camera & image detection

apps/detector_Kozitsin.cpp

@@ -0,0 +1,126 @@
+#include <iostream>
+#include <string>
+#include <vector>
+
+#include "opencv2/core/core.hpp"
+#include "opencv2/highgui/highgui.hpp"
+#include "opencv2/objdetect/objdetect.hpp"
+
+using namespace std;
+using namespace cv;
+
+const char* params =
+     "{ h | help     | false | print usage                                   }"
+     "{   | detector |       | XML file with a cascade detector              }"
+     "{   | image    |       | image to detect objects on                    }"
+     "{   | video    |       | video file to detect on                       }"
+     "{   | camera   | false | whether to detect on video stream from camera }";
+
+
+void drawDetections(const vector<Rect>& detections,
+                    const Scalar& color,
+                    Mat& image)
+{
+    for (size_t i = 0; i < detections.size(); ++i)
+    {
+        rectangle(image, detections[i], color, 2);
+    }
+}
+
+const Scalar red(0, 0, 255);
+const Scalar green(0, 255, 0);
+const Scalar blue(255, 0, 0);
+const Scalar colors[] = {red, green, blue};
+
+int main(int argc, char** argv)
+{
+    // Parse command line arguments.
+    CommandLineParser parser(argc, argv, params);
+    // If help flag is present, print help message and exit.
+    if (parser.get<bool>("help"))
+    {
+        parser.printParams();
+        return 0;
+    }
+
+    string detector_file = parser.get<string>("detector");
+    CV_Assert(!detector_file.empty());
+    string image_file = parser.get<string>("image");
+    string video_file = parser.get<string>("video");
+    bool use_camera = parser.get<bool>("camera");
+
+    // Load detector.
+    CascadeClassifier unnLogoCascade;
+    CascadeClassifier unnOldLogoCascade;
+    CascadeClassifier openCVLogoCascade;
+    CascadeClassifier itseezFullCascade;
+    CascadeClassifier itseezEyeCascade;
+
+    // TODO: change paths!!!
+    if (!unnLogoCascade.load(detector_file))
+    {
+        cout << "UNN not ready!";
+        return -1;
+    }
+    /*if (!unnOldLogoCascade.load(pathToCascade))
+    {
+        cout << "UNN old not ready!";
+        return -1;
+    }
+    if (!openCVLogoCascade.load(pathToCascade))
+    {
+        cout << "openCV not ready!";
+        return -1;
+    }
+    if (!itseezFullCascade.load(pathToCascade))
+    {
+        cout << "itseez full not ready!";
+        return -1;
+    }
+    if (!itseezEyeCascade.load(pathToCascade))
+    {
+        cout << "itseez eye not ready!";
+        return -1;
+    }*/
+
+
+
+
+    if (!image_file.empty())
+    {
+        // TODO: Detect objects on image.
+        vector<Rect> unnLogos;
+        Mat image = imread(image_file);
+
+        unnLogoCascade.detectMultiScale(image, unnLogos);
+        cout << "Num of images found: " << unnLogos.size() << endl;
+    }
+    else if (!video_file.empty())
+    {
+        // TODO: Detect objects on every frame of a video.
+
+    }
+    else if (use_camera)
+    {
+        // TODO: Detect objects on a live video stream from camera.
+        Mat frame;
+        VideoCapture capture;
+        capture.open(0);
+        capture.grab();
+
+        while (true)
+        {
+            capture >> frame;
+
+            vector<Rect> unnLogos;
+            unnLogoCascade.detectMultiScale(frame, unnLogos);
+            cout << unnLogos.size() << endl;
+
+        }
+    }
+
+    return 0;
+}
+
+
+

scripts/train_cascade_detector.py

@@ -24,23 +24,23 @@
 vec_file_path = base_name + ".vec"
 negatives_list_file = join(project_root_dir, "images", "negatives.txt")
 
-create_samples_call = "{0} -img {1} -bgthresh -1 -bg {2} -maxxangle 0.7 -maxyangle 0.7 -maxzangle 0.5 " \
-                      "-num 1000 -w {3} -h {4} -vec {5}".format(join(opencv_bin_dir, create_samples_app),
-                                                                args.image, negatives_list_file, args.width,
-                                                                args.height, vec_file_path)
+create_samples_call = '"{0}" -img "{1}" -bgthresh -1 -bg "{2}" -maxxangle 0.7 -maxyangle 0.7 -maxzangle 0.5 ' \
+                      '-num 1000 -w {3} -h {4} -vec "{5}"'.format(join(opencv_bin_dir, create_samples_app),
+                                                                  args.image, negatives_list_file, args.width,
+                                                                  args.height, vec_file_path)
 print("call: " + create_samples_call)
 call(create_samples_call, shell=True)
 
 cascade_dir = base_name + "_cascade"
 if not isdir(cascade_dir):
     makedirs(cascade_dir)
-train_cascade_call = "{0} -vec {1} -numPos 500 -bg {2} -numNeg 500 -feature_type {3} -maxFalseAlarmRate 0.1 " \
-                     "-numStages 5 -w {4} -h {5} -data {6}".format(join(opencv_bin_dir, train_cascade_app),
-                                                                   vec_file_path, negatives_list_file, args.features,
-                                                                   args.width, args.height, cascade_dir)
+train_cascade_call = '"{0}" -vec "{1}" -numPos 500 -bg "{2}" -numNeg 500 -featureType {3} -maxFalseAlarmRate 0.1 ' \
+                     '-numStages 5 -w {4} -h {5} -data "{6}"'.format(join(opencv_bin_dir, train_cascade_app),
+                                                                     vec_file_path, negatives_list_file, args.features,
+                                                                     args.width, args.height, cascade_dir)
 print("call: " + train_cascade_call)
 call(train_cascade_call, shell=True)
 
 detector_file = join(cascade_dir, "cascade.xml")
 if isfile(detector_file):
-    print("cascade detector has been successfully trained and located at '{}'.".format(detector_file))
+    print("cascade detector has been successfully trained and located at '{}'.".format(detector_file))