From 7ac0322b22bdb17433f659a3c0859b25e7540c78 Mon Sep 17 00:00:00 2001 From: zhxing Date: Wed, 23 May 2018 10:22:01 +0800 Subject: [PATCH] =?UTF-8?q?KCF=20python=E7=89=88=E6=9C=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 先放到这里，可能需要用 --- KCFpy-master/LICENSE | 21 +++ KCFpy-master/README.md | 38 +++++ KCFpy-master/fhog.py | 308 ++++++++++++++++++++++++++++++++++++ KCFpy-master/kcftracker.py | 316 +++++++++++++++++++++++++++++++++++++ KCFpy-master/run.py | 97 ++++++++++++ 5 files changed, 780 insertions(+) create mode 100644 KCFpy-master/LICENSE create mode 100644 KCFpy-master/README.md create mode 100644 KCFpy-master/fhog.py create mode 100644 KCFpy-master/kcftracker.py create mode 100644 KCFpy-master/run.py diff --git a/KCFpy-master/LICENSE b/KCFpy-master/LICENSE new file mode 100644 index 0000000..e6ba000 --- /dev/null +++ b/KCFpy-master/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2016 uoip + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/KCFpy-master/README.md b/KCFpy-master/README.md new file mode 100644 index 0000000..b77ec80 --- /dev/null +++ b/KCFpy-master/README.md @@ -0,0 +1,38 @@ +# KCF tracker in Python + +Python implementation of +> [High-Speed Tracking with Kernelized Correlation Filters](http://www.robots.ox.ac.uk/~joao/publications/henriques_tpami2015.pdf)
+> J. F. Henriques, R. Caseiro, P. Martins, J. Batista
+> TPAMI 2015 + +It is translated from [KCFcpp](https://github.com/joaofaro/KCFcpp) (Authors: Joao Faro, Christian Bailer, Joao F. Henriques), a C++ implementation of Kernelized Correlation Filters. Find more references and code of KCF at http://www.robots.ox.ac.uk/~joao/circulant/ + +### Requirements +- Python 2.7 +- NumPy +- Numba (needed if you want to use the hog feature) +- OpenCV (ensure that you can `import cv2` in python) + +Actually, I have installed Anaconda(for Python 2.7), and OpenCV 3.1(from [opencv.org](http://opencv.org/)). + +### Use +Download the sources and execute +```shell +git clone https://github.com/uoip/KCFpy.git +cd KCFpy +python run.py +``` +It will open the default camera of your computer, you can also open a different camera or a video +```shell +python run.py 2 +``` +```shell +python run.py ./test.avi +``` +Try different options (hog/gray, fixed/flexible window, singlescale/multiscale) of KCF tracker by modifying the arguments in line `tracker = kcftracker.KCFTracker(False, True, False) # hog, fixed_window, multiscale` in run.py. + + +### Peoblem +I have struggled to make this python implementation as fast as possible, but it's still 2 ~ 3 times slower than its C++ counterpart, furthermore, the use of Numba introduce some unpleasant delay when initializing tracker (***NEW:*** the problem has been solved in [KCFnb](https://github.com/uoip/KCFnb) by using AOT compilation). + +***NEWER:*** I write a python wrapper for KCFcpp, see [KCFcpp-py-wrapper](https://github.com/uoip/KCFcpp-py-wrapper), so we can benefit from C++'s speed in python now. diff --git a/KCFpy-master/fhog.py