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refactor logs

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pull/137/head
marina.kolpakova 12 years ago
parent b6081438fa
commit 26af7d7389
1 changed files with 53 additions and 73 deletions
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  1. 126
      modules/objdetect/src/softcascade.cpp

126
modules/objdetect/src/softcascade.cpp
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@ -47,8 +47,8 @@
#include <vector>
#include <string>
#include <iostream>
#include <string>
#include <cstdio>
#include <stdarg.h>
namespace {
@ -61,6 +61,13 @@ char *itoa(long i, char* s, int /*dummy_radix*/)
// used for noisy printfs
// #define WITH_DEBUG_OUT
#if defined WITH_DEBUG_OUT
# define dprintf(format, ...) \
do { printf(format, __VA_ARGS__); } while (0)
#else
# define dprintf(format, ...)
#endif
struct Octave
{
int index;
@ -169,9 +176,6 @@ struct CascadeIntrinsics
static float getFor(int channel, float scaling)
{
CV_Assert(channel < 10);
#if defined WITH_DEBUG_OUT
printf("QQQQQQQQQQQQQQQq: %f %f\n", scaling, fabs(scaling - 1.f));
#endif
if (fabs(scaling - 1.f) < FLT_EPSILON)
// if (scaling == 1.f)
@ -193,9 +197,7 @@ struct CascadeIntrinsics
float a = A[(int)(scaling >= 1)][(int)(channel > 6)];
float b = B[(int)(scaling >= 1)][(int)(channel > 6)];
#if defined WITH_DEBUG_OUT
printf("!!! scaling: %f %f %f %f\n", scaling, a, b, a * pow(scaling, b));
#endif
dprintf("scaling: %f %f %f %f\n", scaling, a, b, a * pow(scaling, b));
return a * pow(scaling, b);
}
};
@ -269,6 +271,7 @@ struct Decimate {
};
// use previous stored integrals for regression testing
// #define USE_REFERENCE_VALUES
struct ChannelStorage
@ -279,14 +282,14 @@ struct ChannelStorage
enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
ChannelStorage() {}
ChannelStorage(cv::Mat& colored, int shr) : shrinkage(shr)
ChannelStorage(const cv::Mat& colored, int shr) : shrinkage(shr)
{
hog.clear();
Decimate<uchar> decimate(shr);
#if defined USE_REFERENCE_VALUES
cv::FileStorage imgs("/home/kellan/testInts.xml", cv::FileStorage::READ);
char buff[33];
cv::FileStorage imgs("/home/kellan/testInts.xml", cv::FileStorage::READ);
for(int i = 0; i < HOG_LUV_BINS; ++i)
{
@ -310,10 +313,10 @@ struct ChannelStorage
// shrink and integrate
for (int i = 0; i < (int)splited.size(); i++)
{
cv::Mat shrunk, sum;
decimate(splited[i], shrunk);
cv::integral(shrunk, sum, cv::noArray(), CV_32S);
luvs.push_back(sum);
cv::Mat shrunk, sum;
decimate(splited[i], shrunk);
cv::integral(shrunk, sum, cv::noArray(), CV_32S);
luvs.push_back(sum);
}
// convert to grey
@ -353,12 +356,12 @@ struct ChannelStorage
cv::integral(shrMag, mag, cv::noArray(), CV_32S);
// create hog channels
angle /= 60;
angle /= 60.f;
std::vector<cv::Mat> hist;
for (int bin = 0; bin < 6; ++bin)
for (int bin = 0; bin < HOG_BINS; ++bin)
{
hist.push_back(cv::Mat(colored.rows, colored.cols, CV_8UC1));
hist.push_back(cv::Mat::zeros(saturatedMag.rows, saturatedMag.cols, CV_8UC1));
}
for (int y = 0; y < saturatedMag.rows; ++y)
@ -375,7 +378,6 @@ struct ChannelStorage
for(int i = 0; i < HOG_BINS; ++i)
{
cv::Mat shrunk, sum;
decimate(hist[i], shrunk);
cv::integral(shrunk, sum, cv::noArray(), CV_32S);
hog.push_back(sum);
@ -385,7 +387,6 @@ struct ChannelStorage
hog.insert(hog.end(), luvs.begin(), luvs.end());
CV_Assert(hog.size() == 10);
