string url = "https://www.cnblogs.com/cplemom/?id=15&data={'id':12,'name':'今天天气不错'}";
var uri = new Uri(url);
var collection= HttpUtility.ParseQueryString(uri.Query);//默认采用UTF-8编码，当然也可以传入特定编码进行解析//var collection= HttpUtility.ParseQueryString(uri.Query,Encoding.ASCII);
Console.WriteLine(collection["data"]);//输出结果： {'id':12,'name':'今天天气不错'}
该方法有几个要注意的点
uri.Query是?开头的，但是在转为键值对以后被自动过滤掉了，且只会过滤一个?字符
参数内容会自动使用UTF-8解码
对于传递的数组参数(?ids=1&ids=2),会通过,拼接
在参数字符中通过&划分后，只会把第一个=前的字符串作为key
基于上述一些要求就可以自己写一个解析参数的方式了。
publicstatic Dictionary<string, string> ParseQueryString(string url)
{
if (string.IsNullOrWhiteSpace(url))
{
thrownew ArgumentNullException("url");
}
var uri = new Uri(url);
if (string.IsNullOrWhiteSpace(uri.Query))
{
returnnew Dictionary<string, string>();
}
//1.去除第一个前导?字符var dic = uri.Query.Substring(1)
//2.通过&划分各个参数
.Split(newchar[] { '&' }, StringSplitOptions.RemoveEmptyEntries)
//3.通过=划分参数key和value,且保证只分割第一个=字符
.Select(param => param.Split(newchar[] { '=' }, 2, StringSplitOptions.RemoveEmptyEntries))
//4.通过相同的参数key进行分组
.GroupBy(part => part[0], part => part.Length > 1 ? part[1] : string.Empty)
//5.将相同key的value以,拼接
.ToDictionary(group => group.Key, group => string.Join(",", group));
return dic;
}
# start the video stream threadprint("[INFO] starting video stream thread...")vs = VideoStream(src=args["webcam"]).start()time.sleep(1.0)# loop over frames from the video streamwhile True: # grab the frame from the threaded video file stream, resize # it, and convert it to grayscale # channels) frame = vs.read() frame = imutils.resize(frame, width=450) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # detect faces in the grayscale frame rects = detector(gray, 0)
实例化 VideoStream。
暂停一秒钟，让相机传感器预热。
开始遍历视频流中的帧。
读取下一帧，然后我们通过将其大小调整为 450 像素的宽度并将其转换为灰度进行预处理。
应用 dlib 的人脸检测器来查找和定位图像中的人脸。
下一步是应用面部标志检测来定位面部的每个重要区域：
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# loop over the face detectionsfor rect in rects: # determine the facial landmarks for the face region, then # convert the facial landmark (x, y)-coordinates to a NumPy # array shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape) # extract the left and right eye coordinates, then use the # coordinates to compute the eye aspect ratio for both eyes leftEye = shape[lStart:lEnd] rightEye = shape[rStart:rEnd] leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratio(rightEye) # average the eye aspect ratio together for both eyes ear = (leftEAR + rightEAR) / 2.0
循环遍历检测到的每个人脸——在我们的实现中（特别与司机睡意有关），我们假设只有一张脸——司机——但我把这个 for 循环留在这里以防万一你想应用多张脸视频的技术。
# check to see if the eye aspect ratio is below the blink# threshold, and if so, increment the blink frame counterif ear < EYE_AR_THRESH: COUNTER += 1 # if the eyes were closed for a sufficient number of # then sound the alarm if COUNTER >= EYE_AR_CONSEC_FRAMES: # if the alarm is not on, turn it on if not ALARM_ON: ALARM_ON = True # check to see if an alarm file was supplied, # and if so, start a thread to have the alarm # sound played in the background if args["alarm"] != "": t = Thread(target=sound_alarm, args=(args["alarm"],)) t.deamon = True t.start() # draw an alarm on the frame frame=cv2ImgAddText(frame,"醒醒，别睡!",10,30,(255, 0, 0),30)# otherwise, the eye aspect ratio is not below the blink# threshold, so reset the counter and alarmelse: COUNTER = 0 ALARM_ON = False
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