Enable tf.image.ssim to run on tensor-like inputs and add SSIM name space.

PiperOrigin-RevId: 317676190
Change-Id: I08408eaf397ace235b5f50513096cbd9ba46d5a8

tensorflow/python/ops/image_ops_impl.py

@@ -3642,6 +3642,11 @@ def ssim(img1,
     values are in range (-1, 1], when pixel values are non-negative. Returns
     a tensor with shape: broadcast(img1.shape[:-3], img2.shape[:-3]).
   """
+  with ops.name_scope(None, 'SSIM', [img1, img2]):
+    # Convert to tensor if needed.
+    img1 = ops.convert_to_tensor(img1, name='img1')
+    img2 = ops.convert_to_tensor(img2, name='img2')
+    # Shape checking.
     _, _, checks = _verify_compatible_image_shapes(img1, img2)
     with ops.control_dependencies(checks):
       img1 = array_ops.identity(img1)

tensorflow/python/ops/image_ops_test.py

@@ -4865,6 +4865,29 @@ class SSIMTest(test_util.TensorFlowTestCase):
     with self.cached_session(use_gpu=True):
       self.assertAllClose(expected, self.evaluate(ssim), atol=1e-4)
 
+  def testBatchNumpyInputs(self):
+    img = self._LoadTestImages()
+    expected = self._ssim[np.triu_indices(3, k=1)]
+
+    img1, img2 = zip(*itertools.combinations(img, 2))
+    img1 = np.concatenate(img1)
+    img2 = np.concatenate(img2)
+
+    with self.cached_session(use_gpu=True):
+      img1 = self.evaluate(constant_op.constant(img1))
+      img2 = self.evaluate(constant_op.constant(img2))
+
+    ssim = image_ops.ssim(
+        img1,
+        img2,
+        1.0,
+        filter_size=11,
+        filter_sigma=1.5,
+        k1=0.01,
+        k2=0.03)
+    with self.cached_session(use_gpu=True):
+      self.assertAllClose(expected, self.evaluate(ssim), atol=1e-4)
+
   def testBroadcast(self):
     img = self._LoadTestImages()[:2]
     expected = self._ssim[:2, :2]