人脸检测一种主流的方法就是类haar+adaboosting，opencv中也是用的这种方法。这种方法可以推广到刚性物体的检测，前提是要训练好级联分类器(比如说用类haar特征)，一旦训练数据弄好了，直接调用opencv中的类CascadeClassifier，用它的几个简单的成员函数就可以完成检测功能。

Haar分类器的概念

以 Haar 特征分类器为基础的对象检测技术是一种非常有效的对象检测技术（2001 年 Paul_Viola 和 Michael_Jones 提出）。它是基于机器学习的， 通过使用大量的正负样本图像训练得到一个 cascade_function，最后再用它 来做对象检测。 现在我们来学习面部检测。开始时，算法需要大量的正样本图像（面部图像）和负样本图像（不含面部的图像）来训练分类器。我们需要从其中提取特 征。下图中的 Haar 特征会被使用。它们就像我们的卷积核。每一个特征是一 个值，这个值等于黑色矩形中的像素值之后减去白色矩形中的像素值之和。

Haar级联器的数据

OpenCV的源码
sources\data\haarcascades
复制到你的工程

静态图像人脸检测

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# coding:utf-8
import cv2
filename = './img/face.jpg'
def detect(filename):
    # 级联分类器
    face_cascade = cv2.CascadeClassifier(
            './cascades/haarcascade_frontalface_default.xml')
    img = cv2.imread(filename)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    # 绘制矩形
    for (x, y, w, h) in faces:
        img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
    cv2.namedWindow('Vikings Detected!!')
    cv2.imshow('Vikings Detected!!', img)
    cv2.imwrite('./img/detect_face.jpg', img)
    cv2.waitKey(0)

Python: cv2.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) → objects
Parameters:

• cascade – Haar classifier cascade (OpenCV 1.x API only). It can be loaded from XML or YAML file using Load(). When the cascade is not needed anymore, release it using cvReleaseHaarClassifierCascade(&cascade).
• image – Matrix of the type CV_8U containing an image where objects are detected.
• objects – Vector of rectangles where each rectangle contains the detected object.
• scaleFactor – Parameter specifying how much the image size is reduced at each image scale.
• minNeighbors – Parameter specifying how many neighbors each candidate rectangle should have to retain it.
• flags – Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
• minSize – Minimum possible object size. Objects smaller than that are ignored.
• maxSize – Maximum possible object size. Objects larger than that are ignored.

  视频中的人脸检测

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  # coding:utf-8 import cv2 def detect(): face_cascade = cv2.CascadeClassifier('./cascades/haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier('./cascades/haarcascade_eye.xml') camera = cv2.VideoCapture(1) while True: ret, frame = camera.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.1, 5) for (x, y, w, h) in faces: img = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) roi_gray = gray[y:y + h, x:x + w] roi_color = img[y:y + h, x:x + w] eyes = eye_cascade.detectMultiScale(roi_gray, 1.3, 6, 0, (40, 40)) for (ex, ey, ew, eh) in eyes: cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (255, 255, 255), 2) cv2.imshow("camera", frame) if cv2.waitKey(1000 / 12) & 0xff == ord("q"): break camera.release() cv2.destroyAllWindows() if __name__ == "__main__": detect()

不上图