remove debug detect at scale method

modules/gpu/doc/object_detection.rst

@@ -248,7 +248,6 @@ Implementation of soft (stageless) cascaded detector. ::
         virtual ~SCascade();
         virtual bool load(const FileNode& fn);
         virtual void detect(InputArray image, InputArray rois, OutputArray objects, Stream& stream = Stream::Null()) const;
-        virtual void detect(InputArray image, InputArray rois, OutputArray objects, const int level, Stream& stream = Stream::Null()) const;
         void genRoi(InputArray roi, OutputArray mask, Stream& stream = Stream::Null()) const;
     };
 
@@ -292,7 +291,6 @@ SCascade::detect
 Apply cascade to an input frame and return the vector of Decection objcts.
 
 .. ocv:function:: void detect(InputArray image, InputArray rois, OutputArray objects, Stream& stream = Stream::Null()) const
-.. ocv:function:: void detect(InputArray image, InputArray rois, OutputArray objects, const int level, Stream& stream = Stream::Null()) const
 
     :param image: a frame on which detector will be applied.
 
@@ -302,8 +300,6 @@ Apply cascade to an input frame and return the vector of Decection objcts.
 
     :param stream: a high-level CUDA stream abstraction used for asynchronous execution.
 
-    :param level: used for execution cascade on specific scales pyramid level.
-
 
 SCascade::genRoi
 --------------------------

modules/gpu/include/opencv2/gpu/gpu.hpp

@@ -1577,9 +1577,7 @@ public:
     // Param objects is an output array of Detections represented as GpuMat of detections (SCascade::Detection)
     //    The first element of the matrix is  actually a count of detections.
     // Param stream is stream is a high-level CUDA stream abstraction used for asynchronous execution
-    // Param level used for execution cascade on specific scales pyramid level.
     virtual void detect(InputArray image, InputArray rois, OutputArray objects, Stream& stream = Stream::Null()) const;
-    virtual void detect(InputArray image, InputArray rois, OutputArray objects, const int level, Stream& stream = Stream::Null()) const;
 
     // Convert ROI matrix into the suitable for detect method.
     // Param roi is an input matrix of the same size as the image.

modules/gpu/src/cuda/isf-sc.cu

@@ -45,15 +45,6 @@
 #include <stdio.h>
 #include <float.h>
 
-// #define LOG_CUDA_CASCADE
-
-#if defined LOG_CUDA_CASCADE
-# define dprintf(format, ...) \
-            do { printf(format, __VA_ARGS__); } while (0)
-#else
-# define dprintf(format, ...)
-#endif
-
 namespace cv { namespace gpu { namespace device {
 namespace icf {
 
@@ -254,12 +245,12 @@ __global__ void soft_cascade(const CascadeInvoker<Policy> invoker, Detection* ob
 
 template<typename Policy>
 void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv,
-    PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const int scale, const cudaStream_t& stream) const
+    PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const cudaStream_t& stream) const
 {
     int fw = roi.rows;
     int fh = roi.cols;
 
-    dim3 grid(fw, fh / Policy::STA_Y, (scale == -1) ? downscales : 1);
+    dim3 grid(fw, fh / Policy::STA_Y, downscales);
 
     uint* ctr = (uint*)(counter.ptr(0));
     Detection* det = (Detection*)objects.ptr();
@@ -268,26 +259,16 @@ void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi&
     cudaChannelFormatDesc desc = cudaCreateChannelDesc<int>();
     cudaSafeCall( cudaBindTexture2D(0, thogluv, hogluv.data, desc, hogluv.cols, hogluv.rows, hogluv.step));
 
-    cudaChannelFormatDesc desc_roi = cudaCreateChannelDesc<float2>();
-    cudaSafeCall( cudaBindTexture2D(0, troi, roi.data, desc_roi, roi.cols / 8, roi.rows, roi.step));
+    cudaChannelFormatDesc desc_roi = cudaCreateChannelDesc<typename Policy::roi_type>();
+    cudaSafeCall( cudaBindTexture2D(0, troi, roi.data, desc_roi, roi.cols / Policy::STA_Y, roi.rows, roi.step));
 
     const CascadeInvoker<Policy> inv = *this;
 
