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kcftracker.py
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import numpy as np
import cv2
import fhog
# ffttools
def fftd(img, backwards=False):
# shape of img can be (m,n), (m,n,1) or (m,n,2)
# in my test, fft provided by numpy and scipy are slower than cv2.dft
return cv2.dft(np.float32(img), flags = ((cv2.DFT_INVERSE | cv2.DFT_SCALE) if backwards else cv2.DFT_COMPLEX_OUTPUT)) # 'flags =' is necessary!
def real(img):
return img[:,:,0]
def imag(img):
return img[:,:,1]
def complexMultiplication(a, b):
res = np.zeros(a.shape, a.dtype)
res[:,:,0] = a[:,:,0]*b[:,:,0] - a[:,:,1]*b[:,:,1]
res[:,:,1] = a[:,:,0]*b[:,:,1] + a[:,:,1]*b[:,:,0]
return res
def complexDivision(a, b):
res = np.zeros(a.shape, a.dtype)
divisor = 1. / (b[:,:,0]**2 + b[:,:,1]**2)
res[:,:,0] = (a[:,:,0]*b[:,:,0] + a[:,:,1]*b[:,:,1]) * divisor
res[:,:,1] = (a[:,:,1]*b[:,:,0] + a[:,:,0]*b[:,:,1]) * divisor
return res
def rearrange(img):
return np.fft.fftshift(img, axes=(0,1))
#assert(img.ndim==2)
#img_ = np.zeros(img.shape, img.dtype)
#xh, yh = int(img.shape[1]/2), int(img.shape[0]/2)
#print(xh,yh,img.shape[0],img.shape[1])
#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]
#img_[0:yh,xh:img.shape[1]], img_[yh:img.shape[0],0:xh] = img[yh:img.shape[0],0:xh], img[0:yh,xh:img.shape[1]]
#return img_
# recttools
def x2(rect):
return rect[0] + rect[2]
def y2(rect):
return rect[1] + rect[3]
def limit(rect, limit):
if(rect[0]+rect[2] > limit[0]+limit[2]):
rect[2] = limit[0]+limit[2]-rect[0]
if(rect[1]+rect[3] > limit[1]+limit[3]):
rect[3] = limit[1]+limit[3]-rect[1]
if(rect[0] < limit[0]):
rect[2] -= (limit[0]-rect[0])
rect[0] = limit[0]
if(rect[1] < limit[1]):
rect[3] -= (limit[1]-rect[1])
rect[1] = limit[1]
if(rect[2] < 0):
rect[2] = 0
if(rect[3] < 0):
rect[3] = 0
return rect
def getBorder(original, limited):
res = [0,0,0,0]
res[0] = limited[0] - original[0]
res[1] = limited[1] - original[1]
res[2] = x2(original) - x2(limited)
res[3] = y2(original) - y2(limited)
assert(np.all(np.array(res) >= 0))
return res
def subwindow(img, window, borderType=cv2.BORDER_CONSTANT):
cutWindow = [x for x in window]
limit(cutWindow, [0,0,img.shape[1],img.shape[0]]) # modify cutWindow
assert(cutWindow[2]>0 and cutWindow[3]>0)
border = getBorder(window, cutWindow)
res = img[cutWindow[1]:cutWindow[1]+cutWindow[3], cutWindow[0]:cutWindow[0]+cutWindow[2]]
if(border != [0,0,0,0]):
res = cv2.copyMakeBorder(res, border[1], border[3], border[0], border[2], borderType)
return res
# KCF tracker
class KCFTracker:
def __init__(self, hog=False, fixed_window=True, multiscale=False):
self.lambdar = 0.0001 # regularization
self.padding = 2.5 # extra area surrounding the target
self.output_sigma_factor = 0.125 # bandwidth of gaussian target
