Comments (5)
Hi,
I need to use Augmentor to process images file-by-file (i am generating artifical dataset and I have to do more operations than just augmenting). Im doing exactly what you posted above, however for
augmentation_pipeline.sample_with_array(np.asarray(img))
(a numpy array) I get error:
return images[0] # Here we return only the first image for the generators.
IndexError: list index out of range
from augmentor.
you can do it like this:
import Augmentor
import numpy as np
from PIL import Image
# Create a Pipeline object without specifying a directory:
p = Augmentor.Pipeline()
# Add operations to the pipeline object as per usual:
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)
And then change your image:
p.sample_with_array((X_train[0].reshape(480,480)))
If it does not work, check this:
#90
from augmentor.
Hi there, the best thing to do would be to pass the single image as an array. So create a pipeline object without specifying an image directory:
import Augmentor
import numpy as np
from PIL import Image
# Create a Pipeline object without specifying a directory:
p = Augmentor.Pipeline()
# Add operations to the pipeline object as per usual:
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)
and then pass an image as an array to the function sample_with_array()
:
img = Image.open("/tmp/test.JPEG")
img_array = np.asarray(img)
img_augmented = p.sample_with_array(img_array)
The image contained in img_augmented
is an image in PIL format.
Admittedly this is not very intuitive, I'll try to fix that for a future version. Also the documentation for this functionality is sparse, I'll fix that too in an upcoming version.
M.
from augmentor.
Thank you.
Look forward to the new version.
from augmentor.
I used p._execute_with_array
instead
img = Image.open("/tmp/test.JPEG")
img_array = np.asarray(img)
img_augmented = p._execute_with_array(img_array)
from augmentor.
Related Issues (20)
- How to read and output uint16 images with Augmentor?
- moudle 'Augmentor' has no attribute 'Pipeline'
- int() argument must be a string, a bytes-like object or a number, not 'Image' HOT 4
- why random_erasing sometimes cause error, but sometimes not. HOT 1
- OSError: cannot write mode RGBA as JPEG HOT 2
- What augments should you use?
- label interpolate issue
- p.ground_truth not work for p.add_operation HOT 3
- Not cropping skewed image to original image size HOT 1
- Random Ereasing areas too big HOT 5
- Question - Does it updated the labels too? HOT 3
- Use for Semantic Segmentation HOT 4
- Output dataset format Labelme JSON
- ValueError: image has wrong mode HOT 1
- ZeroDivisionError: float division by zero while performing augmentor.sample(num_samples)
- DataPipeline使用sample的时候会报错
- Incompatible with numpy >= 1.20 due to use of type aliases. HOT 1
- Rotation degrees are opposite. HOT 3
- Project dependencies may have API risk issues
- fail to build augmentor 0.2.11 due to a minor typo HOT 1
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from augmentor.