def test_image(image_to_check,
known_names,
known_face_encodings,
tolerance=0.6,
show_distance=False):
unknown_image = face_recognition.load_image_file(image_to_check)
# Scale down image if it's giant so things run a little faster
if unknown_image.shape[1] > 1600:
scale_factor = 1600.0 / unknown_image.shape[1]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
unknown_image = scipy.misc.imresize(unknown_image, scale_factor)
unknown_encodings = face_recognition.face_encodings(unknown_image)
for unknown_encoding in unknown_encodings:
distances = face_recognition.face_distance(known_face_encodings,
unknown_encoding)
result = list(distances <= tolerance)
if True in result:
[
print_result(image_to_check, name, distance, show_distance) for
is_match, name, distance in zip(result, known_names, distances)
if is_match
]
else:
print_result(image_to_check, "unknown_person", None, show_distance)
def detect_faces_in_image(file_stream):
# Pre-calculated face encoding of Obama generated with face_recognition.face_encodings(img)
known_face_encoding = [
-0.09634063, 0.12095481, -0.00436332, -0.07643753, 0.0080383,
0.01902981, -0.07184699, -0.09383309, 0.18518871, -0.09588896,
0.23951106, 0.0986533, -0.22114635, -0.1363683, 0.04405268, 0.11574756,
-0.19899382, -0.09597053, -0.11969153, -0.12277931, 0.03416885,
-0.00267565, 0.09203379, 0.04713435, -0.12731361, -0.35371891,
-0.0503444, -0.17841317, -0.00310897, -0.09844551, -0.06910533,
-0.00503746, -0.18466514, -0.09851682, 0.02903969, -0.02174894,
0.02261871, 0.0032102, 0.20312519, 0.02999607, -0.11646006, 0.09432904,
0.02774341, 0.22102901, 0.26725179, 0.06896867, -0.00490024,
-0.09441824, 0.11115381, -0.22592428, 0.06230862, 0.16559327,
0.06232892, 0.03458837, 0.09459756, -0.18777156, 0.00654241,
0.08582542, -0.13578284, 0.0150229, 0.00670836, -0.08195844,
-0.04346499, 0.03347827, 0.20310158, 0.09987706, -0.12370517,
-0.06683611, 0.12704916, -0.02160804, 0.00984683, 0.00766284,
-0.18980607, -0.19641446, -0.22800779, 0.09010898, 0.39178532,
0.18818057, -0.20875394, 0.03097027, -0.21300618, 0.02532415,
0.07938635, 0.01000703, -0.07719778, -0.12651891, -0.04318593,
0.06219772, 0.09163868, 0.05039065, -0.04922386, 0.21839413,
-0.02394437, 0.06173781, 0.0292527, 0.06160797, -0.15553983,
-0.02440624, -0.17509389, -0.0630486, 0.01428208, -0.03637431,
0.03971229, 0.13983178, -0.23006812, 0.04999552, 0.0108454,
-0.03970895, 0.02501768, 0.08157793, -0.03224047, -0.04502571,
0.0556995, -0.24374914, 0.25514284, 0.24795187, 0.04060191, 0.17597422,
0.07966681, 0.01920104, -0.01194376, -0.02300822, -0.17204897,
-0.0596558, 0.05307484, 0.07417042, 0.07126575, 0.00209804
]
# Load the uploaded image file
img = face_recognition.load_image_file(file_stream)
# Get face encodings for any faces in the uploaded image
unknown_face_encodings = face_recognition.face_encodings(img)
face_found = False
is_obama = False
if len(unknown_face_encodings) > 0:
face_found = True
# See if the first face in the uploaded image matches the known face of Obama
match_results = face_recognition.compare_faces(
[known_face_encoding], unknown_face_encodings[0])
if match_results[0]:
is_obama = True
# Return the result as json
result = {
"face_found_in_image": face_found,
"is_picture_of_obama": is_obama
}
return jsonify(result)
def scan_known_people(known_people_folder):
known_names = []
known_face_encodings = []
for file in image_files_in_folder(known_people_folder):
basename = os.path.splitext(os.path.basename(file))[0]
img = face_recognition.load_image_file(file)
encodings = face_recognition.face_encodings(img)
if len(encodings) > 1:
click.echo(
"WARNING: More than one face found in {}. Only considering the first face."
