/**
 * @license
 * Copyright 2020 Google LLC. 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 { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';
export const rotateWithOffsetConfig = {
    kernelName: RotateWithOffset,
    backendName: 'cpu',
    kernelFunc: ({ inputs, attrs, backend }) => {
        const { image } = inputs;
        const { radians, fillValue, center } = attrs;
        const cpuBackend = backend;
        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));
        const [batch, imageHeight, imageWidth, numChannels] = image.shape;
        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);
        const fullOpacityValue = 255;
        const sinFactor = Math.sin(radians);
        const cosFactor = Math.cos(radians);
        const imageVals = cpuBackend.data.get(image.dataId).values;
        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
            for (let row = 0; row < imageHeight; row++) {
                const rowOffset = row * (imageWidth * numChannels);
                for (let col = 0; col < imageWidth; col++) {
                    const colOffset = col * numChannels;
                    for (let channel = 0; channel < numChannels; channel++) {
                        const coords = [batch, row, col, channel];
                        const x = coords[2];
                        const y = coords[1];
                        // coordX/coordY are the result of rotating and translating x/y.
                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
                        coordX = Math.round(coordX + centerX);
                        coordY = Math.round(coordY + centerY);
                        let outputValue = fillValue;
                        if (typeof fillValue !== 'number') {
                            if (channel === 3) {
                                outputValue = fullOpacityValue;
                            }
                            else {
                                outputValue = fillValue[channel];
                            }
                        }
                        // If the coordinate position falls within the image boundaries...
                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&
                            coordY < imageHeight) {
                            // set the output to the image value at the coordinate position.
                            const rotatedRowOffset = coordY * (imageWidth * numChannels);
                            const rotatedColOffset = coordX * numChannels;
                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;
                            outputValue = imageVals[imageIdx];
                        }
                        const outIdx = batchOffset + rowOffset + colOffset + channel;
                        output[outIdx] = outputValue;
                    }
                }
            }
        }
        const dataId = cpuBackend.write(output, image.shape, image.dtype);
        return { dataId, shape: image.shape, dtype: image.dtype };
    }
};
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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 {KernelConfig, NumericDataType, TypedArray} from '@tensorflow/tfjs-core';\nimport {backend_util, RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const rotateWithOffsetConfig: KernelConfig = {\n  kernelName: RotateWithOffset,\n  backendName: 'cpu',\n  kernelFunc: ({inputs, attrs, backend}) => {\n    const {image} = inputs as RotateWithOffsetInputs;\n    const {radians, fillValue, center} =\n      attrs as unknown as RotateWithOffsetAttrs;\n    const cpuBackend = backend as MathBackendCPU;\n\n    const output = util.getTypedArrayFromDType(\n        image.dtype as NumericDataType, util.sizeFromShape(image.shape));\n    const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n\n    const [centerX, centerY] =\n        backend_util.getImageCenter(center, imageHeight, imageWidth);\n    const fullOpacityValue = 255;\n\n    const sinFactor = Math.sin(radians);\n    const cosFactor = Math.cos(radians);\n    const imageVals = cpuBackend.data.get(image.dataId).values as TypedArray;\n\n    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n\n      for (let row = 0; row < imageHeight; row++) {\n        const rowOffset = row * (imageWidth * numChannels);\n\n        for (let col = 0; col < imageWidth; col++) {\n          const colOffset = col * numChannels;\n\n          for (let channel = 0; channel < numChannels; channel++) {\n            const coords = [batch, row, col, channel];\n\n            const x = coords[2];\n            const y = coords[1];\n\n            // coordX/coordY are the result of rotating and translating x/y.\n            let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n            let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n            coordX = Math.round(coordX + centerX);\n            coordY = Math.round(coordY + centerY);\n\n            let outputValue = fillValue;\n            if (typeof fillValue !== 'number') {\n              if (channel === 3) {\n                outputValue = fullOpacityValue;\n              } else {\n                outputValue = fillValue[channel];\n              }\n            }\n\n            // If the coordinate position falls within the image boundaries...\n            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                coordY < imageHeight) {\n              // set the output to the image value at the coordinate position.\n              const rotatedRowOffset = coordY * (imageWidth * numChannels);\n              const rotatedColOffset = coordX * numChannels;\n              const imageIdx =\n                  batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n              outputValue = imageVals[imageIdx];\n            }\n\n            const outIdx = batchOffset + rowOffset + colOffset + channel;\n            output[outIdx] = outputValue as number;\n          }\n        }\n      }\n    }\n\n    const dataId = cpuBackend.write(output, image.shape, image.dtype);\n    return {dataId, shape: image.shape, dtype: image.dtype};\n  }\n};\n"]}