modify dataset

@@ -245,6 +245,8 @@ def build_dataset(img_dir, go_res_dir, label_dir, top_text_list, skip_list, save
         X = list()
         y_true = list()
 
+        X_no_text = list()
+
         # dim = 1 + 5 + 8
 
         # text_vec_max_lens = 15 * 50
@@ -260,22 +262,31 @@ def build_dataset(img_dir, go_res_dir, label_dir, top_text_list, skip_list, save
                 X.append([0. for _ in range(dim)])
                 y_true.append([0 for _ in range(num_classes)])
 
+                X_no_text.append([0. for _ in range(dim)])
+
             elif i in top_text_idx_set:
                 (x0, y0, x1, y1, x2, y2, x3, y3), text = go_res_list[i]
                 feature_vec = [1.]
                 feature_vec.extend(simple_word2vec(text))
-                feature_vec.extend([(x0/w)*2-1, (y0/h)*2-1, (x1/w)*2-1, (y1/h)*2-1, (x2/w)*2-1, (y2/h)*2-1, (x3/w)*2-1, (y3/h)*2-1])
+                feature_vec.extend([(x0/w), (y0/h), (x1/w), (y1/h), (x2/w), (y2/h), (x3/w), (y3/h)])
                 # feature_vec.extend(jwq_word2vec(text, text_vec_max_lens))
                 feature_vec.extend(jieba_and_tencent_word2vec(text, max_jieba_char))
                 X.append(feature_vec)
 
                 y_true.append([0 for _ in range(num_classes)])
 
+                feature_vec_no_text = [1.]
+                feature_vec_no_text.extend([0. for _ in range(5)])
+                feature_vec_no_text.extend([(x0/w), (y0/h), (x1/w), (y1/h), (x2/w), (y2/h), (x3/w), (y3/h)])
+                # feature_vec.extend(jwq_word2vec(text, text_vec_max_lens))
+                feature_vec_no_text.extend([0. for _ in range(text_vec_max_lens)])
+                X_no_text.append(feature_vec_no_text)                
+
             elif i in label_idx_dict:
                 (x0, y0, x1, y1, x2, y2, x3, y3), text = go_res_list[i]
-                feature_vec = [-1.]
+                feature_vec = [0.]
                 feature_vec.extend(simple_word2vec(text))
-                feature_vec.extend([(x0/w)*2-1, (y0/h)*2-1, (x1/w)*2-1, (y1/h)*2-1, (x2/w)*2-1, (y2/h)*2-1, (x3/w)*2-1, (y3/h)*2-1])
+                feature_vec.extend([(x0/w), (y0/h), (x1/w), (y1/h), (x2/w), (y2/h), (x3/w), (y3/h)])
                 # feature_vec.extend(jwq_word2vec(text, text_vec_max_lens))
                 feature_vec.extend(jieba_and_tencent_word2vec(text, max_jieba_char))
                 X.append(feature_vec)
@@ -283,23 +294,43 @@ def build_dataset(img_dir, go_res_dir, label_dir, top_text_list, skip_list, save
                 base_label_list = [0 for _ in range(num_classes)]
                 base_label_list[label_idx_dict[i]] = 1
                 y_true.append(base_label_list)
+
+                feature_vec_no_text = [0.]
+                feature_vec_no_text.extend([0. for _ in range(5)])
+                feature_vec_no_text.extend([(x0/w), (y0/h), (x1/w), (y1/h), (x2/w), (y2/h), (x3/w), (y3/h)])
+                # feature_vec.extend(jwq_word2vec(text, text_vec_max_lens))
+                feature_vec_no_text.extend([0. for _ in range(text_vec_max_lens)])
+                X_no_text.append(feature_vec_no_text) 
+
             else:
                 (x0, y0, x1, y1, x2, y2, x3, y3), text = go_res_list[i]
-                feature_vec = [-1.]
+                feature_vec = [0.]
                 feature_vec.extend(simple_word2vec(text))
-                feature_vec.extend([(x0/w)*2-1, (y0/h)*2-1, (x1/w)*2-1, (y1/h)*2-1, (x2/w)*2-1, (y2/h)*2-1, (x3/w)*2-1, (y3/h)*2-1])
+                feature_vec.extend([(x0/w), (y0/h), (x1/w), (y1/h), (x2/w), (y2/h), (x3/w), (y3/h)])
                 # feature_vec.extend(jwq_word2vec(text, text_vec_max_lens))
                 feature_vec.extend(jieba_and_tencent_word2vec(text, max_jieba_char))
                 X.append(feature_vec)
 
                 y_true.append([0 for _ in range(num_classes)])
 
+                feature_vec_no_text = [0.]
+                feature_vec_no_text.extend([0. for _ in range(5)])
+                feature_vec_no_text.extend([(x0/w), (y0/h), (x1/w), (y1/h), (x2/w), (y2/h), (x3/w), (y3/h)])
+                # feature_vec.extend(jwq_word2vec(text, text_vec_max_lens))
+                feature_vec_no_text.extend([0. for _ in range(text_vec_max_lens)])
+                X_no_text.append(feature_vec_no_text) 
+
         all_data = [X, y_true, valid_lens]
+        all_data_no_text = [X_no_text, y_true, valid_lens]
 
         save_json_name = '{0}.json'.format(uuid.uuid3(uuid.NAMESPACE_DNS, img_name))
         with open(os.path.join(save_dir, save_json_name), 'w') as fp:
             json.dump(all_data, fp)
 
+        save_json_name_2 = '{0}.json'.format(uuid.uuid3(uuid.NAMESPACE_DNS, '{0}_no_text'.format(img_name)))
+        with open(os.path.join(save_dir, save_json_name_2), 'w') as fp:
+            json.dump(all_data_no_text, fp)
+
         if is_create_map:
             create_map[img_name] = {
                 'x_y_valid_lens': save_json_name, 
@@ -333,7 +364,7 @@ def build_dataset(img_dir, go_res_dir, label_dir, top_text_list, skip_list, save
 if __name__ == '__main__':
     base_dir = '/Users/zhouweiqi/Downloads/gcfp/data'
     go_dir = os.path.join(base_dir, 'go_res')
-    dataset_save_dir = os.path.join(base_dir, 'dataset160x414')
+    dataset_save_dir = os.path.join(base_dir, 'dataset160x414x10-no-text')
     label_dir = os.path.join(base_dir, 'labeled')
 
     train_go_path = os.path.join(go_dir, 'train')

@@ -27,12 +27,12 @@ def simple_word2vec(text):
         else:
             other_num += 1
     
-    vec = [(text_len/100)*2 - 1,
-           (cn_num/text_len)*2 - 1,
-           (en_num/text_len)*2 - 1,
-           (digit_num/text_len)*2 - 1,
+    vec = [text_len/100,
+           cn_num/text_len,
+           en_num/text_len,
+           digit_num/text_len,
            # space_num/text_len,
-           (other_num/text_len)*2 - 1,
+           other_num/text_len,
            ]
 
     # print(text)