Create utils/fault_detection.py

utils/fault_detection.py

@@ -0,0 +1,57 @@
+import torch
+import cv2
+from PIL import Image
+from torchvision import transforms
+import numpy as np
+
+# Dummy models – replace with your own
+def load_model(path):
+    model = torch.load(path, map_location="cpu")
+    model.eval()
+    return model
+
+thermal_model = load_model("models/thermal_fault_model.pt")
+crack_model = load_model("models/crack_detector_model.pt")
+dust_model = load_model("models/dust_detector_model.pt")
+
+def detect_faults(image):
+    transform = transforms.Compose([
+        transforms.ToPILImage(),
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+
+    img_tensor = transform(image).unsqueeze(0)
+
+    results = []
+    output_img = image.copy()
+
+    # Simulated prediction
+    if torch.rand(1).item() > 0.5:
+        results.append({
+            "type": "Thermal Fault",
+            "location": "Panel 12-B",
+            "confidence": 0.93
+        })
+        cv2.putText(output_img, "Thermal Fault", (50, 50),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+    if torch.rand(1).item() > 0.5:
+        results.append({
+            "type": "Crack Detected",
+            "location": "Panel 9-C",
+            "confidence": 0.88
+        })
+        cv2.putText(output_img, "Crack", (150, 100),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
+
+    if torch.rand(1).item() > 0.5:
+        results.append({
+            "type": "Dust Coverage",
+            "location": "Panel 3-A",
+            "confidence": 0.91
+        })
+        cv2.putText(output_img, "Dust", (200, 150),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+
+    return results, output_img