static void Main(string[] args)
{
FolderBrowserDialog fbd = new FolderBrowserDialog();
string allImagesTesting = "";
if (fbd.ShowDialog() == DialogResult.OK)
{
allImagesTesting = fbd.SelectedPath;
}
string protoTxtPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "deploy.prototxt");
string mdlBinPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "resnet_50_1by2_nsfw.caffemodel");
var classifier = new ImageClassifier(protoTxtPath, mdlBinPath);
Console.WriteLine("Loaded classifier.");
Console.WriteLine("Classifier has cutoff of {0}.", classifier.Cutoff);
/*
* string badImagesTesting = @"E:\img_malas";
* string goodImagesTesting = @"E:\img_buenas";
* string allImagesTesting = @"G:\Temp\$Temp\pics";
*/
//string allImagesTesting = @"G:\Temp\$Temp\pics";
//classifier.Cutoff = 0.15;
//var badImgs = Directory.GetFiles(badImagesTesting).ToList();
//var goodImgs = Directory.GetFiles(goodImagesTesting).ToList();
//Esto lista todas las .jpg en la carpeta seleccionada y todas sus subcarpetas
var allImgs = Directory.GetFiles(allImagesTesting, "*.jpg", SearchOption.AllDirectories).ToList();
var total = allImgs.Count;
//Creo subcarpeta para mover los falsos negativos
Directory.CreateDirectory(Path.Combine(allImagesTesting, "wrong"));
// Cut down our test base so it's not so huge.
/*
* var min = Math.Min(badImgs.Count, goodImgs.Count);
*
* if(min > 1000)
* {
* min = 1000;
* }
*
* badImgs = badImgs.GetRange(0, min);
* goodImgs = goodImgs.GetRange(0, min);
*
*
* // Shuffle things up a bit.
* Random r = new Random();
* badImgs = badImgs.OrderBy(x => r.Next()).ToList();
* goodImgs = goodImgs.OrderBy(x => r.Next()).ToList();
*/
int goodRight = 0, goodWrong = 0, badRight = 0, badWrong = 0;
var sw = new Stopwatch();
sw.Start();
foreach (var img in allImgs)
{
var imgData = File.ReadAllBytes(img);
if (classifier.ClassifyImage(imgData))
{
++badRight;
}
else
{
// If you feel like inspecting false negatives.
var outPath = Path.Combine(allImagesTesting, "wrong", Path.GetFileName(img));
//File.Move(img, outPath);
File.Copy(img, outPath);
++badWrong;
}
//Publica avance cada 10 imágenes procesadas
if ((badRight + badWrong) % 10 == 0)
{
Console.WriteLine("Classified {0} of {1} bad images.", badRight + badWrong, total);
}
}
/*
*
* foreach(var img in badImgs)
* {
* var imgData = File.ReadAllBytes(img);
*
* if(classifier.ClassifyImage(imgData))
* {
++badRight;
* }
* else
* {
* // If you feel like inspecting false negatives.
* // var outPath = Path.Combine(SOME_BASE_PATH_FOR_FALSE_NEGATIVES, Path.GetFileName(img));
* // File.Move(img, outPath);
* var outPath = Path.Combine(badImagesTesting,"wrong", Path.GetFileName(img));
* File.Move(img, outPath);
++badWrong;
* }
*
* if((badRight + badWrong) % 10 == 0)
* {
* Console.WriteLine("Classified {0} of {1} bad images.", badRight + badWrong, min);
* }
* }
*
* foreach(var img in goodImgs)
* {
* var imgData = File.ReadAllBytes(img);
*
* if(classifier.ClassifyImage(imgData))
* {
++goodWrong;
* }
* else
* {
++goodRight;
* }
*
* if((goodWrong + goodRight) % 10 == 0)
* {
* Console.WriteLine("Classified {0} of {1} good images.", goodWrong + goodRight, min);
* }
* }
*
*/
sw.Stop();
Console.WriteLine("Classified pornographic images with an accuracy of {0}%.", 100d * ((double)badRight / (double)(badRight + badWrong)));
Console.WriteLine("Classified non-pornographic images with an accuracy of {0}%.", 100d * ((double)goodRight / (double)(goodRight + goodWrong)));
//Console.WriteLine("Took an average of {0} msec per image to classify.", sw.ElapsedMilliseconds / (double)(min * 2));
Console.WriteLine("Took an average of {0} msec per image to classify.", sw.ElapsedMilliseconds / (double)(total));
Console.ReadLine(); //Pausa para que no cierre consola
}
private static void TestClassifier(ImageClassifier classifier, ClassifierType type, bool verbose = false)
{
Console.WriteLine("{0} classifier has cutoff of {1}.", type, classifier.Cutoff);
string badImagesTesting = @"SOME BAD STUFF BRO";
string goodImagesTesting = @"SOME GOOD STUFF KIDDO";
var badImgs = Directory.GetFiles(badImagesTesting, "*.jpg", SearchOption.AllDirectories).ToList();
var goodImgs = Directory.GetFiles(goodImagesTesting, "*.jpg", SearchOption.AllDirectories).ToList();
// Cut down our test base so it's not so huge.
