facial_landmarks.zip

# import the necessary packages from imutils.video import VideoStream from imutils import face_utils import argparse import imutils import time import dlib import cv2 # 构造参数 parse 并解析参数 ap = argparse.ArgumentParser() ap.add_argument("-p", "--shape-predictor", required=True, help="path to facial landmark predictor") ap.add_argument("-r", "--picamera", type=int, default=-1, help="whether or not the Raspberry Pi camera should be used") args = vars(ap.parse_args()) # 初始化dlib的人脸检测器（基于HOG）然后创建 # 面部标志预测器 print("[INFO] loading facial landmark predictor...") detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(args["shape_predictor"]) # 初始化视频流并让相机传感器预热 print("[INFO] camera sensor warming up...") vs = VideoStream(usePiCamera=args["picamera"] > 0).start() time.sleep(2.0) # 循环视频流中的帧 while True: # 从线程视频流中抓取帧，将其调整为 # 最大宽度为 400 像素，并将其转换为 # 灰度 frame = vs.read() frame = imutils.resize(frame, width=400) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 在灰度帧中检测人脸 rects = detector(gray, 0) # 循环面部检测 for rect in rects: # 确定面部区域的面部标志，然后 # 将面部标志 (x, y) 坐标转换为 NumPy数组 shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape) # 循环面部标志的 (x, y) 坐标 # 并将它们绘制在图像上 for (x, y) in shape: cv2.circle(frame, (x, y), 1, (0, 0, 255), -1) # 显示帧 cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # 如果按下 `q` 键，则跳出循环 if key == ord("q"): break