-    if (scale == -1)
-    {
-        soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, 0);
-        cudaSafeCall( cudaGetLastError());
+    soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, 0);
+    cudaSafeCall( cudaGetLastError());
 
-        grid = dim3(fw, fh / Policy::STA_Y, scales - downscales);
-        soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, downscales);
-    }
-    else
-    {
-        if (scale >= downscales)
-            soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, scale);
-        else
-            soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, scale);
-    }
+    grid = dim3(fw, fh / Policy::STA_Y, scales - downscales);
+    soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, downscales);
 
     if (!stream)
     {
@@ -297,7 +278,7 @@ void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi&
 }
 
 template void CascadeInvoker<GK107PolicyX4>::operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv,
-    PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const int scale, const cudaStream_t& stream) const;
+    PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const cudaStream_t& stream) const;
 
 }
 }}}
\ No newline at end of file

modules/gpu/src/icf.hpp

@@ -118,9 +118,10 @@ struct __align__(16) Detection
 struct GK107PolicyX4
 {
     enum {WARP = 32, STA_X = WARP, STA_Y = 8, SHRINKAGE = 4};
+    typedef float2 roi_type;
     static const dim3 block()
     {
-        return dim3(GK107PolicyX4::STA_X, GK107PolicyX4::STA_Y);
+        return dim3(STA_X, STA_Y);
     }
 };
 
@@ -146,7 +147,7 @@ struct CascadeInvoker
     int scales;
 
     void operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv, PtrStepSz<uchar4> objects,
-        PtrStepSzi counter, const int downscales, const int csale = -1, const cudaStream_t& stream = 0) const;
+        PtrStepSzi counter, const int downscales, const cudaStream_t& stream = 0) const;
 
     template<bool isUp>
     __device void detect(Detection* objects, const uint ndetections, uint* ctr, const int downscales) const;

modules/gpu/src/softcascade.cpp

@@ -311,13 +311,13 @@ struct cv::gpu::SCascade::Fields
         leaves.upload(hleaves);
     }
 
-    void detect(int scale, const cv::gpu::GpuMat& roi, const cv::gpu::GpuMat& count, cv::gpu::GpuMat& objects, const cudaStream_t& stream) const
+    void detect(const cv::gpu::GpuMat& roi, const cv::gpu::GpuMat& count, cv::gpu::GpuMat& objects, const cudaStream_t& stream) const
     {
         cudaMemset(count.data, 0, sizeof(Detection));
         cudaSafeCall( cudaGetLastError());
         device::icf::CascadeInvoker<device::icf::GK107PolicyX4> invoker
         = device::icf::CascadeInvoker<device::icf::GK107PolicyX4>(levels, octaves, stages, nodes, leaves);
-        invoker(roi, hogluv, objects, count, downscales, scale, stream);
+        invoker(roi, hogluv, objects, count, downscales, stream);
     }
 
     void preprocess(const cv::gpu::GpuMat& colored, Stream& s)
@@ -521,36 +521,7 @@ void cv::gpu::SCascade::detect(InputArray image, InputArray _rois, OutputArray _
     objects = GpuMat(objects, cv::Rect( sizeof(Detection), 0, objects.cols -  sizeof(Detection), 1));
     cudaStream_t stream = StreamAccessor::getStream(s);
 
