openCV tracker

Author: lcosicnus

CMS.py

@@ -144,4 +144,4 @@ def trackMultipleObjects():
 	cv2.destroyAllWindows()
 
 if __name__ == '__main__':
-	trackMultipleObjects()
+	trackMultipleObjects()

CMS_camshift.py

@@ -0,0 +1,105 @@
+import cv2
+import numpy as np
+
+video = 'video//prvi.mkv'
+cascade_src = 'myhaar.xml'
+width = 854
+height = 480
+red = (0, 0, 200)
+
+cap = cv2.VideoCapture(video)
+cascade = cv2.CascadeClassifier(cascade_src)
+termination = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)
+
+def resize(frame):
+	resized_frame = cv2.resize(frame, (width, height), interpolation = cv2.INTER_AREA)
+	return resized_frame
+
+def rgb2gray(frame):
+	gray_frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+	return gray_frame
+
+def detect(frame):
+	all_roi = []
+	gray_frame = rgb2gray(frame)
+	cars = cascade.detectMultiScale(gray_frame, 1.1, 13, 0, (24,24))
+	for x, y, w, h in cars:
+		all_roi.append((x, y, x + w, y + h))
+		
+	cv2.imshow('video', frame)
+	cv2.waitKey(1)
+	return all_roi
+
+def track(all_roi, all_roi_hist):
+	for k in range(0, 30):
+		ret, frame = cap.read()
+		if type(frame) == type(None):
+			return -1
+		i = 0
+		hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+		
+		for roi_hist in all_roi_hist:
+			back_proj = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
+			(r, all_roi[i]) = cv2.CamShift(back_proj, all_roi[i], termination)
+			
+			for j in range(0, 4):
+				if all_roi[i][j] < 0:
+					return -1
+			pts = np.int32(cv2.cv.BoxPoints(r))
+			cv2.polylines(frame, [pts], True, red, 2)
+			i += 1
+		
+		cv2.imshow('video', frame)
+		cv2.waitKey(1)
+	return 1
+		
+
+def calc_hist(frame, all_roi):
+	all_roi_hist = []
+	for pts in all_roi:
+		roi = frame[pts[1]:pts[-1], pts[0]:pts[2]]
+		roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
+		roiHist = cv2.calcHist([roi], [0], None, [16], [0, 180])
+		roiHist = cv2.normalize(roiHist, roiHist, 0, 255, cv2.NORM_MINMAX)
+		all_roi_hist.append(roiHist)
+	return all_roi_hist
+
+def show():
+	for k in range(0, 2):
+		ret, frame = cap.read()
+		if type(frame) == type(None):
+			break
+		cv2.imshow('video', frame)
+		if cv2.waitKey(1) == 27:
+			break
+def main():
+	i = 0
+	while True:
+		if i % 2 == 0:
+			all_roi = []
+			all_hsv_roi = []
+			ret, frame = cap.read()
+			if type(frame) == type(None):
+				cap.release()
+				cv2.destroyAllWindows()
+				return
+			
+			frame = resize(frame)
+			all_roi = detect(frame)
+				
+			if len(all_roi) != 0:
+				all_roi_hist = calc_hist(frame, all_roi)
+				i += 1
+			else:
+				show()
+		else:
+			error = track(all_roi, all_roi_hist)
+			if error == -1:
+				cap.release()
+				cv2.destroyAllWindows()
+				return
+			i += 1
+		if cv2.waitKey(1) & 0xFF == ord('q'):
+			break
+if __name__ == '__main__':
+	main()

carDet.py

@@ -4,7 +4,7 @@
 
 #path to classifier
 #path to video file
-cascade_src = 'myhaar6.xml'
+cascade_src = 'myhaar.xml'
 video_src = 'video//drugi.mkv'
 
 #read video
@@ -39,17 +39,12 @@
 	#Detect objects with different dimensions in frame
 	#output of function is list of rectangles
 	#parameters: image, scaleFactior, minNeighbors, flags, minSize, maxSize
-	cars = car_cascade.detectMultiScale(gray, 1.1, 7, 0, (24, 24))
+	cars = car_cascade.detectMultiScale(gray, 1.1, 13, 0, (24, 24))
 
