fiftyone.utils.image¶

Image utilities.

Copyright 2017-2022, Voxel51, Inc.
voxel51.com

Functions:

`reencode_image`(input_path, output_path)	Re-encodes the image to the format specified by the given output path.
`reencode_images`(sample_collection[, ext, …])	Re-encodes the images in the sample collection to the given format.
`transform_image`(input_path, output_path[, …])	Transforms the image according to the provided parameters.
`transform_images`(sample_collection[, size, …])	Transforms the images in the sample collection according to the provided parameters.

fiftyone.utils.image.reencode_images(sample_collection, ext='.png', force_reencode=True, media_field='filepath', output_field=None, output_dir=None, rel_dir=None, delete_originals=False, num_workers=None, skip_failures=False)¶

Re-encodes the images in the sample collection to the given format.

Note

This method will not update the metadata field of the collection after transforming. You can repopulate the metadata field if needed by calling:

sample_collection.compute_metadata(overwrite=True)

Parameters

sample_collection – a fiftyone.core.collections.SampleCollection
ext (".png") – the image format to use (e.g., “.png” or “.jpg”)
force_reencode (True) – whether to re-encode images whose extension already matches ext
media_field ("filepath") – the input field containing the image paths to transform
output_field (None) – an optional field in which to store the paths to the transformed images. By default, media_field is updated in-place
output_dir (None) – an optional output directory in which to write the transformed images. If none is provided, the images are updated in-place
rel_dir (None) – an optional relative directory to strip from each input filepath to generate a unique identifier that is joined with output_dir to generate an output path for each image. This argument allows for populating nested subdirectories in output_dir that match the shape of the input paths
delete_originals (False) – whether to delete the original images after re-encoding. This parameter has no effect if the images are being updated in-place
num_workers (None) – the number of worker processes to use. By default, multiprocessing.cpu_count() is used
skip_failures (False) – whether to gracefully continue without raising an error if an image cannot be re-encoded

fiftyone.utils.image.transform_images(sample_collection, size=None, min_size=None, max_size=None, interpolation=None, ext=None, force_reencode=False, media_field='filepath', output_field=None, output_dir=None, rel_dir=None, delete_originals=False, num_workers=None, skip_failures=False)¶

Transforms the images in the sample collection according to the provided parameters.

Note

This method will not update the metadata field of the collection after transforming. You can repopulate the metadata field if needed by calling:

sample_collection.compute_metadata(overwrite=True)

Parameters

sample_collection – a fiftyone.core.collections.SampleCollection
size (None) – an optional (width, height) for each image. One dimension can be -1, in which case the aspect ratio is preserved
min_size (None) – an optional minimum (width, height) for each image. A dimension can be -1 if no constraint should be applied. The images are resized (aspect-preserving) if necessary to meet this constraint
max_size (None) – an optional maximum (width, height) for each image. A dimension can be -1 if no constraint should be applied. The images are resized (aspect-preserving) if necessary to meet this constraint
interpolation (None) – an optional interpolation argument for cv2.resize()
ext (None) – an optional image format to re-encode the source images into (e.g., “.png” or “.jpg”)
force_reencode (False) – whether to re-encode images whose parameters already match the specified values
media_field ("filepath") – the input field containing the image paths to transform
output_field (None) – an optional field in which to store the paths to the transformed images. By default, media_field is updated in-place
output_dir (None) – an optional output directory in which to write the transformed images. If none is provided, the images are updated in-place
rel_dir (None) – an optional relative directory to strip from each input filepath to generate a unique identifier that is joined with output_dir to generate an output path for each image. This argument allows for populating nested subdirectories in output_dir that match the shape of the input paths
delete_originals (False) – whether to delete the original images if any transformation was applied. This parameter has no effect if the images are being updated in-place
num_workers (None) – the number of worker processes to use. By default, multiprocessing.cpu_count() is used
skip_failures (False) – whether to gracefully continue without raising an error if an image cannot be transformed

fiftyone.utils.image.reencode_image(input_path, output_path)¶

Re-encodes the image to the format specified by the given output path.

Parameters

input_path – the path to the input image
output_path – the path to write the output image

fiftyone.utils.image.transform_image(input_path, output_path, size=None, min_size=None, max_size=None, interpolation=None)¶

Transforms the image according to the provided parameters.

Parameters

input_path – the path to the input image
output_path – the path to write the output image
size (None) – an optional (width, height) for the image. One dimension can be -1, in which case the aspect ratio is preserved
min_size (None) – an optional minimum (width, height) for the image. A dimension can be -1 if no constraint should be applied. The image is resized (aspect-preserving) if necessary to meet this constraint
max_size (None) – an optional maximum (width, height) for the image. A dimension can be -1 if no constraint should be applied. The image is resized (aspect-preserving) if necessary to meet this constraint
interpolation (None) – an optional interpolation argument for cv2.resize()