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Commit fcec053d authored by Alexander Alekhin's avatar Alexander Alekhin
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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

parents d073215f a63f66c9
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Showing with 220 additions and 130 deletions
modules/core/src/ocl.cpp
View file @ fcec053d
......@@ -2993,7 +2993,11 @@ int Kernel::set(int i, const KernelArg& arg)
if( !p || !p->handle )
return -1;
if (i < 0)
{
CV_LOG_ERROR(NULL, cv::format("OpenCL: Kernel(%s)::set(arg_index=%d): negative arg_index",
p->name.c_str(), (int)i));
return i;
}
if( i == 0 )
p->cleanupUMats();
cl_int status = 0;
......@@ -3002,10 +3006,19 @@ int Kernel::set(int i, const KernelArg& arg)
AccessFlag accessFlags = ((arg.flags & KernelArg::READ_ONLY) ? ACCESS_READ : static_cast<AccessFlag>(0)) |
((arg.flags & KernelArg::WRITE_ONLY) ? ACCESS_WRITE : static_cast<AccessFlag>(0));
bool ptronly = (arg.flags & KernelArg::PTR_ONLY) != 0;
if (ptronly && arg.m->empty())
{
cl_mem h_null = (cl_mem)NULL;
status = clSetKernelArg(p->handle, (cl_uint)i, sizeof(h_null), &h_null);
CV_OCL_DBG_CHECK_RESULT(status, cv::format("clSetKernelArg('%s', arg_index=%d, cl_mem=NULL)", p->name.c_str(), (int)i).c_str());
return i + 1;
}
cl_mem h = (cl_mem)arg.m->handle(accessFlags);
if (!h)
{
CV_LOG_ERROR(NULL, cv::format("OpenCL: Kernel(%s)::set(arg_index=%d, flags=%d): can't create cl_mem handle for passed UMat buffer (addr=%p)",
p->name.c_str(), (int)i, (int)arg.flags, arg.m));
p->release();
p = 0;
return -1;
......
modules/dnn/src/layers/mvn_layer.cpp
View file @ fcec053d
......@@ -138,9 +138,12 @@ public:
UMat& bnorm_weight = umat_scale;
UMat& bnorm_bias = umat_shift;
const unsigned LOCAL_SIZE = 128;
bool use_half = (inputs[0].depth() == CV_16S);
String opts = format(" -DT=%s -DT4=%s -Dconvert_T=%s", use_half ? "half" : "float",
use_half ? "half4" : "float4", use_half ? "convert_half4" : "convert_float4");
String opts = format(" -DT=%s -DT4=%s -Dconvert_T=%s -DLOCAL_SIZE=%u", use_half ? "half" : "float",
use_half ? "half4" : "float4", use_half ? "convert_half4" : "convert_float4",
LOCAL_SIZE
);
int splitDim = (acrossChannels) ? 1 : 2;
for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
......@@ -155,8 +158,8 @@ public:
float alpha = 1.0f / s[1];
String buildopt = "-DNUM=4" + opts;
ocl::Kernel k("mean_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
size_t localsize[] = { 128 };
ocl::Kernel k("mean_fuse4", ocl::dnn::mvn_oclsrc, buildopt + " -DKERNEL_MEAN_FUSE");
size_t localsize[] = { LOCAL_SIZE };
size_t globalsize[] = { (size_t)s[0] / 4 * localsize[0] };
int argId = 0;
......@@ -165,7 +168,6 @@ public:
k.set(argId++, alpha);
k.set(argId++, ocl::KernelArg::PtrWriteOnly(meanMat));
k.set(argId++, ocl::KernelArg::PtrWriteOnly(tmpMat));
k.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
bool ret = k.run(1, globalsize, localsize, false);
if (!ret)
return false;
......@@ -173,7 +175,7 @@ public:
buildopt += format(" %s %s", (fuse_batch_norm) ? "-DFUSE_BATCH_NORM" : "",
(fuse_relu) ? "-DFUSE_RELU" : "");
ocl::Kernel k1("mvn_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
ocl::Kernel k1("mvn_fuse4", ocl::dnn::mvn_oclsrc, buildopt + " -DKERNEL_MVN_FUSE");
argId = 0;
k1.set(argId++, ocl::KernelArg::PtrReadOnly(tmpMat));
k1.set(argId++, ocl::KernelArg::PtrReadOnly(inpMat));
......@@ -185,7 +187,6 @@ public:
k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_weight));
k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_bias));
