faceFind.py

#!/usr/bin/env python3
import face_recognition
import cv2
import numpy as np
import os
import glob
import pickle
#Based on facerec_from_webcam_faster.py
#Reads file from userData folder

video_capture = cv2.VideoCapture(0)

known_face_encodings = []
known_face_names = []

print("Loading user data...")
userDataPath = os.path.join(os.getcwd(), 'userData') # Sets location for userData
for filename in glob.glob(os.path.join(userDataPath, '*.userData')): # Loads *.userData files
    with open(filename, "rb") as fp: # Unpickling and appending to Known Lists
        temp = pickle.load(fp)
        known_face_names.append(temp[0])
        known_face_encodings.append(temp[1])
        print("Loaded data for " + "\"{}\"".format(temp[0]))
print("\nLoaded {} total user".format(len(known_face_names)))




# 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)
    small_frame = frame
    # 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()