pi_face_recognition_hog.py

from imutils.video import VideoStream
from imutils.video import FPS
import face_recognition
import imutils
import pickle
import cv2
import sys, time
import os

# This program processes frames from live video to detect faces and compares againts a list of known face encodings. 
print("[INFO] loading encodings + face detector...")
data = pickle.loads(open("encodings.pickle", "rb").read())

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
#vs = VideoStream(src=0).start()
#vs = VideoStream(usePiCamera=True).start()
vs = VideoStream(usePiCamera=True, resolution = (720,480)).start()
time.sleep(2.0)

# start the FPS counter
fps = FPS().start()


while True:
    # Grab a single frame of video
    frame = vs.read()
    #frame = imutils.resize(frame, width=500)

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    
    

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
        face_names = []

        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(data["encodings"], face_encoding, 0.55)
            #distances = face_recognition.face_distance(data["encodings"], face_encoding)
	    
            name = "Unknown"

            # check to see if we have found a match
            if True in matches:
                # find the indexes of all matched faces then initialize a
                # dictionary to count the total number of times each face
                # was matched
                matchedIdxs = [i for (i, b) in enumerate(matches) if b]
                counts = {}

                # loop over the matched indexes and maintain a count for
                # each recognized face face
                for i in matchedIdxs:
                    name = data["names"][i]
                    counts[name] = counts.get(name, 0) + 1

                # determine the recognized face with the largest number
                # of votes (note: in the event of an unlikely tie Python
                # will select first entry in the dictionary)
                name = max(counts, key=counts.get)
            
            # update the list of names
            face_names.append(name)

    #process_this_frame = not process_this_frame


    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name above the face
        cv2.rectangle(frame, (left, top), (right, bottom),
            (0, 255, 0), 2)
        y = top - 15 if top - 15 > 15 else top + 15
        cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
            0.75, (0, 255, 0), 2)
       
    # Display the resulting image
    
    cv2.imshow('Camera', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == 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()))

# Release handle to the webcam
vs.stop()
cv2.destroyAllWindows()