Create utils/detector.py

utils/detector.py

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+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image
+import torch
+import numpy as np
+
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+def detect_faults(image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    intrusion_detected = any(label == 1 for label in results["labels"].tolist())
+
+    # Simulated thermal detection (average red channel > 200 = overheat)
+    red_mean = np.array(image)[:, :, 0].mean()
+    overheating = red_mean > 200
+
+    # Simulated shade (brightness < 100 on average = dusty/shaded)
+    brightness = np.array(image).mean()
+    dusty = brightness < 100
+
+    return {
+        "Intrusion Detected": intrusion_detected,
+        "Overheating Panel": overheating,
+        "Dust/Shade Fault": dusty
+    }