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10 // License Agreement
11 // For Open Source Computer Vision Library
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40 //M*/
41
42 #include "precomp.hpp"
43 #include "opencv2/photo.hpp"
44 #include "opencv2/imgproc.hpp"
45 #include "hdr_common.hpp"
46
47 namespace cv
48 {
49
50 class MergeDebevecImpl : public MergeDebevec
51 {
52 public:
MergeDebevecImpl()53 MergeDebevecImpl() :
54 name("MergeDebevec"),
55 weights(tringleWeights())
56 {
57 }
58
process(InputArrayOfArrays src,OutputArray dst,InputArray _times,InputArray input_response)59 void process(InputArrayOfArrays src, OutputArray dst, InputArray _times, InputArray input_response)
60 {
61 std::vector<Mat> images;
62 src.getMatVector(images);
63 Mat times = _times.getMat();
64
65 CV_Assert(images.size() == times.total());
66 checkImageDimensions(images);
67 CV_Assert(images[0].depth() == CV_8U);
68
69 int channels = images[0].channels();
70 Size size = images[0].size();
71 int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
72
73 dst.create(images[0].size(), CV_32FCC);
74 Mat result = dst.getMat();
75
76 Mat response = input_response.getMat();
77
78 if(response.empty()) {
79 response = linearResponse(channels);
80 response.at<Vec3f>(0) = response.at<Vec3f>(1);
81 }
82 log(response, response);
83 CV_Assert(response.rows == LDR_SIZE && response.cols == 1 &&
84 response.channels() == channels);
85
86 Mat exp_values(times);
87 log(exp_values, exp_values);
88
89 result = Mat::zeros(size, CV_32FCC);
90 std::vector<Mat> result_split;
91 split(result, result_split);
92 Mat weight_sum = Mat::zeros(size, CV_32F);
93
94 for(size_t i = 0; i < images.size(); i++) {
95 std::vector<Mat> splitted;
96 split(images[i], splitted);
97
98 Mat w = Mat::zeros(size, CV_32F);
99 for(int c = 0; c < channels; c++) {
100 LUT(splitted[c], weights, splitted[c]);
101 w += splitted[c];
102 }
103 w /= channels;
104
105 Mat response_img;
106 LUT(images[i], response, response_img);
107 split(response_img, splitted);
108 for(int c = 0; c < channels; c++) {
109 result_split[c] += w.mul(splitted[c] - exp_values.at<float>((int)i));
110 }
111 weight_sum += w;
112 }
113 weight_sum = 1.0f / weight_sum;
114 for(int c = 0; c < channels; c++) {
115 result_split[c] = result_split[c].mul(weight_sum);
116 }
117 merge(result_split, result);
118 exp(result, result);
119 }
120
process(InputArrayOfArrays src,OutputArray dst,InputArray times)121 void process(InputArrayOfArrays src, OutputArray dst, InputArray times)
122 {
123 process(src, dst, times, Mat());
124 }
125
126 protected:
127 String name;
128 Mat weights;
129 };
130
createMergeDebevec()131 Ptr<MergeDebevec> createMergeDebevec()
132 {
133 return makePtr<MergeDebevecImpl>();
134 }
135
136 class MergeMertensImpl : public MergeMertens
137 {
138 public:
MergeMertensImpl(float _wcon,float _wsat,float _wexp)139 MergeMertensImpl(float _wcon, float _wsat, float _wexp) :
140 name("MergeMertens"),
141 wcon(_wcon),
142 wsat(_wsat),
143 wexp(_wexp)
144 {
145 }
146
process(InputArrayOfArrays src,OutputArrayOfArrays dst,InputArray,InputArray)147 void process(InputArrayOfArrays src, OutputArrayOfArrays dst, InputArray, InputArray)
148 {
149 process(src, dst);
150 }
151
process(InputArrayOfArrays src,OutputArray dst)152 void process(InputArrayOfArrays src, OutputArray dst)
153 {
154 std::vector<Mat> images;
155 src.getMatVector(images);
156 checkImageDimensions(images);
157
158 int channels = images[0].channels();
159 CV_Assert(channels == 1 || channels == 3);
160 Size size = images[0].size();
161 int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
162
163 std::vector<Mat> weights(images.size());
164 Mat weight_sum = Mat::zeros(size, CV_32F);
165
166 for(size_t i = 0; i < images.size(); i++) {
167 Mat img, gray, contrast, saturation, wellexp;
168 std::vector<Mat> splitted(channels);
169
170 images[i].convertTo(img, CV_32F, 1.0f/255.0f);
171 if(channels == 3) {
172 cvtColor(img, gray, COLOR_RGB2GRAY);
173 } else {
174 img.copyTo(gray);
175 }
176 split(img, splitted);
177
178 Laplacian(gray, contrast, CV_32F);
179 contrast = abs(contrast);
180
181 Mat mean = Mat::zeros(size, CV_32F);
182 for(int c = 0; c < channels; c++) {
183 mean += splitted[c];
184 }
185 mean /= channels;
186
187 saturation = Mat::zeros(size, CV_32F);
188 for(int c = 0; c < channels; c++) {
189 Mat deviation = splitted[c] - mean;
190 pow(deviation, 2.0f, deviation);
191 saturation += deviation;
192 }
193 sqrt(saturation, saturation);
194
195 wellexp = Mat::ones(size, CV_32F);
196 for(int c = 0; c < channels; c++) {
197 Mat exp = splitted[c] - 0.5f;
198 pow(exp, 2.0f, exp);
