- use online color classification

- show camera settings



git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@318 b431acfa-c32f-4a4a-93f1-934dc6c82436

beadDetect.py

@@ -21,7 +21,20 @@ def colordistance(color1, color2):
 	d2 = (color1[2]-color2[2])
 	return np.math.sqrt(d0*d0 + d1*d1 + d2*d2)
 
-
+def mindistance(colorList, color):
+	minDist = 1000
+	minIndex = 0
+	index = 0
+	for c in colorList:
+		dist = colordistance(c, color)
+		if dist < minDist:
+			minDist = dist
+			minIndex = index
+			
+		index += 1
+		
+	return (minDist, minIndex)
+	
 class FindObjects():
 	def __init__(self):
 		self.innerOuterRatio = 0.6	# const
@@ -135,6 +148,7 @@ fps = FPS().start()
 findObjects = FindObjects()
 
 beadColors = []
+numColorClasses = 0
 
 # loop over some frames...this time using the threaded stream
 while fps._numFrames < args["num_frames"]:
@@ -144,14 +158,13 @@ while fps._numFrames < args["num_frames"]:
 	else:
 		ret, frame = vs.read()
 
+	kernel = np.ones((3,3),np.uint8)
+	frame_dilated = cv2.dilate(frame,kernel,iterations = 1)
 	gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
 	gray_blurred = cv2.medianBlur(gray,5)
 
 	# Canny edge detection
 	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(gray_blurred,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
@@ -183,14 +196,14 @@ while fps._numFrames < args["num_frames"]:
 				img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(255,0,0),2)
 				thickness = obj['thickness']-maskCenter
 				diameter = member['diameter']+thickness+maskCenter
-				roi = frame[y-thickness:y+diameter, x-thickness:x+diameter]
+				roi = frame_dilated[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]})
+					beads.append({'pos' : pos, 'diameter' : diameter, 'color' : [mean_color[0]/255, mean_color[1]/255, mean_color[2]/255]})
 #					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)
@@ -199,9 +212,27 @@ while fps._numFrames < args["num_frames"]:
 
 	for bead in beads:
 		beadColors.append(bead['color'])
-
+			
+			
+	colorClasses = []
+	for bead in beads:
+		if not colorClasses:
+			colorClasses.append(bead['color'])
+		else:
+			d, i = mindistance(colorClasses, bead['color'])
+			if d > 0.14:
+				colorClasses.append(bead['color'])
+			else:
+				colorClasses[i][0] = 0.5*colorClasses[i][0] + 0.5*bead['color'][0]
+				colorClasses[i][1] = 0.5*colorClasses[i][1] + 0.5*bead['color'][1]
+				colorClasses[i][2] = 0.5*colorClasses[i][2] + 0.5*bead['color'][2]
+				
+	if numColorClasses  != len(colorClasses):
+		numColorClasses  = len(colorClasses)
+		print("Found " + str(numColorClasses) + " color classes")
+	
 #	cv2.imshow('Thresholded',img1_thr)
-	cv2.imshow('Contours', img1_contours)
+	cv2.imshow('Contours', frame_dilated)
 	cv2.imshow('Colors', img1_colors)
 	cv2.imshow('Canny',img1_canny)
 	cv2.imshow('Objects',img1_objects)
@@ -237,9 +268,9 @@ 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
+	blue[i] = color[0]
+	green[i] = color[1]
+	red[i] = color[2]
 	plotColors[i] = [red[i], green[i], blue[i]]
 	i += 1
 
@@ -275,3 +306,4 @@ plt.show()
 # do a bit of cleanup
 cv2.destroyAllWindows()
 
+