forked from tensorflow/tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add ops that perform color transforms (including changing value, satu…
…ration and hue) in YIQ space. PiperOrigin-RevId: 168897736
- Loading branch information
1 parent
30868ef
commit bc68dc8
Showing
7 changed files
with
792 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
172 changes: 172 additions & 0 deletions
172
tensorflow/contrib/image/kernels/adjust_hsv_in_yiq_op.cc
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,172 @@ | ||
| /* Copyright 2017 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 <cmath> | ||
| #include <memory> | ||
| #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" | ||
| #include "tensorflow/core/framework/op_kernel.h" | ||
| #include "tensorflow/core/framework/register_types.h" | ||
| #include "tensorflow/core/framework/tensor.h" | ||
| #include "tensorflow/core/framework/tensor_shape.h" | ||
| #include "tensorflow/core/framework/types.h" | ||
| #include "tensorflow/core/lib/core/status.h" | ||
| #include "tensorflow/core/platform/logging.h" | ||
| #include "tensorflow/core/util/work_sharder.h" | ||
|
|
||
| namespace tensorflow { | ||
|
|
||
| typedef Eigen::ThreadPoolDevice CPUDevice; | ||
| typedef Eigen::GpuDevice GPUDevice; | ||
|
|
||
| class AdjustHsvInYiqOpBase : public OpKernel { | ||
| protected: | ||
| explicit AdjustHsvInYiqOpBase(OpKernelConstruction* context) | ||
| : OpKernel(context) {} | ||
|
|
||
| struct ComputeOptions { | ||
| const Tensor* input = nullptr; | ||
| const Tensor* delta_h = nullptr; | ||
| const Tensor* scale_s = nullptr; | ||
| const Tensor* scale_v = nullptr; | ||
| Tensor* output = nullptr; | ||
| int64 channel_count = 0; | ||
| }; | ||
|
|
||
| virtual void DoCompute(OpKernelContext* context, | ||
| const ComputeOptions& options) = 0; | ||
|
|
||
| void Compute(OpKernelContext* context) override { | ||
| const Tensor& input = context->input(0); | ||
| const Tensor& delta_h = context->input(1); | ||
| const Tensor& scale_s = context->input(2); | ||
| const Tensor& scale_v = context->input(3); | ||
| OP_REQUIRES(context, input.dims() >= 3, | ||
| errors::InvalidArgument("input must be at least 3-D, got shape", | ||
| input.shape().DebugString())); | ||
| OP_REQUIRES(context, TensorShapeUtils::IsScalar(delta_h.shape()), | ||
| errors::InvalidArgument("delta_h must be scalar: ", | ||
| delta_h.shape().DebugString())); | ||
| OP_REQUIRES(context, TensorShapeUtils::IsScalar(scale_s.shape()), | ||
| errors::InvalidArgument("scale_s must be scalar: ", | ||
| scale_s.shape().DebugString())); | ||
| OP_REQUIRES(context, TensorShapeUtils::IsScalar(scale_v.shape()), | ||
| errors::InvalidArgument("scale_v must be scalar: ", | ||
| scale_v.shape().DebugString())); | ||
| auto channels = input.dim_size(input.dims() - 1); | ||
| OP_REQUIRES( | ||
| context, channels == 3, | ||
| errors::InvalidArgument("input must have 3 channels but instead has ", | ||
| channels, " channels.")); | ||
|
|
||
| Tensor* output = nullptr; | ||
| OP_REQUIRES_OK(context, | ||
| context->allocate_output(0, input.shape(), &output)); | ||
|
|
||
| if (input.NumElements() > 0) { | ||
| const int64 channel_count = input.NumElements() / channels; | ||
| ComputeOptions options; | ||
| options.input = &input; | ||
| options.delta_h = &delta_h; | ||
| options.scale_s = &scale_s; | ||
| options.scale_v = &scale_v; | ||
| options.output = output; | ||
| options.channel_count = channel_count; | ||
| DoCompute(context, options); | ||
| } | ||
| } | ||
| }; | ||
|
|
||
| template <class Device> | ||
| class AdjustHsvInYiqOp; | ||
|
|
||
| template <> | ||
| class AdjustHsvInYiqOp<CPUDevice> : public AdjustHsvInYiqOpBase { | ||
| public: | ||
| explicit AdjustHsvInYiqOp(OpKernelConstruction* context) | ||
| : AdjustHsvInYiqOpBase(context) {} | ||
|
|
||
| void DoCompute(OpKernelContext* context, | ||
| const ComputeOptions& options) override { | ||
| const Tensor* input = options.input; | ||
| Tensor* output = options.output; | ||
| const int64 channel_count = options.channel_count; | ||
| static const int kChannelSize = 3; | ||
| auto input_data = input->shaped<float, 2>({channel_count, kChannelSize}); | ||
| const float delta_h = options.delta_h->scalar<float>()(); | ||
| const float scale_s = options.scale_s->scalar<float>()(); | ||
| const float scale_v = options.scale_v->scalar<float>()(); | ||
| auto output_data = output->shaped<float, 2>({channel_count, kChannelSize}); | ||
| const int kCostPerChannel = 10; | ||
| const DeviceBase::CpuWorkerThreads& worker_threads = | ||
| *context->device()->tensorflow_cpu_worker_threads(); | ||
| Shard(worker_threads.num_threads, worker_threads.workers, channel_count, | ||
