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Python implementation of KCF tracking algorithm

License: MIT License

Python 100.00%

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cory8249 sshanu ugv-tracking yangshiyu89 xchenjh 7ychris ronyuzhang liulj13 prsntmaurya tqdavid luolongqiang bozorgtabar gstearmit flamehaze1115 forandwhile jgraving blake51 genadz sy-xuan chonspqx feifannaner mudiaoxu zhenxili96 curiositycreations ganghu1993 absorbguo sunshinezhihuo ztilottama plutojia jbhewitt12 dltensor dansonc ml-lab nosaydomore feiward chenyutcmn stromwine chen849157649 ieee820 satoshirobatofujimoto xiyang1012 fengkai11 xahidbuffon xuexia7023 00001101-xt ninny8151 zhangbinggang ravi-teja-mullapudi ich-bin-schnappi trzp erensu zhangsanfengbygit fayeinseu sisung alvarohurtadobo yinxiaomao yibin-chn whklwhkl trasperj greencandle969 toleranceli wdf1994 liziyun bao2lei windmeeeeee lunar-r xujiao3628 kewenjing1020 trevol smallpeachofchangan sidtalia khaa5866 skyfengbiubiu juliusqv waveyan xiongqing1998 mingshengcai1997 paranoth archerprince jianyushu ve450su2020-group23 dongzhou-1996 woodsgao unsky dynos01 yingjun-zhang wangyoucaocxl moliangdouble swan2015 tongru meta-morphosis lixingwei1106 qianrui99 light1211 tk-233 caojiahuicode zhangl2auto codylcs wang-tf zxf864823150

kcfpy's Issues

how to pulse the video so i can choose the perfect object i want to track?

why is it not useful when i run the code

multi-target tracking

I ran the code, it can track single-target, how to improve the code to achieve multi-target tracking

I think the code has some question in ' def getFeature()'

def getFeatures(self, image, inithann, scale_adjust=1.0):
	extracted_roi = [0,0,0,0]   #[int,int,int,int]
	cx = self._roi[0] + self._roi[2]/2  #float
	cy = self._roi[1] + self._roi[3]/2  #float

	if(inithann):
		padded_w = self._roi[2] * self.padding
		padded_h = self._roi[3] * self.padding

		if(self.template_size > 1):
			if(padded_w >= padded_h):
				self._scale = padded_w / float(self.template_size)
			else:
				self._scale = padded_h / float(self.template_size)
			self._tmpl_sz[0] = int(padded_w / self._scale)
			self._tmpl_sz[1] = int(padded_h / self._scale)
		else:
			self._tmpl_sz[0] = int(padded_w)
			self._tmpl_sz[1] = int(padded_h)
			self._scale = 1.

		if(self._hogfeatures):
			self._tmpl_sz[0] = int(self._tmpl_sz[0]) / (2*self.cell_size) * 2*self.cell_size + 2*self.cell_size
			self._tmpl_sz[1] = int(self._tmpl_sz[1]) / (2*self.cell_size) * 2*self.cell_size + 2*self.cell_size
		else:
			self._tmpl_sz[0] = int(self._tmpl_sz[0]) / 2 * 2
			self._tmpl_sz[1] = int(self._tmpl_sz[1]) / 2 * 2

	extracted_roi[2] = int(scale_adjust * self._scale * self._tmpl_sz[0])
	extracted_roi[3] = int(scale_adjust * self._scale * self._tmpl_sz[1])
	extracted_roi[0] = int(cx - extracted_roi[2]/2)
	extracted_roi[1] = int(cy - extracted_roi[3]/2)

	z = subwindow(image, extracted_roi, cv2.BORDER_REPLICATE)
	if(z.shape[1]!=self._tmpl_sz[0] or z.shape[0]!=self._tmpl_sz[1]):
		z = cv2.resize(z, tuple(self._tmpl_sz))

	if(self._hogfeatures):
		mapp = {'sizeX':0, 'sizeY':0, 'numFeatures':0, 'map':0}
		mapp = fhog.getFeatureMaps(z, self.cell_size, mapp)
		mapp = fhog.normalizeAndTruncate(mapp, 0.2)
		mapp = fhog.PCAFeatureMaps(mapp)
		self.size_patch = map(int, [mapp['sizeY'], mapp['sizeX'], mapp['numFeatures']])
		FeaturesMap = mapp['map'].reshape((self.size_patch[0]*self.size_patch[1], self.size_patch[2])).T   # (size_patch[2], size_patch[0]*size_patch[1])
	else:
		if(z.ndim==3 and z.shape[2]==3):
			FeaturesMap = cv2.cvtColor(z, cv2.COLOR_BGR2GRAY)   # z:(size_patch[0], size_patch[1], 3)  FeaturesMap:(size_patch[0], size_patch[1])   #np.int8  #0~255
		elif(z.ndim==2):
			FeaturesMap = z   #(size_patch[0], size_patch[1]) #np.int8  #0~255
		FeaturesMap = FeaturesMap.astype(np.float32) / 255.0 - 0.5
		self.size_patch = [z.shape[0], z.shape[1], 1]

	if(inithann):
		self.createHanningMats()  # createHanningMats need size_patch

	FeaturesMap = self.hann * FeaturesMap
	return FeaturesMap

for those who need python3 version

Thanks for sharing with us. If somebody wants to use python3 version, you can take reference on https://github.com/LCorleone/KCF_py3

How to deal with the situation of target lost

I found when the target is lost,it is hard to retrack the lost target,how to solve this problem?

