nodes_available = {2: True}
max_concurrency = 4
for each_node in nodeid_map:
if (each_node == nodeid):
continue
client = lib.FileClient('localhost:' + nodeid_map[each_node], nodeid)
node_client[each_node] = client
images_filepath = '/home/vm1/Desktop/ODM/grpc_stages/node1' #file path of current node images
file_path = images_filepath + '/'
opensfm_config = opensfm_interface.setup_opensfm_config(file_path)
active_number_of_nodes = 2
photos_name = collections.defaultdict(lambda : "None")
photo_list = os.listdir(os.path.join(images_filepath, 'images'))
print(photo_list)
image_sent_nodes = collections.defaultdict(lambda : 'none')
cluster_size = len(photo_list) / active_number_of_nodes # images per node
distance_overlap = 10 # meters overlap of images
#directories created:
#exif
#features
def __init__(self):
self.nodeid = 1
#configuration
# args = config.config()
# args.project_path = "./dataset/images"
self.datapath = 'dataset'
# data = ODMLoadDatasetStage(self.datapath , args, progress=5.0,
# verbose=args.verbose)
#distance to include the image in meters
self.include_distance = 10
#run the dataset layer
#data.run()
print('sfm')
print(self.datapath)
#opensfm configuration
opensfm_config = opensfm_interface.setup_opensfm_config(self.datapath)
#extract metadata
# camera_models = {}
# current_path = '/home/j/ODM-master/grpc_stages/node1/'
# ref_image = ['DJI_0019.JPG', 'DJI_0018.JPG','DJI_0020.JPG','DJI_0021.JPG','DJI_0022.JPG','DJI_0023.JPG','DJI_0024.JPG'
# ,'DJI_0025.JPG','DJI_0026.JPG','DJI_0027.JPG','DJI_0028.JPG','DJI_0029.JPG','DJI_0030.JPG','DJI_0031.JPG','DJI_0032.JPG','DJI_0033.JPG','DJI_0034.JPG','DJI_0035.JPG']
# cand_images = ['DJI_0019.JPG', 'DJI_0018.JPG','DJI_0020.JPG','DJI_0021.JPG','DJI_0022.JPG','DJI_0023.JPG','DJI_0024.JPG'
# ,'DJI_0025.JPG','DJI_0026.JPG','DJI_0027.JPG','DJI_0028.JPG','DJI_0029.JPG','DJI_0030.JPG','DJI_0031.JPG','DJI_0032.JPG','DJI_0033.JPG','DJI_0034.JPG','DJI_0035.JPG']
# for each in ref_image:
# d = opensfm_interface.extract_metadata_image('/home/j/ODM-master/grpc_stages/node1/'+each, opensfm_config)
# if d['camera'] not in camera_models:
# camera = exif.camera_from_exif_metadata(d, opensfm_config)
# camera_models[d['camera']] = camera
# opensfm_interface.save_exif('/home/j/ODM-master/grpc_stages/node1/exif', each,d)
# opensfm_interface.save_camera_models('/home/j/ODM-master/grpc_stages/node1/', camera_models)
# #c = opensfm_interface.extract_metadata_image('/home/j/ODM-master/grpc_stages/node1/DJI_0019.JPG', opensfm_config)
# print(d)
# print(camera_models)
# #opensfm_interface.save_exif('/home/j/ODM-master/grpc_stages/node1/', 'DJI_0019.JPG', c)
# #save the exif metadata to file in a folder
# # send extracted metedata and camera model back
# #feature extraction
# for each in ref_image:
# opensfm_interface.detect(current_path+'features', current_path+each,each ,opensfm_config)
# #opensfm_interface.detect(current_path+'features', current_path+'DJI_0019.JPG','DJI_0019.JPG' ,opensfm_config)
# #feature matching
# pairs_matches, preport = new_matching.match_images(current_path, ref_image, cand_images, opensfm_config)
# new_matching.save_matches(current_path, ref_image, pairs_matches)
# print('matching')
# #create tracks first
# features, colors = tracking.load_features(current_path+'features', ref_image, opensfm_config)
# matches = tracking.load_matches(current_path, ref_image)
# graph = tracking.create_tracks_graph(features, colors, matches,
# opensfm_config)
# opensfm_interface.save_tracks_graph(graph, current_path)
# #reconstruction
# # load tracks graph
# graph = opensfm_interface.load_tracks_graph(current_path)
# report, reconstructions = reconstruction.incremental_reconstruction(current_path, graph, opensfm_config)
# opensfm_interface.save_reconstruction(current_path,reconstructions)
# #opensfm_interface.save_report(io.json_dumps(report), 'reconstruction.json')
# outputs = {}
# photos = []
# from opendm import