finalization of pointbase functionality, removed old scripts, removed camera scripts, they will be added into their own folder

src/cam.py

@@ -1,60 +0,0 @@
-import cv2
-import numpy as np
-import os
-
-
-def variance_of_laplacian(image):
-	return cv2.Laplacian(image, cv2.CV_64F).var()
-
-
-
-os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"]="rtsp_transport;tcp"
-
-gstreamerstr = "tcpclientsrc host=192.168.0.144 port=5000 ! jpegdec ! videoconvert ! appsink"
-
-
-
-#if not capture.isOpened() : print("CANNOT OPEN STREAM")
-capture = cv2.VideoCapture("vid.mp4")
-keypressNo = 1
-while(True):
-    #capture = cv2.VideoCapture(gstreamerstr,cv2.CAP_GSTREAMER)
-   
-    ret, frame = capture.read()
-    if not ret:
-        print('fail')
-        break
-
-    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    variance = variance_of_laplacian(frame)
-
-    cv2.putText(frame, "{}: {:.2f}".format("Variance:", variance), (10, 30),
-		cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
-    cv2.imshow('frame',frame)
-    #cv2.waitKey(0)
-    
-    if cv2.waitKey(70) == ord('v'):
-        frames=[]
-        maxVarIdx = 0
-        maxVar = 0
-        for i in range(5):
-            ret, frame = capture.read()
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            frames.append(frame)
-            var = variance_of_laplacian(frame)
-            if var > maxVar:
-                 maxVar = var
-                 maxVarIdx = i
-            cv2.waitKey(20)
-            #test to see if the image with the highest variance gets picked
-            #cv2.putText(frame, "{}: {:.2f}".format("Variance:", var), (10, 30),
-		        #cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
-            #writeString="imgtest"+str(keypressNo)+str(i)+".jpg"
-            #cv2.imwrite(writeString, frames[i])
-        writeString="img"+str(keypressNo)+".jpg"
-        cv2.imwrite(writeString, frames[maxVarIdx])
-        keypressNo=keypressNo+1
-        
-
-capture.release()
-cv2.destroyAllWindows()

src/imgvar.py

@@ -1,38 +0,0 @@
-import cv2
-import numpy as np
-import os
-from imutils import paths
-import argparse
-
-
-def variance_of_laplacian(image):
-	return cv2.Laplacian(image, cv2.CV_64F).var()
-
-
-def minVarInFolder(path):
-	minVar = 90000
-	minPath=""
-	files = os.listdir(path)
-	for p in files:
-		if ".png" in p and ".mask" not in p:
-			print(path+p)
-			img = cv2.imread(path+p)
-			cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-			var = variance_of_laplacian(img)
-			if var < minVar:
-				minPath = path+p
-				minVar = var
-	return minPath,minVar
-
-ap = argparse.ArgumentParser()
-ap.add_argument("-i", "--images", required=True,
-	help="path to image")
-args = vars(ap.parse_args())
-
-path = args["images"]
-
-#img = cv2.imread(path)
-#cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-#var = variance_of_laplacian(img)
-
-print(minVarInFolder(path))

src/runThisOnRPI.txt

@@ -1 +0,0 @@
-gst-launch-1.0 -v v4l2src device=/dev/video0 num-buffers=-1 ! video/x-raw,width=640,height=480, framerate=30/1 ! videoconvert ! jpegenc ! tcpserversink  host=192.168.0.144 port=5000

src/test-kmeans.cpp

@@ -1,71 +0,0 @@
-#include "opencv2/highgui.hpp"
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-#include <iostream>
-using namespace cv;
-using namespace std;
-// static void help()
-// {
-//     cout << "\nThis program demonstrates kmeans clustering.\n"
-//             "It generates an image with random points, then assigns a random number of cluster\n"
-//             "centers and uses kmeans to move those cluster centers to their representitive location\n"
-//             "Call\n"
-//             "./kmeans\n" << endl;
-// }
-int main( int /*argc*/, char** /*argv*/ )
-{
-    const int MAX_CLUSTERS = 5;
-    Scalar colorTab[] =
-    {
-        Scalar(0, 0, 255),
-        Scalar(0,255,0),
-        Scalar(255,100,100),
-        Scalar(255,0,255),
-        Scalar(0,255,255)
-    };
-    Mat img(500, 500, CV_8UC3);
-    RNG rng(12345);
-    for(;;)
-    {
-        int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
-        int i, sampleCount = rng.uniform(1, 1001);
-        Mat points(sampleCount, 1, CV_32FC2), labels;
-        clusterCount = MIN(clusterCount, sampleCount);
-        std::vector<Point2f> centers;
-        /* generate random sample from multigaussian distribution */
-        for( k = 0; k < clusterCount; k++ )
-        {
-            Point center;
-            center.x = rng.uniform(0, img.cols);
-            center.y = rng.uniform(0, img.rows);
-            Mat pointChunk = points.rowRange(k*sampleCount/clusterCount,
-                                             k == clusterCount - 1 ? sampleCount :
-                                             (k+1)*sampleCount/clusterCount);
-            rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
-        }
-        randShuffle(points, 1, &rng);
-        double compactness = kmeans(points, clusterCount, labels,
-            TermCriteria( TermCriteria::EPS+TermCriteria::COUNT, 10, 1.0),
-               3, KMEANS_PP_CENTERS, centers);
-        img = Scalar::all(0);
-        for( i = 0; i < sampleCount; i++ )
-        {
-            int clusterIdx = labels.at<int>(i);
-            Point ipt = points.at<Point2f>(i);
-            circle( img, ipt, 2, colorTab[clusterIdx], FILLED, LINE_AA );
-        }
-        for (i = 0; i < (int)centers.size(); ++i)
-        {
-            Point2f c = centers[i];
-            circle( img, c, 40, colorTab[i], 1, LINE_AA );
-        }
-        cout << "Compactness: " << compactness << endl;
-        cout << "Labels rows" << labels.rows;
-        cout << "Labels cols" << labels.cols;
-        imshow("clusters", img);
-        char key = (char)waitKey();
-        if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'
-            break;
-    }
-    return 0;
-}

