qurator-spk/sbb_pixelwise_segmentation
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Code Issues Releases Wiki Activity

inference script is added

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vahidrezanezhad 2024-05-07 13:34:03 +02:00
parent 38db3e9289
commit 8d1050ec30
4 changed files with 537 additions and 42 deletions
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30
utils.py
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@ -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)