asdTools-ReID Rank可视化

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Github - 开源代码及Readme
Blog - 工具介绍

摘要：使用markdown实现ReID的Rank可视化的工具介绍。

场景

• 想为科研添砖加瓦，因此需要一个好看的可视化。
• 或者只是想了解一下模型的效果。
• 希望快一点。
  • 因为使用markdown作为展示图片的方式，因此无需画图，速度大幅提高。
  • 我之前写了一个，一秒钟大概合成几张图，慢死，我自己都不用。
  • 现在这个只用九秒钟，3543x3384的大小。
  • 此外，同时提供各图像对应的ID信息。

使用示例

• 开源地址：asdTools/asdTools/Tools/Image/VisualizeRankOfReID.py at main
• 处理结果示例：asdTools/Sample/VisualizeRankOfReID at main
• 在test代码的evaluate()函数前，实例化一个VisualizeRankOfReID对象，将 距离矩阵、queryloader、galleryloader 传入即可。
  • 或者说，计算CMC和mAP前。
  • 使用示例见if __name__ == "__main__":

    from asdTools.Classes.Tool.MarkdownTable import MarkdownTable
    from asdTools.Classes.Base.BaseModel import BaseModel
    import numpy as np
    
    class VisualizeRankOfReID(BaseModel):
        """ Sample: Sample/VisualizeRankOfReID
        使用markdown表格进行ReID的Rank可视化。速度快，我的笔记本运行一次3543x3384(query-gallery)的可视化耗时9秒。
        Visualize ReID Rank by markdown tables. Fast - It takes only 9 seconds to generate visualizations on my laptop for a 3543x3384 query-gallery."
        """
        def __init__(self, **kwargs) -> None:
            super().__init__(multipleFiles=True, **kwargs)
    
        def __call__(self, 
                distmat, 
                query_loader, 
                gallery_loader, 
                top_k:int=10,
                pid_index:int=1,
                camid_index:int=2) -> str:
            self.run(distmat, query_loader, gallery_loader, top_k, pid_index, camid_index)
    
        def run(self, 
                distmat, 
                query_loader, 
                gallery_loader, 
                top_k,
                pid_index,
                camid_index) -> str:
            self.begining(isSimple=True)
            if top_k == -1: 
                self.warning("top_k is set to -1, all images will be shown, may too large.")
                top_k = distmat.shape[0]
            index2path = ""
            # ---------- Init Guery Datainfo ----------
            g_imgs = []
            g_pids = []
            g_camids = []
            for i, data_info in enumerate(gallery_loader.dataset.dataset):
                g_imgs.append(data_info[0])
                g_pids.append(data_info[pid_index])
                g_camids.append(data_info[camid_index])
            # ---------- Start Visualazation ----------
            for i, data_info in enumerate(query_loader.dataset.dataset):
                # ---------- Init Query Datainfo ----------
                q_img_path = data_info[0]
                q_pid = data_info[pid_index]
                q_camid = data_info[camid_index]
                # ---------- Sort Rank ----------
                distances = distmat[i]
                sorted_indices = np.argsort(distances)
                sorted_indices = [index for index in sorted_indices if g_camids[index] != q_camid]
                sorted_indices = [index for index in sorted_indices if g_pids[index] != q_pid]
                # ---------- Save Rank ----------
                # ----- map i_query to img_path of i_query
                index2path += f"{i+1}: \t {self.convert_path_to_abspath(q_img_path)} \n"
                # ----- rank gallery of i_query
                # rank gallery imgs
                g_imgs_path = [g_imgs[index] for index in sorted_indices[:top_k]]
                # log imgs
                md_table = MarkdownTable(["i", "query"] + [f"top_{j+1}" for j in range(top_k)])
                row_imgs = [i + 1]
                row_imgs.append(md_table.convert_imgPath_to_MDImgPath(self.convert_path_to_abspath(q_img_path)))
                row_imgs.extend([md_table.convert_imgPath_to_MDImgPath(self.convert_path_to_abspath(g_imgs_path[j])) for j in range(top_k)])
                md_table.add_row(row_imgs)
                # log id
                row_ID = ["data info"]
                row_ID.append(data_info[1:])
                row_ID.extend([gallery_loader.dataset.dataset[j][1:] for j in sorted_indices[:top_k]])
                md_table.add_row(row_ID)
                # save markdown table
                md_table_path = self.generate_output_path(output_middle_dir=self._time_start, output_file=f"{i+1}-rank_table.md")
                self.save_file(md_table.output(), md_table_path)
            # ---------- Save Mapping ----------
            index2path_path = self.generate_output_path(output_middle_dir=self._time_start, output_file=f"0_index2path.txt")
            self.save_file(index2path, index2path_path)
            self.log(f"For find image faster, see mapping in {index2path_path}")
            self.done(isSimple=True)
    
    if __name__ == "__main__":
        # All you need is place the codes before evaluate(distmat, ...) in test()
    
        # ---------- Sample 1 ----------
        """ When specify log_dir
    
    def test_prcc(model, queryloader_same, queryloader_diff, galleryloader, dataset):
        logger = logging.getLogger('reid.test')
        ...
    
        from asdTools.Tools.Image.VisualizeRankOfReID import VisualizeRankOfReID
        rank_vis = VisualizeRankOfReID()
        log_dir = rank_vis.get_loggingLogger_path(logger)
        log_dir = rank_vis.join(log_dir, "Rank Visualization")
        VisualizeRankOfReID(log_dir=log_dir)(distmat, queryloader, galleryloader)
    
        logger.info("Computing CMC and mAP for the same clothes setting")
        cmc, mAP = evaluate(distmat_same, qs_pids, g_pids, qs_camids, g_camids)
        """
    
        # ---------- Sample 2 ----------
        """ Just visualization
    
    def test_prcc(model, queryloader_same, queryloader_diff, galleryloader, dataset):
    
        from asdTools.Tools.Image.VisualizeRankOfReID import VisualizeRankOfReID
        VisualizeRankOfReID()(distmat, queryloader, galleryloader)
    
        logger.info("Computing CMC and mAP for the same clothes setting")
        cmc, mAP = evaluate(distmat_same, qs_pids, g_pids, qs_camids, g_camids)
        """