k1.set(argId++, ocl::KernelArg::PtrWriteOnly(outMat));
k1.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
ret = k1.run(1, globalsize, localsize, false);
if (!ret)
return false;
......@@ -243,7 +244,7 @@ public:
if (normVariance)
{
String kname = format("calc_mean%d", number);
ocl::Kernel kernel(kname.c_str(), ocl::dnn::mvn_oclsrc, buildopt);
ocl::Kernel kernel(kname.c_str(), ocl::dnn::mvn_oclsrc, buildopt + " -DKERNEL_MEAN");
if (kernel.empty())
return false;
......@@ -263,7 +264,7 @@ public:
}
String kname = format("mvn%d", number);
buildopt += format("%s%s%s", (normVariance) ? " -DNORM_VARIANCE" : "",
buildopt += format("%s%s%s -DKERNEL_MVN", (normVariance) ? " -DNORM_VARIANCE" : "",
(fuse_batch_norm) ? " -DFUSE_BATCH_NORM" : "",
(fuse_relu) ? " -DFUSE_RELU" : "");
ocl::Kernel kernel1(kname.c_str(), ocl::dnn::mvn_oclsrc, buildopt);
......
modules/dnn/src/opencl/mvn.cl
View file @ fcec053d
......@@ -74,6 +74,8 @@
#define MVN_FUSE mvn_fuse1
#endif
#ifdef KERNEL_MEAN
__kernel void CALC_MEAN(__global const Dtype* src,
const int rows,
const int cols,
......@@ -94,6 +96,8 @@ __kernel void CALC_MEAN(__global const Dtype* src,
store(dst_vec, dst, index);
}
#elif defined KERNEL_MVN
__kernel void MVN(__global const Dtype* src,
const int rows,
const int cols,
......@@ -140,12 +144,13 @@ __kernel void MVN(__global const Dtype* src,
store(dst_vec, dst, index);
}
#elif defined KERNEL_MEAN_FUSE
__kernel void MEAN_FUSE(__global const T * A,
unsigned int A_col_size,
float alpha,
__global T4 * mean,
__global Dtype * tmp,
__local Dtype4 * work)
__global Dtype * tmp)
{
unsigned int row_gid = get_group_id(0);
unsigned int lid = get_local_id(0);
......@@ -168,15 +173,16 @@ __kernel void MEAN_FUSE(__global const T * A,
dot2 += convert_float4(a2);
dot3 += convert_float4(a3);
i += get_local_size(0);
i += LOCAL_SIZE;
}
__local Dtype4 work[LOCAL_SIZE];
work[lid].s0 = dot(dot0, b0);
work[lid].s1 = dot(dot1, b0);
work[lid].s2 = dot(dot2, b0);
work[lid].s3 = dot(dot3, b0);
for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1)
for(unsigned int stride=LOCAL_SIZE/2 ; stride>0 ; stride>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < stride)
......@@ -212,10 +218,12 @@ __kernel void MEAN_FUSE(__global const T * A,
vstore4(dot2, i, dst0_read + 2 * A_col_size);
vstore4(dot3, i, dst0_read + 3 * A_col_size);
i += get_local_size(0);
i += LOCAL_SIZE;
}
}
#elif defined KERNEL_MVN_FUSE
__kernel void MVN_FUSE(__global const Dtype * tmp,
__global const T * A,
__global const T4 * mean,
......@@ -225,8 +233,7 @@ __kernel void MVN_FUSE(__global const Dtype * tmp,
const float relu_slope,
__global const Dtype4 * bnorm_weight,
__global const Dtype4 * bnorm_bias,
__global T * B,
__local Dtype4 * work)
__global T * B)
{
unsigned int row_gid = get_group_id(0);
unsigned int lid = get_local_id(0);
......@@ -250,15 +257,16 @@ __kernel void MVN_FUSE(__global const Dtype * tmp,
dot2 += a2;
dot3 += a3;
i += get_local_size(0);
i += LOCAL_SIZE;
}
__local Dtype4 work[LOCAL_SIZE];
work[lid].s0 = dot(dot0, b0);
work[lid].s1 = dot(dot1, b0);
work[lid].s2 = dot(dot2, b0);
work[lid].s3 = dot(dot3, b0);
for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1)
for(unsigned int stride=LOCAL_SIZE/2 ; stride>0 ; stride>>=1)
{
barrier(CLK_LOCAL_MEM_FENCE);
if(lid < stride)
......@@ -314,6 +322,10 @@ __kernel void MVN_FUSE(__global const Dtype * tmp,
vstore4(convert_T(dot2), i, dst0_read + 2 * A_col_size);
vstore4(convert_T(dot3), i, dst0_read + 3 * A_col_size);
i += get_local_size(0);
i += LOCAL_SIZE;
}
}
#else
#error "Configuration error!"
