SSD的人脸检测

源代码detect_faces.py

import numpy as np
import argparse
import cv2
import imutils
import time
import imutils
from imutils.video import VideoStream
ap = argparse.ArgumentParser()
ap.add_argument("-s","--stream",type=int,default = 0,help="path to input image")
ap.add_argument("-p","--prototxt",required = True,help="path to caffe 'deploy' prototxt file")
ap.add_argument("-m","--model",required=True,help="path to caffe pre-trained model")
ap.add_argument("-c","--confidence",type=float,default=0.5,help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
print("[INFO] load models...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"],args["model"])
vs = VideoStream(0).start()
time.sleep(2.0)
while True:
    frame = vs.read()
    frame = imutils.resize(frame,width = 400)
    (h,w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame,(300,300)),1.0,(300,300),(104.0,127.0,123.0))
    net.setInput(blob)
    detections = net.forward()
    for i in range(0,detections.shape[2]):
        confidence = detections[0,0,i,2]
        if confidence > args["confidence"]:
            box = detections[0,0,i,3:7]*np.array([w,h,w,h])
            (startX,startY,endX,endY)=box.astype("int")
            text = "{:.2f}".format(confidence*100)
            y = startY-10 if startY-10>10 else startX +10
            cv2.rectangle(frame,(startX,startY),(endX,endY),(0,0,255),2)
            cv2.putText(frame,text,(startX,y),cv2.FONT_HERSHEY_SIMPLEX,0.45,(0,0,255),2)
    cv2.imshow("Output",frame)
    k = cv2.waitKey(1)
    if k == 27:
        break
vs.stop()
cv2.destroyAllWindows()