/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import { buffer, CropAndResize, util } from '@tensorflow/tfjs-core';
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export function cropAndResize(args) {
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const { inputs, backend, attrs } = args;
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const { image, boxes, boxInd } = inputs;
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const { cropSize, method, extrapolationValue } = attrs;
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const [batch, imageHeight, imageWidth, numChannels] = image.shape;
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const numBoxes = boxes.shape[0];
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const [cropHeight, cropWidth] = cropSize;
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const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');
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const boxVals = backend.data.get(boxes.dataId).values;
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const boxIndVals = backend.data.get(boxInd.dataId).values;
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const imageVals = backend.data.get(image.dataId).values;
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const inStride = util.computeStrides(image.shape); // to calculate flat indexes into image
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const outStride = util.computeStrides(output.shape); // to calculate flat indexes into output
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// Reference implementation
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// tslint:disable-next-line:max-line-length
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// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc
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for (let b = 0; b < numBoxes; b++) {
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const startInd = b * 4;
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const y1 = boxVals[startInd];
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const x1 = boxVals[startInd + 1];
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const y2 = boxVals[startInd + 2];
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const x2 = boxVals[startInd + 3];
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const bInd = boxIndVals[b];
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if (bInd >= batch) {
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continue;
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}
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const heightScale = (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
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const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
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for (let y = 0; y < cropHeight; y++) {
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const yInd = (cropHeight > 1) ?
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y1 * (imageHeight - 1) + y * (heightScale) :
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0.5 * (y1 + y2) * (imageHeight - 1);
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if (yInd < 0 || yInd > imageHeight - 1) {
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for (let x = 0; x < cropWidth; x++) {
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for (let c = 0; c < numChannels; c++) {
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const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
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output.values[ind] = extrapolationValue;
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}
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}
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continue;
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}
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if (method === 'bilinear') {
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const topInd = Math.floor(yInd);
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const bottomInd = Math.ceil(yInd);
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const yLerp = yInd - topInd;
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for (let x = 0; x < cropWidth; x++) {
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const xInd = (cropWidth > 1) ?
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x1 * (imageWidth - 1) + x * widthScale :
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0.5 * (x1 + x2) * (imageWidth - 1);
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if (xInd < 0 || xInd > imageWidth - 1) {
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for (let c = 0; c < numChannels; c++) {
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const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
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output.values[ind] = extrapolationValue;
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}
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continue;
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}
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const leftInd = Math.floor(xInd);
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const rightInd = Math.ceil(xInd);
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const xLerp = xInd - leftInd;
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for (let c = 0; c < numChannels; c++) {
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let ind = c + leftInd * inStride[2] + topInd * inStride[1] +
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bInd * inStride[0];
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const topLeft = imageVals[ind];
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ind = c + rightInd * inStride[2] + topInd * inStride[1] +
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bInd * inStride[0];
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const topRight = imageVals[ind];
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ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +
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bInd * inStride[0];
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const bottomLeft = imageVals[ind];
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ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +
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bInd * inStride[0];
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const bottomRight = imageVals[ind];
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const top = topLeft + (topRight - topLeft) * xLerp;
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const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
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ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
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output.values[ind] = top + ((bottom - top) * yLerp);
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}
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}
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}
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else { // method == "nearest"
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for (let x = 0; x < cropWidth; ++x) {
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const xInd = (cropWidth > 1) ?