#endif
modules/imgproc/src/color_yuv.cpp
View file @ fcec053d
This diff is collapsed.
modules/js/src/core_bindings.cpp
View file @ fcec053d
......@@ -289,13 +289,16 @@ namespace binding_utils
float radius;
};
#ifdef HAVE_OPENCV_IMGPROC
Circle minEnclosingCircle(const cv::Mat& points)
{
Circle circle;
cv::minEnclosingCircle(points, circle.center, circle.radius);
return circle;
}
#endif
#ifdef HAVE_OPENCV_VIDEO
emscripten::val CamShiftWrapper(const cv::Mat& arg1, Rect& arg2, TermCriteria arg3)
{
RotatedRect rotatedRect = cv::CamShift(arg1, arg2, arg3);
......@@ -313,6 +316,7 @@ namespace binding_utils
result.call<void>("push", arg2);
return result;
}
#endif // HAVE_OPENCV_VIDEO
std::string getExceptionMsg(const cv::Exception& e) {
return e.msg;
......@@ -551,19 +555,25 @@ EMSCRIPTEN_BINDINGS(binding_utils)
function("exceptionFromPtr", &binding_utils::exceptionFromPtr, allow_raw_pointers());
#ifdef HAVE_OPENCV_IMGPROC
function("minEnclosingCircle", select_overload<binding_utils::Circle(const cv::Mat&)>(&binding_utils::minEnclosingCircle));
#endif
function("minMaxLoc", select_overload<binding_utils::MinMaxLoc(const cv::Mat&, const cv::Mat&)>(&binding_utils::minMaxLoc));
function("minMaxLoc", select_overload<binding_utils::MinMaxLoc(const cv::Mat&)>(&binding_utils::minMaxLoc_1));
#ifdef HAVE_OPENCV_IMGPROC
function("morphologyDefaultBorderValue", &cv::morphologyDefaultBorderValue);
#endif
function("CV_MAT_DEPTH", &binding_utils::cvMatDepth);
#ifdef HAVE_OPENCV_VIDEO
function("CamShift", select_overload<emscripten::val(const cv::Mat&, Rect&, TermCriteria)>(&binding_utils::CamShiftWrapper));
function("meanShift", select_overload<emscripten::val(const cv::Mat&, Rect&, TermCriteria)>(&binding_utils::meanShiftWrapper));
#endif
function("getBuildInformation", &binding_utils::getBuildInformation);
......
modules/js/src/embindgen.py
View file @ fcec053d
......@@ -140,7 +140,7 @@ features2d = {'Feature2D': ['detect', 'compute', 'detectAndCompute', 'descriptor
'AKAZE': ['create', 'setDescriptorType', 'getDescriptorType', 'setDescriptorSize', 'getDescriptorSize', 'setDescriptorChannels', 'getDescriptorChannels', 'setThreshold', 'getThreshold', 'setNOctaves', 'getNOctaves', 'setNOctaveLayers', 'getNOctaveLayers', 'setDiffusivity', 'getDiffusivity', 'getDefaultName'],
'DescriptorMatcher': ['add', 'clear', 'empty', 'isMaskSupported', 'train', 'match', 'knnMatch', 'radiusMatch', 'clone', 'create'],
'BFMatcher': ['isMaskSupported', 'create'],
'': ['FAST', 'AGAST', 'drawKeypoints', 'drawMatches']}
'': ['drawKeypoints', 'drawMatches']}
photo = {'': ['createAlignMTB', 'createCalibrateDebevec', 'createCalibrateRobertson', \
'createMergeDebevec', 'createMergeMertens', 'createMergeRobertson', \
......
