| 29 | SSize GetBiggestImage(); |
| 30 | |
| 31 | void FillRandomImg(CSimpleImage& Image, int ImageNo) |
| 32 | { |
| 33 | static bool Initialized = false; |
| 34 | const static int NbImages = 2; |
| 35 | static CSimpleImage Images[NbImages]; |
| 36 | static CImage<float> FloatImages[NbImages]; |
| 37 | |
| 38 | //assert(ImageNo >= 0 && ImageNo < NbImages); |
| 39 | |
| 40 | if (!Initialized) |
| 41 | { |
| 42 | // Random image generation is time consuming |
| 43 | // So we do it only once |
| 44 | |
| 45 | std::mt19937 Rand; // Mersenne twister pseudo random number generator |
| 46 | |
| 47 | // Uniform distribution that can set all but the leftmost bit |
| 48 | std::uniform_int_distribution<unsigned char> Dist(0, 0xFF); |
| 49 | |
| 50 | SSize Big = GetBiggestImage(); |
| 51 | |
| 52 | for (int i = 0; i < NbImages; i++) |
| 53 | { |
| 54 | Images[i].Create(Big.Width, Big.Height, 1, SImage::S32); |
| 55 | uint uByteWidth = Images[i].BytesWidth(); |
| 56 | for (uint y = 0; y < Images[i].Height; y++) |
| 57 | for (uint b = 0; b < uByteWidth; b++) |
| 58 | Images[i].Data(y)[b] = Dist(Rand); |
| 59 | } |
| 60 | |
| 61 | |
| 62 | std::normal_distribution<float> FloatDist(0, 1); |
| 63 | |
| 64 | for (int i = 0; i < NbImages; i++) |
| 65 | { |
| 66 | FloatImages[i].Create(Big.Width, Big.Height, 1, SImage::F32); |
| 67 | for (uint y = 0; y < FloatImages[i].Height; y++) |
| 68 | for (uint x = 0; x < FloatImages[i].Width; x++) |
| 69 | FloatImages[i](x, y) = FloatDist(Rand); |
| 70 | } |
| 71 | |
| 72 | Initialized = true; |
| 73 | } |
| 74 | |
| 75 | uint uByteWidth = Image.BytesWidth(); |
| 76 | |
| 77 | if (Image.Type == Image.F32) |
| 78 | { |
| 79 | for (uint y = 0; y < Image.Height; y++) |
| 80 | memcpy(Image.Data(y), FloatImages[ImageNo].Data(y), uByteWidth); |
| 81 | |
| 82 | return; |
| 83 | } |
| 84 | |
| 85 | for (uint y = 0; y < Image.Height; y++) |
| 86 | memcpy(Image.Data(y), Images[ImageNo].Data(y), uByteWidth); |
| 87 | } |