merge the visualize.py

inference/tools/visualize.py

@@ -9,12 +9,11 @@ color_map = [[128, 64, 128], [244, 35, 231], [69, 69, 69], [102, 102, 156],
              [0, 0, 230], [119, 10, 32]]
 
 im = cv2.imread(sys.argv[1])
-
-# Please note (224, 224) just for daheng model
+shape = im.shape
 print("visualizing...")
-for i in range(0, 224):
-    for j in range(0, 224):
+for i in range(0, shape[0]):
+    for j in range(0, shape[1]):
         im[i, j] = color_map[im[i, j, 0]]
 
-cv2.imwrite(sys.argv[1], im)
+cv2.imwrite(sys.argv[2], im)
 print("visualizing done!")

serving/seg-serving/op/reader_op.cpp

@@ -123,25 +123,15 @@ int ReaderOp::inference() {
     const int HH = _image_8u_tmp.rows;
     const int WW = _image_8u_tmp.cols;
     const int CC = _image_8u_tmp.channels();
+
    //HH: cols WW:rows 
     in_width_vec->push_back(HH);
     in_height_vec->push_back(WW);
 
-    // resize/crop
+    // resize
     if (_image_8u_tmp.cols != resize.width ||
         _image_8u_tmp.rows != resize.height) {
-//      int short_egde = std::min<int>(_image_8u_tmp.cols, _image_8u_tmp.rows);
-//      int yy = static_cast<int>((_image_8u_tmp.rows - short_egde) / 2);
-//      int xx = static_cast<int>((_image_8u_tmp.cols - short_egde) / 2);
-//      _image_8u_tmp =
-//          cv::Mat(_image_8u_tmp, cv::Rect(xx, yy, short_egde, short_egde));
-//      if (_image_8u_tmp.cols != resize.width ||
-//          _image_8u_tmp.rows != resize.height) {
           cv::Mat resize_image;
-//        cv::resize(_image_8u_tmp, resize_image, resize);
-//        _image_8u_tmp = resize_image;
-//      }
-//
           cv::resize(_image_8u_tmp, resize_image, resize);
           _image_8u_tmp = resize_image;
           LOG(INFO) << "Succ crop one image[CHW=" << _image_8u_tmp.channels()