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/*
 * Copyright 2016 The BigDL Authors.
 *
 * 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.
 */

package com.intel.analytics.bigdl.dataset.image

import com.intel.analytics.bigdl.dataset.Transformer
import org.apache.log4j.Logger
import org.apache.spark.mllib.linalg.DenseVector

import scala.collection.Iterator

object BGRImgToImageVector {
  val logger = Logger.getLogger(getClass)

  def apply(): BGRImgToImageVector = {
    new BGRImgToImageVector()
  }
}

/**
 * Convert a BGR image to dense vector of spark mllib
 */
class BGRImgToImageVector()
  extends Transformer[LabeledBGRImage, DenseVector] {

  private var featureData: Array[Float] = null

  override def apply(prev: Iterator[LabeledBGRImage]): Iterator[DenseVector] = {
    prev.map(
      img => {
        if (null == featureData) {
          featureData = new Array[Float](3 * img.height() * img.width())
        }
        img.copyTo(featureData, 0, true)
        new DenseVector(featureData.map(_.toDouble))
      }
    )
  }
}




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