#endif
// exit(10);
}
float get(const int x, const int y, const int channel, const cv::Rect& area) const
@ -393,26 +394,23 @@ struct ChannelStorage
CV_Assert(channel < HOG_LUV_BINS);
const cv::Mat m = hog[channel];
#if defined WITH_DEBUG_OUT
printf("feature box %d %d %d %d ", area.x, area.y, area.width, area.height);
printf("get for channel %d\n", channel);
printf("!! %d\n", m.depth());
dprintf("feature box %d %d %d %d ", area.x, area.y, area.width, area.height);
dprintf("get for channel %d\n", channel);
dprintf("!! %d\n", m.depth());
printf("extract feature for: [%d %d] [%d %d] [%d %d] [%d %d]\n",
dprintf("extract feature for: [%d %d] [%d %d] [%d %d] [%d %d]\n",
x + area.x, y + area.y, x + area.width,y + area.y, x + area.width,y + area.height,
x + area.x, y + area.height);
printf("at point %d %d with offset %d\n", x, y, 0);
#endif
dprintf("at point %d %d with offset %d\n", x, y, 0);
int a = m.ptr<int>(y + area.y)[x + area.x];
int b = m.ptr<int>(y + area.y)[x + area.width];
int c = m.ptr<int>(y + area.height)[x + area.width];
int d = m.ptr<int>(y + area.height)[x + area.x];
#if defined WITH_DEBUG_OUT
printf(" retruved integral values: %d %d %d %d\n", a, b, c, d);
#endif
dprintf(" retruved integral values: %d %d %d %d\n", a, b, c, d);
return (a - b + c - d);
}
};
@ -444,12 +442,10 @@ struct cv::SoftCascade::Filds
float scaling = CascadeIntrinsics::getFor(feature.channel, relScale);
scaledRect = feature.rect;
#if defined WITH_DEBUG_OUT
printf("feature %d box %d %d %d %d\n", feature.channel, scaledRect.x, scaledRect.y,
dprintf("feature %d box %d %d %d %d\n", feature.channel, scaledRect.x, scaledRect.y,
scaledRect.width, scaledRect.height);
std::cout << "rescale: " << feature.channel << " " << relScale << " " << scaling << std::endl;
#endif
dprintf("rescale: %d %f %f\n",feature.channel, relScale, scaling);
float farea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
// rescale
@ -458,14 +454,9 @@ struct cv::SoftCascade::Filds
scaledRect.width = cvRound(relScale * scaledRect.width);
scaledRect.height = cvRound(relScale * scaledRect.height);
#if defined WITH_DEBUG_OUT
printf("feature %d box %d %d %d %d\n", feature.channel, scaledRect.x, scaledRect.y,
dprintf("feature %d box %d %d %d %d\n", feature.channel, scaledRect.x, scaledRect.y,
scaledRect.width, scaledRect.height);
std::cout << " new rect: " << scaledRect.x << " " << scaledRect.y
<< " " << scaledRect.width << " " << scaledRect.height << " ";
#endif
float sarea = (scaledRect.width - scaledRect.x) * (scaledRect.height - scaledRect.y);
float approx = 1.f;
@ -474,20 +465,14 @@ struct cv::SoftCascade::Filds
const float expected_new_area = farea * relScale * relScale;
approx = expected_new_area / sarea;
#if defined WITH_DEBUG_OUT
std::cout << " rel areas " << expected_new_area << " " << sarea << std::endl;
#endif
dprintf(" rel areas %f %f\n", expected_new_area, sarea);
}
// compensation areas rounding
float rootThreshold = threshold / approx;
rootThreshold *= scaling;
#if defined WITH_DEBUG_OUT
std::cout << "approximation " << approx << " " << threshold << " -> " << rootThreshold
<< " " << scaling << std::endl;
#endif
dprintf("approximation %f %f -> %f %f\n", approx, threshold, rootThreshold, scaling);
return rootThreshold;
}
@ -495,26 +480,21 @@ struct cv::SoftCascade::Filds