var min = Math.Min(badImgs.Count, goodImgs.Count);
if (min > 1000)
{
min = 1000;
}
Console.WriteLine("Testing against a total of {0} images, {1} from each class.", min * 2, min);
badImgs = badImgs.GetRange(0, min);
goodImgs = goodImgs.GetRange(0, min);
// Shuffle things up a bit.
Random r = new Random();
badImgs = badImgs.OrderBy(x => r.Next()).ToList();
goodImgs = goodImgs.OrderBy(x => r.Next()).ToList();
int goodRight = 0, goodWrong = 0, badRight = 0, badWrong = 0;
var sw = new Stopwatch();
sw.Start();
foreach (var img in badImgs)
{
var imgData = File.ReadAllBytes(img);
if (classifier.ClassifyImage(imgData))
{
++badRight;
}
else
{
// If you feel like inspecting false negatives.
//var outPath = Path.Combine(SOME PLACE FOR FALSE NEGATIVES, Path.GetFileName(img));
//File.Move(img, outPath);
++badWrong;
}
if (verbose && (badRight + badWrong) % 10 == 0)
{
Console.WriteLine("Classified {0} of {1} bad images.", badRight + badWrong, min);
}
}
foreach (var img in goodImgs)
{
var imgData = File.ReadAllBytes(img);
if (classifier.ClassifyImage(imgData))
{
++goodWrong;
}
else
{
++goodRight;
}
if (verbose && (goodWrong + goodRight) % 10 == 0)
{
Console.WriteLine("Classified {0} of {1} good images.", goodWrong + goodRight, min);
}
}
sw.Stop();
Console.WriteLine("Classifier with type {0} classified pornographic images with an accuracy of {1}%.", type, 100d * ((double)badRight / (double)(badRight + badWrong)));
Console.WriteLine("Classifier with type {0} classified non-pornographic images with an accuracy of {1}%.", type, 100d * ((double)goodRight / (double)(goodRight + goodWrong)));
Console.WriteLine("Classifier with type {0} took an average of {1} msec per image to classify.", type, sw.ElapsedMilliseconds / (double)(min * 2));
}
private static void RunClassifier(ImageClassifier classifier, string ClassifierPath,
bool classifyPorn = true, bool verbose = false, bool copyWrongs = false, string wrongsPath = "", double cutoff = 0)
{
//cutoff = 2 --> usar default
if (cutoff != 2)
{
classifier.Cutoff = cutoff;
}
Console.WriteLine("Classifier has cutoff of {0}.", classifier.Cutoff);
//REVISAR falla al encontrar directorios con acceso denegado (ej. SVI ó RECYCLE.BIN)
// Y es lento para grandes tamaños, ver exploración recursiva y EnumerateFiles en su lugar
//var allImgs = Directory.GetFiles(ClassifierPath, "*.jpg", SearchOption.AllDirectories).ToList();
//var allImgs = Directory.GetFiles(ClassifierPath, "*.png", SearchOption.AllDirectories).ToList();
//var allImgs = Directory.GetFiles(ClassifierPath, "*.jpg").ToList();
var allImgs = Directory.GetFiles(ClassifierPath).ToList();
var total = allImgs.Count;
bool tPorn = classifyPorn;
Console.WriteLine("{0} imágenes a clasificar.", total);
int imgRight = 0, imgWrong = 0;
var sw = new Stopwatch();
sw.Start();
foreach (var img in allImgs)
{
var imgData = File.ReadAllBytes(img);
var outPath = Path.Combine(wrongsPath, Path.GetFileName(img));
//var outPath = Path.Combine(@"D:\img_wrong", Path.GetFileName(img).Replace(".png", "_" + ctStr + score.ToString("F6") + ".png")); //Path.Combine(img, "wrong", Path.GetFileName(img));
try
{
if (classifier.ClassifyImage(imgData))
{
if (tPorn)
{
++imgRight;
}
else
{
++imgWrong;
if (copyWrongs)
{
File.Copy(img, outPath);
}
}
}
else
{
// If you feel like inspecting false negatives.
