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41 //M*/
42
43 #include "perf_precomp.hpp"
44
45 namespace opencv_test { namespace {
46
47 //////////////////////////////////////////////////////////////////////
48 // HoughLines
49
50 namespace
51 {
52 struct Vec4iComparator
53 {
operator ()opencv_test::__anonf87657fc0111::__anonf87657fc0211::Vec4iComparator54 bool operator()(const cv::Vec4i& a, const cv::Vec4i b) const
55 {
56 if (a[0] != b[0]) return a[0] < b[0];
57 else if(a[1] != b[1]) return a[1] < b[1];
58 else if(a[2] != b[2]) return a[2] < b[2];
59 else return a[3] < b[3];
60 }
61 };
62 struct Vec3fComparator
63 {
operator ()opencv_test::__anonf87657fc0111::__anonf87657fc0211::Vec3fComparator64 bool operator()(const cv::Vec3f& a, const cv::Vec3f b) const
65 {
66 if(a[0] != b[0]) return a[0] < b[0];
67 else if(a[1] != b[1]) return a[1] < b[1];
68 else return a[2] < b[2];
69 }
70 };
71 struct Vec2fComparator
72 {
operator ()opencv_test::__anonf87657fc0111::__anonf87657fc0211::Vec2fComparator73 bool operator()(const cv::Vec2f& a, const cv::Vec2f b) const
74 {
75 if(a[0] != b[0]) return a[0] < b[0];
76 else return a[1] < b[1];
77 }
78 };
79 }
80
PERF_TEST_P(Sz,HoughLines,CUDA_TYPICAL_MAT_SIZES)81 PERF_TEST_P(Sz, HoughLines,
82 CUDA_TYPICAL_MAT_SIZES)
83 {
84 declare.time(30.0);
85
86 const cv::Size size = GetParam();
87
88 const float rho = 1.0f;
89 const float theta = static_cast<float>(CV_PI / 180.0);
90 const int threshold = 300;
91
92 cv::Mat src(size, CV_8UC1, cv::Scalar::all(0));
93 cv::line(src, cv::Point(0, 100), cv::Point(src.cols, 100), cv::Scalar::all(255), 1);
94 cv::line(src, cv::Point(0, 200), cv::Point(src.cols, 200), cv::Scalar::all(255), 1);
95 cv::line(src, cv::Point(0, 400), cv::Point(src.cols, 400), cv::Scalar::all(255), 1);
96 cv::line(src, cv::Point(100, 0), cv::Point(100, src.rows), cv::Scalar::all(255), 1);
97 cv::line(src, cv::Point(200, 0), cv::Point(200, src.rows), cv::Scalar::all(255), 1);
98 cv::line(src, cv::Point(400, 0), cv::Point(400, src.rows), cv::Scalar::all(255), 1);
99
100 if (PERF_RUN_CUDA())
101 {
102 const cv::cuda::GpuMat d_src(src);
103 cv::cuda::GpuMat d_lines;
104
105 cv::Ptr<cv::cuda::HoughLinesDetector> hough = cv::cuda::createHoughLinesDetector(rho, theta, threshold);
106
107 TEST_CYCLE() hough->detect(d_src, d_lines);
108
109 cv::Mat gpu_lines(d_lines.row(0));
110 cv::Vec2f* begin = gpu_lines.ptr<cv::Vec2f>(0);
111 cv::Vec2f* end = begin + gpu_lines.cols;
112 std::sort(begin, end, Vec2fComparator());
113 SANITY_CHECK(gpu_lines);
114 }
115 else
116 {
117 std::vector<cv::Vec2f> cpu_lines;
118
119 TEST_CYCLE() cv::HoughLines(src, cpu_lines, rho, theta, threshold);
120
121 SANITY_CHECK(cpu_lines);
122 }
123 }
124
125 //////////////////////////////////////////////////////////////////////
126 // HoughLinesP
127
128 DEF_PARAM_TEST_1(Image, std::string);
129
130 PERF_TEST_P(Image, HoughLinesP,
131 testing::Values("cv/shared/pic5.png", "stitching/a1.png"))
132 {
133 declare.time(30.0);
134
135 const std::string fileName = getDataPath(GetParam());
