1. 上一篇文章写了yolo的分类模型的训练，写篇文章基于yolo分类模型开发分类软件。
2. 开发环境:pycharm，PySide6 6.6.1 ，PySide6-Addons 6.6.1，PySide6-Essentials 6.6.1，torch 2.3.1+cu121，torchaudio 2.3.1+cu121，torchvision 0.18.1+cu121，onnx 1.16.1，onnxruntime 1.17.3，opencv-contrib-python 4.10.0.82，opencv-python 4.10.0.82，opencv-python-headless 4.7.0.72
3. 分类使用的数据集，halcon的pill分类demo的数据集
   
4. 软件界面
   
   
   
5. 核心代码
   yolo推理代码

  results = self.Model.predict(image_mat_)
            if(len(results)>0):
                ###获取分类名
                names_=results[0].names
                probs_=results[0].probs
                class_id_=probs_.top1
                class_socre_=probs_.top1conf.item()
                self.Classification=names_[class_id_]
                self.Sorce=str(class_socre_)

onnx推理核心代码

 			target_image_height_ = input_onnx_.shape[2]###获取输入的目标维度
            target_image_width_ = input_onnx_.shape[3]

            # image_mat_=image_mat_.resize(target_image_width_,target_image_height_)###缩放图片
            scale_image_mat_=cv2.resize(image_mat_,(target_image_width_,target_image_height_))
            print("ImageMatShape:", scale_image_mat_.shape)

            image_np_ = np.array(scale_image_mat_)  ###图片转成np数组
            print("ImageNpShape:", image_np_.shape)

            image_np_ = image_np_.transpose(2, 0, 1)  ##转成通道在前面的维度
            print("ImageNpShape:", image_np_.shape)

            resized_width = image_np_.shape[2]  ##获取图片输入输出
            resized_height = image_np_.shape[1]

            image_np_ = image_np_.reshape(1, 3, target_image_height_, target_image_width_)  ##添加一个新维度
            print(image_np_[0, 0, 0, 0])
            print("ImageNpShape:", image_np_.shape)

            image_np_ = image_np_.astype(np.float32)

            image_np_ = image_np_ / 255.0  ##数据归一化
            print(image_np_[0, 0, 0, 0])

            ###获取输出点
            outputs = self.OnnxModel.get_outputs()
            output_onnx_ = outputs[0]
            print("Name:", output_onnx_.name)
            print("Type:", output_onnx_.type)
            print("Shape:", output_onnx_.shape)

            ###运行推理
            outputs = self.OnnxModel.run(["output0"], {"images": image_np_})

            if(len(outputs)>0):
                ng_sorce_=outputs[0][0][0]
                ok_sorce_=outputs[0][0][1]

                if(ng_sorce_>ok_sorce_):
                    self.Classification="ng"
                    self.Sorce=str(ng_sorce_)
                else:
                    self.Classification="ok"
                    self.Sorce = str(ok_sorce_)

6. 推理的效果显示