//var outPath = Path.Combine(SOME PLACE FOR FALSE NEGATIVES, Path.GetFileName(img));
//File.Move(img, outPath);
//var outPath = Path.Combine(img, "wrong", Path.GetFileName(img));
//File.Copy(img, outPath);
if (tPorn)
{
++imgWrong;
//if (verbose) Console.WriteLine("score: {0}", classifier.GetPositiveProbability(imgData));
if (copyWrongs)
{
File.Copy(img, outPath);
}
}
else
{
++imgRight;
}
}
if (verbose && (imgRight + imgWrong) % 100 == 0)
{
if (tPorn)
{
Console.WriteLine("Classified {0} of {1} pornographic images.", imgRight + imgWrong, total);
}
else
{
Console.WriteLine("Classified {0} of {1} non-pornographic images.", imgRight + imgWrong, total);
}
}
//Console.WriteLine("score: {0}", score);
}
catch (Exception e)
{
// Extract some information from this exception, and then
// throw it to the parent method.
if (e.Source != null)
{
Console.WriteLine("Exception source: {0}", e.Source);
}
throw;
}
}
sw.Stop();
if (tPorn)
{
Console.WriteLine("Classified {0} pornographic images with an accuracy of {1}%.", imgRight + imgWrong, 100d * ((double)imgRight / (double)(imgRight + imgWrong)));
}
else
{
Console.WriteLine("Classified {0} non-pornographic images with an accuracy of {1}%.", imgRight + imgWrong, 100d * ((double)imgRight / (double)(imgRight + imgWrong)));
}
Console.WriteLine("Classifier took an average of {0} msec per image to classify.", sw.ElapsedMilliseconds / (double)(total * 2));
}
static void Main(string[] args)
{
string protoTxtPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "deploy.prototxt");
string mdlBinPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "resnet_50_1by2_nsfw.caffemodel");
var classifier = new ImageClassifier(protoTxtPath, mdlBinPath);
Console.WriteLine("Loaded classifier.");
Console.WriteLine("Classifier has cutoff of {0}.", classifier.Cutoff);
//string badImagesTesting = PATH_TO_BAD_IMAGES;
//string goodImagesTesting = PATH_TO_GOOD_IMAGES;
string badImagesTesting = @"E:\img_malas";
string goodImagesTesting = @"E:\img_malas";
var badImgs = Directory.GetFiles(badImagesTesting).ToList();
var goodImgs = Directory.GetFiles(goodImagesTesting).ToList();
// Cut down our test base so it's not so huge.
var min = Math.Min(badImgs.Count, goodImgs.Count);
if (min > 1000)
{
min = 1000;
}
badImgs = badImgs.GetRange(0, min);
goodImgs = goodImgs.GetRange(0, min);
// Shuffle things up a bit.
Random r = new Random();
badImgs = badImgs.OrderBy(x => r.Next()).ToList();
goodImgs = goodImgs.OrderBy(x => r.Next()).ToList();
int goodRight = 0, goodWrong = 0, badRight = 0, badWrong = 0;
var sw = new Stopwatch();
sw.Start();
foreach (var img in badImgs)
{
var imgData = File.ReadAllBytes(img);
if (classifier.ClassifyImage(imgData))
{
++badRight;
}
else
{
// If you feel like inspecting false negatives.
// var outPath = Path.Combine(SOME_BASE_PATH_FOR_FALSE_NEGATIVES, Path.GetFileName(img));
// File.Move(img, outPath);
++badWrong;
}
if ((badRight + badWrong) % 10 == 0)
{
Console.WriteLine("Classified {0} of {1} bad images.", badRight + badWrong, min);
}
}
foreach (var img in goodImgs)
{
var imgData = File.ReadAllBytes(img);
if (classifier.ClassifyImage(imgData))
{
++goodWrong;
}
else
{
++goodRight;
}
if ((goodWrong + goodRight) % 10 == 0)
{
Console.WriteLine("Classified {0} of {1} good images.", goodWrong + goodRight, min);
}
}
sw.Stop();
Console.WriteLine("Classified pornographic images with an accuracy of {0}%.", 100d * ((double)badRight / (double)(badRight + badWrong)));
Console.WriteLine("Classified non-pornographic images with an accuracy of {0}%.", 100d * ((double)goodRight / (double)(goodRight + goodWrong)));
Console.WriteLine("Took an average of {0} msec per image to classify.", sw.ElapsedMilliseconds / (double)(min * 2));
//Console.WriteLine("min = {0}", min);
Console.ReadLine();
}