Update example_eval.rst

docs/source/example_eval.rst

@@ -6,10 +6,22 @@ Evaluation
 Data Preparation
 ^^^^^^^^^^^^^^^^
 
-The first step of evaluation is to prepare your own data.
+The first step of evaluation is to prepare your own results.
 You need to run your code and generate a `GraspGroup` for each image in each scene.
 Then call the `save_npy` function of `GraspGroup` to dump the results.
 
+To generate a `GraspGroup` and save it, you can directly input a 2D numpy array for the `GraspGroup` class:
+::
+
+  gg=GraspGroup(np.array([[score_1, width_1, height_1, depth_1, rotation_matrix_1(9), translation_1(3), object_id_1],
+                          [score_2, width_2, height_2, depth_2, rotation_matrix_2(9), translation_2(3), object_id_2],
+                          ...,
+                          [score_N, width_N, height_N, depth_N, rotation_matrix_N(9), translation_N(3), object_id_N]]
+                ))
+  gg.save_npy(save_path)
+
+where your algorithm predicts N grasp poses for an image. For the meaning of each entry, you should refer to the doc for Grasp Label Format-API Loaded Labels
+
 The file structure of dump folder should be as follows:
 
 ::
@@ -30,7 +42,7 @@ The file structure of dump folder should be as follows:
         |
         --- scene_0189
 
-You can only generate dump for one camera, there will be no error for doing that.
+You can choose to generate dump files for only one camera, there will be no error for doing that.
 
 Evaluation API
 ^^^^^^^^^^^^^^