 	#drawing rectangle around detected object
 	#defining regions where object will show up
 	for (x, y, w, h) in cars:
-		if x > 135 and x < 295:
-			cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
-		if x >= 190 and x < 505:
-			cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
-		if x >= 260 and x < 719:
-			cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
+		cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
 
 	#calculation of frame rate
 	fps = 1.0/(time.time() - start_time)
@@ -63,4 +58,4 @@
 		break;
 
 cv2.destroyAllWindows()
-cap.release()
+cap.release()

opencv_tracker.py

@@ -0,0 +1,106 @@
+import cv2
+
+carCascade = cv2.CascadeClassifier('myhaar.xml')
+video = cv2.VideoCapture('video//prvi.mkv')
+
+#WIDTH = 854
+#HEIGHT = 480
+
+#def distance(x1, y1, x2, y2):
+	
+	
+
+def tracker():
+	rectangleColor = (0, 0, 255)
+	frameCounter = 0
+	currentCarID = 0
+	
+	carTracker = {}
+	
+	while True:
+		rc, image = video.read()
+		if type(image) == type(None):
+			break
+		
+#		image = cv2.resize(image, (WIDTH, HEIGHT))
+		image = image[150:720, 0:1000]
+		resultImage = image.copy()
+		
+		frameCounter = frameCounter + 1
+		
+		carIDtoDelete = []
+
+		for carID in carTracker.keys():
+			trackedPosition = carTracker[carID].update(image)
+			#print(trackedPosition)
+			t_x, t_y, t_w, t_h = trackedPosition[1]
+			t_x = int(t_x)
+			t_y = int(t_y)
+			t_w = int(t_w)
+			t_h = int(t_h)
+			
+			if t_x + t_w >= 800:
+				carIDtoDelete.append(carID)
+			if t_y <= 0:
+				carIDtoDelete.append(carID)
+
+				
+		for carID in carIDtoDelete:
+			print('Tracker deleted: ' + str(carID) + '.')
+			carTracker.pop(carID, None)
+		
+		if not (frameCounter % 10):
+			gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+			cars = carCascade.detectMultiScale(gray, 1.1, 13)
+			for (_x, _y, _w, _h) in cars:
+				x = int(_x) + 5
+				y = int(_y) + 5
+				w = int(_w) - 5
+				h = int(_h) - 5
+			
+				x_bar = x + 0.5 * w
+				y_bar = y + 0.5 * h
+				
+				matchCarID = None
+			
+				for carID in carTracker.keys():
+					trackedPosition = carTracker[carID].update(image)
+					t_x, t_y, t_w, t_h = trackedPosition[1]
+					t_x = int(t_x)
+					t_y = int(t_y)
+					t_w = int(t_w)
+					t_h = int(t_h)
+
+					t_x_bar = t_x + 0.5 * t_w
+					t_y_bar = t_y + 0.5 * t_h
+				
+					if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
+						matchCarID = carID
+				
+				if matchCarID is None:
+					bbox = (x, y, w, h)
+					#tracker = cv2.Tracker_create('BOOSTING') #tracker
+					#tracker = cv2.TrackerBoosting_create() #tracker
+					tracker = cv2.TrackerMedianFlow_create() #tracker
+					tracker.init(image, bbox)
+					carTracker[currentCarID] = tracker	
+					currentCarID = currentCarID + 1
+					
+		for carID in carTracker.keys():
+			trackedPosition = carTracker[carID].update(image)
+			t_x, t_y, t_w, t_h = trackedPosition[1]
+			t_x = int(t_x)
+			t_y = int(t_y)
+			t_w = int(t_w)
+			t_h = int(t_h)
+			
+			cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), rectangleColor, 2)
+		cv2.imshow('image', resultImage)
+				
+		if cv2.waitKey(33) == 27:
+			break
+	
+	cv2.destroyAllWindows()
+
+if __name__ == '__main__':
+	tracker()