.format(file))
if len(encodings) == 0:
click.echo(
"WARNING: No faces found in {}. Ignoring file.".format(file))
else:
known_names.append(basename)
known_face_encodings.append(encodings[0])
return known_names, known_face_encodings
# Often instead of just checking if two faces match or not (True or False), it's helpful to see how similar they are.
# You can do that by using the face_distance function.
# The model was trained in a way that faces with a distance of 0.6 or less should be a match. But if you want to
# be more strict, you can look for a smaller face distance. For example, using a 0.55 cutoff would reduce false
# positive matches at the risk of more false negatives.
# Note: This isn't exactly the same as a "percent match". The scale isn't linear. But you can assume that images with a
# smaller distance are more similar to each other than ones with a larger distance.
# Load some images to compare against
known_obama_image = face_recognition.load_image_file("obama.jpg")
known_biden_image = face_recognition.load_image_file("biden.jpg")
# Get the face encodings for the known images
obama_face_encoding = face_recognition.face_encodings(known_obama_image)[0]
biden_face_encoding = face_recognition.face_encodings(known_biden_image)[0]
known_encodings = [
obama_face_encoding,
biden_face_encoding
]
# Load a test image and get encondings for it
image_to_test = face_recognition.load_image_file("obama2.jpg")
image_to_test_encoding = face_recognition.face_encodings(image_to_test)[0]
# See how far apart the test image is from the known faces
face_distances = face_recognition.face_distance(known_encodings, image_to_test_encoding)
for i, face_distance in enumerate(face_distances):
from cv.face_recognition import face_recognition
import picamera
import numpy as np
# Get a reference to the Raspberry Pi camera.
# If this fails, make sure you have a camera connected to the RPi and that you
# enabled your camera in raspi-config and rebooted first.
camera = picamera.PiCamera()
camera.resolution = (320, 240)
output = np.empty((240, 320, 3), dtype=np.uint8)
# Load a sample picture and learn how to recognize it.
print("Loading known face image(s)")
obama_image = face_recognition.load_image_file("obama_small.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Initialize some variables
face_locations = []
face_encodings = []
while True:
print("Capturing image.")
# Grab a single frame of video from the RPi camera as a numpy array
camera.capture(output, format="rgb")
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(output)
print("Found {} faces in image.".format(len(face_locations)))
face_encodings = face_recognition.face_encodings(output, face_locations)
from cv.face_recognition import face_recognition
# Load the jpg files into numpy arrays
biden_image = face_recognition.load_image_file("biden.jpg")
obama_image = face_recognition.load_image_file("obama.jpg")
unknown_image = face_recognition.load_image_file("obama2.jpg")
# Get the face encodings for each face in each image file
# Since there could be more than one face in each image, it returns a list of encodings.
# But since I know each image only has one face, I only care about the first encoding in each image, so I grab index 0.
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
unknown_face_encoding = face_recognition.face_encodings(unknown_image)[0]
known_faces = [
biden_face_encoding,
obama_face_encoding
]
# results is an array of True/False telling if the unknown face matched anyone in the known_faces array
results = face_recognition.compare_faces(known_faces, unknown_face_encoding)
print("Is the unknown face a picture of Biden? {}".format(results[0]))
print("Is the unknown face a picture of Obama? {}".format(results[1]))
print("Is the unknown face a new person that we've never seen before? {}".format(not True in results))
# 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]
# 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)
# Only process every other frame of video to save time
#
# 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.
# Open the input movie file
input_movie = cv2.VideoCapture("hamilton_clip.mp4")
length = int(input_movie.get(cv2.CAP_PROP_FRAME_COUNT))
# Create an output movie file (make sure resolution/frame rate matches input video!)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
output_movie = cv2.VideoWriter('output.avi', fourcc, 29.97, (640, 360))
# Load some sample pictures and learn how to recognize them.
lmm_image = face_recognition.load_image_file("lin-manuel-miranda.png")
lmm_face_encoding = face_recognition.face_encodings(lmm_image)[0]
al_image = face_recognition.load_image_file("alex-lacamoire.png")
al_face_encoding = face_recognition.face_encodings(al_image)[0]
known_faces = [lmm_face_encoding, al_face_encoding]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
frame_number = 0
while True:
# Grab a single frame of video
ret, frame = input_movie.read()