【发布时间】：2020-11-06 22:51:51
【问题描述】：
我想用yolov3基于openCV做物体检测。目前我正在使用windows，在JAVA中使用eclipse。
我的代码基于： https://github.com/suddh123/YOLO-object-detection-in-java/blob/code/yolo.java
当我执行程序时出现错误：
Exception in thread "main" CvException [org.opencv.core.CvException: cv::Exception: OpenCV(4.5.0) C:\build\master_winpack-bindings-win64-vc14-static\opencv\modules\dnn\src\darknet\darknet_importer.cpp:207: error: (-212:Parsing error) Failed to parse NetParameter file: C:\Projects\detection\opencv\yolov3.cfg in function 'cv::dnn::dnn4_v20200908::readNetFromDarknet'
]
at org.opencv.dnn.Dnn.readNetFromDarknet_0(Native Method)
at org.opencv.dnn.Dnn.readNetFromDarknet(Dnn.java:543)
at clement.yolo.main(yolo.java:67)
我的程序代码如下所示：
package clement;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.List;
import javax.imageio.ImageIO;
import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Net;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.utils.Converters;
import org.opencv.videoio.VideoCapture;
public class yolo {
private static List<String> getOutputNames(Net net) {
List<String> names = new ArrayList<>();
List<Integer> outLayers = net.getUnconnectedOutLayers().toList();
List<String> layersNames = net.getLayerNames();
outLayers.forEach((item) -> names.add(layersNames.get(item - 1)));//unfold and create R-CNN layers from the loaded YOLO model//
return names;
}
public static void main(String[] args) throws InterruptedException {
System.load("C:\\Projects\\detection\\opencv_new\\opencv\\build\\java\\x64\\opencv_java450.dll"); // Load the openCV 4.0 dll //
// System.load("C:\\Projects\\detection\\opencv\\build\\java\\x64\\opencv_java3412.dll"); // Load the openCV 4.0 dll //
String modelWeights = "C:\\Projects\\detection\\opencv\\yolov3.weights"; //Download and load only wights for YOLO , this is obtained from official YOLO site//
String modelConfiguration = "C:\\Projects\\detection\\opencv\\yolov3.cfg";//Download and load cfg file for YOLO , can be obtained from official site//
String filePath = "c:\\clement\\uwe.mp4"; //My video file to be analysed//
VideoCapture cap = new VideoCapture(filePath);// Load video using the videocapture method//
Mat frame = new Mat(); // define a matrix to extract and store pixel info from video//
Mat dst = new Mat ();
//cap.read(frame);
JFrame jframe = new JFrame("Video"); // the lines below create a frame to display the resultant video with object detection and localization//
JLabel vidpanel = new JLabel();
jframe.setContentPane(vidpanel);
jframe.setSize(600, 600);
jframe.setVisible(true);// we instantiate the frame here//
Net net = Dnn.readNetFromDarknet(modelConfiguration, modelWeights); //OpenCV DNN supports models trained from various frameworks like Caffe and TensorFlow. It also supports various networks architectures based on YOLO//
//Thread.sleep(5000);
//Mat image = Imgcodecs.imread("D:\\yolo-object-detection\\yolo-object-detection\\images\\soccer.jpg");
Size sz = new Size(288,288);
List<Mat> result = new ArrayList<>();
List<String> outBlobNames = getOutputNames(net);
while (true) {
if (cap.read(frame)) {
Mat blob = Dnn.blobFromImage(frame, 0.00392, sz, new Scalar(0), true, false); // We feed one frame of video into the network at a time, we have to convert the image to a blob. A blob is a pre-processed image that serves as the input.//
net.setInput(blob);
net.forward(result, outBlobNames); //Feed forward the model to get output //
// outBlobNames.forEach(System.out::println);
// result.forEach(System.out::println);
float confThreshold = 0.6f; //Insert thresholding beyond which the model will detect objects//
List<Integer> clsIds = new ArrayList<>();
List<Float> confs = new ArrayList<>();
List<Rect> rects = new ArrayList<>();
for (int i = 0; i < result.size(); ++i)
{
// each row is a candidate detection, the 1st 4 numbers are
// [center_x, center_y, width, height], followed by (N-4) class probabilities
Mat level = result.get(i);
for (int j = 0; j < level.rows(); ++j)
{
Mat row = level.row(j);
Mat scores = row.colRange(5, level.cols());
Core.MinMaxLocResult mm = Core.minMaxLoc(scores);
float confidence = (float)mm.maxVal;
Point classIdPoint = mm.maxLoc;
if (confidence > confThreshold)
{
int centerX = (int)(row.get(0,0)[0] * frame.cols()); //scaling for drawing the bounding boxes//
int centerY = (int)(row.get(0,1)[0] * frame.rows());
int width = (int)(row.get(0,2)[0] * frame.cols());
int height = (int)(row.get(0,3)[0] * frame.rows());
int left = centerX - width / 2;
int top = centerY - height / 2;
clsIds.add((int)classIdPoint.x);
confs.add((float)confidence);
rects.add(new Rect(left, top, width, height));
}
}
}
float nmsThresh = 0.5f;
MatOfFloat confidences = new MatOfFloat(Converters.vector_float_to_Mat(confs));
Rect[] boxesArray = rects.toArray(new Rect[0]);
MatOfRect boxes = new MatOfRect(boxesArray);
MatOfInt indices = new MatOfInt();
// Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThresh, indices); //We draw the bounding boxes for objects here//
int [] ind = indices.toArray();
int j=0;
for (int i = 0; i < ind.length; ++i)
{
int idx = ind[i];
Rect box = boxesArray[idx];
Imgproc.rectangle(frame, box.tl(), box.br(), new Scalar(0,0,255), 2);
//i=j;
System.out.println(idx);
}
// Imgcodecs.imwrite("D://out.png", image);
//System.out.println("Image Loaded");
ImageIcon image = new ImageIcon(Mat2bufferedImage(frame)); //setting the results into a frame and initializing it //
vidpanel.setIcon(image);
vidpanel.repaint();
// System.out.println(j);
// System.out.println("Done");
}
}
}
// }
private static BufferedImage Mat2bufferedImage(Mat image) { // The class described here takes in matrix and renders the video to the frame //
MatOfByte bytemat = new MatOfByte();
Imgcodecs.imencode(".jpg", image, bytemat);
byte[] bytes = bytemat.toArray();
InputStream in = new ByteArrayInputStream(bytes);
BufferedImage img = null;
try {
img = ImageIO.read(in);
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
return img;
}
}
谁能帮帮我？
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