Finish Projective Inverse Warp algorithm

unsupervised/warp.py

@@ -53,7 +53,6 @@ def pose_vec2mat(vec):
     ry = tf.slice(vec, [0, 4], [-1, 1])
     rz = tf.slice(vec, [0, 5], [-1, 1])
     rot_mat = euler_to_matrix(rx, ry, rz)
-    rot_mat = tf.squeeze(rot_mat, axis=[1])
     transform_mat = tf.concat([rot_mat, translation], axis=2)
     return transform_mat
 
@@ -76,10 +75,10 @@ def image_coordinate(batch, height, width):
 
     stacked = tf.stack([x_mesh, y_mesh, ones_mesh], axis=2)
 
-    return tf.repeat(tf.expand_dims(stacked, axis=0), batch, axis=0)
+    return tf.cast(tf.repeat(tf.expand_dims(stacked, axis=0), batch, axis=0), dtype=tf.float32)
 
 
-def projective_inverse_warp(target_img, source_img, depth, pose, intrinsics, coordinates):
+def projective_inverse_warp(source_img, depth, pose, intrinsics, coordinates):
     """
     Calculate the reprojected image from the source to the target, based on the given depth, pose and intrinsics
 
@@ -92,12 +91,11 @@ def projective_inverse_warp(target_img, source_img, depth, pose, intrinsics, coo
     the source image in pixel coordinates (K.T(t->s).{3d coord}), then using the projected coordinates we sample 
     the pixels in the source image (ps) to reconstruct the target image.
 
-    :param target_img: Tensor (batch, height, width, 3)
+    :param source_img: Tensor (batch, height, width, 3)
-    :param source_img: Tensor, same shape as target_img
+    :param depth: Tensor, (batch, height, width)
-    :param depth: Tensor, (batch, height, width, 1)
     :param pose: (batch, 6)
     :param intrinsics: (batch, 3, 3) TODO: Intrinsics per image (per source/target image)?
-    :param coordinates: (batch, height, width, 3) - coordinates for the image. Pass this in so it doesn't need to be
+    :param coordinates: (batch, 3, height * width) - coordinates for the image. Pass this in so it doesn't need to be
         calculated on every warp step
     :return: The source image reprojected to the target
     """
@@ -109,23 +107,117 @@ def projective_inverse_warp(target_img, source_img, depth, pose, intrinsics, coo
     # Calculate inverse of the 4x4 intrinsics matrix
     intrinsics_inverse = tf.linalg.inv(intrinsics)
 
-    # Create grid of homogenous coordinates
+    depth_flat = tf.reshape(depth, [depth.shape[0], depth.shape[1] * depth.shape[2]])
-    grid_coords = image_coordinate(*depth.shape)
-    # Flatten the image coords to [B, 3, height * width] so each point can be used in calculations
-    grid_coords = tf.transpose(tf.reshape(grid_coords, [0, 2, 1]))
-
-    # TODO: Do we need to transpose?
-    depth_flat = tf.transpose(tf.reshape(depth, [0, 2, 1]))
 
     # Do the function
     sample_coordinates = tf.matmul(tf.matmul(intrinsics, pose_3x4),
-                                   tf.concat([depth_flat * tf.matmul(intrinsics_inverse, grid_coords),
+                                   tf.concat([depth_flat * tf.matmul(intrinsics_inverse, coordinates),
-                                              tf.ones(depth_flat.shape)], axis=1))
+                                              tf.ones([depth_flat.shape[0], 1, depth_flat.shape[1]])], axis=1))
 
     # Normalise the x/y axes (divide by z axis)
+    sample_coordinates = sample_coordinates[:, 0:2] / sample_coordinates[:, 2]
 
     # Reshape back to image coordinates
+    sample_coordinates = tf.reshape(tf.transpose(sample_coordinates, [0, 2, 1]),
+                                    [depth.shape[0], depth.shape[1], depth.shape[2], 2])
 
     # sample from the source image using the coordinates applied by the function
+    return bilinear_sampler(source_img, sample_coordinates)
 
