为啥 Numpy 会抛出这个错误 ValueError: operands could not be broadcast together with shapes (3,0) (128,)

Posted 2023-03-16

【中文标题】为啥 Numpy 会抛出这个错误 ValueError: operands could not be broadcast together with shapes (3,0) (128,)

【英文标题】：Why does Numpy throw this error ValueError: operands could not be broadcast together with shapes (3,0) (128,)为什么 Numpy 会抛出这个错误 ValueError: operands could not be broadcast together with shapes (3,0) (128,)

【发布时间】：2021-06-29 12:28:13

【问题描述】：

python 文件正常读取视频文件并在桌面上播放。它引发了一个我无法解决的错误，因此非常感谢任何帮助和指针。视频是 mp4 格式，编解码器也可能会导致问题，因为我看到有些人转换为 avi，或者这与播放正常有关。

最后一个错误调用来自第 57 行：

    face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)```

Any help, much appreciated!!

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("/home/dave/Desktop/smots_1.mp4")

#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])


face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # Capture a single frame of video
    ret, frame = video_capture.read()

    # Resize video frame
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    rgb_small_frame = small_frame[:, :, ::-1]

    # Process every 2nd frame
    if process_this_frame:

        # Find all the faces & face encodings in video Frame
        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:
            # Check face match in known faces
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"

        
            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


      # Show Result
      for (top, right, bottom, left), name in zip(face_locations, face_names):
      # Scale back up 
        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 for 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 video
    cv2.imshow('Video', frame)

    # 'q' quits the video window
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

 # Release video handle
 video_capture.release()

【参考方案1】：

感谢 Aakash 的回复，从那以后我就明白了。不再支持不规则的嵌套数组。 第 57 行缺少 np.array='object'：

            matches = face_recognition.compare_faces(known_face_encodings, face_encoding) ```