// 在图像上绘制人脸边界框for(size_t i =0; i < faces.size(); i++) { cv::rectangle(frame, faces[i], cv::Scalar(0,255,0),2); } // 显示结果图像cv::imshow("Face Detection", frame); // 按下ESC键退出循环if(cv::waitKey(1) ==27)
第一类基于级联的人脸检测方法(如Cascade CNN、MTCNN)运行速度较快、检测性能适中,适用于算力有限、背景简单且人脸数量较少的场景。 第二类两阶段人脸检测方法一般基于Faster-RCNN框架,在第一阶段生成候选区域,然后在第二阶段对候选区域进行分类和回归,其检测准确率较高,缺点是检测速度较慢,代表方法有Face R-CNN、Sca...
faceRects = classfier.detectMultiScale(grey, scaleFactor = 1.2, minNeighbors = 3, minSize = (32, 32)) if len(faceRects) > 0: # 大于0则检测到人脸 for faceRect in faceRects: # 单独框出每一张人脸 x, y, w, h = faceRect cv2.rectangle(frame, (x - 10, y - 10), (x + w ...
(startX, startY, endX, endY) = box.astype("int")# extract the face ROI and grab the ROI dimensionsface = image[startY:endY, startX:endX] (fH, fW) = face.shape[:2]# ensure the face width and height are sufficiently largeiffW <20orfH <20:continue# construct a blob for the f...
faces=detector(gray_image)# 对每个人脸进行关键点定位forfaceinfaces:landmarks=predictor(gray_image,face)forninrange(68):x=landmarks.part(n).x y=landmarks.part(n).y cv2.circle(image,(x,y),2,(0,255,0),-1)# 显示带有关键点的图像 ...
JavaFaceDetectionface_detector=newJavaFaceDetection(model_file,pb_txt_file,0.5f); face_detector.infer_image(inputImage); HighGui.imshow("OpenCVJava深度学习人脸检测演示",inputImage); HighGui.waitKey(0); VideoCapturecapture=newVideoCapture; capture.open(0); ...
for faceRect in faces:x,y,w,h = faceRectcv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2,8,0)roi_gray = gray[y:y+h,x:x+w]roi_color = img[y:y+h,x:x+w]eyes = eye_cascade.detectMultiScale(roi_gray,1.1,1,cv2.CASCADE_SCALE_IMAGE,(2,2))for (ex,ey,ew,eh) in ...
本文介绍最基本的用OpenCV实现人脸检测的方法。 一.人脸检测算法原理 Viola-Jones人脸检测方法 参考文献:Paul Viola, Michael J. Jones. Robust Real-Time Face Detection[J]. International Journal of Computer Vision,2004,57(2):137-154. 该算法的主要贡献有三: ...
for x,y,w,h in faces: cv.rectangle(image,(x,y),(x+w,y+h),(0,0,255),2) cv.imshow("face_detection",image) src = cv.imread("./d.png") #读取图片 cv.namedWindow("input image",cv.WINDOW_AUTOSIZE) #创建GUI窗口,形式为自适应 ...
faces = faceCascade.detectMultiScale( gray, scaleFactor= 1.1, minNeighbors=8, minSize=(55, 55), flags=cv2.CASCADE_SCALE_IMAGE ) # 画出每一个人脸,提取出人脸所在区域 for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2) ...