Mac 上的人脸识别系统

Question

我在这里学习了人脸和眼睛识别的教程，https://towardsdatascience.com/a-beginners-guide-to-building-your-own-face-recognition-system-to-creep-out-your-friends-df3f4c471d55。
但是，当我执行 python3 detect_blinks.py 时，会出现一些错误，我不知道如何解决。 第一次尝试，出现错误1。多次尝试相同的命令（python3 detect_blinks.py.）后，错误变为2。

1.

qt.qpa.plugin：在“”中找不到Qt平台插件“cocoa” 此应用程序无法启动，因为没有 Qt 平台插件可以 被初始化。重新安装应用程序可能会解决此问题。

2.

Traceback（最近一次调用最后一次）：文件“detect_blinks.py”，行 70，在 best_match_index = np.argmin(face_distances) 文件“array_function internals>”，第 5 行，在 argmin 文件中 “/Users/maurice/Dev/newcvtest/lib/python3.8/site-packages/numpy/core/fromnumeric.py”， 第 1267 行，在 argmin 中 return _wrapfunc(a, 'argmin', 轴=轴, 输出=输出) 文件“/Users/maurice/Dev/newcvtest/lib/python3.8/site-packages/numpy/core/fromnumeric.py”， 第 61 行，在 _wrapfunc 中 return bound(*args, **kwds) ValueError: 尝试获取空序列的 argmin

这是我的python代码：

    #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

当您的 known_face_encodings 填充了一个空数组时，会发生此错误。这可能是由于未正确设置工作目录，因此无法从文件夹中提取图像并对其进行编码并与实时处理的帧进行匹配。

因此，请检查您的工作目录文件夹并正确设置“路径”变量，而不是 path = os.path.join(dirname, 'known_people/')