If we set 'hog=False', Tracking is very unstable

Zoomed-in bounding boxes are never getting smaller.

TypeError: 'map' object is not subscriptable

is there any one have this problem when run the code in python3 environment ?
can you give me help to solve this problem.

What will change it will do to my systerm if I install KCFcpp-py-wrapper ?

Dear uoip:
First of all, thanks for your work very much. You write a python wrapper for KCFcpp. I want to make use of it. But I have a question that what kind of change will python setup.py build_ext --inplace will have on my systerm? How can I withdraw the changes?
I am new to github. I cannot find the issues column in the uoip/KCFcpp-py-wrapper, so I asked here. Looking forward to get your response ^_^

I have question about the KCFcpp-py-wrapper ,how can I find the target wether lost?

I konw the opencv API have the flag .
such as --> retval, boundingBox = cv.Tracker.update( image )
the flag is retval to find the object wehter lost .

-->Problem ONE :
but the API has a big disadvantages that it cant re-init the Tracker(in other word ,we cant init a tracker 2 times ).
And I need the function (re-init ),so i must use the kcf (joaofaro/KCFcpp),this code has the function ,we can init the Tracker many times.

-->Problem TWO :
so can you give me some advice for how to use the peak_value (just like use the flag in opencv API )?

-->Problem THREE :
uoip/KCFcpp-py-wrapper
I use the python kcf wrappered from c++ kcf ( joao .f).
do you have any good ideas about how can I find the target wether lost?

Some Question related to program logic

Thanks a lot for great code, I still have some questions
Does it use SVM?
What is the “peak value” returned and what is its range?
where actually it look in the new frame for the object?

I believe there are some bugs in the code

in kcftracker.py：
209-213 should be to make sure self._tmpl_sz can be divisible by self.cell_size：
if(self._hogfeatures):
self._tmpl_sz[0] = int(self._tmpl_sz[0] / (2self.cell_size)) * 2self.cell_size + 2self.cell_size
self._tmpl_sz[1] = int(self._tmpl_sz[1] / (2self.cell_size)) * 2self.cell_size + 2self.cell_size
else:
self._tmpl_sz[0] = int(self._tmpl_sz[0] / 2) * 2
self._tmpl_sz[1] = int(self._tmpl_sz[1] / 2) * 2

TypeError: 'map' object is not subscriptable

Traceback (most recent call last):
File "run.py", line 75, in
tracker.init([ix,iy,w,h], frame)
File "/home/hp/zjc/nk_PyCharm/PyCharm_project/nk_KCF/3_uoip-KCFpy
/KCFpy/kcftracker.py", line 269, in init
self._tmpl = self.getFeatures(image, 1)
File "/home/hp/zjc/nk_PyCharm/PyCharm_project/nk_KCF/3_uoip-KCFpy
/KCFpy/kcftracker.py", line 185, in getFeatures
cx = self._roi[0] + self._roi[2]/2 #float
TypeError: 'map' object is not subscriptable

Faster version (Dual Correlation Filter - DCF)

Hi, thank you for sharing this awesome python implementation.

Can you please implement the DCF function in python?
In the paper, the author clams that DCF is 70% faster than KCF, with only little accuracy lose.

The original matlab code does implement both KCF & DCF
http://www.robots.ox.ac.uk/~joao/circulant/

It seems that there is only slightly changes between 2 correlation functions.

It is not tracking anything, it is just front camera is opened

AssertionError

hello,when i change the code designed by python2 to python3 ,there is the following ploblem:

assert(cutWindow[2]>0 and cutWindow[3]>0)
AssertionError

So,how can i deal with this problem?
thank you!

The gap between KCFcpp and KCFpy?

I have run your code, but I found the performance has some gap between these cpp and py version.

1. the cpp version has a better scale invariance
2. the py version is easily tend to lose target

and I still run some other version of cpp code such as vojirt/kcf and foolwood/KCF, they remain the same problems.

Do you have worked out this weird problem?

A bug about @jit decorator in numba.

Hello, I met a problem when running the code as follows:TypeError: _jit_decorator() takes exactly 1 argument (7 given). But I have checked the fhog.py and find the func1 does provide 7 arguments. Does anybody have some idea and help me?

slice indices must be integers

Traceback (most recent call last):
File "/Users/guomuqi/Downloads/KCFpy-master/run.py", line 75, in
tracker.init([ix,iy,w,h], frame)
File "/Users/guomuqi/Downloads/KCFpy-master/kcftracker.py", line 274, in init
self.train(self.tmpl, 1.0)
File "/Users/guomuqi/Downloads/KCFpy-master/kcftracker.py", line 261, in train
k = self.gaussianCorrelation(x, x)
File "/Users/guomuqi/Downloads/KCFpy-master/kcftracker.py", line 171, in gaussianCorrelation
c = rearrange(c)
File "/Users/guomuqi/Downloads/KCFpy-master/kcftracker.py", line 38, in rearrange
img[0:yh,0:xh], img_[yh:img.shape[0],xh:img.shape[1]] = img[yh:img.shape[0],xh:img.shape[1]], img[0:yh,0:xh]
TypeError: slice indices must be integers or None or have an index method

Did anyone else run into this problem?