modules/js/test/test_features2d.js 0 → 100644
View file @ fcec053d
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
if (typeof module !== 'undefined' && module.exports) {
// The envrionment is Node.js
var cv = require('./opencv.js'); // eslint-disable-line no-var
}
function generateTestFrame(width, height) {
let w = width || 200;
let h = height || 200;
let img = new cv.Mat(h, w, cv.CV_8UC1, new cv.Scalar(0, 0, 0, 0));
let s = new cv.Scalar(255, 255, 255, 255);
let s128 = new cv.Scalar(128, 128, 128, 128);
let rect = new cv.Rect(w / 4, h / 4, w / 2, h / 2);
img.roi(rect).setTo(s);
img.roi(new cv.Rect(w / 2 - w / 8, h / 2 - h / 8, w / 4, h / 4)).setTo(s128);
cv.rectangle(img, new cv.Point(w / 8, h / 8), new cv.Point(w - w / 8, h - h / 8), s, 5);
cv.rectangle(img, new cv.Point(w / 5, h / 5), new cv.Point(w - w / 5, h - h / 5), s128, 3);
cv.line(img, new cv.Point(-w, 0), new cv.Point(w / 2, h / 2), s128, 5);
cv.line(img, new cv.Point(2*w, 0), new cv.Point(w / 2, h / 2), s, 5);
return img;
}
QUnit.module('Features2D', {});
QUnit.test('Detectors', function(assert) {
let image = generateTestFrame();
let kp = new cv.KeyPointVector();
let orb = new cv.ORB();
orb.detect(image, kp);
assert.equal(kp.size(), 67, 'ORB');
let mser = new cv.MSER();
mser.detect(image, kp);
assert.equal(kp.size(), 7, 'MSER');
let brisk = new cv.BRISK();
brisk.detect(image, kp);
assert.equal(kp.size(), 191, 'BRISK');
let ffd = new cv.FastFeatureDetector();
ffd.detect(image, kp);
assert.equal(kp.size(), 12, 'FastFeatureDetector');
let afd = new cv.AgastFeatureDetector();
afd.detect(image, kp);
assert.equal(kp.size(), 67, 'AgastFeatureDetector');
let gftt = new cv.GFTTDetector();
gftt.detect(image, kp);
assert.equal(kp.size(), 168, 'GFTTDetector');
let kaze = new cv.KAZE();
kaze.detect(image, kp);
assert.equal(kp.size(), 159, 'KAZE');
let akaze = new cv.AKAZE();
akaze.detect(image, kp);
assert.equal(kp.size(), 52, 'AKAZE');
});
QUnit.test('BFMatcher', function(assert) {
// Generate key points.
let image = generateTestFrame();
let kp = new cv.KeyPointVector();
let descriptors = new cv.Mat();
let orb = new cv.ORB();
orb.detectAndCompute(image, new cv.Mat(), kp, descriptors);
assert.equal(kp.size(), 67);
// Run a matcher.
let dm = new cv.DMatchVector();
let matcher = new cv.BFMatcher();
matcher.match(descriptors, descriptors, dm);
assert.equal(dm.size(), 67);
});
modules/js/test/tests.html
View file @ fcec053d
......@@ -29,6 +29,7 @@
<script type="application/javascript" src="test_objdetect.js"></script>
<script type="application/javascript" src="test_video.js"></script>
<script type="application/javascript" src="test_photo.js"></script>
<script type="application/javascript" src="test_features2d.js"></script>
<script type='text/javascript'>
QUnit.config.autostart = false;
......@@ -69,11 +70,5 @@
};
};
</script>
<!--
TODO
<script type="application/javascript" src="test_features2d.js"></script>
-->
</body>
</html>
modules/js/test/tests.js
View file @ fcec053d
......@@ -45,7 +45,8 @@ testrunner.run(
{
code: 'opencv.js',
tests: ['test_mat.js', 'test_utils.js', 'test_imgproc.js',
'test_objdetect.js', 'test_video.js', 'test_photo.js'],
'test_objdetect.js', 'test_video.js', 'test_features2d.js',
'test_photo.js'],
},
function(err, report) {
console.log(report.failed + ' failed, ' + report.passed + ' passed');
......
