com.intel.analytics.bigdl.dataset.image.GreyImgCropper.scala Maven / Gradle / Ivy
/*
* 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 scala.collection.Iterator
object GreyImgCropper {
def apply(cropWidth: Int, cropHeight: Int) : GreyImgCropper = {
new GreyImgCropper(cropWidth, cropHeight)
}
}
/**
* Crop an area from a grey image. The crop area width and height must be smaller than grey image
* width and height. The area position is random.
* @param cropWidth
* @param cropHeight
*/
class GreyImgCropper(cropWidth: Int, cropHeight: Int)
extends Transformer[LabeledGreyImage, LabeledGreyImage] {
import com.intel.analytics.bigdl.utils.RandomGenerator.RNG
private val buffer = new LabeledGreyImage(cropWidth, cropHeight)
override def apply(prev: Iterator[LabeledGreyImage]): Iterator[LabeledGreyImage] = {
prev.map(img => {
val width = img.width()
val height = img.height()
val startW = RNG.uniform(0, width - cropWidth).toInt
val startH = RNG.uniform(0, height - cropHeight).toInt
val startIndex = startW + startH * width
val frameLength = cropWidth * cropHeight
val source = img.content
val target = buffer.content
var i = 0
while (i < frameLength) {
target(i) = source(startIndex + (i / cropWidth) * width +
(i % cropWidth))
i += 1
}
buffer.setLabel(img.label())
})
}
}