python配合yolo分类模型开发分类软件
- 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。
- 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
- 分类使用的数据集，halcon的pill分类demo的数据集
- 软件界面
- 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].namesprobs_=results[0].probsclass_id_=probs_.top1class_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_)
- 推理的效果显示
