from keras.src import backend from keras.src.api_export import keras_export from keras.src.layers.input_spec import InputSpec from keras.src.layers.layer import Layer from keras.src.utils import argument_validation @keras_export("keras.layers.Cropping2D") class Cropping2D(Layer): """Cropping layer for 2D input (e.g. picture). It crops along spatial dimensions, i.e. height and width. Example: >>> input_shape = (2, 28, 28, 3) >>> x = np.arange(np.prod(input_shape)).reshape(input_shape) >>> y = keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x) >>> y.shape (2, 24, 20, 3) Args: cropping: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. - If int: the same symmetric cropping is applied to height and width. - If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: `(symmetric_height_crop, symmetric_width_crop)`. - If tuple of 2 tuples of 2 ints: interpreted as `((top_crop, bottom_crop), (left_crop, right_crop))`. data_format: A string, one of `"channels_last"` (default) or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch_size, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch_size, channels, height, width)`. When unspecified, uses `image_data_format` value found in your Keras config file at `~/.keras/keras.json` (if exists). Defaults to `"channels_last"`. Input shape: 4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch_size, height, width, channels)` - If `data_format` is `"channels_first"`: `(batch_size, channels, height, width)` Output shape: 4D tensor with shape: - If `data_format` is `"channels_last"`: `(batch_size, cropped_height, cropped_width, channels)` - If `data_format` is `"channels_first"`: `(batch_size, channels, cropped_height, cropped_width)` """ def __init__(self, cropping=((0, 0), (0, 0)), data_format=None, **kwargs): super().__init__(**kwargs) self.data_format = backend.standardize_data_format(data_format) if isinstance(cropping, int): if cropping < 0: raise ValueError( "`cropping` cannot be negative. " f"Received: cropping={cropping}." ) self.cropping = ((cropping, cropping), (cropping, cropping)) elif hasattr(cropping, "__len__"): if len(cropping) != 2: raise ValueError( "`cropping` should have two elements. " f"Received: cropping={cropping}." ) height_cropping = argument_validation.standardize_tuple( cropping[0], 2, "1st entry of cropping", allow_zero=True ) width_cropping = argument_validation.standardize_tuple( cropping[1], 2, "2nd entry of cropping", allow_zero=True ) self.cropping = (height_cropping, width_cropping) else: raise ValueError( "`cropping` should be either an int, a tuple of 2 ints " "(symmetric_height_crop, symmetric_width_crop), " "or a tuple of 2 tuples of 2 ints " "((top_crop, bottom_crop), (left_crop, right_crop)). " f"Received: cropping={cropping}." ) self.input_spec = InputSpec(ndim=4) def compute_output_shape(self, input_shape): if self.data_format == "channels_first": if ( input_shape[2] is not None and sum(self.cropping[0]) >= input_shape[2] ) or ( input_shape[3] is not None and sum(self.cropping[1]) >= input_shape[3] ): raise ValueError( "Values in `cropping` argument should be smaller than the " "corresponding spatial dimension of the input. Received: " f"input_shape={input_shape}, cropping={self.cropping}" ) return ( input_shape[0], input_shape[1], ( input_shape[2] - self.cropping[0][0] - self.cropping[0][1] if input_shape[2] is not None else None ), ( input_shape[3] - self.cropping[1][0] - self.cropping[1][1] if input_shape[3] is not None else None ), ) else: if ( input_shape[1] is not None and sum(self.cropping[0]) >= input_shape[1] ) or ( input_shape[2] is not None and sum(self.cropping[1]) >= input_shape[2] ): raise ValueError( "Values in `cropping` argument should be smaller than the " "corresponding spatial dimension of the input. Received: " f"input_shape={input_shape}, cropping={self.cropping}" ) return ( input_shape[0], ( input_shape[1] - self.cropping[0][0] - self.cropping[0][1] if input_shape[1] is not None else None ), ( input_shape[2] - self.cropping[1][0] - self.cropping[1][1] if input_shape[2] is not None else None ), input_shape[3], ) def call(self, inputs): if self.data_format == "channels_first": if ( inputs.shape[2] is not None and sum(self.cropping[0]) >= inputs.shape[2] ) or ( inputs.shape[3] is not None and sum(self.cropping[1]) >= inputs.shape[3] ): raise ValueError( "Values in `cropping` argument should be smaller than the " "corresponding spatial dimension of the input. Received: " f"inputs.shape={inputs.shape}, cropping={self.cropping}" ) if self.cropping[0][1] == self.cropping[1][1] == 0: return inputs[ :, :, self.cropping[0][0] :, self.cropping[1][0] : ] elif self.cropping[0][1] == 0: return inputs[ :, :, self.cropping[0][0] :, self.cropping[1][0] : -self.cropping[1][1], ] elif self.cropping[1][1] == 0: return inputs[ :, :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] :, ] return inputs[ :, :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] : -self.cropping[1][1], ] else: if ( inputs.shape[1] is not None and sum(self.cropping[0]) >= inputs.shape[1] ) or ( inputs.shape[2] is not None and sum(self.cropping[1]) >= inputs.shape[2] ): raise ValueError( "Values in `cropping` argument should be smaller than the " "corresponding spatial dimension of the input. Received: " f"inputs.shape={inputs.shape}, cropping={self.cropping}" ) if self.cropping[0][1] == self.cropping[1][1] == 0: return inputs[ :, self.cropping[0][0] :, self.cropping[1][0] :, : ] elif self.cropping[0][1] == 0: return inputs[ :, self.cropping[0][0] :, self.cropping[1][0] : -self.cropping[1][1], :, ] elif self.cropping[1][1] == 0: return inputs[ :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] :, :, ] return inputs[ :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] : -self.cropping[1][1], :, ] def get_config(self): config = {"cropping": self.cropping, "data_format": self.data_format} base_config = super().get_config() return {**base_config, **config}