face_detector.py

# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import face_recognition
import argparse
import imutils
import pickle
import time
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--cascade", default="haarcascade_frontalface_default.xml",
	help = "path to where the face cascade resides")
args = vars(ap.parse_args())


# load the known faces and embeddings along with OpenCV's Haar
# cascade for face detection
print("[INFO] loading encodings + face detector...")
detector = cv2.CascadeClassifier(args["cascade"])
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = cv2.VideoCapture("face.mp4")
# vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
# start the FPS counter
fps = FPS().start()

# loop over frames from the video file stream
while True:
	# grab the frame from the threaded video stream and resize it
	# to 500px (to speedup processing)
	ret, frame = vs.read()
	frame = imutils.resize(frame, width=500)
	
	# convert the input frame from (1) BGR to grayscale (for face
	# detection) and (2) from BGR to RGB (for face recognition)
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
	# detect faces in the grayscale frame
	rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
		minNeighbors=3, minSize=(30, 30))
	# OpenCV returns bounding box coordinates in (x, y, w, h) order
	# but we need them in (top, right, bottom, left) order, so we
	# need to do a bit of reordering

	for (x, y, w, h) in rects:
		cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
	
	# display the image to our screen
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF
	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break
	# update the FPS counter
	fps.update()

    # stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()