Instantiate a subclass of torch::dataset() for geospatial semantic segmentation
Usage
defineSegDataSet(
chpDF,
folder,
normalize = FALSE,
rescaleFactor = 1,
mskRescale = 1,
mskAdd = 0,
bands = c(1, 2, 3),
bMns = 1,
bSDs = 1,
doAugs = FALSE,
maxAugs = 0,
probVFlip = 0,
probHFlip = 0,
probBrightness = 0,
probContrast = 0,
probGamma = 0,
probHue = 0,
probSaturation = 0,
brightFactor = c(0.8, 1.2),
contrastFactor = c(0.8, 1.2),
gammaFactor = c(0.8, 1.2, 1),
hueFactor = c(-0.2, 0.2),
saturationFactor = c(0.8, 1.2)
)Arguments
- chpDF
Data frame of image chip and mask paths created using makeChipsDF().
- folder
Full path or path relative to the working directory to the folder containing the image chips and associated masks. You must include the final forward slash in the path (e.g., "C:/data/chips/").
- normalize
TRUE or FALSE. Whether to apply normalization. If FALSE, bMns and bSDs are ignored. Default is FALSE. If TRUE, you must provide bMns and bSDs.
- rescaleFactor
A rescaling factor to rescale the bands to 0 to 1. For example, this could be set to 255 to rescale 8-bit data. Default is 1 or no rescaling.
- mskRescale
Can be used to rescale binary masks that are not scaled from 0 to 1. For example, if masks are scaled from 0 and 255, you can divide by 255 to obtain a 0 to 1 scale. Default is 1 or no rescaling.
- mskAdd
Value to add to mask class numeric codes. For example, if class indices start are 0, 1 can be added so that indices start at 1. Default is 0 (return original class codes). Note that several other functions in this package have a zeroStart parameter. If class codes start at 0, this argument should be set to TRUE. If they start at 1, this argument should be set to FALSE. The importance of this arises from the use of one-hot encoding internally, which requires that class indices start at 1.
- bands
Vector of bands to include. The default is to only include the first 3 bands. If you want to use a different subset of bands, you must provide a vector of band indices here to override the default.
- bMns
Vector of band means. Length should be the same as the number of bands. Normalization is applied before any rescaling within the function.
- bSDs
Vector of band standard deviations. Length should be the same as the number of bands. Normalization is applied before any rescaling.
- doAugs
TRUE or FALSE. Whether or not to apply data augmentations to combat overfitting. If FALSE, all augmentations parameters are ignored. Data augmentations are generally only applied to the training set. Default is FALSE.
- maxAugs
0 to 7. Maximum number of random augmentations to apply. Default is 0 or no augmentations. Must be changed if augmentations are desired.
- probVFlip
0 to 1. Probability of applying vertical flips. Default is 0 or no augmentations. Must be changed if augmentations are desired.
- probHFlip
0 to 1. Probability of applying horizontal flips. Default is 0 or no augmentations. Must be changed if augmentations are desired.
- probBrightness
0 to 1. Probability of applying brightness augmentation. Default is 0 or no augmentations. Must be changed if augmentations are desired.
- probContrast
0 to 1. Probability of applying contrast augmentations. Default is 0 or no augmentations. Must be changed if augmentations are desired.
- probGamma
0 to 1. Probability of applying gamma augmentations. Default is 0 or no augmentations. Must be changed if augmentations are desired.
- probHue
0 to 1. Probability of applying hue augmentations. Default is 0 or no augmentations. Must be changed if augmentations are desired. This is only applicable to RGB data.
- probSaturation
0 to 1. Probability of applying saturation augmentations. Default is 0 or no augmentations. Must be changed if augmentations are desired. This is only applicable to RGB data.
- brightFactor
Vector of smallest and largest brightness adjustment factors. Random value will be selected between these extremes. The default is 0.8 to 1.2. Can be any non negative number. For example, 0 gives a black image, 1 gives the original image, and 2 increases the brightness by a factor of 2.
- contrastFactor
Vector of smallest and largest contrast adjustment factors. Random value will be selected between these extremes. The default is 0.8 to 1.2. Can be any non negative number. For example, 0 gives a solid gray image, 1 gives the original image, and 2 increases the contrast by a factor of 2.
- gammaFactor
Vector of smallest and largest gamma values and gain value for a total of 3 values. Random value will be selected between these extremes. The default gamma value range is 0.8 to 1.2 and the default gain is 1. The gain is not randomly altered, only the gamma. Non negative real number. A gamma larger than 1 makes the shadows darker while a gamma smaller than 1 makes dark regions lighter.
- hueFactor
Vector of smallest and largest hue adjustment factors. Random value will be selected between these extremes. The default is -0.2 to 0.2. Should be in range -0.5 to 0.5. 0.5 and -0.5 give complete reversal of hue channel in HSV space in positive and negative direction, respectively. 0 means no shift. Therefore, both -0.5 and 0.5 will give an image with complementary colors while 0 gives the original image.
- saturationFactor
Vector of smallest and largest saturation adjustment factors. Random value will be selected between these extremes. The default is 0.8 to 1.2. For example, 0 will give a black-and-white image, 1 will give the original image, and 2 will enhance the saturation by a factor of 2.
Details
This function instantiates a subclass of torch::dataset() that loads data generated using the makeChips() or makeChipsMultiClass() function. Can also define random augmentations to combat overfitting. Note that horizontal and vertical flips will affect the alignment of the image and associated mask chips. As a result, the same augmentation will be applied to both the image and the mask. Changes in brightness, contrast, gamma, hue, and saturation will not be applied to the masks since alignment is not impacted by these transformations. Predictor variables are generated with three dimensions (channel/variable, width, height) regardless of the number of channels/variables. Masks are generated as three dimensional tensors (class index, width, height).
Examples
if (FALSE) {
#Define training dataset and augmentations
trainDS <- defineSegDataSet(
chpDF=trainDF,
folder="PATH TO CHIPS FOLDER",
normalize = FALSE,
rescaleFactor = 255,
mskRescale= 255,
bands = c(1,2,3),
mskAdd=1,
doAugs = TRUE,
maxAugs = 1,
probVFlip = .5,
probHFlip = .5,
probBrightness = 0,
probContrast = 0,
probGamma = 0,
probHue = 0,
probSaturation = 0,
brightFactor = c(.9,1.1),
contrastFactor = c(.9,1.1),
gammaFactor = c(.9, 1.1, 1),
hueFactor = c(-.1, .1),
saturationFactor = c(.9, 1.1))
}