I was following the tutorial of face and eyes recognition here, https://towardsdatascience.com/a-beginners-guide-to-building-your-own-face-recognition-system-to-creep-out-your-friends-df3f4c471d55.
However, when I do python3 detect_blinks.py, some errors occurre, and I don't know how to fix that.
The first time I tried, the error 1 occurred. After several times trying the same command (python3 detect_blinks.py.), the error become 2.
1.
qt.qpa.plugin: Could not find the Qt platform plugin "cocoa" in ""
This application failed to start because no Qt platform plugin could
be initialized. Reinstalling the application may fix this problem.
2.
Traceback (most recent call last): File "detect_blinks.py", line
70, in
best_match_index = np.argmin(face_distances) File "<array_function internals>", line 5, in argmin File
"/Users/maurice/Dev/newcvtest/lib/python3.8/site-packages/numpy/core/fromnumeric.py",
line 1267, in argmin
return _wrapfunc(a, 'argmin', axis=axis, out=out)
File "/Users/maurice/Dev/newcvtest/lib/python3.8/site-packages/numpy/core/fromnumeric.py",
line 61, in _wrapfunc
return bound(*args, **kwds)
ValueError: attempt to get argmin of an empty sequence
this is my python code:
#code forked and tweaked from https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py
#to extend, just add more people into the known_people folder
import face_recognition
import cv2
import numpy as np
import os
import glob
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
#make array of sample pictures with encodings
known_face_encodings = []
known_face_names = []
dirname = os.path.dirname(__file__)
path = os.path.join(dirname, 'known_people/')
#make an array of all the saved jpg files' paths
list_of_files = [f for f in glob.glob(path+'*.jpg')]
#find number of known faces
number_files = len(list_of_files)
names = list_of_files.copy()
for i in range(number_files):
globals()['image_{}'.format(i)] = face_recognition.load_image_file(list_of_files[i])
globals()['image_encoding_{}'.format(i)] = face_recognition.face_encodings(globals()['image_{}'.format(i)])[0]
known_face_encodings.append(globals()['image_encoding_{}'.format(i)])
# Create array of known names
names[i] = names[i].replace("known_people/", "")
known_face_names.append(names[i])
# 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()
This error occurs when your known_face_encodings is filled with an empty array. This may be due to not setting the working directory properly, thus not able to pick up the images from the folder and encode them and match from the real-time processed frame.
So, check your working directory folder and set the "path" variable properly instead of path = os.path.join(dirname, 'known_people/')
Related
I am trying to use a raspberry pi cam but it is not being detected. I have tried using a different cam to see if it was broken and it isn't. I had got it working beforehand but for some reason, it's now not working.
I'm using a pi 4 B
I get This error
Traceback (most recent call last): File "/home/pi/Desktop/FaceDec_Robot/Robot-python/main.py", line 39, in <module>
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) cv2.error: OpenCV(4.5.1) ../modules/imgproc/src/resize.cpp:4051: error: (-215:Assertion failed) !ssize.empty() in function 'resize'
meaning the array is empty but dont know why.
code:
import cv2
import os
import face_recognition
import numpy as np
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture("/dev/video0", cv2.CAP_V4L)
#loop over all the images in the folder and put them in a list
known_face_encodings = []
known_face_names = []
for file in os.listdir("Tested/known/"):
if file.endswith(".jpg"):
#load the image
known_image = face_recognition.load_image_file("Tested/known/" + file)
#get the face encoding
known_face_encoding = face_recognition.face_encodings(known_image)[0]
#add the encoding to the list
known_face_encodings.append(known_face_encoding)
#add the name to the list
known_face_names.append(file[:-4])
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
#loop over the frames
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]
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)
#save the image
cv2.imwrite("Tested/test.jpg", frame)
I have also tried setting the video_capture to 0 but that still does not work
How can I integrate pickled face_data.dat to python face-recognition default example which shows real-time webcam view?
import face_recognition
import cv2
import numpy as np
# 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"
]
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]
# Find all the faces and face enqcodings in the frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# Loop through each face in this frame of video
for (top, right, bottom, left), face_encoding in zip(face_locations, 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]
# 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()
I've pickled the images and tried modifying the program as follows but It's showing the wrong names.
import face_recognition
import cv2
import numpy as np
import pickle
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load face encodings
with open('dataset_faces.dat', 'rb') as f:
all_face_encodings = pickle.load(f)
# Grab the list of names and the list of encodings
face_names = list(all_face_encodings.keys())
face_encodings = np.array(list(all_face_encodings.values()))
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]
# Find all the faces and face enqcodings in the frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# Loop through each face in this frame of video
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(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 = 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(face_encodings, face_encoding)
# best_match_index = np.argmin(face_distances)
# if matches[best_match_index]:
# name = face_names[best_match_index]
# 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()
I've checked pickled data using this script. It is showing the correct output to the relevant image.
