利用OpenCV的人脸检测给头像带上圣诞帽

2024-06-25 19:14| 来源: 网络整理| 查看: 265

我们来看下效果

原图：

效果：

原理其实很简单：

采用一张圣诞帽的png图像作为素材，

利用png图像背景是透明的，贴在背景图片上就是戴帽子的效果了。

人脸检测的目的主要是为了确定贴帽子的位置，类似ps中自由变换的功能，检测到人脸中间的位置，resize圣诞帽子和人脸大小匹配，确定位置，贴上去，ok！

代码：非常简洁，根据参考博客给出的代码，由OpenCV自带的人脸检测代码经过简单修改即可。

// getheader.cpp : 定义控制台应用程序的入口点。 // #include "stdafx.h" #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include #include using namespace std; using namespace cv; #pragma comment(lib,"opencv_core2410d.lib") #pragma comment(lib,"opencv_highgui2410d.lib") #pragma comment(lib,"opencv_objdetect2410d.lib") #pragma comment(lib,"opencv_imgproc2410d.lib") /** Function Headers */ void detectAndDisplay( Mat frame ); /** Global variables */ //-- Note, either copy these two files from opencv/data/haarscascades to your current folder, or change these locations String face_cascade_name = "D:\\Program Files\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml"; String eyes_cascade_name = "D:\\Program Files\\opencv\\sources\\data\\haarcascades\\haarcascade_eye_tree_eyeglasses.xml"; CascadeClassifier face_cascade; CascadeClassifier eyes_cascade; string window_name = "Capture - Face detection"; RNG rng(12345); const int FRAME_WIDTH = 1280; const int FRAME_HEIGHT = 240; /** * @function main */ int main( void ) { CvCapture* capture; //VideoCapture capture; Mat frame; //-- 1. Load the cascades if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; }; if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; }; frame = imread("19.jpg");//背景图片 //-- 3. Apply the classifier to the frame if( !frame.empty() ) { detectAndDisplay( frame ); } waitKey(0); return 0; } void mapToMat(const cv::Mat &srcAlpha, cv::Mat &dest, int x, int y) { int nc = 3; int alpha = 0; for (int j = 0; j < srcAlpha.rows; j++) { for (int i = 0; i < srcAlpha.cols*3; i += 3) { alpha = srcAlpha.ptr(j)[i / 3*4 + 3]; //alpha = 255-alpha; if(alpha != 0) //4通道图像的alpha判断 { for (int k = 0; k < 3; k++) { // if (src1.ptr(j)[i / nc*nc + k] != 0) if( (j+y < dest.rows) && (j+y>=0) && ((i+x*3) / 3*3 + k < dest.cols*3) && ((i+x*3) / 3*3 + k >= 0) && (i/nc*4 + k < srcAlpha.cols*4) && (i/nc*4 + k >=0) ) { dest.ptr(j+y)[(i+x*nc) / nc*nc + k] = srcAlpha.ptr(j)[(i) / nc*4 + k]; } } } } } } /** * @function detectAndDisplay */ void detectAndDisplay( Mat frame ) { std::vector faces; Mat frame_gray; Mat hatAlpha; hatAlpha = imread("2.png",-1);//圣诞帽的图片 cvtColor( frame, frame_gray, COLOR_BGR2GRAY ); equalizeHist( frame_gray, frame_gray ); //-- Detect faces face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) ); for( size_t i = 0; i < faces.size(); i++ ) { Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 ); // ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 255 ), 2, 8, 0 ); // line(frame,Point(faces[i].x,faces[i].y),center,Scalar(255,0,0),5); Mat faceROI = frame_gray( faces[i] ); std::vector eyes; //-- In each face, detect eyes eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); for( size_t j = 0; j < eyes.size(); j++ ) { Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 ); int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 ); // circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 3, 8, 0 ); } // if(eyes.size()) { resize(hatAlpha,hatAlpha,Size(faces[i].width, faces[i].height),0,0,INTER_LANCZOS4); // mapToMat(hatAlpha,frame,center.x+2.5*faces[i].width,center.y-1.3*faces[i].height); mapToMat(hatAlpha,frame,faces[i].x,faces[i].y-0.8*faces[i].height); } } //-- Show what you got imshow( window_name, frame ); imwrite("merry christmas.jpg",frame); }

下面是摄像头实时戴帽子，改下主函数就好了：

int main( void ) { CvCapture* capture; //VideoCapture capture; Mat frame; //-- 1. Load the cascades if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; }; if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; }; // frame = imread("19.jpg");//背景图片 VideoCapture cap(0); //打开默认的摄像头号 if(!cap.isOpened()) //检测是否打开成功 return -1; Mat edges; //namedWindow("edges",1); for(;;) { Mat frame; cap >> frame; // 从摄像头中获取新的一帧 detectAndDisplay( frame ); //imshow("edges", frame); if(waitKey(30) >= 0) break; } //摄像头会在VideoCapture的析构函数中释放 waitKey(0); return 0; }

