def assign_matches_vsmany(args, qcx, cx2_kpts, cx2_desc, cx2_rchip_size):
'''Matches cx2_desc[qcx] vs all database descriptors using
Input:
qcx - query chip index
cx2_desc - chip descriptor lookup table
args - class with FLANN index of database descriptors
Output:
cx2_fm - C x Mx2 array of matching feature indexes
cx2_fs - C x Mx1 array of matching feature scores'''
# args = hs.matcher.vsmany_args
#helpers.println('Assigning vsmany feature matches from qcx=%d to %d chips'\ % (qcx, len(cx2_desc)))
isQueryIndexed = True
k_vsmany = args.K + isQueryIndexed
ax2_cx = args.ax2_cx
ax2_fx = args.ax2_fx
ax2_desc = args.ax2_desc
# Find each query descriptor's k+1 nearest neighbors
nn_args = (args, qcx, cx2_kpts, cx2_desc, cx2_rchip_size, k_vsmany+1)
nn_result = vsmany_nearest_neighbors(*nn_args)
(qfx2_ax, qfx2_dists, qfx2_valid) = nn_result
score_fn = scoring_func_map[params.__VSMANY_SCORE_FN__]
qfx2_valid = qfx2_valid[:, 0:k_vsmany]
vote_dists = qfx2_dists[:, 0:k_vsmany]
norm_dists = qfx2_dists[:, k_vsmany] # k+1th descriptor for normalization
# Score the feature matches
qfx2_score = np.array([score_fn(_vdist.T, norm_dists)
for _vdist in vote_dists.T]).T
# Vote using the inverted file
qfx2_cx = ax2_cx[qfx2_ax[:, 0:k_vsmany]]
qfx2_fx = ax2_fx[qfx2_ax[:, 0:k_vsmany]]
# Build feature matches
cx2_fm = [[] for _ in xrange(len(cx2_desc))]
cx2_fs = [[] for _ in xrange(len(cx2_desc))]
nQuery = len(qfx2_ax)
qfx2_qfx = helpers.tiled_range(nQuery, k_vsmany)
#iter_matches = izip(qfx2_qfx.flat, qfx2_cx.flat, qfx2_fx.flat, qfx2_score.flat)
iter_matches = izip(qfx2_qfx[qfx2_valid],
qfx2_cx[qfx2_valid],
qfx2_fx[qfx2_valid],
qfx2_score[qfx2_valid])
for qfx, cx, fx, score in iter_matches:
if qcx == cx:
continue # dont vote for yourself
cx2_fm[cx].append((qfx, fx))
cx2_fs[cx].append(score)
# Convert to numpy
for cx in xrange(len(cx2_desc)):
fm = np.array(cx2_fm[cx], dtype=FM_DTYPE)
fm = fm.reshape(len(fm), 2)
cx2_fm[cx] = fm
for cx in xrange(len(cx2_desc)):
fs = np.array(cx2_fs[cx], dtype=FS_DTYPE)
#fs.shape = (len(fs), 1)
cx2_fs[cx] = fs
cx2_fm = np.array(cx2_fm)
cx2_fs = np.array(cx2_fs)
cx2_score = np.array([np.sum(fs) for fs in cx2_fs])
return cx2_fm, cx2_fs, cx2_score
# Find query's Nearest Neighbors in data
data_flann = quick_flann_index(data)
(qfx2_dx, qfx2_dists) = data_flann.nn_index(query, K, checks=checks)
qx2_nn = data[qfx2_dx]
# get k-reciprocal nearest neighbors max distance
qx2_nn.shape = (nQuery * K, dim)
(nn2_dx, nn2_dists) = data_flann.nn_index(qx2_nn, K, checks=checks)
nn2_data = data[nn2_dx] # data's nearest neighbors
nn2_dists.shape = (nQuery, K, K)
qx2_nn.shape = (nQuery, K, dim)
qfx2_maxdist = nn2_dists.max(2)
# A neighbor is a K reciprocal if you are within the
# max distance of the assigned points K nearest neighbors
isReciprocal = qfx2_dists < qfx2_maxdist
krx2_nn = qx2_nn[isReciprocal]
krx2_qfx = helpers.tiled_range(nQuery, K)[isReciprocal]
krx2_query = query[krx2_qfx]
# Enforce viewable dimensionality
if dim != tdim:
import sklearn.decomposition
print('Plotting pca.transform dimensionality')
pca = sklearn.decomposition.PCA(copy=True, n_components=tdim, whiten=False)
pca.fit(data)
query_ = pca.transform(query)
data_ = pca.transform(data)
nn2_data_ = pca.transform(nn2_data)
qx2_nn_ = pca.transform(qx2_nn)
krx2_query_ = pca.transform(krx2_query)
krx2_nn_ = pca.transform(krx2_nn)
else:
# Find query's Nearest Neighbors in data
data_flann = quick_flann_index(data)
(qfx2_dx, qfx2_dists) = data_flann.nn_index(query, K, checks=checks)
qx2_nn = data[qfx2_dx]
# get k-reciprocal nearest neighbors max distance
qx2_nn.shape = (nQuery*K, dim)
(nn2_dx, nn2_dists) = data_flann.nn_index(qx2_nn, K, checks=checks)
nn2_data = data[nn2_dx] # data's nearest neighbors
nn2_dists.shape = (nQuery, K, K)
qx2_nn.shape = (nQuery, K, dim)
qfx2_maxdist = nn2_dists.max(2)
# A neighbor is a K reciprocal if you are within the
# max distance of the assigned points K nearest neighbors
isReciprocal = qfx2_dists < qfx2_maxdist
krx2_nn = qx2_nn[isReciprocal]
krx2_qfx = helpers.tiled_range(nQuery, K)[isReciprocal]
krx2_query = query[krx2_qfx]
# Enforce viewable dimensionality
if dim != tdim:
import sklearn.decomposition
print('Plotting pca.transform dimensionality')
pca = sklearn.decomposition.PCA(copy=True, n_components=tdim, whiten=False)
pca.fit(data)
query_ = pca.transform(query)
data_ = pca.transform(data)
nn2_data_ = pca.transform(nn2_data)
qx2_nn_ = pca.transform(qx2_nn)
krx2_query_ = pca.transform(krx2_query)
krx2_nn_ = pca.transform(krx2_nn)
