- improved

git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@323 b431acfa-c32f-4a4a-93f1-934dc6c82436

PiVideoStream.py

@@ -14,8 +14,24 @@ class PiVideoStream:
 		self.camera.exposure_mode = 'auto'
 #		self.camera.image_effect = 'colorbalance'
 #		self.camera.image_effect_params = (0,1,1,1,0,0)
+		self.camera.exposure_mode = 'auto'
+		self.camera.awb_mode = 'off'
+		self.camera.awb_gains = (1.5, 1.5)
+#		self.camera.analog_gain = 1.75
+#		self.camera.digital_gain = 1.00
+		self.rawCapture = PiRGBArray(self.camera, size=resolution)
+		self.stream = self.camera.capture_continuous(self.rawCapture,
+			format="bgr", use_video_port=True)
 
+		# initialize the frame and the variable used to indicate
+		# if the thread should be stopped
+		self.frame = None
+		self.stopped = False
+		self.fps = None
+		self.thread = None
+		self.startup = True
 
+	def printSettings(self):
 		awb_gains = self.camera.awb_gains
 		print ("sensor_mode          : " + str(self.camera.sensor_mode))
 		print ("resolution           : " + str(self.camera.resolution))
@@ -38,18 +54,6 @@ class PiVideoStream:
 		print ("image_effect_params  : " + str(self.camera.image_effect_params))
 		print ("sharpness            : " + str(self.camera.sharpness))
 
-		self.rawCapture = PiRGBArray(self.camera, size=resolution)
-		self.stream = self.camera.capture_continuous(self.rawCapture,
-			format="bgr", use_video_port=True)
-
-		# initialize the frame and the variable used to indicate
-		# if the thread should be stopped
-		self.frame = None
-		self.stopped = False
-		self.fps = None
-		self.thread = None
-		self.startup = True
-
 	def start(self):
 		# start the thread to read frames from the video stream
 		self.thread = Thread(target=self.update, args=())
@@ -59,6 +63,11 @@ class PiVideoStream:
 	def update(self):
 		# keep looping infinitely until the thread is stopped
 		for f in self.stream:
+			if self.camera.exposure_mode == 'auto':
+				if self.camera.analog_gain > 1.75 and self.camera.digital_gain == 1.0:
+					self.camera.exposure_mode = 'off'
+					self.printSettings()
+
 			if self.startup:
 				self.fps = FPS().start()
 				self.startup = False

beadDetect.py

@@ -62,7 +62,8 @@ class FindObjects():
 		temp_objects = []
 		objectsCandIds = []
 		
-		if hierachy is not None:
+		try:
+			if hierachy.any():
 				while count != -1:
 					family = FindObjects.getFamily([], count, hierachy)
 	
@@ -111,6 +112,8 @@ class FindObjects():
 						self.meanThickness = int(abs(obj[0]['diameter'] - obj[1]['diameter'])/2)
 	
 					objects.append({'thickness' : self.meanThickness, 'members' : obj})
+		except:
+			pass
 
 		return objects
 
@@ -175,15 +178,18 @@ if args["calibrate"] == 'on':
 	params.append(0)
 
 	cv2.imwrite("lens_corr.png", np.uint8(lens_corr*127.0), params)
+	cv2.imwrite("lens_shot.png", frame, params)
 
-	sys.exit()
+	fps.stop()
+	if videoFile == "piCamera":
+		vs.stop()
 
+else:
+	frame = np.ones((height, width, 3), np.float32)
+	lens_corr = np.float32(cv2.imread("lens_corr.png"))/128.0
 
-frame = np.ones((height, width, 3), np.float32)
+	# loop over some frames...this time using the threaded stream
-lens_corr = np.float32(cv2.imread("lens_corr.png"))/140.0
+	while fps._numFrames < args["num_frames"]:
-
-# loop over some frames...this time using the threaded stream
-while fps._numFrames < args["num_frames"]:
 		# grab the frame from the threaded video stream
 
 		if videoFile == "piCamera":
@@ -203,9 +209,9 @@ while fps._numFrames < args["num_frames"]:
 		img1_canny = cv2.Canny(gray_blurred, 100, 50)
 
 		# Thresholding
-#	ret,img1_thr = cv2.threshold(gray_blurred,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+	#	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_MEAN_C, cv2.THRESH_BINARY,11,2)
-#	img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_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)
@@ -214,7 +220,7 @@ while fps._numFrames < args["num_frames"]:
 	
 		objects = findObjects.find(contours, hierachy)
 	
-#	print (ids)
+	#	print (ids)
 
 		img1_objects = frame.copy()
 		img1_colors = np.zeros((height,width,3), np.uint8)
@@ -240,7 +246,7 @@ while fps._numFrames < args["num_frames"]:
 						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]/255, mean_color[1]/255, mean_color[2]/255]})
-#					img1_objects = cv2.circle(img1_objects,member['pos'],int(diameter/2),(255,255,255),thickness)
+	#					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)
 
@@ -276,26 +282,24 @@ while fps._numFrames < args["num_frames"]:
 		if videoFile == "piCamera":	
 			cv2.waitKey(1)
 		else:
-#		cv2.waitKey(0)
+	#		cv2.waitKey(0)
 			cv2.waitKey(int(1000.0/framerate))
 		
 
 		# update the FPS counter
 		fps.update()
 
-# stop the timer and display FPS information
+	# stop the timer and display FPS information
-fps.stop()
+	fps.stop()
-if videoFile == "piCamera":
+	if videoFile == "piCamera":
 		vs.stop()
 
-print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
+	print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
-print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
+	print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
-if videoFile == "piCamera":
+	if videoFile == "piCamera":
 		print("[INFO] Camera : elasped time: {:.2f}".format(vs.getfps().elapsed()))
 		print("[INFO] Camera: approx. FPS: {:.2f}".format(vs.getfps().fps()))
 
-if numColorClasses:
-	
 	# Output stats
 	findObjects.printStats()
 
@@ -318,7 +322,7 @@ if numColorClasses:
 
 	# Define criteria = ( type, max_iter, epsilon )
 	criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.1)
-	ret,label,center=cv2.kmeans(np.float32(plotColors), numColorClasses, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
+	ret,label,center=cv2.kmeans(Z,numColorClasses,None,criteria,10,cv2.KMEANS_PP_CENTERS)
 
 	print (center)
 
@@ -341,9 +345,6 @@ if numColorClasses:
 
 	plt.show()
 
-else:
-	print("Sorry, no colors have been detected")
-
 # do a bit of cleanup
 cv2.destroyAllWindows()