def generate_obs(img_num, n_img, db_rows, obs_out, neogeo_out, flann_time,
flann):
t0 = time.time()
ft = f5.get_node('/images/sift_features/img_%d' % img_num)
tret = time.time() - t0
t1 = time.time()
windowed = ft.read_where('size > 0.5')
obs_desc = windowed['descriptor'][-n_img:, :]
obs_desc = obs_desc.astype(np.float32)
tsort = time.time() - t1
t2 = time.time()
idx, dist = flann.nn_index(obs_desc, 2)
d_vec = dist[:, 0] / dist[:, 1]
img_idx = np.where(d_vec < 0.7)[0]
db_idx = idx[img_idx, 0].astype(np.int)
flann_time[img_num] = time.time() - t2
if db_idx.shape[0] > 0:
pid = db_rows[db_idx]['pair_id']
flannid, fidx, flanncount = np.unique(pid,
return_counts=True,
return_index=True)
flannxy = np.array([pdb.unpair(tt) for tt in flannid]).astype(np.int)
img = utils.plot_on_grid(flannxy[:, 0], flannxy[:, 1], flanncount, xb,
yb)
neogeo_out[img_num] = np.copy(img)
obs_likelihood = np.zeros_like(flanncount, dtype=np.float64)
for fii in np.arange(flannid.shape[0]):
obs_likelihood[fii] = (1 -
d_vec[img_idx][pid == flannid[fii]]).sum()
img2 = utils.plot_on_grid(flannxy[:, 0], flannxy[:, 1], obs_likelihood,
xb, yb)
obs_out[img_num] = np.copy(img2)
def generate_obs(img_num, n_img, db_rows, obs_out, neogeo_out, flann_time, flann):
t0 = time.time()
ft = pd.read_hdf('/media/sean/D2F2E7B2F2E798CD/Users/student/AIEoutput2/feat/' + feat_path.iloc[img_num])
ftdesc = bcolz.open('/media/sean/D2F2E7B2F2E798CD/Users/student/AIEoutput2/feat/' + descripath.iloc[img_num], 'r')
tret = time.time() - t0
t1 = time.time()
# Here, we need to make sure that we sort the values by response
n_img = n_img.astype(np.int)
ft = ft.sort_values(by='response', ascending=False).iloc[:n_img]
# Then pull the descriptors by the index
obs_desc = ftdesc[ft.index, :]
# windowedindex = ft[ft['size'] > 0.5].index
# windowed = ftdesc[windowedindex,:]
# #obs_desc = windowed['descriptor'][-n_img:, :]
# n_img = n_img.astype(np.int)
# obs_desc = windowed[-n_img:, :]
## print(n_img)
# print(windowed)
# print(obs_desc)
obs_desc = obs_desc.astype(np.float32)
tsort = time.time() - t1
t2 = time.time()
idx, dist = flann.nn_index(obs_desc, 2)
d_vec = dist[:, 0] / dist[:, 1]
img_idx = np.where(d_vec < 0.7)[0]
# print[img_idx.size]
db_idx = idx[img_idx, 0].astype(np.int)
# print[db_idx.size]
flann_time[img_num] = time.time() - t2
# print(db_idx.shape[0])brisk
if db_idx.shape[0] > 0:
pid = db_rows[db_idx]['pair_id']
# print(1-d_vec[img_idx])
dbfeatwlla = np.array(
(1 - d_vec[img_idx], db_rows[db_idx]['lat'], db_rows[db_idx]['lon'], db_rows[db_idx]['height'])).transpose()
# print(dbfeatwlla)
obs_out[img_num, 0:(dbfeatwlla.shape[0]), :] = np.copy(dbfeatwlla)
print(obs_out[img_num, 0:(dbfeatwlla.shape[0] + 1), :])
# dbloc[img_num][0:dbfeatwlla.shape[0]] = dbfeatwlla
flannid, fidx, flanncount = np.unique(
pid, return_counts=True, return_index=True)
flannxy = np.array([pdb.unpair(tt)
for tt in flannid]).astype(np.int)
img = utils.plot_on_grid(
flannxy[:, 0], flannxy[:, 1], flanncount, xb, yb)
# print(flannid)
# print(flanncount)
neogeo_out[img_num] = np.copy(img)
txmax = dbt.cols.x[int(dbt.colindexes['x'][-1])]
tymin = dbt.cols.y[int(dbt.colindexes['y'][0])]
tymax = dbt.cols.y[int(dbt.colindexes['y'][-1])]
xbounds = np.array((txmin, txmax), dtype=np.int64)
ybounds = np.array((tymin, tymax), dtype=np.int64)
xb, yb = neoextent.pad_grid(xbounds, ybounds)
# Full aggregate tile id
tid, tidcount = np.unique(dbf.root.sift_db.unique_tiles[:],
return_counts=True)
extent = neoextent.SearchExtent(6, xb, yb, tid, tidcount)
# Reshape the observations to fit the grid.
obs_out = np.zeros((obs.shape[0], extent.grid_size, extent.grid_size))
for ii, i_obs in enumerate(obs):
obs_out[ii] = nutils.plot_on_grid(obs_xy[:, 0], obs_xy[:, 1], i_obs,
xb, yb)
neo = core.NeoGeo()
neo.init_px_from_extent(extent)
posterior = np.zeros_like(obs_out)
for ii in np.arange(0, obs_out.shape[0]):
dpos = np.copy(ned_vel[ii, 0:2])
dpos_sig = 0.10 * np.linalg.norm(dpos) * np.ones(2)
neo.motion_model(dpos, dpos_sig)
neo.update(obs_out[ii])
posterior[ii] = np.copy(neo.p_x)
fig = plt.figure(figsize=(10, 10))
ax = plt.gca()
oimg = ax.imshow(neo.p_x, cmap='hot', interpolation='Nearest')
img_resp = windowed['response']
img_sort = np.argsort(img_resp)
obs_desc = windowed['descriptor'][img_sort[-10000:], :]
# obs_desc = windowed['descriptor']
obs_desc = obs_desc.astype(np.float32)
result, dist = flann.nn_index(obs_desc, 1)
obs_words = np.unique(result, return_counts=True)
obs_tf_vec = np.zeros(cluster_centers.shape[0])
obs_tf_vec[obs_words[0]] = obs_words[1]
obs_tf_vec = obs_tf_vec[mask] / obs_tf_vec[mask].sum()
obs_tf_idf = obs_tf_vec * idf_term[mask]
obs_tf_idf = obs_tf_idf / np.linalg.norm(obs_tf_idf)
d2 = 2 - 2 * np.dot(tf_idf_mask, obs_tf_idf)
d3 = np.zeros_like(d2)
d3[np.argsort(d2)[:25]] = 1.0
img = neoutils.plot_on_grid(tidxy[:, 0], tidxy[:, 1], d2, xb, yb)
vocab_out[img_num] = np.copy(img)
print('%d / %d' % (img_num, img_times.nrows))
backup = np.copy(vocab_out)
vocab_out[np.isnan(vocab_out)] = 2.0
vocab_out[vocab_out == 0.0] = 2.0
vocab_out = 2.0 - vocab_out
vocab_out[vocab_out == 0.0] = vocab_out[vocab_out > 0.0].min()
import neogeo.core as core
reload(core)
neo = core.NeoGeo()
neo.init_px_from_extent(extent)
neo.pix_size_m = neoextent.get_average_tile_size(neo.extent)