-    pass
+
+def bilinear_sampler(imgs, coords):
+    """Construct a new image by bilinear sampling from the input image.
+
+    Points falling outside the source image boundary have value 0.
+
+    Args:
+      imgs: source image to be sampled from [batch, height_s, width_s, channels]
+      coords: coordinates of source pixels to sample from [batch, height_t,
+        width_t, 2]. height_t/width_t correspond to the dimensions of the output
+        image (don't need to be the same as height_s/width_s). The two channels
+        correspond to x and y coordinates respectively.
+    Returns:
+      A new sampled image [batch, height_t, width_t, channels]
+    """
+
+    def _repeat(x, n_repeats):
+        rep = tf.transpose(
+            tf.expand_dims(tf.ones(shape=tf.stack([
+                n_repeats,
+            ])), 1), [1, 0])
+        rep = tf.cast(rep, 'float32')
+        x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
+        return tf.reshape(x, [-1])
+
+    coords_x, coords_y = tf.split(coords, [1, 1], axis=3)
+    inp_size = imgs.get_shape()
+    coord_size = coords.get_shape()
+    out_size = coords.get_shape().as_list()
+    out_size[3] = imgs.get_shape().as_list()[3]
+
+    coords_x = tf.cast(coords_x, 'float32')
+    coords_y = tf.cast(coords_y, 'float32')
+
+    x0 = tf.floor(coords_x)
+    x1 = x0 + 1
+    y0 = tf.floor(coords_y)
+    y1 = y0 + 1
+
+    y_max = tf.cast(tf.shape(imgs)[1] - 1, 'float32')
+    x_max = tf.cast(tf.shape(imgs)[2] - 1, 'float32')
+    zero = tf.zeros([1], dtype='float32')
+
+    x0_safe = tf.clip_by_value(x0, zero, x_max)
+    y0_safe = tf.clip_by_value(y0, zero, y_max)
+    x1_safe = tf.clip_by_value(x1, zero, x_max)
+    y1_safe = tf.clip_by_value(y1, zero, y_max)
+
+    # bilinear interp weights, with points outside the grid having weight 0
+    # wt_x0 = (x1 - coords_x) * tf.cast(tf.equal(x0, x0_safe), 'float32')
+    # wt_x1 = (coords_x - x0) * tf.cast(tf.equal(x1, x1_safe), 'float32')
+    # wt_y0 = (y1 - coords_y) * tf.cast(tf.equal(y0, y0_safe), 'float32')
+    # wt_y1 = (coords_y - y0) * tf.cast(tf.equal(y1, y1_safe), 'float32')
+
+    wt_x0 = x1_safe - coords_x
+    wt_x1 = coords_x - x0_safe
+    wt_y0 = y1_safe - coords_y
+    wt_y1 = coords_y - y0_safe
+
+    # indices in the flat image to sample from
+    dim2 = tf.cast(inp_size[2], 'float32')
+    dim1 = tf.cast(inp_size[2] * inp_size[1], 'float32')
+    base = tf.reshape(
+        _repeat(
+            tf.cast(tf.range(coord_size[0]), 'float32') * dim1,
+            coord_size[1] * coord_size[2]),
+        [out_size[0], out_size[1], out_size[2], 1])
+
+    base_y0 = base + y0_safe * dim2
+    base_y1 = base + y1_safe * dim2
+    idx00 = tf.reshape(x0_safe + base_y0, [-1])
+    idx01 = x0_safe + base_y1
+    idx10 = x1_safe + base_y0
+    idx11 = x1_safe + base_y1
+
+    # sample from imgs
+    imgs_flat = tf.reshape(imgs, tf.stack([-1, inp_size[3]]))
+    imgs_flat = tf.cast(imgs_flat, 'float32')
+    im00 = tf.reshape(
+        tf.gather(imgs_flat, tf.cast(idx00, 'int32')), out_size)
+    im01 = tf.reshape(
+        tf.gather(imgs_flat, tf.cast(idx01, 'int32')), out_size)
+    im10 = tf.reshape(
+        tf.gather(imgs_flat, tf.cast(idx10, 'int32')), out_size)
+    im11 = tf.reshape(
+        tf.gather(imgs_flat, tf.cast(idx11, 'int32')), out_size)
+
+    w00 = wt_x0 * wt_y0
+    w01 = wt_x0 * wt_y1
+    w10 = wt_x1 * wt_y0
+    w11 = wt_x1 * wt_y1
+
+    output = tf.add_n([
+        w00 * im00, w01 * im01,
+        w10 * im10, w11 * im11
+    ])
+    return output

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()