update docs

contrib/RealTimeHumanSeg/humanseg.cc → contrib/RealTimeHumanSeg/cpp/humanseg.cc

@@ -44,7 +44,9 @@ void LoadModel(
     std::unique_ptr<paddle::PaddlePredictor>* predictor) {
   // Config the model info
   paddle::AnalysisConfig config;
-  config.SetModel(model_dir);
+  auto prog_file = model_dir + "/__model__";
+  auto params_file = model_dir + "/__params__";
+  config.SetModel(prog_file, params_file);
   if (use_gpu) {
       config.EnableUseGpu(100, 0);
   } else {
@@ -60,7 +62,8 @@ void LoadModel(
 void HumanSeg::Preprocess(const cv::Mat& image_mat) {
   // Clone the image : keep the original mat for postprocess
   cv::Mat im = image_mat.clone();
-  cv::resize(im, im, cv::Size(192, 192), 0.f, 0.f, cv::INTER_LINEAR);
+  auto eval_wh = cv::Size(eval_size_[0], eval_size_[1]);
+  cv::resize(im, im, eval_wh, 0.f, 0.f, cv::INTER_LINEAR);
 
   im.convertTo(im, CV_32FC3, 1.0);
   int rc = im.channels();

contrib/RealTimeHumanSeg/humanseg.h → contrib/RealTimeHumanSeg/cpp/humanseg.h

@@ -37,9 +37,11 @@ class HumanSeg {
   explicit HumanSeg(const std::string& model_dir,
                         const std::vector<float>& mean,
                         const std::vector<float>& scale,
+                        const std::vector<int>& eval_size,
                         bool use_gpu = false) :
       mean_(mean),
-      scale_(scale) {
+      scale_(scale),
+      eval_size_(eval_size) {
     LoadModel(model_dir, use_gpu, &predictor_);
   }
 
@@ -60,4 +62,5 @@ class HumanSeg {
   std::vector<uchar> segout_data_;
   std::vector<float> mean_;
   std::vector<float> scale_;
+  std::vector<int> eval_size_;
 };

contrib/RealTimeHumanSeg/main.cc → contrib/RealTimeHumanSeg/cpp/main.cc

@@ -78,7 +78,8 @@ int main(int argc, char* argv[]) {
   // Init Model
   std::vector<float> means = {104.008, 116.669, 122.675};
   std::vector<float> scale = {1.000, 1.000, 1.000};
-  HumanSeg seg(model_dir, means, scale, use_gpu);
+  std::vector<int> eval_sz = {192, 192};
+  HumanSeg seg(model_dir, means, scale, eval_sz, use_gpu);
 
   // Call ImagePredict while input_path is a image file path
   // The output will be saved as result.jpeg