Fixed inversed order in HPatches homography preprocessing

lzb863 · Nov 6, 2019 · 163d030 · 163d030
1 parent 13c678d
commit 163d030
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Showing 2 changed files with 9 additions and 16 deletions.
diff --git a/notebooks/visualize_hpatches.ipynb b/notebooks/visualize_hpatches.ipynb
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "scrolled": false
    },
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -285,13 +285,6 @@
     "    superimposed = cv2.addWeighted(img1, 0.5, warped_img0, 0.5, 0.)\n",
     "    plot_imgs([img0, img1, warped_img0, superimposed], titles=['image0', 'image1', 'warped image0', 'superimposed'], dpi=200, cmap='gray')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/superpoint/datasets/patches_dataset.py b/superpoint/datasets/patches_dataset.py
@@ -68,27 +68,27 @@ def _adapt_homography_to_preprocessing(zip_data):
 
             # Compute the scaling ratio due to the resizing for both images
             s = tf.reduce_max(tf.divide(target_size, source_size))
-            up_scale = tf.diag(tf.stack([s, s, tf.constant(1.)]))
+            up_scale = tf.diag(tf.stack([1. / s, 1. / s, tf.constant(1.)]))
             warped_s = tf.reduce_max(tf.divide(target_size, source_warped_size))
-            down_scale = tf.diag(tf.stack([1 / warped_s, 1 / warped_s, tf.constant(1.)]))
+            down_scale = tf.diag(tf.stack([warped_s, warped_s, tf.constant(1.)]))
 
             # Compute the translation due to the crop for both images
             pad_y = tf.to_int32(((source_size[0] * s - target_size[0]) / tf.constant(2.0)))
             pad_x = tf.to_int32(((source_size[1] * s - target_size[1]) / tf.constant(2.0)))
-            translation = tf.stack([tf.constant(1), tf.constant(0), -pad_x, 
-                                    tf.constant(0), tf.constant(1), -pad_y,
+            translation = tf.stack([tf.constant(1), tf.constant(0), pad_x, 
+                                    tf.constant(0), tf.constant(1), pad_y,
                                     tf.constant(0),tf.constant(0), tf.constant(1)])
             translation = tf.to_float(tf.reshape(translation, [3,3]))
             pad_y = tf.to_int32(((source_warped_size[0] * warped_s - target_size[0])
                                  / tf.constant(2.0)))
             pad_x = tf.to_int32(((source_warped_size[1] * warped_s - target_size[1])
                                  / tf.constant(2.0)))
-            warped_translation = tf.stack([tf.constant(1), tf.constant(0), pad_x, 
-                                           tf.constant(0), tf.constant(1), pad_y,
+            warped_translation = tf.stack([tf.constant(1), tf.constant(0), -pad_x, 
+                                           tf.constant(0), tf.constant(1), -pad_y,
                                            tf.constant(0),tf.constant(0), tf.constant(1)])
             warped_translation = tf.to_float(tf.reshape(warped_translation, [3,3]))
 
-            H = translation @ up_scale @ H @ down_scale @ warped_translation
+            H = warped_translation @ down_scale @ H @ up_scale @ translation
             return H
 
         def _get_shape(image):