Create thermal_anomaly_detection.py

models/thermal_anomaly_detection.py

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+import torch
+from torchvision import models, transforms
+from PIL import Image
+import numpy as np
+
+# Load a pretrained ResNet50 model for thermal anomaly detection
+model = models.resnet50(pretrained=True)
+model.eval()
+
+def detect_thermal_anomalies(image_path):
+    """
+    Detect thermal anomalies in solar panels using thermal images.
+    Args:
+    - image_path (str): Path to the thermal image file
+
+    Returns:
+    - anomaly (str): Description of the detected thermal anomaly
+    """
+    # Image preprocessing for ResNet
+    transform = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    image = Image.open(image_path)
+    image_tensor = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(image_tensor)
+        _, predicted_class = torch.max(output, 1)
+
+    # Simulate anomaly detection (you can replace this with actual anomaly labels)
+    anomaly = "Overheating detected" if predicted_class == 0 else "Normal condition"
+    return anomaly