def computescoreandthre(self):
# filter = np.ones(self.leftgridsize)
# self.leftgridkpoints = Func.conv2withstride(self.leftimg, filter, stride=self.leftgridsize, start=None,
# gridnum=self.lgn)
# Func.imagesc(self.leftgridkpoints)
# print((self.leftgridkpoints>1).sum())
#*************测试卷积是否正确*********************
self.check = []
# 计算阈值
self.thre1 = Func.conv2withstride(self.leftimg,
self.kernel[:, :, 0, 0],
stride=self.stride[1:3],
start=self.start,
gridnum=self.lgn)
# De = self.neibor ** 2
# self.thre1 = self.TreshFactor * np.sqrt(self.thre1 / De) # 阈值计算公式
self.check.append(self.thre1.reshape(self.lgn, self.lgn, 1))
self.convset = []
self.cposition = np.asarray([[], [], [], []], np.int32)
self.leftmap = []
# 计算阈值
h, w, c = self.img1.shape
self.convset.append(self.leftimg.reshape(h, w, 1))
# 计算打分
self.score = np.zeros((self.lgn, self.lgn))
self.lgshape = (self.lgn, self.lgn)
nnd = 1
for i in range(self.lgn): # r
for j in range(self.lgn): # c
leftvalue = Func.index2value((i, j), self.lgshape) + 1
bestmatchgrid = self.leftmatchgrid[self.leftimglabel ==
leftvalue]
if bestmatchgrid.size < 100:
continue # 点数小于阈值则不计算，默认为不匹配
number, n_counts = np.unique(bestmatchgrid, return_counts=True)
rbestindex = number[np.argsort(n_counts)[-1]]
index = (self.leftimglabel == leftvalue) & (self.leftmatchgrid
== rbestindex)
neiborsindex = (((self.leftimglabel - self.leftmatchgrid)
== (leftvalue - rbestindex))
& self.leftimg.astype(np.bool)).astype(
np.float32)
self.convset.append(neiborsindex.reshape(h, w, 1))
self.leftmap.append(index)
self.cposition = np.concatenate(
(self.cposition, [[nnd], [i], [j], [0]]), 1)
nnd += 1
neiborsindexconv = Func.conv2withstride(
neiborsindex,
self.kernel[:, :, 0, 0],
stride=self.stride[1:3],
start=self.start,
gridnum=self.lgn)
self.check.append(
neiborsindexconv.reshape(self.lgn, self.lgn, 1))
# self.score[i, j] = neiborsindexconv[i, j]
# if self.DEBUG:
# print("calc grid(%d,%d)\n thre=%f,index.sum=%d,neiborsindex.sum=%d,score=%f"
# % (i, j,self.thre[i,j],index.sum(),neiborsindex.sum(),self.score[i, j]))
# if neiborsindexconv[i, j] < self.thre[i, j]:
# continue
# self.TrueMatches += index
#卷积计算
self.convset = np.asarray(self.convset, np.float32)
if self.shift[0] == 1:
self.convset = np.roll(self.convset,
int(self.leftgridsize[0] / 2),
axis=1)
if self.shift[1] == 1:
self.convset = np.roll(self.convset,
int(self.leftgridsize[1] / 2),
axis=2)
t = time.time()
res = Func.batchconv4d(self.convset,
np.asarray(self.kernel, np.float32),
stride=self.stride)
print("batchsize is %d,batch conv cost %fs" % (nnd, time.time() - t))
tmp = np.asarray(self.check, np.float32) - res
# Func.imagesc(tmp.reshape(self.lgn,self.lgn),'conv sta whin 4D conv and 2Dconv')
print("conv sta whin 4D conv and 2Dconv is:sum %f,mean %f,std %f" %
(tmp.sum(), tmp.mean(), tmp.std()))
self.thre = res[0, :, :, 0]
De = self.neibor**2
self.thre = self.TreshFactor * np.sqrt(self.thre / De) # 阈值计算公式
self.cposition = list(self.cposition)
tmp = res[self.cposition]
sindex = self.cposition[1:3]
for i in range(len(tmp)):
if self.thre[sindex[0][i], sindex[1][i]] > tmp[i]:
continue
self.TrueMatches += self.leftmap[i]
