コンピュータビジョンにおけるターゲット追跡の問題

import cv2

def color_tracking(frame, target):
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, target.lower_bound, target.upper_bound)
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    if len(contours) > 0:
        max_contour = max(contours, key=cv2.contourArea)
        x, y, w, h = cv2.boundingRect(max_contour)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        
    return frame

# 定义目标物体的颜色范围
class Target:
    def __init__(self, lower_bound, upper_bound):
        self.lower_bound = lower_bound
        self.upper_bound = upper_bound

# 初始化目标物体的颜色范围
target = Target((0, 100, 100), (10, 255, 255))

# 目标跟踪主程序
def main():
    cap = cv2.VideoCapture(0)
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        frame = color_tracking(frame, target)
        cv2.imshow("Tracking", frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn

# 定义目标跟踪模型
class TrackingModel(nn.Module):
    def __init__(self):
        super(TrackingModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.fc1 = nn.Linear(128 * 8 * 8, 512)
        self.fc2 = nn.Linear(512, 2)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = x.view(-1, 128 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 初始化目标跟踪模型
model = TrackingModel()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 加载数据集
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

# 训练目标跟踪模型
def train():
    for epoch in range(10):  # 迭代次数
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            if i % 2000 == 1999:    # 打印loss值
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

    print('Finished Training')

if __name__ == '__main__':
    train()