| /* |
| * Copyright (c) 2016, 2017 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/runtime/NEON/NEFunctions.h" |
| |
| #include "arm_compute/core/Types.h" |
| #include "utils/Utils.h" |
| |
| using namespace arm_compute; |
| using namespace utils; |
| |
| /** Gaussian 3x3 matrix |
| */ |
| const int16_t gaussian3x3[] = |
| { |
| 1, 2, 1, |
| 2, 4, 2, |
| 1, 2, 1 |
| }; |
| |
| /** Gaussian 5x5 matrix |
| */ |
| const int16_t gaussian5x5[] = |
| { |
| 1, 4, 6, 4, 1, |
| 4, 16, 24, 16, 4, |
| 6, 24, 36, 24, 6, |
| 4, 16, 24, 16, 4, |
| 1, 4, 6, 4, 1 |
| }; |
| |
| void main_neon_convolution(int argc, const char **argv) |
| { |
| /** [Accurate padding] **/ |
| PPMLoader ppm; |
| Image src, tmp, dst; |
| |
| if(argc < 2) |
| { |
| // Print help |
| std::cout << "Usage: ./build/neon_convolution [input_image.ppm]\n\n"; |
| std::cout << "No input_image provided, creating a dummy 640x480 image\n"; |
| // Initialize just the dimensions and format of your buffers: |
| src.allocator()->init(TensorInfo(640, 480, Format::U8)); |
| } |
| else |
| { |
| ppm.open(argv[1]); |
| // Initialize just the dimensions and format of your buffers: |
| ppm.init_image(src, Format::U8); |
| } |
| |
| // Initialize just the dimensions and format of the temporary and destination images: |
| tmp.allocator()->init(*src.info()); |
| dst.allocator()->init(*src.info()); |
| |
| NEConvolution3x3 conv3x3; |
| NEConvolution5x5 conv5x5; |
| |
| // Apply a Gaussian 3x3 filter to the source image followed by a Gaussian 5x5: |
| // The function will automatically update the padding information inside input and output to match its requirements |
| conv3x3.configure(&src, &tmp, gaussian3x3, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED); |
| conv5x5.configure(&tmp, &dst, gaussian5x5, 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED); |
| |
| // Now that the padding requirements are known we can allocate the images: |
| src.allocator()->allocate(); |
| tmp.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| // Fill the input image with the content of the PPM image if a filename was provided: |
| if(ppm.is_open()) |
| { |
| ppm.fill_image(src); |
| } |
| |
| //Execute the functions: |
| conv3x3.run(); |
| conv5x5.run(); |
| |
| // Save the result to file: |
| if(ppm.is_open()) |
| { |
| const std::string output_filename = std::string(argv[1]) + "_out.ppm"; |
| save_to_ppm(dst, output_filename); |
| } |
| /** [Accurate padding] **/ |
| } |
| |
| /** Main program for convolution test |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( [optional] Path to PPM image to process ) |
| */ |
| int main(int argc, const char **argv) |
| { |
| return utils::run_example(argc, argv, main_neon_convolution); |
| } |