b/KCFpy-master/fhog.py new file mode 100644 index 0000000..3e2c48d --- /dev/null +++ b/KCFpy-master/fhog.py @@ -0,0 +1,308 @@ +import numpy as np +import cv2 +from numba import jit + +# constant +NUM_SECTOR = 9 +FLT_EPSILON = 1e-07 + + +@jit +def func1(dx, dy, boundary_x, boundary_y, height, width, numChannels): + r = np.zeros((height, width), np.float32) + alfa = np.zeros((height, width, 2), np.int) + + for j in xrange(1, height-1): + for i in xrange(1, width-1): + c = 0 + x = dx[j, i, c] + y = dy[j, i, c] + r[j, i] = np.sqrt(x*x + y*y) + + for ch in xrange(1, numChannels): + tx = dx[j, i, ch] + ty = dy[j, i, ch] + magnitude = np.sqrt(tx*tx + ty*ty) + if(magnitude > r[j, i]): + r[j, i] = magnitude + c = ch + x = tx + y = ty + + mmax = boundary_x[0]*x + boundary_y[0]*y + maxi = 0 + + for kk in xrange(0, NUM_SECTOR): + dotProd = boundary_x[kk]*x + boundary_y[kk]*y + if(dotProd > mmax): + mmax = dotProd + maxi = kk + elif(-dotProd > mmax): + mmax = -dotProd + maxi = kk + NUM_SECTOR + + alfa[j, i, 0] = maxi % NUM_SECTOR + alfa[j, i, 1] = maxi + return r, alfa + +@jit +def func2(dx, dy, boundary_x, boundary_y, r, alfa, nearest, w, k, height, width, sizeX, sizeY, p, stringSize): + mapp = np.zeros((sizeX*sizeY*p), np.float32) + for i in xrange(sizeY): + for j in xrange(sizeX): + for ii in xrange(k): + for jj in xrange(k): + if((i * k + ii > 0) and (i * k + ii < height - 1) and (j * k + jj > 0) and (j * k + jj < width - 1)): + mapp[i*stringSize + j*p + alfa[k*i+ii,j*k+jj,0]] += r[k*i+ii,j*k+jj] * w[ii,0] * w[jj,0] + mapp[i*stringSize + j*p + alfa[k*i+ii,j*k+jj,1] + NUM_SECTOR] += r[k*i+ii,j*k+jj] * w[ii,0] * w[jj,0] + if((i + nearest[ii] >= 0) and (i + nearest[ii] <= sizeY - 1)): + mapp[(i+nearest[ii])*stringSize + j*p + alfa[k*i+ii,j*k+jj,0]] += r[k*i+ii,j*k+jj] * w[ii,1] * w[jj,0] + mapp[(i+nearest[ii])*stringSize + j*p + alfa[k*i+ii,j*k+jj,1] + NUM_SECTOR] += r[k*i+ii,j*k+jj] * w[ii,1] * w[jj,0] + if((j + nearest[jj] >= 0) and (j + nearest[jj] <= sizeX - 1)): + mapp[i*stringSize + (j+nearest[jj])*p + alfa[k*i+ii,j*k+jj,0]] += r[k*i+ii,j*k+jj] * w[ii,0] * w[jj,1] + mapp[i*stringSize + (j+nearest[jj])*p + alfa[k*i+ii,j*k+jj,1] + NUM_SECTOR] += r[k*i+ii,j*k+jj] * w[ii,0] * w[jj,1] + if((i + nearest[ii] >= 0) and (i + nearest[ii] <= sizeY - 1) and (j + nearest[jj] >= 0) and (j + nearest[jj] <= sizeX - 1)): + mapp[(i+nearest[ii])*stringSize + (j+nearest[jj])*p + alfa[k*i+ii,j*k+jj,0]] += r[k*i+ii,j*k+jj] * w[ii,1] * w[jj,1] + mapp[(i+nearest[ii])*stringSize + (j+nearest[jj])*p + alfa[k*i+ii,j*k+jj,1] + NUM_SECTOR] += r[k*i+ii,j*k+jj] * w[ii,1] * w[jj,1] + return mapp + +@jit +def func3(partOfNorm, mappmap, sizeX, sizeY, p, xp, pp): + newData = np.zeros((sizeY*sizeX*pp), np.float32) + for i in xrange(1, sizeY+1): + for j in xrange(1, sizeX+1): + pos1 = i * (sizeX+2) * xp + j * xp + pos2 = (i-1) * sizeX * pp + (j-1) * pp + + valOfNorm = np.sqrt(partOfNorm[(i )*(sizeX + 2) + (j )] + + partOfNorm[(i )*(sizeX + 2) + (j + 1)] + + partOfNorm[(i + 1)*(sizeX + 2) + (j )] + + partOfNorm[(i + 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON + newData[pos2:pos2+p] = mappmap[pos1:pos1+p] / valOfNorm + newData[pos2+4*p:pos2+6*p] = mappmap[pos1+p:pos1+3*p] / valOfNorm + + valOfNorm = np.sqrt(partOfNorm[(i )*(sizeX + 2) + (j )] + + partOfNorm[(i )*(sizeX + 2) + (j + 1)] + + partOfNorm[(i - 1)*(sizeX + 2) + (j )] + + partOfNorm[(i - 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON + newData[pos2+p:pos2+2*p] = mappmap[pos1:pos1+p] / valOfNorm + newData[pos2+6*p:pos2+8*p] = mappmap[pos1+p:pos1+3*p] / valOfNorm + + valOfNorm = np.sqrt(partOfNorm[(i )*(sizeX + 2) + (j )] + + partOfNorm[(i )*(sizeX + 2) + (j - 1)] + + partOfNorm[(i + 1)*(sizeX + 2) + (j )] + + partOfNorm[(i + 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON + newData[pos2+2*p:pos2+3*p] = mappmap[pos1:pos1+p] / valOfNorm + newData[pos2+8*p:pos2+10*p] = mappmap[pos1+p:pos1+3*p] / valOfNorm + + valOfNorm = np.sqrt(partOfNorm[(i )*(sizeX + 2) + (j )] + + partOfNorm[(i )*(sizeX + 2) + (j - 1)] + + partOfNorm[(i - 1)*(sizeX + 2) + (j )] + + partOfNorm[(i - 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON + newData[pos2+3*p:pos2+4*p] = mappmap[pos1:pos1+p] / valOfNorm + newData[pos2+10*p:pos2+12*p] = mappmap[pos1+p:pos1+3*p] / valOfNorm + return newData + +@jit +def func4(mappmap, p, sizeX, sizeY, pp, yp, xp, nx, ny): + newData = np.zeros((sizeX*sizeY*pp), np.float32) + for i in xrange(sizeY): + for j in xrange(sizeX): + pos1 = (i*sizeX + j) * p + pos2 = (i*sizeX + j) * pp + + for jj in xrange(2 * xp): # 2*9 + newData[pos2 + jj] = np.sum(mappmap[pos1 + yp*xp + jj : pos1 + 3*yp*xp + jj : 2*xp]) * ny + for jj in xrange(xp): # 9 + newData[pos2 + 2*xp + jj] = np.sum(mappmap[pos1 + jj : pos1 + jj + yp*xp : xp]) * ny + for ii in xrange(yp): # 4 + newData[pos2 + 3*xp + ii] = np.sum(mappmap[pos1 + yp*xp + ii*xp*2 : pos1 + yp*xp + ii*xp*2 + 2*xp]) * nx + return newData + + + +def getFeatureMaps(image, k, mapp): + kernel = np.array([[-1., 0., 1.]], np.float32) + + height = image.shape[0] + width = image.shape[1] + assert(image.ndim==3 and image.shape[2]) + numChannels = 3 #(1 if image.ndim==2 else image.shape[2]) + + sizeX = width / k + sizeY = height / k + px = 3 * NUM_SECTOR + p = px + stringSize = sizeX * p + + mapp['sizeX'] = sizeX + mapp['sizeY'] = sizeY + mapp['numFeatures'] = p + mapp['map'] = np.zeros((mapp['sizeX']*mapp['sizeY']*mapp['numFeatures']), np.float32) + + dx = cv2.filter2D(np.float32(image), -1, kernel) # np.float32(...) is necessary + dy = cv2.filter2D(np.float32(image), -1, kernel.T) + + arg_vector = np.arange(NUM_SECTOR+1).astype(np.float32) * np.pi / NUM_SECTOR + boundary_x = np.cos(arg_vector) + boundary_y = np.sin(arg_vector) + + ''' + ### original implementation + r, alfa = func1(dx, dy, boundary_x, boundary_y, height, width, numChannels) #func1 without @jit ### + + ### 40x speedup + magnitude = np.sqrt(dx**2 + dy**2) + r = np.max(magnitude, axis=2) + c = np.argmax(magnitude, axis=2) + idx = (np.arange(c.shape[0])[:,np.newaxis], np.arange(c.shape[1]), c) + x, y = dx[idx], dy[idx] + + dotProd = x[:,:,np.newaxis] * boundary_x[np.newaxis,np.newaxis,:] + y[:,:,np.newaxis] * boundary_y[np.newaxis,np.newaxis,:] + dotProd = np.concatenate((dotProd, -dotProd), axis=2) + maxi = np.argmax(dotProd, axis=2) + alfa = np.dstack((maxi % NUM_SECTOR, maxi)) ### + ''' + ### 200x speedup + r, alfa = func1(dx, dy, boundary_x, boundary_y, height, width, numChannels) #with @jit + ### ~0.001s + + nearest = np.ones((k), np.int) + nearest[0:k/2] = -1 + + w = np.zeros((k, 2), np.float32) + a_x = np.concatenate((k/2 - np.arange(k/2) - 0.5, np.arange(k/2,k) - k/2 + 0.5)).astype(np.float32) + b_x = np.concatenate((k/2 + np.arange(k/2) + 0.5, -np.arange(k/2,k) + k/2 - 0.5 + k)).astype(np.float32) + w[:, 0] = 1.0 / a_x * ((a_x*b_x) / (a_x+b_x)) + w[:, 1] = 1.0 / b_x * ((a_x*b_x) / (a_x+b_x)) + + ''' + ### original implementation + mapp['map'] = func2(dx, dy, boundary_x, boundary_y, r, alfa, nearest, w, k, height, width, sizeX, sizeY, p, stringSize) #func2 without @jit ### + ''' + ### 500x speedup + mapp['map'] = func2(dx, dy, boundary_x, boundary_y, r, alfa, nearest, w, k, height, width, sizeX, sizeY, p, stringSize) #with @jit + ### ~0.001s + + return mapp + + +def normalizeAndTruncate(mapp, alfa): + sizeX = mapp['sizeX'] + sizeY = mapp['sizeY'] + + p = NUM_SECTOR + xp = NUM_SECTOR * 3 + pp = NUM_SECTOR * 12 + + ''' + ### original implementation + partOfNorm = np.zeros((sizeY*sizeX), np.float32) + + for i in xrange(sizeX*sizeY): + pos = i * mapp['numFeatures'] + partOfNorm[i] = np.sum(mapp['map'][pos:pos+p]**2) ### + ''' + ### 50x speedup + idx = np.arange(0, sizeX*sizeY*mapp['numFeatures'], mapp['numFeatures']).reshape((sizeX*sizeY, 1)) + np.arange(p) + partOfNorm = np.sum(mapp['map'][idx] ** 2, axis=1) ### ~0.0002s + + sizeX, sizeY = sizeX-2, sizeY-2 + + + ''' + ### original implementation + newData = func3(partOfNorm, mapp['map'], sizeX, sizeY, p, xp, pp) #func3 without @jit ### + + ### 30x speedup + newData = np.zeros((sizeY*sizeX*pp), np.float32) + idx = (np.arange(1,sizeY+1)[:,np.newaxis] * (sizeX+2) + np.arange(1,sizeX+1)).reshape((sizeY*sizeX, 1)) # much faster than it's List Comprehension counterpart (see next line) + #idx = np.array([[i*(sizeX+2) + j] for i in xrange(1,sizeY+1) for j in xrange(1,sizeX+1)]) + pos1 = idx * xp + pos2 = np.arange(sizeY*sizeX)[:,np.newaxis] * pp + + valOfNorm1 = np.sqrt(partOfNorm[idx] + partOfNorm[idx+1] + partOfNorm[idx+sizeX+2] + partOfNorm[idx+sizeX+2+1]) + FLT_EPSILON + valOfNorm2 = np.sqrt(partOfNorm[idx] + partOfNorm[idx+1] + partOfNorm[idx-sizeX-2] + partOfNorm[idx+sizeX-2+1]) + FLT_EPSILON + valOfNorm3 = np.sqrt(partOfNorm[idx] + partOfNorm[idx-1] + partOfNorm[idx+sizeX+2] + partOfNorm[idx+sizeX+2-1]) + FLT_EPSILON + valOfNorm4 = np.sqrt(partOfNorm[idx] + partOfNorm[idx-1] + partOfNorm[idx-sizeX-2] + partOfNorm[idx+sizeX-2-1]) + FLT_EPSILON + + map1 = mapp['map'][pos1 + np.arange(p)] + map2 = mapp['map'][pos1 + np.arange(p,3*p)] + + newData[pos2 + np.arange(p)] = map1 / valOfNorm1 + newData[pos2 + np.arange(4*p,6*p)] = map2 / valOfNorm1 + newData[pos2 + np.arange(p,2*p)] = map1 / valOfNorm2 + newData[pos2 + np.arange(6*p,8*p)] = map2 / valOfNorm2 + newData[pos2 + np.arange(2*p,3*p)] = map1 / valOfNorm3 + newData[pos2 + np.arange(8*p,10*p)] = map2 / valOfNorm3 + newData[pos2 + np.arange(3*p,4*p)] = map1 / valOfNorm4 + newData[pos2 + np.arange(10*p,12*p)] = map2 / valOfNorm4 ### + ''' + ### 30x speedup + newData = func3(partOfNorm, mapp['map'], sizeX, sizeY, p, xp, pp) #with @jit + ### + + # truncation + newData[newData > alfa] = alfa + + mapp['numFeatures'] = pp + mapp['sizeX'] = sizeX + mapp['sizeY'] = sizeY + mapp['map'] = newData + + return mapp + + +def PCAFeatureMaps(mapp): + sizeX = mapp['sizeX'] + sizeY = mapp['sizeY'] + + p = mapp['numFeatures'] + pp = NUM_SECTOR * 3 + 4 + yp = 4 + xp = NUM_SECTOR + + nx = 1.0 / np.sqrt(xp*2) + ny = 1.0 / np.sqrt(yp) + + ''' + ### original implementation + newData = func4(mapp['map'], p, sizeX, sizeY, pp, yp, xp, nx, ny) #func without @jit ### + + ### 7.5x speedup + newData = np.zeros((sizeX*sizeY*pp), np.float32) + idx1 = np.arange(2*xp).reshape((2*xp, 1)) + np.arange(xp*yp, 3*xp*yp, 2*xp) + idx2 = np.arange(xp).reshape((xp, 1)) + np.arange(0, xp*yp, xp) + idx3 = np.arange(0, 2*xp*yp, 2*xp).reshape((yp, 1)) + np.arange(xp*yp, xp*yp+2*xp) + + for i in xrange(sizeY): + for j in xrange(sizeX): + pos1 = (i*sizeX + j) * p + pos2 = (i*sizeX + j) * pp + + newData[pos2 : pos2+2*xp] = np.sum(mapp['map'][pos1 + idx1], axis=1) * ny + newData[pos2+2*xp : pos2+3*xp] = np.sum(mapp['map'][pos1 + idx2], axis=1) * ny + newData[pos2+3*xp : pos2+3*xp+yp] = np.sum(mapp['map'][pos1 + idx3], axis=1) * nx ### + + ### 120x speedup + newData = np.zeros((sizeX*sizeY*pp), np.float32) + idx01 = (np.arange(0,sizeX*sizeY*pp,pp)[:,np.newaxis] + np.arange(2*xp)).reshape((sizeX*sizeY*2*xp)) + idx02 = (np.arange(0,sizeX*sizeY*pp,pp)[:,np.newaxis] + np.arange(2*xp,3*xp)).reshape((sizeX*sizeY*xp)) + idx03 = (np.arange(0,sizeX*sizeY*pp,pp)[:,np.newaxis] + np.arange(3*xp,3*xp+yp)).reshape((sizeX*sizeY*yp)) + + idx11 = (np.arange(0,sizeX*sizeY*p,p)[:,np.newaxis] + np.arange(2*xp)).reshape((sizeX*sizeY*2*xp, 1)) + np.arange(xp*yp, 3*xp*yp, 2*xp) + idx12 = (np.arange(0,sizeX*sizeY*p,p)[:,np.newaxis] + np.arange(xp)).reshape((sizeX*sizeY*xp, 1)) + np.arange(0, xp*yp, xp) + idx13 = (np.arange(0,sizeX*sizeY*p,p)[:,np.newaxis] + np.arange(0, 2*xp*yp, 2*xp)).reshape((sizeX*sizeY*yp, 1)) + np.arange(xp*yp, xp*yp+2*xp) + + newData[idx01] = np.sum(mapp['map'][idx11], axis=1) * ny + newData[idx02] = np.sum(mapp['map'][idx12], axis=1) * ny + newData[idx03] = np.sum(mapp['map'][idx13], axis=1) * nx ### + ''' + ### 190x speedup + newData = func4(mapp['map'], p, sizeX, sizeY, pp, yp, xp, nx, ny) #with @jit + ### + + mapp['numFeatures'] = pp + mapp['map'] = newData + + return mapp diff --git a/KCFpy-master/kcftracker.py b/KCFpy-master/kcftracker.py