Working frontend cascade with some (very) rough edges

Jordan Cheney
1 parent 39f5a23e
Showing 4 changed files with 83 additions and 87 deletions
openbr/openbr_plugin.h
openbr/plugins/classification/boostedforest.cpp
openbr/plugins/classification/cascade.cpp
openbr/plugins/imgproc/slidingwindow.cpp
@@ -1423,11 +1423,10 @@ public:
     static Classifier *make(QString str, QObject *parent); /*!< \brief Make a classifier from a string. */
     virtual void train(const QList<cv::Mat> &images, const QList<float> &labels) = 0;
-    // By convention, classify should return a value normalized such that the threshold is 0. Negative values
-    // can be interpreted as a negative classification and positive values as a positive classification.
-    virtual float classify(const cv::Mat &image) const = 0;
+    virtual float classify(const cv::Mat &image, float &confidence) const = 0;
     // Slots for representation
+    virtual cv::Mat preprocess(const cv::Mat &image) const = 0;
     virtual cv::Size windowSize() const = 0;
     // OpenCV compatibility
@@ -129,12 +129,9 @@ class BoostedForestClassifier : public Classifier
         }
     }
-    float classify(const Mat &_image) const
+    float classify(const Mat &image, float &confidence) const
     {
-        Mat image;
-        representation->preprocess(_image, image);
-
-        float sum = 0;
+        confidence = 0;
         for (int i = 0; i < classifiers.size(); i++) {
             Node *node = classifiers[i];
@@ -147,11 +144,10 @@ class BoostedForestClassifier : public Classifier
                     node = val <= node->threshold ? node->left : node->right;
                 }
             }
-            qDebug("value: %f", node->value);
-            sum += node->value;
+            confidence += node->value;
         }
-        return sum < threshold - THRESHOLD_EPS ? -std::abs(sum) : std::abs(sum);
+        return confidence < threshold - THRESHOLD_EPS ? 0.0f : 1.0f;
     }
     int numFeatures() const
@@ -164,6 +160,13 @@ class BoostedForestClassifier : public Classifier
         return representation->maxCatCount();
     }
+    Mat preprocess(const Mat &image) const
+    {
+        Mat dst;
+        representation->preprocess(image, dst);
+        return dst;
+    }
+
     Size windowSize() const
     {
         return representation->preWindowSize();
@@ -152,18 +152,23 @@ class CascadeClassifier : public Classifier
         }
     }
-    float classify(const Mat &image) const
+    float classify(const Mat &image, float &confidence) const
     {
-        if (stages.empty())
-             return 1.0f;
+        if (stages.empty()) {
+            confidence = 0.0f;
+            return 1.0f;
+        }
-        float val = 0.0f;
         for (int i = 0; i < stages.size(); i++) {
-            val = stages[i]->classify(image);
-            if (val < 0.0f)
-                return stages.size() - i < 4 ? i * val : 0.0f;
+            float result = stages[i]->classify(image, confidence);
+            if (result == 0.0f) {
+                //confidence *= i;
+                return i;
+            }
         }
-        return stages.size() * val;
+
+        //confidence *= stages.size();
+        return stages.size();
     }
     int numFeatures() const
@@ -176,7 +181,12 @@ class CascadeClassifier : public Classifier
         return stages.first()->maxCatCount();
     }
-    cv::Size windowSize() const
+    Mat preprocess(const Mat &image) const
+    {
+        return stages.first()->preprocess(image);
+    }
+
+    Size windowSize() const
     {
         return stages.first()->windowSize();
     }
@@ -207,12 +217,14 @@ private:
     {
         imgHandler.restart();
+        float confidence = 0.0f; // not used;
+
         while (images.size() < numPos) {
             Mat pos(imgHandler.winSize, CV_8UC1);
             if (!imgHandler.getPos(pos))
                 qFatal("Cannot get another positive sample!");
-            if (classify(pos) > 0.0f) {
+            if (classify(pos, confidence) > 0.0f) {
                 printf("POS current samples: %d\r", images.size());
                 images.append(pos);
                 labels.append(1.0f);
@@ -228,7 +240,7 @@ private:
             if (!imgHandler.getNeg(neg))
                 qFatal("Cannot get another negative sample!");
-            if (classify(neg) > 0.0f) {
+            if (classify(neg, confidence) > 0.0f) {
                 printf("NEG current samples: %d\r", images.size() - posCount);
                 images.append(neg);
                 labels.append(0.0f);
@@ -14,15 +14,10 @@
  * limitations under the License.                                            *
  * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
-#include <fstream>
-
 #include <openbr/plugins/openbr_internal.h>
+#include <openbr/core/cascade.h>
 #include <openbr/core/opencvutils.h>
 #include <openbr/core/qtutils.h>
-#include <openbr/core/cascade.h>
-
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
 using namespace cv;
@@ -31,22 +26,23 @@ namespace br
 /*!