modules/video/src/optflowgf.cpp
View file @ fcec053d
......@@ -42,6 +42,7 @@
#include "precomp.hpp"
#include "opencl_kernels_video.hpp"
#include "opencv2/core/hal/intrin.hpp"
#if defined __APPLE__ || defined __ANDROID__
#define SMALL_LOCALSIZE
......@@ -433,13 +434,11 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1,
for( i = 0; i <= m; i++ )
kernel[i] = (float)(kernel[i]*s);
#if CV_SSE2
#if CV_SIMD128
float* simd_kernel = alignPtr(kernel + m+1, 16);
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE);
if( useSIMD )
{
for( i = 0; i <= m; i++ )
_mm_store_ps(simd_kernel + i*4, _mm_set1_ps(kernel[i]));
v_store(simd_kernel + i*4, v_setall_f32(kernel[i]));
}
#endif
......@@ -457,54 +456,53 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1,
}
x = 0;
#if CV_SSE2
if( useSIMD )
#if CV_SIMD128
{
for( ; x <= width*5 - 16; x += 16 )
{
const float *sptr0 = srow[m], *sptr1;
__m128 g4 = _mm_load_ps(simd_kernel);
__m128 s0, s1, s2, s3;
s0 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x), g4);
s1 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x + 4), g4);
s2 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x + 8), g4);
s3 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x + 12), g4);
v_float32x4 g4 = v_load(simd_kernel);
v_float32x4 s0, s1, s2, s3;
s0 = v_load(sptr0 + x) * g4;
s1 = v_load(sptr0 + x + 4) * g4;
s2 = v_load(sptr0 + x + 8) * g4;
s3 = v_load(sptr0 + x + 12) * g4;
for( i = 1; i <= m; i++ )
{
__m128 x0, x1;
v_float32x4 x0, x1;
sptr0 = srow[m+i], sptr1 = srow[m-i];
g4 = _mm_load_ps(simd_kernel + i*4);
x0 = _mm_add_ps(_mm_loadu_ps(sptr0 + x), _mm_loadu_ps(sptr1 + x));
x1 = _mm_add_ps(_mm_loadu_ps(sptr0 + x + 4), _mm_loadu_ps(sptr1 + x + 4));
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, g4));
s1 = _mm_add_ps(s1, _mm_mul_ps(x1, g4));
x0 = _mm_add_ps(_mm_loadu_ps(sptr0 + x + 8), _mm_loadu_ps(sptr1 + x + 8));
x1 = _mm_add_ps(_mm_loadu_ps(sptr0 + x + 12), _mm_loadu_ps(sptr1 + x + 12));
s2 = _mm_add_ps(s2, _mm_mul_ps(x0, g4));
s3 = _mm_add_ps(s3, _mm_mul_ps(x1, g4));
g4 = v_load(simd_kernel + i*4);
x0 = v_load(sptr0 + x) + v_load(sptr1 + x);
x1 = v_load(sptr0 + x + 4) + v_load(sptr1 + x + 4);
s0 = v_muladd(x0, g4, s0);
s1 = v_muladd(x1, g4, s1);
x0 = v_load(sptr0 + x + 8) + v_load(sptr1 + x + 8);
x1 = v_load(sptr0 + x + 12) + v_load(sptr1 + x + 12);
s2 = v_muladd(x0, g4, s2);
s3 = v_muladd(x1, g4, s3);
}
_mm_store_ps(vsum + x, s0);
_mm_store_ps(vsum + x + 4, s1);
_mm_store_ps(vsum + x + 8, s2);
_mm_store_ps(vsum + x + 12, s3);
v_store(vsum + x, s0);
v_store(vsum + x + 4, s1);
v_store(vsum + x + 8, s2);
