diff --git a/color_detect.py b/color_detect.py
index e31e3aa..8eb0bb8 100755
--- a/color_detect.py
+++ b/color_detect.py
@@ -4,28 +4,75 @@
 
 cap = cv2.VideoCapture(0)
 
-def greenCircleDetect():
+#function to detect green color. It returns the frame after drawing the contour on it.
+def greenCircleDetect(frame):
 
+	# converting from BGR to HSV color space
+	hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
 
-def redCircleDetect():
+	# HSV Range for green
+	lower_green = np.array([36,25,25])
+	upper_green = np.array([75,255,255])
+	mask = cv2.inRange(hsv, lower_green, upper_green) # generating a mask in the image to determine the region of the detected color
+	kernel = np.ones((5,5),np.uint8)
+	erosion = cv2.erode(mask,kernel,iterations = 2) #morphological operation done on the image to remove the false positives.
 
+	_, contours, _  = cv2.findContours(erosion, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #extracting the contours from the image
+	output  = cv2.drawContours(frame, contours, -1, (255,0,0),3) #drawing all possible detected contours with blue color.
+	return output
 
-def blueCircleDetect():
+#function to detect red color. It returns the frame after drawing the contour on it.
+def redCircleDetect(frame):
 
+	# converting from BGR to HSV color space
+	hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
 
-while True:
-	ret, frame = cap.read()
+	# HSV Range for lower red
+	lower_red = np.array([0,230,20])
+	upper_red = np.array([1,255,255])
+	mask1 = cv2.inRange(hsv, lower_red, upper_red)
 
-	greenCircleDetect()
+	# HSV Range for upper red
+	lower_red = np.array([170,120,20])
+	upper_red = np.array([180,255,255])
+	mask2 = cv2.inRange(hsv,lower_red,upper_red)
 
-	redCircleDetect()
+	# Generating the final mask to detect red color
+	mask1 = mask1+mask2
+	kernel = np.ones((5,5),np.uint8)
+	erosion = cv2.erode(mask1,kernel,iterations = 2) #morphological operation done on the image to remove the false positives.
 
-	blueCircleDetect()
+	_, contours, _  = cv2.findContours(erosion, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #extracting all possible contours from the image
+	output  = cv2.drawContours(frame, contours, -1, (255,0,0),3) #drawing all possible detected contours with blue color.
+	return output
 
 
-	cv2.imshow('frame', frame)
+#function to detect blue color. It returns the frame after drawing the contour on it.
+def blueCircleDetect(frame):
+
+	# converting from BGR to HSV color space
+	hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
+
+	# HSV Range for blue
+	lower_blue = np.array([85,86,0])
+	upper_blue = np.array([130,255,255])
+	mask = cv2.inRange(hsv, lower_blue, upper_blue) # generating a mask in the image to determine the region of the detected color
+	kernel = np.ones((5,5),np.uint8)
+	erosion = cv2.erode(mask,kernel,iterations = 2) #morphological operation done on the image to remove the false positives.
+
+	_, contours, _  = cv2.findContours(erosion, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #extracting all possible contours from the image
+	output  = cv2.drawContours(frame, contours, -1, (255,0,0),3) #drawing all possible detected contours with blue color.
+	return output
 
+while True:
+	ret, frame = cap.read()
+
+	green = greenCircleDetect(frame)
+	red = redCircleDetect(frame)
+	blue = blueCircleDetect(frame)
+
+	cv2.imshow('frame', frame)
 	cv2.waitKey(3)
 
 cap.release()
-cv2.destroyAllWindows()
\ No newline at end of file
+cv2.destroyAllWindows()