public RgbAlphaPixel Operator(string str) { if (this._Colors.TryGetValue(str, out var value)) { return(value); } var pix = new HsiPixel { H = Reverse((byte)this._Colors.Count), S = byte.MaxValue, I = 150 }; var result = new RgbAlphaPixel(); Dlib.AssignPixel(ref result, pix); this._Colors[str] = result; return(result); }
public void GetRowColumn() { const int width = 150; const int height = 100; var tests = new[] { new { Type = ImageTypes.RgbPixel, ExpectResult = true }, new { Type = ImageTypes.RgbAlphaPixel, ExpectResult = true }, new { Type = ImageTypes.UInt8, ExpectResult = true }, new { Type = ImageTypes.UInt16, ExpectResult = true }, new { Type = ImageTypes.UInt32, ExpectResult = true }, new { Type = ImageTypes.Int8, ExpectResult = true }, new { Type = ImageTypes.Int16, ExpectResult = true }, new { Type = ImageTypes.Int32, ExpectResult = true }, new { Type = ImageTypes.HsiPixel, ExpectResult = true }, new { Type = ImageTypes.Float, ExpectResult = true }, new { Type = ImageTypes.Double, ExpectResult = true } }; foreach (var test in tests) { var array2D = CreateArray2D(test.Type, height, width); switch (array2D.ImageType) { case ImageTypes.UInt8: { var array = (Array2D <byte>)array2D; Dlib.AssignAllPpixels(array, 255); using (var row = array[0]) Assert.AreEqual(row[0], 255, "Array<byte> failed"); } break; case ImageTypes.UInt16: { var array = (Array2D <ushort>)array2D; Dlib.AssignAllPpixels(array, 255); using (var row = array[0]) Assert.AreEqual(row[0], 255, "Array<ushort> failed"); } break; case ImageTypes.UInt32: { var array = (Array2D <uint>)array2D; Dlib.AssignAllPpixels(array, 255); using (var row = array[0]) Assert.AreEqual(row[0], 255u, "Array<uint> failed"); } break; case ImageTypes.Int8: { var array = (Array2D <sbyte>)array2D; Dlib.AssignAllPpixels(array, 127); using (var row = array[0]) Assert.AreEqual(row[0], 127, "Array<sbyte> failed"); } break; case ImageTypes.Int16: { var array = (Array2D <short>)array2D; Dlib.AssignAllPpixels(array, 255); using (var row = array[0]) Assert.AreEqual(row[0], 255, "Array<short> failed"); } break; case ImageTypes.Int32: { var array = (Array2D <int>)array2D; Dlib.AssignAllPpixels(array, 255); using (var row = array[0]) Assert.AreEqual(row[0], 255, "Array<int> failed"); } break; case ImageTypes.Float: { var array = (Array2D <float>)array2D; Dlib.AssignAllPpixels(array, 255); using (var row = array[0]) Assert.AreEqual(row[0], 255, "Array<float> failed"); } break; case ImageTypes.Double: { var array = (Array2D <double>)array2D; Dlib.AssignAllPpixels(array, 255); using (var row = array[0]) Assert.AreEqual(row[0], 255, "Array<double> failed"); } break; case ImageTypes.RgbPixel: { var array = (Array2D <RgbPixel>)array2D; var pixel = new RgbPixel { Red = 255, Blue = 255, Green = 255 }; Dlib.AssignAllPpixels(array, pixel); using (var row = array[0]) { var t = row[0]; Assert.AreEqual(t.Red, 255, "Array<RgbPixel> failed"); Assert.AreEqual(t.Blue, 255, "Array<RgbPixel> failed"); Assert.AreEqual(t.Green, 255, "Array<RgbPixel> failed"); } } break; case ImageTypes.RgbAlphaPixel: { var array = (Array2D <RgbAlphaPixel>)array2D; var pixel = new RgbAlphaPixel { Red = 255, Blue = 255, Green = 255, Alpha = 255 }; Dlib.AssignAllPpixels(array, pixel); using (var row = array[0]) { var t = row[0]; Assert.AreEqual(t.Red, 255, "Array<RgbAlphaPixel> failed"); Assert.AreEqual(t.Blue, 255, "Array<RgbAlphaPixel> failed"); Assert.AreEqual(t.Green, 255, "Array<RgbAlphaPixel> failed"); Assert.AreEqual(t.Alpha, 255, "Array<RgbAlphaPixel> failed"); } } break; case ImageTypes.HsiPixel: { var array = (Array2D <HsiPixel>)array2D; var pixel = new HsiPixel { H = 255, S = 255, I = 255 }; Dlib.AssignAllPpixels(array, pixel); using (var row = array[0]) { var t = row[0]; Assert.AreEqual(t.H, 255, "Array<HsiPixel> failed"); Assert.AreEqual(t.S, 255, "Array<HsiPixel> failed"); Assert.AreEqual(t.I, 255, "Array<HsiPixel> failed"); } } break; default: throw new ArgumentOutOfRangeException(nameof(array2D.ImageType), array2D.ImageType, null); } this.DisposeAndCheckDisposedState(array2D); } }
