private static ImageIndex fromImage(SURFDescriptor descriptor, bool noimages, string outPath, Crew crew, Mat image)
{
image = image.SubMat(0, image.Rows * 7 / 10, 0, image.Cols);
if (!noimages)
{
Cv2.ImWrite(Path.Combine(outPath, "data", "traindata", $"feat_{crew.symbol}.png"), image);
}
KeyPoint[] keypoints;
var features = descriptor.Describe(image, out keypoints);
if ((features.Rows > 0) && (features.Cols > 0))
{
float[] featuresBytes = new float[features.Rows * features.Cols];
features.GetArray(out featuresBytes);
return(new ImageIndex()
{
Features = featuresBytes,
MType = features.Type(),
Cols = features.Cols,
Rows = features.Rows,
Symbol = crew.symbol,
Rarity = crew.max_rarity
});
}
return(null);
}
public static void ParseSingleImage(string imagePath, string symbol, string outPath)
{
SURFDescriptor descriptor = new SURFDescriptor();
List <ImageIndex> imgIndexes = new List <ImageIndex>();
Mat image = Cv2.ImRead(imagePath, ImreadModes.Unchanged);
KeyPoint[] keypoints;
var features = descriptor.Describe(image, out keypoints);
if ((features.Rows > 0) && (features.Cols > 0))
{
float[] featuresBytes = new float[features.Rows * features.Cols];
features.GetArray(0, 0, featuresBytes);
var imgIdx = new List <ImageIndex>();
imgIdx.Add(new ImageIndex()
{
Features = featuresBytes,
MType = features.Type(),
Cols = features.Cols,
Rows = features.Rows,
Symbol = symbol,
Rarity = 0
});
SaveToDisk(imgIdx, outPath);
}
}
public static void ParseDataset(string datacorepath, string outPath, bool noimages, Action <string> progress)
{
var crewPath = Path.Combine(datacorepath, "static", "structured", "crew.json");
var assetPath = Path.Combine(datacorepath, "static", "media", "assets");
Crew[] allcrew = JsonConvert.DeserializeObject <Crew[]>(File.ReadAllText(crewPath));
SURFDescriptor descriptor = new SURFDescriptor();
List <ImageIndex> imgIndexes = new List <ImageIndex>();
foreach (Crew crew in allcrew)
{
if (crew.max_rarity < 4)
{
continue;
}
progress($"Parsing {crew.name}...");
string fileName = Path.Combine(assetPath, crew.imageUrlFullBody);
Mat image = Cv2.ImRead(fileName, ImreadModes.Unchanged);
image = image.SubMat(0, image.Rows * 7 / 10, 0, image.Cols);
if (!noimages)
{
Cv2.ImWrite(Path.Combine(outPath, "data", "traindata", $"feat_{crew.symbol}.png"), image);
}
KeyPoint[] keypoints;
var features = descriptor.Describe(image, out keypoints);
if ((features.Rows > 0) && (features.Cols > 0))
{
float[] featuresBytes = new float[features.Rows * features.Cols];
features.GetArray(0, 0, featuresBytes);
imgIndexes.Add(new ImageIndex()
{
Features = featuresBytes,
MType = features.Type(),
Cols = features.Cols,
Rows = features.Rows,
Symbol = crew.symbol,
Rarity = crew.max_rarity
});
}
}
SaveToDisk(imgIndexes, outPath);
}
private ImageIndex MatchCrew(Mat crew)
{
KeyPoint[] keypoints;
Mat crew3Features = new Mat();
_descriptor.Describe(crew, out keypoints).ConvertTo(crew3Features, MatType.CV_32F);
var goodMatches = _descriptorMatcher.Match(crew3Features);
var indexBest = goodMatches.GroupBy(x => (x.ImgIdx)).Select(
g => new
{
Key = g.Key,
Value = g.Count()
}).OrderByDescending(x => (x.Value)).First();
var result = _indexedDatset.Where(x => x.index == indexBest.Key).FirstOrDefault();
result.score = indexBest.Value;
return(result);
}
