visioncpp/examples/anisotropic_diffusion.cpp at master · codeplaysoftware/visioncpp · GitHub

This repository was archived by the owner on Apr 29, 2024. It is now read-only.

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

// This file is part of VisionCPP, a lightweight C++ template library

// for computer vision and image processing.

// Contact: visioncpp@codeplay.com

// 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.

/// \file anisotropic_diffusion.cpp

///

/// ---------Example---------

/// Simplified Anisotropic Diffusion

///

/// \brief This example implements a simplified version of the Perona-Malik

/// Anisotropic Diffusion.

/// This technique removes noise from an image preserving the edges.

/// \param k - the edge preserving parameter [the smaller is the k, less things

/// are considered edges, the bigger, more things are considered as edges]

/// \param iters - number of iterations [it controls how blurry the image will

/// become, a higher number means a more blurry image]

// include OpenCV for camera display

#include <opencv2/opencv.hpp>

// include VisionCpp

#include <visioncpp.hpp>

// tunable parameters

constexpr float k{15.0f}; // edge preserving parameter

constexpr size_t iters{15}; // controls the blur

// operator which implements the simplified anisotropic diffusion

struct AniDiff {

template <typename T>

visioncpp::pixel::F32C3 operator()(T nbr) {

// init output pixel

cl::sycl::float4 out(0, 0, 0, 0);

// init sum variable, which is used to normalize

cl::sycl::float4 sum_w(0, 0, 0, 0);

// get center pixel

cl::sycl::float4 p1(nbr.at(nbr.I_c, nbr.I_r)[0],

nbr.at(nbr.I_c, nbr.I_r)[1],

nbr.at(nbr.I_c, nbr.I_r)[2], 0);

// iterate over a 3x3 neighbourhood

for (int i = -1; i <= 1; i++) {

for (int j = -1; j <= 1; j++) {

// get neighbour pixel

cl::sycl::float4 p2(nbr.at(nbr.I_c + i, nbr.I_r + j)[0],

nbr.at(nbr.I_c + i, nbr.I_r + j)[1],

nbr.at(nbr.I_c + i, nbr.I_r + j)[2], 0);

// computes the weight which basically is the difference between pixels

cl::sycl::float4 w = cl::sycl::exp((-k) * cl::sycl::fabs(p1 - p2));

// sum the weights for normalization

sum_w += w;

// store the output

out += w * p2;

}

// normalize output and return

out = out / sum_w;

return visioncpp::pixel::F32C3(out.x(), out.y(), out.z());

}

};

// main program

int main(int argc, char **argv) {

// open video or camera

cv::VideoCapture cap;

if (argc == 1) {

cap.open(0);

std::cout << "To use video" << std::endl;

std::cout << "example>: ./example path/to/video.avi" << std::endl;

} else if (argc > 1) {

cap.open(argv[1]);

}

// check if we succeeded

if (!cap.isOpened()) {

std::cout << "Opening Camera/Video Failed." << std::endl;

return -1;

}

// selecting device using sycl as backend

auto dev = visioncpp::make_device<visioncpp::backend::sycl,

visioncpp::device::cpu>();

// defining size constants

constexpr size_t COLS = 640;

constexpr size_t ROWS = 480;

constexpr size_t CHNS = 3;

// initialising output pointer

std::shared_ptr<uchar> output(new uchar[COLS * ROWS * CHNS],

[](uchar *dataMem) { delete[] dataMem; });

// initializing input and output image

cv::Mat input;

cv::Mat outImage(ROWS, COLS, CV_8UC(CHNS), output.get());

This example contains a small expression tree

but it uses a device memory which stores a temporary computation.

So it is possible to create a loop during the computation

and the data is always in the device. It just comes to the host in the last

execute.

Below is the expression tree used for this example

(in_node)

(frgb) [OP_U8C3ToF32C3] (convert uchar to float)

---->(anidiff) [AniDiff] (It iterates several times in the same node

| | applying the anisotropic diffusion serveral times)

-------

(urgb) [OP_F32C3ToU8C3] (convert float to uchar to display)

for (;;) {

// Starting building the tree (use {} during the creation of the tree)

{

// read frame

cap.read(input);

// check if image was loaded

if (!input.data) {

break;

}

// resize image to the desirable size

cv::resize(input, input, cv::Size(COLS, ROWS), 0, 0, cv::INTER_CUBIC);

auto in_node =

visioncpp::terminal<visioncpp::pixel::U8C3, COLS, ROWS,

visioncpp::memory_type::Buffer2D>(input.data);

auto out_node =

visioncpp::terminal<visioncpp::pixel::U8C3, COLS, ROWS,

visioncpp::memory_type::Buffer2D>(output.get());

// device only memory

auto device_memory =

visioncpp::terminal<visioncpp::pixel::F32C3, COLS, ROWS,

visioncpp::memory_type::Buffer2D>();

// convert to float

auto frgb =

visioncpp::point_operation<visioncpp::OP_U8C3ToF32C3>(in_node);

// assign to temporary device memory

auto exec1 = visioncpp::assign(device_memory, frgb);

// apply anisotropic diffusion

auto anidiff =

visioncpp::neighbour_operation<AniDiff, 1, 1, 1, 1>(device_memory);

// assign to the temporary device memory

auto exec2 = visioncpp::assign(device_memory, anidiff);

// convert to uchar

auto urgb =

visioncpp::point_operation<visioncpp::OP_F32C3ToU8C3>(device_memory);

// assign to the host memory

auto exec3 = visioncpp::assign(out_node, urgb);

// execution (convert to float)

visioncpp::execute<visioncpp::policy::Fuse, 32, 32, 16, 16>(exec1, dev);

// apply anisotropic diffusion several times

for (int i = 0; i < iters; i++) {

visioncpp::execute<visioncpp::policy::Fuse, 32, 32, 16, 16>(exec2, dev);

}

// return image to host memory

visioncpp::execute<visioncpp::policy::Fuse, 32, 32, 16, 16>(exec3, dev);

}

// display results

cv::imshow("Reference Image", input);

cv::imshow("Simplified Anisotropic Diffusion", outImage);

// wait for key to finalize program

if (cv::waitKey(1) >= 0) break;

}

// release video/camera

cap.release();

return 0;

}

visioncpp/examples/anisotropic_diffusion.cpp at master · codeplaysoftware/visioncpp · GitHub

Repository navigation

Expand file tree

Breadcrumbs

anisotropic_diffusion.cpp

Latest commit

History

Breadcrumbs

anisotropic_diffusion.cpp

File metadata and controls