def GetPointData(info):
reconstruction = Info.ReadReconstructionData(info)['shots']
reconstruction_data = []
latlon_data = []
frame_lst = []
f = open(Info.GetMatchLstFileName(info), 'r')
for line in f:
line = line[0:-1].split('\t')
frame_name = line[0]
pano_name = line[1]
try:
[x, y, z] = optical_center(reconstruction[frame_name])
except:
continue
lat = float(line[2])
lon = float(line[3])
frame_lst.append(line[0])
reconstruction_data.append([x, y])
latlon_data.append([lat, lon])
f.close()
reconstruction_data = np.array(reconstruction_data, dtype=np.float32)
latlon_data = np.array(latlon_data, dtype=np.float32)
#plt.figure()
#plt.subplot('111')
#plt.plot(reconstruction_data[:,0], reconstruction_data[:,1], 'o')
#plt.show()
return [frame_lst, reconstruction_data, latlon_data]
def RunRANSAC(ID):
print ID
info = Info.GetVideoInfo(ID)
subprocess.call('rm %s' % (Info.GetGCPFileName(info)), shell=True)
[frame_lst, reconstruct_set, latlon_set] = GetPointData(info)
if len(reconstruct_set) == 0 or len(latlon_set) == 0:
return
#try:
[M, model] = RANSAC_2D(reconstruct_set,
latlon_set,
iteration=1000,
tolerance=Info.Config.STEP_TEN_METER)
print M
#except:
# return
if not M is None and not model is None:
reconstruct = Info.ReadReconstructionData(info)['shots']
f = open(Info.GetGCPFileName(info), 'w')
frame_lst = sorted(reconstruct.keys())
for index, frame in enumerate(frame_lst):
[x, y, z] = optical_center(reconstruct[frame])
point = np.dot(M,
np.transpose(np.array([x, y, 1], dtype=np.float32)))
s = '%s\t%f\t%f\n' % (frame, point[0], point[1])
f.write(s)
f.close()
def RansacPointExtract(ID):
info = Info.GetVideoInfo(ID)
f_name = Info.GetTrackFileName(info)
f = open(f_name, 'r')
obj, graph_feature_data, graph_track_data = dataset.load_tracks_graph(f) #####
#print graph_track_data
f.close()
track_nodes, image_nodes = nx.algorithms.bipartite.sets(obj) ####
buf = Info.ReadReconstructionData(info)
image_lst = buf['shots'].keys() #####
reconstruct_data = buf['points'] ####
reconstruct_data_key_lst = reconstruct_data.keys() #####
match_data = np.load(Info.GetMatchResultFileName(info)).item()
#print match_data.keys()
f = open(Info.GetMatchResultPointFileName(info), 'r')
pano_data = json.load(f)
f.close()
#print pano_data.keys()
new_pano_data = {}
for index, key in enumerate(pano_data):
loc_key = pano_data[key].keys()[0]
pano_key = match_data[key].keys()[0]
tmp = np.reshape(pano_data[key][loc_key], (-1,3))
try:
new_pano_data[key][pano_key] = tmp
except:
new_pano_data[key] = {}
new_pano_data[key][pano_key] = tmp
recontruct_point = []
pano_point = []
for index, key in enumerate(match_data):
#key_tmp = key + '.jpg'
key_tmp = key
if key_tmp in image_lst:
pano_key = match_data[key].keys()[0]
for index, image_index in enumerate(match_data[key][pano_key][0]):
if image_index in graph_feature_data[key_tmp]:
track_id = graph_track_data[key_tmp][str(image_index)]
if track_id in reconstruct_data_key_lst:
point1 = reconstruct_data[track_id]['coordinates']
point2 = new_pano_data[key_tmp][pano_key][index, :]
if point2[np.abs(point2) > 10000].size == 0:
recontruct_point.append(point1)
pano_point.append(point2)
recontruct_point = np.array(recontruct_point, dtype=np.float32)
#length = recontruct_point.shape[0]
#recontruct_point = np.array([recontruct_point[:,0], recontruct_point[:,1], recontruct_point[:,2], np.ones([length])], dtype=np.float32)
pano_point = np.array(pano_point, dtype = np.float32)
#pano_point = np.array([pano_point[:,0], pano_point[:,1], pano_point[:,2], np.ones([length])], dtype=np.float32)
np.save(Info.GetMatchResultExtractPointFileName(info), [recontruct_point, pano_point])
print recontruct_point.shape
print pano_point.shape
print '%s extract point finish'%ID
def RunRansac(ID):
print ID
info = Info.GetVideoInfo(ID)
data = np.load(Info.GetMatchResultExtractPointFileName(info))
#data2 = data[1]
#data[0] = data[0][np.abs(data[1]) <= 3000]
#data[1] = data[1][np.abs(data[1]) <= 3000]
#data2 = data[1i]
try:
M, align_data = RANSAC.RANSAC_affine(data[0], data[1], 10000, 5)
except:
return
if M is None:
return
print M
#print np.reshape(M, [12])
try:
M = np.reshape(M, [12]).astype(float)
except:
return
gcp = Info.ReadGCPData(info)
data = Info.ReadReconstructionData(info)
trajectory = {}
for shot in data['shots']:
[x, y, z] = optical_center(data['shots'][shot])
trajectory[shot] = [x, y, z]
data['gcp'] = gcp
#print gcp
data['transformation'] = list(M)
#print type(M[0])
data['trajectory'] = trajectory
#print trajectory
f_name = Info.Get3DRansacFileName(info)
f = open(f_name, 'w')
f.write(json.dumps(data, indent=4))
f.close()
print '%s ransac finish' % ID