-    flds.detect(-1, rois, tmp, objects, stream);
-}
-
-void cv::gpu::SCascade::detect(InputArray image, InputArray _rois, OutputArray _objects, const int level, Stream& s) const
-{
-    CV_Assert(fields);
-
-    const GpuMat colored = image.getGpuMat();
-    // only color images are supperted
-    CV_Assert(colored.type() == CV_8UC3 || colored.type() == CV_32SC1);
-
-    Fields& flds = *fields;
-    if (colored.type() == CV_8UC3)
-    {
-        // only this window size allowed
-        // CV_Assert(colored.cols == Fields::FRAME_WIDTH && colored.rows == Fields::FRAME_HEIGHT);
-        flds.preprocess(colored, s);
-    }
-    else
-    {
-        colored.copyTo(flds.hogluv);
-    }
-
-    GpuMat rois = _rois.getGpuMat(), objects = _objects.getGpuMat();
-
-    GpuMat tmp = GpuMat(objects, cv::Rect(0, 0, sizeof(Detection), 1));
-    objects = GpuMat(objects, cv::Rect( sizeof(Detection), 0, objects.cols -  sizeof(Detection), 1));
-    cudaStream_t stream = StreamAccessor::getStream(s);
-
-    flds.detect(level, rois, tmp, objects, stream);
+    flds.detect(rois, tmp, objects, stream);
 }
 
 void cv::gpu::SCascade::genRoi(InputArray _roi, OutputArray _mask, Stream& stream) const

modules/gpu/test/test_softcascade.cpp

@@ -195,55 +195,55 @@ GPU_TEST_P(SCascadeTestRoi, detect,
 
 }
 
-typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestLevel;
-GPU_TEST_P(SCascadeTestLevel, detect,
-        testing::Combine(
-        ALL_DEVICES,
-        testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
-        testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
-        testing::Range(0, 47)
-        ))
-{
-    cv::gpu::setDevice(GET_PARAM(0).deviceID());
-
-    cv::gpu::SCascade cascade;
-
-    cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
-    ASSERT_TRUE(fs.isOpened());
-
-    ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
-
-    cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
-    ASSERT_FALSE(coloredCpu.empty());
-
-    typedef cv::gpu::SCascade::Detection Detection;
-    GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
-    rois.setTo(1);
-
-    cv::gpu::GpuMat trois;
-    cascade.genRoi(rois, trois);
-    objectBoxes.setTo(0);
-    int level = GET_PARAM(3);
-    cascade.detect(colored, trois, objectBoxes, level);
-
-    cv::Mat dt(objectBoxes);
-
-    Detection* dts = ((Detection*)dt.data) + 1;
-    int* count = dt.ptr<int>(0);
-
-    cv::Mat result(coloredCpu);
-
-    printTotal(std::cout, *count);
-    for (int i = 0; i  < *count; ++i)
-    {
-        Detection d = dts[i];
-        print(std::cout, d);
-        cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
-    }
-
-    writeResult(result, level);
-    SHOW(result);
-}
+// typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestLevel;
+// GPU_TEST_P(SCascadeTestLevel, detect,
+//         testing::Combine(
+//         ALL_DEVICES,
+//         testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
+//         testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
+//         testing::Range(0, 47)
+//         ))
+// {
+//     cv::gpu::setDevice(GET_PARAM(0).deviceID());
+
+//     cv::gpu::SCascade cascade;
+
+//     cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
+//     ASSERT_TRUE(fs.isOpened());
+
+//     ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
+
+//     cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
+//     ASSERT_FALSE(coloredCpu.empty());
+
+//     typedef cv::gpu::SCascade::Detection Detection;
+//     GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
+//     rois.setTo(1);
+
+//     cv::gpu::GpuMat trois;
+//     cascade.genRoi(rois, trois);
+//     objectBoxes.setTo(0);
+//     int level = GET_PARAM(3);
+//     cascade.detect(colored, trois, objectBoxes, level);
+
+//     cv::Mat dt(objectBoxes);
+
+//     Detection* dts = ((Detection*)dt.data) + 1;
+//     int* count = dt.ptr<int>(0);
+
+//     cv::Mat result(coloredCpu);
+
+//     printTotal(std::cout, *count);
+//     for (int i = 0; i  < *count; ++i)
+//     {
+//         Detection d = dts[i];
+//         print(std::cout, d);
+//         cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
+//     }
+
+//     writeResult(result, level);
+//     SHOW(result);
+// }
 
 TEST(SCascadeTest, readCascade)
 {