diff --git a/img6.jpg b/img6.jpg
new file mode 100644
index 0000000..1acea35
Binary files /dev/null and b/img6.jpg differ
diff --git a/img7.jpg b/img7.jpg
new file mode 100644
index 0000000..dfc464d
Binary files /dev/null and b/img7.jpg differ
diff --git a/main.py b/main.py
index 5e56c07..55d1e6e 100644
--- a/main.py
+++ b/main.py
@@ -6,6 +6,7 @@ import argparse
 import imutils
 import cv2
 import math
+import time 
 
 itemw = 0
 itemh = 0
@@ -44,6 +45,11 @@ def smaller(a, b):
 		return a
 	else: 
 		return b
+
+def near(a, b, close):
+	if abs(a-b) < close:
+		return True
+	return False
 # construct the argument parse and parse the arguments
 ap = argparse.ArgumentParser()
 ap.add_argument("-i", "--image", required=True,
@@ -70,16 +76,13 @@ if args2.show:
 	cv2.waitKey(0)
 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 gray = cv2.GaussianBlur(gray, (7, 7), 0)
-if args2.show:
-	cv2.imshow("Item Sorter", gray)
-	cv2.waitKey(0)
 
 
 # perform edge detection, then perform a dilation + erosion to
 # close gaps in between object edges
 edged = cv2.Canny(gray, 50, 100)
 edged = cv2.dilate(edged, None, iterations=1)
-edged = cv2.erode(edged, None, iterations=1)
+#edged = cv2.erode(edged, None, iterations=1)
 
 if args2.show:
 	cv2.imshow("Item Sorter", edged)
@@ -131,14 +134,20 @@ for c in cnts:
 	else:
 		circular = True
 		cv2.circle(orig,(int(x),int(y)),int(radius),(0,255,0),2)
-
-	if pixelsPerMetric is None and circular is True:
+		mask = np.zeros(gray.shape,np.uint8)
+		cv2.drawContours(mask,[c],0,255,-1)
+		#pixelpoints = np.transpose(np.nonzero(mask))
+		mean_val = cv2.mean(orig,mask = mask)
+		#print(str(mean_val))
+	if pixelsPerMetric is None and circular is True and near(mean_val[0], 63, 30) is True and near(mean_val[1], 108, 30) is True and near(mean_val[2], 104, 30) is True:
 		pixelsPerMetric = smaller(dA, dB) / args["width"]
+		
 
 
 orig = image.copy()
 # loop over the contours individually
 for c in cnts:
+	
 	num += 1
 	# if the contour is not sufficiently large, ignore it
 	if cv2.contourArea(c) < 100 or pixelsPerMetric is None:
@@ -233,7 +242,7 @@ for c in cnts:
 			#print(str(convexness) + " % fill")
 			#if not cv2.isContourConvex(approx):
 			#if cv2.matchShapes(hull, c, 1, 0.0) > 1:
-			if defects.size > 5 and (convexness < 0.9 or boxiness < 0.75):
+			if defects.size > 5 and (convexness < 0.9 and boxiness < 0.7):
 				objtype = "Screw"
 				iteml = larger(dimA, dimB)
 				#print("Screw Length (RAW): " + str(iteml))
@@ -249,7 +258,7 @@ for c in cnts:
 					iteml = (radius * 2 / pixelsPerMetric + itemw) / 2
 
 				
-		print(str(iteml))
+		#print(str(iteml))
 		# draw the object sizes on the image
 		if args2.show:
 			#cv2.putText(orig, "{:.5f}in".format(itemhr),
@@ -258,6 +267,10 @@ for c in cnts:
 			cv2.putText(orig, str(objtype),
 				(int(trbrX + 20), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
 				0.65, (255, 255, 255), 2)
+			if objtype == "Unknown":
+				cv2.putText(orig, "{:.2f}in".format(itemw) + " x {:.2f}in".format(itemh), # print axle length
+					(int(trbrX + 20), int(trbrY + 20)), cv2.FONT_HERSHEY_SIMPLEX,
+					0.65, (255, 255, 255), 2)
 			if objtype == "Screw":
 				cv2.putText(orig, str(iteml) + "in thread", # print screw length
 					(int(trbrX + 20), int(trbrY + 20)), cv2.FONT_HERSHEY_SIMPLEX,
@@ -272,5 +285,5 @@ for c in cnts:
 					0.65, (255, 255, 255), 2)
  
 	# show the output image
-			cv2.imshow("Item Sorter", orig)
-			cv2.waitKey(0)
\ No newline at end of file
+cv2.imshow("Item Sorter", orig)
+cv2.waitKey(0)
\ No newline at end of file