How to return batches of augmented images in an image preprocessor?
0
$begingroup$
I've written the following class that generates augmented images one by one. However, I'd like to be able to generate batches of 8 images each (or any number really). The way I'm hoping to do it is:
- Create batches of resized images (store them in a tensor, perhaps?)
- Iterate through batches (tensors?) and apply augmentation (the augmenter would return the augmented images)
- Add the augmented images to a batch (new tensor) of their own and yield this new batch
Is this a valid approach and if so, how could I go about doing this? And if not, how else might it be done?
class ImagePreprocessor(object):
def __init__(self, path_to_input_images):
if not os.path.exists(path_to_input_images):
raise ValueError('path(s) doesn't exist!')
self.input_root, self.input_folders, self.input_files = next(os.walk(path_to_input_images))
def load_image_from_file(self, filename):
filepath = os.path.join(self.input_root, filename)
img = skio.imread(filepath) # returns image as ndarray
return img
def create_augmenter(self):
# (augmenter details removed for brevity; I know the augmenter works)
return augmenter
def augment(self, img):
augmenter = self.create_augmenter()
augmented_image = augmenter.augment_image(img)
return augmented_image
def generate_data(self):
images = self.input_files
i = 0
shuffle(images)
while True:
# If all the images have been gone through, shuffle and restart
if i >= len(images):
i = 0
shuffle(images)
# Here is where I would like to create batches of images so that
# I can yield entire batches rather than just one image at a time
im = self.load_image_from_file(images[i])
resized_im = cv2.resize(im,(2000,2000))
aug_img = self.augment(resized_im)
i += 1
yield aug_img
```
data-augmentation image-preprocessing
asked 5 mins ago
cloud_colourscloud_colours
1
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add a comment |
0
$begingroup$
I've written the following class that generates augmented images one by one. However, I'd like to be able to generate batches of 8 images each (or any number really). The way I'm hoping to do it is:
- Create batches of resized images (store them in a tensor, perhaps?)
- Iterate through batches (tensors?) and apply augmentation (the augmenter would return the augmented images)
- Add the augmented images to a batch (new tensor) of their own and yield this new batch
Is this a valid approach and if so, how could I go about doing this? And if not, how else might it be done?
class ImagePreprocessor(object):
def __init__(self, path_to_input_images):
if not os.path.exists(path_to_input_images):
raise ValueError('path(s) doesn't exist!')
self.input_root, self.input_folders, self.input_files = next(os.walk(path_to_input_images))
def load_image_from_file(self, filename):
filepath = os.path.join(self.input_root, filename)
img = skio.imread(filepath) # returns image as ndarray
return img
def create_augmenter(self):
# (augmenter details removed for brevity; I know the augmenter works)
return augmenter
def augment(self, img):
augmenter = self.create_augmenter()
augmented_image = augmenter.augment_image(img)
return augmented_image
def generate_data(self):
images = self.input_files
i = 0
shuffle(images)
while True:
# If all the images have been gone through, shuffle and restart
if i >= len(images):
i = 0
shuffle(images)
# Here is where I would like to create batches of images so that
# I can yield entire batches rather than just one image at a time
im = self.load_image_from_file(images[i])
resized_im = cv2.resize(im,(2000,2000))
aug_img = self.augment(resized_im)
i += 1
yield aug_img
```
data-augmentation image-preprocessing
asked 5 mins ago
cloud_colourscloud_colours
1
New contributor
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
I've written the following class that generates augmented images one by one. However, I'd like to be able to generate batches of 8 images each (or any number really). The way I'm hoping to do it is:
- Create batches of resized images (store them in a tensor, perhaps?)
- Iterate through batches (tensors?) and apply augmentation (the augmenter would return the augmented images)
- Add the augmented images to a batch (new tensor) of their own and yield this new batch
Is this a valid approach and if so, how could I go about doing this? And if not, how else might it be done?
class ImagePreprocessor(object):
def __init__(self, path_to_input_images):
if not os.path.exists(path_to_input_images):
raise ValueError('path(s) doesn't exist!')
self.input_root, self.input_folders, self.input_files = next(os.walk(path_to_input_images))
def load_image_from_file(self, filename):
filepath = os.path.join(self.input_root, filename)
img = skio.imread(filepath) # returns image as ndarray
return img
def create_augmenter(self):
# (augmenter details removed for brevity; I know the augmenter works)
return augmenter
def augment(self, img):
augmenter = self.create_augmenter()
augmented_image = augmenter.augment_image(img)
return augmented_image
def generate_data(self):
images = self.input_files
i = 0
shuffle(images)
while True:
# If all the images have been gone through, shuffle and restart
if i >= len(images):
i = 0
shuffle(images)
# Here is where I would like to create batches of images so that
# I can yield entire batches rather than just one image at a time
im = self.load_image_from_file(images[i])
resized_im = cv2.resize(im,(2000,2000))
aug_img = self.augment(resized_im)
i += 1
yield aug_img
```
data-augmentation image-preprocessing
asked 5 mins ago
cloud_colourscloud_colours
1
New contributor
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I've written the following class that generates augmented images one by one. However, I'd like to be able to generate batches of 8 images each (or any number really). The way I'm hoping to do it is:
- Create batches of resized images (store them in a tensor, perhaps?)
- Iterate through batches (tensors?) and apply augmentation (the augmenter would return the augmented images)
- Add the augmented images to a batch (new tensor) of their own and yield this new batch
Is this a valid approach and if so, how could I go about doing this? And if not, how else might it be done?
class ImagePreprocessor(object):
def __init__(self, path_to_input_images):
if not os.path.exists(path_to_input_images):
raise ValueError('path(s) doesn't exist!')
self.input_root, self.input_folders, self.input_files = next(os.walk(path_to_input_images))
def load_image_from_file(self, filename):
filepath = os.path.join(self.input_root, filename)
img = skio.imread(filepath) # returns image as ndarray
return img
def create_augmenter(self):
# (augmenter details removed for brevity; I know the augmenter works)
return augmenter
def augment(self, img):
augmenter = self.create_augmenter()
augmented_image = augmenter.augment_image(img)
return augmented_image
def generate_data(self):
images = self.input_files
i = 0
shuffle(images)
while True:
# If all the images have been gone through, shuffle and restart
if i >= len(images):
i = 0
shuffle(images)
# Here is where I would like to create batches of images so that
# I can yield entire batches rather than just one image at a time
im = self.load_image_from_file(images[i])
resized_im = cv2.resize(im,(2000,2000))
aug_img = self.augment(resized_im)
i += 1
yield aug_img
```
data-augmentation image-preprocessing
data-augmentation image-preprocessing
asked 5 mins ago
cloud_colourscloud_colours
1
New contributor
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
cloud_colourscloud_colours
1
New contributor
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
cloud_colourscloud_colours
1
New contributor
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
cloud_colourscloud_colours
1
asked 5 mins ago
cloud_colourscloud_colours
1
1
New contributor
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
cloud_colours is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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