Create crack_detection_service.py

services/crack_detection_service.py

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+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+from PIL import Image
+import cv2
+import random
+
+# Load model
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+def detect_cracks(frame):
+    image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
+
+    cracks = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        if score >= 0.9:
+            severity = random.choice(['Minor', 'Moderate', 'Severe'])  # Simulate severity
+            cracks.append({
+                'box': box.tolist(),
+                'severity': severity,
+                'confidence': score.item()
+            })
+
+    return cracks