1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
|
// 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
|
