Add tile_images

chainercv/utils/__init__.py

@@ -4,6 +4,7 @@
 from chainercv.utils.download import download_model  # NOQA
 from chainercv.utils.download import extractall  # NOQA
 from chainercv.utils.image import read_image  # NOQA
+from chainercv.utils.image import tile_images  # NOQA
 from chainercv.utils.image import write_image  # NOQA
 from chainercv.utils.iterator import apply_prediction_to_iterator  # NOQA
 from chainercv.utils.iterator import unzip  # NOQA

chainercv/utils/image/__init__.py

@@ -1,2 +1,3 @@
 from chainercv.utils.image.read_image import read_image  # NOQA
+from chainercv.utils.image.tile_images import tile_images  # NOQA
 from chainercv.utils.image.write_image import write_image  # NOQA

chainercv/utils/image/tile_images.py

@@ -0,0 +1,49 @@
+from __future__ import division
+
+import math
+import numpy as np
+
+
+def tile_images(imgs, n_col, pad=2, fill=0):
+    """Make a tile of images
+
+    Args:
+        imgs (numpy.ndarray): A batch of images whose shape is BCHW.
+        n_col (int): The number of columns in a tile.
+        pad (int): Amount of pad. The default value is 2.
+        fill (float, tuple or ~numpy.ndarray): The value of padded pixels.
+            If it is :class:`numpy.ndarray`,
+            its shape should be :math:`(C, 1, 1)`,
+            where :math:`C` is the number of channels of :obj:`img`.
+
+    Returns:
+        ~numpy.ndarray:
+        An image array in CHW format.
+        The size of this image is
+        :math:`((H + pad) \\times \\lceil B / n_{n_{col}} \\rceil,
+        (W + pad) \\times n_{col})`.
+
+    """
+    B, C, H, W = imgs.shape
+    n_col = min(n_col, B)
+    n_row = int(math.ceil(B / n_col))
+
+    shape = (C,
+             (H + pad) * n_row,
+             (W + pad) * n_col)
+    tile = np.empty(shape, dtype=imgs.dtype)
+    tile[:] = np.array(fill).reshape((-1, 1, 1))
+
+    k = 0
+    for y in range(n_row):
+        for x in range(n_col):
+            if k >= B:
+                break
+            start_y = y * (H + pad) + pad // 2
+            start_x = x * (W + pad) + pad // 2
+            tile[:,
+                 start_y: start_y + H,
+                 start_x: start_x + W] = imgs[k]
+            k += 1
+
+    return tile

docs/source/reference/utils.rst

@@ -39,6 +39,10 @@ read_image
 ~~~~~~~~~~
 .. autofunction:: read_image
 
+tile_images
+~~~~~~~~~~~
+.. autofunction:: tile_images
+
 write_image
 ~~~~~~~~~~~
 .. autofunction:: write_image

tests/utils_tests/image_tests/test_tile_images.py

@@ -0,0 +1,37 @@
+from __future__ import division
+import math
+import numpy as np
+import unittest
+
+from chainer import testing
+
+from chainercv.utils import tile_images
+
+
+@testing.parameterize(*testing.product({
+    'fill': [128, (104, 117, 123), np.random.uniform(255, size=(3, 1, 1))],
+    'pad': [0, 1, 2, 3]
+}))
+class TestTileImages(unittest.TestCase):
+
+    def test_tile_images(self):
+        B = np.random.randint(10, 20)
+        n_col = np.random.randint(2, 5)
+        H = 30
+        W = 40
+
+        imgs = np.random.uniform(255, size=(B, 3, H, W))
+        tile = tile_images(imgs, n_col, self.pad, fill=self.fill)
+
+        n_row = int(math.ceil(B / n_col))
+        self.assertTrue(n_col >= 1 and n_row >= 1)
+        start_y_11 = H + self.pad + self.pad // 2
+        start_x_11 = W + self.pad + self.pad // 2
+        tile_11 = tile[:,
+                       start_y_11:start_y_11 + H,
+                       start_x_11:start_x_11 + W]
+
+        np.testing.assert_equal(tile_11, imgs[(n_col - 1) + 2])
+
+
+testing.run_module(__name__, __file__)