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syntax = "proto2";
package object_detection.protos;
// Configuration proto for image resizing operations.
// See builders/image_resizer_builder.py for details.
message ImageResizer {
oneof image_resizer_oneof {
KeepAspectRatioResizer keep_aspect_ratio_resizer = 1;
FixedShapeResizer fixed_shape_resizer = 2;
IdentityResizer identity_resizer = 3;
ConditionalShapeResizer conditional_shape_resizer = 4;
PadToMultipleResizer pad_to_multiple_resizer = 5;
}
}
// Enumeration type for image resizing methods provided in TensorFlow.
enum ResizeType {
BILINEAR = 0; // Corresponds to tf.image.ResizeMethod.BILINEAR
NEAREST_NEIGHBOR = 1; // Corresponds to tf.image.ResizeMethod.NEAREST_NEIGHBOR
BICUBIC = 2; // Corresponds to tf.image.ResizeMethod.BICUBIC
AREA = 3; // Corresponds to tf.image.ResizeMethod.AREA
}
message IdentityResizer {
}
// Configuration proto for image resizer that keeps aspect ratio.
message KeepAspectRatioResizer {
// Desired size of the smaller image dimension in pixels.
optional int32 min_dimension = 1 [default = 600];
// Desired size of the larger image dimension in pixels.
optional int32 max_dimension = 2 [default = 1024];
// Desired method when resizing image.
optional ResizeType resize_method = 3 [default = BILINEAR];
// Whether to pad the image with zeros so the output spatial size is
// [max_dimension, max_dimension]. Note that the zeros are padded to the
// bottom and the right of the resized image.
optional bool pad_to_max_dimension = 4 [default = false];
// Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
optional bool convert_to_grayscale = 5 [default = false];
// Per-channel pad value. This is only used when pad_to_max_dimension is True.
// If unspecified, a default pad value of 0 is applied to all channels.
repeated float per_channel_pad_value = 6;
}
// Configuration proto for image resizer that resizes to a fixed shape.
message FixedShapeResizer {
// Desired height of image in pixels.
optional int32 height = 1 [default = 300];
// Desired width of image in pixels.
optional int32 width = 2 [default = 300];
// Desired method when resizing image.
optional ResizeType resize_method = 3 [default = BILINEAR];
// Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
optional bool convert_to_grayscale = 4 [default = false];
}
// Configuration proto for image resizer that resizes only if input image height
// or width is greater or smaller than a certain size.
// Aspect ratio is maintained.
message ConditionalShapeResizer {
// Enumeration for the condition on which to resize an image.
enum ResizeCondition {
INVALID = 0; // Default value.
GREATER = 1; // Resizes image if a dimension is greater than specified size.
SMALLER = 2; // Resizes image if a dimension is smaller than specified size.
}
// Condition which must be true to resize the image.
optional ResizeCondition condition = 1 [default = GREATER];
// Threshold for the image size. If any image dimension is above or below this
// (as specified by condition) the image will be resized so that it meets the
// threshold.
optional int32 size_threshold = 2 [default = 300];
// Desired method when resizing image.
optional ResizeType resize_method = 3 [default = BILINEAR];
// Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
optional bool convert_to_grayscale = 4 [default = false];
}
// An image resizer which resizes inputs by zero padding them such that their
// spatial dimensions are divisible by a specified multiple. This is useful
// when you want to concatenate or compare the input to an output of a
// fully convolutional network.
message PadToMultipleResizer {
// The multiple to which the spatial dimensions will be padded to.
optional int32 multiple = 1 [default = 1];
// Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
optional bool convert_to_grayscale = 4 [default = false];
}
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