Python 代码详解：DetectionPredictor 类 - 基于检测模型的预测

This code defines a class called 'DetectionPredictor' that extends the 'BasePredictor' class. It is used for prediction based on a detection model.

The 'postprocess' method is overridden in this class to post-process the predictions and return a list of 'Results' objects.

The 'preds' argument represents the predictions made by the model, 'img' represents the input image, and 'orig_imgs' represents the original images.

In the method, the 'preds' are processed using non-maximum suppression ('ops.non_max_suppression') to filter out overlapping bounding boxes. The parameters used for non-maximum suppression are obtained from 'self.args'.

The 'results' list is initialized to store the post-processed results. The variable 'is_list' is used to check if the 'orig_imgs' are a list of images or a single image.

For each prediction in 'preds', the corresponding original image is retrieved from 'orig_imgs'. If 'orig_imgs' is a list, the bounding box coordinates in the prediction are scaled according to the size difference between 'img' and 'orig_img'.

The 'img_path' is obtained from the batch of inputs, and a 'Results' object is created and appended to the 'results' list. The 'Results' object contains the original image, its path, the class names from the model, and the post-processed bounding box predictions.

Finally, the 'results' list is returned.