if(hog): # HOG feature
# VOT
self.interp_factor = 0.012 # linear interpolation factor for adaptation
self.sigma = 0.6 # gaussian kernel bandwidth
# TPAMI #interp_factor = 0.02 #sigma = 0.5
self.cell_size = 4 # HOG cell size
self._hogfeatures = True
else: # raw gray-scale image # aka CSK tracker
self.interp_factor = 0.075
self.sigma = 0.2
self.cell_size = 1
self._hogfeatures = False
if(multiscale):
self.template_size = 96 # template size
self.scale_step = 1.05 # scale step for multi-scale estimation
self.scale_weight = 0.96 # to downweight detection scores of other scales for added stability
elif(fixed_window):
self.template_size = 96
self.scale_step = 1
else:
self.template_size = 1
self.scale_step = 1
self._tmpl_sz = [0,0] # cv::Size, [width,height] #[int,int]
self._roi = [0.,0.,0.,0.] # cv::Rect2f, [x,y,width,height] #[float,float,float,float]
self.size_patch = [0,0,0] #[int,int,int]
self._scale = 1. # float
self._alphaf = None # numpy.ndarray (size_patch[0], size_patch[1], 2)
self._prob = None # numpy.ndarray (size_patch[0], size_patch[1], 2)
self._tmpl = None # numpy.ndarray raw: (size_patch[0], size_patch[1]) hog: (size_patch[2], size_patch[0]*size_patch[1])
self.hann = None # numpy.ndarray raw: (size_patch[0], size_patch[1]) hog: (size_patch[2], size_patch[0]*size_patch[1])
def subPixelPeak(self, left, center, right):
divisor = 2*center - right - left #float
return (0 if abs(divisor)<1e-3 else 0.5*(right-left)/divisor)
def createHanningMats(self):
hann2t, hann1t = np.ogrid[0:self.size_patch[0], 0:self.size_patch[1]]
hann1t = 0.5 * (1 - np.cos(2*np.pi*hann1t/(self.size_patch[1]-1)))
hann2t = 0.5 * (1 - np.cos(2*np.pi*hann2t/(self.size_patch[0]-1)))
hann2d = hann2t * hann1t
if(self._hogfeatures):
hann1d = hann2d.reshape(self.size_patch[0]*self.size_patch[1])
self.hann = np.zeros((self.size_patch[2], 1), np.float32) + hann1d
else:
self.hann = hann2d
self.hann = self.hann.astype(np.float32)
def createGaussianPeak(self, sizey, sizex):
syh, sxh = sizey/2, sizex/2
output_sigma = np.sqrt(sizex*sizey) / self.padding * self.output_sigma_factor
mult = -0.5 / (output_sigma*output_sigma)
y, x = np.ogrid[0:sizey, 0:sizex]
y, x = (y-syh)**2, (x-sxh)**2
res = np.exp(mult * (y+x))
return fftd(res)
def gaussianCorrelation(self, x1, x2):
if(self._hogfeatures):
c = np.zeros((self.size_patch[0], self.size_patch[1]), np.float32)
for i in xrange(self.size_patch[2]):
x1aux = x1[i, :].reshape((self.size_patch[0], self.size_patch[1]))
x2aux = x2[i, :].reshape((self.size_patch[0], self.size_patch[1]))
caux = cv2.mulSpectrums(fftd(x1aux), fftd(x2aux), 0, conjB = True)
caux = real(fftd(caux, True))
#caux = rearrange(caux)
c += caux
c = rearrange(c)
else:
c = cv2.mulSpectrums(fftd(x1), fftd(x2), 0, conjB = True) # 'conjB=' is necessary!
