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parallel_identify_from_rtmp_2324.py
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parallel_identify_from_rtmp_2324.py
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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.
# 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 yang')
# Create arrays of known face encodings and their names
known_face_encodings = [
obama_face_encoding,
biden_face_encoding
]
known_face_names = [
"Yang",
"Zhu"
]
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(
images_to_check,
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 = []
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)
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:
# 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()