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>
#include <opencv2/highgui/highgui.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)
Q_PROPERTY(float minNeighbors READ get_minNeighbors WRITE set_minNeighbors RESET reset_minNeighbors STORED false)
Q_PROPERTY(bool group READ get_group WRITE set_group RESET reset_group STORED false)
Q_PROPERTY(int shrinkingFactor READ get_shrinkingFactor WRITE set_shrinkingFactor RESET reset_shrinkingFactor 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)
BR_PROPERTY(int, minNeighbors, 3)
BR_PROPERTY(bool, group, true)
BR_PROPERTY(int, shrinkingFactor, 1)
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;
}
// SlidingWindow assumes that all matricies in a template represent
// different channels of the same image!
const Size imageSize = t.m().size();
const int minSize = t.file.get<int>("MinSize", this->minSize);
QList<Rect> rects;
QList<float> confidences;
int dx, dy;
const Size classifierSize = classifier->windowSize(&dx, &dy);
for (double factor = 1; ; factor *= scaleFactor) {
// TODO: This should support non-square sizes
// Compute the size of the window in which we will detect faces
const Size detectionSize(cvRound(minSize*factor),cvRound(minSize*factor));
// Stop if detection size is bigger than the image itself
if (detectionSize.width > imageSize.width || detectionSize.height > imageSize.height)
break;
const float widthScale = (float)classifierSize.width/detectionSize.width;
const float heightScale = (float)classifierSize.height/detectionSize.height;
// Scale the image such that the detection size within the image corresponds to the respresentation size
const Size scaledImageSize(cvRound(imageSize.width*widthScale), cvRound(imageSize.height*heightScale));
Template rep(t.file);
foreach (const Mat &m, t) {
Mat scaledImage;
resize(m, scaledImage, scaledImageSize, 0, 0, CV_INTER_AREA);
rep.append(scaledImage);
}
rep = classifier->preprocess(rep);
// Pre-allocate the window to avoid constructing this every iteration
Template window(t.file);
for (int i=0; i<rep.size(); i++)
window.append(Mat());
const int step = factor > 2.0 ? shrinkingFactor : shrinkingFactor*2;
for (int y = 0; y < scaledImageSize.height-classifierSize.height; y += step) {
for (int x = 0; x < scaledImageSize.width-classifierSize.width; x += step) {
for (int i=0; i<rep.size(); i++)
window[i] = rep[i](Rect(Point(x, y), Size(classifierSize.width+dx, classifierSize.height+dy))).clone();
float confidence = 0;
int result = classifier->classify(window, false, &confidence);
if (result == 1) {
rects.append(Rect(cvRound(x/widthScale), cvRound(y/heightScale), detectionSize.width, detectionSize.height));
confidences.append(confidence);
}
// TODO: Add non ROC mode
if (result == 0)
x += step;
}
}
}
if (group)
OpenCVUtils::group(rects, confidences, confidenceThreshold, minNeighbors, eps);
if (!enrollAll && rects.empty()) {
rects.append(Rect(0, 0, imageSize.width, imageSize.height));
confidences.append(-std::numeric_limits<float>::max());
}
for (int j=0; j<rects.size(); j++) {
Template u = t;
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"