Crude impl of glue layer, not sure anything works

make_dataset.py

@@ -10,6 +10,7 @@ import h5py
 CWD = os.getcwd()
 
 def pre_dataset_sh():
+    dataset_name = "ShanghaiTech"
     root = CWD + "/synchronous/dataset/" + dataset_name + "/"
 
     part_A_train = os.path.join(root, "part_A_final/train_data", "images")
@@ -53,8 +54,7 @@ def pre_dataset_sh():
         gt_data[:, 0] = gt_data[:, 0] * rate_x
         gt_data[:, 1] = gt_data[:, 1] * rate_y
 
-        # Compute gt_count from density map (gt_data)
-        # XXX: what does it do exactly?
+        # Compute 0/1 counts from density map
         kpoint = np.zeros((img_data.shape[0], img_data.shape[1]))
         for i in range(len(gt_data)):
             if (    int(gt_data[i][1]) < img_data.shape[0]
@@ -65,15 +65,14 @@ def pre_dataset_sh():
         root_path = img_path.split("IMG_")[0].replace("images", "images_crop")
 
         # Likewise, we do not crop to patched sequences here...
-        # Skip directly to saving fixed-size data & gt_count.
+        # Skip directly to saving fixed-size data & kpoint.
         img_path = img_path.replace("images", "images_crop")
         cv2.imwrite(img_path, img_data)
-        gt_count = np.sum(kpoint)
         with h5py.File(
             img_path.replace('.jpg', '.h5').replace('images', 'gt_density_map'),
-            'w'
+            mode='w'
         ) as hf:
-            hf["gt_count"] = gt_count
+            hf["kpoint"] = kpoint
 
 
 def make_npydata():