image1 =image[y1:y1+h1, x1:x1+w1]

这是一个图像处理中的切割操作，它会从原始图像中截取出一个子图像。在这里，image是原始图像，[y1:y1+h1, x1:x1+w1]表示要截取子图像的区域范围。具体来说，y1是起始行坐标，h1是要截取的行数（高度），x1是起始列坐标，w1是要截取的列数（宽度）。通过这个操作，你可以获取到原始图像中指定区域的子图像。

相关问题

import face_recognition import cv2 import os unknow_people_list = [i for i in os.listdir('unknow_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))] know_people_list = [i for i in os.listdir('know_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))] def face_select(): for unknow_people in unknow_people_list: # 读取待识别图片 unknow = face_recognition.load_image_file('unknow_people/' + unknow_people) # 将待识别图片转化为特征向量 unknow_encode = face_recognition.face_encodings(unknow)[0] flag = False for know_people in know_people_list: # 读取计算机已经认识的图片 know = face_recognition.load_image_file('know_people/' + know_people) # 获得面部位置 face_location1 = face_recognition.face_locations(know) face_location2 = face_recognition.face_locations(unknow) # 提取面部关键点 face_landmarks_list1 = face_recognition.face_landmarks(know) face_landmarks_list2 = face_recognition.face_landmarks(unknow) # 图片转化为特征向量 know_encode = face_recognition.face_encodings(know)[0] # 两张图片进行比较的结果 res = face_recognition.compare_faces([know_encode], unknow_encode, tolerance=0.5) if res[0]: flag = True name = know_people.split(".")[0] break if flag: print(f'{name}匹配成功！') else: print(f'匹配失败') name = "UNKNOWN" # 绘制人脸特征点和矩形框 for (x1, y1, w1, h1) in face_location1: cv2.rectangle(know, (y1, x1), (h1, w1), (255, 0, 0), 2) cv2.putText(know, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2) for face_landmarks in face_landmarks_list1: for facial_feature in face_landmarks.keys(): for pt_pos in face_landmarks[facial_feature]: cv2.circle(know, pt_pos, 1, (192, 192, 192), 2) for (x1, y1, w1, h1) in face_location2: cv2.rectangle(unknow, (y1, x1), (h1, w1), (255, 0, 0), 2) cv2.putText(unknow, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2) for face_landmarks in face_landmarks_list2: for facial_feature in face_landmarks.keys(): for pt_pos in face_landmarks[facial_feature]: cv2.circle(unknow, pt_pos, 1, (192, 192, 192), 2) # 显示图片 cv2.imshow("known", know) cv2.imshow("unknown", unknow) cv2.waitKey(0) if __name__ == '__main__': face_select()

这段代码使用 face_recognition 和 OpenCV 库来进行人脸识别。它将 "know_people" 和 "unknow_people" 两个文件夹中的图像进行比较，以查找已知人脸。以下是代码的主要步骤：

1. 导入所需库。

import face_recognition
import cv2
import os

2. 定义要识别的人脸图像列表。

unknow_people_list = [i for i in os.listdir('unknow_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))]
know_people_list = [i for i in os.listdir('know_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))]

3. 定义人脸识别函数。

def face_select():
    for unknow_people in unknow_people_list:
        # 读取待识别图片
        unknow = face_recognition.load_image_file('unknow_people/' + unknow_people)
        # 将待识别图片转化为特征向量
        unknow_encode = face_recognition.face_encodings(unknow)[0]
        flag = False
        for know_people in know_people_list:
            # 读取计算机已经认识的图片
            know = face_recognition.load_image_file('know_people/' + know_people)
            # 获得面部位置
            face_location1 = face_recognition.face_locations(know)
            face_location2 = face_recognition.face_locations(unknow)
            # 提取面部关键点
            face_landmarks_list1 = face_recognition.face_landmarks(know)
            face_landmarks_list2 = face_recognition.face_landmarks(unknow)
            # 图片转化为特征向量
            know_encode = face_recognition.face_encodings(know)[0]
            # 两张图片进行比较的结果
            res = face_recognition.compare_faces([know_encode], unknow_encode, tolerance=0.5)
            if res[0]:
                flag = True
                name = know_people.split(".")[0]
                break
        if flag:
            print(f'{name}匹配成功！')
        else:
            print(f'匹配失败')
            name = "UNKNOWN"
        # 绘制人脸特征点和矩形框
        for (x1, y1, w1, h1) in face_location1:
            cv2.rectangle(know, (y1, x1), (h1, w1), (255, 0, 0), 2)
            cv2.putText(know, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)
            for face_landmarks in face_landmarks_list1:
                for facial_feature in face_landmarks.keys():
                    for pt_pos in face_landmarks[facial_feature]:
                        cv2.circle(know, pt_pos, 1, (192, 192, 192), 2)
        for (x1, y1, w1, h1) in face_location2:
            cv2.rectangle(unknow, (y1, x1), (h1, w1), (255, 0, 0), 2)
            cv2.putText(unknow, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)
            for face_landmarks in face_landmarks_list2:
                for facial_feature in face_landmarks.keys():
                    for pt_pos in face_landmarks[facial_feature]:
                        cv2.circle(unknow, pt_pos, 1, (192, 192, 192), 2)
        # 显示图片
        cv2.imshow("known", know)
        cv2.imshow("unknown", unknow)
        cv2.waitKey(0)

该函数首先将待识别图片转化为特征向量，然后将其与已知人脸图像进行比较，以查找匹配。如果找到匹配，则在图像上绘制人脸特征点和矩形框，并显示人名。如果未找到匹配，则在图像上显示 "UNKNOWN"。

4. 在主函数中调用人脸识别函数。

if __name__ == '__main__':
    face_select()

在主函数中，我们只需要调用 `face_select()` 函数即可开始人脸识别。

MATLAB怎么重叠多张图片，并在重叠图片中心画十字线

您可以使用以下代码来将多张图片叠加，并在中心绘制十字线：

% 读取图片并调整大小
img1 = imresize(imread('image1.jpg'), 0.5);
img2 = imresize(imread('image2.jpg'), 0.5);

% 计算每张图片的大小和位置
[h1, w1, ~] = size(img1);
[h2, w2, ~] = size(img2);
x1 = round((w2 - w1) / 2);
y1 = round((h2 - h1) / 2);
x2 = x1 + w1 - 1;
y2 = y1 + h1 - 1;

% 将两张图片叠加
img = zeros(h2, w2, 3);
img(y1:y2, x1:x2, :) = img1;
img(y1:y2, x1:x2, :) = img(y1:y2, x1:x2, :) + img2;

% 在图片中心绘制十字线
img(round(h2/2), :, :) = 255;
img(:, round(w2/2), :) = 255;

% 显示结果
imshow(uint8(img));

在这个例子中，我们假设有两张图片要叠加，分别为 `image1.jpg` 和 `image2.jpg`。我们使用 `imread` 函数读取图片，并使用 `imresize` 函数调整图片大小为原来的一半。然后，我们计算每张图片的大小和位置，使用 `zeros` 函数创建一张新的空白图片，并将两张图片叠加到新图片上。最后，在图片中心绘制十字线，并使用 `imshow` 函数显示结果。