KCF

leizhu1989 · May 18, 2018 · 253fc97 · 253fc97
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diff --git a/KCF_interpolation/.vs/KCF_WQ/v14/.suo b/KCF_interpolation/.vs/KCF_WQ/v14/.suo
diff --git a/KCF_interpolation/KCF_WQ.sdf b/KCF_interpolation/KCF_WQ.sdf
diff --git a/KCF_interpolation/KCF_WQ.sln b/KCF_interpolation/KCF_WQ.sln
@@ -0,0 +1,48 @@
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diff --git a/KCF_interpolation/KCF_WQ/KCF_WQ.vcxproj b/KCF_interpolation/KCF_WQ/KCF_WQ.vcxproj
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diff --git a/KCF_interpolation/KCF_WQ/KCF_WQ.vcxproj.filters b/KCF_interpolation/KCF_WQ/KCF_WQ.vcxproj.filters
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diff --git a/KCF_interpolation/KCF_WQ/PosLoc.txt b/KCF_interpolation/KCF_WQ/PosLoc.txt
diff --git a/KCF_interpolation/KCF_WQ/Pos_zx.xlsx b/KCF_interpolation/KCF_WQ/Pos_zx.xlsx
diff --git a/KCF_interpolation/KCF_WQ/TemplateTracking.h b/KCF_interpolation/KCF_WQ/TemplateTracking.h
@@ -0,0 +1,29 @@
+#pragma once
+
+#include<opencv2\opencv.hpp>
+using namespace std;
+using namespace cv;
+
+
+void tracking(Mat frame, Mat &model, Rect &trackBox)
+{
+	Mat gray;
+	cvtColor(frame, gray, CV_RGB2GRAY);
+
+	Rect searchWindow;
+	searchWindow.width = trackBox.width * 3;
+	searchWindow.height = trackBox.height * 3;
+	searchWindow.x = trackBox.x + trackBox.width * 0.5 - searchWindow.width * 0.5;
+	searchWindow.y = trackBox.y + trackBox.height * 0.5 - searchWindow.height * 0.5;
+	searchWindow &= Rect(0, 0, frame.cols, frame.rows);
+
+	Mat similarity;
+	matchTemplate(gray(searchWindow), model, similarity, CV_TM_CCOEFF_NORMED);
+
+	double mag_r;
+	Point point;
+	minMaxLoc(similarity, 0, &mag_r,  0,&point);
+	trackBox.x = point.x + searchWindow.x;
+	trackBox.y = point.y + searchWindow.y;
+	model = 0.95*model+0.05*gray(trackBox);          
+}
diff --git a/KCF_interpolation/KCF_WQ/fhog.hpp b/KCF_interpolation/KCF_WQ/fhog.hpp
@@ -0,0 +1,93 @@
+/*
+    - c++ wrapper for the piotr toolbox
+    Created by Tomas Vojir, 2014
+*/
+
+
+#ifndef FHOG_HEADER_7813784354687
+#define FHOG_HEADER_7813784354687
+
+#include <vector>
+#include <opencv2/opencv.hpp>
+
+#include "gradientMex.h"
+
+
+class FHoG
+{
+public:
+    //description: extract hist. of gradients(use_hog == 0), hog(use_hog == 1) or fhog(use_hog == 2)
+    //input: float one channel image as input, hog type    //输入是单通道的图像
+    //return: computed descriptor
+    std::vector<cv::Mat> extract(const cv::Mat & img, int use_hog = 2, int bin_size = 4, int n_orients = 9, int soft_bin = -1, float clip = 0.2)
+    {
+        // d image dimension -> gray image d = 1
+        // h, w -> height, width of image
+        // full -> ??
+        // I -> input image, M, O -> mag, orientation OUTPUT
+        int h = img.rows, w = img.cols,d = 1;
+        bool full = true;
+        if (h < 2 || w < 2) {
+            std::cerr << "I must be at least 2x2." << std::endl;
+            return std::vector<cv::Mat>();
+        }
+
+        float * I = new float[h*w];
+        for (int x = 0; x < w; ++x) {
+            for (int y = 0; y < h; ++y) {
+                I[x*h + y] = img.at<float>(y, x);
+            }
+        }      //得到每个点的像素值存入数组
+
+        float *M = new float[h*w], *O = new float[h*w];
+        gradMag(I, M, O, h, w, d, full);
+		//这个算出每个点幅值的大小（M）和方向(O)
+
+        int n_chns = (use_hog == 0) ? n_orients : (use_hog==1 ? n_orients*4 : n_orients*3+5);
+		//通道数,看是否用0是9维，1是hog36维，2是fhog一共32维（最后一维是全零）
+        int hb = h/bin_size, wb = w/bin_size;   //bin_size是每个cell的大小应该是
+
+        float *H = new float[hb*wb*n_chns];  //看需要多少存储空间来存这个
+        memset(H, 0, hb*wb*n_chns*sizeof(float)); //分配空间
+
+        if (use_hog == 0) 
+		{
+            full = false;   //by default
+            gradHist( M, O, H, h, w, bin_size, n_orients, soft_bin, full );   //这个函数是算梯度统计中方图
+        } else if (use_hog == 1) {
+            full = false;   //by default
+            hog( M, O, H, h, w, bin_size, n_orients, soft_bin, full, clip );
+        } else {
+            fhog( M, O, H, h, w, bin_size, n_orients, soft_bin, clip );
+        }
+
+        //convert, assuming row-by-row-by-channel storage
+        std::vector<cv::Mat> res;
+        int n_res_channels = (use_hog == 2) ? n_chns-1 : n_chns;    //last channel all zeros for fhog
+        res.reserve(n_res_channels);    //需要这么多通道，这个是分配空间用
+        for (int i = 0; i < n_res_channels; ++i) {
+            //output rows-by-rows
+//            cv::Mat desc(hb, wb, CV_32F, (H+hb*wb*i));
+
+            //output cols-by-cols
+            cv::Mat desc(hb, wb, CV_32F);
+            for (int x = 0; x < wb; ++x) {
+                for (int y = 0; y < hb; ++y) {
+                    desc.at<float>(y,x) = H[i*hb*wb + x*hb + y];
+                }
+            }
+            res.push_back(desc.clone());
+        }
+
+        //clean
+        delete [] I;
+        delete [] M;
+        delete [] O;
+        delete [] H;
+
+        return res;
+    }
+};
+
+#endif 
+//FHOG_HEADER_7813784354687