def blockStat(bs, match, indexlist1, indexlist2, image1, image2):
print("match:", len(match), "\t bs:", bs * 2 + 1)
try:
matchtemp = random.sample(match, 500)
except:
matchtemp = match
print("too less sample")
print("extract 1000 points for calculation")
possibilityMatrix = gauss.getGuassPossiblityTensor()
# 正确匹配
midloss_1 = []
for i in range(len(matchtemp)):
mapindex1 = indexlist1[matchtemp[i][0]]
mapindex2 = indexlist2[matchtemp[i][1]]
x1, y1 = decompose(mapindex1, image1.shape)
x2, y2 = decompose(mapindex2, image2.shape) #
image1xmin = max(x1 - bs, 0)
image1ymin = max(y1 - bs, 0)
image2xmin = max(x2 - bs, 0)
image2ymin = max(y2 - bs, 0)
# print(x1,x2,y1,y2)
# get the block
temp1 = image1[image1xmin:(x1 + bs), image1ymin:(y1 + bs)]
temp2 = image2[image2xmin:(x2 + bs), image2ymin:(y2 + bs)]
temp1 = temp1.reshape(-1, 3)
temp2 = temp2.reshape(-1, 3)
# find the color center => Guass seperate
##initialize
tensor1 = np.zeros((256, 256, 256))
tensor2 = np.zeros((256, 256, 256))
##collection information from two small pixel-blocks
for j in range(len(temp1)):
try:
tensor1 = gauss.blockReplace(tensor1, temp1[j],
possibilityMatrix)
except:
print(temp1[j])
for j in range(len(temp2)):
try:
tensor2 = gauss.blockReplace(tensor2, temp2[j],
possibilityMatrix)
except:
print(temp2[j])
tensor1 = tensor1 / (np.sum(tensor1) + 1e-7)
tensor2 = tensor2 / (np.sum(tensor2) + 1e-7)
loss = np.linalg.norm(tensor1.flatten() - tensor2.flatten())
# print("loss",i," ",loss)
midloss_1.append(loss)
plt.hist(midloss_1, bins=100)
plt.ylabel(str(bs * 2 + 1) + 'right match')
#plt.show()
plt.savefig(str(bs * 2 + 1) + 'right match.jpg')
store_vec(str(bs * 2 + 1) + '_right_distribute.txt', midloss_1)
# select
# 错误匹配
midloss_2 = []
for i in range(len(matchtemp)):
mapindex1 = indexlist1[matchtemp[i][0]]
mapindex2 = indexlist2[matchtemp[(i + 3) % len(matchtemp)][1]]
x1, y1 = decompose(mapindex1, image1.shape)
x2, y2 = decompose(mapindex2, image2.shape)
image1xmin = max(x1 - bs, 0)
image1ymin = max(y1 - bs, 0)
image2xmin = max(x2 - bs, 0)
image2ymin = max(y2 - bs, 0)
# print(x1,x2,y1,y2)
# get the block
temp1 = image1[image1xmin:(x1 + bs), image1ymin:(y1 + bs)]
temp2 = image2[image2xmin:(x2 + bs), image2ymin:(y2 + bs)]
# find the color center => Guass seperate
##initialize
tensor1 = np.zeros((256, 256, 256))
tensor2 = np.zeros((256, 256, 256))
temp1 = temp1.reshape(-1, 3)
temp2 = temp2.reshape(-1, 3)
##collection information from two small pixel-blocks
for j in range(len(temp1)):
try:
tensor1 = gauss.blockReplace(tensor1, temp1[j],
possibilityMatrix)
except:
print(temp1[j])
for j in range(len(temp2)):
try:
tensor2 = gauss.blockReplace(tensor2, temp2[j],
possibilityMatrix)
except:
print(temp2[j])
tensor1 = tensor1 / (np.sum(tensor1) + 1e-7)
tensor2 = tensor2 / (np.sum(tensor2) + 1e-7)
loss = np.linalg.norm(tensor1.flatten() - tensor2.flatten())
# print("loss",i," ",loss)
midloss_2.append(loss)
plt.hist(midloss_2, bins=100)
plt.ylabel(str(bs * 2 + 1) + 'wrong match')
# plt.show()
plt.savefig(str(bs * 2 + 1) + 'teeth wrong match.jpg')
store_vec(str(bs * 2 + 1) + '_wrong_distribute.txt', midloss_2)
# 计算区分度
for i in range(99):
gt = np.percentile(np.array(midloss_1), i + 1) # right
wm = np.percentile(np.array(midloss_2), 99 - i) # wrong
if (gt - wm > 0):
print(i)
# print("hopeing",abs(gt-wm))
break
return
def blockStat(bs,match,indexlist1,indexlist2,image1,image2):
print("match:", len(match),"\t bs:",bs*2+1)
j=0############
try:
matchtemp = random.sample(match,500)
except:
matchtemp = match
print("too less sample")
print("extract 1000 points for calculation")
possibilityMatrix = gauss.getGuassPossiblityTensor()
# 正确匹配
colorblocksize = (bs+1)*2
print("right match")
midloss_1 = []
j=0
for i in range(len(matchtemp)):
imagefile = "./true_image/"
mapindex1 = indexlist1[matchtemp[i][0]]
mapindex2 = indexlist2[matchtemp[i][1]]
x1, y1 = decompose(mapindex1, image1.shape)
x2, y2 = decompose(mapindex2, image2.shape) #
image1xmin = max(x1 - bs, 0)
image1ymin = max(y1 - bs, 0)
image2xmin = max(x2 - bs, 0)
image2ymin = max(y2 - bs, 0)
# print(x1,x2,y1,y2)
# get the block
temp1 = image1[image1xmin:(x1 + bs+2), image1ymin:(y1 + bs + 2)]
temp2 = image2[image2xmin:(x2 + bs+2), image2ymin:(y2 + bs + 2)]
if (temp1.shape ==(colorblocksize,colorblocksize,3))&(temp2.shape ==(colorblocksize,colorblocksize,3)):
save_image(imagefile+str(j)+"_1.jpg",temp1)
save_image(imagefile+str(j)+"_2.jpg",temp2)
j=j+1
else:
print("temp1.shape:",temp1.shape)
print("temp2.shape:",temp2.shape)
print("generate "+str(j))
print("wrong match:")
j=0
for i in range(len(matchtemp)):
imagefile = "./wrong_image/"
mapindex1 = indexlist1[matchtemp[i][0]]
mapindex2 = indexlist2[matchtemp[(i + 3) % len(matchtemp)][1]]
x1, y1 = decompose(mapindex1, image1.shape)
x2, y2 = decompose(mapindex2, image2.shape)
image1xmin = max(x1 - bs, 0)
image1ymin = max(y1 - bs, 0)
image2xmin = max(x2 - bs, 0)
image2ymin = max(y2 - bs, 0)
temp1 = image1[image1xmin:(x1 + bs+2), image1ymin:(y1 + bs + 2)]
temp2 = image2[image2xmin:(x2 + bs+2), image2ymin:(y2 + bs + 2)]
if (temp1.shape ==(colorblocksize,colorblocksize,3))&(temp2.shape ==(colorblocksize,colorblocksize,3)):
save_image(imagefile+str(j)+"_1.jpg",temp1)
save_image(imagefile+str(j)+"_2.jpg",temp2)
j=j+1
else:
print("temp1.shape:",temp1.shape)
print("temp2.shape:",temp2.shape)
print("generate "+str(j))
print("end")
return