from PIL import Image
import torchvision.transforms as transforms

# لود مدل YOLOv5
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# پیش‌پردازش تصویر
def preprocess_image(image_path):
    image = Image.open(image_path)
    transform = transforms.Compose([
        transforms.Resize((640, 640)),
        transforms.ToTensor()
    ])
    return transform(image).unsqueeze(0)

# آموزش مدل
def train_model(data_dir, epochs=10):
    # آماده‌سازی داده‌ها
    dataset = ...  # خواندن داده‌ها از data_dir
    dataloader = ...  # ایجاد DataLoader

    # تنظیم پارامترهای آموزش
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    criterion = torch.nn.CrossEntropyLoss()

    for epoch in range(epochs):
        for images, labels in dataloader:
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
        print(f'Epoch {epoch+1}/{epochs}, Loss: {loss.item()}')

# تشخیص مناطق دارای گپ
def detect_gaps(image_path):
    image = preprocess_image(image_path)
    results = model(image)
    return results

# مثال استفاده
image_path = '/content/Sugarcane-Cultivation-in-Tamil-Nadu-1.jpg'
results = detect_gaps(image_path)
print(results)