public void LoadImageData2() { const int cols = 512; const int rows = 512; const int steps = 512; var tests = new[] { new { Type = ImageTypes.UInt8, ExpectResult = true }, new { Type = ImageTypes.UInt16, ExpectResult = true }, new { Type = ImageTypes.Int16, ExpectResult = true }, new { Type = ImageTypes.Int32, ExpectResult = true }, new { Type = ImageTypes.HsiPixel, ExpectResult = true }, new { Type = ImageTypes.RgbPixel, ExpectResult = true }, new { Type = ImageTypes.RgbAlphaPixel, ExpectResult = true }, new { Type = ImageTypes.Float, ExpectResult = true }, new { Type = ImageTypes.Double, ExpectResult = true } }; var random = new Random(0); foreach (var test in tests) { TwoDimentionObjectBase image; using (var win = new ImageWindow()) { switch (test.Type) { case ImageTypes.UInt8: { var data = new byte[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = (byte)random.Next(0, 255); } } image = Dlib.LoadImageData <byte>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <byte>)image); win.WaitUntilClosed(); } } break; case ImageTypes.UInt16: { var data = new ushort[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = (ushort)random.Next(0, 255); } } image = Dlib.LoadImageData <ushort>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <ushort>)image); win.WaitUntilClosed(); } } break; case ImageTypes.Int16: { var data = new short[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = (short)random.Next(0, 255); } } image = Dlib.LoadImageData <short>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <short>)image); win.WaitUntilClosed(); } } break; case ImageTypes.Int32: { var data = new int[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = random.Next(0, 255); } } image = Dlib.LoadImageData <int>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <int>)image); win.WaitUntilClosed(); } } break; case ImageTypes.Float: { var data = new float[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = (float)random.NextDouble(); } } image = Dlib.LoadImageData <float>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <float>)image); win.WaitUntilClosed(); } } break; case ImageTypes.Double: { var data = new double[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = (double)random.NextDouble(); } } image = Dlib.LoadImageData <double>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <double>)image); win.WaitUntilClosed(); } } break; case ImageTypes.HsiPixel: { var data = new HsiPixel[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = new HsiPixel { H = (byte)random.Next(0, 255), S = (byte)random.Next(0, 255), I = (byte)random.Next(0, 255) } } } ; image = Dlib.LoadImageData <HsiPixel>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <HsiPixel>)image); win.WaitUntilClosed(); } } break; case ImageTypes.RgbPixel: { var data = new RgbPixel[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = new RgbPixel { Red = (byte)random.Next(0, 255), Green = (byte)random.Next(0, 255), Blue = (byte)random.Next(0, 255) } } } ; image = Dlib.LoadImageData <RgbPixel>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <RgbPixel>)image); win.WaitUntilClosed(); } } break; case ImageTypes.RgbAlphaPixel: { var data = new RgbAlphaPixel[rows * cols]; for (var r = 0; r < rows; r++) { for (var c = 0; c < cols; c++) { data[steps * r + c] = new RgbAlphaPixel { Red = (byte)random.Next(0, 255), Green = (byte)random.Next(0, 255), Blue = (byte)random.Next(0, 255), Alpha = (byte)random.Next(0, 255) } } } ; image = Dlib.LoadImageData <RgbAlphaPixel>(data, rows, cols, steps); if (this.CanGuiDebug) { win.SetImage((Array2D <RgbAlphaPixel>)image); win.WaitUntilClosed(); } } break; default: throw new ArgumentOutOfRangeException(); } } Assert.AreEqual(image.Columns, cols, $"Failed to load {test.Type}."); Assert.AreEqual(image.Rows, rows, $"Failed to load {test.Type}."); this.DisposeAndCheckDisposedState(image); } }
public void Indexer3() { try { using (var matrix = new Matrix <byte>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (byte)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(byte)}"); } try { using (var matrix = new Matrix <ushort>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (ushort)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(ushort)}"); } try { using (var matrix = new Matrix <uint>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (uint)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(uint)}"); } try { using (var matrix = new Matrix <sbyte>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (sbyte)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(sbyte)}"); } try { using (var matrix = new Matrix <short>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (short)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(short)}"); } try { using (var matrix = new Matrix <int>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (int)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(int)}"); } try { using (var matrix = new Matrix <float>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (float)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(float)}"); } try { using (var matrix = new Matrix <double>(1, 3)) { for (var index = 0; index < 3; index++) { var v = (double)(index); matrix[index] = v; Assert.AreEqual(v, matrix[index]); } } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(double)}"); } try { using (var matrix = new Matrix <RgbPixel>(1, 3)) for (var index = 0; index < 3; index++) { var b = (byte)(index); var v = new RgbPixel { Red = b, Blue = b, Green = b }; matrix[index] = v; Assert.AreEqual(v.Red, matrix[index].Red); Assert.AreEqual(v.Blue, matrix[index].Blue); Assert.AreEqual(v.Green, matrix[index].Green); } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(RgbPixel)}"); } try { using (var matrix = new Matrix <RgbAlphaPixel>(1, 3)) for (var index = 0; index < 3; index++) { var b = (byte)(index); var v = new RgbAlphaPixel { Red = b, Blue = b, Green = b }; matrix[index] = v; Assert.AreEqual(v.Red, matrix[index].Red); Assert.AreEqual(v.Blue, matrix[index].Blue); Assert.AreEqual(v.Green, matrix[index].Green); } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(RgbPixel)}"); } try { using (var matrix = new Matrix <HsiPixel>(1, 3)) for (var index = 0; index < 3; index++) { var b = (byte)(index); var v = new HsiPixel { H = b, S = b, I = b }; matrix[index] = v; Assert.AreEqual(v.H, matrix[index].H); Assert.AreEqual(v.S, matrix[index].S); Assert.AreEqual(v.I, matrix[index].I); } } catch (Exception) { Assert.Fail($"Failed to access for Type: {typeof(HsiPixel)}"); } }
private static void Main() { try { // You can get this file from http://dlib.net/files/mmod_rear_end_vehicle_detector.dat.bz2 // This network was produced by the dnn_mmod_train_find_cars_ex.cpp example program. // As you can see, the file also includes a separately trained shape_predictor. To see // a generic example of how to train those refer to train_shape_predictor_ex.cpp. using (var deserialize = new ProxyDeserialize("mmod_rear_end_vehicle_detector.dat")) using (var net = LossMmod.Deserialize(deserialize, 1)) using (var sp = ShapePredictor.Deserialize(deserialize)) using (var img = Dlib.LoadImageAsMatrix <RgbPixel>("mmod_cars_test_image.jpg")) using (var win = new ImageWindow()) { win.SetImage(img); // Run the detector on the image and show us the output. var dets = net.Operator(img).First(); foreach (var d in dets) { // We use a shape_predictor to refine the exact shape and location of the detection // box. This shape_predictor is trained to simply output the 4 corner points of // the box. So all we do is make a rectangle that tightly contains those 4 points // and that rectangle is our refined detection position. var fd = sp.Detect(img, d); var rect = Rectangle.Empty; for (var j = 0u; j < fd.Parts; ++j) { rect += fd.GetPart(j); } win.AddOverlay(rect, new RgbPixel(255, 0, 0)); } Console.WriteLine("Hit enter to view the intermediate processing steps"); Console.ReadKey(); // Now let's look at how the detector works. The high level processing steps look like: // 1. Create an image pyramid and pack the pyramid into one big image. We call this // image the "tiled pyramid". // 2. Run the tiled pyramid image through the CNN. The CNN outputs a new image where // bright pixels in the output image indicate the presence of cars. // 3. Find pixels in the CNN's output image with a value > 0. Those locations are your // preliminary car detections. // 4. Perform non-maximum suppression on the preliminary detections to produce the // final output. // // We will be plotting the images from steps 1 and 2 so you can visualize what's // happening. For the CNN's output image, we will use the jet colormap so that "bright" // outputs, i.e. pixels with big values, appear in red and "dim" outputs appear as a // cold blue color. To do this we pick a range of CNN output values for the color // mapping. The specific values don't matter. They are just selected to give a nice // looking output image. const float lower = -2.5f; const float upper = 0.0f; Console.WriteLine($"jet color mapping range: lower={lower} upper={upper}"); // Create a tiled pyramid image and display it on the screen. // Get the type of pyramid the CNN used //using pyramid_type = std::remove_reference < decltype(input_layer(net)) >::type::pyramid_type; // And tell create_tiled_pyramid to create the pyramid using that pyramid type. using (var inputLayer = new InputRgbImagePyramid <PyramidDown>(6)) { net.TryGetInputLayer(inputLayer); var padding = inputLayer.GetPyramidPadding(); var outerPadding = inputLayer.GetPyramidOuterPadding(); Dlib.CreateTiledPyramid <RgbPixel, PyramidDown>(img, padding, outerPadding, 6, out var tiledImg, out var rects); using (var winpyr = new ImageWindow(tiledImg, "Tiled pyramid")) { // This CNN detector represents a sliding window detector with 3 sliding windows. Each // of the 3 windows has a different aspect ratio, allowing it to find vehicles which // are either tall and skinny, squarish, or