diff --git a/preprocess_data.py b/preprocess_data.py
index 767dbaa..d1066c8 100644
--- a/preprocess_data.py
+++ b/preprocess_data.py
@@ -23,6 +23,7 @@ import random
 import numpy as np
 import cv2
 import scipy.io as io
+import scipy.sparse as sparse
 import h5py
 
 CWD = os.getcwd()
@@ -75,16 +76,25 @@ def pre_dataset_sh():
         gt_data[:, 1] = gt_data[:, 1] * rate_y
 
         if is_portrait:
-            print("Portrait img: \'{}\' -- rotating 90 deg clockwise...".format(img_path))
-            img_data = cv2.rotate(img_data, cv2.ROTATE_90_CLOCKWISE)
-
+            print("Portrait img: \'{}\' -- transposing...".format(img_path))
+            img_data = cv2.transpose(img_data)
+            gt_data = gt_data[:, ::-1]
 
         # 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]
-                and int(gt_data[i][0]) < img_data.shape[1]):
-                kpoint[int(gt_data[i][1]), int(gt_data[i][0])] = 1
+        assert img_data.shape[:2] == (768, 1152)
+        coordinates = gt_data.round().astype(int)           # To integer coords
+        coordinates[:, 0] = np.clip(coordinates[:, 0], a_min=0, a_max=1151)
+        coordinates[:, 1] = np.clip(coordinates[:, 1], a_min=0, a_max=767)
+        assert max(coordinates[:, 0]) < 1152
+        assert max(coordinates[:, 1]) < 768
+        sparse_mat = sparse.coo_matrix((
+                np.ones(coordinates.shape[0]),              # data
+                (coordinates[:, 1], coordinates[:, 0]),     # (i, j)
+            ),  # N.B. all k |- ret[i[k], j[k]] = data[k]
+            shape=(768, 1152),
+            dtype=int,
+        )   # To same shape as image, so i, j flipped wrt. coordinates
+        kpoint = sparse_mat.toarray()
 
         fname = img_path.split("/")[-1]
         root_path = img_path.split("IMG_")[0].replace("images", "images_crop")