lib.mve_dense_reconstruction(submodel_path ,max_concurrency, True, file_path)
end = timer()
mve_dense_reconstruction_time = end - start
start = timer()
lib.mve_scene2pset_function(submodel_path, max_concurrency, True, file_path)
end = timer()
mve_mve_scene2pset_time = end - start
start = timer()
lib.mve_cleanmesh_function(submodel_path, max_concurrency)
end = timer()
mve_mve_cleanmesh_time = end - start
start = timer()
lib.odm_filterpoints_function(submodel_path, max_concurrency)
end = timer()
odm_filterpoint_time = end - start
start = timer()
def do_task(job_queue):
while True:
try:
task = job_queue.get() # returns a map
print("task: " + str(task))
if task['clientid'] == task['nodeid']:
print('equals self node id')
response = False
if task['title'] == 'upload_image':
#check if there is a is a submodel folder
print('no need to upload for self')
if task['title'] == 'extract_exif':
#image path, opensfm_config, node_file_path
print('extract exif in self')
# file_path
print(task['node_file_path'])
image_path = os.path.join(task['node_file_path'], 'images')
response = lib.sfm_extract_metadata_list_of_images(image_path, task['opensfm_config'], task['node_file_path'], task['imagelist'])
print('response in self')
print(response)
elif task['title'] == 'detect_features':
print('detect features in task queue in self')
# needs images
# current path, ref images, opensfm_config
current_path = task['node_file_path']
ref_image = os.listdir(current_path + '/images')
response = lib.sfm_detect_features(ref_image, current_path ,task['opensfm_config'], task['imagelist'])
elif task['title'] == 'feature_matching':
print('feature matching')
# needs images
# image path
# list of images
# candidate images
# opensfm config
response = lib.sfm_feature_matching(task['node_file_path'], task['ref_image'], task['cand_images'], task['opensfm_config'])
elif task['title'] == 'feature_matching_pairs':
print('in task here ' + str(task['pair']))
pair = task['pair']
#send the pair of feature files
feature_0 = 'features/' + pair[0] + '.features.npz'
feature_1 = 'features/' + pair[1] + '.features.npz'
#response = task['client'].upload(task['filepath'], [feature_0, feature_1])
exif_0 = 'exif/' + pair[0] + '.exif'
exif_1 = 'exif/' + pair[1] + '.exif'
image_list = [pair[0], pair[1]]
pairs_matches = lib.sfm_feature_matching_pairs(task['filepath'],image_list,image_list ,task['opensfm_config'])
filename = pair[0]+'-'+pair[1]
# save pair matches in a file
# send it to client
opensfm_interface.save_matches(task['filepath'], filename, pairs_matches)
#filename = pair[0]+'-'+pair[1]
matches = opensfm_interface.load_matches(task['filepath'],filename)
#print('load matches')
#print(matches)
task['lock'].acquire()
try:
print('lock acquired')
tup = (pair[0], pair[1])
task['results'].update(matches)
print('updated matches')
except Exception as e:
print(e.message)
pass
task['lock'].release()
print('lock release')
elif task['title'] == 'sfm_create_tracks':
# needs images
# current path
#submodel version
print(task['cluster'])
sending_match_pairs = {}
print(task['results'].keys())
print('here')
for key, value in task['results'].items():
#print(key)
if key[0] in task['cluster'] and key[1] in task['cluster']:
print('key:' + str(key))
sending_match_pairs[key] = value
print(len(sending_match_pairs))
print(sending_match_pairs)
print(os.path.join(task['filepath'], task['submodel_path']))
matches_filepath =os.path.join(task['filepath'], task['submodel_path'])
# maybe save the results in a results file and send it
submodel_path = task['submodel_path']
print(submodel_path)
#opensfm_interface.save_matches(matches_filepath, submodel_matches, sending_match_pairs)
#matches = opensfm_interface.load_matches(matches_filepath, submodel_matches)
#print(matches)
response = lib.sfm_create_tracks(submodel_path, task['cluster'],task['opensfm_config'], sending_match_pairs, True, task['filepath'])
start = timer()
# # need the exifs
lib.sfm_opensfm_reconstruction(submodel_path, opensfm_config, True,task['filepath'] )
end = timer()
sfm_opensfm_reconstruction_time = end - start
start = timer()
lib.sfm_undistort_image(submodel_path, opensfm_config, True, task['filepath'])
end = timer()
sfm_undistort_image_time = end - start
start = timer()
lib.sfm_export_visual_sfm(submodel_path, opensfm_config ,True, task['filepath'])
end = timer()
sfm_export_visualsfm_time = end - start
start = timer()
#test load
udata = opensfm_interface.UndistortedDataSet(task['filepath'], task['opensfm_config'], 'undistorted')