136
137 const float rho = 1.0f;
138 const float theta = static_cast<float>(CV_PI / 180.0);
139 const int threshold = 100;
140 const int minLineLength = 50;
141 const int maxLineGap = 5;
142
143 const cv::Mat image = cv::imread(fileName, cv::IMREAD_GRAYSCALE);
144 ASSERT_FALSE(image.empty());
145
146 cv::Mat mask;
147 cv::Canny(image, mask, 50, 100);
148
149 if (PERF_RUN_CUDA())
150 {
151 const cv::cuda::GpuMat d_mask(mask);
152 cv::cuda::GpuMat d_lines;
153
154 cv::Ptr<cv::cuda::HoughSegmentDetector> hough = cv::cuda::createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap);
155
156 TEST_CYCLE() hough->detect(d_mask, d_lines);
157
158 cv::Mat gpu_lines(d_lines);
159 cv::Vec4i* begin = gpu_lines.ptr<cv::Vec4i>();
160 cv::Vec4i* end = begin + gpu_lines.cols;
161 std::sort(begin, end, Vec4iComparator());
162 SANITY_CHECK(gpu_lines);
163 }
164 else
165 {
166 std::vector<cv::Vec4i> cpu_lines;
167
168 TEST_CYCLE() cv::HoughLinesP(mask, cpu_lines, rho, theta, threshold, minLineLength, maxLineGap);
169
170 SANITY_CHECK(cpu_lines);
171 }
172 }
173
174 //////////////////////////////////////////////////////////////////////
175 // HoughCircles
176
177 DEF_PARAM_TEST(Sz_Dp_MinDist, cv::Size, float, float);
178
179 PERF_TEST_P(Sz_Dp_MinDist, HoughCircles,
180 Combine(CUDA_TYPICAL_MAT_SIZES,
181 Values(1.0f, 2.0f, 4.0f),
182 Values(1.0f)))
183 {
184 declare.time(30.0);
185
186 const cv::Size size = GET_PARAM(0);
187 const float dp = GET_PARAM(1);
188 const float minDist = GET_PARAM(2);
189
190 const int minRadius = 10;
191 const int maxRadius = 30;
192 const int cannyThreshold = 100;
193 const int votesThreshold = 15;
194
195 cv::Mat src(size, CV_8UC1, cv::Scalar::all(0));
196 cv::circle(src, cv::Point(100, 100), 20, cv::Scalar::all(255), -1);
197 cv::circle(src, cv::Point(200, 200), 25, cv::Scalar::all(255), -1);
198 cv::circle(src, cv::Point(200, 100), 25, cv::Scalar::all(255), -1);
199
200 if (PERF_RUN_CUDA())
201 {
202 const cv::cuda::GpuMat d_src(src);
203 cv::cuda::GpuMat d_circles;
204
205 cv::Ptr<cv::cuda::HoughCirclesDetector> houghCircles = cv::cuda::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
206
207 TEST_CYCLE() houghCircles->detect(d_src, d_circles);
208
209 cv::Mat gpu_circles(d_circles);
210 cv::Vec3f* begin = gpu_circles.ptr<cv::Vec3f>(0);
211 cv::Vec3f* end = begin + gpu_circles.cols;
212 std::sort(begin, end, Vec3fComparator());
213 SANITY_CHECK(gpu_circles);
214 }
215 else
216 {
217 std::vector<cv::Vec3f> cpu_circles;
218
219 TEST_CYCLE() cv::HoughCircles(src, cpu_circles, cv::HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
220
221 SANITY_CHECK(cpu_circles);
222 }
223 }
224
225 //////////////////////////////////////////////////////////////////////
226 // GeneralizedHough
227
PERF_TEST_P(Sz,GeneralizedHoughBallard,CUDA_TYPICAL_MAT_SIZES)228 PERF_TEST_P(Sz, GeneralizedHoughBallard, CUDA_TYPICAL_MAT_SIZES)
229 {
230 declare.time(10);
231
232 const cv::Size imageSize = GetParam();
233
234 const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