我的系统的是win10 64位的系统，之前摄像头出来都是黑的，发现需要用vs2010配置一下x64版本方可使用，查了半天还是自己之前写的博客靠谱：

就是按照win7 x64来配置，完美运行

http://blog.csdn.net/wangyaninglm/article/details/16325283

效果：

参考文献：

http://blog.csdn.net/lonelyrains/article/details/50388999

http://docs.opencv.org/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html

我调试好的工程：

点击打开链接

一个python版本的代码：

https://github.com/LiuXiaolong19920720/Add-Christmas-Hat import numpy as np import cv2 import dlib # 给img中的人头像加上圣诞帽，人脸最好为正脸 def add_hat(img,hat_img): # 分离rgba通道，合成rgb三通道帽子图，a通道后面做mask用 r,g,b,a = cv2.split(hat_img) rgb_hat = cv2.merge((r,g,b)) cv2.imwrite("hat_alpha.jpg",a) # ------------------------- 用dlib的人脸检测代替OpenCV的人脸检测----------------------- # # 灰度变换 # gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # # 用opencv自带的人脸检测器检测人脸 # face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") # faces = face_cascade.detectMultiScale(gray,1.05,3,cv2.CASCADE_SCALE_IMAGE,(50,50)) # ------------------------- 用dlib的人脸检测代替OpenCV的人脸检测----------------------- # dlib人脸关键点检测器 predictor_path = "shape_predictor_5_face_landmarks.dat" predictor = dlib.shape_predictor(predictor_path) # dlib正脸检测器 detector = dlib.get_frontal_face_detector() # 正脸检测 dets = detector(img, 1) # 如果检测到人脸 if len(dets)>0: for d in dets: x,y,w,h = d.left(),d.top(), d.right()-d.left(), d.bottom()-d.top() # x,y,w,h = faceRect # cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2,8,0) # 关键点检测，5个关键点 shape = predictor(img, d) # for point in shape.parts(): # cv2.circle(img,(point.x,point.y),3,color=(0,255,0)) # cv2.imshow("image",img) # cv2.waitKey() # 选取左右眼眼角的点 point1 = shape.part(0) point2 = shape.part(2) # 求两点中心 eyes_center = ((point1.x+point2.x)//2,(point1.y+point2.y)//2) # cv2.circle(img,eyes_center,3,color=(0,255,0)) # cv2.imshow("image",img) # cv2.waitKey() # 根据人脸大小调整帽子大小 factor = 1.5 resized_hat_h = int(round(rgb_hat.shape[0]*w/rgb_hat.shape[1]*factor)) resized_hat_w = int(round(rgb_hat.shape[1]*w/rgb_hat.shape[1]*factor)) if resized_hat_h > y: resized_hat_h = y-1 # 根据人脸大小调整帽子大小 resized_hat = cv2.resize(rgb_hat,(resized_hat_w,resized_hat_h)) # 用alpha通道作为mask mask = cv2.resize(a,(resized_hat_w,resized_hat_h)) mask_inv = cv2.bitwise_not(mask) # 帽子相对与人脸框上线的偏移量 dh = 0 dw = 0 # 原图ROI # bg_roi = img[y+dh-resized_hat_h:y+dh, x+dw:x+dw+resized_hat_w] bg_roi = img[y+dh-resized_hat_h:y+dh,(eyes_center[0]-resized_hat_w//3):(eyes_center[0]+resized_hat_w//3*2)] # 原图ROI中提取放帽子的区域 bg_roi = bg_roi.astype(float) mask_inv = cv2.merge((mask_inv,mask_inv,mask_inv)) alpha = mask_inv.astype(float)/255 # 相乘之前保证两者大小一致（可能会由于四舍五入原因不一致） alpha = cv2.resize(alpha,(bg_roi.shape[1],bg_roi.shape[0])) # print("alpha size: ",alpha.shape) # print("bg_roi size: ",bg_roi.shape) bg = cv2.multiply(alpha, bg_roi) bg = bg.astype('uint8') cv2.imwrite("bg.jpg",bg) # cv2.imshow("image",img) # cv2.waitKey() # 提取帽子区域 hat = cv2.bitwise_and(resized_hat,resized_hat,mask = mask) cv2.imwrite("hat.jpg",hat) # cv2.imshow("hat",hat) # cv2.imshow("bg",bg) # print("bg size: ",bg.shape) # print("hat size: ",hat.shape) # 相加之前保证两者大小一致（可能会由于四舍五入原因不一致） hat = cv2.resize(hat,(bg_roi.shape[1],bg_roi.shape[0])) # 两个ROI区域相加 add_hat = cv2.add(bg,hat) # cv2.imshow("add_hat",add_hat) # 把添加好帽子的区域放回原图 img[y+dh-resized_hat_h:y+dh,(eyes_center[0]-resized_hat_w//3):(eyes_center[0]+resized_hat_w//3*2)] = add_hat # 展示效果 # cv2.imshow("img",img ) # cv2.waitKey(0) return img # 读取帽子图，第二个参数-1表示读取为rgba通道，否则为rgb通道 hat_img = cv2.imread("hat2.png",-1) # 读取头像图 img = cv2.imread("test.jpg") output = add_hat(img,hat_img) # 展示效果 cv2.imshow("output",output ) cv2.waitKey(0) cv2.imwrite("output.jpg",output) # import glob as gb # img_path = gb.glob("./images/*.jpg") # for path in img_path: # img = cv2.imread(path) # # 添加帽子 # output = add_hat(img,hat_img) # # 展示效果 # cv2.imshow("output",output ) # cv2.waitKey(0) cv2.destroyAllWindows()

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