Face Recognition System on Mac

Question

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()

Answer 1

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/')