解释h1, w1 = src_rgb.shape[:2]

这行代码是用来获取一个 RGB 图像的高度和宽度。它使用 Python 的切片（slice）操作符，将 src_rgb.shape 的前两个元素取出来并赋值给变量 h1 和 w1。因为 RGB 图像是一个三维数组，其 shape 属性返回一个元组，其中第一个元素是高度，第二个元素是宽度，第三个元素是颜色通道数（通常为 3，分别代表红、绿、蓝三个颜色通道）。因此，src_rgb.shape[:2] 就是取出前两个元素，即高度和宽度。

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。

class DyCAConv(nn.Module): def __init__(self, inp, oup, kernel_size, stride, reduction=32): super(DyCAConv, self).__init__() self.pool_h = nn.AdaptiveAvgPool2d((None, 1)) self.pool_w = nn.AdaptiveAvgPool2d((1, None)) self.pool_h1 = nn.MaxPool2d((None, 1)) self.pool_w1 = nn.MaxPool2d((1, None)) mip = max(8, inp // reduction) self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(mip) self.act = h_swish() self.conv_h = nn.Conv2d(mip, inp, kernel_size=1, stride=1, padding=0) self.conv_w = nn.Conv2d(mip, inp, kernel_size=1, stride=1, padding=0) self.conv = nn.Sequential(nn.Conv2d(inp, oup, kernel_size, padding=kernel_size // 2, stride=stride), nn.BatchNorm2d(oup), nn.SiLU()) self.dynamic_weight_fc = nn.Sequential( nn.Linear(inp, 2), nn.Softmax(dim=1) ) def forward(self, x): identity = x n, c, h, w = x.size() x_h = self.pool_h(x) x_w = self.pool_w(x).permute(0, 1, 3, 2) x_h1 = self.pool_h1(x) x_w1 = self.pool_w1(x).permute(0, 1, 3, 2) y = torch.cat([x_h, x_w, x_h1, x_w1], dim=2) y = self.conv1(y) y = self.bn1(y) y = self.act(y) x_h, x_w, _, _ = torch.split(y, [h, w, h, w], dim=2) x_w = x_w.permute(0, 1, 3, 2) x_w1 = x_w1.permute(0, 1, 3, 2) a_h = self.conv_h(x_h).sigmoid() a_w = self.conv_w(x_w).sigmoid() a_w1 = self.conv_w(x_w1).sigmoid() # Compute dynamic weights x_avg_pool = nn.AdaptiveAvgPool2d(1)(x) x_avg_pool = x_avg_pool.view(x.size(0), -1) dynamic_weights = self.dynamic_weight_fc(x_avg_pool) out = identity * (dynamic_weights[:, 0].view(-1, 1, 1, 1) * a_w + dynamic_weights[:, 1].view(-1, 1, 1, 1) * a_h + dynamic_weights[:, 1].view(-1, 1, 1, 1) * a_w1) return self.conv(out)在里面修改一下，换成这个y = torch.cat([x_h+x_h1, x_w+x_w1], dim=2)

如果您想将原来代码中的y拼接成x_h+x_h1和x_w+x_w1，可以按照以下方式进行修改：

# 将原来的y拆分成x_h, x_w, x_h1, x_w1
x_h, x_w, x_h1, x_w1 = torch.split(y, [h, w, h, w], dim=2)

# 将x_h和x_h1相加，x_w和x_w1相加
x_h = x_h + x_h1
x_w = x_w + x_w1

# 将x_h和x_w拼接起来
y = torch.cat([x_h, x_w], dim=2)

这样就可以将x_h和x_h1相加，x_w和x_w1相加，并将它们拼接起来，作为新的y输出了。