slidingwindow.cpp
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/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
* Copyright 2012 The MITRE Corporation *
* *
* Licensed under the Apache License, Version 2.0 (the "License"); *
* you may not use this file except in compliance with the License. *
* You may obtain a copy of the License at *
* *
* http://www.apache.org/licenses/LICENSE-2.0 *
* *
* Unless required by applicable law or agreed to in writing, software *
* distributed under the License is distributed on an "AS IS" BASIS, *
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. *
* See the License for the specific language governing permissions and *
* limitations under the License. *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
#include <openbr/plugins/openbr_internal.h>
#include <openbr/core/opencvutils.h>
#include <openbr/core/qtutils.h>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;
namespace br
{
/*!
* \ingroup transforms
* \brief Sliding Window Framework for object detection. Performs an exhaustive search of an image by sliding a window of a given size around the image and then resizing the image and repeating until terminating conditions are met.
* \author Jordan Cheney \cite jcheney
* \author Scott Klum \cite sklum
* \br_property Classifier* classifier The classifier that determines if a given window is a positive or negative sample. The size of the window is determined using the classifiers *windowSize* method.
* \br_property int minSize The smallest sized object to detect in pixels
* \br_property int maxSize The largest sized object to detect in pixels. A negative value will set maxSize == image size
* \br_property float scaleFactor The factor to scale the image by during each resize.
* \br_property float confidenceThreshold A threshold for positive detections. Positive detections returned by the classifier that have confidences below this threshold are considered negative detections.
* \br_property float eps Parameter for non-maximum supression
*/
class SlidingWindowTransform : public MetaTransform
{
Q_OBJECT
Q_PROPERTY(br::Classifier* classifier READ get_classifier WRITE set_classifier RESET reset_classifier STORED false)
Q_PROPERTY(int minSize READ get_minSize WRITE set_minSize RESET reset_minSize STORED false)
Q_PROPERTY(int maxSize READ get_maxSize WRITE set_maxSize RESET reset_maxSize STORED false)
Q_PROPERTY(float scaleFactor READ get_scaleFactor WRITE set_scaleFactor RESET reset_scaleFactor STORED false)
Q_PROPERTY(float confidenceThreshold READ get_confidenceThreshold WRITE set_confidenceThreshold RESET reset_confidenceThreshold STORED false)
Q_PROPERTY(float eps READ get_eps WRITE set_eps RESET reset_eps STORED false)
BR_PROPERTY(br::Classifier*, classifier, NULL)
BR_PROPERTY(int, minSize, 20)
BR_PROPERTY(int, maxSize, -1)
BR_PROPERTY(float, scaleFactor, 1.2)
BR_PROPERTY(float, confidenceThreshold, 10)
BR_PROPERTY(float, eps, 0.2)
void train(const TemplateList &data)
{
classifier->train(data);
}
void project(const Template &src, Template &dst) const
{
TemplateList temp;
project(TemplateList() << src, temp);
if (!temp.isEmpty()) dst = temp.first();
}
void project(const TemplateList &src, TemplateList &dst) const
{
foreach (const Template &t, src) {
// As a special case, skip detection if the appropriate metadata already exists
if (t.file.contains("Face")) {
Template u = t;
u.file.setRects(QList<QRectF>() << t.file.get<QRectF>("Face"));
u.file.set("Confidence", t.file.get<float>("Confidence", 1));
dst.append(u);
continue;
}
const bool enrollAll = t.file.getBool("enrollAll");
// Mirror the behavior of ExpandTransform in the special case
// of an empty template.
if (t.empty() && !enrollAll) {
dst.append(t);
continue;
}
const int minSize = t.file.get<int>("MinSize", this->minSize);
Size minObjectSize(minSize, minSize);
Size maxObjectSize;
for (int i=0; i<t.size(); i++) {
Mat m;
OpenCVUtils::cvtUChar(t[i], m);
QList<Rect> rects;
QList<float> confidences;
if (maxObjectSize.height == 0 || maxObjectSize.width == 0)
maxObjectSize = m.size();
Mat imageBuffer(m.rows + 1, m.cols + 1, CV_8U);
for (double factor = 1; ; factor *= scaleFactor) {
int dx, dy;
Size originalWindowSize = classifier->windowSize(&dx, &dy);
Size windowSize(cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
Size scaledImageSize(cvRound(m.cols/factor ), cvRound(m.rows/factor));
Size processingRectSize(scaledImageSize.width - originalWindowSize.width, scaledImageSize.height - originalWindowSize.height);
if (processingRectSize.width <= 0 || processingRectSize.height <= 0)
break;
if (windowSize.width > maxObjectSize.width || windowSize.height > maxObjectSize.height)
break;
if (windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height)
continue;
Mat scaledImage(scaledImageSize, CV_8U, imageBuffer.data);
resize(m, scaledImage, scaledImageSize, 0, 0, CV_INTER_LINEAR);
Template repImage(t.file, scaledImage);
repImage = classifier->preprocess(repImage);
int step = factor > 2. ? 1 : 2;
for (int y = 0; y < processingRectSize.height; y += step) {
for (int x = 0; x < processingRectSize.width; x += step) {
Mat window = repImage.m()(Rect(Point(x, y), Size(originalWindowSize.width + dx, originalWindowSize.height + dy))).clone();
Template t(window);
float confidence = 0;
int result = classifier->classify(t, false, &confidence);
if (result == 1) {
rects.append(Rect(cvRound(x*factor), cvRound(y*factor), windowSize.width, windowSize.height));
confidences.append(confidence);
}
// TODO: Add non ROC mode
if (result == 0)
x += step;
}
}
}
OpenCVUtils::group(rects, confidences, confidenceThreshold, eps);
if (!enrollAll && rects.empty()) {
rects.append(Rect(0, 0, m.cols, m.rows));
confidences.append(-std::numeric_limits<float>::max());
}
for (int j=0; j<rects.size(); j++) {
Template u(t.file, m);
u.file.set("Confidence", confidences[j]);
const QRectF rect = OpenCVUtils::fromRect(rects[j]);
u.file.appendRect(rect);
u.file.set("Face", rect);
dst.append(u);
}
}
}
}
void load(QDataStream &stream)
{
classifier->load(stream);
}
void store(QDataStream &stream) const
{
classifier->store(stream);
}
};
BR_REGISTER(Transform, SlidingWindowTransform)
} // namespace br
#include "imgproc/slidingwindow.moc"