I'm writing a network for Image Segmentation. I have my ImageDataGenerator for my masks (which are RGB images with only 0 and 255 as values, black and white) which is:
train_mask_data_gen = ImageDataGenerator(rotation_range=10,
width_shift_range=10,
height_shift_range=10,
zoom_range=0.3,
horizontal_flip=True,
vertical_flip=True,
fill_mode='nearest',#interpolation used for augmenting the image
cval=0,
rescale=1./255)
And flow_from_directory:
train_mask_gen = train_mask_data_gen.flow_from_directory(os.path.join(training_dir, 'masks'),
target_size=(img_h, img_w),
batch_size=bs,
class_mode=None, # Because we have no class subfolders in this case
shuffle=True,
interpolation='nearest',#interpolation used for resizing
#color_mode='grayscale',
seed=SEED)
The code works fine, the only problem is that, when i'm applying data augmentation to the masks, i won't have binary images anymore, but i get some values between 0 and 1 (normalized). For example, if i print my output matrix (the image) i get something like this:
[[0. 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]
...
[1. 1. 1. ]
[1. 1. 1. ]
[1. 1. 1. ]]
...
[[0. 0. 0. ]
[0.3457849 0.3457849 0.3457849 ]
[1. 1. 1. ]
...
[0. 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]]
Which contains also those "extra" values due to augmentation. If i don't apply any augmentation i get binary images as i wanted.
How can i embedd the casting to integer? (in order to get values which are only 0 or 1)
I tried to use the field dtype=int
in the ImageDataGenerator
, but it doesn't do anything, i keep getting the same results.
setting the dtype argument to 'uint8' worked for me:
Original:
datagen = ImageDataGenerator(dtype = 'float32')
val_set = datagen.flow_from_directory(data_dir, batch_size=1, target_size = (257,144))
Output:
[[[ 52. 58. 61.]
[ 53. 53. 61.]
[ 54. 57. 66.]
...
[ 5. 12. 0.]
[ 19. 26. 12.]
[ 1. 15. 0.]]]
New:
datagen = ImageDataGenerator(dtype = 'uint8')
val_set = datagen.flow_from_directory(data_dir, batch_size=1, target_size = (257,144))
output:
[[[ 52 58 61]
[ 53 53 61]
[ 54 57 66]
...
[ 5 12 0]
[ 19 26 12]
[ 1 15 0]]]