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// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
#include <stdlib.h>
#include <cstdio>
#include <set>
#include "tensorflow/contrib/ffmpeg/ffmpeg_lib.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/logging.h"
namespace tensorflow {
namespace ffmpeg {
class DecodeVideoOp : public OpKernel {
public:
explicit DecodeVideoOp(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
OP_REQUIRES(
context, context->num_inputs() == 1,
errors::InvalidArgument("DecodeVideo requires exactly 1 input."));
const Tensor& contents_tensor = context->input(0);
OP_REQUIRES(context, TensorShapeUtils::IsScalar(contents_tensor.shape()),
errors::InvalidArgument(
"contents must be a rank-0 tensor but got shape ",
contents_tensor.shape().DebugString()));
const tensorflow::StringPiece contents = contents_tensor.scalar<string>()();
// Write the input data to a temp file.
string extension;
const string temp_filename = io::GetTempFilename(extension);
OP_REQUIRES_OK(context, WriteFile(temp_filename, contents));
FileDeleter deleter(temp_filename);
uint32 width = 0;
uint32 height = 0;
uint32 frames = 0;
// Run FFmpeg on the data and verify results.
std::vector<uint8> output_data;
const Status result = ffmpeg::ReadVideoFile(temp_filename, &output_data,
&width, &height, &frames);
if (result.code() == error::Code::NOT_FOUND) {
OP_REQUIRES(
context, result.ok(),
errors::Unavailable("FFmpeg must be installed to run this op. FFmpeg "
"can be found at http://www.ffmpeg.org."));
} else if (result.code() == error::UNKNOWN) {
LOG(ERROR) << "Ffmpeg failed with error '" << result.error_message()
<< "'. Returning empty tensor.";
Tensor* output = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, TensorShape({0, 0}), &output));
return;
} else {
OP_REQUIRES_OK(context, result);
}
OP_REQUIRES(context, !output_data.empty(),
errors::Unknown("No output created by FFmpeg."));
OP_REQUIRES(
context, output_data.size() == (frames * height * width * 3),
errors::Unknown("Output created by FFmpeg [", output_data.size(),
"] does not match description [", frames, ", ", height,
", ", width, ", 3]"));
Tensor* output = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(
0, TensorShape({frames, height, width, 3}), &output));
auto output_flat = output->flat<uint8>();
std::copy_n(output_data.begin(), output_data.size(), &output_flat(0));
}
};
REGISTER_KERNEL_BUILDER(Name("DecodeVideo").Device(DEVICE_CPU), DecodeVideoOp);
REGISTER_OP("DecodeVideo")
.Input("contents: string")
.Output("output: uint8")
.SetShapeFn([](shape_inference::InferenceContext* c) {
c->set_output(0, c->UnknownShapeOfRank(4));
return Status::OK();
})
.Doc(R"doc(
Processes the contents of an video file into a tensor using FFmpeg to decode
the file.
contents: The binary contents of the video file to decode. This is a
scalar.
output: A rank-4 `Tensor` that has `[frames, height, width, 3]` RGB as output.
)doc");
} // namespace ffmpeg
} // namespace tensorflow
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