Update yolo_Anomaly

yolo_Anomaly

@@ -0,0 +1,64 @@
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from PIL import Image
+
+# Load YOLO model
+def load_model(model_path="best.pt"):
+    """Load the YOLO model"""
+    model = YOLO(model_path)
+    return model
+
+# Process image
+def process_image(image, model):
+    """Run YOLO prediction on the given image"""
+    if isinstance(image, Image.Image):
+        image_array = np.array(image)
+    else:
+        image_array = image
+
+    results = model.predict(image_array)
+    return results[0]  # return first result
+
+# Draw all predictions
+def draw_predictions(image, results):
+    """Draw bounding boxes and labels"""
+    if isinstance(image, Image.Image):
+        image_array = np.array(image)
+    else:
+        image_array = image
+
+    plotted_image = results.plot()  # YOLO's built-in plotting
+    return Image.fromarray(plotted_image)
+
+# Group predictions by class/condition
+def group_predictions_by_condition(results):
+    """Organize detections by class"""
+    condition_groups = {}
+    if len(results.boxes) > 0:
+        for box in results.boxes:
+            class_id = int(box.cls[0])
+            class_name = results.names[class_id]
+            confidence = float(box.conf[0])
+            if class_name not in condition_groups:
+                condition_groups[class_name] = []
+            condition_groups[class_name].append({
+                'box': box,
+                'confidence': confidence
+            })
+    return condition_groups
+
+# Example usage (standalone)
+if __name__ == "__main__":
+    model = load_model("best.pt")
+    img_path = "your_test_image.jpg"
+    image = Image.open(img_path)
+
+    results = process_image(image, model)
+    grouped = group_predictions_by_condition(results)
+
+    print("Grouped Predictions:", grouped)
+
+    # Save output visualization
+    output_img = draw_predictions(image, results)
+    output_img.save("predicted_output.jpg")