抖动调整大小层

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JitteredResize class

keras_cv.layers.JitteredResize(
    target_size,
    scale_factor,
    crop_size=None,
    bounding_box_format=None,
    interpolation="bilinear",
    seed=None,
    **kwargs
)

JitteredResize implements resize with scale distortion.

JitteredResize takes a three-step approach to size-distortion based image augmentation. This technique is specifically tuned for object detection pipelines. The layer takes an input of images and bounding boxes, both of which may be ragged. It outputs a dense image tensor, ready to feed to a model for training. As such this layer will commonly be the final step in an augmentation pipeline.

The augmentation process is as follows:

The image is first scaled according to a randomly sampled scale factor. The width and height of the image are then resized according to the sampled scale. This is done to introduce noise into the local scale of features in the image. A subset of the image is then cropped randomly according to crop_size. This crop is then padded to be target_size. Bounding boxes are translated and scaled according to the random scaling and random cropping.

Arguments

• target_size: A tuple representing the output size of images.
• scale_factor: A tuple of two floats or a keras_cv.FactorSampler. For each augmented image a value is sampled from the provided range. This factor is used to scale the input image. To replicate the results of the MaskRCNN paper pass (0.8, 1.25).
• crop_size: (Optional) the size of the image to crop from the scaled image, defaults to target_size when not provided.
• bounding_box_format: The format of bounding boxes of input boxes. Refer to https://github.com/keras-team/keras-cv/blob/master/keras_cv/bounding_box/converters.py for more details on supported bounding box formats.
• interpolation: String, the interpolation method, defaults to "bilinear". Supports "bilinear", "nearest", "bicubic", "area", "lanczos3", "lanczos5", "gaussian", "mitchellcubic".
• seed: (Optional) integer to use as the random seed.

Example

train_ds = load_object_detection_dataset()
jittered_resize = layers.JitteredResize(
    target_size=(640, 640),
    scale_factor=(0.8, 1.25),
    bounding_box_format="xywh",
)
train_ds = train_ds.map(
    jittered_resize, num_parallel_calls=tf.data.AUTOTUNE
)
# images now are (640, 640, 3)

# an example using crop size
train_ds = load_object_detection_dataset()
jittered_resize = layers.JitteredResize(
    target_size=(640, 640),
    crop_size=(250, 250),
    scale_factor=(0.8, 1.25),
    bounding_box_format="xywh",
)
train_ds = train_ds.map(
    jittered_resize, num_parallel_calls=tf.data.AUTOTUNE
)
# images now are (640, 640, 3), but they were resized from a 250x250 crop.