c = fftd(c, True)
c = real(c)
c = rearrange(c)
if(x1.ndim==3 and x2.ndim==3):
d = (np.sum(x1[:,:,0]*x1[:,:,0]) + np.sum(x2[:,:,0]*x2[:,:,0]) - 2.0*c) / (self.size_patch[0]*self.size_patch[1]*self.size_patch[2])
elif(x1.ndim==2 and x2.ndim==2):
d = (np.sum(x1*x1) + np.sum(x2*x2) - 2.0*c) / (self.size_patch[0]*self.size_patch[1]*self.size_patch[2])
d = d * (d>=0)
d = np.exp(-d / (self.sigma*self.sigma))
return d
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]):
self._tmpl_sz[0]=int(self._tmpl_sz[0])
self._tmpl_sz[1]=int(self._tmpl_sz[1])
print(self._tmpl_sz)
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
def detect(self, z, x):
k = self.gaussianCorrelation(x, z)
res = real(fftd(complexMultiplication(self._alphaf, fftd(k)), True))
_, pv, _, pi = cv2.minMaxLoc(res) # pv:float pi:tuple of int
p = [float(pi[0]), float(pi[1])] # cv::Point2f, [x,y] #[float,float]
if(pi[0]>0 and pi[0]<res.shape[1]-1):
p[0] += self.subPixelPeak(res[pi[1],pi[0]-1], pv, res[pi[1],pi[0]+1])
if(pi[1]>0 and pi[1]<res.shape[0]-1):
p[1] += self.subPixelPeak(res[pi[1]-1,pi[0]], pv, res[pi[1]+1,pi[0]])
p[0] -= res.shape[1] / 2.
p[1] -= res.shape[0] / 2.
return p, pv
def train(self, x, train_interp_factor):
k = self.gaussianCorrelation(x, x)
alphaf = complexDivision(self._prob, fftd(k)+self.lambdar)
self._tmpl = (1-train_interp_factor)*self._tmpl + train_interp_factor*x
self._alphaf = (1-train_interp_factor)*self._alphaf + train_interp_factor*alphaf
def init(self, roi, image):
self._roi = list(map(float, roi))
assert(roi[2]>0 and roi[3]>0)
self._tmpl = self.getFeatures(image, 1)
self._prob = self.createGaussianPeak(self.size_patch[0], self.size_patch[1])
self._alphaf = np.zeros((self.size_patch[0], self.size_patch[1], 2), np.float32)
self.train(self._tmpl, 1.0)
def update(self, image):
if(self._roi[0]+self._roi[2] <= 0): self._roi[0] = -self._roi[2] + 1
if(self._roi[1]+self._roi[3] <= 0): self._roi[1] = -self._roi[2] + 1
if(self._roi[0] >= image.shape[1]-1): self._roi[0] = image.shape[1] - 2
if(self._roi[1] >= image.shape[0]-1): self._roi[1] = image.shape[0] - 2
cx = self._roi[0] + self._roi[2]/2.
cy = self._roi[1] + self._roi[3]/2.
loc, peak_value = self.detect(self._tmpl, self.getFeatures(image, 0, 1.0))
if(self.scale_step != 1):
# Test at a smaller _scale
new_loc1, new_peak_value1 = self.detect(self._tmpl, self.getFeatures(image, 0, 1.0/self.scale_step))
# Test at a bigger _scale
new_loc2, new_peak_value2 = self.detect(self._tmpl, self.getFeatures(image, 0, self.scale_step))
if(self.scale_weight*new_peak_value1 > peak_value and new_peak_value1>new_peak_value2):
loc = new_loc1
peak_value = new_peak_value1
self._scale /= self.scale_step
self._roi[2] /= self.scale_step
self._roi[3] /= self.scale_step
elif(self.scale_weight*new_peak_value2 > peak_value):
loc = new_loc2
peak_value = new_peak_value2
self._scale *= self.scale_step
self._roi[2] *= self.scale_step
self._roi[3] *= self.scale_step
self._roi[0] = cx - self._roi[2]/2.0 + loc[0]*self.cell_size*self._scale
self._roi[1] = cy - self._roi[3]/2.0 + loc[1]*self.cell_size*self._scale
if(self._roi[0] >= image.shape[1]-1): self._roi[0] = image.shape[1] - 1
if(self._roi[1] >= image.shape[0]-1): self._roi[1] = image.shape[0] - 1
if(self._roi[0]+self._roi[2] <= 0): self._roi[0] = -self._roi[2] + 2
if(self._roi[1]+self._roi[3] <= 0): self._roi[1] = -self._roi[3] + 2
assert(self._roi[2]>0 and self._roi[3]>0)
x = self.getFeatures(image, 0, 1.0)
self.train(x, self.interp_factor)
return self._roi