diff --git a/src/main/python/__pycache__/detector.cpython-38.pyc b/src/main/python/__pycache__/detector.cpython-38.pyc
index 802fe1f..66288f9 100644
Binary files a/src/main/python/__pycache__/detector.cpython-38.pyc and b/src/main/python/__pycache__/detector.cpython-38.pyc differ
diff --git a/src/main/python/detector.py b/src/main/python/detector.py
index 4115374..caedfd9 100644
--- a/src/main/python/detector.py
+++ b/src/main/python/detector.py
@@ -7,12 +7,8 @@
 
 # define orange hsv values (opencv range is h:0-180, s:0-255, v:0-255)
 # NOTE: BELOW VALS REPRESENT RED ACTUALLY BC I ONLY HAD A RED SPONGE TO USE 
-LOWER_ORANGE_HSV = np.array([3, 80, 80])
-UPPER_ORANGE_HSV = np.array([6, 255, 255])
-
-LOW_THRESHOLD = 110
-HIGH_THRESHOLD = 255
-
+# LOWER_ORANGE_HSV = np.array([3, 80, 80])
+# UPPER_ORANGE_HSV = np.array([6, 255, 255])
 
 # orange hsv values
 # LOWER_ORANGE_HSV = np.array([3, 80, 80])
@@ -28,11 +24,11 @@ class Detector:
     def __init__(self):
         pass
 
-    def detectOrange(self, grayscale_image, threshold):     
+    def detectOrange(self, grayscale_image, low_threshold, high_threshold):     
         """NOTE: to threshold with monochrome image (single-channel, grayscale) to create a binary mask, 
                 can specify a SINGLE scalar value for lower/upper bounds
                 returns: binary image (single-channel, 8-bit)"""
-        return cv2.inRange(grayscale_image, LOW_THRESHOLD, HIGH_THRESHOLD)
+        return cv2.inRange(grayscale_image, low_threshold, high_threshold)
         #return np.where(grayscale_image > threshold, 255, 0).astype(np.uint8)    
     
     def find_largest_orange_contour(self, orange_mask: np.ndarray) -> np.ndarray:
diff --git a/src/main/python/test.py b/src/main/python/test.py
index 320ee2f..1404813 100644
--- a/src/main/python/test.py
+++ b/src/main/python/test.py
@@ -7,6 +7,9 @@
 cap = cv2.VideoCapture(0)
 counter = 0
 
+LOW_THRESHOLD = 110
+HIGH_THRESHOLD = 255
+
 while(True):
 
     ret, frame = cap.read()
@@ -24,7 +27,7 @@
     # convert tuple from (height, width, # of channels) to just (height, width)
     frame_single_channel = frame[:,:,0]
 
-    orange_mask = d.detectOrange(frame_single_channel, threshold=100)
+    orange_mask = d.detectOrange(frame_single_channel, LOW_THRESHOLD, HIGH_THRESHOLD)
     contour = d.find_largest_orange_contour(orange_mask)
     #cv2.drawContours(frame, contour, 0, [255, 0, 0], 2)
     if contour is not None and d.contour_is_note(contour):