代码实现vgg行人检测
时间: 2023-08-03 15:07:11 浏览: 96
行人检测是计算机视觉中的一个重要应用,其中基于深度学习算法的 VGG 行人检测模型具有较高的准确率和鲁棒性。下面是使用 PyTorch 实现 VGG 行人检测的示例代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(512 * 7 * 7, 4096)
self.fc2 = nn.Linear(4096, 4096)
self.fc3 = nn.Linear(4096, 1000)
self.fc4 = nn.Linear(1000, 2)
self.dropout = nn.Dropout(p=0.5)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = self.pool1(x)
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = self.pool2(x)
x = F.relu(self.conv5(x))
x = F.relu(self.conv6(x))
x = F.relu(self.conv7(x))
x = self.pool3(x)
x = F.relu(self.conv8(x))
x = F.relu(self.conv9(x))
x = F.relu(self.conv10(x))
x = self.pool4(x)
x = F.relu(self.conv11(x))
x = F.relu(self.conv12(x))
x = F.relu(self.conv13(x))
x = self.pool5(x)
x = x.view(-1, 512 * 7 * 7)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.relu(self.fc2(x))
x = self.dropout(x)
x = F.relu(self.fc3(x))
x = self.dropout(x)
x = self.fc4(x)
return x
```
上述代码中定义了一个 VGG16 类,继承自 nn.Module 类。其中,VGG16 模型包含了 13 个卷积层和 5 个最大池化层。最后使用了 4 个全连接层,其中最后一个全连接层输出的是行人检测的结果。
接下来,我们可以使用该模型进行行人检测,示例代码如下:
```python
import cv2
import numpy as np
# 加载行人检测模型
model = VGG16()
model.load_state_dict(torch.load('vgg16.pth'))
# 加载图像并进行预处理
image = cv2.imread('test.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (224, 224))
image = np.transpose(image, (2, 0, 1))
image = np.expand_dims(image, axis=0)
image = image.astype(np.float32) / 255.0
# 运行模型并进行行人检测
with torch.no_grad():
output = model(torch.from_numpy(image))
output = F.softmax(output, dim=1)
prob = output[0, 1].item()
if prob > 0.5:
print('有行人')
else:
print('无行人')
```
上述代码中,首先加载了训练好的 VGG16 行人检测模型,并加载了需要进行检测的图像。然后对图像进行预处理,并将其转换为 PyTorch 张量。接下来,使用模型对图像进行预测,并对输出结果进行后处理,得到行人检测的结果。
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