import face_recognition
import pickle
import numpy as np
# Load face encodings
with open('dataset_faces.dat', 'rb') as f:
all_face_encodings = pickle.load(f)
# Grab the list of names and the list of encodings
face_names = list(all_face_encodings.keys())
face_encodings = np.array(list(all_face_encodings.values()))
# Try comparing an unknown image
unknown_image = face_recognition.load_image_file("obama.jpg")
unknown_face = face_recognition.face_encodings(unknown_image)
result = face_recognition.compare_faces(face_encodings, unknown_face)
# Print the result as a list of names with True/False
names_with_result = list(zip(face_names, result))
print(names_with_result)
Hi I try to connect OpenCV, Face recognition Library with my RSTP CCTV. But when i run my code i getting an error like below. Hope someone can help me regarding on this issue. I attach here my code
Error:
[h264 # 000002a9659dba00] error while decoding MB 10 94, bytestream -5
[h264 # 000002a953c1e2c0] Invalid NAL unit 8, skipping. Traceback
(most recent call last): File "face-present.py", line 125, in
cv2.imshow('Video', frame) cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv\modules\highgui\src\window.cpp:350:
error: (-215:Assertion failed) size.width>0 && size.height>0 in
function 'cv::imshow'
mycode.py
import face_recognition
import cv2
video_capture = cv2.VideoCapture("rtsp://admin:adam12345#192.168.0.158:554/Streaming/channels/101")
roy_image = face_recognition.load_image_file("images/roy1.jpg")
roy_face_encoding = face_recognition.face_encodings(roy_image,num_jitters=100)[0]
# Load a second sample picture and learn how to recognize it.
henrik_image = face_recognition.load_image_file("images/Mr_henrik.jpg")
henrik_face_encoding = face_recognition.face_encodings(henrik_image,num_jitters=100)[0]
stefan_image = face_recognition.load_image_file("images/stefan.jpg")
stefan_face_encoding = face_recognition.face_encodings(stefan_image,num_jitters=100)[0]
hairi_image = face_recognition.load_image_file("images/Hairi.jpeg")
hairi_face_encoding = face_recognition.face_encodings(hairi_image,num_jitters=100)[0]
syam_image = face_recognition.load_image_file("images/syam1.jpeg")
syam_face_encoding = face_recognition.face_encodings(syam_image,num_jitters=100)[0]
#print(syam_face_encoding)
# Create arrays of known face encodings and their names
known_face_encodings = [
roy_face_encoding,
stefan_face_encoding,
henrik_face_encoding,
hairi_face_encoding,
syam_face_encoding
]
known_face_names = [
"roy",
"stefan",
"henrik",
"hairi",
"syam"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# # Process video frame frequency
# process_frame_freq = 4
# process_this_frame = process_frame_freq
while True:
if video_capture.isOpened():
# Grab a single frame of video
ret, frame = video_capture.read()
if ret:
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, None, 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)
if face_locations: # prevent manipulation of null variable
top, right, bottom, left = face_locations[0]
# faces_recognized += 1
# print("[%i] Face recognized..." % faces_recognized)
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cropped_face = frame[top:bottom, left:right]
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
#print(face_encodings)
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,tolerance=0.5)
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]
print(name)
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()
i have a code for face recognition by open cv python
import face_recognition as fr
import os
import cv2
import face_recognition
import numpy as np
from time import sleep
def get_encoded_faces():
"""
looks through the faces folder and encodes all
the faces
:return: dict of (name, image encoded)
"""
encoded = {}
for dirpath, dnames, fnames in os.walk("./faces"):
for f in fnames:
if f.endswith(".jpg") or f.endswith(".png"):
face = fr.load_image_file("faces/" + f)
encoding = fr.face_encodings(face)[0]
encoded[f.split(".")[0]] = encoding
return encoded
def unknown_image_encoded(img):
"""
encode a face given the file name
"""
face = fr.load_image_file("faces/" + img)
encoding = fr.face_encodings(face)[0]
return encoding
def classify_face(im):
"""
will find all of the faces in a given image and label
them if it knows what they are
:param im: str of file path
:return: list of face names
"""
faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())
img = cv2.imread(im, 1)
#img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
#img = img[:,:,::-1]
face_locations = face_recognition.face_locations(img)
unknown_face_encodings = face_recognition.face_encodings(img, face_locations)
face_names = []
for face_encoding in unknown_face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(faces_encoded, face_encoding)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(faces_encoded, 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)
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Draw a box around the face
cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
# Display the resulting image
while True:
cv2.imshow('Video', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
return face_names
print(classify_face("test-image");
it is taking image that we save to test but i want it to take image from camera then recognize it So any one here can please tell how i can change test image so it will take test image from camera not what we save in data base to test ...........