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x1 * (imageWidth - 1) + x * widthScale :
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0.5 * (x1 + x2) * (imageWidth - 1);
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if (xInd < 0 || xInd > imageWidth - 1) {
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for (let c = 0; c < numChannels; c++) {
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const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
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output.values[ind] = extrapolationValue;
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}
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continue;
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}
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const closestX = Math.round(xInd);
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const closestY = Math.round(yInd);
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for (let c = 0; c < numChannels; c++) {
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const inInd = c + closestX * inStride[2] + closestY * inStride[1] +
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bInd * inStride[0];
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const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
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output.values[outInd] = imageVals[inInd];
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}
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}
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}
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}
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}
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return backend.makeTensorInfo(output.shape, output.dtype, output.values);
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}
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export const cropAndResizeConfig = {
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kernelName: CropAndResize,
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backendName: 'cpu',
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kernelFunc: cropAndResize
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};
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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {buffer, CropAndResize, CropAndResizeAttrs, CropAndResizeInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport function cropAndResize(args: {\n  inputs: CropAndResizeInputs,\n  backend: MathBackendCPU,\n  attrs: CropAndResizeAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {image, boxes, boxInd} = inputs;\n  const {cropSize, method, extrapolationValue} = attrs;\n\n  const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n  const numBoxes = boxes.shape[0];\n\n  const [cropHeight, cropWidth] = cropSize;\n  const output =\n      buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n\n  const boxVals = backend.data.get(boxes.dataId).values as TypedArray;\n  const boxIndVals = backend.data.get(boxInd.dataId).values as TypedArray;\n  const imageVals = backend.data.get(image.dataId).values as TypedArray;\n\n  const inStride =\n      util.computeStrides(image.shape);  // to calculate flat indexes into image\n  const outStride = util.computeStrides(\n      output.shape);  // to calculate flat indexes into output\n\n  // Reference implementation\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n  for (let b = 0; b < numBoxes; b++) {\n    const startInd = b * 4;\n    const y1 = boxVals[startInd];\n    const x1 = boxVals[startInd + 1];\n    const y2 = boxVals[startInd + 2];\n    const x2 = boxVals[startInd + 3];\n\n    const bInd: number = boxIndVals[b];\n    if (bInd >= batch) {\n      continue;\n    }\n\n    const heightScale =\n        (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;\n    const widthScale =\n        (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n\n    for (let y = 0; y < cropHeight; y++) {\n      const yInd: number = (cropHeight > 1) ?\n          y1 * (imageHeight - 1) + y * (heightScale) :\n          0.5 * (y1 + y2) * (imageHeight - 1);\n\n      if (yInd < 0 || yInd > imageHeight - 1) {\n        for (let x = 0; x < cropWidth; x++) {\n          for (let c = 0; c < numChannels; c++) {\n            const ind =\n                c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = extrapolationValue;\n          }\n        }\n        continue;\n      }\n\n      if (method === 'bilinear') {\n        const topInd = Math.floor(yInd);\n        const bottomInd = Math.ceil(yInd);\n        const yLerp = yInd - topInd;\n\n        for (let x = 0; x < cropWidth; x++) {\n          const xInd = (cropWidth > 1) ?\n              x1 * (imageWidth - 1) + x * widthScale :\n              0.5 * (x1 + x2) * (imageWidth - 1);\n\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n\n          const leftInd = Math.floor(xInd);\n          const rightInd = Math.ceil(xInd);\n          const xLerp = xInd - leftInd;\n\n          for (let c = 0; c < numChannels; c++) {\n            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                bInd * inStride[0];\n            const topLeft = imageVals[ind];\n\n            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                bInd * inStride[0];\n            const topRight = imageVals[ind];\n\n            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                bInd * inStride[0];\n            const bottomLeft = imageVals[ind];\n\n            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                bInd * inStride[0];\n            const bottomRight = imageVals[ind];\n\n            const top = topLeft + (topRight - topLeft) * xLerp;\n            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n\n            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = top + ((bottom - top) * yLerp);\n          }\n        }\n      } else {  // method == \"nearest\"\n        for (let x = 0; x < cropWidth; ++x) {\n          const xInd = (cropWidth > 1) ?\n              x1 * (imageWidth - 1) + x * widthScale :\n              0.5 * (x1 + x2) * (imageWidth - 1);\n\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n\n          const closestX = Math.round(xInd);\n          const closestY = Math.round(yInd);\n          for (let c = 0; c < numChannels; c++) {\n            const inInd = c + closestX * inStride[2] + closestY * inStride[1] +\n                bInd * inStride[0];\n            const outInd =\n                c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[outInd] = imageVals[inInd];\n          }\n        }\n      }\n    }\n  }\n\n  return backend.makeTensorInfo(output.shape, output.dtype, output.values);\n}\n\nexport const cropAndResizeConfig: KernelConfig = {\n  kernelName: CropAndResize,\n  backendName: 'cpu',\n  kernelFunc: cropAndResize as unknown as KernelFunc\n};\n"]}
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