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// 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/png/png_io.h"
namespace tensorflow {
// Decode the contents of a PNG file
class DecodePngOp : public OpKernel {
public:
explicit DecodePngOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("channels", &channels_));
OP_REQUIRES(context, channels_ == 0 || channels_ == 1 || channels_ == 3 ||
channels_ == 4,
errors::InvalidArgument("channels must be 0, 1, 3, or 4, got ",
channels_));
}
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()));
// Start decoding image to get shape details
const StringPiece data = contents.scalar<string>()();
png::DecodeContext decode;
OP_REQUIRES(
context, png::CommonInitDecode(data, channels_, 8, &decode),
errors::InvalidArgument("Invalid PNG header, data size ", data.size()));
// Verify that width and height don't overflow int
const int width = decode.width;
const int height = decode.height;
if (width != static_cast<int64>(decode.width) ||
height != static_cast<int64>(decode.height)) {
png::CommonFreeDecode(&decode);
OP_REQUIRES(context, false,
errors::InvalidArgument("PNG size too large for int: ",
decode.width, " by ", decode.height));
}
// Allocate tensor
Tensor* output = nullptr;
const auto status = context->allocate_output(
0, TensorShape({height, width, decode.channels}), &output);
if (!status.ok()) png::CommonFreeDecode(&decode);
OP_REQUIRES_OK(context, status);
// Finish decoding image
OP_REQUIRES(
context, png::CommonFinishDecode(output->flat<uint8>().data(),
decode.channels * width, &decode),
errors::InvalidArgument("Invalid PNG data, size ", data.size()));
}
private:
int channels_;
};
REGISTER_KERNEL_BUILDER(Name("DecodePng").Device(DEVICE_CPU), DecodePngOp);
} // namespace tensorflow
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