There is a problem with the hr_fft_batch function in the utils_sig.py file

[BugMaker2002]

Some errors occurred when I used the following code to calculateMAE, RMSE,and Pearson coefficients:

for h5_path in test_list:
        h5_path = str(h5_path)

        with h5py.File(h5_path, 'r') as f:
            imgs = f['imgs']
            subject_name = os.path.basename(h5_path)[:-3]
            bvp_path = f"/share2/data/zhouwenqing/UBFC_rPPG/dataset2/{subject_name}/ground_truth.txt"
            bvp = np.loadtxt(bvp_path).reshape((-1, 1))
            # bvppeak = f['bvp_peak']
            fs = config_train['fs']
            
            # duration表示秒数，fs表示frame per seccond 
            duration = np.min([imgs.shape[0], bvp.shape[0]]) / fs
            num_blocks = int(duration // time_interval)
            # 从整个视频当中截取出num_blocks个视频片段，这些片段之间是连续的（指从原视频当中截取的方式）
            rppg_list = []
            bvp_list = []
            # bvppeak_list = []
            for b in range(num_blocks):
                rppg_clip = dl_model(imgs[b*time_interval*fs:(b+1)*time_interval*fs])
                rppg_list.append(rppg_clip)

                bvp_list.append(bvp[b*time_interval*fs:(b+1)*time_interval*fs])
                # bvppeak_list.append(bvppeak[b*time_interval*fs:(b+1)*time_interval*fs])

            rppg_list = np.array(rppg_list)
            bvp_list = np.array(bvp_list)
            results = {'rppg_list': rppg_list, 'bvp_list': bvp_list}
            np.save(pred_exp_dir+'/'+h5_path.split('/')[-1][:-3], results)
            
            bvp_list = bvp_list.reshape(num_blocks, -1)
  
            
            # 通带滤波
            hr_pred = butter_bandpass_batch(rppg_list, lowcut=0.6, highcut=4, fs=30)
            hr_gt = butter_bandpass_batch(bvp_list, lowcut=0.6, highcut=4, fs=30)
            print("通带滤波 hr_pred.shape: ", hr_pred.shape)
            print("通带滤波 hr_gt.shape: ", hr_gt.shape)
            
            # 计算预测心率和地面真值心率
            hr_pred = torch.tensor(hr_fft_batch(hr_pred, 30))
            hr_gt = torch.tensor(hr_fft_batch(hr_gt, 30))
            print("hr_fft_batch hr_pred.shape: ", hr_pred.shape)
            print("hr_fft_batch hr_gt.shape: ", hr_gt.shape)
            
            mae_all = mae_loss_func(hr_pred, hr_gt)
            mse_all = mse_loss_func(hr_pred, hr_gt)
            rmse = np.sqrt(mse_all)
            correlation_coefficients = []
            for i in range(num_blocks):
                # 计算每个维度的皮尔逊相关系数
                correlation_coefficient, _ = pearsonr(hr_pred[i].flatten(), hr_gt[i].flatten())
                correlation_coefficients.append(correlation_coefficient)
            print("Evaluation Result\n MAE: {:.4f}; RMSE: {:.4f}; R: {:.4f};".format(
                mae_all, rmse, sum(correlation_coefficients) / len(correlation_coefficients)))

The output of the above code shows: a tensor with shape of (2, 900) after hr_fft_batch() function shape changed to (2,), and then I applied the output tensor to the scipy.stats.pearsonr functionto find the Pearson coefficient, but error:

File "test.py", line 132, in my_main
correlation_coefficient, _ = pearsonr(hr_pred[i].flatten(), hr_gt[i].flatten())
The File "/ share1 / home/zhouwenqing/anaconda3 / envs/RPPG/lib/python3.6 / site - packages/scipy/stats/stats. Py", line 3838, in pearsonr
raise ValueError('x and y must have length at least 2.')
ValueError: x and y must have length at least 2.

That is, thepearsonr()function requires that the input arrays x and y have at least two elements, because the Pearson correlation coefficient is a statistic that measures the strength and direction of the linear relationship between two variables. What should I do about it?