通过YOLOV5实现：王者荣耀百里守约自瞄

**王者荣耀百里守约自瞄系统**

**概述**

本文将介绍如何使用YOLOv5（You Only Look Once v5）实现王者荣耀百里守约自瞄系统。百里守约是王者荣耀中的一名英雄，擅长远程攻击和防御。自瞄系统的目标是自动识别并追踪敌方英雄，并在必要时进行攻击。

**环境准备**

* **Python**:3.7+
* **PyTorch**:1.9+
* **OpenCV**:4.5+
* **YOLOv5**: 最新版本**数据集准备**

首先，我们需要准备一个包含王者荣耀英雄图像的数据集。我们可以使用现有的数据集或自行收集数据。

假设我们的数据集已经准备好，保存在`data/`目录下。

import osfrom torch.utils.data import Datasetclass KingGloryDataset(Dataset):
 def __init__(self, root_dir, transform=None):
 self.root_dir = root_dir self.transform = transform self.images = []
 for file in os.listdir(root_dir):
 if file.endswith(".jpg"):
 self.images.append(os.path.join(root_dir, file))

 def __len__(self):
 return len(self.images)

 def __getitem__(self, index):
 image_path = self.images[index]
 image = cv2.imread(image_path)
 if self.transform:
 image = self.transform(image)
 return image

from torchvision import transformstransform = transforms.Compose([
 transforms.ToTensor(),
 transforms.RandomHorizontalFlip(p=0.5),
 transforms.RandomVerticalFlip(p=0.5),
 transforms.Resize((416,416)),
])

from yolov5.models import YOLOv5model = YOLOv5("yolov5s")

train_dataset = KingGloryDataset(root_dir="data/train", transform=transform)
test_dataset = KingGloryDataset(root_dir="data/test")

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
 for images, labels in train_loader:
 optimizer.zero_grad()
 outputs = model(images)
 loss = criterion(outputs, labels)
 loss.backward()
 optimizer.step()

 print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

def predict(model, image):
 outputs = model(image)
 _, predicted = torch.max(outputs,1)
 return predictedimage_path = "data/test/image.jpg"
image = cv2.imread(image_path)

predicted = predict(model, image)
print(predicted)