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detect_person.py
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detect_person.py
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# python2
import face_recognition
import cv2
import numpy as np
import os
def extract_known_faces(folder):
images, names = load_images_from_folder(folder)
known_face_encodings = []
for image in images:
known_face_encodings.append(face_recognition.face_encodings(image)[0])
return known_face_encodings, names
def load_images_from_folder(folder):
images = []
filenames = []
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder,filename))
if img is not None:
images.append(img)
filenames.append(os.path.splitext(os.path.basename(filename))[0])
return images, filenames
def identify_faces(frame, known_face_encodings, known_face_names):
# 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
# 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"
# 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)
return face_locations, face_names
def display_identified(frame, face_locations, face_names):
# 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)
if __name__ == "__main__":
# config
facecam_id = 0
# PART 1: extract the faces
script_dir = os.path.dirname(os.path.realpath(__file__))
folder = script_dir + '/faces'
known_face_encodings, known_face_names = extract_known_faces(folder)
# PART2: capture webcam frames
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(facecam_id)
# PART3: recognise people from video frames
process_this_frame = True
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Identify faces and display them
# added ret check to make more robust.
if ret:
if process_this_frame: face_locations, face_names = identify_faces(frame, known_face_encodings, known_face_names)
display_identified(frame, face_locations, face_names)
process_this_frame = not process_this_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()