ret = self.original_func(*self.args, **self.kwargs) ret = self.original_func(*self.args, **self.kwargs)
时间: 2023-09-28 15:11:28 浏览: 69
这段代码是一个装饰器的实现方式,用于在执行函数之前和之后添加额外的逻辑。
其中,self.original_func指代被装饰的函数,*self.args和**self.kwargs是传递给被装饰函数的参数。
在执行被装饰函数之前,会先执行第一行代码,然后再执行被装饰函数;在执行被装饰函数之后,会执行第二行代码。最终返回的是被装饰函数的返回值ret。
相关问题
import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable class Bottleneck(nn.Module): def init(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer): super(Bottleneck, self).init() self.out_planes = out_planes self.dense_depth = dense_depth self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False) self.bn2 = nn.BatchNorm2d(in_planes) self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(out_planes+dense_depth) self.shortcut = nn.Sequential() if first_layer: self.shortcut = nn.Sequential( nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_planes+dense_depth) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) x = self.shortcut(x) d = self.out_planes out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1) out = F.relu(out) return out class DPN(nn.Module): def init(self, cfg): super(DPN, self).init() in_planes, out_planes = cfg['in_planes'], cfg['out_planes'] num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth'] self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.last_planes = 64 self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1) self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2) self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2) self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2) self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)dense_depth[3], 10) def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride): strides = [stride] + 1 layers = [] for i,stride in (strides): layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0)) self.last_planes = out_planes + (i+2) * dense_depth return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) return out def DPN92(): cfg = { 'in_planes': (96,192,384,768), 'out_planes': (256,512,1024,2048), 'num_blocks': (3,4,20,3), 'dense_depth': (16,32,24,128) } return DPN(cfg)基于这个程序改成对摄像头采集的图像检测与分类输出坐标、大小和种类
首先,需要将输入的图像大小和通道数进行调整以适应模型输入。假设模型需要的输入大小为224x224,通道数为3,则可以使用OpenCV库中的resize函数将摄像头采集的图像缩放至指定大小。同时,需要将图像转换成PyTorch中的Tensor格式,并对像素值进行归一化操作。
代码如下:
```python
import cv2
import numpy as np
# 调整图像大小和通道数
img_size = (224, 224)
img_channels = 3
# 初始化摄像头
cap = cv2.VideoCapture(0)
while True:
# 读取摄像头图像
ret, frame = cap.read()
if not ret:
break
# 缩放图像
frame = cv2.resize(frame, img_size)
# 转换为Tensor格式,并进行归一化
frame = np.transpose(frame, (2, 0, 1)).astype(np.float32) / 255.0
frame = np.expand_dims(frame, axis=0)
frame = torch.from_numpy(frame)
# 调用模型进行预测
output = model(frame)
# TODO: 解析输出结果,得到分类、坐标和大小信息
# 显示图像
cv2.imshow('frame', frame)
# 按下q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
cap.release()
cv2.destroyAllWindows()
```
接下来,需要解析模型的输出结果,得到分类、坐标和大小信息。由于代码中没有给出模型输出的具体格式,这里需要根据模型输出结果进行相应的解析。
假设模型输出一个长度为4的向量,其中前两个元素表示物体的左上角坐标,后两个元素表示物体的宽度和高度,第三个元素表示物体的类别。则可以使用以下代码进行解析:
```python
# 解析模型输出结果
x, y, w, h, cls = output[0]
# 计算物体的右下角坐标
x2 = x + w
y2 = y + h
# 显示分类、坐标和大小信息
class_names = ['class1', 'class2', 'class3', 'class4', 'class5']
print('Class:', class_names[cls])
print('Position: ({}, {})-({}, {})'.format(x, y, x2, y2))
print('Size: {}x{}'.format(w, h))
```
最后,将以上代码整合到一起,即可完成对摄像头采集的图像进行检测与分类输出坐标、大小和种类的任务。
完整代码如下:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import cv2
import numpy as np
class Bottleneck(nn.Module):
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
self.dense_depth = dense_depth
self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
self.bn2 = nn.BatchNorm2d(in_planes)
self.conv3 = nn.Conv2d(in_planes, out_planes * dense_depth, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_planes * dense_depth)
self.shortcut = nn.Sequential()
if first_layer:
self.shortcut = nn.Sequential(
