import face_recognition

import cv2

import numpy as np

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the

# other example, but it includes some basic performance tweaks to make things run a lot faster:

#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)

#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.

# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this

# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one)

video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.

obama_image = face_recognition.load_image_file("obama.jpg")

obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

# Load a second sample picture and learn how to recognize it.

biden_image = face_recognition.load_image_file("biden.jpg")

biden_face_encoding = face_recognition.face_encodings(biden_image)[0]

# Create arrays of known face encodings and their names

known_face_encodings = [

    obama_face_encoding,

    biden_face_encoding

]

known_face_names = [

    "Barack Obama",

    "Joe Biden"

]

# Initialize some variables

face_locations = []

face_encodings = []

face_names = []

process_this_frame = True

while True:

    # Grab a single frame of video

    ret, frame = video_capture.read()

    # 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(known_face_encodings, face_encoding)

            name = "Unknown"

            # # If a match was found in known_face_encodings, just use the first one.

            # if True in matches:

            #     first_match_index = matches.index(True)

            #     name = known_face_names[first_match_index]

            # Or instead, use the known face with the smallest distance to the new face

            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)

            best_match_index = np.argmin(face_distances)

            if matches[best_match_index]:

                name = known_face_names[best_match_index]

            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 below the face

        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)

        font = cv2.FONT_HERSHEY_DUPLEX

        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image

    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

# Release handle to the webcam

video_capture.release()

cv2.destroyAllWindows()　　

import cv2

import threading

import face_recognition

import numpy as np

import os

class camThread(threading.Thread):

    def __init__(self, previewName, camID):

        threading.Thread.__init__(self)

        self.previewName = previewName

        self.camID = camID

    def run(self):

        print("Starting " + self.previewName)

        camPreview(self.previewName, self.camID)

def camPreview(previewName, camID):

    cv2.namedWindow(previewName)

    video_capture = cv2.VideoCapture(camID)

    if video_capture.isOpened():

        rval, frame = video_capture.read()

    else:

        rval = False

    known_face_encodings = []

    known_face_names = []

    imagelist = os.listdir('./face/')

    for imagename in imagelist:

        image = face_recognition.load_image_file("./face/"+imagename)

        face_encoding = face_recognition.face_encodings(image)[0]

        known_face_encodings.append(face_encoding)

        subname=imagename.split('.')[0]

        known_face_names.append(subname)

        face_locations = []

        face_encodings = []

        face_names = []

        process_this_frame = True

    while rval:

        #cv2.imshow(previewName, frame)

        rval, frame = video_capture.read()

        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

        rgb_small_frame = small_frame[:, :, ::-1]

        if process_this_frame:

            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:

                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

                name = "Unknown"

                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)

                best_match_index = np.argmin(face_distances)

                if matches[best_match_index]:

                    name = known_face_names[best_match_index]

                face_names.append(name)

        process_this_frame = not process_this_frame

        for (top, right, bottom, left), name in zip(face_locations, face_names):

            top *= 4

            right *= 4

            bottom *= 4

            left *= 4

            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)

            font = cv2.FONT_HERSHEY_DUPLEX

            cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

        cv2.imshow(previewName, frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):

            break

    cv2.destroyWindow(previewName)

thread1 = camThread("Camera 1", 0)

thread2 = camThread("Camera 2", 1)

thread1.start()

thread2.start()