def _read_image_and_resize(img_entry: Union[str,
'numpy.array'], img_width: int,
img_height: int, should_resize: bool,
num_channels: int, resize_method: str,
user_specified_num_channels: int):
"""
:param img_entry Union[str, 'numpy.array']: if str file path to the
image else numpy.array of the image itself
:param img_width: expected width of the image
:param img_height: expected height of the image
:param should_resize: Should the image be resized?
:param resize_method: type of resizing method
:param num_channels: expected number of channels in the first image
:param user_specified_num_channels: did the user specify num channels?
:return: image object
Helper method to read and resize an image according to model defn.
If the user doesn't specify a number of channels, we use the first image
in the dataset as the source of truth. If any image in the dataset
doesn't have the same number of channels as the first image,
raise an exception.
If the user specifies a number of channels, we try to convert all the
images to the specifications by dropping channels/padding 0 channels
"""
img = read_image(img_entry)
if img is None:
logger.info(f"{img_entry} cannot be read")
return None
img_num_channels = num_channels_in_image(img)
if img_num_channels == 1:
img = img.reshape((img.shape[0], img.shape[1], 1))
if should_resize:
img = resize_image(img, (img_height, img_width), resize_method)
if user_specified_num_channels is True:
# convert to greyscale if needed
if num_channels == 1 and (img_num_channels == 3
or img_num_channels == 4):
img = greyscale(img)
img_num_channels = 1
# Number of channels is specified by the user
img_padded = np.zeros((img_height, img_width, num_channels),
dtype=np.uint8)
min_num_channels = min(num_channels, img_num_channels)
img_padded[:, :, :min_num_channels] = img[:, :, :min_num_channels]
img = img_padded
if img_num_channels != num_channels:
logger.warning(
"Image has {0} channels, where as {1} "
"channels are expected. Dropping/adding channels "
"with 0s as appropriate".format(img_num_channels,
num_channels))
else:
# If the image isn't like the first image, raise exception
if img_num_channels != num_channels:
raise ValueError(
'Image has {0} channels, unlike the first image, which '
'has {1} channels. Make sure all the images have the same '
'number of channels or use the num_channels property in '
'image preprocessing'.format(img_num_channels,
num_channels))
if img.shape[0] != img_height or img.shape[1] != img_width:
raise ValueError(
"Images are not of the same size. "
"Expected size is {0}, "
"current image size is {1}."
"Images are expected to be all of the same size "
"or explicit image width and height are expected "
"to be provided. "
"Additional information: "
"https://ludwig-ai.github.io/ludwig-docs/user_guide/#image-features-preprocessing"
.format([img_height, img_width, num_channels], img.shape))
return img
def _read_image_and_resize(filepath, img_width, img_height, should_resize,
num_channels, resize_method,
user_specified_num_channels):
"""
:param filepath: path to the image
:param img_width: expected width of the image
:param img_height: expected height of the image
:param should_resize: Should the image be resized?
:param resize_method: type of resizing method
:param num_channels: expected number of channels in the first image
:param user_specified_num_channels: did the user specify num channels?
:return: image object
Helper method to read and resize an image according to model defn.
If the user doesn't specify a number of channels, we use the first image
in the dataset as the source of truth. If any image in the dataset
doesn't have the same number of channels as the first image,
raise an exception.
If the user specifies a number of channels, we try to convert all the
images to the specifications by dropping channels/padding 0 channels
"""
try:
from skimage.io import imread
except ImportError:
logger.error(
' scikit-image is not installed. '
'In order to install all image feature dependencies run '
'pip install ludwig[image]')
sys.exit(-1)
img = imread(filepath)
img_num_channels = num_channels_in_image(img)
if img_num_channels == 1:
img = img.reshape((img.shape[0], img.shape[1], 1))
if should_resize:
img = resize_image(img, (img_height, img_width), resize_method)
if user_specified_num_channels is True:
# convert to greyscale if needed
if num_channels == 1 and (img_num_channels == 3
or img_num_channels == 4):
img = greyscale(img)
img_num_channels = 1
# Number of channels is specified by the user
img_padded = np.zeros((img_height, img_width, num_channels),
dtype=np.uint8)
min_num_channels = min(num_channels, img_num_channels)
img_padded[:, :, :min_num_channels] = img[:, :, :min_num_channels]
img = img_padded
if img_num_channels != num_channels:
logger.warning(
"Image {0} has {1} channels, where as {2}"
" channels are expected. Dropping/adding channels"
"with 0s as appropriate".format(filepath, img_num_channels,
num_channels))
else:
# If the image isn't like the first image, raise exception
if img_num_channels != num_channels:
raise ValueError(
'Image {0} has {1} channels, unlike the first image, which'
' has {2} channels. Make sure all the iamges have the same'
'number of channels or use the num_channels property in'
'image preprocessing'.format(filepath, img_num_channels,
num_channels))
if img.shape[0] != img_height or img.shape[1] != img_width:
raise ValueError(
"Images are not of the same size. "
"Expected size is {0}, "
"current image size is {1}."
"Images are expected to be all of the same size"
"or explicit image width and height are expected"
"to be provided. "
"Additional information: "
"https://ludwig-ai.github.io/ludwig-docs/user_guide/#image-features-preprocessing"
.format([img_height, img_width, num_channels], img.shape))
return img
def _read_image_and_resize(filepath, img_width, img_height, should_resize,
num_channels, resize_method,
user_specified_num_channels):
"""
:param filepath: path to the image
:param img_width: expected width of the image
:param img_height: expected height of the image
:param should_resize: Should the image be resized?
:param resize_method: type of resizing method
:param num_channels: expected number of channels in the first image
:param user_specified_num_channels: did the user specify num channels?
:return: image object
Helper method to read and resize an image according to model defn.
If the user doesn't specify a number of channels, we use the first image
in the dataset as the source of truth. If any image in the dataset
doesn't have the same number of channels as the first image,
raise an exception.
If the user specifies a number of channels, we try to convert all the
images to the specifications by dropping channels/padding 0 channels
"""
img = imread(filepath)
img_num_channels = num_channels_in_image(img)
if img_num_channels == 1:
img = img.reshape((img.shape[0], img.shape[1], 1))
if should_resize:
img = resize_image(img, (img_height, img_width), resize_method)
if user_specified_num_channels is True:
# convert to greyscale if needed
if num_channels == 1 and (img_num_channels == 3
or img_num_channels == 4):
img = greyscale(img)
img_num_channels = 1
# Number of channels is specified by the user
img_padded = np.zeros((img_height, img_width, num_channels),
dtype=np.uint8)
min_num_channels = min(num_channels, img_num_channels)
img_padded[:, :, :min_num_channels] = img[:, :, :min_num_channels]
img = img_padded
if img_num_channels != num_channels:
logger.warning(
"Image {0} has {1} channels, where as {2}"
" channels are expected. Dropping/adding channels"
"with 0s as appropriate".format(filepath, img_num_channels,
num_channels))
else:
# If the image isn't like the first image, raise exception
if img_num_channels != num_channels:
raise ValueError(
'Image {0} has {1} channels, unlike the first image, which'
' has {2} channels. Make sure all the iamges have the same'
'number of channels or use the num_channels property in'
'image preprocessing'.format(filepath, img_num_channels,
num_channels))
return img
