fix_inference_in_ttfnet (#1063)

deploy/cpp/include/preprocess_op.h

@@ -39,6 +39,8 @@ class ImageBlob {
   std::vector<float> ori_im_size_f_;
   // Evaluation image width and height
   std::vector<float>  eval_im_size_f_;
+  // Scale factor for image size to origin image size
+  std::vector<float> scale_factor_f_;
 };
 
 // Abstraction of preprocessing opration class

deploy/cpp/src/object_detector.cc

@@ -140,7 +140,7 @@ void ObjectDetector::Postprocess(
     int ymax = (output_data_[5 + j * 6] * rh);
     int wd = xmax - xmin;
     int hd = ymax - ymin;
-    if (score > threshold_) {
+    if (score > threshold_ && class_id > -1) {
       ObjectResult result_item;
       result_item.rect = {xmin, xmax, ymin, ymax};
       result_item.class_id = class_id;
@@ -172,6 +172,9 @@ void ObjectDetector::Predict(const cv::Mat& im,
     } else if (tensor_name == "im_shape") {
       in_tensor->Reshape({1, 3});
       in_tensor->copy_from_cpu(inputs_.ori_im_size_f_.data());
+    } else if (tensor_name == "scale_factor") {
+      in_tensor->Reshape({1, 4});
+      in_tensor->copy_from_cpu(inputs_.scale_factor_f_.data());
     }
   }
   // Run predictor

deploy/cpp/src/preprocess_op.cc

@@ -78,6 +78,12 @@ void Resize::Run(cv::Mat* im, ImageBlob* data) {
     static_cast<float>(im->cols),
     resize_scale.first
   };
+  data->scale_factor_f_ = {
+    resize_scale.first,
+    resize_scale.second,
+    resize_scale.first,
+    resize_scale.second
+  };
 }
 
 std::pair<float, float> Resize::GenerateScale(const cv::Mat& im) {

tools/infer.py

@@ -178,6 +178,8 @@ def main():
             for k, v in zip(keys, outs)
         }
         logger.info('Infer iter {}'.format(iter_id))
+        if 'TTFNet' in cfg.architecture:
+            res['bbox'][1].append([len(res['bbox'][0])])
 
         bbox_results = None
         mask_results = None