人脸检测（二）

       本篇博客接上一篇博客，是人脸检测算法的第二部分，讲述如何检测视频中的人脸，也是为了巩固一下所学到的知识。

       本文中的视频有两种：其一为本地保存的视频，其二为来自于电脑摄像头的视频。有了上一篇博客作为基础，所以操作起来比较容易。所用到的的文件挂在Guihub

导入类库

# USAGE
# python webcam.py qq

# import the necessary packages
from pyimagesearch.facedetector import FaceDetector
from pyimagesearch import imutils
# from picamera.array import PiRGBArray
# from picamera import PiCamera
import argparse
import time
import cv2

       这里的imutils包中包含用于执行基本图像操作的便捷功能，例如调整大小。

传参

       这里仍然用解析命令行。如下：

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", required = True,
    help = "path to where the face cascade resides")
ap.add_argument("-v", "--video",
    help = "path to the (optional) video file")
args = vars(ap.parse_args())

实例化算法

       下面我们进行基于视频人脸检测算法的实例化，这里视频主要来源于本地或者电脑的摄像头。

if not args.get('video', False):
    fd = FaceDetector(args["face"])
    time.sleep(0.1)
    camera = cv2.VideoCapture(0)
    while True:
        # Capture frame-by-frame
        ret, frame = camera.read()
        frame = imutils.resize(frame, width = 300)

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faceRects = fd.detect(gray, scaleFactor = 1.1, 
        minNeighbors = 5,minSize = (30, 30))
        frameClone = frame.copy()


        for (fX, fY, fW, fH) in faceRects:
            cv2.rectangle(frameClone, (fX, fY), (fX+fW, fY+fH), (0,255,0), 2)

        cv2.imshow("Face", frameClone)

        if cv2.waitKey(1) & 0xFF==ord('q'):
            break

    camera.release()
    cv2.destroyAllWindows()    

else: 
    fd = FaceDetector(args["face"])
    camera = cv2.VideoCapture(args["video"])
    # keep looping
    while True:
        #grabing the next frame in the video by calling the read() method of camera
        (grabbed, frame) = camera.read()
        # if we are viewing a video and we did not grab a
        # frame, then we have reached the end of the video
        if args.get("video") and not grabbed:
            break

        # resize the frame and convert it to grayscale
        frame = imutils.resize(frame, width = 300)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # detect faces in the image and then clone the frame
        # so that we can draw on it
        faceRects = fd.detect(gray, scaleFactor = 1.1, minNeighbors = 5,
            minSize = (30, 30))
        frameClone = frame.copy()

        # Loop over the face bounding boxes and draw them
        for (fX, fY, fW, fH) in faceRects:
            cv2.rectangle(frameClone, (fX, fY), (fX+fW, fY+fH), (0,255,0), 2)

        # show our detected faces
        cv2.imshow("Face", frameClone)
        # cleanup the camera and close any open windows
        camera.release()
        cv2.destroyAllWindows()

       代码段里使用了一个if语句来实现判断视频是源自本地还是摄像头。其中，if not args.get(‘video’, False):…，else:..表示如果解析命令行参数是没有–vedio参数，则打开摄像头，获取实时视频，否则读取本地视频。
效果如下：