199 exp = -exp / 0.08f;
200 wellexp = wellexp.mul(exp);
201 }
202
203 pow(contrast, wcon, contrast);
204 pow(saturation, wsat, saturation);
205 pow(wellexp, wexp, wellexp);
206
207 weights[i] = contrast;
208 if(channels == 3) {
209 weights[i] = weights[i].mul(saturation);
210 }
211 weights[i] = weights[i].mul(wellexp) + 1e-12f;
212 weight_sum += weights[i];
213 }
214 int maxlevel = static_cast<int>(logf(static_cast<float>(min(size.width, size.height))) / logf(2.0f));
215 std::vector<Mat> res_pyr(maxlevel + 1);
216
217 for(size_t i = 0; i < images.size(); i++) {
218 weights[i] /= weight_sum;
219 Mat img;
220 images[i].convertTo(img, CV_32F, 1.0f/255.0f);
221
222 std::vector<Mat> img_pyr, weight_pyr;
223 buildPyramid(img, img_pyr, maxlevel);
224 buildPyramid(weights[i], weight_pyr, maxlevel);
225
226 for(int lvl = 0; lvl < maxlevel; lvl++) {
227 Mat up;
228 pyrUp(img_pyr[lvl + 1], up, img_pyr[lvl].size());
229 img_pyr[lvl] -= up;
230 }
231 for(int lvl = 0; lvl <= maxlevel; lvl++) {
232 std::vector<Mat> splitted(channels);
233 split(img_pyr[lvl], splitted);
234 for(int c = 0; c < channels; c++) {
235 splitted[c] = splitted[c].mul(weight_pyr[lvl]);
236 }
237 merge(splitted, img_pyr[lvl]);
238 if(res_pyr[lvl].empty()) {
239 res_pyr[lvl] = img_pyr[lvl];
240 } else {
241 res_pyr[lvl] += img_pyr[lvl];
242 }
243 }
244 }
245 for(int lvl = maxlevel; lvl > 0; lvl--) {
246 Mat up;
247 pyrUp(res_pyr[lvl], up, res_pyr[lvl - 1].size());
248 res_pyr[lvl - 1] += up;
249 }
250 dst.create(size, CV_32FCC);
251 res_pyr[0].copyTo(dst.getMat());
252 }
253
getContrastWeight() const254 float getContrastWeight() const { return wcon; }
setContrastWeight(float val)255 void setContrastWeight(float val) { wcon = val; }
256
getSaturationWeight() const257 float getSaturationWeight() const { return wsat; }
setSaturationWeight(float val)258 void setSaturationWeight(float val) { wsat = val; }
259
getExposureWeight() const260 float getExposureWeight() const { return wexp; }
setExposureWeight(float val)261 void setExposureWeight(float val) { wexp = val; }
262
write(FileStorage & fs) const263 void write(FileStorage& fs) const
264 {
265 fs << "name" << name
266 << "contrast_weight" << wcon
267 << "saturation_weight" << wsat
268 << "exposure_weight" << wexp;
269 }
270
read(const FileNode & fn)271 void read(const FileNode& fn)
272 {
273 FileNode n = fn["name"];
274 CV_Assert(n.isString() && String(n) == name);
275 wcon = fn["contrast_weight"];
276 wsat = fn["saturation_weight"];
277 wexp = fn["exposure_weight"];
278 }
279
280 protected:
281 String name;
282 float wcon, wsat, wexp;
283 };
284
createMergeMertens(float wcon,float wsat,float wexp)285 Ptr<MergeMertens> createMergeMertens(float wcon, float wsat, float wexp)
286 {
287 return makePtr<MergeMertensImpl>(wcon, wsat, wexp);
288 }
289
290 class MergeRobertsonImpl : public MergeRobertson
291 {
292 public:
MergeRobertsonImpl()293 MergeRobertsonImpl() :
294 name("MergeRobertson"),
295 weight(RobertsonWeights())
296 {
297 }
298
process(InputArrayOfArrays src,OutputArray dst,InputArray _times,InputArray input_response)299 void process(InputArrayOfArrays src, OutputArray dst, InputArray _times, InputArray input_response)
300 {
301 std::vector<Mat> images;
302 src.getMatVector(images);
303 Mat times = _times.getMat();
304
305 CV_Assert(images.size() == times.total());
306 checkImageDimensions(images);
307 CV_Assert(images[0].depth() == CV_8U);
308
309 int channels = images[0].channels();
310 int CV_32FCC = CV_MAKETYPE(CV_32F, channels);
311
312 dst.create(images[0].size(), CV_32FCC);
313 Mat result = dst.getMat();
314
315 Mat response = input_response.getMat();
316 if(response.empty()) {
317 float middle = LDR_SIZE / 2.0f;
318 response = linearResponse(channels) / middle;
319 }
320 CV_Assert(response.rows == LDR_SIZE && response.cols == 1 &&
321 response.channels() == channels);
322
323 result = Mat::zeros(images[0].size(), CV_32FCC);
324 Mat wsum = Mat::zeros(images[0].size(), CV_32FCC);
325 for(size_t i = 0; i < images.size(); i++) {
326 Mat im, w;
327 LUT(images[i], weight, w);
328 LUT(images[i], response, im);
329
330 result += times.at<float>((int)i) * w.mul(im);
331 wsum += times.at<float>((int)i) * times.at<float>((int)i) * w;
332 }
333 result = result.mul(1 / wsum);
334 }
335
process(InputArrayOfArrays src,OutputArray dst,InputArray times)336 void process(InputArrayOfArrays src, OutputArray dst, InputArray times)
337 {
338 process(src, dst, times, Mat());
339 }
340
341 protected:
342 String name;
343 Mat weight;
344 };
345
createMergeRobertson()346 Ptr<MergeRobertson> createMergeRobertson()
347 {
348 return makePtr<MergeRobertsonImpl>();
349 }
350
351 }
352