SKTexture nearest filtering mode doesn't work (Making Pixel Art) - swift

I used a relatively small image (44pixel tall) and scaled it up to have a pixel art look. I tried to change the filtering mode to nearest so the anti-aliasing would disappear but it didn't. It appears with blurry edges. I tried the same with a background getting the same result.
let myNode = SKSpriteNode()
class GameScene: SKScene {
func makeNode() {
myNode.position = CGPoint(x: self.frame.size.width/2, y: self.frame.size.height/2)
myNode.size = CGSize(width: 200, height: 200)
myNode.texture = SKTexture(imageNamed: "image")
myNode.texture!.filteringMode = .Nearest
return self.addChild(myNode)
}
}
override func didMoveToView(view: SKView) {
makeNode()
}

UPDATE:
SKTexture nearest filtering mode is now working in Xcode 7 GM. No need to manually scale images as I've suggested below.
I am experiencing this same problem in Xcode 7 beta 5 with beta iOS 9 ; the Nearest filtering mode is seemingly ignored. In my project, scaling with nearest-neighbor scaling was previously working in Xcode 6 on iOS 8.
In the event that the bug is not resolved before the final release of iOS 9 I am pre-scaling images before I place them in their respective atlases.
To do this I wrote a simple python script to recursively find png files and scale them using imagmagick.
If you don’t have imagemagick installed, you can install it using macports like so:
sudo port install ImageMagick
If you have homebrew it looks like this:
brew install imagemagick
I just place this script (below) in a file called imgscaler.py within the directory above my atlas files (which I want to scale by 400% in my case) and kick it off from the terminal:
python imgscaler.py
The script looks like this:
import subprocess
import os
import sys
import fnmatch
def main():
execution_folder_name = os.getcwd()
file_extension_name = "png"
#
# Collect names of files that will be manipulated
matches = []
for root, dirNames, fileNames in os.walk(execution_folder_name):
for filename in fnmatch.filter(fileNames, '*.' + file_extension_name):
full_name = os.path.join(root, filename)
matches.append(full_name)
scale_percentage = "400"
if not __query_yes_no("This script will scale images by " + scale_percentage + "% recursively from directory " + execution_folder_name + ". Proceed?"):
return
#
# Scale the images
for match in matches:
execution_str = "convert " + match + " -interpolate Nearest -filter point -resize " + scale_percentage + "% " + match + "\n"
sys.stdout.write(execution_str)
__run_command(execution_str)
def __run_command(input_cmd):
"""Runs a command on the terminal, and returns the output in a string array.
"""
process = subprocess.Popen(input_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
output = []
while True:
line = process.stdout.readline()
if line != '':
output.append(line)
else:
break
return output
def __query_yes_no(question, default="yes"):
"""Asks a yes or no question, returns a true is answered "yes".
"""
valid = {"yes": True, "y": True, "ye": True,
"no": False, "n": False}
if default is None:
prompt = " [y/n] "
elif default == "yes":
prompt = " [Y/n] "
elif default == "no":
prompt = " [y/N] "
else:
raise ValueError("invalid default answer: '%s'" % default)
while True:
sys.stdout.write(question + prompt)
sys.stdout.flush()
choice = raw_input().lower()
if default is not None and choice == '':
sys.stdout.write(default)
sys.stdout.write('\n')
sys.stdout.flush()
return valid[default]
elif choice in valid:
sys.stdout.write(choice)
sys.stdout.write('\n')
sys.stdout.flush()
return valid[choice]
else:
sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n")
sys.stdout.flush()
main()
Change scale_percentage to whatever percentage you want to scale by.
BTW, I am guessing this scaling issue will eventually get resolved. I’m currently updating my code with this assumption in mind. This is just a bandaid in the event the fix to nearest-neighbor scaling in SpriteKit arrives later than iOS 9.0.

This is a bug in Xcode7 beta / iOS9. It works if you use Xcode 6.

