Python GetDataSet.get_class_id_to_name_dict 예제들

예제 #1

파일: train_tdid_COMB_mtargets_stepeval.py 프로젝트: chengyangfu/instance_detection

            ]]

#pick which objects to include
#will be further refined by the name_to_id_map loaded later
#excluded_cids = [53,76,78,79,82,86,16,   1,2,18,21,25]
#excluded_cids = [53,76,78,79,82,86,16,33,32,   18,50,79,94,96] #10, 5,11,12,14]
excluded_cids = []
chosen_ids =  [x for x in range(0,32) if x not in excluded_cids]
#val_chosen_ids = [[4,5,17,19,23],[18,50,79,94,96]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing
#val_chosen_ids = [[18,50,79,94,96,5,10,12,14,21],[5,10,12,14,21]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing
val_chosen_ids = [[1,5,9,13,17,21,25,28,29],[28,29]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing

max_difficulty = 4 

#get a map from instance name to id, and back
id_to_name = GetDataSet.get_class_id_to_name_dict(data_path, file_name=id_map_fname)
name_to_id = {}
for cid in id_to_name.keys():
    name_to_id[id_to_name[cid]] = cid


##prepare target images (gather paths to the images)
#
target_images ={} 
#means to subtract from each channel of target image
means = np.array([[[102.9801, 115.9465, 122.7717]]])

#path that holds dirs of all targets
#i.e. target_path/target_0/* has one type of target image for each object
#     target_path/target_1/* has another type of target image
#type of target image can mean different things, 

예제 #2

파일: train_tdid_COMB_iter.py 프로젝트: mike-shvets/instance_detection

    'Gen_004_2',
]

#pick which objects to include
#will be further refined by the name_to_id_map loaded later
#excluded_cids = [53,76,78,79,82,86,16,   1,2,18,21,25]
excluded_cids = [53, 76, 78, 79, 82, 86, 16, 33, 32, 10, 5, 11, 12, 14]
chosen_ids = [x for x in range(0, 111) if x not in excluded_cids]
val_chosen_ids = [[4, 5, 17, 19], [
    5, 11, 12, 14
]]  #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing

max_difficulty = 4

#get a map from instance name to id, and back
id_to_name = GetDataSet.get_class_id_to_name_dict(data_path)
name_to_id = {}
for cid in id_to_name.keys():
    name_to_id[id_to_name[cid]] = cid

##prepare target images (gather paths to the images)
#
target_images = {}
#means to subtract from each channel of target image
means = np.array([[[102.9801, 115.9465, 122.7717]]])

#path that holds dirs of all targets
#i.e. target_path/target_0/* has one type of target image for each object
#     target_path/target_1/* has another type of target image
#type of target image can mean different things,
#probably different type is different view