short and wide. The aspect ratio of a // detection is determined by which channel in the output image triggers the detection. // Here we are just going to max pool the channels together to get one final image for // our display. In this image, a pixel will be bright if any of the sliding window // detectors thinks there is a car at that location. using (var subnet = net.GetSubnet()) { var output = subnet.Output; Console.WriteLine($"Number of channels in final tensor image: {output.K}"); var networkOutput = Dlib.ImagePlane(output); for (var k = 1; k < output.K; k++) { using (var tmpNetworkOutput = Dlib.ImagePlane(output, 0, k)) { var maxPointWise = Dlib.MaxPointWise(networkOutput, tmpNetworkOutput); networkOutput.Dispose(); networkOutput = maxPointWise; } } // We will also upsample the CNN's output image. The CNN we defined has an 8x // downsampling layer at the beginning. In the code below we are going to overlay this // CNN output image on top of the raw input image. To make that look nice it helps to // upsample the CNN output image back to the same resolution as the input image, which // we do here. var networkOutputScale = img.Columns / (double)networkOutput.Columns; Dlib.ResizeImage(networkOutput, networkOutputScale); // Display the network's output as a color image. using (var jet = Dlib.Jet(networkOutput, upper, lower)) using (var winOutput = new ImageWindow(jet, "Output tensor from the network")) { // Also, overlay network_output on top of the tiled image pyramid and display it. for (var r = 0; r < tiledImg.Rows; ++r) { for (var c = 0; c < tiledImg.Columns; ++c) { var tmp = new DPoint(c, r); tmp = Dlib.InputTensorToOutputTensor(net, tmp); var dp = networkOutputScale * tmp; tmp = new DPoint((int)dp.X, (int)dp.Y); if (Dlib.GetRect(networkOutput).Contains((int)tmp.X, (int)tmp.Y)) { var val = networkOutput[(int)tmp.Y, (int)tmp.X]; // alpha blend the network output pixel with the RGB image to make our // overlay. var p = new RgbAlphaPixel(); Dlib.AssignPixel(ref p, Dlib.ColormapJet(val, lower, upper)); p.Alpha = 120; var rgb = new RgbPixel(); Dlib.AssignPixel(ref rgb, p); tiledImg[r, c] = rgb; } } } // If you look at this image you can see that the vehicles have bright red blobs on // them. That's the CNN saying "there is a car here!". You will also notice there is // a certain scale at which it finds cars. They have to be not too big or too small, // which is why we have an image pyramid. The pyramid allows us to find cars of all // scales. using (var winPyrOverlay = new ImageWindow(tiledImg, "Detection scores on image pyramid")) { // Finally, we can collapse the pyramid back into the original image. The CNN doesn't // actually do this step, since it's enough to threshold the tiled pyramid image to get // the detections. However, it makes a nice visualization and clearly indicates that // the detector is firing for all the cars. using (var collapsed = new Matrix <float>(img.Rows, img.Columns)) using (var inputTensor = new ResizableTensor()) { inputLayer.ToTensor(img, 1, inputTensor); for (var r = 0; r < collapsed.Rows; ++r) { for (var c = 0; c < collapsed.Columns; ++c) { // Loop over a bunch of scale values and look up what part of network_output // corresponds to the point(c,r) in the original image, then take the max // detection score over all the scales and save it at pixel point(c,r). var maxScore = -1e30f; for (double scale = 1; scale > 0.2; scale *= 5.0 / 6.0) { // Map from input image coordinates to tiled pyramid coordinates. var tensorSpace = inputLayer.ImageSpaceToTensorSpace(inputTensor, scale, new DRectangle(new DPoint(c, r))); var tmp = tensorSpace.Center; // Now map from pyramid coordinates to network_output coordinates. var dp = networkOutputScale * Dlib.InputTensorToOutputTensor(net, tmp); tmp = new DPoint((int)dp.X, (int)dp.Y); if (Dlib.GetRect(networkOutput).Contains((int)tmp.X, (int)tmp.Y)) { var val = networkOutput[(int)tmp.Y, (int)tmp.X]; if (val > maxScore) { maxScore = val; } } } collapsed[r, c] = maxScore; // Also blend the scores into the original input image so we can view it as // an overlay on the cars. var p = new RgbAlphaPixel(); Dlib.AssignPixel(ref p, Dlib.ColormapJet(maxScore, lower, upper)); p.Alpha = 120; var rgb = new RgbPixel(); Dlib.AssignPixel(ref rgb, p); img[r, c] = rgb; } } using (var jet2 = Dlib.Jet(collapsed, upper, lower)) using (var winCollapsed = new ImageWindow(jet2, "Collapsed output tensor from the network")) using (var winImgAndSal = new ImageWindow(img, "Collapsed detection scores on raw image")) { Console.WriteLine("Hit enter to end program"); Console.ReadKey(); } } } } } } } } } catch (Exception e) { Console.WriteLine(e); } }