pos_data.append(pos_tem)
feature_data.append(feature_particle)
patch_data.append(patch_particle)#存放每一帧patch的34*32HOG
count = j-1#有效粒子计数
'''
计算每个有效patch的权重w
'''
res =[]
res.append(feature_particle)
feature_2=np.array(res)
print(feature_2.shape)
tem_w=cnc_rnn.predict(feature_2)
print tem_w
w = 1-((1-tem_w[0][0])**2+(0- tem_w[0][1])**2)**(0.5)
#gc.collect()
print i,j
print w
#print (w)
w_data.append(w)#存放每一帧patch的权重w（未归一化）
wx = pos_tem[0]*w#每一个patch的权重w 乘以 对应patch的x坐标
wy = pos_tem[1]*w#每一个patch的权重w 乘以 对应patch的y坐标
wxim.append(wx)#存放当前im的所有patch带权坐标x
wyim.append(wy)#存放当前im的所有patch带权坐标y
iniw[count] = iniw[count] + w#这里权值向量iniw在无效粒子处权值w为0
import hog
import cv2
import numpy as np
import sample
raw = 16
col = 32
img_dir = './balltrain/test.jpg'
image = cv2.imread(img_dir, cv2.IMREAD_GRAYSCALE)
feature = sample.HOG_feature(image)
res = []
res.append(feature)
feature_2 = np.array(res)
print(image.shape)
print(feature_2.shape)
'''
example:
feature_2=[ [image] [image] [image] ]
[image]=[ [+++++]
[+++++]
[+++++]
]
feature_2.shape=(1,9,32)
'''
import cnc_rnn
prediction = cnc_rnn.predict(feature_2)
print(prediction)
print(prediction[0][0])
except:
print(i)
print(j)
pass
#计算当前im期望位置prex prey，归一化权重
data = np.array(feature_data)
print(data.shape)
print(i)
print(len(feature_particle))
try:
tem_w=cnc_rnn.predict(data)
except:
print(data)
print(data.shape)
print(type(feature_particle))
for each_one in data:
print(type(each_one))
break
#break
#for each_one in tem_w:
#print(each_one)
'''
wsum = 0