From 4fb15f2147bb7d6d249149ac57f2a327d30471f2 Mon Sep 17 00:00:00 2001
From: Jens Ahrensfeld <jens@jayfield.org>
Date: Sat, 17 Sep 2016 13:34:00 +0000
Subject: [PATCH] - improved color detector

git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@311 b431acfa-c32f-4a4a-93f1-934dc6c82436
---
 circledetect_threaded.py | 73 +++++++++++++++++++++++++++-------------
 1 file changed, 49 insertions(+), 24 deletions(-)

diff --git a/circledetect_threaded.py b/circledetect_threaded.py
index 9b39b74..f1ddb32 100755
--- a/circledetect_threaded.py
+++ b/circledetect_threaded.py
@@ -10,6 +10,8 @@ import imutils
 import time
 import cv2
 import pprint
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
 
 width = 320
 height = 240
@@ -90,7 +92,6 @@ class FindObjects():
 
 			objects.append({'thickness' : self.meanThickness, 'members' : obj})
 						
-#		self.pp.pprint(objects)
 		return objects
 
 	def printStats(self):
@@ -114,20 +115,25 @@ time.sleep(2.0)
 fps = FPS().start()
 findObjects = FindObjects()
 
+beadColors = []
+
 # loop over some frames...this time using the threaded stream
 while fps._numFrames < args["num_frames"]:
 	# grab the frame from the threaded video stream
 	frame = vs.read()
 	gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
-	gray_bluured = cv2.medianBlur(gray,5)
+	gray_blurred = cv2.medianBlur(gray,5)
 
 	# Canny edge detection
-	img1_canny = cv2.Canny(gray_bluured, 100, 50)
+	img1_canny = cv2.Canny(gray_blurred, 100, 50)
+	kernel = np.ones((1,1),np.uint8)
+#	img1_canny = cv2.blur(img1_canny,(3,3))
+#	img1_canny = cv2.dilate(img1_canny,kernel,iterations = 1)
 
 	# Thresholding
-#	ret,img1_thr = cv2.threshold(gray1,120,255,cv2.THRESH_BINARY)
-#	img1_thr = cv2.adaptiveThreshold(gray1,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
-#	img1_thr = cv2.adaptiveThreshold(gray1,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
+#	ret,img1_thr = cv2.threshold(gray_blurred,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+#	img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
+#	img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
 
 	# Contours
 	(_, contours, hierachy) = cv2.findContours(img1_canny.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
@@ -138,8 +144,9 @@ while fps._numFrames < args["num_frames"]:
 
 	img1_objects = frame.copy()
 	img1_colors = np.zeros((height,width,3), np.uint8)
-	maskCenter = 2
+	maskCenter = 4
 	roides = []
+	beads = []
 	for obj in objects:
 		memberCount = 0
 		radius = 0
@@ -150,28 +157,26 @@ while fps._numFrames < args["num_frames"]:
 			if member['isHole']:
 				img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(255,0,0),2)
 				thickness = obj['thickness']-maskCenter
-				radius = int((member['diameter']+thickness+maskCenter)/2)
-				roi = frame[y-thickness:y+2*radius, x-thickness:x+2*radius]
-				roides.append({'roi' : roi, 'pos' : member['pos'], 'radius' : radius, 'thickness' : thickness})
+				diameter = member['diameter']+thickness+maskCenter
+				roi = frame[y-thickness:y+diameter, x-thickness:x+diameter]
+				pos = member['pos']
+				if diameter > 0:
+					mask = np.zeros((diameter,diameter,1), np.uint8)
+					mask = cv2.circle(mask,(int(diameter/2), int(diameter/2)),radius,255,thickness)
+					mean_color = cv2.mean(roi)
+					img1_colors = cv2.circle(img1_colors,(int(pos[0]),int(pos[1])),int(diameter/2),mean_color,-1)
+					beads.append({'pos' : pos, 'diameter' : diameter, 'color' : mean_color[0:3]})
+					img1_objects = cv2.circle(img1_objects,member['pos'],int(diameter/2),(255,255,255),thickness)
 			else:
 				img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(0,0,255),2)
 
 		memberCount += 1
 
-	for r in roides:
-		if radius > 0:
-			radius = r['radius']
-			thickness = r['thickness']
-			roi = r['roi']
-			pos = r['pos']
-			mask = np.zeros((2*radius,2*radius,1), np.uint8)
-			mask = cv2.circle(mask,(radius, radius),radius,255,thickness)
-			mean_color = cv2.mean(roi)
-			center = (int(pos[0]),int(pos[1]))
-			img1_colors = cv2.circle(img1_colors,center,radius,mean_color,thickness)
+	for bead in beads:
+		beadColors.append(bead['color'])
 
-	cv2.imshow('detected colors', img1_colors)
 #	cv2.imshow('Thresholded',img1_thr)
+	cv2.imshow('detected colors', img1_colors)
 	cv2.imshow('Canny',img1_canny)
 	cv2.imshow('Objects',img1_objects)
 	cv2.waitKey(1)
@@ -179,8 +184,6 @@ while fps._numFrames < args["num_frames"]:
    	# update the FPS counter
 	fps.update()
 
-findObjects.printStats()
- 
 # stop the timer and display FPS information
 fps.stop()
 vs.stop()
@@ -189,6 +192,28 @@ print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
 print("[INFO] Camera : elasped time: {:.2f}".format(vs.getfps().elapsed()))
 print("[INFO] Camera: approx. FPS: {:.2f}".format(vs.getfps().fps()))
 
+# Output stats
+findObjects.printStats()
+
+# Scatter plot of colors
+numObjects = len(beadColors);
+blue = np.zeros(numObjects)
+green = np.zeros(numObjects)
+red = np.zeros(numObjects)
+plotColors = np.zeros((numObjects,3))
+i = 0
+for color in beadColors:
+	blue[i] = color[0]/255
+	green[i] = color[1]/255
+	red[i] = color[2]/255
+	plotColors[i] = [red[i], green[i], blue[i]]
+	i += 1
+
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+ax.scatter(blue, green, red, zdir='z', s=50, facecolors=plotColors, lw = 0, depthshade=True)
+plt.show()
+
 # do a bit of cleanup
 cv2.destroyAllWindows()