  * \ingroup transforms
- * \brief Applies a classifier to a sliding window.
- * \author Jordan Cheney \cite JordanCheney
+ * \brief Sliding Window Framework
+ * \author Jordan Cheney
  */
-
-class SlidingWindowTransform : public Transform
+class SlidingWindowTransform : public UntrainableMetaTransform
 {
     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(int minNeighbors READ get_minNeighbors WRITE set_minNeighbors RESET reset_minNeighbors STORED false)
     Q_PROPERTY(float eps READ get_eps WRITE set_eps RESET reset_eps STORED false)
-    Q_PROPERTY(QString cascadeDir READ get_cascadeDir WRITE set_cascadeDir RESET reset_cascadeDir STORED false)
+    Q_PROPERTY(QString model READ get_model WRITE set_model RESET reset_model STORED false)
+
     BR_PROPERTY(br::Classifier *, classifier, NULL)
     BR_PROPERTY(int, minSize, 20)
     BR_PROPERTY(int, maxSize, -1)
@@ -54,7 +50,13 @@ class SlidingWindowTransform : public Transform
     BR_PROPERTY(int, minNeighbors, 5)
     BR_PROPERTY(float, eps, 0.2)
-    BR_PROPERTY(QString, cascadeDir, "")
+    BR_PROPERTY(QString, model, "")
+
+    void init()
+    {
+        QDataStream stream;
+        load(stream);
+    }
     void train(const TemplateList &data)
     {
@@ -70,6 +72,9 @@ class SlidingWindowTransform : public Transform
     void project(const TemplateList &src, TemplateList &dst) const
     {
+        Size minObjectSize(minSize, minSize);
+        Size maxObjectSize;
+
         foreach (const Template &t, src) {
             const bool enrollAll = t.file.getBool("enrollAll");
@@ -80,26 +85,23 @@ class SlidingWindowTransform : public Transform
                 continue;
             }
-            for (int i = 0; i < t.size(); i++) {
-                Mat image;
-                OpenCVUtils::cvtUChar(t[i], image);
-
+            for (int i=0; i<t.size(); i++) {
+                Mat m;
+                OpenCVUtils::cvtUChar(t[i], m);
                 std::vector<Rect> rects;
                 std::vector<int> rejectLevels;
                 std::vector<double> levelWeights;
-                Size minObjectSize(minSize, minSize);
-                Size maxObjectSize(maxSize, maxSize);
-                if (maxObjectSize.height <= 0 || maxObjectSize.width <= 0)
-                    maxObjectSize = image.size();
+                if (maxObjectSize.height == 0 || maxObjectSize.width == 0)
+                    maxObjectSize = m.size();
-                Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U);
+                Mat imageBuffer(m.rows + 1, m.cols + 1, CV_8U);
                 for (double factor = 1; ; factor *= scaleFactor) {
-                    Size originalWindowSize = classifier->windowSize();
+                    Size originalWindowSize(24, 24);
                     Size windowSize(cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
-                    Size scaledImageSize(cvRound(image.cols/factor ), cvRound(image.rows/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)
@@ -110,22 +112,26 @@ class SlidingWindowTransform : public Transform
                         continue;
                     Mat scaledImage(scaledImageSize, CV_8U, imageBuffer.data);
-                    resize(image, scaledImage, scaledImageSize, 0, 0, CV_INTER_LINEAR);
+                    resize(m, scaledImage, scaledImageSize, 0, 0, CV_INTER_LINEAR);
+
+                    Mat repImage = classifier->preprocess(scaledImage);
+
+                    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(Rect(Point(x, y), Size(25,25))).clone();