v_store(vsum + x + 12, s3);
}
for( ; x <= width*5 - 4; x += 4 )
{
const float *sptr0 = srow[m], *sptr1;
__m128 g4 = _mm_load_ps(simd_kernel);
__m128 s0 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x), g4);
v_float32x4 g4 = v_load(simd_kernel);
v_float32x4 s0 = v_load(sptr0 + x) * g4;
for( i = 1; i <= m; i++ )
{
sptr0 = srow[m+i], sptr1 = srow[m-i];
g4 = _mm_load_ps(simd_kernel + i*4);
__m128 x0 = _mm_add_ps(_mm_loadu_ps(sptr0 + x), _mm_loadu_ps(sptr1 + x));
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, g4));
g4 = v_load(simd_kernel + i*4);
v_float32x4 x0 = v_load(sptr0 + x) + v_load(sptr1 + x);
s0 = v_muladd(x0, g4, s0);
}
_mm_store_ps(vsum + x, s0);
v_store(vsum + x, s0);
}
}
#endif
......@@ -525,28 +523,25 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1,
// horizontal blur
x = 0;
#if CV_SSE2
if( useSIMD )
#if CV_SIMD128
{
for( ; x <= width*5 - 8; x += 8 )
{
__m128 g4 = _mm_load_ps(simd_kernel);
__m128 s0 = _mm_mul_ps(_mm_loadu_ps(vsum + x), g4);
__m128 s1 = _mm_mul_ps(_mm_loadu_ps(vsum + x + 4), g4);
v_float32x4 g4 = v_load(simd_kernel);
v_float32x4 s0 = v_load(vsum + x) * g4;
v_float32x4 s1 = v_load(vsum + x + 4) * g4;
for( i = 1; i <= m; i++ )
{
g4 = _mm_load_ps(simd_kernel + i*4);
__m128 x0 = _mm_add_ps(_mm_loadu_ps(vsum + x - i*5),
_mm_loadu_ps(vsum + x + i*5));
__m128 x1 = _mm_add_ps(_mm_loadu_ps(vsum + x - i*5 + 4),
_mm_loadu_ps(vsum + x + i*5 + 4));
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, g4));
s1 = _mm_add_ps(s1, _mm_mul_ps(x1, g4));
g4 = v_load(simd_kernel + i*4);
v_float32x4 x0 = v_load(vsum + x - i*5) + v_load(vsum + x+ i*5);
v_float32x4 x1 = v_load(vsum + x - i*5 + 4) + v_load(vsum + x+ i*5 + 4);
s0 = v_muladd(x0, g4, s0);
s1 = v_muladd(x1, g4, s1);
}
_mm_store_ps(hsum + x, s0);
_mm_store_ps(hsum + x + 4, s1);
v_store(hsum + x, s0);
v_store(hsum + x + 4, s1);
}
}
#endif
......
platforms/js/build_js.py
View file @ fcec053d
......@@ -113,6 +113,7 @@ class Builder:
"-DWITH_GPHOTO2=OFF",
"-DWITH_LAPACK=OFF",
"-DWITH_ITT=OFF",
"-DWITH_QUIRC=OFF",
"-DBUILD_ZLIB=ON",
"-DBUILD_opencv_apps=OFF",
"-DBUILD_opencv_calib3d=ON", # No bindings provided. This module is used as a dependency for other modules.
......@@ -130,9 +131,11 @@ class Builder:
"-DBUILD_opencv_superres=OFF",
"-DBUILD_opencv_stitching=OFF",
"-DBUILD_opencv_java=OFF",
"-DBUILD_opencv_java_bindings_generator=OFF",
"-DBUILD_opencv_js=ON",
"-DBUILD_opencv_python2=OFF",
"-DBUILD_opencv_python3=OFF",
"-DBUILD_opencv_python_bindings_generator=OFF",
"-DBUILD_EXAMPLES=OFF",
"-DBUILD_PACKAGE=OFF",
"-DBUILD_TESTS=OFF",
......