If you want to check single frame from camera then simply use
cap = cv2.VideoCapture(0)
status, img = cap.read()
instead of
img = cv2.imread(im, 1)
If you want to check faces in stream then you need to put all in loop
def classify_face(im):
faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())
cap = cv2.VideoCapture(0)
while True:
status, img = cap.read()
face_locations = face_recognition.face_locations(img)
unknown_face_encodings = face_recognition.face_encodings(img, face_locations)
face_names = []
for face_encoding in unknown_face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(faces_encoded, face_encoding)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(faces_encoded, 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)
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Draw a box around the face
cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
cv2.imshow('Video', img)
print(face_names)
if cv2.waitKey(1) & 0xFF == ord('q'):
return
EDIT: Full code (with small changes) for work with video stream. It works for me. But if previous function did works for you then this version may not help you - function classify_face is almost the same as in previous example.
import os
import cv2
import face_recognition as fr
import numpy as np
def get_encoded_faces(folder="./faces"):
"""
looks through the faces folder and encodes all
the faces
:return: dict of (name, image encoded)
"""
encoded = {}
for dirpath, dnames, fnames in os.walk(folder):
for f in fnames:
if f.lower().endswith(".jpg") or f.lower().endswith(".png"):
fullpath = os.path.join(dirpath, f)
face = fr.load_image_file(fullpath)
# normally face_encodings check if face is on image - and it can get empty result
height, width = face.shape[:2]
encoding = fr.face_encodings(face, known_face_locations=[(0, width, height, 0)])
if len(encoding) > 0:
encoding = encoding[0]
encoded[f.split(".")[0]] = encoding
return encoded
def classify_face(im):
"""
will find all of the faces in a given image and label
them if it knows what they are
:param im: str of file path
:return: list of face names
"""
faces = get_encoded_faces()
faces_encoded = list(faces.values())
known_face_names = list(faces.keys())
cap = cv2.VideoCapture(0)
while True:
status, img = cap.read()
#print('status:', status)
face_locations = fr.face_locations(img)
unknown_face_encodings = fr.face_encodings(img, face_locations)
face_names = []
for location, face_encoding in zip(face_locations, unknown_face_encodings): # I moved `zip()` in this place
# See if the face is a match for the known face(s)
matches = fr.compare_faces(faces_encoded, face_encoding)
name = "Unknown"
# use the known face with the smallest distance to the new face
face_distances = fr.face_distance(faces_encoded, 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)
top, right, bottom, left = location
# Draw a box around the face
cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
print('face_names:', face_names)
cv2.imshow('Video', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
return face_names
# --- main ---
print(classify_face("test-image"))
cv2.destroyAllWindows()
I have some code here and it is a little sloppy, is there any way that I could put the images, encodings, and names into separate files and import them into the main code for use? I have tried putting them into a separate file and then importing them, but it still shows a not defined error? Can anyone help me find out why, or how to fix it.
main code
import cv2
import numpy as np
# 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)
me_image = face_recognition.load_image_file("me.jpg")
me_face_encoding = face_recognition.face_encodings(me_image)[0]
mom_image = face_recognition.load_image_file("mom.jpg")
mom_face_encoding = face_recognition.face_encodings(mom_image)[0]
mattm_image = face_recognition.load_image_file("mattm.jpg")
mattm_face_encoding = face_recognition.face_encodings(mattm_image)[0]
soph_image = face_recognition.load_image_file("soph.jpg")
soph_face_encoding = face_recognition.face_encodings(soph_image)[0]
known_face_encodings = [
me_face_encoding,
mom_face_encoding,
mattm_face_encoding,
soph_face_encoding
]
known_face_names = [
"Jacob North",
"Shelly North",
"Matt Mersino",
"Sophia North"
]
# 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()
Code I Wish To Separate
me_image = face_recognition.load_image_file("me.jpg")
me_face_encoding = face_recognition.face_encodings(me_image)[0]
mom_image = face_recognition.load_image_file("mom.jpg")
mom_face_encoding = face_recognition.face_encodings(mom_image)[0]
mattm_image = face_recognition.load_image_file("mattm.jpg")
mattm_face_encoding = face_recognition.face_encodings(mattm_image)[0]
soph_image = face_recognition.load_image_file("soph.jpg")
soph_face_encoding = face_recognition.face_encodings(soph_image)[0]
known_face_encodings = [
me_face_encoding,
mom_face_encoding,
mattm_face_encoding,
soph_face_encoding
]
known_face_names = [
"Jacob North",
"Shelly North",
"Matt Mersino",
"Sophia North"
]
I just want to make it neater and easier to access.