/*Author: Afag Rizayeva
  Date:11-January-2022
  Purpose: 1. Merge the separate exported texture layers into one for each of the chosen second-order texture metrics:
              Angular Second Moment
              Entropy
              Homogeneity

  Usage: Convert the code naming in the code to your corresponding Corona image by pressing Ctrl+F twice
        and entering "10111040" without quotes in the first line and "your Corona image code" in the second line
          
        Create a geometry corresponding to your region
          
        Then run the code and excuate "RUN" in the Tasks tab to export the results into GLCM folder in your assets
        Based on the following code, you should have 6 total processes to execute (two per each second-order texture metric)
          
  Parameters: 
              assetId: the directory where you store your separate texture layers exported earlier to be merged by this code
              geometry:
*/


//     Import two Corona images and build the image geometry
var image2 = ee.Image('users/rizayeva/corona_2_5m/DS1011-1040DA_2_5m_JPEG_1band_EPSG32638');
var image3 = ee.Image('users/rizayeva/corona_2_5m/DS1011-1040DF_2_5m_JPEG_1band_EPSG32638');
var geometry10111040 = image2.addBands(image3).geometry();
Map.addLayer(image2, {min: 0, max: 200}, 'DS1011-1040DA', false);
Map.addLayer(image3, {min: 0, max: 200}, 'DS1011-1040DF', false);
Map.addLayer(geometry10111040, {}, 'geometry10111040', false);

//     Angular Second Moment for backward-facing camera
var imageVisParam = {bands: ['ds10111040da_asm'], min: 0.015071922070983398, max: 0.06105865647962591,  gamma: [1]};
var assetId='users/rizayeva/GLCM_ASM_DA_10111040';

//     Make a list that contains all the imagery in the asset directory
var assetList = ee.List(ee.data.getList({'id':assetId}));
// print(assetList);
var n=assetList.size().getInfo();

//     Generate an imagery collection that contains all the land cover maps on the list
var listocollection = function(assetList,size){
  var col=ee.ImageCollection([]);
  for (var i=0; i<size; i++){
    var value=ee.Dictionary(assetList.get(i));
    var imgID=ee.String(value.get('id')).getInfo();
    var imgs=ee.Image(imgID);
    col=col.merge(imgs);
  }
  return col;
};

//    Check the number of rasters in collection
var collection=listocollection(assetList,n);
print('AngularSecondMomentDA', collection);

//     Mosaic the rasters within collection
var angularSecondMoment_DA = collection.mosaic();
print(angularSecondMoment_DA);
// Map.addLayer(angularSecondMoment_DF, imageVisParam, 'angularSecondMoment_DF');
Map.addLayer(angularSecondMoment_DA, imageVisParam, 'angularSecondMoment_DA');

//     Export the final texture metric file
Export.image.toAsset({
  image: angularSecondMoment_DA,
  description: "angularSecondMoment_DA_10111040",
  assetId: 'GLCM/angularSecondMoment_DA_10111040',
  scale: 2.5,
  region: geometry10111040,
  maxPixels:1e13,
});


//     Angular Second Moment for forwaqrd-facing camera
var imageVisParam = {bands: ['ds10111040df_asm'], min: 0.015071922070983398, max: 0.06105865647962591,  gamma: [1]};
var assetId='users/rizayeva/GLCM_ASM_DF_10111040';

//     Make a list that contains all the imagery in the asset directory
var assetList = ee.List(ee.data.getList({'id':assetId}));
// print(assetList);
var n=assetList.size().getInfo();

//     Generate an imagery collection that contains all the land cover maps on the list
var listocollection = function(assetList,size){
  var col=ee.ImageCollection([]);
  for (var i=0; i<size; i++){
    var value=ee.Dictionary(assetList.get(i));
    var imgID=ee.String(value.get('id')).getInfo();
    var imgs=ee.Image(imgID);
    col=col.merge(imgs);
  }
  return col;
};

//    Check the number of rasters in collection
var collection=listocollection(assetList,n);
print('AngularSecondMomentDF', collection);

//     Mosaic the rasters within collection
var angularSecondMoment_DF = collection.mosaic();
print(angularSecondMoment_DF);
// Map.addLayer(angularSecondMoment_DF, imageVisParam, 'angularSecondMoment_DF');
Map.addLayer(angularSecondMoment_DF, imageVisParam, 'angularSecondMoment_DF');

//     Export the final texture metric file
Export.image.toAsset({
  image: angularSecondMoment_DF,
  description: "angularSecondMoment_DF_10111040",
  assetId: 'GLCM/angularSecondMoment_DF_10111040',
  scale: 2.5,
  region: geometry10111040,
  maxPixels:1e13,
});


//     Entropy for backward-facing camera
var imageVisParam = {bands: ['ds10111040da_ent'], min: 3.0209101473712536, max: 3.9960161288948637,  gamma: [1]};
var assetId='users/rizayeva/GLCM_ENT_DA_10111040';

//     Make a list that contains all the imagery in the asset directory
var assetList = ee.List(ee.data.getList({'id':assetId}));
// print(assetList);
var n=assetList.size().getInfo();

//     Generate an imagery collection that contains all the land cover maps on the list
var listocollection = function(assetList,size){
  var col=ee.ImageCollection([]);
  for (var i=0; i<size; i++){
    var value=ee.Dictionary(assetList.get(i));
    var imgID=ee.String(value.get('id')).getInfo();
    var imgs=ee.Image(imgID);
    col=col.merge(imgs);
  }
  return col;
};

