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Image

Processors for image transformations

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CastImage

paz.processors.image.CastImage(dtype)

Cast image to given dtype.

Arguments

  • dtype: Str or np.dtype

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SubtractMeanImage

paz.processors.image.SubtractMeanImage(mean)

Subtract channel-wise mean to image.

Arguments

  • mean: List of length 3, containing the channel-wise mean.

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AddMeanImage

paz.processors.image.AddMeanImage(mean)

Adds channel-wise mean to image.

Arguments

  • mean: List of length 3, containing the channel-wise mean.

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NormalizeImage

paz.processors.image.NormalizeImage()

Normalize image by diving all values by 255.0.

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DenormalizeImage

paz.processors.image.DenormalizeImage()

Denormalize image by multiplying all values by 255.0.

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LoadImage

paz.processors.image.LoadImage(num_channels=3)

Loads image.

Arguments

  • num_channels: Integer, valid integers are: 1, 3 and 4.

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RandomSaturation

paz.processors.image.RandomSaturation(lower=0.3, upper=1.5)

Applies random saturation to an image in RGB space.

Arguments

  • lower: Float, lower bound for saturation factor.
  • upper: Float, upper bound for saturation factor.

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RandomBrightness

paz.processors.image.RandomBrightness(delta=32)

Adjust random brightness to an image in RGB space.

Arguments

  • max_delta: Float.

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RandomContrast

paz.processors.image.RandomContrast(lower=0.5, upper=1.5)

Applies random contrast to an image in RGB

Arguments

  • lower: Float, indicating the lower bound of the random number to be multiplied with the BGR/RGB image.
  • upper: Float, indicating the upper bound of the random number

to be multiplied with the BGR/RGB image.

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RandomHue

paz.processors.image.RandomHue(delta=18)

Applies random hue to an image in RGB space.

Arguments

  • delta: Int, indicating the range (-delta, delta ) of possible hue values.

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ResizeImages

paz.processors.image.ResizeImages(shape)

Resize list of images.

Arguments

  • size: List of two ints.

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ResizeImages

paz.processors.image.ResizeImages(shape)

Resize list of images.

Arguments

  • size: List of two ints.

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RandomImageBlur

paz.processors.image.RandomImageBlur(probability=0.5)

Randomizes image quality

Arguments

  • probability: Float between [0, 1]. Assigns probability of how often a random image blur is applied.

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RandomGaussianBlur

paz.processors.image.RandomGaussianBlur(kernel_size=(5, 5), probability=0.5)

Randomizes image quality

Arguments

  • probability: Float between [0, 1]. Assigns probability of how often a random image blur is applied.

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RandomFlipImageLeftRight

paz.processors.image.RandomFlipImageLeftRight()

Randomly flip the image left or right

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ConvertColorSpace

paz.processors.image.ConvertColorSpace(flag)

Converts image to a different color space.

Arguments

  • flag: Flag found in processorsindicating transform e.g. pr.BGR2RGB

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ShowImage

paz.processors.image.ShowImage(window_name='image', wait=True)

Shows image in a separate window.

Arguments

  • window_name: String. Window name.
  • wait: Boolean

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ImageDataProcessor

paz.processors.image.ImageDataProcessor(generator)

Wrapper for Keras ImageDataGenerator

Arguments

  • generator: An instantiated Keras ImageDataGenerator

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AlphaBlending

paz.processors.image.AlphaBlending()

Blends image to background using the image's alpha channel.

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RandomImageCrop

paz.processors.image.RandomImageCrop(crop_factor=0.3, probability=0.5)

Crops randomly a rectangle from an image.

Arguments

  • crop_factor: Float between [0, 1].
  • probability: Float between [0, 1].

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RandomShapeCrop

paz.processors.image.RandomShapeCrop(shape)

Randomly crops a part of an image of always the same given shape.

Arguments

  • shape: List of two ints [height, width]. Dimensions of image to be cropped.

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MakeRandomPlainImage

paz.processors.image.MakeRandomPlainImage(shape)

Makes random plain image by randomly sampling an RGB color.

Arguments

  • shape: List of two ints [height, width]. Dimensions of plain image to be generated.

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ConcatenateAlphaMask

paz.processors.image.ConcatenateAlphaMask()

Concatenates alpha mask to original image.

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BlendRandomCroppedBackground

paz.processors.image.BlendRandomCroppedBackground(background_paths)

Blends image with a randomly cropped background.

Arguments

  • background_paths: List of strings. Each element of the list is a full-path to an image used for cropping a background.

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AddOcclusion

paz.processors.image.AddOcclusion(max_radius_scale=0.5, probability=0.5)

Adds a random occlusion to image by generating random vertices and drawing a polygon.

Arguments

  • max_radius_scale: Float between [0, 1]. Value multiplied with largest image dimension to obtain the maximum radius possible of a vertex in the occlusion polygon.
  • probability: Float between [0, 1]. Assigns probability of how often an occlusion to an image is generated.

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TranslateImage

paz.processors.geometric.TranslateImage(fill_color=None)

Applies a translation of image. The translation is a list of length two indicating the x, y values.

Arguments

  • fill_color: List of three integers indicating the color values e.g. [0, 0, 0]

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ImageToNormalizedDeviceCoordinates

paz.processors.image.ImageToNormalizedDeviceCoordinates()

Map image value from [0, 255] -> [-1, 1].

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NormalizedDeviceCoordinatesToImage

paz.processors.image.NormalizedDeviceCoordinatesToImage()

Map normalized value from [-1, 1] -> [0, 255].

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ReplaceLowerThanThreshold

paz.processors.image.ReplaceLowerThanThreshold(threshold=1e-08, replacement=0.0)

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GetNonZeroValues

paz.processors.image.GetNonZeroValues()

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GetNonZeroArguments

paz.processors.image.GetNonZeroArguments()

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FlipLeftRightImage

paz.processors.image.FlipLeftRightImage()

Flips an image left and right.

Arguments

  • image: Numpy array.

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DivideStandardDeviationImage

paz.processors.image.DivideStandardDeviationImage(standard_deviation)

Divide channel-wise standard deviation to image.

Arguments

  • standard_deviation: List of length 3, containing the channel-wise standard deviation.

Properties

  • standard_deviation: List.

Methods

call()

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ScaledResize

paz.processors.image.ScaledResize(image_size)

Resizes image by returning the scales to original image.

Arguments

  • image_size: Int, desired size of the model input.

Properties

  • image_size: Int.

Methods

call()

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BufferImages

paz.processors.image.BufferImages(input_size, stride=25)

Buffers an image to store and process multiple images.

Arguments

  • input_size: Tuple of integers. Input shape to the model in following format: (frames, height, width, channels) e.g. (38, 96, 96, 3).
  • stride: Integer, specifies after how many images the buffer will return the all buffered images. In a scenario with an already full buffer and a stride of 10, after each 10th call the buffer will be returned. The stride must be smaller than the frames (the first argument of the input_size).

Methods

call()

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PadImage

paz.processors.image.PadImage(size, mode='constant')

Pads the image to the final size size.

Arguments

  • size: Int, final size of maximum dimension of the image.
  • mode: Str, specifying the type of padding.

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EqualizeHistogram

paz.processors.image.EqualizeHistogram(probability=0.5)

The Efficientpose implementation uses Histogram equalization algorithm from python Pillow library. This version of Histogram equalization produces slightly different results from that used in the paper.

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InvertColors

paz.processors.image.InvertColors(probability=0.5)

Performs color / gray value inversion on a given image.

Arguments

  • probability: Float, probability of data transformation.

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Posterize

paz.processors.image.Posterize(probability=0.5, num_bits=4)

Performs posterization on a given image. This is achieved by reducing the bit depth of the gray value.

Arguments

  • probability: Float, probability of data transformation.
  • num_bits: Int, final bit depth after posterization.

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Solarize

paz.processors.image.Solarize(probability=0.5, threshold=225)

Performs solarization on a given image. This is achieved by inverting those pixels whose gray values lie above a certain threshold.

Arguments

  • probability: Float, probability of data transformation.
  • threshold: Int, threshold value.

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SharpenImage

paz.processors.image.SharpenImage(probability=0.5)

Performs image sharpening by applying a high pass filter.

Arguments

  • probability: Float, probability of data transformation.
  • kernel: Array, the high pass filter.

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Cutout

paz.processors.image.Cutout(probability=0.5, size=16, fill=128)

Cuts out a square of size size x size at a random location in the image and fills it with fill value.

Arguments

  • probability: Float, probability of data transformation.
  • size: Int, size of cutout square.
  • fill: Int, value to fill cutout with.

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AddGaussianNoise

paz.processors.image.AddGaussianNoise(probability=0.5, mean=0, scale=0.2)

Adds Gaussian noise defined by mean and scale to the image.

Arguments

  • probability: Float, probability of data transformation.
  • mean: Int, mean of Gaussian noise.
  • scale: Int, percent of variance relative to 255 (max gray value of 8 bit image).