parallel_identify_from_rtmp_0602.py

import face_recognition
import cv2
import PIL.Image
import numpy as np
import multiprocessing
import click
import sys
import itertools

# 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.

def process_images_in_process_pool(rgb_small_frame, known_names, known_face_encodings, number_of_cpus, tolerance, show_distance):
    if number_of_cpus == -1:
        processes = None
    else:
        processes = number_of_cpus

    # macOS will crash due to a bug in libdispatch if you don't use 'forkserver'
    context = multiprocessing
    if "forkserver" in multiprocessing.get_all_start_methods():
        context = multiprocessing.get_context("forkserver")

    pool = context.Pool(processes=processes)

    function_parameters = zip(
        rgb_small_frame,
        itertools.repeat(known_names),
        itertools.repeat(known_face_encodings),
        itertools.repeat(tolerance),
        itertools.repeat(show_distance)
    )

    pool.starmap(test_image, function_parameters)

def test_image(rgb_small_frame, known_names, known_face_encodings, tolerance=0.6, show_distance=False):
    #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 = []
    print('2')
    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)

# @click.command()
# @click.argument('known_people_folder')
# @click.option('--rtmp_addr', default='rtmp://server.blackant.org:1935/live_2710/hello')
# @click.option('--cpus', default=1, help='number of CPU cores to use in parallel (can speed up processing lots of images). -1 means "use all in system"')
# @click.option('--tolerance', default=0.6, help='Tolerance for face comparisons. Default is 0.6. Lower this if you get multiple matches for the same person.')
# @click.option('--show-distance', default=False, type=bool, help='Output face distance. Useful for tweaking tolerance setting.')
def main():
    rtmp_addr = 'rtmp://server.blackant.org:1935/live_2710/hello'
    cpus = 4
    tolerance = 0.6
    show_distance = False

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


    # Multi-core processing only supported on Python 3.4 or greater
    if (sys.version_info < (3, 4)) and cpus != 1:
        click.echo("WARNING: Multi-processing support requires Python 3.4 or greater. Falling back to single-threaded processing!")
        cpus = 1

    # Get a reference to webcam #0 (the default rtmp_addr)
    video_capture = cv2.VideoCapture(rtmp_addr)
    #video_capture = cv2.VideoCapture(0)

    # Load a sample picture and learn how to recognize it.
    obama_image = face_recognition.load_image_file("./known_people/yang.png")
    obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
    print('finished training yang')

    # Load a second sample picture and learn how to recognize it.
    biden_image = face_recognition.load_image_file("./known_people/zhu.png")
    biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
    print('finished training zhu')

    # Create arrays of known face encodings and their names
    known_face_encodings = [
        obama_face_encoding,
        biden_face_encoding
    ]
    known_face_names = [
        "Yang",
        "Zhu"
    ]


    while True:
        # Grab a single frame of video
        ret, frame = video_capture.read()
        print('1')

        # 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:
            if cpus == 1:
                test_image(rgb_small_frame, known_face_names, known_face_encodings, tolerance, show_distance)
            else:
                process_images_in_process_pool(rgb_small_frame, known_face_names, known_face_encodings, cpus, tolerance, show_distance)
            # test_image(rgb_small_frame, known_face_names, known_face_encodings, tolerance, show_distance)


        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()


if __name__ == "__main__":
    main()