Finish Projective Inverse Warp algorithm

unsupervised/warp_tests.py

@@ -42,6 +42,22 @@ class MyTestCase(unittest.TestCase):
         self.assertEqual(coords[0, height - 1, width - 1, 1], height - 1)
         self.assertEqual(coords[0, height - 1, width - 1, 2], 1)
 
+    def test_warp(self):
+        height = 1000
+        width = 2000
+        coords = warp.image_coordinate(1, height, width)
+        coords = tf.reshape(coords, [1, height * width, 3])
+        coords = tf.transpose(coords, [0, 2, 1])
+        # source image to sample from
+        img = tf.random.uniform([1, height, width, 3]) * 255
+
+        intrinsics = tf.constant([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]], dtype=tf.float32)
+
+        disp = tf.random.uniform([1, height, width]) * 255
+        pose = tf.random.uniform([1, 6])
+
+        warp.projective_inverse_warp(img, disp, pose, intrinsics, coords)
+
 
 if __name__ == '__main__':
     unittest.main()