Related

Raspberry Pi 4 Aborts SSH Connection When TensorFlow Lite Has Initialized After Running Python 3 Script

I want to use object detection using tensorflow lite in order to detect a clear face or a covered face where the statement "door opens" is printed when a clear face is detected. I could run this code smoothly previously but later after rebooting raspberry pi 4, although the tensorflow lite runtime is initialized, the raspberry pi 4 disconnects with the ssh completely. The following is the code:
######## Webcam Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 10/27/19
# Description:
# This program uses a TensorFlow Lite model to perform object detection on a live webcam
# feed. It draws boxes and scores around the objects of interest in each frame from the
# webcam. To improve FPS, the webcam object runs in a separate thread from the main program.
# This script will work with either a Picamera or regular USB webcam.
#
# This code is based off the TensorFlow Lite image classification example at:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/label_image.py
#
# I added my own method of drawing boxes and labels using OpenCV.
# Import packages
import os
import argparse
import cv2
import numpy as np
import sys
import time
from threading import Thread
import importlib.util
import simpleaudio as sa
# Define VideoStream class to handle streaming of video from webcam in separate processing thread
# Source - Adrian Rosebrock, PyImageSearch: https://www.pyimagesearch.com/2015/12/28/increasing-raspberry-pi-fps-with-python-and-opencv/
class VideoStream:
"""Camera object that controls video streaming from the Picamera"""
def __init__(self,resolution=(640,480),framerate=30):
# Initialize the PiCamera and the camera image stream
self.stream = cv2.VideoCapture(0)
ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
ret = self.stream.set(3,resolution[0])
ret = self.stream.set(4,resolution[1])
# Read first frame from the stream
(self.grabbed, self.frame) = self.stream.read()
# Variable to control when the camera is stopped
self.stopped = False
def start(self):
# Start the thread that reads frames from the video stream
Thread(target=self.update,args=()).start()
return self
def update(self):
# Keep looping indefinitely until the thread is stopped
while True:
# If the camera is stopped, stop the thread
if self.stopped:
# Close camera resources
self.stream.release()
return
# Otherwise, grab the next frame from the stream
(self.grabbed, self.frame) = self.stream.read()
def read(self):
# Return the most recent frame
return self.frame
def stop(self):
# Indicate that the camera and thread should be stopped
self.stopped = True
# Define and parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--modeldir', help='Folder the .tflite file is located in',
required=True)
parser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite',
default='masktracker.tflite')
parser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt',
default='facelabelmap.txt')
parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',
default=0.5)
parser.add_argument('--resolution', help='Desired webcam resolution in WxH. If the webcam does not support the resolution entered, errors may occur.',
default='640x480')
parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',
action='store_true')
args = parser.parse_args()
MODEL_NAME = args.modeldir
GRAPH_NAME = args.graph
LABELMAP_NAME = args.labels
min_conf_threshold = float(args.threshold)
resW, resH = args.resolution.split('x')
imW, imH = int(resW), int(resH)
use_TPU = args.edgetpu
# Import TensorFlow libraries
# If tflite_runtime is installed, import interpreter from tflite_runtime, else import from regular tensorflow
# If using Coral Edge TPU, import the load_delegate library
pkg = importlib.util.find_spec('tflite_runtime')
if pkg:
from tflite_runtime.interpreter import Interpreter
if use_TPU:
from tflite_runtime.interpreter import load_delegate
else:
from tensorflow.lite.python.interpreter import Interpreter
if use_TPU:
from tensorflow.lite.python.interpreter import load_delegate
# If using Edge TPU, assign filename for Edge TPU model
if use_TPU:
# If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
if (GRAPH_NAME == 'masktracker.tflite'):
GRAPH_NAME = 'edgetpu.tflite'
