// See docs in ../ops/image_ops.cc

#include <memory>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/public/status.h"
#include "tensorflow/core/public/tensor.h"
#include "tensorflow/core/public/tensor_shape.h"
#include "tensorflow/core/lib/jpeg/jpeg_mem.h"

namespace tensorflow {

// Decode the contents of a JPEG file
class DecodeJpegOp : public OpKernel {
 public:
  explicit DecodeJpegOp(OpKernelConstruction* context) : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("channels", &flags_.components));
    OP_REQUIRES(context, flags_.components == 0 || flags_.components == 1 ||
                             flags_.components == 3,
                errors::InvalidArgument("channels must be 0, 1, or 3, got ",
                                        flags_.components));
    OP_REQUIRES_OK(context, context->GetAttr("ratio", &flags_.ratio));
    OP_REQUIRES(context, flags_.ratio == 1 || flags_.ratio == 2 ||
                             flags_.ratio == 4 || flags_.ratio == 8,
                errors::InvalidArgument("ratio must be 1, 2, 4, or 8, got ",
                                        flags_.ratio));
    OP_REQUIRES_OK(
        context, context->GetAttr("fancy_upscaling", &flags_.fancy_upscaling));
    OP_REQUIRES_OK(context,
                   context->GetAttr("try_recover_truncated",
                                    &flags_.try_recover_truncated_jpeg));
    OP_REQUIRES_OK(context, context->GetAttr("acceptable_fraction",
                                             &flags_.min_acceptable_fraction));
  }

  void Compute(OpKernelContext* context) override {
    const Tensor& contents = context->input(0);
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(contents.shape()),
                errors::InvalidArgument("contents must be scalar, got shape ",
                                        contents.shape().ShortDebugString()));
    const StringPiece input = contents.scalar<string>()();
    OP_REQUIRES(context, input.size() <= std::numeric_limits<int>::max(),
                errors::InvalidArgument("JPEG contents are too large for int: ",
                                        input.size()));

    // Decode image, allocating tensor once the image size is known
    Tensor* output = NULL;
    OP_REQUIRES(
        context,
        jpeg::Uncompress(
            input.data(), input.size(), flags_, NULL,
            [=, &output](int width, int height, int channels) -> uint8* {
              Status status(context->allocate_output(
                  0, TensorShape({height, width, channels}), &output));
              if (!status.ok()) {
                VLOG(1) << status;
                context->SetStatus(status);
                return nullptr;
              }
              return output->flat<uint8>().data();
            }),
        errors::InvalidArgument("Invalid JPEG data, size ", input.size()));
  }

 private:
  jpeg::UncompressFlags flags_;
};
REGISTER_KERNEL_BUILDER(Name("DecodeJpeg").Device(DEVICE_CPU), DecodeJpegOp);

}  // namespace tensorflow