235 ASSERT_FALSE(templ.empty());
236
237 cv::Mat image(imageSize, CV_8UC1, cv::Scalar::all(0));
238 templ.copyTo(image(cv::Rect(50, 50, templ.cols, templ.rows)));
239
240 cv::Mat edges;
241 cv::Canny(image, edges, 50, 100);
242
243 cv::Mat dx, dy;
244 cv::Sobel(image, dx, CV_32F, 1, 0);
245 cv::Sobel(image, dy, CV_32F, 0, 1);
246
247 if (PERF_RUN_CUDA())
248 {
249 cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::cuda::createGeneralizedHoughBallard();
250
251 const cv::cuda::GpuMat d_edges(edges);
252 const cv::cuda::GpuMat d_dx(dx);
253 const cv::cuda::GpuMat d_dy(dy);
254 cv::cuda::GpuMat positions;
255
256 alg->setTemplate(cv::cuda::GpuMat(templ));
257
258 TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions);
259
260 CUDA_SANITY_CHECK(positions);
261 }
262 else
263 {
264 cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::createGeneralizedHoughBallard();
265
266 cv::Mat positions;
267
268 alg->setTemplate(templ);
269
270 TEST_CYCLE() alg->detect(edges, dx, dy, positions);
271
272 CPU_SANITY_CHECK(positions);
273 }
274 }
275
PERF_TEST_P(Sz,DISABLED_GeneralizedHoughGuil,CUDA_TYPICAL_MAT_SIZES)276 PERF_TEST_P(Sz, DISABLED_GeneralizedHoughGuil, CUDA_TYPICAL_MAT_SIZES)
277 {
278 declare.time(10);
279
280 const cv::Size imageSize = GetParam();
281
282 const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
283 ASSERT_FALSE(templ.empty());
284
285 cv::Mat image(imageSize, CV_8UC1, cv::Scalar::all(0));
286 templ.copyTo(image(cv::Rect(50, 50, templ.cols, templ.rows)));
287
288 cv::RNG rng(123456789);
289 const int objCount = rng.uniform(5, 15);
290 for (int i = 0; i < objCount; ++i)
291 {
292 double scale = rng.uniform(0.7, 1.3);
293 bool rotate = 1 == rng.uniform(0, 2);
294
295 cv::Mat obj;
296 cv::resize(templ, obj, cv::Size(), scale, scale);
297 if (rotate)
298 obj = obj.t();
299
300 cv::Point pos;
301
302 pos.x = rng.uniform(0, image.cols - obj.cols);
303 pos.y = rng.uniform(0, image.rows - obj.rows);
304
305 cv::Mat roi = image(cv::Rect(pos, obj.size()));
306 cv::add(roi, obj, roi);
307 }
308
309 cv::Mat edges;
310 cv::Canny(image, edges, 50, 100);
311
312 cv::Mat dx, dy;
313 cv::Sobel(image, dx, CV_32F, 1, 0);
314 cv::Sobel(image, dy, CV_32F, 0, 1);
315
316 if (PERF_RUN_CUDA())
317 {
318 cv::Ptr<cv::GeneralizedHoughGuil> alg = cv::cuda::createGeneralizedHoughGuil();
319 alg->setMaxAngle(90.0);
320 alg->setAngleStep(2.0);
321
322 const cv::cuda::GpuMat d_edges(edges);
323 const cv::cuda::GpuMat d_dx(dx);
324 const cv::cuda::GpuMat d_dy(dy);
325 cv::cuda::GpuMat positions;
326
327 alg->setTemplate(cv::cuda::GpuMat(templ));
328
329 TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions);
330 }
331 else
332 {
333 cv::Ptr<cv::GeneralizedHoughGuil> alg = cv::createGeneralizedHoughGuil();
334 alg->setMaxAngle(90.0);
335 alg->setAngleStep(2.0);
336
337 cv::Mat positions;
338
339 alg->setTemplate(templ);
340
341 TEST_CYCLE() alg->detect(edges, dx, dy, positions);
342 }
343
344 // The algorithm is not stable yet.
345 SANITY_CHECK_NOTHING();
346 }
347
348 }} // namespace
349