samples/dnn/object_detection.cpp
View file @ fcec053d
......@@ -153,51 +153,39 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect> boxes;
if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
if (outLayerType == "DetectionOutput")
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
// [batchId, classId, confidence, left, top, right, bottom]
CV_Assert(outs.size() == 1);
float* data = (float*)outs[0].data;
for (size_t i = 0; i < outs[0].total(); i += 7)
CV_Assert(outs.size() > 0);
for (size_t k = 0; k < outs.size(); k++)
{
float confidence = data[i + 2];
if (confidence > confThreshold)
float* data = (float*)outs[k].data;
for (size_t i = 0; i < outs[k].total(); i += 7)
{
int left = (int)data[i + 3];
int top = (int)data[i + 4];
int right = (int)data[i + 5];
int bottom = (int)data[i + 6];
int width = right - left + 1;
int height = bottom - top + 1;
classIds.push_back((int)(data[i + 1]) - 1); // Skip 0th background class id.
boxes.push_back(Rect(left, top, width, height));
confidences.push_back(confidence);
}
}
}
else if (outLayerType == "DetectionOutput")
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
// [batchId, classId, confidence, left, top, right, bottom]
CV_Assert(outs.size() == 1);
float* data = (float*)outs[0].data;
for (size_t i = 0; i < outs[0].total(); i += 7)
{
float confidence = data[i + 2];
if (confidence > confThreshold)
{
int left = (int)(data[i + 3] * frame.cols);
int top = (int)(data[i + 4] * frame.rows);
int right = (int)(data[i + 5] * frame.cols);
int bottom = (int)(data[i + 6] * frame.rows);
int width = right - left + 1;
int height = bottom - top + 1;
classIds.push_back((int)(data[i + 1]) - 1); // Skip 0th background class id.
boxes.push_back(Rect(left, top, width, height));
confidences.push_back(confidence);
float confidence = data[i + 2];
if (confidence > confThreshold)
{
int left = (int)data[i + 3];
int top = (int)data[i + 4];
int right = (int)data[i + 5];
int bottom = (int)data[i + 6];
int width = right - left + 1;
int height = bottom - top + 1;
if (width * height <= 1)
{
left = (int)(data[i + 3] * frame.cols);
top = (int)(data[i + 4] * frame.rows);
right = (int)(data[i + 5] * frame.cols);
bottom = (int)(data[i + 6] * frame.rows);
width = right - left + 1;
height = bottom - top + 1;
}
classIds.push_back((int)(data[i + 1]) - 1); // Skip 0th background class id.
boxes.push_back(Rect(left, top, width, height));
confidences.push_back(confidence);
}
}
}
}
......
samples/dnn/object_detection.py
View file @ fcec053d
......@@ -102,7 +102,7 @@ def postprocess(frame, outs):
classIds = []
confidences = []
boxes = []
if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
if lastLayer.type == 'DetectionOutput':
# Network produces output blob with a shape 1x1xNx7 where N is a number of
# detections and an every detection is a vector of values
# [batchId, classId, confidence, left, top, right, bottom]
......@@ -116,23 +116,13 @@ def postprocess(frame, outs):
bottom = int(detection[6])
width = right - left + 1
height = bottom - top + 1
classIds.append(int(detection[1]) - 1) # Skip background label
confidences.append(float(confidence))
boxes.append([left, top, width, height])
elif lastLayer.type == 'DetectionOutput':
# Network produces output blob with a shape 1x1xNx7 where N is a number of
# detections and an every detection is a vector of values
# [batchId, classId, confidence, left, top, right, bottom]
for out in outs:
for detection in out[0, 0]:
confidence = detection[2]
if confidence > confThreshold:
left = int(detection[3] * frameWidth)
top = int(detection[4] * frameHeight)
right = int(detection[5] * frameWidth)
bottom = int(detection[6] * frameHeight)
width = right - left + 1
height = bottom - top + 1
if width * height <= 1:
left = int(detection[3] * frameWidth)
top = int(detection[4] * frameHeight)
right = int(detection[5] * frameWidth)
bottom = int(detection[6] * frameHeight)
width = right - left + 1
height = bottom - top + 1
classIds.append(int(detection[1]) - 1) # Skip background label
confidences.append(float(confidence))
boxes.append([left, top, width, height])
......
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