face_recognition.face_locations(frame)

C知道：这段代码使用了face_recognition库中的face_locations函数来检测图像中人脸的位置。它接受一个图像帧作为输入，并返回一个列表，其中包含检测到的每个人脸的位置坐标。这些坐标可以用于在图像上绘制边界框或进行其他人脸相关的任务。请注意，这个函数使用了面部识别算法来进行人脸检测，因此你需要确保你已经安装了face_recognition库并导入了相应的模块。如果你还有其他问题，我会很乐意帮助你！

相关问题

import cv2 import face_recognition import numpy as np from PIL import Image, ImageDraw,ImageFont video_capture = cv2.VideoCapture(r'C:/Users/ALIENWARE/123.mp4')#如果输入是(0)为摄像头输入 #现输入为MP4进行识别检测人脸 first_image = face_recognition.load_image_file("1.jpg") first_face_encoding = face_recognition.face_encodings(first_image)[0] Second_image = face_recognition.load_image_file("2.jpg") Second_face_encoding = face_recognition.face_encodings(Second_image)[0] third_image = face_recognition.load_image_file("3.jpg") third_face_encoding = face_recognition.face_encodings(third_image)[0] inside_face_encodings = [first_face_encoding,Second_face_encoding,third_face_encoding] inside_face_names = ['A','B','C'] face_locations = [] face_encodings = [] face_names = [] process_this_frame = True while True: ret, frame = video_capture.read() small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) rgb_small_frame = small_frame[:, :, ::-1] if process_this_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: matches = face_recognition.compare_faces(inside_face_encodings, face_encoding) name = '未录入人脸' if True in matches: first_match_index = matches.index(True) name = inside_face_names[first_match_index] face_names.append(name) process_this_frame = not process_this_frame for (top, right, bottom, left), name in zip(face_locations, face_names): top *= 4 right *= 4 bottom *= 4 left *= 4 cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) img_pil = Image.fromarray(frame) draw = ImageDraw.Draw(img_pil) fontStyle = ImageFont.truetype("C:/Windows/Fonts/simsun.ttc", 32, encoding="utf-8") draw.text((left + 6, bottom - 6), name, (0, 200, 0), font=fontStyle) frame = np.asarray(np.array(img_pil)) cv2.imshow('face_out', frame) if cv2.waitKey(1) & 0xFF == ord('q'): #退出需要按下Q键否则内核会崩溃 break video_capture.release() cv2.destroyAllWindows()

以下是修改后的代码，主要是添加了注释和一些规范化的修改：

import cv2
import face_recognition
import numpy as np
from PIL import Image, ImageDraw, ImageFont

# 加载要识别的人脸图片
first_image = face_recognition.load_image_file("1.jpg")
first_face_encoding = face_recognition.face_encodings(first_image)[0]

Second_image = face_recognition.load_image_file("2.jpg")
Second_face_encoding = face_recognition.face_encodings(Second_image)[0]

third_image = face_recognition.load_image_file("3.jpg")
third_face_encoding = face_recognition.face_encodings(third_image)[0]

# 存储要检测的人脸编码及对应的人名
inside_face_encodings = [first_face_encoding, Second_face_encoding, third_face_encoding]
inside_face_names = ['A', 'B', 'C']

# 打开视频流
video_capture = cv2.VideoCapture(r'C:/Users/ALIENWARE/123.mp4')

# 循环读取视频帧
while True:
    # 读取一帧
    ret, frame = video_capture.read()

    # 降低帧率，加快处理速度
    if not ret:
        break

    # 缩小图片尺寸，加快处理速度
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    rgb_small_frame = small_frame[:, :, ::-1]

    # 处理每一帧图片
    face_locations = face_recognition.face_locations(rgb_small_frame)
    face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
    face_names = []

    # 遍历每个检测到的人脸
    for face_encoding in face_encodings:
        # 比对人脸编码
        matches = face_recognition.compare_faces(inside_face_encodings, face_encoding)
        name = '未录入人脸'

        # 找到匹配的人脸
        if True in matches:
            first_match_index = matches.index(True)
            name = inside_face_names[first_match_index]
        face_names.append(name)

    # 在画面上标记人脸及名字
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # 将坐标放大4倍，因为之前缩小了4倍
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # 画出人脸框
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # 添加名字
        img_pil = Image.fromarray(frame)
        draw = ImageDraw.Draw(img_pil)
        fontStyle = ImageFont.truetype("C:/Windows/Fonts/simsun.ttc", 32, encoding="utf-8")
        draw.text((left + 6, bottom - 6), name, (0, 200, 0), font=fontStyle)
        frame = np.asarray(np.array(img_pil))

    # 显示画面
    cv2.imshow('face_out', frame)

    # 按下q键退出程序
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放视频流
video_capture.release()

# 关闭所有窗口
cv2.destroyAllWindows()

self.face_encodings = face_recognition.face_encodings(small_frame, self.face_locations)

这行代码是使用 face_recognition 库中的 face_encodings 函数来计算图像中所有面部的128维编码。其中，small_frame 是一个包含被检测到的人脸的小图像，face_locations 是一个包含人脸位置的元组列表。这个函数会返回一个包含所有面部编码的列表。这些编码可以用于比较两张面部图像，以确定它们是否属于同一个人。