v1.4.t1

added object detection code base on yolov5 machine vision model. also
added a stat file so motion and object detection values can be monitored
in real time if used with the 'watch' command.

install.sh

@@ -1,3 +1,6 @@
 #!/bin/sh
 
+mkdir -p /etc/mow
+cp ./etc/yolov5s.onnx /etc/mow/yolov5s.onnx
+cp ./etc/classes.txt /etc/mow/classes.txt
 cp ./.build-mow/mow /usr/bin/mow

setup.sh

@@ -3,11 +3,17 @@
 apt update
 apt install -y cmake g++ wget unzip git ffmpeg libavcodec-dev libavformat-dev libavutil-dev libswscale-dev
 cd ./src
-git clone https://github.com/opencv/opencv.git
+if [ -d "./opencv" ]
+then
+    cd ./opencv
+    git pull origin
+    cd ..
+else
+    git clone https://github.com/opencv/opencv.git
+fi
 cd ..
 mkdir -p ./.build-opencv
 cd ./.build-opencv
 cmake ../src/opencv
 make -j4
 make install
-

src/common.cpp

@@ -181,6 +181,21 @@ void rdLine(const string &param, const string &line, int *value)
     }
 }
 
+vector<string> loadClassList()
+{
+    vector<string> ret;
+
+    ifstream ifs("/etc/mow/classes.txt");
+    string   line;
+
+    while (getline(ifs, line))
+    {
+        ret.push_back(line);
+    }
+
+    return ret;
+}
+
 bool rdConf(shared_t *share)
 {
     ifstream varFile(share->conf.c_str());
@@ -209,6 +224,11 @@ bool rdConf(shared_t *share)
         share->vidExt       = "mp4";
         share->recLoopWait  = false;
         share->skipCmd      = false;
+        share->network      = dnn::readNet("/etc/mow/yolov5s.onnx");
+        share->classNames   = loadClassList();
+
+        share->network.setPreferableBackend(dnn::DNN_BACKEND_OPENCV);
+        share->network.setPreferableTarget(dnn::DNN_TARGET_CPU);
 
         do
         {

src/common.h

@@ -34,30 +34,42 @@ using namespace std;
 using namespace std::filesystem;
 
 #define BUF_SZ  10
-#define APP_VER "1.4"
+#define APP_VER "1.4.t1"
+
+#define ST_LEN         7
+#define ST_PIXDIFF     0
+#define ST_BLOCK_POS_X 1
+#define ST_BLOCK_POS_Y 2
+#define ST_BLOCK_LEN_X 3
+#define ST_BLOCK_LEN_Y 4
+#define ST_CONFIDENCE  5
+#define ST_CLASS_SCORE 6
 
 struct shared_t
 {
-    string recordUrl;
+    vector<string> stat;
-    string outDir;
+    vector<string> classNames;
-    string postCmd;
+    dnn::Net       network;
-    string conf;
+    string         recordUrl;
-    string buffDir;
+    string         outDir;
-    string concatTxtTmp;
+    string         postCmd;
-    string concatShTmp;
+    string         conf;
-    string createShTmp;
+    string         buffDir;
-    string vidExt;
+    string         concatTxtTmp;
-    bool   init;
+    string         concatShTmp;
-    bool   recLoopWait;
+    string         createShTmp;
-    bool   skipCmd;
+    string         vidExt;
-    int    tmpId;
+    bool           init;
-    int    colorThresh;
+    bool           recLoopWait;
-    int    secs;
+    bool           skipCmd;
-    int    blockThresh;
+    int            tmpId;
-    int    blockX;
+    int            colorThresh;
-    int    blockY;
+    int            secs;
-    int    maxDays;
+    int            blockThresh;
-    int    retCode;
+    int            blockX;
+    int            blockY;
+    int            maxDays;
+    int            retCode;
 };
 
 string         genTmpFile(const string &dirOut, const string &ext, shared_t *share);
@@ -77,5 +89,6 @@ bool           rdConf(shared_t *share);
 bool           capPair(Mat &prev, Mat &next, VideoCapture &capture, shared_t *share);
 Mat            toGray(const Mat &src);
 vector<string> lsFilesInDir(const string &path, const string &ext);
+vector<string> loadClassList();
 