void detectAt(const Level& level, const int dx, const int dy, const ChannelStorage& storage,
std::vector<Object>& detections) const
{
#if defined WITH_DEBUG_OUT
std::cout << "detect at: " << dx << " " << dy << std::endl;
#endif
dprintf("detect at: %d %d\n", dx, dy);
float detectionScore = 0.f;
const Octave& octave = *(level.octave);
int stBegin = octave.index * octave.stages, stEnd = stBegin + octave.stages;
#if defined WITH_DEBUG_OUT
std::cout << " octave stages: " << stBegin << " to " << stEnd << " index " << octave.index << " "
<< octave.scale << " level " << level.origScale << std::endl;
#endif
dprintf(" octave stages: %d to %d index %d %f level %f\n",
stBegin, stEnd, octave.index, octave.scale, level.origScale);
int st = stBegin;
for(; st < stEnd; ++st)
{
#if defined WITH_DEBUG_OUT
printf("index: %d\n", st);
#endif
dprintf("index: %d\n", st);
const Stage& stage = stages[st];
{
@ -529,15 +509,11 @@ struct cv::SoftCascade::Filds
float sum = storage.get(dx, dy, feature.channel, scaledRect);
#if defined WITH_DEBUG_OUT
printf("root feature %d %f\n",feature.channel, sum);
#endif
dprintf("root feature %d %f\n",feature.channel, sum);
int next = (sum >= threshold)? 2 : 1;
#if defined WITH_DEBUG_OUT
printf("go: %d (%f >= %f)\n\n" ,next, sum, threshold);
#endif
dprintf("go: %d (%f >= %f)\n\n" ,next, sum, threshold);
// leaves
const Node& leaf = nodes[nId + next];
@ -549,23 +525,24 @@ struct cv::SoftCascade::Filds
int lShift = (next - 1) * 2 + ((sum >= threshold) ? 1 : 0);
float impact = leaves[(st * 4) + lShift];
#if defined WITH_DEBUG_OUT
printf("decided: %d (%f >= %f) %d %f\n\n" ,next, sum, threshold, lShift, impact);
#endif
dprintf("decided: %d (%f >= %f) %d %f\n\n" ,next, sum, threshold, lShift, impact);
detectionScore += impact;
}
dprintf("extracted stage:\n");
dprintf("ct %f\n", stage.threshold);
dprintf("computed score %f\n\n", detectionScore);
#if defined WITH_DEBUG_OUT
printf("extracted stage:\n");
printf("ct %f\n", stage.threshold);
printf("computed score %f\n\n", detectionScore);
if (st - stBegin > 50 ) break;
#endif
if (detectionScore <= stage.threshold) break;
}
printf("x %d y %d: %d\n", dx, dy, st - stBegin);
dprintf("x %d y %d: %d\n", dx, dy, st - stBegin);
if (st == stEnd)
{
@ -793,7 +770,7 @@ void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::R
const Filds& fld = *filds;
cv::Mat image1;
cv::cvtColor(image, image1, CV_RGB2RGBA);
cv::cvtColor(image, image1, CV_BGR2RGB);
#if defined DEBUG_STORE_IMAGES
cv::FileStorage fs("/home/kellan/opencvInputImage.xml", cv::FileStorage::WRITE);
@ -812,8 +789,11 @@ void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::R
#endif
cv::imshow("!!", image1);
cv::waitKey(0);
// create integrals
ChannelStorage storage(image1, fld.shrinkage);
ChannelStorage storage(image, fld.shrinkage);
// object candidates
std::vector<Object> detections;
@ -826,6 +806,8 @@ void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::R
#if defined WITH_DEBUG_OUT
std::cout << "================================ " << l++ << std::endl;
#else
(void)l;
#endif
// int dx = 79; int dy = 76;
for (int dy = 0; dy < level.workRect.height; ++dy)
@ -834,9 +816,7 @@ void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::R
{
fld.detectAt(level, dx, dy, storage, detections);
total++;
// break;
}
// break;
}
cv::Mat out = image.clone();

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