# Get path to current working directory
CWD_PATH = os.getcwd()
# Path to .tflite file, which contains the model that is used for object detection
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)
# Load the label map
with open(PATH_TO_LABELS, 'r') as f:
labels = [line.strip() for line in f.readlines()]
# Have to do a weird fix for label map if using the COCO "starter model" from
# https://www.tensorflow.org/lite/models/object_detection/overview
# First label is '???', which has to be removed.
if labels[0] == '???':
del(labels[0])
# Load the Tensorflow Lite model.
# If using Edge TPU, use special load_delegate argument
if use_TPU:
interpreter = Interpreter(model_path=PATH_TO_CKPT,
experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
print(PATH_TO_CKPT)
else:
interpreter = Interpreter(model_path=PATH_TO_CKPT)
interpreter.allocate_tensors()
# Get model details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
floating_model = (input_details[0]['dtype'] == np.float32)
input_mean = 127.5
input_std = 127.5
# Initialize frame rate calculation
frame_rate_calc = 1
freq = cv2.getTickFrequency()
global image_capture
image_capture = 0
# Initialize video stream
videostream = VideoStream(resolution=(imW,imH),framerate=30).start()
time.sleep(1)
#for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
while True:
# Start timer (for calculating frame rate)
t1 = cv2.getTickCount()
# Grab frame from video stream
frame1 = videostream.read()
# Acquire frame and resize to expected shape [1xHxWx3]
frame = frame1.copy()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (width, height))
input_data = np.expand_dims(frame_resized, axis=0)
# Normalize pixel values if using a floating model (i.e. if model is non-quantized)
if floating_model:
input_data = (np.float32(input_data) - input_mean) / input_std
# Perform the actual detection by running the model with the image as input
interpreter.set_tensor(input_details[0]['index'],input_data)
interpreter.invoke()
# Retrieve detection results
boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects
#num = interpreter.get_tensor(output_details[3]['index'])[0] # Total number of detected objects (inaccurate and not needed)
# Loop over all detections and draw detection box if confidence is above minimum threshold
for i in range(len(scores)):
if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):
# Get bounding box coordinates and draw box
# Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
ymin = int(max(1,(boxes[i][0] * imH)))
xmin = int(max(1,(boxes[i][1] * imW)))
ymax = int(min(imH,(boxes[i][2] * imH)))
xmax = int(min(imW,(boxes[i][3] * imW)))
# Draw label
object_name = labels[int(classes[i])] # Look up object name from "labels" array using class index
if (object_name=="face unclear" ):
color = (0, 255, 0)
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax),color, 2)
print("Face Covered: Door Not Opened")
if(image_capture == 0):
path = r'/home/pi/Desktop/tflite_1/photographs'
date_string = time.strftime("%Y-%m-%d_%H%M%S")
#print(date_string)
cv2.imwrite(os.path.join(path, (date_string + ".jpg")),frame)
#cv2.imshow("Photograph",frame)
#mp3File = input(alert_audio.mp3)
print("Photo Taken")
#w_object = sa.WaveObject.from_wave_file('alert_audio.wav')
#p_object = w_object.play()
#p_object.wait_done()
image_capture = 1
else:
color = (0, 0, 255)
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax),color, 2)
print("Face Clear: Door Opened")
image_capture = 0
#cv2.rectangle(frame, (xmin,ymin), (xmax,ymax),color, 2)
#image = np.asarray(ImageGrab.grab())
label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text
if ((scores[0] < min_conf_threshold)):
cv2.putText(frame,"No Face Detected",(260,260),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color=(255,0,0))
print("No Face Detected")
image_capture = 0
# Draw framerate in corner of frame
cv2.putText(frame,'FPS: {0:.2f}'.format(frame_rate_calc),(30,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA)
# All the results have been drawn on the frame, so it's time to display it.
cv2.imshow('Object detector', frame)
# Calculate framerate
t2 = cv2.getTickCount()
time1 = (t2-t1)/freq
frame_rate_calc= 1/time1
# Press 'q' to quit
if cv2.waitKey(1) == ord('q'):
break
# Clean up
cv2.destroyAllWindows()
videostream.stop()
Any help is appreciated.
Regards,
MD