for image_name in task['cluster']:
try:
#print(image_name)
ret = udata.load_raw_depthmap(image_name)
except Exception as e:
print(e.message)
print('image name bad ' + str(image_name))
lib.sfm_compute_depthmaps(submodel_path, opensfm_config, True, task['filepath'])
end = timer()
sfm_compute_depthmaps_time = end - start
# split merge here no problem
start = timer()
max_concurrency = 2
#need images
# delete from makescene
#lib.mve_makescene_function(submodel_path, max_concurrency, True, task['file_path'])
end = timer()
mve_makescene_function_time = end - start
start = timer()
#lib.mve_dense_reconstruction(submodel_path ,max_concurrency, True, task['file_path'])
end = timer()
mve_dense_reconstruction_time = end - start
start = timer()
#lib.mve_scene2pset_function(submodel_path, max_concurrency)
end = timer()
mve_mve_scene2pset_time = end - start
start = timer()
# lib.mve_cleanmesh_function(submodel_path, max_concurrency)
end = timer()
mve_mve_cleanmesh_time = end - start
start = timer()
# lib.odm_filterpoints_function(submodel_path, max_concurrency)
end = timer()
odm_filterpoint_time = end - start
start = timer()
# lib.odm_mesh_function(submodel_path, max_concurrency)
end = timer()
odm_mesh_time = end - start
print('OpenSfm Reconstruction Total Time: ' + str(sfm_opensfm_reconstruction_time))
print('OpenSfm Undistort Image Total Time: ' + str(sfm_undistort_image_time))
print('OpenSfm Export Visual Sfm Total Time: ' + str(sfm_export_visualsfm_time))
print('OpenSfm Compute DepthMaps Sfm Total Time: ' + str(sfm_compute_depthmaps_time))
print('Mve Makescene Sfm Total Time: ' + str(mve_makescene_function_time))
print('Mve Dense Reconstruction Sfm Total Time: ' + str(mve_dense_reconstruction_time))
print('Mve Scene 2 Pset Sfm Total Time: ' + str(mve_mve_scene2pset_time))
print('Mve Cleanmesh Sfm Total Time: ' + str(mve_mve_cleanmesh_time))
print('ODM Filterpoints Total Time: ' + str(odm_filterpoint_time))
print('ODM Mesh Total Time: ' + str(odm_mesh_time))
elif task['title'] == 'opensfm_reconstruction':
# needs create tracks . csv
# return reconstruction.json
response = lib.sfm_opensfm_reconstruction(task['node_file_path'], task['opensfm_config'])
elif task['title'] == 'sfm_undistort_image':
response = lib.sfm_undistort_image(task['node_file_path'], task['opensfm_config'])
elif task['title'] == 'export_visual_sfm':
#nvm file
response = lib.sfm_export_visual_sfm(task['node_file_path'],task['opensfm_config'])
elif task['title'] == 'compute_depthmaps':
response = lib.sfm_compute_depthmaps(task['node_file_path']. task['opensfm_config'])
elif task['title'] == 'dense_reconstruction':
response = lib.mve_dense_reconstruction(task['node_file_path'])
elif task['title'] == 'makescene_function':
response = lib.mve_makescene_function(task['node_file_path'], task['max_concurreny'])
elif task['title'] == 'scene2pset':
response = lib.mve_scene2pset_function(task['node_file_path'], task['max_concurreny'])
elif task['title'] == 'cleanmesh':
response = lib.mve_cleanmesh_function(task['node_file_path'], task['max_concurreny'])
elif task['title'] == 'filterpoints':
response = lib.odm_filterpoints_function(task['node_file_path'], task['max_concurreny'])
elif task['title'] == 'mesh':
response = lib.odm_mesh_function(task['node_file_path'], task['max_concurreny'])
elif task['title'] == 'texturing':
response = lib.odm_texturing_function(task['node_file_path'])
else:
if task['title'] == 'upload_image':
print('upload')
response = task['client'].upload(task['filepath'], task['list'])
print(response)
elif task['title'] == 'extract_exif':
print(str(task))
print('extract exif ')
response = task['client'].sendTask('exif', task['nodeid'], '/exif')
print(response)
elif task['title'] == 'detect_features':
print('detect features in task queue')
# needs images
response = task['client'].sendTask('detect_features', task['nodeid'], '/features')
elif task['title'] == 'feature_matching':
print('feature matching')
# needs images
respone = task['client'].sendTask('feature_matching', task['nodeid'], '/matches')
elif task['title'] == 'sfm_create_tracks':
#needs images
print(task['cluster'])
sending_match_pairs = {}
print(task['results'].keys())
print('here')
for key, value in task['results'].items():
#print(key)
if key[0] in task['cluster'] and key[1] in task['cluster']:
print('key:' + str(key))
sending_match_pairs[key] = value
print(len(sending_match_pairs))
print(sending_match_pairs)
print(os.path.join(task['filepath'], task['submodel_path']))
