YOLOv5 得到boxes, scores, 类, nums
YOLOv5 get boxes, scores, classes, nums
我正在尝试在我的项目中将对象跟踪与深度排序绑定,我需要获取框、分数、类、nums。
正在加载预训练的 Yolov5 模型:
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
model.eval()
获得预测:
result = model(img)
print(result.shape)
print(result)
torch.Size([8, 6])
tensor([[277.50000, 379.25000, 410.50000, 478.75000, 0.90625, 2.00000],
[404.00000, 205.12500, 498.50000, 296.00000, 0.88623, 2.00000],
[262.50000, 247.75000, 359.50000, 350.25000, 0.88281, 2.00000],
[210.50000, 177.75000, 295.00000, 261.75000, 0.83154, 2.00000],
[195.50000, 152.50000, 257.75000, 226.00000, 0.78223, 2.00000],
[137.00000, 146.75000, 168.00000, 162.00000, 0.55713, 2.00000],
[ 96.00000, 130.12500, 132.50000, 161.12500, 0.54199, 2.00000],
[ 43.56250, 89.56250, 87.68750, 161.50000, 0.50146, 5.00000]], device='cuda:0')
tensor([[277.50000, 379.25000, 410.50000, 478.75000, 0.90625, 2.00000],
[404.00000, 205.12500, 498.50000, 296.00000, 0.88623, 2.00000],
[262.50000, 247.75000, 359.50000, 350.25000, 0.88281, 2.00000],
[210.50000, 177.75000, 295.00000, 261.75000, 0.83154, 2.00000],
[195.50000, 152.50000, 257.75000, 226.00000, 0.78223, 2.00000],
[137.00000, 146.75000, 168.00000, 162.00000, 0.55713, 2.00000],
[ 96.00000, 130.12500, 132.50000, 161.12500, 0.54199, 2.00000],
[ 43.56250, 89.56250, 87.68750, 161.50000, 0.50146, 5.00000]], device='cuda:0')
所以现在我的问题是如何获取每个变量中的方框、分数、类、数值?
我需要它用于对象跟踪
我用 Pytorch 文档上的例子试了一次:
result.xyxy[0]
但在我的案例中我得到了一个错误:
Tensor has no attribute xyxy
模型的输出是火炬张量,没有 xyxy 方法。您需要手动提取值。要么你一个一个过一遍每一个检测:
import torch
det = torch.rand(8, 6)
for *xyxy, conf, cls in det:
print(*xyxy)
print(conf)
print(cls)
或者您可以通过以下方式对检测张量进行切片:
xyxy = det[:, 0:4]
conf = det[:, 4]
cls = det[:, 5]
print(xyxy)
print(conf)
print(cls)
我正在尝试在我的项目中将对象跟踪与深度排序绑定,我需要获取框、分数、类、nums。
正在加载预训练的 Yolov5 模型:
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
model.eval()
获得预测:
result = model(img)
print(result.shape)
print(result)
torch.Size([8, 6])
tensor([[277.50000, 379.25000, 410.50000, 478.75000, 0.90625, 2.00000],
[404.00000, 205.12500, 498.50000, 296.00000, 0.88623, 2.00000],
[262.50000, 247.75000, 359.50000, 350.25000, 0.88281, 2.00000],
[210.50000, 177.75000, 295.00000, 261.75000, 0.83154, 2.00000],
[195.50000, 152.50000, 257.75000, 226.00000, 0.78223, 2.00000],
[137.00000, 146.75000, 168.00000, 162.00000, 0.55713, 2.00000],
[ 96.00000, 130.12500, 132.50000, 161.12500, 0.54199, 2.00000],
[ 43.56250, 89.56250, 87.68750, 161.50000, 0.50146, 5.00000]], device='cuda:0')
tensor([[277.50000, 379.25000, 410.50000, 478.75000, 0.90625, 2.00000],
[404.00000, 205.12500, 498.50000, 296.00000, 0.88623, 2.00000],
[262.50000, 247.75000, 359.50000, 350.25000, 0.88281, 2.00000],
[210.50000, 177.75000, 295.00000, 261.75000, 0.83154, 2.00000],
[195.50000, 152.50000, 257.75000, 226.00000, 0.78223, 2.00000],
[137.00000, 146.75000, 168.00000, 162.00000, 0.55713, 2.00000],
[ 96.00000, 130.12500, 132.50000, 161.12500, 0.54199, 2.00000],
[ 43.56250, 89.56250, 87.68750, 161.50000, 0.50146, 5.00000]], device='cuda:0')
所以现在我的问题是如何获取每个变量中的方框、分数、类、数值? 我需要它用于对象跟踪
我用 Pytorch 文档上的例子试了一次:
result.xyxy[0]
但在我的案例中我得到了一个错误:
Tensor has no attribute xyxy
模型的输出是火炬张量,没有 xyxy 方法。您需要手动提取值。要么你一个一个过一遍每一个检测:
import torch
det = torch.rand(8, 6)
for *xyxy, conf, cls in det:
print(*xyxy)
print(conf)
print(cls)
或者您可以通过以下方式对检测张量进行切片:
xyxy = det[:, 0:4]
conf = det[:, 4]
cls = det[:, 5]
print(xyxy)
print(conf)
print(cls)