Preview app doesn't open since I installed MACOS Catalina

import PIL
img = PIL.Image.new("RGB", (100,100))
img.show()
The error message:
FSPathMakeRef(/Applications/Preview.app) failed with error -43.
Following from Sean True's answer, an even quicker but temporary fix is to simply make a symbolic link to Preview.app in the old location. In the terminal run
ln -s /System/Applications/Preview.app /Applications/Preview.app
This fixed the problem for me.
There's an official fix in github for Pillow 7, but I'm still on 6.
This appears to be a PIL ImageShow issue, with the PIL MacViewer using /Applications/Preview.app as an absolute path to the OSX Preview app.
It's not there in Catalina. I did a quick hack to ImageShow.py changing /Applications/Preview.app to just Preview.app and the issue went away. That might or might not still work on pre-Catalina OSX, but I don't have an easy way to test.
It has apparently moved to /System/Applications/Preview.app so a quick check at run time would probably cover both cases.
elif sys.platform == "darwin":
class MacViewer(Viewer):
format = "PNG"
options = {'compress_level': 1}
preview_locations = ["/System/Applications/Preview.app","/Applications/Preview.app"]
preview_location = None
def get_preview_application(self):
if self.preview_location is None:
for pl in self.preview_locations:
if os.path.exists(pl):
self.preview_location = pl
break
if self.preview_location is None:
raise RuntimeError("Can't find Preview.app in %s" % self.preview_locations)
return self.preview_location
def get_command(self, file, **options):
# on darwin open returns immediately resulting in the temp
# file removal while app is opening
pa = self.get_preview_application()
command = "open -a %s" % pa
command = "(%s %s; sleep 20; rm -f %s)&" % (command, quote(file),
quote(file))
return command
def show_file(self, file, **options):
"""Display given file"""
pa = self.get_preview_application()
fd, path = tempfile.mkstemp()
with os.fdopen(fd, 'w') as f:
f.write(file)
with open(path, "r") as f:
subprocess.Popen([
'im=$(cat);'
'open %s $im;'
'sleep 20;'
'rm -f $im' % pa
], shell=True, stdin=f)
os.remove(path)
return 1

Importing two text files to compare as lists sequentially

Student trying to compare two .txt files of string "answers" from a multiple choice test a,c,d,b, etc. I've found some information on different parts of the problems I'm having and found a possible way to get the comparisons I want, but the guide was meant for in script strings and not pulling a list from a file.
For the import of the two files and comparing them, I'm basing my code on my textbook and this video here: Video example
I've got the code up and running, but for some reason I'm only getting 0.0% match when I want to a 100.0% match, at least for the two text files I'm using with identical answer lists.
import difflib
answer_sheet = "TestAnswerList.txt"
student_sheet = "StudentAnswerList.txt"
ans_list = open(answer_sheet).readlines()
stu_list = open(student_sheet).readlines()
sequence = difflib.SequenceMatcher(isjunk=None, a=ans_list, b=stu_list)
check_list = sequence.ratio()*100
check_list = round(check_list,1)
print(str(check_list) + "% match")
if check_list == 100:
print('This grade is Plus Ultra!')
elif check_list >= 75:
print('Good job, you pass!')
else:
print('Please study harder for your next test.')
# not the crux of my issue, but will accept advice all the same
answer_sheet.close
student_sheet.close
If I add in the close statement at the end for both of the text files, then I receive this error:
Traceback (most recent call last): File
"c:/Users/jaret/Documents/Ashford U/CPT 200/Python Code/Wk 5 Int Assg
- Tester code.py", line 18, in
answer_sheet.close AttributeError: 'str' object has no attribute 'close'
I had to re-look at how my files were being opened and realized that the syntax was for Python 2 not 3. I chose to go w/ basic open and later close to reduce any potential errors on my novice part.
import difflib
f1 = open('TestAnswerList.txt')
tst_ans = f1.readlines()
f2 = open('StudentAnswerList.txt')
stu_ans = f2.readlines()
sequence = difflib.SequenceMatcher(isjunk=None, a=stu_ans, b=tst_ans)
check_list = sequence.ratio()*100
check_list = round(check_list,1)
print(str(check_list) + "% match") # Percentage correct
if check_list == 100:
print('This grade is Plus Ultra!')
elif check_list >= 75:
print('Good job, you pass!')
else:
print('Please study harder for your next test.')
# Visual Answer match-up
print('Test Answers: ', tst_ans)
print('Student Answers:', stu_ans)
f1.close()
f2.close()

Storage values using os.stat(filename)

