Python人脸识别程序检测到人脸即崩溃的问题排查求助

DevOps 小满

2026-7-11

Python人脸识别程序捕捉人脸时崩溃问题排查

问题描述

我开发的基于Python的人脸识别程序，每当摄像头捕捉到人脸时就会崩溃。尝试更换不同人脸图片、甚至不加载图片，问题依然存在，恳请帮忙排查崩溃原因。

程序代码

import face_recognition
import os, sys
import cv2
import numpy as np
import math


def face_confidence(face_distance, face_match_threshold=0.6):
    range = (1.0-face_match_threshold)
    linear_valu=(1.0-face_distance)/(range*2.0)

    if face_distance> face_match_threshold:
        return str(round(linear_valu*100/2))+'%'
    else:
        value= (linear_valu+((1-linear_valu)*math.pow((linear_valu-0.5)*2,0.2)))*100
        return str(round(value,2))+"%"

class FaceRecognition:
    face_location=[]
    face_encodings=[]
    face_names=[]
    known_face_encodings=[]
    known_face_names=[]
    process_current_frame= True

    def __init__(self):
        self.encode_faces()

    def encode_faces(self):
        for image in os.listdir('faces'):
            face_image= face_recognition.load_image_file(f'faces/{image}')
            face_encoding= face_recognition.face_encodings(face_image)[0]

            self.known_face_encodings.append(face_encoding)
            self.known_face_encodings.append(image)

        print(self.known_face_names)

    def run_recognition(self):
        video_capture= cv2.VideoCapture(0)

        if not video_capture.isOpened():
            sys.exit("Video source not found")

        while True:
            ret, frame= video_capture.read()

            if self.process_current_frame:
                small_frame=cv2.resize(frame,(0,0),fx=0.25,fy=0.25)
                rgb_small_frame= small_frame[:,:,::-1]

                #find all faces in frame
                self.face_location= face_recognition.face_locations(rgb_small_frame)
                self.face_encodings=face_recognition.face_encodings(rgb_small_frame, self.face_location)

                self.face_names=[]
                for face_encoding in self.face_encodings:
                    matches= face_recognition.compare_faces(self.known_face_encodings, face_encoding)
                    name= 'Unknown'
                    confidence= 'Unknown'

                    face_distances= face_recognition.face_distance(self.known_face_encodings,face_encoding)
                    best_match_index=np.argmin(face_distances)

                    if matches[best_match_index]:
                        name= self.known_face_names[best_match_index]
                        confidence=face_confidence(face_distances[best_match_index])

                    self.face_names.append(f'{name}({confidence}')
            self.process_current_frame = not self.process_current_frame

            #desplay annotaations
            for(top,right,bottom,left), name in zip(self.face_location, self.face_names):
                top*=4
                right*=4
                bottom*=4
                left*=4

                cv2.rectangle(frame,(left,top),(right,bottom),(0,0,255),2)
                cv2.rectangle(frame,(left,bottom-35),(right,bottom),(0,0,255),-1)
                cv2.putText(frame,name,(left+6,bottom-6),cv2.FONT_HERSHEY_DUPLEX,0.8,(255,255,255),1)

            cv2.imshow("face recognition",frame)

            if cv2.waitKey(1)== ord('q'):
                break
        video_capture.release()
        cv2.destroyAllWindows()

if __name__=='__main__':
    fr = FaceRecognition()
    fr.run_recognition()

报错信息

Traceback (most recent call last):
  File "/Users/youssefmajdalani/PycharmProjects/faceproj/main.py", line 96, in <module>
    fr.run_recognition()
  File "/Users/youssefmajdalani/PycharmProjects/faceproj/main.py", line 56, in run_recognition
    self.face_encodings=face_recognition.face_encodings(rgb_small_frame, self.face_location)
  File "/Users/youssefmajdalani/PycharmProjects/faceproj/venv/lib/python3.9/site-packages/face_recognition/api.py", line 214, in face_encodings
    return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
  File "/Users/youssefmajdalani/PycharmProjects/faceproj/venv/lib/python3.9/site-packages/face_recognition/api.py", line 214, in <listcomp>
    return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
TypeError: compute_face_descriptor(): incompatible function arguments. The following argument types are supported:
    1. (self: _dlib_pybind11.face_recognition_model_v1, img: numpy.ndarray[(rows,cols,3),numpy.uint8], face: _dlib_pybind11.full_object_detection, num_jitters: int = 0, padding: float = 0.25) -> _dlib_pybind11.vector
    2. (self: _dlib_pybind11.face_recognition_model_v1, img: numpy.ndarray[(rows,cols,3),numpy.uint8], num_jitters: int = 0) -> _dlib_pybind11.vector
    3. (self: _dlib_pybind11.face_recognition_model_v1, img: numpy.ndarray[(rows,cols,3),numpy.uint8], faces: _dlib_pybind11.full_object_detections, num_jitters: int = 0, padding: float = 0.25) -> _dlib_pybind11.vectors
    4. (self: _dlib_pybind11.face_recognition_model_v1, batch_img: List[numpy.ndarray[(rows,cols,3),numpy.uint8]], batch_faces: List[_dlib_pybind11.full_object_detections], num_jitters: int = 0, padding: float = 0.25) -> _dlib_pybind11.vectorss
    5. (self: _dlib_pybind11.face_recognition_model_v1, batch_img: List[numpy.ndarray[(rows,cols,3),numpy.uint8]], num_jitters: int = 0) -> _dlib_pybind11.vectors