new file mode 100644 index 0000000..dbaf5d8 --- /dev/null +++ b/KCFpy-master/kcftracker.py @@ -0,0 +1,316 @@ +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 = img.shape[1]/2, img.shape[0]/2 + 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]): + 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]0 and pi[1]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 diff --git a/KCFpy-master/run.py b/KCFpy-master/run.py new file mode 100644 index 0000000..729304b --- /dev/null +++ b/KCFpy-master/run.py @@ -0,0 +1,97 @@ +import numpy as np +import cv2 +import sys +from time import time + +import kcftracker + +selectingObject = False +initTracking = False +onTracking = False +ix, iy, cx, cy = -1, -1, -1, -1 +w, h = 0, 0 + +inteval = 1 +duration = 0.01 + +# mouse callback function +def draw_boundingbox(event, x, y, flags, param): + global selectingObject, initTracking, onTracking, ix, iy, cx,cy, w, h + + if event == cv2.EVENT_LBUTTONDOWN: + selectingObject = True + onTracking = False + ix, iy = x, y + cx, cy = x, y + + elif event == cv2.EVENT_MOUSEMOVE: + cx, cy = x, y + + elif event == cv2.EVENT_LBUTTONUP: + selectingObject = False + if(abs(x-ix)>10 and abs(y-iy)>10): + w, h = abs(x - ix), abs(y - iy) + ix, iy = min(x, ix), min(y, iy) + initTracking = True + else: + onTracking = False + + elif event == cv2.EVENT_RBUTTONDOWN: + onTracking = False + if(w>0): + ix, iy = x-w/2, y-h/2 + initTracking = True + + + +if __name__ == '__main__': + + if(len(sys.argv)==1): + cap = cv2.VideoCapture(0) + elif(len(sys.argv)==2): + if(sys.argv[1].isdigit()): # True if sys.argv[1] is str of a nonnegative integer + cap = cv2.VideoCapture(int(sys.argv[1])) + else: + cap = cv2.VideoCapture(sys.argv[1]) + inteval = 30 + else: assert(0), "too many arguments" + + tracker = kcftracker.KCFTracker(True, True, True) # hog, fixed_window, multiscale + #if you use hog feature, there will be a short pause after you draw a first boundingbox, that is due to the use of Numba. + + cv2.namedWindow('tracking') + cv2.setMouseCallback('tracking',draw_boundingbox) + + while(cap.isOpened()): + ret, frame = cap.read() + if not ret: + break + + if(selectingObject): + cv2.rectangle(frame,(ix,iy), (cx,cy), (0,255,255), 1) + elif(initTracking): + cv2.rectangle(frame,(ix,iy), (ix+w,iy+h), (0,255,255), 2) + + tracker.init([ix,iy,w,h], frame) + + initTracking = False + onTracking = True + elif(onTracking): + t0 = time() + boundingbox = tracker.update(frame) + t1 = time() + + boundingbox = map(int, boundingbox) + cv2.rectangle(frame,(boundingbox[0],boundingbox[1]), (boundingbox[0]+boundingbox[2],boundingbox[1]+boundingbox[3]), (0,255,255), 1) + + duration = 0.8*duration + 0.2*(t1-t0) + #duration = t1-t0 + cv2.putText(frame, 'FPS: '+str(1/duration)[:4].strip('.'), (8,20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,255), 2) + + cv2.imshow('tracking', frame) + c = cv2.waitKey(inteval) & 0xFF + if c==27 or c==ord('q'): + break + + cap.release() + cv2.destroyAllWindows()