I'm trying to create an EDUCATIONAL PURPOSES ONLY virus. I do not plan on spreading it. It's purpose is to grow a file to the point your storage is full and slow your computer down. It prints the size of the file every 0.001 seconds. With that, I also want to know how fast it is growing the file. The following code doesn't seem to let it run:
class Vstatus():
def _init_(Status):
Status.countspeed == True
Status.active == True
Status.growingspeed == 0
import time
import os
#Your storage is at risk of over-expansion. Please do not let this file run forever, as your storage will fill continuously.
#This is for educational purposes only.
while Vstatus.Status.countspeed == True:
f = open('file.txt', 'a')
f.write('W')
fsize = os.stat('file.txt')
Key1 = fsize
time.sleep(1)
Key2 = fsize
Vstatus.Status.growingspeed = (Key2 - Key1)
Vstatus.Status.countspeed = False
while Vstatus.Status.active == True:
time.sleep(0.001)
f = open('file.txt', 'a')
f.write('W')
fsize = os.stat('file.txt')
print('size:' + fsize.st_size.__str__() + ' at a speed of ' + Vstatus.Status.growingspeed + 'bytes per second.')
This is for Educational Purposes ONLY
The main error I keep getting when running the file is here:
TypeError: unsupported operand type(s) for -: 'os.stat_result' and 'os.stat_result'
What does this mean? I thought os.stat returned an integer Can I get a fix on this?
Vstatus.Status.growingspeed = (Key2 - Key1)
You can't subtract os.stat objects. Your code also has some other problems. Your loops will run sequentially, meaning that your first loop will try to estimate how quickly the file is being written to without writing anything to the file.
import time # Imports at the top
import os
class VStatus:
def __init__(self): # double underscores around __init__
self.countspeed = True # Assignment, not equality test
self.active = True
self.growingspeed = 0
status = VStatus() # Make a VStatus instance
# You need to do the speed estimation and file appending in the same loop
with open('file.txt', 'a+') as f: # Only open the file once
start = time.time() # Get the current time
starting_size = os.fstat(f.fileno()).st_size
while status.active: # Access the attribute of the VStatus instance
size = os.fstat(f.fileno()).st_size # Send file desciptor to stat
f.write('W') # Writing more than one character at a time will be your biggest speed up
f.flush() # make sure the byte is written
if status.countspeed:
diff = time.time() - start
if diff >= 1: # More than a second has gone by
status.countspeed = False
status.growingspeed = (os.fstat(f.fileno()).st_size - starting_size)/diff # get rate of growth
else:
print(f"size: {size} at a speed of {status.growingspeed}")

How to convert opencv functions to mexopencv functions useable in matlab?

My Problem:
I want to use functions of opencv like the MIL-Tracker or MedianFlow-Tracker in Matlab (these functions are not in mexopencv). But I don't know how or understand how to do this. The documentation of opencv/mexopencv doesn't help me. This doesn't help: how do OpenCV shared libraries in matlab? - because the link in the answer is down.
So is there a way to use these functions in Matlab? And if- How?
Why?: As a part of my bachelor thesis I have to compare different already implemented ways to track people.
If you would like to use these functions specifically in MATLAB you could always write your own MEX file in C/C++ and send the data back/forward between the two calls, however this would require some basic C++ knowledge and understanding creating MEX files.
Personally I would definately recommend trying this with Python and the OpenCV Python interface since its so widely used and more supported than using the calls in MATLAB (plus its always a useful skill to be able to switch between Python and MATLAB as and when needed).
There is a full example with the MIL-Tracker and the MedianFlow-Tracker (and others) here (Which demonstrates using them in C++ and Python!).
Python Example :
import cv2
import sys
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
if __name__ == '__main__' :
# Set up tracker.
# Instead of MIL, you can also use
tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']
tracker_type = tracker_types[2]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
# Read video
video = cv2.VideoCapture("videos/chaplin.mp4")
# Exit if video not opened.
if not video.isOpened():
print "Could not open video"
sys.exit()
# Read first frame.
ok, frame = video.read()
if not ok:
print 'Cannot read video file'
sys.exit()
# Define an initial bounding box
bbox = (287, 23, 86, 320)
# Uncomment the line below to select a different bounding box
bbox = cv2.selectROI(frame, False)
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)
while True:
# Read a new frame
ok, frame = video.read()
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)
else :
# Tracking failure
cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
# Display tracker type on frame
cv2.putText(frame, tracker_type + " Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);
# Display FPS on frame
cv2.putText(frame, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);
# Display result
cv2.imshow("Tracking", frame)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27 : break
I would definately try it using Python (if this is an option). Otherwise if MATLAB is a must then probably try implementing the C++ example code shown in the link before as a MEX file and linking openCV during the compilation i.e.
mex trackerMexOpenCV.cpp 'true filepath location to openCV lib'
I hope this helps!