diff --git a/tensorflow/core/kernels/draw_bounding_box_op.cc b/tensorflow/core/kernels/draw_bounding_box_op.cc
index 618c47e6848..de9223b17b6 100644
--- a/tensorflow/core/kernels/draw_bounding_box_op.cc
+++ b/tensorflow/core/kernels/draw_bounding_box_op.cc
@@ -52,7 +52,6 @@ class DrawBoundingBoxesOp : public OpKernel {
     const int64 batch_size = images.dim_size(0);
     const int64 height = images.dim_size(1);
     const int64 width = images.dim_size(2);
-    const int64 color_table_length = 10;
 
     // 0: yellow
     // 1: blue
@@ -64,7 +63,7 @@ class DrawBoundingBoxesOp : public OpKernel {
     // 7: navy blue
     // 8: aqua
     // 9: fuchsia
-    float color_table[color_table_length][4] = {
+    std::vector<std::array<float, 4>> color_table = {
         {1, 1, 0, 1},     {0, 0, 1, 1},     {1, 0, 0, 1},   {0, 1, 0, 1},
         {0.5, 0, 0.5, 1}, {0.5, 0.5, 0, 1}, {0.5, 0, 0, 1}, {0, 0, 0.5, 1},
         {0, 1, 1, 1},     {1, 0, 1, 1},
@@ -73,10 +72,23 @@ class DrawBoundingBoxesOp : public OpKernel {
     // Reset first color channel to 1 if image is GRY.
     // For GRY images, this means all bounding boxes will be white.
     if (depth == 1) {
-      for (int64 i = 0; i < color_table_length; i++) {
+      for (int64 i = 0; i < color_table.size(); i++) {
         color_table[i][0] = 1;
       }
     }
+    if (context->num_inputs() == 3) {
+      const Tensor& colors_tensor = context->input(2);
+      OP_REQUIRES(context, colors_tensor.shape().dims() == 2, errors::InvalidArgument("colors must be a 2-D matrix", colors_tensor.shape().DebugString()));
+      OP_REQUIRES(context, colors_tensor.shape().dim_size(1) == 4, errors::InvalidArgument("colors must be n x 4 (RGBA)", colors_tensor.shape().DebugString()));
+      if (colors_tensor.NumElements() != 0) {
+        color_table.clear();
+
+        auto colors = colors_tensor.matrix<float>();
+        for (int64 i = 0; i < colors.dimension(0); i++) {
+            color_table.emplace_back(std::array<float, 4>{colors(i, 0), colors(i, 1), colors(i, 2), colors(i, 3)});
+        }
+      }
+    }
     Tensor* output;
     OP_REQUIRES_OK(
         context,
@@ -90,7 +102,7 @@ class DrawBoundingBoxesOp : public OpKernel {
       const int64 num_boxes = boxes.dim_size(1);
       const auto tboxes = boxes.tensor<T, 3>();
       for (int64 bb = 0; bb < num_boxes; ++bb) {
-        int64 color_index = bb % color_table_length;
+        int64 color_index = bb % color_table.size();
         const int64 min_box_row =
             static_cast<float>(tboxes(b, bb, 0)) * (height - 1);
         const int64 min_box_row_clamp = std::max<int64>(min_box_row, int64{0});
@@ -179,6 +191,9 @@ class DrawBoundingBoxesOp : public OpKernel {
 #define REGISTER_CPU_KERNEL(T)                                             \
   REGISTER_KERNEL_BUILDER(                                                 \
       Name("DrawBoundingBoxes").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      DrawBoundingBoxesOp<T>);                                             \
+  REGISTER_KERNEL_BUILDER(                                                 \
+      Name("DrawBoundingBoxesV2").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
       DrawBoundingBoxesOp<T>);
 TF_CALL_half(REGISTER_CPU_KERNEL);
 TF_CALL_float(REGISTER_CPU_KERNEL);