Add Estimator Python API and Inception Example (intel-analytics#1597)

dl/src/main/scala/com/intel/analytics/bigdl/dllib/feature/imageFromPreprocessing/ImageMirror.scala

@@ -0,0 +1,53 @@
+/*
+ * Copyright 2018 Analytics Zoo 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.zoo.feature.image
+
+import com.intel.analytics.bigdl.transform.vision.image.opencv.OpenCVMat
+import com.intel.analytics.bigdl.transform.vision.image.{FeatureTransformer, ImageFeature}
+import org.opencv.core.Core
+
+/**
+ * Flip the image horizontally and vertically
+ */
+class Mirror() extends FeatureTransformer {
+  override def transformMat(feature: ImageFeature): Unit = {
+    Mirror.transform(feature.opencvMat(), feature.opencvMat())
+  }
+}
+
+object Mirror {
+  def apply(): Mirror = new Mirror()
+
+  def transform(input: OpenCVMat, output: OpenCVMat): OpenCVMat = {
+    Core.flip(input, output, -1)
+    output
+  }
+}
+
+/**
+ * Flip the image horizontally and vertically
+ */
+class ImageMirror() extends ImageProcessing {
+  private val internalCrop = new Mirror
+
+  override def apply(prev: Iterator[ImageFeature]): Iterator[ImageFeature] = {
+    internalCrop.apply(prev)
+  }
+}
+
+object ImageMirror {
+  def apply(): ImageMirror = new ImageMirror()
+}

dl/src/main/scala/com/intel/analytics/bigdl/dllib/feature/python/PythonFeatureSet.scala

@@ -17,7 +17,8 @@
 package com.intel.analytics.zoo.feature.python
 
 import com.intel.analytics.bigdl.DataSet
-import com.intel.analytics.bigdl.dataset.Transformer
+import com.intel.analytics.bigdl.dataset.{Transformer, Sample => JSample}
+import com.intel.analytics.bigdl.python.api.Sample
 import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
 import com.intel.analytics.bigdl.transform.vision.image._
 import com.intel.analytics.zoo.common.PythonZoo
@@ -48,6 +49,11 @@ class PythonFeatureSet[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pytho
     FeatureSet.rdd(data, MemoryType.fromString(memoryType))
   }
 
+  def createSampleFeatureSetFromRDD(data: JavaRDD[Sample], memoryType: String)
+  : FeatureSet[JSample[T]] = {
+    FeatureSet.rdd(toJSample(data), MemoryType.fromString(memoryType))
+  }
+
   def transformFeatureSet(featureSet: FeatureSet[Any],
                        transformer: Transformer[Any, Any]): FeatureSet[Any] = {
     featureSet -> transformer

dl/src/main/scala/com/intel/analytics/bigdl/dllib/feature/python/PythonImageFeature.scala

@@ -220,6 +220,11 @@ class PythonImageFeature[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pyt
     ImageBytesToMat(byteKey, imageCodec)
   }
 
+  def createImagePixelBytesToMat(
+      byteKey: String = ImageFeature.bytes): ImagePixelBytesToMat = {
+    ImagePixelBytesToMat(byteKey)
+  }
+
   def createImageBrightness(deltaLow: Double, deltaHigh: Double): ImageBrightness = {
     ImageBrightness(deltaLow, deltaHigh)
   }
@@ -228,6 +233,10 @@ class PythonImageFeature[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pyt
     ImageFeatureToTensor()
   }
 
+  def createImageFeatureToSample(): ImageFeatureToSample[T] = {
+    ImageFeatureToSample()
+  }
+
   def createImageChannelNormalizer(
                                   meanR: Double, meanG: Double, meanB: Double,
                                   stdR: Double = 1, stdG: Double = 1, stdB: Double = 1
@@ -347,6 +356,10 @@ class PythonImageFeature[T: ClassTag](implicit ev: TensorNumeric[T]) extends Pyt
     ImageHFlip()
   }
 
+  def createImageMirror(): ImageMirror = {
+    ImageMirror()
+  }
+
   def createImageSetToSample(inputKeys: JList[String],
                              targetKeys: JList[String],
                              sampleKey: String): ImageSetToSample[T] = {