| kCostPerChannel, | ||
| [channel_count, &input_data, &output_data, delta_h, scale_s, scale_v]( | ||
| int64 start_channel, int64 end_channel) { | ||
| // Using approximate linear transfomation described in: | ||
| // https://beesbuzz.biz/code/hsv_color_transforms.php | ||
| /** Get the constants from sympy | ||
| from sympy import Matrix | ||
| from sympy.abc import u, w | ||
| # Projection matrix to YIQ. http://en.wikipedia.org/wiki/YIQ | ||
| tyiq = Matrix([[0.299, 0.587, 0.114], | ||
| [0.596, -0.274, -0.322], | ||
| [0.211, -0.523, 0.312]]) | ||
| # Hue rotation matrix in YIQ space. | ||
| hue_proj = Matrix(3,3, [v, 0, 0, 0, vsu, -vsw, 0, vsw, vsu]) | ||
| m = tyiq.inv() * hue_proj * tyiq | ||
| **/ | ||
| // TODO(huangyp): directly compute the projection matrix from tyiq. | ||
| static const float t[kChannelSize][kChannelSize][kChannelSize] = { | ||
| {{.299, .701, .16862179492229}, | ||
| {.587, -.587, .329804745287403}, | ||
| {.114, -.114, -0.498426540209694}}, | ||
| {{.299, -.299, -.327963394172371}, | ||
| {.587, .413, .0346106879248821}, | ||
| {.114, -.114, .293352706247489}}, | ||
| {{.299, -.299, 1.24646136576682}, | ||
| {.587, -.587, -1.04322888291964}, | ||
| {.114, .886, -.203232482847173}}}; | ||
| float m[kChannelSize][kChannelSize] = {{0.}}; | ||
| float su = scale_s * std::cos(delta_h); | ||
| float sw = scale_s * std::sin(delta_h); | ||
| for (int q_index = 0; q_index < kChannelSize; q_index++) { | ||
| for (int p_index = 0; p_index < kChannelSize; p_index++) { | ||
| m[q_index][p_index] = scale_v * (t[q_index][p_index][0] + | ||
| t[q_index][p_index][1] * su + | ||
| t[q_index][p_index][2] * sw); | ||
| } | ||
| } | ||
| // Applying projection matrix to input RGB vectors. | ||
| const float* p = input_data.data() + start_channel * kChannelSize; | ||
| float* q = output_data.data() + start_channel * kChannelSize; | ||
| for (int i = start_channel; i < end_channel; i++) { | ||
| for (int q_index = 0; q_index < kChannelSize; q_index++) { | ||
| q[q_index] = 0; | ||
| for (int p_index = 0; p_index < kChannelSize; p_index++) { | ||
| q[q_index] += m[q_index][p_index] * p[p_index]; | ||
| } | ||
| } | ||
| p += kChannelSize; | ||
| q += kChannelSize; | ||
| } | ||
| }); | ||
| } | ||
| }; | ||
|
|
||
| REGISTER_KERNEL_BUILDER(Name("AdjustHsvInYiq").Device(DEVICE_CPU), | ||
| AdjustHsvInYiqOp<CPUDevice>); | ||
|
|
||
| // TODO(huangyp): add the GPU kernel | ||
| } // namespace tensorflow |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,60 @@ | ||
| /* 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 "tensorflow/core/framework/common_shape_fns.h" | ||
| #include "tensorflow/core/framework/op.h" | ||
| #include "tensorflow/core/framework/shape_inference.h" | ||
|
|
||
| namespace tensorflow { | ||
|
|
||
| using shape_inference::InferenceContext; | ||
|
|
||
| // -------------------------------------------------------------------------- | ||
| REGISTER_OP("AdjustHsvInYiq") | ||
| .Input("images: T") | ||
| .Input("delta_h: float") | ||
| .Input("scale_s: float") | ||
| .Input("scale_v: float") | ||
| .Output("output: T") | ||
| .Attr("T: {uint8, int8, int16, int32, int64, half, float, double}") | ||
| .SetShapeFn([](InferenceContext* c) { | ||
| return shape_inference::UnchangedShapeWithRankAtLeast(c, 3); | ||
| }) | ||
| .Doc(R"Doc( | ||
| Adjust the YIQ hue of one or more images. | ||
| `images` is a tensor of at least 3 dimensions. The last dimension is | ||
| interpretted as channels, and must be three. | ||
| We used linear transfomation described in: | ||
| beesbuzz.biz/code/hsv_color_transforms.php | ||
| The input image is considered in the RGB colorspace. Conceptually, the RGB | ||
| colors are first mapped into YIQ space, rotated around the Y channel by | ||
| delta_h in radians, multiplying the chrominance channels (I, Q) by scale_s, | ||
| multiplying all channels (Y, I, Q) by scale_v, and then remapped back to RGB | ||
| colorspace. Each operation described above is a linear transformation. | ||
| images: Images to adjust. At least 3-D. | ||
| delta_h: A float scale that represents the hue rotation amount, in radians. | ||
| Although delta_h can be any float value. | ||
| scale_s: A float scale that represents the factor to multiply the saturation by. | ||
| scale_s needs to be non-negative. | ||
| scale_v: A float scale that represents the factor to multiply the value by. | ||
| scale_v needs to be non-negative. | ||
| output: The hsv-adjusted image or images. No clipping will be done in this op. | ||
| The client can clip them using additional ops in their graph. | ||
| )Doc"); | ||
|
|
||
| } // namespace tensorflow |
Oops, something went wrong.