Hi team, i seem not to be able to get YOLO working despite some step by step guidance stated on:
from mr MagicandLove. I removed libraries and replaced them to try get the error removed.
Is there a tip that can steer me in the correct direction?
I did import the required files in the locations as the site stated, the CVImage file seems to keep conflicting (i do restart pc to refresh )
help to tackle this last step would be helped
basic code from site:
import cvimage.*;
import ipcapture.*;
IPCapture cap;
import processing.video.*;
//import CVImage.*;
import org.opencv.core.*;
import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.dnn.*;
import org.opencv.core.MatOfDouble;
import java.util.*;
final int CAPW = 640, CAPH = 360;
final int W = 416, H = 416;
final float THRESH = 0.85;
private Net net;
//private Capture cap;
private CVImage cv;
private String [] labels;
private ArrayList<String> names;
public void settings() {
size(CAPW, CAPH);
}
public void setup() {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
println(Core.VERSION);
// printArray(Capture.list());
// cap = new Capture(this, CAPW, CAPH);
cap = new IPCapture(this, "http://DESKTOP-1QTI31N:8080/cx","","");
cap.start();
cv = new CVImage(W, H);
loadYOLO();
getOutputsNames(net);
}
private void getOutputsNames(Net n) {
int [] outLayers = n.getUnconnectedOutLayers().toArray();
ArrayList<String> layers = (ArrayList)n.getLayerNames();
names = new ArrayList<String>(outLayers.length);
for (int i=0; i<outLayers.length; i++) {
names.add(layers.get(outLayers[i]-1));
}
for (String s : names) {
println(s);
}
}
private void loadYOLO() {
net = Dnn.readNetFromDarknet(dataPath("yolov3.cfg"),
dataPath("yolov3.weights"));
//net.setPreferableBackend(Dnn.DNN_BACKEND_DEFAULT);
//net.setPreferableTarget(Dnn.DNN_TARGET_OPENCL);
String [] labelFile = loadStrings("object_detection_classes_yolov3.txt");
labels = new String[labelFile.length];
println("Class size: " + labelFile.length);
for (int i=0; i<labelFile.length; i++) {
labels[i] = trim(labelFile[i]);
println(labels[i]);
}
}
public void draw() {
if (cap.available()) {
return;
cap.read();
}
background(0);
cv.copy(cap, 0, 0, cap.width, cap.height,
0, 0, cv.width, cv.height);
cv.copyTo();
image(cap, 0, 0);
Mat blob = Dnn.blobFromImage(cv.getBGR(), 1.0f/255.0f,
new Size(cv.width, cv.height),
new Scalar(0, 0, 0), true, false);
net.setInput(blob);
Mat result = net.forward(names.get(0));
pushStyle();
for (int i=0; i<result.rows(); i++) {
Mat scores = result.row(i).colRange(5, result.cols());
MinMaxLocResult mm = Core.minMaxLoc(scores);
Point classIdPoint = mm.maxLoc;
double confidence = mm.maxVal;
if (confidence > THRESH) {
int idx = (int)classIdPoint.x;
int x = (int)(result.get(i, 0)[0]*cap.width);
int y = (int)(result.get(i, 1)[0]*cap.height);
int w = (int)(result.get(i, 2)[0]*cap.width);
int h = (int)(result.get(i, 3)[0]*cap.height);
noFill();
stroke(255, 0, 0);
rect(x-w/2.0, y-h/2.0, w, h);
noStroke();
fill(255, 0, 0, 200);
rect(x-w/2.0, y-h/2.0, w, 18);
fill(255, 255, 255);
text(labels[idx], x+5-w/2.0, y+12-h/2.0);
}
}
popStyle();
}
error:
