![]() If a single float is used, a value between `0.0` and the passed float is sampled. If a tuple is used, a `factor` is sampled between the two values for every image augmented. Values should be between `0.0` and `1.0`. Values between 0 and 1 result in linear interpolation between the original image and a fully blue image. `factor=0.0` makes this layer perform a no-op operation, while a value of 1.0 uses the degenerated result entirely. `factor` controls the extent to which the image is blue shifted. Args: factor: A tuple of two floats, a single float or a `keras_cv.FactorSampler`. BaseImageAugmentationLayer ): """RandomBlueTint randomly applies a blue tint to images. Get_random_transformation() method alongside with augment_label(),Īugment_target() and augment_bounding_boxes().Īugment_segmentation_map() and others will be added in the future.Ĭlass RandomBlueTint ( keras_cv. To handle this issue, BaseImageAugmentationLayer has an overrideable Luckily, BaseImageAugmentationLayer was designed with this in mind. Your layer will need to have information about what augmentations are taken on the image Now, suppose that your layer impacts the prediction targets: whether they are boundingīoxes, classification labels, or regression targets. NormalFactorSampler, and ConstantFactorSampler. There are various types of FactorSamplers including UniformFactorSampler, numpy ())Īs you can see, the augmentations now are drawn from a normal distributions. NormalFactorSampler ( mean = 0.3, stddev = 0.1, min_value = 0.0, max_value = 1.0 ) layer = RandomBlueTint ( factor = factor ) augmented = layer ( many_elephants ) gallery_show ( augmented. expand_dims ( elephants, axis = 0 ), 9, axis = 0 ) factor = keras_cv. We can give it a range of values to sample from: Now, we can configure the random behavior of ou RandomBlueTint layer. clip_by_value ( blue + blue_shift, 0.0, 255.0 ) return tf. unstack ( image, axis =- 1 ) blue_shift = self. parse_factor ( factor ) def augment_image ( self, image, transformation = None ): = tf. """ def _init_ ( self, factor, ** kwargs ): super (). In order to ensure the value is always the same, please pass a tuple with two identical floats: `(0.5, 0.5)`. ![]() ![]() ![]() In our example, we will use the FactorSampler API, the value_range API, andīaseImageAugmentationLayer to implement a robust, configurable, and correct RandomBlueTint layer.Ĭlass RandomBlueTint ( keras_cv. KerasCV offers the value_range API to simplify the handling of this. KerasCV offers an abstraction to make random sampling end user configurable: theįinally, many augmentation techniques requires some information about the pixel values Most augmentation techniques also must sample from one or more random distributions. Performing automatic vectorization under the hood. This is done by allowing the end user to override an augment_image() method and then This is a common mistake many machine learning practicioners make when implementingīaseImageAugmentation offers a set of clean abstractions to make implementing imageĪugmentation techniques on a sample wise basis much easier. Image augmentation should operate on a sample-wise basis not batch-wise. astype ( int ) for i in range ( 9 ): image = images plt. show () def gallery_show ( images ): images = images. ![]()
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