 #endif // COMMON_H

src/main.cpp

@@ -30,7 +30,7 @@ void detectLoop(shared_t *share)
 
             if (moDetect(fullPath, &blockArea, &blockImg, share))
             {
-                if (objectInImage(blockImg, blockArea))
+                if (objectInImage(blockImg, blockArea, share))
                 {
                     share->skipCmd = true;
 

src/mo_detect.cpp

@@ -44,6 +44,12 @@ void secDiff(Mat imgA, Mat imgB, int rows, int cols, int rowOffs, int colOffs, R
 
                 if (pnts >= share->blockThresh)
                 {
+                    share->stat[ST_PIXDIFF]     = to_string(pnts);
+                    share->stat[ST_BLOCK_POS_X] = to_string(colOffs);
+                    share->stat[ST_BLOCK_POS_Y] = to_string(rowOffs);
+                    share->stat[ST_BLOCK_LEN_X] = to_string(cols);
+                    share->stat[ST_BLOCK_LEN_Y] = to_string(rows);
+
                     block->x = colOffs;
                     block->y = rowOffs;
 
@@ -92,6 +98,8 @@ bool moDetect(const string &buffFile, Rect *block, Mat *img, shared_t *share)
         Mat prev;
         Mat next;
 
+        share->stat = vector<string>(ST_LEN, string("0"));
+
         while (capPair(prev, next, capture, share))
         {
             if (imgDiff(toGray(prev), toGray(next), block, share))
@@ -100,6 +108,19 @@ bool moDetect(const string &buffFile, Rect *block, Mat *img, shared_t *share)
                 mod  = true; break;
             }
         }
+
+        ofstream file(string(cleanDir(share->buffDir) + "/stat").c_str());
+
+        file << "pix_diff:    " << share->stat[ST_PIXDIFF]     << endl;
+        file << "block_pos_x: " << share->stat[ST_BLOCK_POS_X] << endl;
+        file << "block_pos_y: " << share->stat[ST_BLOCK_POS_Y] << endl;
+        file << "block_siz_x: " << share->stat[ST_BLOCK_LEN_X] << endl;
+        file << "block_siz_y: " << share->stat[ST_BLOCK_LEN_Y] << endl;
+        file << "--object_dection:" << endl;
+        file << "  confidence:  " << share->stat[ST_CONFIDENCE]  << endl;
+        file << "  class_score: " << share->stat[ST_CLASS_SCORE] << endl;
+
+        file.close();
     }
     else
     {

src/obj_detect.cpp

@@ -12,8 +12,46 @@
 
 #include "obj_detect.h"
 
-bool objectInImage(const Mat &image, const Rect &area)
+bool objectInImage(const Mat &src, const Rect &area, shared_t *share)
 {
-    // future object detection code. for now this will just return true.
+    // reference: https://github.com/doleron/yolov5-opencv-cpp-python/blob/main/cpp/yolo.cpp
-    return true;
+    Mat blockImage(src, area);
+    Mat blob;
+
+    dnn::blobFromImage(blockImage, blob, 1./255., Size(area.width, area.height), cv::Scalar(), true, false);
+
+    share->network.setInput(blob);
+
+    vector<Mat> outputs;
+
+    share->network.forward(outputs, share->network.getUnconnectedOutLayersNames());
+
+    float *data = (float *)outputs[0].data;
+
+    for (int i = 0; i < 25200; ++i)
+    {
+        // confidence level data[4];
+        if (data[4] >= 0.4)
+        {
+            float *classesScores = data + 5;
+
+            Mat    scores(1, share->classNames.size(), CV_32FC1, classesScores);
+            Point  classId;
+            double maxClassScore;
+
+            minMaxLoc(scores, 0, &maxClassScore, 0, &classId);
+
+            share->stat[ST_CONFIDENCE]  = to_string(data[4]);
+            share->stat[ST_CLASS_SCORE] = to_string(maxClassScore);
+
+            if (maxClassScore > 0.2)
+            {
+                return true;
+            }
+        }
+
+        data += 85;
+    }
+
+    return false;
 }

src/obj_detect.h

@@ -15,6 +15,6 @@
 
 #include "common.h"
 
-bool objectInImage(const Mat &image, const Rect &area);
+bool objectInImage(const Mat &image, const Rect &area, shared_t *share);
 
 #endif // OBJ_DETECT_H