nn.Conv2d(last_planes, out_planes * dense_depth, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_planes * dense_depth)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
x = self.shortcut(x)
d = self.out_planes * self.dense_depth
out = torch.cat([x[:,:d,:,:], out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1)
out = F.relu(out)
return out
class DPN(nn.Module):
def __init__(self, cfg):
super(DPN, self).__init__()
in_planes, out_planes = cfg['in_planes'], cfg['out_planes']
num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth']
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.last_planes = 64
self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
self.linear = nn.Linear(out_planes[3] * (num_blocks[3] + 1) * dense_depth[3], 10)
def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for i, stride in enumerate(strides):
layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0))
self.last_planes = out_planes * dense_depth
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def DPN92():
cfg = {
'in_planes': (96, 192, 384, 768),
'out_planes': (256, 512, 1024, 2048),
'num_blocks': (3, 4, 20, 3),
'dense_depth': (16, 32, 24, 128)
}
return DPN(cfg)
# 调整图像大小和通道数
img_size = (224, 224)
img_channels = 3
# 初始化模型
model = DPN92()
model.load_state_dict(torch.load('dpn92.pth', map_location='cpu'))
model.eval()
# 初始化摄像头
cap = cv2.VideoCapture(0)
while True:
# 读取摄像头图像
ret, frame = cap.read()
if not ret:
break
# 缩放图像
frame = cv2.resize(frame, img_size)
# 转换为Tensor格式,并进行归一化
frame = np.transpose(frame, (2, 0, 1)).astype(np.float32) / 255.0
frame = np.expand_dims(frame, axis=0)
frame = torch.from_numpy(frame)
# 调用模型进行预测
output = model(frame)
# 解析模型输出结果
x, y, w, h, cls = output[0]
x, y, w, h, cls = int(x), int(y), int(w), int(h), int(cls)
# 计算物体的右下角坐标
x2 = x + w
y2 = y + h
# 显示分类、坐标和大小信息
class_names = ['class1', 'class2', 'class3', 'class4', 'class5']
print('Class:', class_names[cls])
print('Position: ({}, {})-({}, {})'.format(x, y, x2, y2))
print('Size: {}x{}'.format(w, h))
# 在图像上绘制矩形框
cv2.rectangle(frame, (x, y), (x2, y2), (0, 255, 0), 2)
# 显示图像
cv2.imshow('frame', frame)
# 按下q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头
cap.release()
cv2.destroyAllWindows()
```
利用 def vedio_dect(self): print(self.ved_path) cap = cv2.VideoCapture(self.ved_path) fps = cap.get(cv2.CAP_PROP_FPS) # 获得视频帧率 start = time.time() while cap.isOpened(): # 读取视频帧:ret bool true(读取成功);frame Numpy数组 ret, frame = cap.read() # print(ret) # print(frame) if ret: # 将视频帧转换为适合模型输入的格式, 将 numpy 数组转换为 PIL 图像对象。 # preprocess_frame = Image.fromarray(np.uint8(frame),mode='RGB') preprocess_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) RGBImg = self.resize_img(preprocess_frame, self.ui.label_show) qt_img = QImage(RGBImg[:], RGBImg.shape[1], RGBImg.shape[0], RGBImg.shape[1] * 3, QImage.Format_RGB888) # 转换为QImage格式 pixmap_img = QPixmap.fromImage(qt_img) self.ui.label_show.setPixmap(pixmap_img) # 使用烟雾检测模型或算法进行烟雾检测 testTime, smoke_result_frame, smoke_region= self.det_thread.test_img(preprocess_frame) # 根据检测结果进行后续处理 self.display_smoke_image(preprocess_frame, smoke_region) cv2.waitKey(int(500 / fps)) #调用它之后的一段时间内暂停程序执行,以等待用户按下键盘上的键或达到指定的时间,ms else: break cap.release() end=time.time() testTime=end-start return testTime 如何把检测完得到的各烟雾帧存储到缓冲空间,并另外调函数实现存储到本地
您可以使用Python中的列表(List)作为缓冲空间,将烟雾帧append到列表中。当列表中的元素个数达到一定的数量时,就可以调用另一个函数将这些帧存储到本地。
以下是示例代码:
1. 在初始化方法中创建一个空的列表,用于存储烟雾帧。
```python
def __init__(self):
self.smoke_frames = []
```
2. 在检测烟雾帧后,将其append到列表中。
```python
testTime, smoke_result_frame, smoke_region= self.det_thread.test_img(preprocess_frame)
self.display_smoke_image(preprocess_frame, smoke_region)
self.smoke_frames.append(smoke_result_frame)
```
3. 当列表中的元素个数达到一定的数量时,调用另一个函数将这些帧存储到本地。
```python
if len(self.smoke_frames) >= 10:
self.save_smoke_frames(self.smoke_frames)
self.smoke_frames = []
def save_smoke_frames(self, frames):
# 将frames写入到本地文件中
pass
```
在 `save_smoke_frames` 函数中,您可以使用OpenCV或Pillow等库将帧写入到本地文件中。