diff --git a/server/nightr/app.py b/server/nightr/app.py
index 7ba6a99..3eecf5e 100644
--- a/server/nightr/app.py
+++ b/server/nightr/app.py
@@ -42,15 +42,16 @@ def probabilities():
         logger.debug("phone_data:\n%s", json.dumps(phone_data, indent=2))
         context = Context(**phone_data["data"])
 
-    logger.debug("Context: %s", context)
+    #logger.debug("Context: %s", context)
 
     predictions: List[dict] = []
     for name, strategy in strategies.items():
         try:
+            logger.debug("Executing %s..", name)
             start = timeit.default_timer()
             prediction = strategy(context)
             stop = timeit.default_timer()
-            logger.debug("Execution time for %s: %ss", name, stop - start)
+            logger.debug("Execution time for %s: %ss", name, round(stop - start, 3 ))
         except Exception as e:
             logger.warning("Strategy '%s' failed:", name)
             logger.exception(e)
diff --git a/server/nightr/strategies/miloStrats.py b/server/nightr/strategies/miloStrats.py
index 593c981..3fc4c43 100644
--- a/server/nightr/strategies/miloStrats.py
+++ b/server/nightr/strategies/miloStrats.py
@@ -1,8 +1,6 @@
 from datetime import datetime
-from pathlib import Path
-import requests
 
-import cv2
+import requests
 from pytz import timezone
 
 from ..util import Context, Prediction
@@ -12,12 +10,11 @@ def camImgStrat(context : Context) -> Prediction:
     """
     The contents of the camera image
     """
-    img = cv2.imread(str(Path(__file__).parent.joinpath("night.jpg")), 0)
+    img = context.image
     average = img.mean(axis=0).mean(axis=0)
     p = Prediction()
     p.weight = 0.7
-
-    if average < 100: 
+    if average < 100:
         p.probability = 1.0
         p.reasons.append('Image was dark')
     else:
diff --git a/server/nightr/util.py b/server/nightr/util.py
index 5b2bc3c..ce7d143 100644
--- a/server/nightr/util.py
+++ b/server/nightr/util.py
@@ -1,16 +1,30 @@
+import base64
 from dataclasses import dataclass, field
+from pathlib import Path
 from typing import List, Dict
 
+import cv2
+import numpy as np
+
 
 @dataclass
 class Context:
     battery: float = 1.0
     position: Dict[str, float] = field(default_factory=lambda: {'latitude': 53.0, 'longitude': 9.0})
+    image: np.ndarray = None
 
     # App settings
     in_australia: bool = False
     flat_earth: bool = False
 
+    def __post_init__(self):
+        if self.image is None:  # no image given
+            self.image = cv2.imread(str(Path(__file__).parent.joinpath("gray.png")))
+        else:
+            img_original = base64.b64decode(self.image)
+            img_as_np = np.frombuffer(img_original, dtype=np.uint8)
+            self.image = cv2.imdecode(img_as_np, flags=1)
+
 
 @dataclass
 class Prediction: