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openedon Aug 6, 2024
Why do the following models use the same method but have different results? Is it a C # issue?
How can C # achieve code with the same effect as Python?
// See https://aka.ms/new-console-template for more information
using System.Drawing;
using ConsoleApp3;
using Microsoft.ML.OnnxRuntime;
InferenceSession session = new InferenceSession("D:\\yolov5\\yolov5x.onnx");
List<NamedOnnxValue> inputs = new List<NamedOnnxValue>();
var bit=(Bitmap)Bitmap.FromFile("C:\\Users\\47013\\Desktop\\2.jpeg");
inputs.Add(NamedOnnxValue.CreateFromTensor<float>("images",test.PreprocessImage(bit)));
var results = session.Run(inputs);
var output=results.First().AsTensor<float>();
var boxes = new List<float[]>();
for (int i = 0; i < output.Dimensions[1]; i++)
{
var boxData = new float[85];
for (int j = 0; j < 85; j++)
{
boxData[j] = output[0, i, j];
}
boxes.Add(boxData);
}
var asd=test.DrawBoundingBoxes(bit,boxes,0.1f);
asd.Save("test.jpg");
var m=boxes.Max(s => s[4]);
List<float> ll = new List<float>();
foreach (var box in boxes)
{
float confidence = box[4];
ll.Add(confidence);
}
var sasd=ll.Max();
Console.WriteLine();import datetime
import torch
# 加载模型
model = torch.hub.load('ultralytics/yolov5', 'yolov5x', pretrained=True)
# 设置模型为评估模式
model.eval()
# 图像文件路径
img_path = 'C:\\Users\\47013\\Desktop\\新建文件夹\\2.jpeg' # 替换为你自己的图像路径
start_time = datetime.datetime.now()
# 进行推理
results = model(img_path)
print(datetime.datetime.now() - start_time)
# 解析结果
results.print() # 打印检测结果
results.show() # 显示带检测框的图像
# 访问结果数据
detections = results.xyxy[0] # 访问检测结果，格式为 (x1, y1, x2, y2, confidence, class)
# 打印检测框的详细信息
for *box, conf, cls in detections:
print(f"Detected {model.names[int(cls)]} with confidence {conf:.2f} at [{box[0]:.2f}, {box[1]:.2f}, {box[2]:.2f}, {box[3]:.2f}]")
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