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pf_guass_model.py
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pf_guass_model.py
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from cv2 import cv2
import copy
import numpy as np
from numpy.random import *
from skimage.measure import compare_ssim as ssim
drawing = False
ix,iy,w,h= -1, -1,-1,-1
cropped = None
firstFrame = None
the_firstFrame = None
is_cropped = False
particle_num = 100
image_w,image_h = -1,-1
class Particle(object):
def __init__(self,_x = 0,_y = 0,_s = 0):
self.x = _x
self.y = _y
self.s = _s
self.xp = 0
self.yp = 0
self.sp = 0
self.x0 = 0
self.y0 = 0
self.width = 0
self.height = 0
self.weight = 0
def choose_frame(event, x, y, flags, param):
global ix, iy,w,h,drawing,cropped,is_cropped
if event == cv2.EVENT_LBUTTONDOWN:
print('left button down')
drawing = True
ix, iy = x,y
elif event == cv2.EVENT_MOUSEMOVE:
print('mouse move')
if drawing == True:
cv2.rectangle(firstFrame, (ix, iy), (x,y), (0,255,0), -1)
elif event == cv2.EVENT_LBUTTONUP:
print('left button up')
w,h = x - ix,y - iy
drawing = False
cropped = the_firstFrame[iy:y,ix:x]
is_cropped = True
cv2.destroyAllWindows()
cv2.imshow("cropped",cropped)
cv2.waitKey(10)
def add_noise(mat):
mat = mat + np.random.normal(0,3,mat.shape[0] * mat.shape[1]).reshape(mat.shape)
for i in range(particle_num):
mat[i][0] %= image_w
mat[i][1] %= image_h
return mat
def resample(weights):
weights = sorted(weights, reverse = True)
n = len(weights)
indices = []
C = [0.] + [sum(weights[:i+1]) for i in range(n)]
u0,j = random(),0
for u in [(u0 + i) / n for i in range(n)]:
while u > C[j]:
j+=1
indices.append(j - 1)
return indices
def initial_particle():
weights =[]
x = np.array([[ix,iy] for i in range(particle_num)])
x = add_noise(x)
for now_x in x:
lx,ly = int(now_x[0]),int(now_x[1])
if lx + w > image_w or ly + h > image_h:
noisy_sub = the_firstFrame[ly : ly + h,lx: lx + w]
noisy_sub = cv2.resize(noisy_sub, (w,h), interpolation=cv2.INTER_CUBIC)
weights.append(ssim(cropped,noisy_sub,multichannel=True))
else:
noisy_sub = the_firstFrame[ly : ly + h,lx: lx + w]
weights.append(ssim(cropped,noisy_sub,multichannel=True))
weights = weights / sum(weights)
indice = resample(weights)
y = np.array([x[i] for i in indice])
x = y
return x
def particlefilter(x,frame):
weights = []
x = add_noise(x)
for now_x in x:
lx,ly = int(now_x[0]),int(now_x[1])
if lx + w > image_w or ly + h > image_h:
noisy_sub = the_firstFrame[ly : ly + h,lx: lx + w]
noisy_sub = cv2.resize(noisy_sub, (w,h), interpolation=cv2.INTER_CUBIC)
weights.append(ssim(cropped,noisy_sub,multichannel=True))
else:
noisy_sub = frame[ly : ly + h,lx: lx + w]
weights.append(ssim(cropped,noisy_sub,multichannel=True))
weights = weights / sum(weights)
weights = weights / sum(weights)
indice = resample(weights)
y = np.array([x[i] for i in indice])
x = y
return weights,x,frame
if __name__ == "__main__":
capture = cv2.VideoCapture("video/hockey.avi")
is_destroy = False
x = []
if capture.isOpened():
while(True):
ret,prev = capture.read()
if ret == True:
if is_cropped == False:
firstFrame = copy.deepcopy(prev)
the_firstFrame = copy.deepcopy(prev)
image_w,image_h = prev.shape[1],prev.shape[0]
while(is_cropped == False):
cv2.namedWindow("choose_image",flags = 0)
cv2.resizeWindow('choose_image', 1080, 600)
cv2.setMouseCallback('choose_image', choose_frame)
cv2.imshow('choose_image',firstFrame)
cv2.waitKey(10)&0xff
if is_destroy == False :
cv2.waitKey(1000)
cv2.destroyAllWindows()
is_destroy = True
x = initial_particle()
cv2.rectangle(prev, (ix, iy), (ix + w,iy + h), (0,255,0), 1)
cv2.namedWindow('video', flags=0)
cv2.resizeWindow('video', 1080, 600)
cv2.imshow('video',prev)
else:
weights,x,prev = particlefilter(x,prev)
sx,sy = 0,0
for i,now_x in enumerate(x):
sx += now_x[0] * weights[i]
sy += now_x[1] * weights[i]
sx = int(sx)
sy = int(sy)
print(sx,sy)
cv2.rectangle(prev, (sx, sy), (sx + w,sy + h), (0,255,0), 1)
cv2.imshow('video',prev)
else:
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
capture.release()
cv2.destroyAllWindows()