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| /* | |
| * SPDX-License-Identifier: Apache-2.0 | |
| */ | |
| namespace ONNX_NAMESPACE { | |
| static const char* ImageDecoder_ver20_doc = | |
| R"DOC(Loads and decodes and image from a file. If it can't decode for any reason (e.g. corrupted encoded | |
| stream, invalid format, it will return an empty matrix). | |
| The following image formats are supported: | |
| * BMP | |
| * JPEG (note: Lossless JPEG support is optional) | |
| * JPEG2000 | |
| * TIFF | |
| * PNG | |
| * WebP | |
| * Portable image format (PBM, PGM, PPM, PXM, PNM) | |
| Decoded images follow a channel-last layout: (Height, Width, Channels). | |
| **JPEG chroma upsampling method:** | |
| When upsampling the chroma components by a factor of 2, the pixels are linearly interpolated so that the | |
| centers of the output pixels are 1/4 and 3/4 of the way between input pixel centers. | |
| When rounding, 0.5 is rounded down and up at alternative pixels locations to prevent bias towards | |
| larger values (ordered dither pattern). | |
| Considering adjacent input pixels A, B, and C, B is upsampled to pixels B0 and B1 so that | |
| ``` | |
| B0 = round_half_down((1/4) * A + (3/4) * B) | |
| B1 = round_half_up((3/4) * B + (1/4) * C) | |
| ``` | |
| This method, is the default chroma upsampling method in the well-established libjpeg-turbo library, | |
| also referred as "smooth" or "fancy" upsampling. | |
| )DOC"; | |
| ONNX_OPERATOR_SET_SCHEMA( | |
| ImageDecoder, | |
| 20, | |
| OpSchema() | |
| .SetDoc(ImageDecoder_ver20_doc) | |
| .Attr( | |
| "pixel_format", | |
| "Pixel format. Can be one of \"RGB\", \"BGR\", or \"Grayscale\".", | |
| AttributeProto::STRING, | |
| std::string("RGB")) | |
| .Input(0, "encoded_stream", "Encoded stream", "T1", OpSchema::Single, true, 1, OpSchema::NonDifferentiable) | |
| .Output(0, "image", "Decoded image", "T2", OpSchema::Single, true, 1, OpSchema::NonDifferentiable) | |
| .TypeConstraint("T1", {"tensor(uint8)"}, "Constrain input types to 8-bit unsigned integer tensor.") | |
| .TypeConstraint("T2", {"tensor(uint8)"}, "Constrain output types to 8-bit unsigned integer tensor.") | |
| .TypeAndShapeInferenceFunction([](InferenceContext& ctx) { | |
| if (hasInputShape(ctx, 0)) { | |
| auto& input_shape = getInputShape(ctx, 0); | |
| if (input_shape.dim_size() != 1) { | |
| fail_shape_inference("Input tensor must be 1-dimensional"); | |
| } | |
| } | |
| propagateElemTypeFromDtypeToOutput(ctx, TensorProto::UINT8, 0); | |
| auto output_type = ctx.getOutputType(0); | |
| auto* sh = output_type->mutable_tensor_type()->mutable_shape(); | |
| sh->clear_dim(); | |
| sh->add_dim(); | |
| sh->add_dim(); | |
| sh->add_dim(); | |
| })); | |
| } // namespace ONNX_NAMESPACE | |