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face_recognition_video.py
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import face_recognition
import cv2
import pyttsx3;
#text to speech
engine = pyttsx3.init();
def speakText(textToSpeak):
engine.say(textToSpeak);
engine.runAndWait() ;
video_show=True
# SET FALSE IF RUNNING IN RASPBERRY PI
# 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)
if (video_capture.isOpened() == False):
print("Unable to read camera feed")
else:
print("Passed video capture initialization")
image_size = 128
# Load a sample picture and learn how to recognize it.
william_image = face_recognition.load_image_file("known_people/William.jpg")
x_scale = y_scale = 128/william_image.shape[0]
william_image = cv2.resize(william_image, (0,0), fx=x_scale, fy=y_scale)
william_face_encoding = face_recognition.face_encodings(william_image)[0]
# Load a second sample picture and learn how to recognize it.
kout_image = face_recognition.load_image_file("known_people/Kout.jpg")
x_scale = y_scale = 128/kout_image.shape[0]
kout_image = cv2.resize(kout_image, (0,0), fx=x_scale, fy=y_scale)
kout_face_encoding = face_recognition.face_encodings(kout_image)[0]
# Load a third sample picture and learn how to recognize it.
leandra_image = face_recognition.load_image_file("known_people/Leandra.jpg")
x_scale = y_scale = 128/leandra_image.shape[0]
leandra_image = cv2.resize(leandra_image, (0,0), fx=x_scale, fy=y_scale)
leandra_face_encoding = face_recognition.face_encodings(leandra_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
william_face_encoding,
kout_face_encoding,
leandra_face_encoding
]
known_face_names = [
"William",
"Dimitrios",
"Leandra"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
lastMatchedPerson = None
frame_counter = 0
people_found = []
video_scale = 0.2
while True:
if frame_counter >= 50:
frame_counter=0
people_found = []
frame_counter+=1
# print(frame_counter)
#print("Entered while loop")
# Grab a single frame of video
ret, frame = video_capture.read()
#apply frame rotation
# frame=cv2.transpose(frame)
# frame=cv2.flip(frame,flipCode=0)
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0,0), fx=video_scale, fy=video_scale)
# 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:
print("found face")
# 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]
#if reading first everytime, how does it go on to 2nd, and 3rd person in frame?
# print("Face locations: ",face_locations)
face_names.append(name)
if name not in people_found:
center_x=0.5*(face_locations[face_names.index(name)][3]+face_locations[face_names.index(name)][1])
frame_third = rgb_small_frame.shape[1]/3
if center_x < frame_third:
relative_location = " to the left"
elif center_x > frame_third and center_x < 2*frame_third:
relative_location = "ahead"
elif center_x > 2*frame_third:
relative_location = "to the right"
speakText(name + " is " + relative_location + " of you")
print("This is "+name)
people_found.append(name)
process_this_frame = frame_counter%5==1
# 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 *= int(1/video_scale)
right *= int(1/video_scale)
bottom *= int(1/video_scale)
left *= int(1/video_scale)
# 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
if video_show == True:
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()