//    Check the number of rasters in collection
var collection=listocollection(assetList,n);
print('EntropyDA', collection);

//     Mosaic the rasters within collection
var entropy_DA = collection.mosaic();
print(entropy_DA);
// Map.addLayer(entropy_DA, imageVisParam, 'entropy_DA');
Map.addLayer(entropy_DA, imageVisParam, 'entropy_DA');

//     Export the final texture metric file
Export.image.toAsset({
  image: entropy_DA,
  description: "entropy_DA_10111040",
  assetId: 'GLCM/entropy_DA_10111040',
  scale: 2.5,
  region: geometry10111040,
  maxPixels:1e13,
});


//     Entropy for forward-facing camera
var imageVisParam = {bands: ['ds10111040df_ent'], min: 3.0209101473712536, max: 3.9960161288948637,  gamma: [1]};
var assetId='users/rizayeva/GLCM_ENT_DF_10111040';

//     Make a list that contains all the imagery in the asset directory
var assetList = ee.List(ee.data.getList({'id':assetId}));
// print(assetList);
var n=assetList.size().getInfo();

//     Generate an imagery collection that contains all the land cover maps on the list
var listocollection = function(assetList,size){
  var col=ee.ImageCollection([]);
  for (var i=0; i<size; i++){
    var value=ee.Dictionary(assetList.get(i));
    var imgID=ee.String(value.get('id')).getInfo();
    var imgs=ee.Image(imgID);
    col=col.merge(imgs);
  }
  return col;
};

//    Check the number of rasters in collection
var collection=listocollection(assetList,n);
print('EntropyDF', collection);

//     Mosaic the rasters within collection
var entropy_DF = collection.mosaic();
print(entropy_DF);
// Map.addLayer(entropy_DF, imageVisParam, 'entropy_DF');
Map.addLayer(entropy_DF, imageVisParam, 'entropy_DF');

//     Export the final texture metric file
Export.image.toAsset({
  image: entropy_DF,
  description: "entropy_DF_10111040",
  assetId: 'GLCM/entropy_DF_10111040',
  scale: 2.5,
  region: geometry10111040,
  maxPixels:1e13,
});


//     Homogeneity for backward-facing camera
var imageVisParam = {bands: ['ds10111040da_idm'], min: 0.10173501666498017, max: 0.49671797626021486,  gamma: [1]};
var assetId='users/rizayeva/GLCM_HOM_DA_10111040';

//     Make a list that contains all the imagery in the asset directory
var assetList = ee.List(ee.data.getList({'id':assetId}));
// print(assetList);
var n=assetList.size().getInfo();

//     Generate an imagery collection that contains all the land cover maps on the list
var listocollection = function(assetList,size){
  var col=ee.ImageCollection([]);
  for (var i=0; i<size; i++){
    var value=ee.Dictionary(assetList.get(i));
    var imgID=ee.String(value.get('id')).getInfo();
    var imgs=ee.Image(imgID);
    col=col.merge(imgs);
  }
  return col;
};

//    Check the number of rasters in collection
var collection=listocollection(assetList,n);
print('HomogeneityDA', collection);

//     Mosaic the rasters within collection
var homogeneity_DA = collection.mosaic();
print(homogeneity_DA);
// Map.addLayer(homogeneity_DA, imageVisParam, 'homogeneity_DA');
Map.addLayer(homogeneity_DA, imageVisParam, 'homogeneity_DA');

//     Export the final texture metric file
Export.image.toAsset({
  image: homogeneity_DA,
  description: "homogeneity_DA_10111040",
  assetId: 'GLCM/homogeneity_DA_10111040',
  scale: 2.5,
  region: geometry10111040,
  maxPixels:1e13,
});


//     Homogeneity for forward-facing camera
var imageVisParam = {bands: ['ds10111040df_idm'], min: 0.10173501666498017, max: 0.49671797626021486,  gamma: [1]};
var assetId='users/rizayeva/GLCM_HOM_DF_10111040';
var n=assetList.size().getInfo();

//      Make a list that contains all the imagery in the asset directory
var assetList = ee.List(ee.data.getList({'id':assetId}));
// print(assetList);

//     Generate an imagery collection that contains all the land cover maps on the list
var listocollection = function(assetList,size){
  var col=ee.ImageCollection([]);
  for (var i=0; i<size; i++){
    var value=ee.Dictionary(assetList.get(i));
    var imgID=ee.String(value.get('id')).getInfo();
    var imgs=ee.Image(imgID);
    col=col.merge(imgs);
  }
  return col;
};

//    Check the number of rasters in collection
var collection=listocollection(assetList,n);
print('HomogeneityDF', collection);

//     Mosaic the rasters within collection
var homogeneity_DF = collection.mosaic();
print(homogeneity_DF);
// Map.addLayer(homogeneity_DF, imageVisParam, 'homogeneity_DF');
Map.addLayer(homogeneity_DF, imageVisParam, 'homogeneity_DF');

//     Export the final texture metric file
Export.image.toAsset({
  image: homogeneity_DF,
  description: "homogeneity_DF_10111040",
  assetId: 'GLCM/homogeneity_DF_10111040',
  scale: 2.5,
  region: geometry10111040,
  maxPixels:1e13,
});