face_landmark_detection.py

'''
来源 
	http://dlib.net/face_landmark_detection.py.html
依赖:
	机器视觉
	  opencv
		pip3 install opencv-python
	  dlib
		brew install cmake dlib
	  	pip3 install dlib # 稍等片刻
	摄像头用imutils
		VideoStream

todo:
	考虑树莓派 mobilenet/ssd + opencv
	如何流向scratch
	启动，使用独立的进程，启停，还要能返回数据，不断print
adapter使用
	sys.path 指向python3 一个ai目录 codelab_ai

注意相机 斜向上指向脸部
'''

# USAGE
# python3 video_facial_landmarks.py --shape-predictor shape_predictor_68_face_landmarks.dat
# python3 video_facial_landmarks.py --shape-predictor shape_predictor_68_face_landmarks.dat --picamera 1

# import the necessary packages
from imutils.video import VideoStream
from imutils import face_utils
import datetime
import argparse
import imutils
import time
import dlib
import cv2
 
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
	help="path to facial landmark predictor")
# 加载model路径 dlib’s pre-trained facial landmark detector.
# 同级目录下的文件
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())
 
# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
print("[INFO] loading facial landmark predictor...")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

# initialize the video stream and allow the cammera sensor to warmup
print("[INFO] camera sensor warming up...")
vs = VideoStream(usePiCamera=args["picamera"] > 0).start()
time.sleep(2.0)

# loop over the frames from the video stream
while True:
	# grab the frame from the threaded video stream, resize it to
	# have a maximum width of 400 pixels, and convert it to
	# grayscale
	frame = vs.read()
	frame = imutils.resize(frame, width=480) # 使用scratch的尺寸 480x360
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

	# detect faces in the grayscale frame
	rects = detector(gray, 0)

	key = cv2.waitKey(1) & 0xFF

	# loop over the face detections
	for 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) # 是什么
		shape = face_utils.shape_to_np(shape) # 是否可以用json发过去 shape.tolist()
		if key == ord("i"):
			from IPython import embed;embed()
		# 	找到眼镜所在的位置，索引
		# (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
		# loop over the (x, y)-coordinates for the facial landmarks
		# and draw them on the image
		for (x, y) in shape:
			cv2.circle(frame, (x, y), 1, (0, 0, 255), -1) # 红色小点 不断发布json
	  
	# show the frame
	cv2.imshow("Frame", frame) # 展示出图
	
 
	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break
 
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()