My C++ code is not detecting objects correctly yolov5 - c++

Question

I have a yolov5 onnx file where I trained apples and bananas. I was using python until today, but I decided to switch to c++ to gain some speed. I get correct results when I use yolov5's own onnx files and image in the code I added below. But when I add my own onnx file and my test image it gives me wrong result. You can also find the attached image. What is the problem here?
// Include Libraries.
\#include \<opencv2/opencv.hpp\>
\#include \<fstream\>
// Namespaces.
using namespace cv;
using namespace std;
using namespace cv::dnn;
// Constants.
const float INPUT_WIDTH = 640.0;
const float INPUT_HEIGHT = 640.0;
const float SCORE_THRESHOLD = 0.3;
const float NMS_THRESHOLD = 0.4;
const float CONFIDENCE_THRESHOLD = 0.65;
// Text parameters.
const float FONT_SCALE = 0.7;
const int FONT_FACE = FONT_HERSHEY_SIMPLEX;
const int THICKNESS = 1;
// Colors.
Scalar BLACK = Scalar(0,0,0);
Scalar BLUE = Scalar(255, 178, 50);
Scalar YELLOW = Scalar(0, 255, 255);
Scalar RED = Scalar(0,0,255);
// Draw the predicted bounding box.
void draw_label(Mat& input_image, string label, int left, int top)
{
// Display the label at the top of the bounding box.
int baseLine;
Size label_size = getTextSize(label, FONT_FACE, FONT_SCALE, THICKNESS, &baseLine);
top = max(top, label_size.height);
// Top left corner.
Point tlc = Point(left, top);
// Bottom right corner.
Point brc = Point(left + label_size.width, top + label_size.height + baseLine);
// Draw black rectangle.
rectangle(input_image, tlc, brc, BLACK, FILLED);
// Put the label on the black rectangle.
putText(input_image, label, Point(left, top + label_size.height), FONT_FACE, FONT_SCALE, YELLOW, THICKNESS);
}
vector\<Mat\> pre_process(Mat &input_image, Net &net)
{
// Convert to blob.
Mat blob;
blobFromImage(input_image, blob, 1./255., Size(INPUT_WIDTH, INPUT_HEIGHT), Scalar(), true, false);
net.setInput(blob);
// Forward propagate.
vector<Mat> outputs;
net.forward(outputs, net.getUnconnectedOutLayersNames());
return outputs;
}
Mat post_process(Mat &input_image, vector\<Mat\> &outputs, const vector\<string\> &class_name)
{
// Initialize vectors to hold respective outputs while unwrapping detections.
vector\<int\> class_ids;
vector\<float\> confidences;
vector\<Rect\> boxes;
// Resizing factor.
float x_factor = input_image.cols / INPUT_WIDTH;
float y_factor = input_image.rows / INPUT_HEIGHT;
float *data = (float *)outputs[0].data;
const int dimensions = 85;
const int rows = 25200;
// Iterate through 25200 detections.
for (int i = 0; i < rows; ++i)
{
float confidence = data[4];
// Discard bad detections and continue.
if (confidence >= CONFIDENCE_THRESHOLD)
{
float * classes_scores = data + 5;
// Create a 1x85 Mat and store class scores of 80 classes.
Mat scores(1, class_name.size(), CV_32FC1, classes_scores);
// Perform minMaxLoc and acquire index of best class score.
Point class_id;
double max_class_score;
minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
// Continue if the class score is above the threshold.
if (max_class_score > SCORE_THRESHOLD)
{
// Store class ID and confidence in the pre-defined respective vectors.
confidences.push_back(confidence);
class_ids.push_back(class_id.x);
// Center.
float cx = data[0];
float cy = data[1];
// Box dimension.
float w = data[2];
float h = data[3];
// Bounding box coordinates.
int left = int((cx - 0.5 * w) * x_factor);
int top = int((cy - 0.5 * h) * y_factor);
int width = int(w * x_factor);
int height = int(h * y_factor);
// Store good detections in the boxes vector.
boxes.push_back(Rect(left, top, width, height));
}
}
// Jump to the next column.
data += 85;
}
// Perform Non Maximum Suppression and draw predictions.
vector<int> indices;
NMSBoxes(boxes, confidences, SCORE_THRESHOLD, NMS_THRESHOLD, indices);
for (int i = 0; i < indices.size(); i++)
{
int idx = indices[i];
Rect box = boxes[idx];
int left = box.x;
int top = box.y;
int width = box.width;
int height = box.height;
// Draw bounding box.
rectangle(input_image, Point(left, top), Point(left + width, top + height), BLUE, 3*THICKNESS);
// Get the label for the class name and its confidence.
string label = format("%.2f", confidences[idx]);
label = class_name[class_ids[idx]] + ":" + label;
// Draw class labels.
draw_label(input_image, label, left, top);
//cout<<"The Value is "<<label;
//cout<<endl;
}
return input_image;
}
int main()
{
vector<string> class_list;
ifstream ifs("/Users/admin/Documents/C++/First/obj.names");
string line;
while (getline(ifs, line))
{
class_list.push_back(line);
}
// Load image.
Mat frame;
frame = imread("/Users/admin/Documents/C++/First/test.jpg");
// Load model.
Net net;
net = readNet("/Users/admin/Documents/C++/First/my.onnx");
vector<Mat> detections;
detections = pre_process(frame, net);
Mat img = post_process(frame, detections, class_list);
//Mat img = post_process(frame.clone(), detections, class_list);
// Put efficiency information.
// The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
vector<double> layersTimes;
double freq = getTickFrequency() / 1000;
double t = net.getPerfProfile(layersTimes) / freq;
string label = format("Inference time : %.2f ms", t);
putText(img, label, Point(20, 40), FONT_FACE, FONT_SCALE, RED);
imshow("Output", img);
waitKey(0);
return 0;
}
The photos I use are 640x480. I played around with the size of the photo, thinking it might be related, but the same problem persisted.

Answer 1

The Yolov5 output format is xyxy as can be seen here:
https://github.com/ultralytics/yolov5/blob/bfa1f23045c7c4136a9b8ced9d6be8249ed72692/detect.py#L161
Not xywh as you are assuming in your code