photo
# from opendm import types
# for each in ref_image:
# photos += [types.ODM_Photo(current_path+each)]
# # get match image sizes
# outputs['undist_image_max_size'] = max(
# gsd.image_max_size(photos, 5.0, current_path+'reconstruction.json'),
# 0.1
# )
# print(outputs)
# #undistort image dataset:
# opensfm_interface.opensfm_undistort(current_path, opensfm_config)
# #export visualsfm
# opensfm_interface.open_export_visualsfm(current_path, opensfm_config)
# #compute depthmaps
# opensfm_interface.open_compute_depthmaps(current_path, opensfm_config)
# #mve stage 1 makescene
# #input compute depthmaps file
# mve_file_path = '/home/j/ODM-master/grpc_stages/node1/mve'
# nvm_file = '/home/j/ODM-master/grpc_stages/node1/undistorted/reconstruction.nvm'
# mve_interface.mve_makescene(nvm_file, mve_file_path, 2)
# #mve stage 2 dense reconstruction
# mve_interface.mve_dense_recon(outputs['undist_image_max_size'], mve_file_path, 2)
# #mve stage 3 scene2pset_path
# mve_model = io.join_paths(mve_file_path, 'mve_dense_point_cloud.ply')
# mve_interface.mve_scene2pset(mve_file_path, mve_model,outputs['undist_image_max_size'],2)
# #mve stage 4 clean_mesh
# mve_interface.mve_cleanmesh(0.6, mve_model, 2)
# # filterpoint cloud
# odm_filterpoints = '/home/j/ODM-master/grpc_stages/node1/filterpoints'
# filterpoint_cloud = io.join_paths(odm_filterpoints, "point_cloud.ply")
# filterpoint_interface.filter_points(odm_filterpoints, mve_model, filterpoint_cloud,2)
# #meshing stage
# odm_mesh_folder= '/home/j/ODM-master/grpc_stages/node1/mesh'
# odm_mesh_ply = io.join_paths(odm_mesh_folder, "odm_mesh.ply")
# mesh_interface.mesh_3d(odm_mesh_folder, odm_mesh_ply, filterpoint_cloud, 2)
# #texturing stage
# mvs_folder= '/home/j/ODM-master/grpc_stages/node1/mvs'
# mvs_texturing.mvs_texturing(odm_mesh_ply, mvs_folder, nvm_file)
#https://stackoverflow.com/questions/45071567/how-to-send-custom-header-metadata-with-python-grpc
class Servicer(sendFile_pb2_grpc.FileServiceServicer):
def __init__(self):
self.dataset_dir = './dataset'
self.tmp_file_name = './dataset2/IMG_2359.JPG'
async def upload(self, request_iterator, context):
# client uploads images to this node
#request iterator is the file iterator through the chuncks
#self.tmp_file_name is the name to save the file chucks to
nodeid = ''
filename = ''
for key, value in context.invocation_metadata():
if (key == 'node-id'):
nodeid = value
#check if there is a dir for the node id
system.mkdir_p(nodeid)
if key == 'filename':
filename = value
print('Received initial metadata: key=%s value=%s' %
(key, value))
#print(os.path.dirname(os.path.abspath(__file__)))
if (nodeid != '' and filename != ''):
save_chunks_to_file(request_iterator,
nodeid + '/images/' + filename)
return sendFile_pb2.UploadStatus(
Message=" Successul ",
Code=sendFile_pb2.UploadStatusCode.Ok)
else:
print('bad node id and bad filename')
# reply = sendFile_pb2.UploadStatus()
# reply.Message = " Failed "
# reply.c
return sendFile_pb2.UploadStatus(
Message=" Failure ",
Code=sendFile_pb2.UploadStatusCode.Failed)
def download(self, request, context):
if request.name:
return get_file_chunks(self.tmp_file_name)
def compute(self, request, context):
taskName = request.taskName
#metadata use image name
#use node id
#opensfm compute
# detect feature
#
# if(taskName == "compute_image_feature"):
# elif(taskName == 'compute_matching_two_images'):0
# elif(taskName == 'compute_'):
# elif(taskName == ''):
return 0
self.server = aio.server()
#self.server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
sendFile_pb2_grpc.add_FileServiceServicer_to_server(
Servicer(), self.server)
neighbor_ip = ['50001', '50002']
self.has_neighbor = defaultdict(lambda: "Node not present.")
# tuple (false as any response from neighbor, filelocation)
neighbor_response = 0 #increment neighbors response as each neighbor respond
for each in neighbor_ip:
self.has_neighbor[each] = (False, "")
self.leader = True