mmr/tkDNN
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color fix

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Francesco Gatti
2019-02-19 08:57:51 +00:00
parent c7941666ec
commit 1aa4f0275d
1 changed files with 3 additions and 1 deletions
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src/Yolo3Detection.cpp
+3 -1
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@@ -9,7 +9,7 @@ float get_color(int c, int x, int max)
int i = floor(ratio); int i = floor(ratio);
int j = ceil(ratio); int j = ceil(ratio);
ratio -= i; ratio -= i;
float r = (1-ratio) * _colors[i][c] + ratio*_colors[j][c]; float r = (1-ratio) * _colors[i % 6][c % 3] + ratio*_colors[j % 6][c % 3];
//printf("%f\n", r); //printf("%f\n", r);
return r; return r;
} }
@@ -33,6 +33,8 @@ bool Yolo3Detection::init(std::string tensor_path) {
// make a yolo layer for interpret predictions // make a yolo layer for interpret predictions
yolo[i] = new tk::dnn::Yolo(nullptr, classes, num, nullptr); // yolo without input and bias yolo[i] = new tk::dnn::Yolo(nullptr, classes, num, nullptr); // yolo without input and bias
yolo[i]->mask_h = new dnnType[num];
yolo[i]->bias_h = new dnnType[num*3*2];
memcpy(yolo[i]->mask_h, yRT->mask, sizeof(dnnType)*num); memcpy(yolo[i]->mask_h, yRT->mask, sizeof(dnnType)*num);
memcpy(yolo[i]->bias_h, yRT->bias, sizeof(dnnType)*num*3*2); memcpy(yolo[i]->bias_h, yRT->bias, sizeof(dnnType)*num*3*2);
yolo[i]->input_dim = yolo[i]->output_dim = tk::dnn::dataDim_t(1, yRT->c, yRT->h, yRT->w); yolo[i]->input_dim = yolo[i]->output_dim = tk::dnn::dataDim_t(1, yRT->c, yRT->h, yRT->w);