src/test-stitch.cpp

@@ -1,66 +0,0 @@
-// CPP program to Stitch
-// input images (panorama) using OpenCV
-#include <iostream>
-#include <fstream>
- 
-// Include header files from OpenCV directory
-// required to stitch images.
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/highgui.hpp"
-#include "opencv2/stitching.hpp"
- 
-using namespace std;
-using namespace cv;
- 
-// Define mode for stitching as panorama
-// (One out of many functions of Stitcher)
-Stitcher::Mode mode = Stitcher::SCANS;
- 
-// Array for pictures
-vector<Mat> imgs;
- 
-int main(int argc, char* argv[])
-{
-    // Get all the images that need to be
-    // stitched as arguments from command line
-    for (int i = 1; i < argc; ++i)
-    {
-            // Read the ith argument or image
-            // and push into the image array
-            Mat img = imread(argv[i]);
-            if (img.empty())
-            {
-                // Exit if image is not present
-                cout << "Can't read image '" << argv[i] << "'\n";
-                return -1;
-            }
-            imgs.push_back(img);
-    }
-     
-    // Define object to store the stitched image
-    Mat pano;
-     
-    // Create a Stitcher class object with mode panoroma
-    Ptr<Stitcher> stitcher = Stitcher::create(mode);
-     
-    // Command to stitch all the images present in the image array
-    Stitcher::Status status = stitcher->stitch(imgs, pano);
- 
-    if (status != Stitcher::OK)
-    {
-        // Check if images could not be stitched
-        // status is OK if images are stitched successfully
-        cout << "Can't stitch images\n";
-        return -1;
-    }
-     
-    // Store a new image stitched from the given
-    //set of images as "result.jpg"
-    imwrite("result.jpg", pano);
-     
-    // Show the result
-    imshow("Result", pano);
-     
-    waitKey(0);
-    return 0;
-}

src/test.cpp

@@ -43,12 +43,12 @@ int main(int argc, char** argv)
     //120/2022-11-15_09-35-09
     //155/2022-11-15_07-18-56
     //2/2022-10-13_13-33-22
-    //1/2022-10-13_11-50-12
+    //v
     //pb.load("../1/2022-10-13_11-50-12/snapshots/");
-    pb.load("../data/");
+    pb.load("../155/2022-11-15_07-18-56/snapshots/");
 
     //test print all point info
-    pb.printPoints(0);
+    //pb.printPoints(0);
 
     /* test get path
     path: ../data/0000044.json; 
@@ -57,7 +57,7 @@ int main(int argc, char** argv)
     */
     cout << pb.getPath(206.500,1051.00) << '\n';
 
-    pb.showPointImg();
+    //pb.showPointImg();
 
     /*
     vector<Point2d> res = pb.getAdjacents(Point2d(75.1,3220.80));
@@ -69,8 +69,10 @@ int main(int argc, char** argv)
     */
 
     //pb.stitchImgs();
-    cout <<"IMG HEIGHT:" << pb.imgHeightDeg <<"\n";
+    //cout <<"IMG HEIGHT:" << pb.imgHeightDeg <<"\n";
-    cout <<"IMG WIDTH DEG:" << pb.imgWidthDeg <<"\n";
+    //cout <<"IMG WIDTH DEG:" << pb.imgWidthDeg <<"\n";
-    pb.cumdump(650,525);
+    //pb.cumdump(770,605);
+    pb.patternMatchTry(770,605);
+    //pb.onlineStitchSIFT(1280,720,"online/");
 
 }