generate_mosaic(cv::Mat& src, std::vector<Object>& objects)
{
    
    

    if (objects.empty())return false;

    int step = 10;//步长

    for (int t = 0; t < objects.size(); t++)
    {
    
    
        int x = objects[t].rect.tl().x;
        int y = objects[t].rect.tl().y;
        int width = objects[t].rect.width;
        int height = objects[t].rect.height;

        for (int i = y; i < (y + height); i += step)
        {
    
    
            for (int j = x; j < (x + width); j += step)
            {
    
    
                for (int k = i; k < (step + i); k++)
                {
    
    
                    for (int m = j; m < (step + j); m++)
                    {
    
    
                        for (int c = 0; c < 3; c++)
                        {
    
    
                            src.at<cv::Vec3b>(k, m)[c] = src.at<cv::Vec3b>(i, j)[c];
                        }
                    }
                }
            }
        }
    }

    return true;
}

int main(void)
{
    
    
	YoloFace yolo_face;

    //加载人脸检测模型
	yolo_face.loadModel("models/face/face_lite");
    cv::VideoCapture cap;

    //打开摄像头或者是输入视频
    //cap.open(0);
    cap.open("222.mp4");
    if (!cap.isOpened())
    {
    
    
        return 0;
    }

    cv::Mat cv_src;
    while (1)
    {
    
    
        cap >> cv_src;

        if (cv_src.empty())
        {
    
    
            break;
        }

       std::vector<Object> objects;
       
       //图像边界扩展，这步是为了提高精度
       cv::Mat cv_target;
      // cv::Rect rect = targetResize(cv_src, cv_target, target_w, target_h);

       //人脸检测
       yolo_face.detection(cv_src, objects);

       cv::Mat cv_dst;
       generate_mosaic(cv_src, objects);
       //yolo_face.drawFace(cv_src, cv_dst, objects);
        

        cv::namedWindow("face", 0);
        cv::imshow("face", cv_dst);
        cv::waitKey(20);
    }
    cap.release();
    return 0;
}
cv::Rect targetResize(const cv::Mat& cv_src, cv::Mat& cv_dst, int target_w, int target_h)
{
    
    
    float s;
   
    if (cv_src.cols > cv_src.rows)
    {
    
    
        s = float(target_w) / cv_src.cols;
    }
    else
    {
    
    
        s = float(target_h) / cv_src.rows;
      
    }
    float w = cv_src.cols * s;
    float h = cv_src.rows * s;
    int w_p = (target_w - w) / 2;
    int h_p = (target_h - h) / 2;

    cv::Mat cv_size;
    cv::resize(cv_src, cv_size, cv::Size(w, h));
    cv::copyMakeBorder(cv_size, cv_dst, h_p, h_p, w_p, w_p, cv::BORDER_CONSTANT, 114.f);

    return cv::Rect(w_p, h_p, cv_size.cols, cv_size.rows);
}

打码效果：
在这里插入图片描述

三、源码

1.源码地址：https://mp.csdn.net/mp_download/manage/download/UpDetailed
2.源码配置方法
1.文件目录
在这里插入图片描述
2.配置include和lib路径
在这里插入图片描述
3.添加lib名，就是源码目录里面所有点lib后缀的名称。
在这里插入图片描述
4.IDE的配置
在这里插入图片描述

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