matches_filepath =os.path.join(task['filepath'], task['submodel_path'])
# maybe save the results in a results file and send it
submodel_matches = task['submodel_path']
print(submodel_matches)
opensfm_interface.save_matches(matches_filepath, submodel_matches, sending_match_pairs)
#matches = opensfm_interface.load_matches(matches_filepath, submodel_matches)
print(matches)
print(' upload matches ')
feature_matches_response = task['client'].upload(matches_filepath, [submodel_matches+'_matches.pkl.gz'], submodel_file='matches')
print('here after feature matches send')
# send features
# send matches
feature_list = []
for image_name in task['cluster']:
feature_0 = 'features/' + image_name + '.features.npz'
feature_list.append(feature_0)
print(' send features ')
response = task['client'].upload(task['filepath'], feature_list, 'feature', task['submodel_path'] )
#send images
response = task['client'].upload(task['filepath'], task['cluster'], 'images', task['submodel_path'])
#send exif
response = task['client'].upload(task['filepath'], task['cluster'], 'exif', task['submodel_path'])
#send cameras.json
response = task['client'].upload(task['filepath'], ['camera_models.json'], 'camera_models', task['submodel_path'] )
#
respone = task['client'].sendTask('create_tracks', task['nodeid'], '/', submodel_path=task['submodel_path'], cluster_images = task['cluster'])
elif task['title'] == 'opensfm_reconstruction':
# needs create tracks . csv
# return reconstruction.json
respone = task['client'].sendTask('opensfm_reconstruction', task['nodeid'], '/')
elif task['title'] == 'sfm_undistort_image':
respone = task['client'].sendTask('undistort_image', task['nodeid'], '/undistorted')
elif task['title'] == 'export_visual_sfm':
#nvm file
respone = task['client'].sendTask('export_visual_sfm', task['nodeid'], '/undistorted')
elif task['title'] == 'compute_depthmaps':
respone = task['client'].sendTask('compute_depthmaps', task['nodeid'], '/depthmaps')
elif task['title'] == 'dense_reconstruction':
respone = task['client'].sendTask('dense_reconstrution', task['nodeid'], '/mve')
elif task['title'] == 'makescene_function':
respone = task['client'].sendTask('makescene_function', task['nodeid'], '/mve')
elif task['title'] == 'scene2pset':
respone = task['client'].sendTask('scene2pset', task['nodeid'], '/mve')
elif task['title'] == 'cleanmesh':
respone = task['client'].sendTask('cleanmesh', task['nodeid'], '/mve')
elif task['title'] == 'filterpoints':
respone = task['client'].sendTask('odm_filterpoints', task['nodeid'], '/filterpoints')
elif task['title'] == 'mesh':
respone = task['client'].sendTask('odm_mesh', task['nodeid'], '/mesh')
elif task['title'] == 'texturing':
respone = task['client'].sendTask('odm_texturing', task['nodeid'], '/mvs')
elif task['title'] == 'feature_matching_pairs':
# send over the pair feature files and exif files
print('in task here ' + str(task['pair']))
pair = task['pair']
#send the pair of feature files
feature_0 = 'features/' + pair[0] + '.features.npz'
feature_1 = 'features/' + pair[1] + '.features.npz'
response = task['client'].upload(task['filepath'], [feature_0, feature_1])
exif_0 = 'exif/' + pair[0] + '.exif'
exif_1 = 'exif/' + pair[1] + '.exif'
response = task['client'].upload(task['filepath'], [exif_0, exif_1])
response = task['client'].sendTask('feature_match_pairs', task['nodeid'], '/matches', [pair[0], pair[1]])
filename = pair[0]+'-'+pair[1]
matches = opensfm_interface.load_matches(task['filepath'],filename)
#print('load matches')
#print(matches)
print('try lock')
task['lock'].acquire()
try:
tup = (pair[0], pair[1])
task['results'].update(matches)
print('lock acquired and reuslt done for send ')
except Exception as e:
print(e.message)
pass
task['lock'].release()
print('lock release for send ')
print('done')
job_queue.task_done()
except Exception as e:
print(e.message)
print(traceback.print_exc())
print()
sys.exit(0)
except KeyboardInterrupt:
sys.exit(0)
lib.mve_dense_reconstruction(current_path, max_concurrency)
end = timer()
mve_dense_reconstruction_time = end - start
start = timer()
lib.mve_scene2pset_function(current_path, max_concurrency)
end = timer()
mve_mve_scene2pset_time = end - start
start = timer()
lib.mve_cleanmesh_function(current_path, max_concurrency)
end = timer()
mve_mve_cleanmesh_time = end - start
start = timer()
lib.odm_filterpoints_function(current_path, max_concurrency)
end = timer()
odm_filterpoint_time = end - start
start = timer()
from opendm import io
images_database_file = io.join_paths(current_path, 'images.json')