Invoked with: <_dlib_pybind11.face_recognition_model_v1 object at 0x10533b830>, array([[[134, 105,  82],
        [135, 106,  83],
        [136, 107,  82],
        ...,
        [132, 111,  81],
        [133, 111,  82],
        [132, 110,  81]],

       [[131, 102,  79],
        [136, 107,  84],
        [136, 106,  81],
        ...,
        [131, 111,  81],
        [133, 112,  82],
        [133, 111,  81]],

       [[127, 100,  76],
        [131, 104,  81],
        [133, 104,  80],
        ...,
        [132, 112,  82],
        [132, 111,  81],
        [134, 112,  80]],

       ...,

       [[138, 116,  89],
        [139, 117,  89],
        [139, 116,  87],
        ...,
        [168, 140, 121],
        [ 37,  18,   8],
        [ 44,  30,  17]],

       [[137, 115,  86],
        [138, 116,  89],
        [140, 117,  90],
        ...,
        [146, 105,  97],
        [ 39,  17,   7],
        [ 42,  24,  10]],

       [[134, 115,  85],
        [137, 115,  86],
        [138, 116,  87],
        ...,
        [129,  92,  88],
        [ 45,  24,  12],
        [ 42,  23,   9]]], dtype=uint8), <_dlib_pybind11.full_object_detection object at 0x1078d3d30>, 1

Process finished with exit code 1

问题分析与修复方案

1. 核心崩溃原因：类变量与实例变量混淆+命名错误

代码中类定义的face_location是类变量（所有实例共享），且命名为单数，但face_recognition.face_locations()返回的是人脸位置列表。后续调用face_encodings()时，若类变量被意外修改为单个对象，就会触发参数类型不匹配的报错。

修复：将类变量改为实例变量，统一使用复数命名保持语义一致：

class FaceRecognition:
    def __init__(self):
        self.face_locations = []
        self.face_encodings = []
        self.face_names = []
        self.known_face_encodings = []
        self.known_face_names = []
        self.process_current_frame = True
        self.encode_faces()

同时将run_recognition()中所有self.face_location替换为self.face_locations。

2. 致命逻辑错误：人脸编码与文件名存储混淆

encode_faces()方法中，错误地将图片文件名添加到known_face_encodings列表（本该存储人脸编码numpy数组），导致列表中混合了数组和字符串，后续对比时必然出错。

修复：将文件名存入known_face_names，并去掉文件后缀：

def encode_faces(self):
    for image in os.listdir('faces'):
        face_image= face_recognition.load_image_file(f'faces/{image}')
        # 添加异常处理，避免图片无人脸导致索引错误
        face_encodings = face_recognition.face_encodings(face_image)
        if not face_encodings:
            print(f"警告：图片{image}中未检测到人脸，已跳过")
            continue
        face_encoding= face_encodings[0]

        self.known_face_encodings.append(face_encoding)
        # 存储去掉后缀的文件名作为人名
        self.known_face_names.append(os.path.splitext(image)[0])

    print(self.known_face_names)

3. 其他细节错误修复

拼写错误：face_confidence()中的linear_valu改为linear_value，且避免使用Python内置函数range作为变量名，改为range_val：

def face_confidence(face_distance, face_match_threshold=0.6):
    range_val = (1.0 - face_match_threshold)
    linear_value = (1.0 - face_distance) / (range_val * 2.0)

    if face_distance > face_match_threshold:
        return str(round(linear_value * 100 / 2)) + '%'
    else:
        value = (linear_value + ((1 - linear_value) * math.pow((linear_value - 0.5) * 2, 0.2))) * 100
        return str(round(value, 2)) + "%"

字符串格式错误：self.face_names.append(f'{name}({confidence}')缺少闭合括号，改为：
```
self.face_names.append(f'{name}({confidence})')
```

空列表防护：在计算人脸距离前，先判断known_face_encodings是否为空，避免空列表调用np.argmin()出错：

if self.known_face_encodings:
    face_distances = face_recognition.face_distance(self.known_face_encodings, face_encoding)
    best_match_index = np.argmin(face_distances)
    if matches[best_match_index]:
        name = self.known_face_names[best_match_index]
        confidence = face_confidence(face_distances[best_match_index])

内容的提问来源于stack exchange，提问作者youssef majdalani youssefmaj25

Python人脸识别程序检测到人脸即崩溃的问题排查求助

问题描述 #

程序代码 #

报错信息 #

问题分析与修复方案 #

1. 核心崩溃原因：类变量与实例变量混淆+命名错误 #

2. 致命逻辑错误：人脸编码与文件名存储混淆 #

3. 其他细节错误修复 #