/**
|
* @license
|
* Copyright 2018 Google Inc. All Rights Reserved.
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
* you may not use this file except in compliance with the License.
|
* You may obtain a copy of the License at
|
*
|
* http://www.apache.org/licenses/LICENSE-2.0
|
*
|
* Unless required by applicable law or agreed to in writing, software
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
* See the License for the specific language governing permissions and
|
* limitations under the License.
|
* =============================================================================
|
*/
|
import { Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
|
import { NamedTensorMap } from '../tensor_types';
|
import { TensorLike } from '../types';
|
/**
|
* Bilinear resize a batch of 3D images to a new shape.
|
*
|
* @param images The images, of rank 4 or rank 3, of shape
|
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
|
* @param size The new shape `[newHeight, newWidth]` to resize the
|
* images to. Each channel is resized individually.
|
* @param alignCorners Defaults to False. If true, rescale
|
* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
|
* corners of images and resized images. If false, rescale by
|
* `new_height / height`. Treat similarly the width dimension.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
|
declare function resizeBilinear_<T extends Tensor3D | Tensor4D>(images: T | TensorLike, size: [number, number], alignCorners?: boolean): T;
|
/**
|
* NearestNeighbor resize a batch of 3D images to a new shape.
|
*
|
* @param images The images, of rank 4 or rank 3, of shape
|
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
|
* @param size The new shape `[newHeight, newWidth]` to resize the
|
* images to. Each channel is resized individually.
|
* @param alignCorners Defaults to False. If true, rescale
|
* input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
|
* corners of images and resized images. If false, rescale by
|
* `new_height / height`. Treat similarly the width dimension.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
|
declare function resizeNearestNeighbor_<T extends Tensor3D | Tensor4D>(images: T | TensorLike, size: [number, number], alignCorners?: boolean): T;
|
/**
|
* Performs non maximum suppression of bounding boxes based on
|
* iou (intersection over union).
|
*
|
* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
|
* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
|
* the bounding box.
|
* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
|
* @param maxOutputSize The maximum number of boxes to be selected.
|
* @param iouThreshold A float representing the threshold for deciding whether
|
* boxes overlap too much with respect to IOU. Must be between [0, 1].
|
* Defaults to 0.5 (50% box overlap).
|
* @param scoreThreshold A threshold for deciding when to remove boxes based
|
* on score. Defaults to -inf, which means any score is accepted.
|
* @return A 1D tensor with the selected box indices.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
|
declare function nonMaxSuppression_(boxes: Tensor2D | TensorLike, scores: Tensor1D | TensorLike, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number): Tensor1D;
|
/** This is the async version of `nonMaxSuppression` */
|
declare function nonMaxSuppressionAsync_(boxes: Tensor2D | TensorLike, scores: Tensor1D | TensorLike, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number): Promise<Tensor1D>;
|
/**
|
* Performs non maximum suppression of bounding boxes based on
|
* iou (intersection over union).
|
*
|
* This op also supports a Soft-NMS mode (c.f.
|
* Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
|
* of other overlapping boxes, therefore favoring different regions of the image
|
* with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
|
* parameter to be larger than 0.
|
*
|
* @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
|
* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
|
* the bounding box.
|
* @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
|
* @param maxOutputSize The maximum number of boxes to be selected.
|
* @param iouThreshold A float representing the threshold for deciding whether
|
* boxes overlap too much with respect to IOU. Must be between [0, 1].
|
* Defaults to 0.5 (50% box overlap).
|
* @param scoreThreshold A threshold for deciding when to remove boxes based
|
* on score. Defaults to -inf, which means any score is accepted.
|
* @param softNmsSigma A float representing the sigma parameter for Soft NMS.
|
* When sigma is 0, it falls back to nonMaxSuppression.
|
* @return A map with the following properties:
|
* - selectedIndices: A 1D tensor with the selected box indices.
|
* - selectedScores: A 1D tensor with the corresponding scores for each
|
* selected box.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
|
declare function nonMaxSuppressionWithScore_(boxes: Tensor2D | TensorLike, scores: Tensor1D | TensorLike, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, softNmsSigma?: number): NamedTensorMap;
|
/** This is the async version of `nonMaxSuppressionWithScore` */
|
declare function nonMaxSuppressionWithScoreAsync_(boxes: Tensor2D | TensorLike, scores: Tensor1D | TensorLike, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, softNmsSigma?: number): Promise<NamedTensorMap>;
|
/**
|
* Extracts crops from the input image tensor and resizes them using bilinear
|
* sampling or nearest neighbor sampling (possibly with aspect ratio change)
|
* to a common output size specified by crop_size.
|
*
|
* @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
|
* where imageHeight and imageWidth must be positive, specifying the
|
* batch of images from which to take crops
|
* @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
|
* `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
|
* coordinates of the box in the boxInd[i]'th image in the batch
|
* @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
|
* `[0, batch)` that specifies the image that the `i`-th box refers to.
|
* @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
|
* specifying the size to which all crops are resized to.
|
* @param method Optional string from `'bilinear' | 'nearest'`,
|
* defaults to bilinear, which specifies the sampling method for resizing
|
* @param extrapolationValue A threshold for deciding when to remove boxes based
|
* on score. Defaults to 0.
|
* @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
|
declare function cropAndResize_(image: Tensor4D | TensorLike, boxes: Tensor2D | TensorLike, boxInd: Tensor1D | TensorLike, cropSize: [number, number], method?: 'bilinear' | 'nearest', extrapolationValue?: number): Tensor4D;
|
export declare const resizeBilinear: typeof resizeBilinear_;
|
export declare const resizeNearestNeighbor: typeof resizeNearestNeighbor_;
|
export declare const nonMaxSuppression: typeof nonMaxSuppression_;
|
export declare const nonMaxSuppressionAsync: typeof nonMaxSuppressionAsync_;
|
export declare const nonMaxSuppressionWithScore: typeof nonMaxSuppressionWithScore_;
|
export declare const nonMaxSuppressionWithScoreAsync: typeof nonMaxSuppressionWithScoreAsync_;
|
export declare const cropAndResize: typeof cropAndResize_;
|
export {};
|