-                    int yStep = factor > 2. ? 1 : 2;
-                    for (int y = 0; y < processingRectSize.height; y += yStep) {
-                        for (int x = 0; x < processingRectSize.width; x += yStep) {
-                            Mat window = scaledImage(Rect(Point(x, y), classifier->windowSize())).clone();
+                            float gypWeight;
+                            int result = classifier->classify(window, gypWeight);
-                            float result = classifier->classify(window);
-                            qDebug("result: %f", result);
-                            if (result > 0) {
+                            if (12 - result < 4) {
                                 rects.push_back(Rect(cvRound(x*factor), cvRound(y*factor), windowSize.width, windowSize.height));
-                                rejectLevels.push_back(1);
-                                levelWeights.push_back(result);
+                                rejectLevels.push_back(result);
+                                levelWeights.push_back(gypWeight);
                             }
+
                             if (result == 0)
-                                x = yStep;
+                                x += step;
                         }
                     }
                 }
@@ -133,57 +139,33 @@ class SlidingWindowTransform : public Transform
                 groupRectangles(rects, rejectLevels, levelWeights, minNeighbors, eps);
                 if (!enrollAll && rects.empty())
-                    rects.push_back(Rect(0, 0, image.cols, image.rows));
+                    rects.push_back(Rect(0, 0, m.cols, m.rows));
-                for (size_t j = 0; j < rects.size(); j++) {
-                    Template u(t.file, image);
+                for (size_t j=0; j<rects.size(); j++) {
+                    Template u(t.file, m);
                     if (rejectLevels.size() > j)
                         u.file.set("Confidence", rejectLevels[j]*levelWeights[j]);
                     else
                         u.file.set("Confidence", 1);
                     const QRectF rect = OpenCVUtils::fromRect(rects[j]);
                     u.file.appendRect(rect);
-                    u.file.set("Face", rect);
+                    u.file.set(model, rect);
                     dst.append(u);
                 }
             }
         }
-     }
+    }
     void load(QDataStream &stream)
     {
-        (void) stream;
+        (void)stream;
-        QString filename = Globals->sdkPath + "/share/openbr/models/openbrcascades/" + cascadeDir + "/cascade.xml";
+        QString filename = Globals->sdkPath + "/share/openbr/models/openbrcascades/" + model + "/cascade.xml";
         FileStorage fs(filename.toStdString(), FileStorage::READ);
         if (!fs.isOpened())
             return;
         classifier->read(fs.getFirstTopLevelNode());
-
-        return;
-    }
-
-    void store(QDataStream &stream) const
-    {
-        (void) stream;
-
-        QString path = Globals->sdkPath + "/share/openbr/models/openbrcascades/" + cascadeDir;
-        QtUtils::touchDir(QDir(path));
-
-        QString filename = path + "/cascade.xml";
-        FileStorage fs(filename.toStdString(), FileStorage::WRITE);
-
-        if (!fs.isOpened()) {
-            qWarning("Unable to open file: %s", qPrintable(filename));
-            return;
-        }
-
-        fs << FileStorage::getDefaultObjectName(filename.toStdString()) << "{";
-
-        classifier->write(fs);
-
-        fs << "}";
     }
 };