mirror of
https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
synced 2025-06-09 11:50:04 +02:00
inference script is added
This commit is contained in:
parent
38db3e9289
commit
8d1050ec30
4 changed files with 537 additions and 42 deletions
30
utils.py
30
utils.py
|
@ -21,14 +21,14 @@ def return_number_of_total_training_data(path_classes):
|
|||
|
||||
|
||||
|
||||
def generate_data_from_folder_evaluation(path_classes, height, width, n_classes):
|
||||
sub_classes = os.listdir(path_classes)
|
||||
def generate_data_from_folder_evaluation(path_classes, height, width, n_classes, list_classes):
|
||||
#sub_classes = os.listdir(path_classes)
|
||||
#n_classes = len(sub_classes)
|
||||
all_imgs = []
|
||||
labels = []
|
||||
dicts =dict()
|
||||
indexer= 0
|
||||
for sub_c in sub_classes:
|
||||
#dicts =dict()
|
||||
#indexer= 0
|
||||
for indexer, sub_c in enumerate(list_classes):
|
||||
sub_files = os.listdir(os.path.join(path_classes,sub_c ))
|
||||
sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files]
|
||||
#print( os.listdir(os.path.join(path_classes,sub_c )) )
|
||||
|
@ -37,8 +37,8 @@ def generate_data_from_folder_evaluation(path_classes, height, width, n_classes)
|
|||
|
||||
#print( len(sub_labels) )
|
||||
labels = labels + sub_labels
|
||||
dicts[sub_c] = indexer
|
||||
indexer +=1
|
||||
#dicts[sub_c] = indexer
|
||||
#indexer +=1
|
||||
|
||||
|
||||
categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ]
|
||||
|
@ -64,15 +64,15 @@ def generate_data_from_folder_evaluation(path_classes, height, width, n_classes)
|
|||
|
||||
return ret_x/255., ret_y
|
||||
|
||||
def generate_data_from_folder_training(path_classes, batchsize, height, width, n_classes):
|
||||
sub_classes = os.listdir(path_classes)
|
||||
n_classes = len(sub_classes)
|
||||
def generate_data_from_folder_training(path_classes, batchsize, height, width, n_classes, list_classes):
|
||||
#sub_classes = os.listdir(path_classes)
|
||||
#n_classes = len(sub_classes)
|
||||
|
||||
all_imgs = []
|
||||
labels = []
|
||||
dicts =dict()
|
||||
indexer= 0
|
||||
for sub_c in sub_classes:
|
||||
#dicts =dict()
|
||||
#indexer= 0
|
||||
for indexer, sub_c in enumerate(list_classes):
|
||||
sub_files = os.listdir(os.path.join(path_classes,sub_c ))
|
||||
sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files]
|
||||
#print( os.listdir(os.path.join(path_classes,sub_c )) )
|
||||
|
@ -81,8 +81,8 @@ def generate_data_from_folder_training(path_classes, batchsize, height, width, n
|
|||
|
||||
#print( len(sub_labels) )
|
||||
labels = labels + sub_labels
|
||||
dicts[sub_c] = indexer
|
||||
indexer +=1
|
||||
#dicts[sub_c] = indexer
|
||||
#indexer +=1
|
||||
|
||||
ids = np.array(range(len(labels)))
|
||||
random.shuffle(ids)
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue